From c25ce53203cde78be2b7a73c91b567f023811619 Mon Sep 17 00:00:00 2001
From: GitHub Actions <actions@github.com>
Date: Mon, 21 Sep 2020 17:52:00 +0000
Subject: [PATCH] Update site

---
 static/docs/.nojekyll                         |    1 +
 static/docs/404.html                          |  223 +
 static/docs/CONTRIBUTING.html                 |  260 +
 static/docs/LICENSE-text.html                 |  225 +
 static/docs/LICENSE.html                      |  229 +
 static/docs/articles/examples/mnist-cnn.html  |  259 +
 .../empty-anchor.js                           |   15 +
 .../docs/articles/examples/mnist-dcgan.html   |  340 ++
 .../empty-anchor.js                           |   15 +
 static/docs/articles/examples/mnist-mlp.html  |  247 +
 .../empty-anchor.js                           |   15 +
 static/docs/articles/extending-autograd.html  |  265 +
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 .../articles/getting-started/assets/mnist.png |  Bin 0 -> 42703 bytes
 .../articles/getting-started/autograd.html    |  350 ++
 .../empty-anchor.js                           |   15 +
 .../control-flow-and-weight-sharing.html      |  292 +
 .../empty-anchor.js                           |   15 +
 .../articles/getting-started/custom-nn.html   |  276 +
 .../empty-anchor.js                           |   15 +
 .../getting-started/neural-networks.html      |  409 ++
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 .../new-autograd-functions.html               |  284 +
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 static/docs/articles/getting-started/nn.html  |  267 +
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 .../docs/articles/getting-started/optim.html  |  269 +
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 .../getting-started/tensors-and-autograd.html |  262 +
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 .../docs/articles/getting-started/warmup.html |  240 +
 .../empty-anchor.js                           |   15 +
 .../getting-started/what-is-torch.html        |  411 ++
 .../empty-anchor.js                           |   15 +
 static/docs/articles/index.html               |  254 +
 static/docs/articles/indexing.html            |  379 ++
 .../empty-anchor.js                           |   15 +
 static/docs/articles/loading-data.html        |  390 ++
 .../empty-anchor.js                           |   15 +
 static/docs/articles/tensor-creation.html     |  316 +
 .../empty-anchor.js                           |   15 +
 static/docs/articles/tensor/index.html        | 3612 +++++++++++
 .../empty-anchor.js                           |   15 +
 static/docs/articles/using-autograd.html      |  373 ++
 .../empty-anchor.js                           |   15 +
 static/docs/authors.html                      |  238 +
 static/docs/bootstrap-toc.css                 |   60 +
 static/docs/bootstrap-toc.js                  |  159 +
 static/docs/dev/.nojekyll                     |    1 +
 static/docs/dev/404.html                      |  223 +
 static/docs/dev/CONTRIBUTING.html             |  260 +
 static/docs/dev/LICENSE-text.html             |  225 +
 static/docs/dev/LICENSE.html                  |  229 +
 .../docs/dev/articles/examples/mnist-cnn.html |  260 +
 .../empty-anchor.js                           |   15 +
 .../dev/articles/examples/mnist-dcgan.html    |  341 ++
 .../empty-anchor.js                           |   15 +
 .../docs/dev/articles/examples/mnist-mlp.html |  248 +
 .../empty-anchor.js                           |   15 +
 .../docs/dev/articles/extending-autograd.html |  266 +
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 .../articles/getting-started/autograd.html    |  351 ++
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 .../articles/getting-started/custom-nn.html   |  277 +
 .../empty-anchor.js                           |   15 +
 .../getting-started/neural-networks.html      |  410 ++
 .../empty-anchor.js                           |   15 +
 .../new-autograd-functions.html               |  285 +
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 .../docs/dev/articles/getting-started/nn.html |  268 +
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 .../dev/articles/getting-started/optim.html   |  270 +
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 .../getting-started/tensors-and-autograd.html |  263 +
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 .../dev/articles/getting-started/tensors.html |  248 +
 .../empty-anchor.js                           |   15 +
 .../dev/articles/getting-started/warmup.html  |  241 +
 .../empty-anchor.js                           |   15 +
 .../getting-started/what-is-torch.html        |  413 ++
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 static/docs/dev/articles/index.html           |  254 +
 static/docs/dev/articles/indexing.html        |  380 ++
 .../empty-anchor.js                           |   15 +
 static/docs/dev/articles/loading-data.html    |  391 ++
 .../empty-anchor.js                           |   15 +
 static/docs/dev/articles/tensor-creation.html |  317 +
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 static/docs/dev/articles/tensor/index.html    | 3613 +++++++++++
 .../empty-anchor.js                           |   15 +
 static/docs/dev/articles/using-autograd.html  |  374 ++
 .../empty-anchor.js                           |   15 +
 static/docs/dev/authors.html                  |  238 +
 static/docs/dev/bootstrap-toc.css             |   60 +
 static/docs/dev/bootstrap-toc.js              |  159 +
 static/docs/dev/docsearch.css                 |  148 +
 static/docs/dev/docsearch.js                  |   85 +
 static/docs/dev/index.html                    |  312 +
 static/docs/dev/link.svg                      |   12 +
 static/docs/dev/news/index.html               |  235 +
 static/docs/dev/pkgdown.css                   |  367 ++
 static/docs/dev/pkgdown.js                    |  108 +
 static/docs/dev/pkgdown.yml                   |   23 +
 .../docs/dev/reference/AutogradContext.html   |  338 ++
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 static/docs/dev/reference/dataloader.html     |  310 +
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 .../docs/dev/reference/dataloader_next.html   |  237 +
 static/docs/dev/reference/dataset.html        |  267 +
 static/docs/dev/reference/default_dtype.html  |  240 +
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 static/docs/dev/reference/enumerate.html      |  241 +
 static/docs/dev/reference/figures/torch.png   |  Bin 0 -> 1697283 bytes
 static/docs/dev/reference/index.html          | 3157 ++++++++++
 static/docs/dev/reference/install_torch.html  |  266 +
 static/docs/dev/reference/is_dataloader.html  |  237 +
 .../docs/dev/reference/is_torch_device.html   |  237 +
 static/docs/dev/reference/is_torch_dtype.html |  237 +
 .../docs/dev/reference/is_torch_layout.html   |  237 +
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 .../docs/dev/reference/is_torch_qscheme.html  |  237 +
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 .../docs/dev/reference/load_state_dict.html   |  250 +
 .../dev/reference/nn_adaptive_avg_pool1d.html |  248 +
 .../dev/reference/nn_adaptive_avg_pool2d.html |  255 +
 .../dev/reference/nn_adaptive_avg_pool3d.html |  255 +
 .../nn_adaptive_log_softmax_with_loss.html    |  335 ++
 .../dev/reference/nn_adaptive_max_pool1d.html |  253 +
 .../dev/reference/nn_adaptive_max_pool2d.html |  260 +
 .../dev/reference/nn_adaptive_max_pool3d.html |  260 +
 static/docs/dev/reference/nn_avg_pool1d.html  |  305 +
 static/docs/dev/reference/nn_avg_pool2d.html  |  318 +
 static/docs/dev/reference/nn_avg_pool3d.html  |  326 +
 .../docs/dev/reference/nn_batch_norm1d.html   |  320 +
 .../docs/dev/reference/nn_batch_norm2d.html   |  319 +
 static/docs/dev/reference/nn_bce_loss.html    |  304 +
 static/docs/dev/reference/nn_bilinear.html    |  290 +
 static/docs/dev/reference/nn_celu.html        |  266 +
 static/docs/dev/reference/nn_conv1d.html      |  377 ++
 static/docs/dev/reference/nn_conv2d.html      |  394 ++
 static/docs/dev/reference/nn_conv3d.html      |  382 ++
 .../dev/reference/nn_conv_transpose1d.html    |  375 ++
 .../dev/reference/nn_conv_transpose2d.html    |  395 ++
 .../dev/reference/nn_conv_transpose3d.html    |  396 ++
 .../dev/reference/nn_cross_entropy_loss.html  |  310 +
 static/docs/dev/reference/nn_dropout.html     |  272 +
 static/docs/dev/reference/nn_dropout2d.html   |  276 +
 static/docs/dev/reference/nn_dropout3d.html   |  276 +
 static/docs/dev/reference/nn_elu.html         |  264 +
 static/docs/dev/reference/nn_embedding.html   |  333 +
 .../reference/nn_fractional_max_pool2d.html   |  276 +
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 static/docs/dev/reference/nn_gelu.html        |  252 +
 static/docs/dev/reference/nn_glu.html         |  259 +
 static/docs/dev/reference/nn_hardshrink.html  |  266 +
 static/docs/dev/reference/nn_hardsigmoid.html |  257 +
 static/docs/dev/reference/nn_hardswish.html   |  260 +
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 static/docs/dev/reference/nn_init_zeros_.html |  248 +
 static/docs/dev/reference/nn_leaky_relu.html  |  273 +
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 static/docs/dev/reference/nn_log_softmax.html |  266 +
 static/docs/dev/reference/nn_lp_pool1d.html   |  292 +
 static/docs/dev/reference/nn_lp_pool2d.html   |  304 +
 static/docs/dev/reference/nn_max_pool1d.html  |  301 +
 static/docs/dev/reference/nn_max_pool2d.html  |  316 +
 static/docs/dev/reference/nn_max_pool3d.html  |  321 +
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 static/docs/dev/reference/nn_relu.html        |  260 +
 static/docs/dev/reference/nn_relu6.html       |  260 +
 static/docs/dev/reference/nn_rnn.html         |  479 ++
 static/docs/dev/reference/nn_rrelu.html       |  283 +
 static/docs/dev/reference/nn_selu.html        |  264 +
 static/docs/dev/reference/nn_sequential.html  |  259 +
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 static/docs/dev/reference/nnf_avg_pool1d.html |  271 +
 static/docs/dev/reference/nnf_avg_pool2d.html |  279 +
 static/docs/dev/reference/nnf_avg_pool3d.html |  279 +
 static/docs/dev/reference/nnf_batch_norm.html |  275 +
 static/docs/dev/reference/nnf_bilinear.html   |  258 +
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 static/docs/dev/reference/nnf_celu.html       |  248 +
 static/docs/dev/reference/nnf_conv1d.html     |  275 +
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 static/docs/dev/reference/nnf_conv_tbc.html   |  253 +
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 static/docs/dev/reference/nnf_ctc_loss.html   |  277 +
 static/docs/dev/reference/nnf_dropout.html    |  254 +
 static/docs/dev/reference/nnf_dropout2d.html  |  258 +
 static/docs/dev/reference/nnf_dropout3d.html  |  258 +
 static/docs/dev/reference/nnf_elu.html        |  259 +
 static/docs/dev/reference/nnf_embedding.html  |  282 +
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 static/docs/dev/reference/nnf_fold.html       |  277 +
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 static/docs/dev/reference/nnf_gelu.html       |  248 +
 static/docs/dev/reference/nnf_glu.html        |  248 +
 .../docs/dev/reference/nnf_grid_sample.html   |  309 +
 static/docs/dev/reference/nnf_group_norm.html |  253 +
 .../dev/reference/nnf_gumbel_softmax.html     |  251 +
 static/docs/dev/reference/nnf_hardshrink.html |  242 +
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 static/docs/dev/reference/nnf_hardswish.html  |  253 +
 static/docs/dev/reference/nnf_hardtanh.html   |  252 +
 .../reference/nnf_hinge_embedding_loss.html   |  258 +
 .../docs/dev/reference/nnf_instance_norm.html |  276 +
 .../docs/dev/reference/nnf_interpolate.html   |  295 +
 static/docs/dev/reference/nnf_kl_div.html     |  248 +
 static/docs/dev/reference/nnf_l1_loss.html    |  248 +
 static/docs/dev/reference/nnf_layer_norm.html |  261 +
 static/docs/dev/reference/nnf_leaky_relu.html |  248 +
 static/docs/dev/reference/nnf_linear.html     |  246 +
 .../reference/nnf_local_response_norm.html    |  257 +
 .../docs/dev/reference/nnf_log_softmax.html   |  253 +
 static/docs/dev/reference/nnf_logsigmoid.html |  238 +
 static/docs/dev/reference/nnf_lp_pool1d.html  |  258 +
 static/docs/dev/reference/nnf_lp_pool2d.html  |  258 +
 .../reference/nnf_margin_ranking_loss.html    |  258 +
 static/docs/dev/reference/nnf_max_pool1d.html |  276 +
 static/docs/dev/reference/nnf_max_pool2d.html |  276 +
 static/docs/dev/reference/nnf_max_pool3d.html |  276 +
 .../docs/dev/reference/nnf_max_unpool1d.html  |  264 +
 .../docs/dev/reference/nnf_max_unpool2d.html  |  264 +
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 static/docs/dev/reference/nnf_mse_loss.html   |  248 +
 .../nnf_multi_head_attention_forward.html     |  366 ++
 .../dev/reference/nnf_multi_margin_loss.html  |  272 +
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 .../nnf_multilabel_soft_margin_loss.html      |  254 +
 static/docs/dev/reference/nnf_nll_loss.html   |  267 +
 static/docs/dev/reference/nnf_normalize.html  |  262 +
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 static/docs/dev/reference/nnf_softmax.html    |  252 +
 static/docs/dev/reference/nnf_softmin.html    |  252 +
 static/docs/dev/reference/nnf_softplus.html   |  250 +
 static/docs/dev/reference/nnf_softshrink.html |  243 +
 static/docs/dev/reference/nnf_softsign.html   |  238 +
 static/docs/dev/reference/nnf_tanhshrink.html |  238 +
 static/docs/dev/reference/nnf_threshold.html  |  252 +
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 static/docs/dev/reference/nnf_unfold.html     |  269 +
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 static/docs/dev/reference/torch_addmm.html    |  283 +
 static/docs/dev/reference/torch_addmv.html    |  284 +
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 static/docs/dev/reference/torch_angle.html    |  254 +
 static/docs/dev/reference/torch_arange.html   |  295 +
 static/docs/dev/reference/torch_argmax.html   |  281 +
 static/docs/dev/reference/torch_argmin.html   |  279 +
 static/docs/dev/reference/torch_argsort.html  |  267 +
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 static/docs/dev/reference/torch_asin.html     |  259 +
 static/docs/dev/reference/torch_atan.html     |  259 +
 static/docs/dev/reference/torch_atan2.html    |  267 +
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 static/docs/dev/reference/torch_baddbmm.html  |  333 +
 .../dev/reference/torch_bartlett_window.html  |  292 +
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 static/docs/dev/reference/torch_bincount.html |  279 +
 .../docs/dev/reference/torch_bitwise_and.html |  248 +
 .../docs/dev/reference/torch_bitwise_not.html |  244 +
 .../docs/dev/reference/torch_bitwise_or.html  |  248 +
 .../docs/dev/reference/torch_bitwise_xor.html |  248 +
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 static/docs/dev/reference/torch_bmm.html      |  319 +
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 static/docs/dev/reference/torch_can_cast.html |  255 +
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 static/docs/dev/reference/torch_cat.html      |  264 +
 static/docs/dev/reference/torch_cdist.html    |  255 +
 static/docs/dev/reference/torch_ceil.html     |  260 +
 static/docs/dev/reference/torch_celu.html     |  247 +
 static/docs/dev/reference/torch_celu_.html    |  247 +
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 static/docs/dev/reference/torch_cholesky.html |  283 +
 .../dev/reference/torch_cholesky_inverse.html |  272 +
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 static/docs/dev/reference/torch_chunk.html    |  254 +
 static/docs/dev/reference/torch_clamp.html    |  301 +
 .../dev/reference/torch_combinations.html     |  270 +
 static/docs/dev/reference/torch_conj.html     |  253 +
 static/docs/dev/reference/torch_conv1d.html   | 4589 ++++++++++++++
 static/docs/dev/reference/torch_conv2d.html   |  342 ++
 static/docs/dev/reference/torch_conv3d.html   |  285 +
 static/docs/dev/reference/torch_conv_tbc.html |  256 +
 .../dev/reference/torch_conv_transpose1d.html | 5206 ++++++++++++++++
 .../dev/reference/torch_conv_transpose2d.html |  347 ++
 .../dev/reference/torch_conv_transpose3d.html |  291 +
 static/docs/dev/reference/torch_cos.html      |  259 +
 static/docs/dev/reference/torch_cosh.html     |  260 +
 .../reference/torch_cosine_similarity.html    |  368 ++
 static/docs/dev/reference/torch_cross.html    |  272 +
 static/docs/dev/reference/torch_cummax.html   |  287 +
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 static/docs/dev/reference/torch_fft.html      |  614 ++
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 static/docs/dev/reference/torch_matmul.html   |  347 ++
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 static/docs/dev/reference/torch_rfft.html     |  334 ++
 static/docs/dev/reference/torch_roll.html     |  269 +
 static/docs/dev/reference/torch_rot90.html    |  271 +
 static/docs/dev/reference/torch_round.html    |  257 +
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 static/docs/dev/reference/torch_std_mean.html |  299 +
 static/docs/dev/reference/torch_stft.html     |  341 ++
 static/docs/dev/reference/torch_sum.html      |  287 +
 static/docs/dev/reference/torch_svd.html      |  298 +
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 static/docs/link.svg                          |   12 +
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 static/docs/pkgdown.css                       |  367 ++
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 static/docs/reference/AutogradContext.html    |  338 ++
 static/docs/reference/Rplot001.png            |  Bin 0 -> 1011 bytes
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 static/docs/reference/index.html              | 3157 ++++++++++
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 .../reference/nn_adaptive_avg_pool3d.html     |  255 +
 .../nn_adaptive_log_softmax_with_loss.html    |  335 ++
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 .../reference/nn_adaptive_max_pool2d.html     |  260 +
 .../reference/nn_adaptive_max_pool3d.html     |  260 +
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 static/docs/reference/nn_conv1d.html          |  377 ++
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 static/docs/reference/nn_conv3d.html          |  382 ++
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 static/docs/reference/nn_relu6.html           |  260 +
 static/docs/reference/nn_rnn.html             |  479 ++
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 static/docs/reference/torch_conj.html         |  253 +
 static/docs/reference/torch_conv1d.html       | 4657 ++++++++++++++
 static/docs/reference/torch_conv2d.html       |  342 ++
 static/docs/reference/torch_conv3d.html       |  285 +
 static/docs/reference/torch_conv_tbc.html     |  256 +
 .../reference/torch_conv_transpose1d.html     | 5342 +++++++++++++++++
 .../reference/torch_conv_transpose2d.html     |  347 ++
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 static/docs/reference/torch_cos.html          |  259 +
 static/docs/reference/torch_cosh.html         |  260 +
 .../reference/torch_cosine_similarity.html    |  368 ++
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 static/docs/reference/torch_expm1.html        |  256 +
 static/docs/reference/torch_eye.html          |  280 +
 static/docs/reference/torch_fft.html          |  614 ++
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 static/docs/reference/torch_manual_seed.html  |  237 +
 .../docs/reference/torch_masked_select.html   |  269 +
 static/docs/reference/torch_matmul.html       |  347 ++
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 static/docs/reference/torch_reshape.html      |  268 +
 static/docs/reference/torch_result_type.html  |  255 +
 static/docs/reference/torch_rfft.html         |  334 ++
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 static/docs/reference/torch_rrelu_.html       |  265 +
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 static/docs/reference/torch_std.html          |  289 +
 static/docs/reference/torch_std_mean.html     |  299 +
 static/docs/reference/torch_stft.html         |  341 ++
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 static/docs/reference/torch_symeig.html       |  290 +
 static/docs/reference/torch_t.html            |  264 +
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+      <h1>Contributing to torch</h1>
+    </div>
+
+<div id="contributing-to-torch" class="section level1">
+
+<p>This outlines how to propose a change to torch. For more detailed info about contributing to this, and other tidyverse packages, please see the <a href="https://rstd.io/tidy-contrib"><strong>development contributing guide</strong></a>.</p>
+<div id="fixing-typos" class="section level2">
+<h2 class="hasAnchor">
+<a href="#fixing-typos" class="anchor"></a>Fixing typos</h2>
+<p>You can fix typos, spelling mistakes, or grammatical errors in the documentation directly using the GitHub web interface, as long as the changes are made in the <em>source</em> file. This generally means you’ll need to edit <a href="https://roxygen2.r-lib.org/articles/roxygen2.html">roxygen2 comments</a> in an <code>.R</code>, not a <code>.Rd</code> file. You can find the <code>.R</code> file that generates the <code>.Rd</code> by reading the comment in the first line.</p>
+<p>See also the [Documentation] section.</p>
+</div>
+<div id="filing-bugs" class="section level2">
+<h2 class="hasAnchor">
+<a href="#filing-bugs" class="anchor"></a>Filing bugs</h2>
+<p>If you find a bug in <code>torch</code> please open an issue <a href="https://github.com/mlverse/torch/issues">here</a>. Please, provide detailed information on how to reproduce the bug. It would be great to also provide a <a href="https://reprex.tidyverse.org/"><code>reprex</code></a>.</p>
+</div>
+<div id="feature-requests" class="section level2">
+<h2 class="hasAnchor">
+<a href="#feature-requests" class="anchor"></a>Feature requests</h2>
+<p>Feel free to open issues <a href="https://github.com/mlverse/torch/issues">here</a> and add the <code>feature-request</code> tag. Try searching if there’s already an open issue for your <code>feature-request</code>, in this case it’s better to comment or upvote it intead of opening a new one.</p>
+</div>
+<div id="examples" class="section level2">
+<h2 class="hasAnchor">
+<a href="#examples" class="anchor"></a>Examples</h2>
+<p>We welcome contributed examples. feel free to open a PR with new examples. The should be placed in the <code>vignettes/examples</code> folder.</p>
+<p>The examples should be an .R file and a .Rmd file with the same name that just renders the code.</p>
+<p>See <a href="https://github.com/mlverse/torch/blob/master/vignettes/examples/mnist-mlp.R">mnist-mlp.R</a> and <a href="https://github.com/mlverse/torch/blob/master/vignettes/examples/mnist-mlp.Rmd">mnist-mlp.Rmd</a></p>
+<p>One must be able to run the example without manually downloading any dataset/file. You should also add an entry to the <a href="https://github.com/mlverse/torch/blob/master/_pkgdown.yml#L24-L25"><code>_pkgdown.yaml</code> file</a>.</p>
+</div>
+<div id="code-contributions" class="section level2">
+<h2 class="hasAnchor">
+<a href="#code-contributions" class="anchor"></a>Code contributions</h2>
+<p>We have many open issues in the <a href="https://github.com/mlverse/torch/issues">github repo</a> if there’s one item that you want to work on, you can comment on it an ask for directions.</p>
+</div>
+<div id="documentation" class="section level2">
+<h2 class="hasAnchor">
+<a href="#documentation" class="anchor"></a>Documentation</h2>
+<p>We use roxygen2 to generate the documentation. IN order to update the docs, edit the file in the <code>R</code> directory. To regenerate and preview the docs, use the custom <code>tools/document.R</code> script, as we need to patch roxygen2 to avoid running the examples on CRAN.</p>
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+    </div>
+
+<pre>YEAR: 2020
+COPYRIGHT HOLDER: Daniel Falbel
+</pre>
+
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+
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+    <div class="page-header toc-ignore">
+      <h1 data-toc-skip>mnist-cnn</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/examples/mnist-cnn.Rmd"><code>vignettes/examples/mnist-cnn.Rmd</code></a></small>
+      <div class="hidden name"><code>mnist-cnn.Rmd</code></div>
+
+    </div>
+
+    
+    
+<div class="sourceCode" id="cb1"><html><body><pre class="r"><span class="no">dir</span> <span class="kw">&lt;-</span> <span class="st">"~/Downloads/mnist"</span>
+
+<span class="no">ds</span> <span class="kw">&lt;-</span> <span class="fu">mnist_dataset</span>(
+  <span class="no">dir</span>,
+  <span class="kw">download</span> <span class="kw">=</span> <span class="fl">TRUE</span>,
+  <span class="kw">transform</span> <span class="kw">=</span> <span class="kw">function</span>(<span class="no">x</span>) {
+    <span class="no">x</span> <span class="kw">&lt;-</span> <span class="no">x</span>$<span class="fu">to</span>(<span class="kw">dtype</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/torch_dtype.html">torch_float</a></span>())/<span class="fl">256</span>
+    <span class="no">x</span>[<span class="no">newaxis</span>,<span class="no">..</span>]
+  }
+)
+<span class="no">dl</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/dataloader.html">dataloader</a></span>(<span class="no">ds</span>, <span class="kw">batch_size</span> <span class="kw">=</span> <span class="fl">32</span>, <span class="kw">shuffle</span> <span class="kw">=</span> <span class="fl">TRUE</span>)
+
+<span class="no">net</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nn_module.html">nn_module</a></span>(
+  <span class="st">"Net"</span>,
+  <span class="kw">initialize</span> <span class="kw">=</span> <span class="kw">function</span>() {
+    <span class="no">self</span>$<span class="no">conv1</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nn_conv2d.html">nn_conv2d</a></span>(<span class="fl">1</span>, <span class="fl">32</span>, <span class="fl">3</span>, <span class="fl">1</span>)
+    <span class="no">self</span>$<span class="no">conv2</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nn_conv2d.html">nn_conv2d</a></span>(<span class="fl">32</span>, <span class="fl">64</span>, <span class="fl">3</span>, <span class="fl">1</span>)
+    <span class="no">self</span>$<span class="no">dropout1</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nn_dropout2d.html">nn_dropout2d</a></span>(<span class="fl">0.25</span>)
+    <span class="no">self</span>$<span class="no">dropout2</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nn_dropout2d.html">nn_dropout2d</a></span>(<span class="fl">0.5</span>)
+    <span class="no">self</span>$<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">9216</span>, <span class="fl">128</span>)
+    <span class="no">self</span>$<span class="no">fc2</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="fl">128</span>, <span class="fl">10</span>)
+  },
+  <span class="kw">forward</span> <span class="kw">=</span> <span class="kw">function</span>(<span class="no">x</span>) {
+    <span class="no">x</span> <span class="kw">&lt;-</span> <span class="no">self</span>$<span class="fu">conv1</span>(<span class="no">x</span>)
+    <span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nnf_relu.html">nnf_relu</a></span>(<span class="no">x</span>)
+    <span class="no">x</span> <span class="kw">&lt;-</span> <span class="no">self</span>$<span class="fu">conv2</span>(<span class="no">x</span>)
+    <span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nnf_relu.html">nnf_relu</a></span>(<span class="no">x</span>)
+    <span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nnf_max_pool2d.html">nnf_max_pool2d</a></span>(<span class="no">x</span>, <span class="fl">2</span>)
+    <span class="no">x</span> <span class="kw">&lt;-</span> <span class="no">self</span>$<span class="fu">dropout1</span>(<span class="no">x</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>$<span class="fu">fc1</span>(<span class="no">x</span>)
+    <span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nnf_relu.html">nnf_relu</a></span>(<span class="no">x</span>)
+    <span class="no">x</span> <span class="kw">&lt;-</span> <span class="no">self</span>$<span class="fu">dropout2</span>(<span class="no">x</span>)
+    <span class="no">x</span> <span class="kw">&lt;-</span> <span class="no">self</span>$<span class="fu">fc2</span>(<span class="no">x</span>)
+    <span class="no">output</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nnf_log_softmax.html">nnf_log_softmax</a></span>(<span class="no">x</span>, <span class="kw">dim</span><span class="kw">=</span><span class="fl">1</span>)
+    <span class="no">output</span>
+  }
+)
+
+<span class="no">model</span> <span class="kw">&lt;-</span> <span class="fu">net</span>()
+<span class="no">optimizer</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/optim_sgd.html">optim_sgd</a></span>(<span class="no">model</span>$<span class="no">parameters</span>, <span class="kw">lr</span> <span class="kw">=</span> <span class="fl">0.01</span>)
+
+<span class="no">epochs</span> <span class="kw">&lt;-</span> <span class="fl">10</span>
+
+<span class="kw">for</span> (<span class="no">epoch</span> <span class="kw">in</span> <span class="fl">1</span>:<span class="fl">10</span>) {
+
+  <span class="no">pb</span> <span class="kw">&lt;-</span> <span class="kw pkg">progress</span><span class="kw ns">::</span><span class="no">progress_bar</span>$<span class="fu">new</span>(
+    <span class="kw">total</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/length.html">length</a></span>(<span class="no">dl</span>),
+    <span class="kw">format</span> <span class="kw">=</span> <span class="st">"[:bar] :eta Loss: :loss"</span>
+  )
+  <span class="no">l</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>()
+
+  <span class="kw">for</span> (<span class="no">b</span> <span class="kw">in</span> <span class="fu"><a href="../../reference/enumerate.html">enumerate</a></span>(<span class="no">dl</span>)) {
+    <span class="no">optimizer</span>$<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>$<span class="fu">item</span>())
+    <span class="no">pb</span>$<span class="fu">tick</span>(<span class="kw">tokens</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="kw">loss</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>(<span class="no">l</span>)))
+  }
+
+  <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/sprintf.html">sprintf</a></span>(<span class="st">"Loss at epoch %d: %3f\n"</span>, <span class="no">epoch</span>, <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>(<span class="no">l</span>)))
+}</pre></body></html></div>
+  </div>
+
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+
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diff --git a/static/docs/articles/examples/mnist-cnn_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/articles/examples/mnist-cnn_files/accessible-code-block-0.0.1/empty-anchor.js
new file mode 100644
index 000000000..ca349fd6a
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+
+document.addEventListener('DOMContentLoaded', function() {
+  const codeList = document.getElementsByClassName("sourceCode");
+  for (var i = 0; i < codeList.length; i++) {
+    var linkList = codeList[i].getElementsByTagName('a');
+    for (var j = 0; j < linkList.length; j++) {
+      if (linkList[j].innerHTML === "") {
+        linkList[j].setAttribute('aria-hidden', 'true');
+      }
+    }
+  }
+});
diff --git a/static/docs/articles/examples/mnist-dcgan.html b/static/docs/articles/examples/mnist-dcgan.html
new file mode 100644
index 000000000..a7a103c5d
--- /dev/null
+++ b/static/docs/articles/examples/mnist-dcgan.html
@@ -0,0 +1,340 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
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+<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+<meta charset="utf-8">
+<meta http-equiv="X-UA-Compatible" content="IE=edge">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>mnist-dcgan • torch</title>
+<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.4.0/united/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous">
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+<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../../pkgdown.css" rel="stylesheet">
+<script src="../../pkgdown.js"></script><meta property="og:title" content="mnist-dcgan">
+<meta property="og:description" content="torch">
+<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+</li>
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+<ul class="nav navbar-nav navbar-right">
+<li>
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+     
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+</div>
+<!--/.nav-collapse -->
+  </div>
+<!--/.container -->
+</div>
+<!--/.navbar -->
+
+      
+
+      </header><script src="mnist-dcgan_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header toc-ignore">
+      <h1 data-toc-skip>mnist-dcgan</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/examples/mnist-dcgan.Rmd"><code>vignettes/examples/mnist-dcgan.Rmd</code></a></small>
+      <div class="hidden name"><code>mnist-dcgan.Rmd</code></div>
+
+    </div>
+
+    
+    
+<div class="sourceCode" id="cb1"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">torch</span>)
+
+<span class="no">dir</span> <span class="kw">&lt;-</span> <span class="st">"~/Downloads/mnist"</span>
+
+<span class="no">ds</span> <span class="kw">&lt;-</span> <span class="fu">mnist_dataset</span>(
+  <span class="no">dir</span>,
+  <span class="kw">download</span> <span class="kw">=</span> <span class="fl">TRUE</span>,
+  <span class="kw">transform</span> <span class="kw">=</span> <span class="kw">function</span>(<span class="no">x</span>) {
+    <span class="no">x</span> <span class="kw">&lt;-</span> <span class="no">x</span>$<span class="fu">to</span>(<span class="kw">dtype</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/torch_dtype.html">torch_float</a></span>())/<span class="fl">256</span>
+    <span class="no">x</span> <span class="kw">&lt;-</span> <span class="fl">2</span>*(<span class="no">x</span> - <span class="fl">0.5</span>)
+    <span class="no">x</span>[<span class="no">newaxis</span>,<span class="no">..</span>]
+  }
+)
+<span class="no">dl</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/dataloader.html">dataloader</a></span>(<span class="no">ds</span>, <span class="kw">batch_size</span> <span class="kw">=</span> <span class="fl">32</span>, <span class="kw">shuffle</span> <span class="kw">=</span> <span class="fl">TRUE</span>)
+
+<span class="no">generator</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nn_module.html">nn_module</a></span>(
+  <span class="st">"generator"</span>,
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+      <span class="fu"><a href="../../reference/nn_relu.html">nn_relu</a></span>(),
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+      <span class="fu"><a href="../../reference/nn_tanh.html">nn_tanh</a></span>()
+    )
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+  <span class="kw">forward</span> <span class="kw">=</span> <span class="kw">function</span>(<span class="no">input</span>) {
+    <span class="no">self</span>$<span class="fu">main</span>(<span class="no">input</span>)
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+      <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>),
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+    )
+    <span class="no">self</span>$<span class="no">linear</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="fl">128</span>, <span class="fl">1</span>)
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+  },
+  <span class="kw">forward</span> <span class="kw">=</span> <span class="kw">function</span>(<span class="no">input</span>) {
+    <span class="no">x</span> <span class="kw">&lt;-</span> <span class="no">self</span>$<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>)
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+    <span class="no">self</span>$<span class="fu">sigmoid</span>(<span class="no">x</span>)
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+<span class="no">plot_gen</span> <span class="kw">&lt;-</span> <span class="kw">function</span>(<span class="no">noise</span>) {
+  <span class="no">img</span> <span class="kw">&lt;-</span> <span class="fu">G</span>(<span class="no">noise</span>)
+  <span class="no">img</span> <span class="kw">&lt;-</span> <span class="no">img</span>$<span class="fu">cpu</span>()
+  <span class="no">img</span> <span class="kw">&lt;-</span> <span class="no">img</span>[<span class="fl">1</span>,<span class="fl">1</span>,,,<span class="no">newaxis</span>]/<span class="fl">2</span> + <span class="fl">0.5</span>
+  <span class="no">img</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_stack.html">torch_stack</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">img</span>, <span class="no">img</span>, <span class="no">img</span>), <span class="kw">dim</span> <span class="kw">=</span> <span class="fl">2</span>)[<span class="no">..</span>,<span class="fl">1</span>]
+  <span class="no">img</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/grDevices/as.raster.html">as.raster</a></span>(<span class="fu"><a href="../../reference/as_array.html">as_array</a></span>(<span class="no">img</span>))
+  <span class="fu"><a href="https://rdrr.io/r/base/plot.html">plot</a></span>(<span class="no">img</span>)
+}
+
+<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">progress_bar</span>$<span class="fu">new</span>(
+    <span class="kw">total</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/length.html">length</a></span>(<span class="no">dl</span>),
+    <span class="kw">format</span> <span class="kw">=</span> <span class="st">"[:bar] :eta Loss D: :lossd Loss G: :lossg"</span>
+  )
+  <span class="no">lossg</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>()
+  <span class="no">lossd</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>()
+
+  <span class="kw">for</span> (<span class="no">b</span> <span class="kw">in</span> <span class="fu"><a href="../../reference/enumerate.html">enumerate</a></span>(<span class="no">dl</span>)) {
+
+    <span class="no">y_real</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span>(<span class="fl">32</span>, <span class="kw">device</span> <span class="kw">=</span> <span class="no">device</span>)
+    <span class="no">y_fake</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">32</span>, <span class="kw">device</span> <span class="kw">=</span> <span class="no">device</span>)
+
+    <span class="no">noise</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">32</span>, <span class="fl">100</span>, <span class="fl">1</span>, <span class="fl">1</span>, <span class="kw">device</span> <span class="kw">=</span> <span class="no">device</span>)
+    <span class="no">fake</span> <span class="kw">&lt;-</span> <span class="fu">G</span>(<span class="no">noise</span>)
+
+    <span class="no">img</span> <span class="kw">&lt;-</span> <span class="no">b</span><span class="kw">[[</span><span class="fl">1</span>]]$<span class="fu">to</span>(<span class="kw">device</span> <span class="kw">=</span> <span class="no">device</span>)
+
+    <span class="co"># train the discriminator ---</span>
+    <span class="no">D_loss</span> <span class="kw">&lt;-</span> <span class="fu">loss</span>(<span class="fu"><a href="https://rdrr.io/r/stats/deriv.html">D</a></span>(<span class="no">img</span>), <span class="no">y_real</span>) + <span class="fu">loss</span>(<span class="fu"><a href="https://rdrr.io/r/stats/deriv.html">D</a></span>(<span class="no">fake</span>$<span class="fu"><a href="https://rdrr.io/r/base/detach.html">detach</a></span>()), <span class="no">y_fake</span>)
+
+    <span class="no">D_optimizer</span>$<span class="fu">zero_grad</span>()
+    <span class="no">D_loss</span>$<span class="fu">backward</span>()
+    <span class="no">D_optimizer</span>$<span class="fu"><a href="https://rdrr.io/r/stats/step.html">step</a></span>()
+
+    <span class="co"># train the generator ---</span>
+
+    <span class="no">G_loss</span> <span class="kw">&lt;-</span> <span class="fu">loss</span>(<span class="fu"><a href="https://rdrr.io/r/stats/deriv.html">D</a></span>(<span class="no">fake</span>), <span class="no">y_real</span>)
+
+    <span class="no">G_optimizer</span>$<span class="fu">zero_grad</span>()
+    <span class="no">G_loss</span>$<span class="fu">backward</span>()
+    <span class="no">G_optimizer</span>$<span class="fu"><a href="https://rdrr.io/r/stats/step.html">step</a></span>()
+
+    <span class="no">lossd</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="no">lossd</span>, <span class="no">D_loss</span>$<span class="fu">item</span>())
+    <span class="no">lossg</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="no">lossg</span>, <span class="no">G_loss</span>$<span class="fu">item</span>())
+    <span class="no">pb</span>$<span class="fu">tick</span>(<span class="kw">tokens</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="kw">lossd</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>(<span class="no">lossd</span>), <span class="kw">lossg</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>(<span class="no">lossg</span>)))
+  }
+  <span class="fu">plot_gen</span>(<span class="no">fixed_noise</span>)
+
+  <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/sprintf.html">sprintf</a></span>(<span class="st">"Epoch %d - Loss D: %3f Loss G: %3f\n"</span>, <span class="no">epoch</span>, <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>(<span class="no">lossd</span>), <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>(<span class="no">lossg</span>)))
+}</pre></body></html></div>
+  </div>
+
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+
+      </div>
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+</div>
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+  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.5.1.</p>
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diff --git a/static/docs/articles/examples/mnist-dcgan_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/articles/examples/mnist-dcgan_files/accessible-code-block-0.0.1/empty-anchor.js
new file mode 100644
index 000000000..ca349fd6a
--- /dev/null
+++ b/static/docs/articles/examples/mnist-dcgan_files/accessible-code-block-0.0.1/empty-anchor.js
@@ -0,0 +1,15 @@
+// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
+// v0.0.1
+// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
+
+document.addEventListener('DOMContentLoaded', function() {
+  const codeList = document.getElementsByClassName("sourceCode");
+  for (var i = 0; i < codeList.length; i++) {
+    var linkList = codeList[i].getElementsByTagName('a');
+    for (var j = 0; j < linkList.length; j++) {
+      if (linkList[j].innerHTML === "") {
+        linkList[j].setAttribute('aria-hidden', 'true');
+      }
+    }
+  }
+});
diff --git a/static/docs/articles/examples/mnist-mlp.html b/static/docs/articles/examples/mnist-mlp.html
new file mode 100644
index 000000000..df53285e9
--- /dev/null
+++ b/static/docs/articles/examples/mnist-mlp.html
@@ -0,0 +1,247 @@
+<!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">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>mnist-mlp • torch</title>
+<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.4.0/united/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous">
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+<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../../pkgdown.css" rel="stylesheet">
+<script src="../../pkgdown.js"></script><meta property="og:title" content="mnist-mlp">
+<meta property="og:description" content="torch">
+<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+      <a href="../../articles/getting-started/tensors-and-autograd.html">Tensors and autograd</a>
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+      <a href="../../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
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+      <a href="../../articles/getting-started/what-is-torch.html">What's torch?</a>
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+      <a href="../../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
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+<li class="dropdown-header">Vision</li>
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+      </header><script src="mnist-mlp_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header toc-ignore">
+      <h1 data-toc-skip>mnist-mlp</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/examples/mnist-mlp.Rmd"><code>vignettes/examples/mnist-mlp.Rmd</code></a></small>
+      <div class="hidden name"><code>mnist-mlp.Rmd</code></div>
+
+    </div>
+
+    
+    
+<div class="sourceCode" id="cb1"><html><body><pre class="r"><span class="no">dir</span> <span class="kw">&lt;-</span> <span class="st">"~/Downloads/mnist"</span>
+
+<span class="no">ds</span> <span class="kw">&lt;-</span> <span class="fu">mnist_dataset</span>(
+  <span class="no">dir</span>,
+  <span class="kw">download</span> <span class="kw">=</span> <span class="fl">TRUE</span>,
+  <span class="kw">transform</span> <span class="kw">=</span> <span class="kw">function</span>(<span class="no">x</span>) {
+    <span class="no">x</span>$<span class="fu">to</span>(<span class="kw">dtype</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/torch_dtype.html">torch_float</a></span>())/<span class="fl">256</span>
+  }
+)
+<span class="no">dl</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/dataloader.html">dataloader</a></span>(<span class="no">ds</span>, <span class="kw">batch_size</span> <span class="kw">=</span> <span class="fl">32</span>, <span class="kw">shuffle</span> <span class="kw">=</span> <span class="fl">TRUE</span>)
+
+<span class="no">net</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nn_module.html">nn_module</a></span>(
+  <span class="st">"Net"</span>,
+  <span class="kw">initialize</span> <span class="kw">=</span> <span class="kw">function</span>() {
+    <span class="no">self</span>$<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">784</span>, <span class="fl">128</span>)
+    <span class="no">self</span>$<span class="no">fc2</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="fl">128</span>, <span class="fl">10</span>)
+  },
+  <span class="kw">forward</span> <span class="kw">=</span> <span class="kw">function</span>(<span class="no">x</span>) {
+    <span class="no">x</span> <span class="kw">%&gt;%</span>
+      <span class="fu"><a href="../../reference/torch_flatten.html">torch_flatten</a></span>(<span class="kw">start_dim</span> <span class="kw">=</span> <span class="fl">2</span>) <span class="kw">%&gt;%</span>
+      <span class="no">self</span>$<span class="fu">fc1</span>() <span class="kw">%&gt;%</span>
+      <span class="fu"><a href="../../reference/nnf_relu.html">nnf_relu</a></span>() <span class="kw">%&gt;%</span>
+      <span class="no">self</span>$<span class="fu">fc2</span>() <span class="kw">%&gt;%</span>
+      <span class="fu"><a href="../../reference/nnf_log_softmax.html">nnf_log_softmax</a></span>(<span class="kw">dim</span> <span class="kw">=</span> <span class="fl">1</span>)
+  }
+)
+
+<span class="no">model</span> <span class="kw">&lt;-</span> <span class="fu">net</span>()
+<span class="no">optimizer</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/optim_sgd.html">optim_sgd</a></span>(<span class="no">model</span>$<span class="no">parameters</span>, <span class="kw">lr</span> <span class="kw">=</span> <span class="fl">0.01</span>)
+
+<span class="no">epochs</span> <span class="kw">&lt;-</span> <span class="fl">10</span>
+
+<span class="kw">for</span> (<span class="no">epoch</span> <span class="kw">in</span> <span class="fl">1</span>:<span class="fl">10</span>) {
+
+  <span class="no">pb</span> <span class="kw">&lt;-</span> <span class="kw pkg">progress</span><span class="kw ns">::</span><span class="no">progress_bar</span>$<span class="fu">new</span>(
+    <span class="kw">total</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/length.html">length</a></span>(<span class="no">dl</span>),
+    <span class="kw">format</span> <span class="kw">=</span> <span class="st">"[:bar] :eta Loss: :loss"</span>
+  )
+  <span class="no">l</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>()
+
+  <span class="kw">for</span> (<span class="no">b</span> <span class="kw">in</span> <span class="fu"><a href="../../reference/enumerate.html">enumerate</a></span>(<span class="no">dl</span>)) {
+    <span class="no">optimizer</span>$<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>$<span class="fu">item</span>())
+    <span class="no">pb</span>$<span class="fu">tick</span>(<span class="kw">tokens</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="kw">loss</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>(<span class="no">l</span>)))
+  }
+
+  <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/sprintf.html">sprintf</a></span>(<span class="st">"Loss at epoch %d: %3f\n"</span>, <span class="no">epoch</span>, <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>(<span class="no">l</span>)))
+}</pre></body></html></div>
+  </div>
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diff --git a/static/docs/articles/examples/mnist-mlp_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/articles/examples/mnist-mlp_files/accessible-code-block-0.0.1/empty-anchor.js
new file mode 100644
index 000000000..ca349fd6a
--- /dev/null
+++ b/static/docs/articles/examples/mnist-mlp_files/accessible-code-block-0.0.1/empty-anchor.js
@@ -0,0 +1,15 @@
+// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
+// v0.0.1
+// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
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diff --git a/static/docs/articles/extending-autograd.html b/static/docs/articles/extending-autograd.html
new file mode 100644
index 000000000..05802b50d
--- /dev/null
+++ b/static/docs/articles/extending-autograd.html
@@ -0,0 +1,265 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
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+<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet">
+<script src="../pkgdown.js"></script><meta property="og:title" content="Extending Autograd">
+<meta property="og:description" content="torch">
+<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+        <a class="navbar-link" href="../index.html">torch</a>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.3</span>
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+<li class="dropdown-header">Beginers guide</li>
+    <li>
+      <a href="../articles/getting-started/warmup.html">Warm-up</a>
+    </li>
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+      <a href="../articles/getting-started/tensors.html">Tensors</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/tensors-and-autograd.html">Tensors and autograd</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/optim.html">optim: optimizers in torch</a>
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+      <a href="../articles/getting-started/custom-nn.html">Custom nn modules</a>
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+      <a href="../articles/getting-started/control-flow-and-weight-sharing.html">Control flow &amp; Weight sharing</a>
+    </li>
+    <li class="dropdown-header">Torch Mechanics</li>
+    <li>
+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
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+      <a href="../articles/getting-started/neural-networks.html">Neural networks</a>
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+<li class="dropdown-header">Tensors</li>
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+      <a href="../articles/tensor-creation.html">Creating tensors</a>
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+    <li>
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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>
+
+    </div>
+
+    
+    
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
+</pre></div>
+<p>Adding operations to autograd requires implementing a new <code>autograd_function</code> for each operation. Recall that <code>autograd_functions</code>s are what <code>autograd</code> uses to compute the results and gradients, and encode the operation history. Every new function requires you to implement 2 methods:</p>
+<ul>
+<li><p><code>forward()</code> - the code that performs the operation. It can take as many arguments as you want, with some of them being optional, if you specify the default values. All kinds of R objects are accepted here. Tensor arguments that track history (i.e., with <code>requires_grad=TRUE</code>) will be converted to ones that don’t track history before the call, and their use will be registered in the graph. Note that this logic won’t traverse lists or any other data structures and will only consider Tensor’s that are direct arguments to the call. You can return either a single Tensor output, or a list of <code>Tensor</code>s if there are multiple outputs. Also, please refer to the docs of <code>autograd_function</code> to find descriptions of useful methods that can be called only from <code>forward()</code>.</p></li>
+<li><p><code>backward()</code> - gradient formula. It will be given as many Tensor arguments as there were outputs, with each of them representing gradient w.r.t. that output. It should return as many <code>Tensor</code>s as there were <code>Tensor's</code> that required gradients in <code>forward</code>, with each of them containing the gradient w.r.t. its corresponding input.</p></li>
+</ul>
+<div id="note" class="section level2">
+<h2 class="hasAnchor">
+<a href="#note" class="anchor"></a>Note</h2>
+<p>It’s the user’s responsibility to use the special functions in the forward’s <code>ctx</code> properly in order to ensure that the new <code>autograd_function</code> works properly with the autograd engine.</p>
+<ul>
+<li><p><code>save_for_backward()</code> must be used when saving input or ouput of the forward to be used later in the backward.</p></li>
+<li><p><code>mark_dirty()</code> must be used to mark any input that is modified inplace by the forward function.</p></li>
+<li><p><code>mark_non_differentiable()</code> must be used to tell the engine if an output is not differentiable.</p></li>
+</ul>
+</div>
+<div id="examples" class="section level2">
+<h2 class="hasAnchor">
+<a href="#examples" class="anchor"></a>Examples</h2>
+<p>Below you can find code for a linear function:</p>
+<div class="sourceCode" id="cb2"><pre class="downlit">
+<span class="kw">linear</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/autograd_function.html">autograd_function</a></span>(
+  forward = <span class="fu">function</span>(<span class="kw">ctx</span>, <span class="kw">input</span>, <span class="kw">weight</span>, <span class="kw">bias</span> = <span class="kw">NULL</span>) {
+    <span class="kw">ctx</span><span class="op">$</span><span class="fu">save_for_backward</span>(input = <span class="kw">input</span>, weight = <span class="kw">weight</span>, bias = <span class="kw">bias</span>)
+    <span class="kw">output</span> <span class="op">&lt;-</span> <span class="kw">input</span><span class="op">$</span><span class="fu">mm</span>(<span class="kw">weight</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span>())
+    <span class="co">if</span> (<span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/NULL.html">is.null</a></span>(<span class="kw">bias</span>))
+      <span class="kw">output</span> <span class="op">&lt;-</span> <span class="kw">output</span> <span class="op">+</span> <span class="kw">bias</span><span class="op">$</span><span class="fu">unsqueeze</span>(<span class="fl">0</span>)<span class="op">$</span><span class="fu">expand_as</span>(<span class="kw">output</span>)
+    
+    <span class="kw">output</span>
+  },
+  backward = <span class="fu">function</span>(<span class="kw">ctx</span>, <span class="kw">grad_output</span>) {
+    
+    <span class="kw">s</span> <span class="op">&lt;-</span> <span class="kw">ctx</span><span class="op">$</span><span class="kw">saved_variables</span>
+    
+    <span class="kw">grads</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(
+      input = <span class="kw">NULL</span>,
+      weight = <span class="kw">NULL</span>,
+      bias = <span class="kw">NULL</span>
+    )
+    
+    <span class="co">if</span> (<span class="kw">ctx</span><span class="op">$</span><span class="kw">needs_input_grad</span><span class="op">$</span><span class="kw">input</span>)
+      <span class="kw">grads</span><span class="op">$</span><span class="kw">input</span> <span class="op">&lt;-</span> <span class="kw">grad_output</span><span class="op">$</span><span class="fu">mm</span>(<span class="kw">s</span><span class="op">$</span><span class="kw">weight</span>)
+    
+    <span class="co">if</span> (<span class="kw">ctx</span><span class="op">$</span><span class="kw">needs_input_grad</span><span class="op">$</span><span class="kw">weight</span>)
+      <span class="kw">grads</span><span class="op">$</span><span class="kw">weight</span> <span class="op">&lt;-</span> <span class="kw">grad_output</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span>()<span class="op">$</span><span class="fu">mm</span>(<span class="kw">s</span><span class="op">$</span><span class="kw">input</span>)
+    
+    <span class="co">if</span> (<span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/NULL.html">is.null</a></span>(<span class="kw">s</span><span class="op">$</span><span class="kw">bias</span>) <span class="op">&amp;&amp;</span> <span class="kw">ctx</span><span class="op">$</span><span class="kw">needs_input_grad</span><span class="op">$</span><span class="kw">bias</span>)
+      <span class="kw">grads</span><span class="op">$</span><span class="kw">bias</span> <span class="op">&lt;-</span> <span class="kw">grad_output</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span>(dim = <span class="fl">0</span>)
+    
+    <span class="kw">grads</span>
+  }
+)
+</pre></div>
+<p>Here, we give an additional example of a function that is parametrized by non-Tensor arguments:</p>
+<div class="sourceCode" id="cb3"><pre class="downlit">
+<span class="kw">mul_constant</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/autograd_function.html">autograd_function</a></span>(
+  forward = <span class="fu">function</span>(<span class="kw">ctx</span>, <span class="kw">tensor</span>, <span class="kw">constant</span>) {
+    <span class="kw">ctx</span><span class="op">$</span><span class="fu">save_for_backward</span>(constant = <span class="kw">constant</span>)
+    <span class="kw">tensor</span> <span class="op">*</span> <span class="kw">constant</span>
+  },
+  backward = <span class="fu">function</span>(<span class="kw">ctx</span>, <span class="kw">grad_output</span>) {
+    <span class="kw">v</span> <span class="op">&lt;-</span> <span class="kw">ctx</span><span class="op">$</span><span class="kw">saved_variables</span>
+    <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(
+      tensor = <span class="kw">grad_output</span> <span class="op">*</span> <span class="kw">v</span><span class="op">$</span><span class="kw">constant</span>
+    )
+  }
+)
+</pre></div>
+<div class="sourceCode" id="cb4"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1</span>, requires_grad = <span class="fl">TRUE</span>)
+<span class="kw">o</span> <span class="op">&lt;-</span> <span class="fu">mul_constant</span>(<span class="kw">x</span>, <span class="fl">2</span>)
+<span class="kw">o</span><span class="op">$</span><span class="fu">backward</span>()
+<span class="kw">x</span><span class="op">$</span><span class="kw">grad</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  2</span>
+<span class="co">#&gt; [ CPUFloatType{1} ]</span>
+</pre></div>
+</div>
+  </div>
+
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+
+        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
+    </nav>
+</div>
+
+</div>
+
+
+
+      <footer><div class="copyright">
+  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+</div>
+
+<div class="pkgdown">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+</div>
+
+      </footer>
+</div>
+
+  
+
+
+  </body>
+</html>
diff --git a/static/docs/articles/extending-autograd_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/articles/extending-autograd_files/accessible-code-block-0.0.1/empty-anchor.js
new file mode 100644
index 000000000..ca349fd6a
--- /dev/null
+++ b/static/docs/articles/extending-autograd_files/accessible-code-block-0.0.1/empty-anchor.js
@@ -0,0 +1,15 @@
+// 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');
+      }
+    }
+  }
+});
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+  <div class="col-md-9 contents">
+    <div class="page-header toc-ignore">
+      <h1 data-toc-skip>Autograd: automatic differentiation</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/autograd.Rmd"><code>vignettes/getting-started/autograd.Rmd</code></a></small>
+      <div class="hidden name"><code>autograd.Rmd</code></div>
+
+    </div>
+
+    
+    
+<blockquote>
+<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/blitz/tensor_tutorial.html#sphx-glr-beginner-blitz-tensor-tutorial-py">here</a>. All credits goes to <a href="http://soumith.ch/">Soumith Chintala</a>.</p>
+</blockquote>
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
+</pre></div>
+<p>Central to all neural networks in torch is the autograd functionality. Let’s first briefly visit this, and we will then go to training our first neural network.</p>
+<p>Autograd provides automatic differentiation for all operations on Tensors. It is a define-by-run framework, which means that your backprop is defined by how your code is run, and that every single iteration can be different.</p>
+<p>Let us see this in more simple terms with some examples.</p>
+<div id="tensor" class="section level2">
+<h2 class="hasAnchor">
+<a href="#tensor" class="anchor"></a>Tensor</h2>
+<p><code>torch_tensor</code> is the central class of the package. If you set its attribute <code>$requires_grad</code> as <code>TRUE</code>, it starts to track all operations on it. When you finish your computation you can call <code>$backward()</code> and have all the gradients computed automatically. The gradient for this tensor will be accumulated into <code>$grad</code> attribute.</p>
+<p>To stop a tensor from tracking history, you can call <code>$detach()</code> to detach it from the computation history, and to prevent future computation from being tracked.</p>
+<p>To prevent tracking history (and using memory), you can also wrap the code block in <code>with_no_grad({&lt;code&gt;})</code>. This can be particularly helpful when evaluating a model because the model may have trainable parameters with <code>requires_grad=TRUE</code>, but for which we don’t need the gradients.</p>
+<p>There’s one more class which is very important for autograd implementation - a <code>autograd_function</code>.</p>
+<p>Tensor and Function are interconnected and build up an acyclic graph, that encodes a complete history of computation. Each tensor has a <code>$grad_fn</code> attribute that references an <code>autograd_function</code> that has created the Tensor (except for Tensors created by the user - their <code>grad_fn</code> is <code>NULL</code>).</p>
+<p>If you want to compute the derivatives, you can call <code>$backward()</code> on a Tensor. If Tensor is a scalar (i.e. it holds a one element data), you don’t need to specify any arguments to <code>backward()</code>, however if it has more elements, you need to specify a gradient argument that is a tensor of matching shape.</p>
+<p>Create a tensor and set <code>requires_grad=TRUE</code> to track computation with it:</p>
+<div class="sourceCode" id="cb2"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span>(<span class="fl">2</span>, <span class="fl">2</span>, requires_grad = <span class="fl">TRUE</span>)
+<span class="kw">x</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  1  1</span>
+<span class="co">#&gt;  1  1</span>
+<span class="co">#&gt; [ CPUFloatType{2,2} ]</span>
+</pre></div>
+<p>Do a tensor operation:</p>
+<div class="sourceCode" id="cb3"><pre class="downlit">
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="kw">x</span> <span class="op">+</span> <span class="fl">2</span>
+<span class="kw">y</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  3  3</span>
+<span class="co">#&gt;  3  3</span>
+<span class="co">#&gt; [ CPUFloatType{2,2} ]</span>
+</pre></div>
+<p><code>y</code> was created as a result of an operation, so it has a <code>grad_fn</code>.</p>
+<div class="sourceCode" id="cb4"><pre class="downlit">
+<span class="kw">y</span><span class="op">$</span><span class="kw">grad_fn</span>
+<span class="co">#&gt; AddBackward1</span>
+</pre></div>
+<p>Do more operations on <code>y</code></p>
+<div class="sourceCode" id="cb5"><pre class="downlit">
+<span class="kw">z</span> <span class="op">&lt;-</span> <span class="kw">y</span> <span class="op">*</span> <span class="kw">y</span> <span class="op">*</span> <span class="fl">3</span>
+<span class="kw">z</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  27  27</span>
+<span class="co">#&gt;  27  27</span>
+<span class="co">#&gt; [ CPUFloatType{2,2} ]</span>
+<span class="kw">out</span> <span class="op">&lt;-</span> <span class="kw">z</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>()
+<span class="kw">out</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt; 27</span>
+<span class="co">#&gt; [ CPUFloatType{} ]</span>
+</pre></div>
+<p><code>$requires_grad_( ... )</code> changes an existing Tensor’s <code>requires_grad</code> flag in-place. The input flag defaults to <code>FALSE</code> if not given.</p>
+<div class="sourceCode" id="cb6"><pre class="downlit">
+<span class="kw">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">2</span>, <span class="fl">2</span>)
+<span class="kw">a</span> <span class="op">&lt;-</span> (<span class="kw">a</span> <span class="op">*</span> <span class="fl">3</span>) <span class="op">/</span> (<span class="kw">a</span> <span class="op">-</span> <span class="fl">1</span>)
+<span class="kw">a</span><span class="op">$</span><span class="kw">requires_grad</span>
+<span class="co">#&gt; [1] FALSE</span>
+<span class="kw">a</span><span class="op">$</span><span class="fu">requires_grad_</span>(<span class="fl">TRUE</span>)
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt; -0.4350  1.4882</span>
+<span class="co">#&gt; -0.5849  9.3457</span>
+<span class="co">#&gt; [ CPUFloatType{2,2} ]</span>
+<span class="kw">a</span><span class="op">$</span><span class="kw">requires_grad</span>
+<span class="co">#&gt; [1] TRUE</span>
+<span class="kw">b</span> <span class="op">&lt;-</span> (<span class="kw">a</span> <span class="op">*</span> <span class="kw">a</span>)<span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span>()
+<span class="kw">b</span><span class="op">$</span><span class="kw">grad_fn</span>
+<span class="co">#&gt; SumBackward0</span>
+</pre></div>
+</div>
+<div id="gradients" class="section level2">
+<h2 class="hasAnchor">
+<a href="#gradients" class="anchor"></a>Gradients</h2>
+<p>Let’s backprop now. Because out contains a single scalar, <code>out$backward()</code> is equivalent to <code>out$backward(torch.tensor(1.))</code>.</p>
+<div class="sourceCode" id="cb7"><pre class="downlit">
+<span class="kw">out</span><span class="op">$</span><span class="fu">backward</span>()
+</pre></div>
+<p>Print gradients d(out)/dx</p>
+<div class="sourceCode" id="cb8"><pre class="downlit">
+<span class="kw">x</span><span class="op">$</span><span class="kw">grad</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  4.5000  4.5000</span>
+<span class="co">#&gt;  4.5000  4.5000</span>
+<span class="co">#&gt; [ CPUFloatType{2,2} ]</span>
+</pre></div>
+<p>You should have got a matrix of <code>4.5</code>. Let’s call the <code>out</code> <em>Tensor</em> <span class="math inline">\(o\)</span>.</p>
+<p>We have that <span class="math inline">\(o = \frac{1}{4}\sum_i z_i\)</span>, <span class="math inline">\(z_i = 3(x_i+2)^2\)</span> and <span class="math inline">\(z_i\bigr\rvert_{x_i=1} = 27\)</span>. Therefore, <span class="math inline">\(\frac{\partial o}{\partial x_i} = \frac{3}{2}(x_i+2)\)</span>, hence <span class="math inline">\(\frac{\partial o}{\partial x_i}\bigr\rvert_{x_i=1} = \frac{9}{2} = 4.5\)</span>.</p>
+<p>Mathematically, if you have a vector valued function <span class="math inline">\(\vec{y}=f(\vec{x})\)</span>, then the gradient of <span class="math inline">\(\vec{y}\)</span> with respect to <span class="math inline">\(\vec{x}\)</span> is a Jacobian matrix:</p>
+<p><span class="math display">\[
+  J=\left(\begin{array}{ccc}
+   \frac{\partial y_{1}}{\partial x_{1}} &amp; \cdots &amp; \frac{\partial y_{1}}{\partial x_{n}}\\
+   \vdots &amp; \ddots &amp; \vdots\\
+   \frac{\partial y_{m}}{\partial x_{1}} &amp; \cdots &amp; \frac{\partial y_{m}}{\partial x_{n}}
+   \end{array}\right)
+\]</span></p>
+<p>Generally speaking, <code>autograd</code> is an engine for computing vector-Jacobian product. That is, given any vector <span class="math inline">\(v=\left(\begin{array}{cccc} v_{1} &amp; v_{2} &amp; \cdots &amp; v_{m}\end{array}\right)^{T}\)</span>, compute the product <span class="math inline">\(v^{T}\cdot J\)</span>. If <span class="math inline">\(v\)</span> happens to be the gradient of a scalar function <span class="math inline">\(l=g\left(\vec{y}\right)\)</span>, that is, <span class="math inline">\(v=\left(\begin{array}{ccc}\frac{\partial l}{\partial y_{1}} &amp; \cdots &amp; \frac{\partial l}{\partial y_{m}}\end{array}\right)^{T}\)</span>, then by the chain rule, the vector-Jacobian product would be the gradient of <span class="math inline">\(l\)</span> with respect to <span class="math inline">\(\vec{x}\)</span>:</p>
+<p><span class="math display">\[
+  J^{T}\cdot v=\left(\begin{array}{ccc}
+   \frac{\partial y_{1}}{\partial x_{1}} &amp; \cdots &amp; \frac{\partial y_{m}}{\partial x_{1}}\\
+   \vdots &amp; \ddots &amp; \vdots\\
+   \frac{\partial y_{1}}{\partial x_{n}} &amp; \cdots &amp; \frac{\partial y_{m}}{\partial x_{n}}
+   \end{array}\right)\left(\begin{array}{c}
+   \frac{\partial l}{\partial y_{1}}\\
+   \vdots\\
+   \frac{\partial l}{\partial y_{m}}
+   \end{array}\right)=\left(\begin{array}{c}
+   \frac{\partial l}{\partial x_{1}}\\
+   \vdots\\
+   \frac{\partial l}{\partial x_{n}}
+   \end{array}\right)
+\]</span></p>
+<p>(Note that <span class="math inline">\(v^{T}\cdot J\)</span> gives a row vector which can be treated as a column vector by taking <span class="math inline">\(J^{T}\cdot v\)</span>.)</p>
+<p>This characteristic of vector-Jacobian product makes it very convenient to feed external gradients into a model that has non-scalar output.</p>
+<p>Now let’s take a look at an example of vector-Jacobian product:</p>
+<div class="sourceCode" id="cb9"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">3</span>, requires_grad=<span class="fl">TRUE</span>)
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="fl">100</span> <span class="op">*</span> <span class="kw">x</span>
+<span class="kw">y</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  -50.4960</span>
+<span class="co">#&gt;  -28.4113</span>
+<span class="co">#&gt;  101.7135</span>
+<span class="co">#&gt; [ CPUFloatType{3} ]</span>
+</pre></div>
+<p>Now in this case y is no longer a scalar. <code>autograd</code> could not compute the full Jacobian directly, but if we just want the vector-Jacobian product, simply pass the vector to backward as argument:</p>
+<div class="sourceCode" id="cb10"><pre class="downlit">
+<span class="kw">v</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">0.1</span>, <span class="fl">1.0</span>, <span class="fl">0.0001</span>))
+<span class="kw">y</span><span class="op">$</span><span class="fu">backward</span>(<span class="kw">v</span>)
+
+<span class="kw">x</span><span class="op">$</span><span class="kw">grad</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  1.0000e+01</span>
+<span class="co">#&gt;  1.0000e+02</span>
+<span class="co">#&gt;  1.0000e-02</span>
+<span class="co">#&gt; [ CPUFloatType{3} ]</span>
+</pre></div>
+<p>You can also stop autograd from tracking history on Tensors with <code>$requires_grad=TRUE</code> either by wrapping the code block in with <code><a href="../../reference/with_no_grad.html">with_no_grad()</a></code>:</p>
+<div class="sourceCode" id="cb11"><pre class="downlit">
+<span class="kw">x</span><span class="op">$</span><span class="kw">requires_grad</span>
+<span class="co">#&gt; [1] TRUE</span>
+(<span class="kw">x</span> <span class="op">**</span> <span class="fl">2</span>)<span class="op">$</span><span class="kw">requires_grad</span>
+<span class="co">#&gt; [1] TRUE</span>
+
+<span class="fu"><a href="../../reference/with_no_grad.html">with_no_grad</a></span>({
+  <span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>((<span class="kw">x</span> <span class="op">**</span> <span class="fl">2</span>)<span class="op">$</span><span class="kw">requires_grad</span>)
+})
+<span class="co">#&gt; [1] FALSE</span>
+</pre></div>
+<div class="sourceCode" id="cb12"><pre class="downlit">
+<span class="kw">x</span><span class="op">$</span><span class="kw">requires_grad</span>
+<span class="co">#&gt; [1] TRUE</span>
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="kw">x</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/detach.html">detach</a></span>()
+<span class="kw">y</span><span class="op">$</span><span class="kw">requires_grad</span>
+<span class="co">#&gt; [1] FALSE</span>
+<span class="kw">x</span><span class="op">$</span><span class="fu">eq</span>(<span class="kw">y</span>)<span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/all.html">all</a></span>()
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt; 1</span>
+<span class="co">#&gt; [ CPUBoolType{} ]</span>
+</pre></div>
+<p>Read Later:</p>
+<p>Document about <code><a href="../../reference/autograd_function.html">help(autograd_function)</a></code>, <code><a href="../../articles/using-autograd.html">vignette("using-autograd")</a></code>, <code><a href="../../articles/extending-autograd.html">vignette("extending-autograd")</a></code>.</p>
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diff --git a/static/docs/articles/getting-started/autograd_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/articles/getting-started/autograd_files/accessible-code-block-0.0.1/empty-anchor.js
new file mode 100644
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--- /dev/null
+++ b/static/docs/articles/getting-started/autograd_files/accessible-code-block-0.0.1/empty-anchor.js
@@ -0,0 +1,15 @@
+// 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");
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diff --git a/static/docs/articles/getting-started/control-flow-and-weight-sharing.html b/static/docs/articles/getting-started/control-flow-and-weight-sharing.html
new file mode 100644
index 000000000..89a344091
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@@ -0,0 +1,292 @@
+<!DOCTYPE html>
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+      <a href="../../articles/getting-started/what-is-torch.html">What's torch?</a>
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+
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+      </header><script src="control-flow-and-weight-sharing_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header toc-ignore">
+      <h1 data-toc-skip>Control flow &amp; Weight sharing</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/control-flow-and-weight-sharing.Rmd"><code>vignettes/getting-started/control-flow-and-weight-sharing.Rmd</code></a></small>
+      <div class="hidden name"><code>control-flow-and-weight-sharing.Rmd</code></div>
+
+    </div>
+
+    
+    
+<blockquote>
+<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
+</blockquote>
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
+</pre></div>
+<p>As an example of dynamic graphs and weight sharing, we implement a very strange model: a fully-connected ReLU network that on each forward pass chooses a random number between 1 and 4 and uses that many hidden layers, reusing the same weights multiple times to compute the innermost hidden layers.</p>
+<p>For this model we can use normal R flow control to implement the loop, and we can implement weight sharing among the innermost layers by simply reusing the same Module multiple times when defining the forward pass.</p>
+<p>We can easily implement this model using <code>nn_module</code>:</p>
+<div class="sourceCode" id="cb2"><pre class="downlit">
+<span class="kw">dynamic_net</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_module.html">nn_module</a></span>(
+   <span class="st">"dynamic_net"</span>,
+   <span class="co"># In the constructor we construct three nn_linear instances that we will use</span>
+   <span class="co"># in the forward pass.</span>
+   initialize = <span class="fu">function</span>(<span class="kw">D_in</span>, <span class="kw">H</span>, <span class="kw">D_out</span>) {
+      <span class="kw">self</span><span class="op">$</span><span class="kw">input_linear</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="kw">D_in</span>, <span class="kw">H</span>)
+      <span class="kw">self</span><span class="op">$</span><span class="kw">middle_linear</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="kw">H</span>, <span class="kw">H</span>)
+      <span class="kw">self</span><span class="op">$</span><span class="kw">output_linear</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="kw">H</span>, <span class="kw">D_out</span>)
+   },
+   <span class="co"># For the forward pass of the model, we randomly choose either 0, 1, 2, or 3</span>
+   <span class="co"># and reuse the middle_linear Module that many times to compute hidden layer</span>
+   <span class="co"># representations.</span>
+   <span class="co"># </span>
+   <span class="co"># Since each forward pass builds a dynamic computation graph, we can use normal</span>
+   <span class="co"># R control-flow operators like loops or conditional statements when</span>
+   <span class="co"># defining the forward pass of the model.</span>
+   <span class="co"># </span>
+   <span class="co"># Here we also see that it is perfectly safe to reuse the same Module many</span>
+   <span class="co"># times when defining a computational graph. This is a big improvement from Lua</span>
+   <span class="co"># Torch, where each Module could be used only once.</span>
+   forward = <span class="fu">function</span>(<span class="kw">x</span>) {
+      <span class="kw">h_relu</span> <span class="op">&lt;-</span> <span class="kw">self</span><span class="op">$</span><span class="fu">input_linear</span>(<span class="kw">x</span>)<span class="op">$</span><span class="fu">clamp</span>(min = <span class="fl">0</span>)
+      <span class="co">for</span> (<span class="kw">i</span> <span class="co">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/sample.html">sample.int</a></span>(<span class="fl">4</span>, size = <span class="fl">1</span>))) {
+         <span class="kw">h_relu</span> <span class="op">&lt;-</span> <span class="kw">self</span><span class="op">$</span><span class="fu">middle_linear</span>(<span class="kw">h_relu</span>)<span class="op">$</span><span class="fu">clamp</span>(min=<span class="fl">0</span>)
+      }
+      <span class="kw">y_pred</span> <span class="op">&lt;-</span> <span class="kw">self</span><span class="op">$</span><span class="fu">output_linear</span>(<span class="kw">h_relu</span>)
+      <span class="kw">y_pred</span>
+   }
+)
+
+
+<span class="co">if</span> (<span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span>()) {
+   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cuda"</span>)
+} <span class="co">else</span> {
+   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cpu"</span>)
+}
+   
+<span class="co"># N is batch size; D_in is input dimension;</span>
+<span class="co"># H is hidden dimension; D_out is output dimension.</span>
+<span class="kw">N</span> <span class="op">&lt;-</span> <span class="fl">64</span>
+<span class="kw">D_in</span> <span class="op">&lt;-</span> <span class="fl">1000</span>
+<span class="kw">H</span> <span class="op">&lt;-</span> <span class="fl">100</span>
+<span class="kw">D_out</span> <span class="op">&lt;-</span> <span class="fl">10</span>
+
+<span class="co"># Create random input and output data</span>
+<span class="co"># Setting requires_grad=FALSE (the default) indicates that we do not need to </span>
+<span class="co"># compute gradients with respect to these Tensors during the backward pass.</span>
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_in</span>, device=<span class="kw">device</span>)
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_out</span>, device=<span class="kw">device</span>)
+
+<span class="co"># Construct our model by instantiating the class defined above</span>
+<span class="kw">model</span> <span class="op">&lt;-</span> <span class="fu">dynamic_net</span>(<span class="kw">D_in</span>, <span class="kw">H</span>, <span class="kw">D_out</span>)
+
+<span class="co"># The nn package also contains definitions of popular loss functions; in this</span>
+<span class="co"># case we will use Mean Squared Error (MSE) as our loss function.</span>
+<span class="kw">loss_fn</span> <span class="op">&lt;-</span> <span class="kw">nnf_mse_loss</span>
+
+<span class="co"># Use the optim package to define an Optimizer that will update the weights of</span>
+<span class="co"># the model for us. Here we will use Adam; the optim package contains many other</span>
+<span class="co"># optimization algorithms. The first argument to the Adam constructor tells the</span>
+<span class="co"># optimizer which Tensors it should update.</span>
+<span class="kw">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-4</span>
+<span class="kw">optimizer</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/optim_sgd.html">optim_sgd</a></span>(<span class="kw">model</span><span class="op">$</span><span class="kw">parameters</span>, lr=<span class="kw">learning_rate</span>, momentum = <span class="fl">0.9</span>)
+
+<span class="co">for</span> (<span class="kw">t</span> <span class="co">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fl">500</span>)) {
+   <span class="co"># Forward pass: compute predicted y by passing x to the model. Module objects</span>
+   <span class="co"># can be called like functions. When doing so you pass a Tensor of input</span>
+   <span class="co"># data to the Module and it produces a Tensor of output data.</span>
+   <span class="kw">y_pred</span> <span class="op">&lt;-</span> <span class="fu">model</span>(<span class="kw">x</span>)
+   
+   <span class="co"># Compute and print loss. We pass Tensors containing the predicted and true</span>
+   <span class="co"># values of y, and the loss function returns a Tensor containing the</span>
+   <span class="co"># loss.</span>
+   <span class="kw">loss</span> <span class="op">&lt;-</span> <span class="fu">loss_fn</span>(<span class="kw">y_pred</span>, <span class="kw">y</span>)
+   <span class="co">if</span> (<span class="kw">t</span> <span class="op">%%</span> <span class="fl">100</span> <span class="op">==</span> <span class="fl">0</span> <span class="op">||</span> <span class="kw">t</span> <span class="op">==</span> <span class="fl">1</span>)
+      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="st">"Step:"</span>, <span class="kw">t</span>, <span class="st">":"</span>, <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="kw">loss</span>), <span class="st">"\n"</span>)
+   
+   <span class="co"># Before the backward pass, use the optimizer object to zero all of the</span>
+   <span class="co"># gradients for the variables it will update (which are the learnable</span>
+   <span class="co"># weights of the model). This is because by default, gradients are</span>
+   <span class="co"># accumulated in buffers( i.e, not overwritten) whenever $backward()</span>
+   <span class="co"># is called. Checkout docs of `autograd_backward` for more details.</span>
+   <span class="kw">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span>()
+
+   <span class="co"># Backward pass: compute gradient of the loss with respect to model</span>
+   <span class="co"># parameters</span>
+   <span class="kw">loss</span><span class="op">$</span><span class="fu">backward</span>()
+
+   <span class="co"># Calling the step function on an Optimizer makes an update to its</span>
+   <span class="co"># parameters</span>
+   <span class="kw">optimizer</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/stats/step.html">step</a></span>()
+}
+<span class="co">#&gt; Step: 1 : 1.054659 </span>
+<span class="co">#&gt; Step: 100 : 1.05705 </span>
+<span class="co">#&gt; Step: 200 : 1.048708 </span>
+<span class="co">#&gt; Step: 300 : 1.052647 </span>
+<span class="co">#&gt; Step: 400 : 1.042869 </span>
+<span class="co">#&gt; Step: 500 : 1.039991</span>
+</pre></div>
+  </div>
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diff --git a/static/docs/articles/getting-started/control-flow-and-weight-sharing_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/articles/getting-started/control-flow-and-weight-sharing_files/accessible-code-block-0.0.1/empty-anchor.js
new file mode 100644
index 000000000..ca349fd6a
--- /dev/null
+++ b/static/docs/articles/getting-started/control-flow-and-weight-sharing_files/accessible-code-block-0.0.1/empty-anchor.js
@@ -0,0 +1,15 @@
+// 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");
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diff --git a/static/docs/articles/getting-started/custom-nn.html b/static/docs/articles/getting-started/custom-nn.html
new file mode 100644
index 000000000..671685baa
--- /dev/null
+++ b/static/docs/articles/getting-started/custom-nn.html
@@ -0,0 +1,276 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
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+      <a href="../../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
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+      </header><script src="custom-nn_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header toc-ignore">
+      <h1 data-toc-skip>Custom nn modules</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/custom-nn.Rmd"><code>vignettes/getting-started/custom-nn.Rmd</code></a></small>
+      <div class="hidden name"><code>custom-nn.Rmd</code></div>
+
+    </div>
+
+    
+    
+<blockquote>
+<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
+</blockquote>
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
+</pre></div>
+<p>Sometimes you will want to specify models that are more complex than a sequence of existing Modules; for these cases you can define your own Modules by using <code>nn_module</code> function and defining a forward which receives input Tensors and produces output Tensors using other modules or other autograd operations on Tensors.</p>
+<p>In this example we implement our two-layer network as a custom Module subclass:</p>
+<div class="sourceCode" id="cb2"><pre class="downlit">
+<span class="kw">two_layer_net</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_module.html">nn_module</a></span>(
+   <span class="st">"two_layer_net"</span>,
+   initialize = <span class="fu">function</span>(<span class="kw">D_in</span>, <span class="kw">H</span>, <span class="kw">D_out</span>) {
+      <span class="kw">self</span><span class="op">$</span><span class="kw">linear1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="kw">D_in</span>, <span class="kw">H</span>)
+      <span class="kw">self</span><span class="op">$</span><span class="kw">linear2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="kw">H</span>, <span class="kw">D_out</span>)
+   },
+   forward = <span class="fu">function</span>(<span class="kw">x</span>) {
+      <span class="kw">x</span> <span class="op">%&gt;%</span> 
+         <span class="kw">self</span><span class="op">$</span><span class="fu">linear1</span>() <span class="op">%&gt;%</span> 
+         <span class="fu"><a href="../../reference/nnf_relu.html">nnf_relu</a></span>() <span class="op">%&gt;%</span> 
+         <span class="kw">self</span><span class="op">$</span><span class="fu">linear2</span>()
+   }
+)
+
+
+<span class="co">if</span> (<span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span>()) {
+   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cuda"</span>)
+} <span class="co">else</span> {
+   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cpu"</span>)
+}
+   
+<span class="co"># N is batch size; D_in is input dimension;</span>
+<span class="co"># H is hidden dimension; D_out is output dimension.</span>
+<span class="kw">N</span> <span class="op">&lt;-</span> <span class="fl">64</span>
+<span class="kw">D_in</span> <span class="op">&lt;-</span> <span class="fl">1000</span>
+<span class="kw">H</span> <span class="op">&lt;-</span> <span class="fl">100</span>
+<span class="kw">D_out</span> <span class="op">&lt;-</span> <span class="fl">10</span>
+
+<span class="co"># Create random input and output data</span>
+<span class="co"># Setting requires_grad=FALSE (the default) indicates that we do not need to </span>
+<span class="co"># compute gradients with respect to these Tensors during the backward pass.</span>
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_in</span>, device=<span class="kw">device</span>)
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_out</span>, device=<span class="kw">device</span>)
+
+<span class="co"># Construct our model by instantiating the class defined above</span>
+<span class="kw">model</span> <span class="op">&lt;-</span> <span class="fu">two_layer_net</span>(<span class="kw">D_in</span>, <span class="kw">H</span>, <span class="kw">D_out</span>)
+
+<span class="co"># The nn package also contains definitions of popular loss functions; in this</span>
+<span class="co"># case we will use Mean Squared Error (MSE) as our loss function.</span>
+<span class="kw">loss_fn</span> <span class="op">&lt;-</span> <span class="kw">nnf_mse_loss</span>
+
+<span class="co"># Use the optim package to define an Optimizer that will update the weights of</span>
+<span class="co"># the model for us. Here we will use Adam; the optim package contains many other</span>
+<span class="co"># optimization algorithms. The first argument to the Adam constructor tells the</span>
+<span class="co"># optimizer which Tensors it should update.</span>
+<span class="kw">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-4</span>
+<span class="kw">optimizer</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/optim_sgd.html">optim_sgd</a></span>(<span class="kw">model</span><span class="op">$</span><span class="kw">parameters</span>, lr=<span class="kw">learning_rate</span>)
+
+<span class="co">for</span> (<span class="kw">t</span> <span class="co">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fl">500</span>)) {
+   <span class="co"># Forward pass: compute predicted y by passing x to the model. Module objects</span>
+   <span class="co"># can be called like functions. When doing so you pass a Tensor of input</span>
+   <span class="co"># data to the Module and it produces a Tensor of output data.</span>
+   <span class="kw">y_pred</span> <span class="op">&lt;-</span> <span class="fu">model</span>(<span class="kw">x</span>)
+   
+   <span class="co"># Compute and print loss. We pass Tensors containing the predicted and true</span>
+   <span class="co"># values of y, and the loss function returns a Tensor containing the</span>
+   <span class="co"># loss.</span>
+   <span class="kw">loss</span> <span class="op">&lt;-</span> <span class="fu">loss_fn</span>(<span class="kw">y_pred</span>, <span class="kw">y</span>)
+   <span class="co">if</span> (<span class="kw">t</span> <span class="op">%%</span> <span class="fl">100</span> <span class="op">==</span> <span class="fl">0</span> <span class="op">||</span> <span class="kw">t</span> <span class="op">==</span> <span class="fl">1</span>)
+      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="st">"Step:"</span>, <span class="kw">t</span>, <span class="st">":"</span>, <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="kw">loss</span>), <span class="st">"\n"</span>)
+   
+   <span class="co"># Before the backward pass, use the optimizer object to zero all of the</span>
+   <span class="co"># gradients for the variables it will update (which are the learnable</span>
+   <span class="co"># weights of the model). This is because by default, gradients are</span>
+   <span class="co"># accumulated in buffers( i.e, not overwritten) whenever $backward()</span>
+   <span class="co"># is called. Checkout docs of `autograd_backward` for more details.</span>
+   <span class="kw">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span>()
+
+   <span class="co"># Backward pass: compute gradient of the loss with respect to model</span>
+   <span class="co"># parameters</span>
+   <span class="kw">loss</span><span class="op">$</span><span class="fu">backward</span>()
+
+   <span class="co"># Calling the step function on an Optimizer makes an update to its</span>
+   <span class="co"># parameters</span>
+   <span class="kw">optimizer</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/stats/step.html">step</a></span>()
+}
+<span class="co">#&gt; Step: 1 : 1.04065 </span>
+<span class="co">#&gt; Step: 100 : 1.026708 </span>
+<span class="co">#&gt; Step: 200 : 1.013019 </span>
+<span class="co">#&gt; Step: 300 : 0.9996911 </span>
+<span class="co">#&gt; Step: 400 : 0.986709 </span>
+<span class="co">#&gt; Step: 500 : 0.9740159</span>
+</pre></div>
+<p>In the <a href="control-flow-and-weight-sharing.html">next example</a> we will about dynamic graphs in torch.</p>
+  </div>
+
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+
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+
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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> 1.6.1.</p>
+</div>
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+</div>
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diff --git a/static/docs/articles/getting-started/custom-nn_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/articles/getting-started/custom-nn_files/accessible-code-block-0.0.1/empty-anchor.js
new file mode 100644
index 000000000..ca349fd6a
--- /dev/null
+++ b/static/docs/articles/getting-started/custom-nn_files/accessible-code-block-0.0.1/empty-anchor.js
@@ -0,0 +1,15 @@
+// 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');
+      }
+    }
+  }
+});
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+  <div class="col-md-9 contents">
+    <div class="page-header toc-ignore">
+      <h1 data-toc-skip>Neural networks</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/neural-networks.Rmd"><code>vignettes/getting-started/neural-networks.Rmd</code></a></small>
+      <div class="hidden name"><code>neural-networks.Rmd</code></div>
+
+    </div>
+
+    
+    
+<blockquote>
+<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/blitz/tensor_tutorial.html#sphx-glr-beginner-blitz-tensor-tutorial-py">here</a>. All credits goes to <a href="http://soumith.ch/">Soumith Chintala</a>.</p>
+</blockquote>
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
+</pre></div>
+<p>Neural networks can be constructed using the <code>nn</code> functionality.</p>
+<p>Now that you had a glimpse of <code>autograd</code>, <code>nn</code> depends on <code>autograd</code> to define models and differentiate them. An nn.Module contains layers, and a method <code>forward(input)</code> that returns the output.</p>
+<p>For example, look at this network that classifies digit images:</p>
+<div class="figure">
+<img src="assets/mnist.png" alt=""><p class="caption">Convnet for mnist classification</p>
+</div>
+<p>It is a simple feed-forward network. It takes the input, feeds it through several layers one after the other, and then finally gives the output.</p>
+<p>A typical training procedure for a neural network is as follows:</p>
+<ul>
+<li>Define the neural network that has some learnable parameters (or weights)</li>
+<li>Iterate over a dataset of inputs</li>
+<li>Process input through the network</li>
+<li>Compute the loss (how far is the output from being correct)</li>
+<li>Propagate gradients back into the network’s parameters</li>
+<li>Update the weights of the network, typically using a simple update rule: <code>weight = weight - learning_rate * gradient</code>.</li>
+</ul>
+<div id="define-the-network" class="section level2">
+<h2 class="hasAnchor">
+<a href="#define-the-network" class="anchor"></a>Define the network</h2>
+<p>Let’s define this network:</p>
+<div class="sourceCode" id="cb2"><pre class="downlit">
+<span class="kw">Net</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_module.html">nn_module</a></span>(
+  initialize = <span class="fu">function</span>() {
+    <span class="kw">self</span><span class="op">$</span><span class="kw">conv1</span> <span class="op">=</span> <span class="fu"><a href="../../reference/nn_conv2d.html">nn_conv2d</a></span>(<span class="fl">1</span>, <span class="fl">6</span>, <span class="fl">3</span>)
+    <span class="kw">self</span><span class="op">$</span><span class="kw">conv2</span> <span class="op">=</span> <span class="fu"><a href="../../reference/nn_conv2d.html">nn_conv2d</a></span>(<span class="fl">6</span>, <span class="fl">16</span>, <span class="fl">3</span>)
+    <span class="co"># an affine operation: y = Wx + b</span>
+    <span class="kw">self</span><span class="op">$</span><span class="kw">fc1</span> <span class="op">=</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="fl">16</span> <span class="op">*</span> <span class="fl">6</span> <span class="op">*</span> <span class="fl">6</span>, <span class="fl">120</span>)  <span class="co"># 6*6 from image dimension</span>
+    <span class="kw">self</span><span class="op">$</span><span class="kw">fc2</span> <span class="op">=</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="fl">120</span>, <span class="fl">84</span>)
+    <span class="kw">self</span><span class="op">$</span><span class="kw">fc3</span> <span class="op">=</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="fl">84</span>, <span class="fl">10</span>)
+  },
+  forward = <span class="fu">function</span>(<span class="kw">x</span>) {
+    <span class="kw">x</span> <span class="op">%&gt;%</span> 
+      
+      <span class="kw">self</span><span class="op">$</span><span class="fu">conv1</span>() <span class="op">%&gt;%</span> 
+      <span class="fu"><a href="../../reference/nnf_relu.html">nnf_relu</a></span>() <span class="op">%&gt;%</span> 
+      <span class="fu"><a href="../../reference/nnf_max_pool2d.html">nnf_max_pool2d</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>,<span class="fl">2</span>)) <span class="op">%&gt;%</span> 
+      
+      <span class="kw">self</span><span class="op">$</span><span class="fu">conv2</span>() <span class="op">%&gt;%</span> 
+      <span class="fu"><a href="../../reference/nnf_relu.html">nnf_relu</a></span>() <span class="op">%&gt;%</span> 
+      <span class="fu"><a href="../../reference/nnf_max_pool2d.html">nnf_max_pool2d</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>,<span class="fl">2</span>)) <span class="op">%&gt;%</span> 
+      
+      <span class="fu"><a href="../../reference/torch_flatten.html">torch_flatten</a></span>(start_dim = <span class="fl">2</span>) <span class="op">%&gt;%</span> 
+      
+      <span class="kw">self</span><span class="op">$</span><span class="fu">fc1</span>() <span class="op">%&gt;%</span> 
+      <span class="fu"><a href="../../reference/nnf_relu.html">nnf_relu</a></span>() <span class="op">%&gt;%</span> 
+      
+      <span class="kw">self</span><span class="op">$</span><span class="fu">fc2</span>() <span class="op">%&gt;%</span> 
+      <span class="fu"><a href="../../reference/nnf_relu.html">nnf_relu</a></span>() <span class="op">%&gt;%</span> 
+      
+      <span class="kw">self</span><span class="op">$</span><span class="fu">fc3</span>()
+  }
+)
+
+<span class="kw">net</span> <span class="op">&lt;-</span> <span class="fu">Net</span>()
+</pre></div>
+<p>You just have to define the <code>forward</code> function, and the <code>backward</code> function (where gradients are computed) is automatically defined for you using <code>autograd.</code> You can use any of the Tensor operations in the <code>forward</code> function.</p>
+<p>The learnable parameters of a model are returned by <code>net$parameters</code>.</p>
+<div class="sourceCode" id="cb3"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/utils/str.html">str</a></span>(<span class="kw">net</span><span class="op">$</span><span class="kw">parameters</span>)
+<span class="co">#&gt; List of 10</span>
+<span class="co">#&gt;  $ conv1.weight:Float [1:6, 1:1, 1:3, 1:3]</span>
+<span class="co">#&gt;  $ conv1.bias  :Float [1:6]</span>
+<span class="co">#&gt;  $ conv2.weight:Float [1:16, 1:6, 1:3, 1:3]</span>
+<span class="co">#&gt;  $ conv2.bias  :Float [1:16]</span>
+<span class="co">#&gt;  $ fc1.weight  :Float [1:120, 1:576]</span>
+<span class="co">#&gt;  $ fc1.bias    :Float [1:120]</span>
+<span class="co">#&gt;  $ fc2.weight  :Float [1:84, 1:120]</span>
+<span class="co">#&gt;  $ fc2.bias    :Float [1:84]</span>
+<span class="co">#&gt;  $ fc3.weight  :Float [1:10, 1:84]</span>
+<span class="co">#&gt;  $ fc3.bias    :Float [1:10]</span>
+</pre></div>
+<p>Let’s try a random 32x32 input. Note: expected input size of this net (LeNet) is 32x32. To use this net on the MNIST dataset, please resize the images from the dataset to 32x32.</p>
+<div class="sourceCode" id="cb4"><pre class="downlit">
+<span class="kw">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">32</span>, <span class="fl">32</span>)
+<span class="kw">out</span> <span class="op">&lt;-</span> <span class="fu">net</span>(<span class="kw">input</span>)
+<span class="kw">out</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt; -0.0560  0.0916  0.0401 -0.1081 -0.0183 -0.0508  0.1250 -0.0574  0.0058  0.0025</span>
+<span class="co">#&gt; [ CPUFloatType{1,10} ]</span>
+</pre></div>
+<p>Zero the gradient buffers of all parameters and backprops with random gradients:</p>
+<div class="sourceCode" id="cb5"><pre class="downlit">
+<span class="kw">net</span><span class="op">$</span><span class="fu">zero_grad</span>()
+<span class="kw">out</span><span class="op">$</span><span class="fu">backward</span>(<span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">1</span>, <span class="fl">10</span>))
+</pre></div>
+<blockquote>
+<p><strong>Note</strong>: <code>nn</code> only supports mini-batches. The entire torch.nn package only supports inputs that are a mini-batch of samples, and not a single sample. For example, <code>nn_conv2d</code> will take in a 4D Tensor of nSamples x nChannels x Height x Width. If you have a single sample, just use <code>input$unsqueeze(1)</code> to add a fake batch dimension.</p>
+</blockquote>
+<p>Before proceeding further, let’s recap all the classes you’ve seen so far.</p>
+<div id="recap" class="section level3">
+<h3 class="hasAnchor">
+<a href="#recap" class="anchor"></a>Recap</h3>
+<ul>
+<li><p><code>torch_tensor</code> - A multi-dimensional array with support for autograd operations like <code>backward()</code>. Also holds the gradient w.r.t. the tensor.</p></li>
+<li><p><code>nn_module</code> - Neural network module. Convenient way of encapsulating parameters, with helpers for moving them to GPU, exporting, loading, etc.</p></li>
+<li><p><code>nn_parameter</code> - A kind of Tensor, that is automatically registered as a parameter when assigned as an attribute to a Module.</p></li>
+<li><p><code>autograd_function</code> - Implements forward and backward definitions of an autograd operation. Every Tensor operation creates at least a single Function node that connects to functions that created a Tensor and encodes its history.</p></li>
+</ul>
+</div>
+<div id="at-this-point-we-covered" class="section level3">
+<h3 class="hasAnchor">
+<a href="#at-this-point-we-covered" class="anchor"></a>At this point, we covered</h3>
+<ul>
+<li>Defining a neural network</li>
+<li>Processing inputs and calling backward</li>
+</ul>
+</div>
+<div id="still-left" class="section level3">
+<h3 class="hasAnchor">
+<a href="#still-left" class="anchor"></a>Still left</h3>
+<ul>
+<li>Computing the loss</li>
+<li>Updating the weights of the network</li>
+</ul>
+</div>
+</div>
+<div id="loss-function" class="section level2">
+<h2 class="hasAnchor">
+<a href="#loss-function" class="anchor"></a>Loss function</h2>
+<p>A loss function takes the (output, target) pair of inputs, and computes a value that estimates how far away the output is from the target.</p>
+<p>There are several different loss functions under the nn package . A simple loss is: <code>nnf_mse_loss</code> which computes the mean-squared error between the input and the target.</p>
+<p>For example:</p>
+<div class="sourceCode" id="cb6"><pre class="downlit">
+<span class="kw">output</span> <span class="op">&lt;-</span> <span class="fu">net</span>(<span class="kw">input</span>)
+<span class="kw">target</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">10</span>)  <span class="co"># a dummy target, for example</span>
+<span class="kw">target</span> <span class="op">&lt;-</span> <span class="kw">target</span><span class="op">$</span><span class="fu">view</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1</span>, <span class="op">-</span><span class="fl">1</span>))  <span class="co"># make it the same shape as output</span>
+
+<span class="kw">loss</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nnf_mse_loss.html">nnf_mse_loss</a></span>(<span class="kw">output</span>, <span class="kw">target</span>)
+<span class="kw">loss</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt; 0.388282</span>
+<span class="co">#&gt; [ CPUFloatType{} ]</span>
+</pre></div>
+<p>Now, if you follow loss in the backward direction, using its <code>$grad_fn</code> attribute, you will see a graph of computations that looks like this:</p>
+<pre><code>input -&gt; conv2d -&gt; relu -&gt; maxpool2d -&gt; conv2d -&gt; relu -&gt; maxpool2d
+      -&gt; view -&gt; linear -&gt; relu -&gt; linear -&gt; relu -&gt; linear
+      -&gt; MSELoss
+      -&gt; loss</code></pre>
+<p>So, when we call <code>loss$backward()</code>, the whole graph is differentiated w.r.t. the loss, and all Tensors in the graph that has requires_grad=True will have their <code>#grad</code> Tensor accumulated with the gradient.</p>
+<p>For illustration, let us follow a few steps backward:</p>
+<div class="sourceCode" id="cb8"><pre class="downlit">
+<span class="kw">loss</span><span class="op">$</span><span class="kw">grad_fn</span>
+<span class="co">#&gt; MseLossBackward</span>
+<span class="kw">loss</span><span class="op">$</span><span class="kw">grad_fn</span><span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]
+<span class="co">#&gt; AddmmBackward</span>
+<span class="kw">loss</span><span class="op">$</span><span class="kw">grad_fn</span><span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]<span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]
+<span class="co">#&gt; torch::autograd::AccumulateGrad</span>
+</pre></div>
+</div>
+<div id="backprop" class="section level2">
+<h2 class="hasAnchor">
+<a href="#backprop" class="anchor"></a>Backprop</h2>
+<p>To backpropagate the error all we have to do is to <code>loss$backward()</code>. You need to clear the existing gradients though, else gradients will be accumulated to existing gradients.</p>
+<p>Now we shall call <code>loss$backward()</code>, and have a look at conv1’s bias gradients before and after the backward.</p>
+<div class="sourceCode" id="cb9"><pre class="downlit">
+<span class="kw">net</span><span class="op">$</span><span class="fu">zero_grad</span>()     <span class="co"># zeroes the gradient buffers of all parameters</span>
+
+<span class="co"># conv1.bias.grad before backward</span>
+<span class="kw">net</span><span class="op">$</span><span class="kw">conv1</span><span class="op">$</span><span class="kw">bias</span><span class="op">$</span><span class="kw">grad</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  0</span>
+<span class="co">#&gt;  0</span>
+<span class="co">#&gt;  0</span>
+<span class="co">#&gt;  0</span>
+<span class="co">#&gt;  0</span>
+<span class="co">#&gt;  0</span>
+<span class="co">#&gt; [ CPUFloatType{6} ]</span>
+
+<span class="kw">loss</span><span class="op">$</span><span class="fu">backward</span>()
+
+<span class="co"># conv1.bias.grad after backward</span>
+<span class="kw">net</span><span class="op">$</span><span class="kw">conv1</span><span class="op">$</span><span class="kw">bias</span><span class="op">$</span><span class="kw">grad</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt; 0.001 *</span>
+<span class="co">#&gt;  2.3567</span>
+<span class="co">#&gt; -1.3589</span>
+<span class="co">#&gt; -0.6749</span>
+<span class="co">#&gt;  5.5939</span>
+<span class="co">#&gt; -4.2062</span>
+<span class="co">#&gt;  0.6161</span>
+<span class="co">#&gt; [ CPUFloatType{6} ]</span>
+</pre></div>
+<p>Now, we have seen how to use loss functions.</p>
+</div>
+<div id="update-the-weights" class="section level2">
+<h2 class="hasAnchor">
+<a href="#update-the-weights" class="anchor"></a>Update the weights</h2>
+<p>The simplest update rule used in practice is the Stochastic Gradient Descent (SGD):</p>
+<p><span class="math display">\[weight = weight - learning_rate * gradient\]</span></p>
+<p>We can implement this using simple R code:</p>
+<div class="sourceCode" id="cb10"><pre class="downlit">
+<span class="kw">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">0.01</span>
+<span class="co">for</span> (<span class="kw">f</span> <span class="co">in</span> <span class="kw">net</span><span class="op">$</span><span class="kw">parameters</span>) {
+  <span class="fu"><a href="../../reference/with_no_grad.html">with_no_grad</a></span>({
+    <span class="kw">f</span><span class="op">$</span><span class="fu">sub_</span>(<span class="kw">f</span><span class="op">$</span><span class="kw">grad</span> <span class="op">*</span> <span class="kw">learning_rate</span>)
+  })
+}
+</pre></div>
+<blockquote>
+<p><strong>Note:</strong> Weight updates here is wraped around <code>with_no_grad</code> as we don’t the updates to be tracked by the autograd engine.</p>
+</blockquote>
+<p>However, as you use neural networks, you want to use various different update rules such as SGD, Nesterov-SGD, Adam, RMSProp, etc.</p>
+<div class="sourceCode" id="cb11"><pre class="downlit">
+<span class="co"># create your optimizer</span>
+<span class="kw">optimizer</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/optim_sgd.html">optim_sgd</a></span>(<span class="kw">net</span><span class="op">$</span><span class="kw">parameters</span>, lr = <span class="fl">0.01</span>)
+
+<span class="co"># in your training loop:</span>
+<span class="kw">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span>()   <span class="co"># zero the gradient buffers</span>
+<span class="kw">output</span> <span class="op">&lt;-</span> <span class="fu">net</span>(<span class="kw">input</span>)
+<span class="kw">loss</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nnf_mse_loss.html">nnf_mse_loss</a></span>(<span class="kw">output</span>, <span class="kw">target</span>)
+<span class="kw">loss</span><span class="op">$</span><span class="fu">backward</span>()
+<span class="kw">optimizer</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/stats/step.html">step</a></span>()    <span class="co"># Does the update</span>
+<span class="co">#&gt; NULL</span>
+</pre></div>
+<blockquote>
+<p><strong>Note:</strong> Observe how gradient buffers had to be manually set to zero using <code>optimizer$zero_grad()</code>. This is because gradients are accumulated as explained in the Backprop section.</p>
+</blockquote>
+</div>
+  </div>
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diff --git a/static/docs/articles/getting-started/new-autograd-functions.html b/static/docs/articles/getting-started/new-autograd-functions.html
new file mode 100644
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+<!--/.navbar -->
+
+      
+
+      </header><script src="new-autograd-functions_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header toc-ignore">
+      <h1 data-toc-skip>Defining new autograd functions</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/new-autograd-functions.Rmd"><code>vignettes/getting-started/new-autograd-functions.Rmd</code></a></small>
+      <div class="hidden name"><code>new-autograd-functions.Rmd</code></div>
+
+    </div>
+
+    
+    
+<blockquote>
+<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
+</blockquote>
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
+</pre></div>
+<p>Under the hood, each primitive autograd operator is really two functions that operate on Tensors. The forward function computes output Tensors from input Tensors. The backward function receives the gradient of the output Tensors with respect to some scalar value, and computes the gradient of the input Tensors with respect to that same scalar value.</p>
+<p>In torch we can easily define our own autograd operator by defining a subclass of <code>autograd_function</code> and implementing the forward and backward functions. We can then use our new autograd operator by constructing an instance and calling it like a function, passing Tensors containing input data.</p>
+<p>In this example we define our own custom autograd function for performing the ReLU nonlinearity, and use it to implement our two-layer network:</p>
+<div class="sourceCode" id="cb2"><pre class="downlit">
+<span class="co"># We can implement our own custom autograd Functions by subclassing</span>
+<span class="co"># autograd_functioon and implementing the forward and backward passes</span>
+<span class="co"># which operate on Tensors.</span>
+<span class="kw">my_relu</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/autograd_function.html">autograd_function</a></span>(
+   <span class="co"># In the forward pass we receive a Tensor containing the input and return</span>
+   <span class="co"># a Tensor containing the output. ctx is a context object that can be used</span>
+   <span class="co"># to stash information for backward computation. You can cache arbitrary</span>
+   <span class="co"># objects for use in the backward pass using the ctx$save_for_backward method.</span>
+   forward = <span class="fu">function</span>(<span class="kw">ctx</span>, <span class="kw">input</span>) {
+      <span class="kw">ctx</span><span class="op">$</span><span class="fu">save_for_backward</span>(input = <span class="kw">input</span>)
+      <span class="kw">input</span><span class="op">$</span><span class="fu">clamp</span>(min = <span class="fl">0</span>)
+   },
+   <span class="co"># In the backward pass we receive a Tensor containing the gradient of the loss</span>
+   <span class="co"># with respect to the output, and we need to compute the gradient of the loss</span>
+   <span class="co"># with respect to the input.</span>
+   backward = <span class="fu">function</span>(<span class="kw">ctx</span>, <span class="kw">grad_output</span>) {
+      <span class="kw">v</span> <span class="op">&lt;-</span> <span class="kw">ctx</span><span class="op">$</span><span class="kw">saved_variables</span>
+      <span class="kw">grad_input</span> <span class="op">&lt;-</span> <span class="kw">grad_output</span><span class="op">$</span><span class="fu">clone</span>()
+      <span class="kw">grad_input</span>[<span class="kw">v</span><span class="op">$</span><span class="kw">input</span> <span class="op">&lt;</span> <span class="fl">0</span>] <span class="op">&lt;-</span> <span class="fl">0</span>
+      <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(input = <span class="kw">grad_input</span>)
+   }
+)
+
+<span class="co">if</span> (<span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span>()) {
+   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cuda"</span>)
+} <span class="co">else</span> {
+   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cpu"</span>)
+}
+   
+<span class="co"># N is batch size; D_in is input dimension;</span>
+<span class="co"># H is hidden dimension; D_out is output dimension.</span>
+<span class="kw">N</span> <span class="op">&lt;-</span> <span class="fl">64</span>
+<span class="kw">D_in</span> <span class="op">&lt;-</span> <span class="fl">1000</span>
+<span class="kw">H</span> <span class="op">&lt;-</span> <span class="fl">100</span>
+<span class="kw">D_out</span> <span class="op">&lt;-</span> <span class="fl">10</span>
+
+<span class="co"># Create random input and output data</span>
+<span class="co"># Setting requires_grad=FALSE (the default) indicates that we do not need to </span>
+<span class="co"># compute gradients with respect to these Tensors during the backward pass.</span>
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_in</span>, device=<span class="kw">device</span>)
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_out</span>, device=<span class="kw">device</span>)
+
+<span class="co"># Randomly initialize weights</span>
+<span class="co"># Setting requires_grad=TRUE indicates that we want to compute gradients with</span>
+<span class="co"># respect to these Tensors during the backward pass.</span>
+<span class="kw">w1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">D_in</span>, <span class="kw">H</span>, device=<span class="kw">device</span>, requires_grad = <span class="fl">TRUE</span>)
+<span class="kw">w2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">H</span>, <span class="kw">D_out</span>, device=<span class="kw">device</span>, requires_grad = <span class="fl">TRUE</span>)
+
+<span class="kw">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-6</span>
+<span class="co">for</span> (<span class="kw">t</span> <span class="co">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fl">500</span>)) {
+   <span class="co"># Forward pass: compute predicted y using operations on Tensors; these</span>
+   <span class="co"># are exactly the same operations we used to compute the forward pass using</span>
+   <span class="co"># Tensors, but we do not need to keep references to intermediate values since</span>
+   <span class="co"># we are not implementing the backward pass by hand.</span>
+   <span class="kw">y_pred</span> <span class="op">&lt;-</span> <span class="fu">my_relu</span>(<span class="kw">x</span><span class="op">$</span><span class="fu">mm</span>(<span class="kw">w1</span>))<span class="op">$</span><span class="fu">mm</span>(<span class="kw">w2</span>)
+   
+   <span class="co"># Compute and print loss using operations on Tensors.</span>
+   <span class="co"># Now loss is a Tensor of shape (1,)</span>
+   <span class="kw">loss</span> <span class="op">&lt;-</span> (<span class="kw">y_pred</span> <span class="op">-</span> <span class="kw">y</span>)<span class="op">$</span><span class="fu">pow</span>(<span class="fl">2</span>)<span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span>()
+   <span class="co">if</span> (<span class="kw">t</span> <span class="op">%%</span> <span class="fl">100</span> <span class="op">==</span> <span class="fl">0</span> <span class="op">||</span> <span class="kw">t</span> <span class="op">==</span> <span class="fl">1</span>)
+      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="st">"Step:"</span>, <span class="kw">t</span>, <span class="st">":"</span>, <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="kw">loss</span>), <span class="st">"\n"</span>)
+   
+   <span class="co"># Use autograd to compute the backward pass. This call will compute the</span>
+   <span class="co"># gradient of loss with respect to all Tensors with requires_grad=True.</span>
+   <span class="co"># After this call w1$grad and w2$grad will be Tensors holding the gradient</span>
+   <span class="co"># of the loss with respect to w1 and w2 respectively.</span>
+   <span class="kw">loss</span><span class="op">$</span><span class="fu">backward</span>()
+   
+   <span class="co"># Manually update weights using gradient descent. Wrap in `with_no_grad`</span>
+   <span class="co"># because weights have requires_grad=TRUE, but we don't need to track this</span>
+   <span class="co"># in autograd.</span>
+   <span class="co"># You can also use optim_sgd to achieve this.</span>
+   <span class="fu"><a href="../../reference/with_no_grad.html">with_no_grad</a></span>({
+      
+      <span class="co"># operations suffixed with an `_` operates on in-place on the tensor.</span>
+      <span class="kw">w1</span><span class="op">$</span><span class="fu">sub_</span>(<span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">w1</span><span class="op">$</span><span class="kw">grad</span>)
+      <span class="kw">w2</span><span class="op">$</span><span class="fu">sub_</span>(<span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">w2</span><span class="op">$</span><span class="kw">grad</span>)
+      
+      <span class="co"># Manually zero the gradients after updating weights</span>
+      <span class="kw">w1</span><span class="op">$</span><span class="kw">grad</span><span class="op">$</span><span class="fu">zero_</span>()
+      <span class="kw">w2</span><span class="op">$</span><span class="kw">grad</span><span class="op">$</span><span class="fu">zero_</span>()
+   })
+}
+<span class="co">#&gt; Step: 1 : 25332368 </span>
+<span class="co">#&gt; Step: 100 : 473.5124 </span>
+<span class="co">#&gt; Step: 200 : 2.001738 </span>
+<span class="co">#&gt; Step: 300 : 0.01279241 </span>
+<span class="co">#&gt; Step: 400 : 0.0002553065 </span>
+<span class="co">#&gt; Step: 500 : 4.130635e-05</span>
+</pre></div>
+<p>In the <a href="nn.html">next example</a> we will learn how to use the neural networks abstractions in torch.</p>
+  </div>
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diff --git a/static/docs/articles/getting-started/new-autograd-functions_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/articles/getting-started/new-autograd-functions_files/accessible-code-block-0.0.1/empty-anchor.js
new file mode 100644
index 000000000..ca349fd6a
--- /dev/null
+++ b/static/docs/articles/getting-started/new-autograd-functions_files/accessible-code-block-0.0.1/empty-anchor.js
@@ -0,0 +1,15 @@
+// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
+// v0.0.1
+// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
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diff --git a/static/docs/articles/getting-started/nn.html b/static/docs/articles/getting-started/nn.html
new file mode 100644
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+<!DOCTYPE html>
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+      <a href="../../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
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+      </header><script src="nn_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header toc-ignore">
+      <h1 data-toc-skip>nn: neural networks with torch</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/nn.Rmd"><code>vignettes/getting-started/nn.Rmd</code></a></small>
+      <div class="hidden name"><code>nn.Rmd</code></div>
+
+    </div>
+
+    
+    
+<blockquote>
+<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
+</blockquote>
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
+</pre></div>
+<p>Computational graphs and autograd are a very powerful paradigm for defining complex operators and automatically taking derivatives; however for large neural networks raw autograd can be a bit too low-level.</p>
+<p>When building neural networks we frequently think of arranging the computation into layers, some of which have learnable parameters which will be optimized during learning.</p>
+<p>In TensorFlow, packages like Keras, TensorFlow-Slim, and TFLearn provide higher-level abstractions over raw computational graphs that are useful for building neural networks.</p>
+<p>In torch, the nn functionality serves this same purpose. The nn feature defines a set of Modules, which are roughly equivalent to neural network layers. A Module receives input Tensors and computes output Tensors, but may also hold internal state such as Tensors containing learnable parameters. The nn collection also defines a set of useful loss functions that are commonly used when training neural networks.</p>
+<p>In this example we use nn to implement our two-layer network:</p>
+<div class="sourceCode" id="cb2"><pre class="downlit">
+<span class="co">if</span> (<span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span>()) {
+   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cuda"</span>)
+} <span class="co">else</span> {
+   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cpu"</span>)
+}
+   
+<span class="co"># N is batch size; D_in is input dimension;</span>
+<span class="co"># H is hidden dimension; D_out is output dimension.</span>
+<span class="kw">N</span> <span class="op">&lt;-</span> <span class="fl">64</span>
+<span class="kw">D_in</span> <span class="op">&lt;-</span> <span class="fl">1000</span>
+<span class="kw">H</span> <span class="op">&lt;-</span> <span class="fl">100</span>
+<span class="kw">D_out</span> <span class="op">&lt;-</span> <span class="fl">10</span>
+
+<span class="co"># Create random input and output data</span>
+<span class="co"># Setting requires_grad=FALSE (the default) indicates that we do not need to </span>
+<span class="co"># compute gradients with respect to these Tensors during the backward pass.</span>
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_in</span>, device=<span class="kw">device</span>)
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_out</span>, device=<span class="kw">device</span>)
+
+<span class="co"># Use the nn package to define our model as a sequence of layers. nn_sequential</span>
+<span class="co"># is a Module which contains other Modules, and applies them in sequence to</span>
+<span class="co"># produce its output. Each Linear Module computes output from input using a</span>
+<span class="co"># linear function, and holds internal Tensors for its weight and bias.</span>
+<span class="kw">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_sequential.html">nn_sequential</a></span>(
+    <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="kw">D_in</span>, <span class="kw">H</span>),
+    <span class="fu"><a href="../../reference/nn_relu.html">nn_relu</a></span>(),
+    <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="kw">H</span>, <span class="kw">D_out</span>)
+)
+
+<span class="co"># The nn package also contains definitions of popular loss functions; in this</span>
+<span class="co"># case we will use Mean Squared Error (MSE) as our loss function.</span>
+<span class="kw">loss_fn</span> <span class="op">&lt;-</span> <span class="kw">nnf_mse_loss</span>
+
+<span class="kw">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-6</span>
+<span class="co">for</span> (<span class="kw">t</span> <span class="co">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fl">500</span>)) {
+   <span class="co"># Forward pass: compute predicted y by passing x to the model. Module objects</span>
+   <span class="co"># can be called like functions. When doing so you pass a Tensor of input</span>
+   <span class="co"># data to the Module and it produces a Tensor of output data.</span>
+   <span class="kw">y_pred</span> <span class="op">&lt;-</span> <span class="fu">model</span>(<span class="kw">x</span>)
+   
+   <span class="co"># Compute and print loss. We pass Tensors containing the predicted and true</span>
+   <span class="co"># values of y, and the loss function returns a Tensor containing the</span>
+   <span class="co"># loss.</span>
+   <span class="kw">loss</span> <span class="op">&lt;-</span> <span class="fu">loss_fn</span>(<span class="kw">y_pred</span>, <span class="kw">y</span>)
+   <span class="co">if</span> (<span class="kw">t</span> <span class="op">%%</span> <span class="fl">100</span> <span class="op">==</span> <span class="fl">0</span> <span class="op">||</span> <span class="kw">t</span> <span class="op">==</span> <span class="fl">1</span>)
+      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="st">"Step:"</span>, <span class="kw">t</span>, <span class="st">":"</span>, <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="kw">loss</span>), <span class="st">"\n"</span>)
+   
+   <span class="co"># Zero the gradients before running the backward pass.</span>
+   <span class="kw">model</span><span class="op">$</span><span class="fu">zero_grad</span>()
+
+   <span class="co"># Backward pass: compute gradient of the loss with respect to all the learnable</span>
+   <span class="co"># parameters of the model. Internally, the parameters of each Module are stored</span>
+   <span class="co"># in Tensors with requires_grad=TRUE, so this call will compute gradients for</span>
+   <span class="co"># all learnable parameters in the model.</span>
+   <span class="kw">loss</span><span class="op">$</span><span class="fu">backward</span>()
+   
+   <span class="co"># Update the weights using gradient descent. Each parameter is a Tensor, so</span>
+   <span class="co"># we can access its gradients like we did before.</span>
+   <span class="fu"><a href="../../reference/with_no_grad.html">with_no_grad</a></span>({
+      <span class="co">for</span> (<span class="kw">param</span> <span class="co">in</span> <span class="kw">model</span><span class="op">$</span><span class="kw">parameters</span>) {
+         <span class="kw">param</span><span class="op">$</span><span class="fu">sub_</span>(<span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">param</span><span class="op">$</span><span class="kw">grad</span>)
+      }
+   })
+}
+<span class="co">#&gt; Step: 1 : 1.04115 </span>
+<span class="co">#&gt; Step: 100 : 1.041026 </span>
+<span class="co">#&gt; Step: 200 : 1.040901 </span>
+<span class="co">#&gt; Step: 300 : 1.040776 </span>
+<span class="co">#&gt; Step: 400 : 1.04065 </span>
+<span class="co">#&gt; Step: 500 : 1.040525</span>
+</pre></div>
+<p>In the <a href="optim.html">next example</a> we will learn how to use optimizers implemented in torch.</p>
+  </div>
+
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+
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+</div>
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diff --git a/static/docs/articles/getting-started/nn_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/articles/getting-started/nn_files/accessible-code-block-0.0.1/empty-anchor.js
new file mode 100644
index 000000000..ca349fd6a
--- /dev/null
+++ b/static/docs/articles/getting-started/nn_files/accessible-code-block-0.0.1/empty-anchor.js
@@ -0,0 +1,15 @@
+// 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");
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+        linkList[j].setAttribute('aria-hidden', 'true');
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+  <div class="col-md-9 contents">
+    <div class="page-header toc-ignore">
+      <h1 data-toc-skip>optim: optimizers in torch</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/optim.Rmd"><code>vignettes/getting-started/optim.Rmd</code></a></small>
+      <div class="hidden name"><code>optim.Rmd</code></div>
+
+    </div>
+
+    
+    
+<blockquote>
+<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
+</blockquote>
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
+</pre></div>
+<p>Up to this point we have updated the weights of our models by manually mutating the Tensors holding learnable parameters (with <code>with_no_grad</code> to avoid tracking history in autograd). This is not a huge burden for simple optimization algorithms like stochastic gradient descent, but in practice we often train neural networks using more sophisticated optimizers like AdaGrad, RMSProp, Adam, etc.</p>
+<p>The optim package in torch abstracts the idea of an optimization algorithm and provides implementations of commonly used optimization algorithms.</p>
+<p>In this example we will use the nn package to define our model as before, but we will optimize the model using the Adam algorithm provided by <code>optim</code>:</p>
+<div class="sourceCode" id="cb2"><pre class="downlit">
+<span class="co">if</span> (<span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span>()) {
+   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cuda"</span>)
+} <span class="co">else</span> {
+   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cpu"</span>)
+}
+   
+<span class="co"># N is batch size; D_in is input dimension;</span>
+<span class="co"># H is hidden dimension; D_out is output dimension.</span>
+<span class="kw">N</span> <span class="op">&lt;-</span> <span class="fl">64</span>
+<span class="kw">D_in</span> <span class="op">&lt;-</span> <span class="fl">1000</span>
+<span class="kw">H</span> <span class="op">&lt;-</span> <span class="fl">100</span>
+<span class="kw">D_out</span> <span class="op">&lt;-</span> <span class="fl">10</span>
+
+<span class="co"># Create random input and output data</span>
+<span class="co"># Setting requires_grad=FALSE (the default) indicates that we do not need to </span>
+<span class="co"># compute gradients with respect to these Tensors during the backward pass.</span>
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_in</span>, device=<span class="kw">device</span>)
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_out</span>, device=<span class="kw">device</span>)
+
+<span class="co"># Use the nn package to define our model as a sequence of layers. nn_sequential</span>
+<span class="co"># is a Module which contains other Modules, and applies them in sequence to</span>
+<span class="co"># produce its output. Each Linear Module computes output from input using a</span>
+<span class="co"># linear function, and holds internal Tensors for its weight and bias.</span>
+<span class="kw">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_sequential.html">nn_sequential</a></span>(
+    <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="kw">D_in</span>, <span class="kw">H</span>),
+    <span class="fu"><a href="../../reference/nn_relu.html">nn_relu</a></span>(),
+    <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="kw">H</span>, <span class="kw">D_out</span>)
+)
+
+<span class="co"># The nn package also contains definitions of popular loss functions; in this</span>
+<span class="co"># case we will use Mean Squared Error (MSE) as our loss function.</span>
+<span class="kw">loss_fn</span> <span class="op">&lt;-</span> <span class="kw">nnf_mse_loss</span>
+
+<span class="co"># Use the optim package to define an Optimizer that will update the weights of</span>
+<span class="co"># the model for us. Here we will use Adam; the optim package contains many other</span>
+<span class="co"># optimization algorithms. The first argument to the Adam constructor tells the</span>
+<span class="co"># optimizer which Tensors it should update.</span>
+<span class="kw">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-4</span>
+<span class="kw">optimizer</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/optim_adam.html">optim_adam</a></span>(<span class="kw">model</span><span class="op">$</span><span class="kw">parameters</span>, lr=<span class="kw">learning_rate</span>)
+
+<span class="co">for</span> (<span class="kw">t</span> <span class="co">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fl">500</span>)) {
+   <span class="co"># Forward pass: compute predicted y by passing x to the model. Module objects</span>
+   <span class="co"># can be called like functions. When doing so you pass a Tensor of input</span>
+   <span class="co"># data to the Module and it produces a Tensor of output data.</span>
+   <span class="kw">y_pred</span> <span class="op">&lt;-</span> <span class="fu">model</span>(<span class="kw">x</span>)
+   
+   <span class="co"># Compute and print loss. We pass Tensors containing the predicted and true</span>
+   <span class="co"># values of y, and the loss function returns a Tensor containing the</span>
+   <span class="co"># loss.</span>
+   <span class="kw">loss</span> <span class="op">&lt;-</span> <span class="fu">loss_fn</span>(<span class="kw">y_pred</span>, <span class="kw">y</span>)
+   <span class="co">if</span> (<span class="kw">t</span> <span class="op">%%</span> <span class="fl">100</span> <span class="op">==</span> <span class="fl">0</span> <span class="op">||</span> <span class="kw">t</span> <span class="op">==</span> <span class="fl">1</span>)
+      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="st">"Step:"</span>, <span class="kw">t</span>, <span class="st">":"</span>, <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="kw">loss</span>), <span class="st">"\n"</span>)
+   
+   <span class="co"># Before the backward pass, use the optimizer object to zero all of the</span>
+   <span class="co"># gradients for the variables it will update (which are the learnable</span>
+   <span class="co"># weights of the model). This is because by default, gradients are</span>
+   <span class="co"># accumulated in buffers( i.e, not overwritten) whenever $backward()</span>
+   <span class="co"># is called. Checkout docs of `autograd_backward` for more details.</span>
+   <span class="kw">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span>()
+
+   <span class="co"># Backward pass: compute gradient of the loss with respect to model</span>
+   <span class="co"># parameters</span>
+   <span class="kw">loss</span><span class="op">$</span><span class="fu">backward</span>()
+
+   <span class="co"># Calling the step function on an Optimizer makes an update to its</span>
+   <span class="co"># parameters</span>
+   <span class="kw">optimizer</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/stats/step.html">step</a></span>()
+}
+<span class="co">#&gt; Step: 1 : 1.03194 </span>
+<span class="co">#&gt; Step: 100 : 0.08338322 </span>
+<span class="co">#&gt; Step: 200 : 0.001254716 </span>
+<span class="co">#&gt; Step: 300 : 3.605265e-06 </span>
+<span class="co">#&gt; Step: 400 : 2.155708e-09 </span>
+<span class="co">#&gt; Step: 500 : 5.439427e-13</span>
+</pre></div>
+<p>In the <a href="custom-nn.html">next example</a> we will learn how to create custom <code>nn_modules</code>.</p>
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+  <div class="col-md-9 contents">
+    <div class="page-header toc-ignore">
+      <h1 data-toc-skip>Tensors and autograd</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/tensors-and-autograd.Rmd"><code>vignettes/getting-started/tensors-and-autograd.Rmd</code></a></small>
+      <div class="hidden name"><code>tensors-and-autograd.Rmd</code></div>
+
+    </div>
+
+    
+    
+<blockquote>
+<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
+</blockquote>
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
+</pre></div>
+<p>In the previous examples, we had to manually implement both the forward and backward passes of our neural network. Manually implementing the backward pass is not a big deal for a small two-layer network, but can quickly get very hairy for large complex networks.</p>
+<p>Thankfully, we can use automatic differentiation to automate the computation of backward passes in neural networks. The autograd feature in torch provides exactly this functionality. When using autograd, the forward pass of your network will define a computational graph; nodes in the graph will be Tensors, and edges will be functions that produce output Tensors from input Tensors. Backpropagating through this graph then allows you to easily compute gradients.</p>
+<p>This sounds complicated, it’s pretty simple to use in practice. Each Tensor represents a node in a computational graph. If x is a Tensor that has <code>x$requires_grad=TRUE</code> then <code>x$grad</code> is another Tensor holding the gradient of x with respect to some scalar value.</p>
+<p>Here we use torch Tensors and autograd to implement our two-layer network; now we no longer need to manually implement the backward pass through the network:</p>
+<div class="sourceCode" id="cb2"><pre class="downlit">
+<span class="co">if</span> (<span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span>()) {
+   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cuda"</span>)
+} <span class="co">else</span> {
+   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cpu"</span>)
+}
+   
+<span class="co"># N is batch size; D_in is input dimension;</span>
+<span class="co"># H is hidden dimension; D_out is output dimension.</span>
+<span class="kw">N</span> <span class="op">&lt;-</span> <span class="fl">64</span>
+<span class="kw">D_in</span> <span class="op">&lt;-</span> <span class="fl">1000</span>
+<span class="kw">H</span> <span class="op">&lt;-</span> <span class="fl">100</span>
+<span class="kw">D_out</span> <span class="op">&lt;-</span> <span class="fl">10</span>
+
+<span class="co"># Create random input and output data</span>
+<span class="co"># Setting requires_grad=FALSE (the default) indicates that we do not need to </span>
+<span class="co"># compute gradients with respect to these Tensors during the backward pass.</span>
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_in</span>, device=<span class="kw">device</span>)
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_out</span>, device=<span class="kw">device</span>)
+
+<span class="co"># Randomly initialize weights</span>
+<span class="co"># Setting requires_grad=TRUE indicates that we want to compute gradients with</span>
+<span class="co"># respect to these Tensors during the backward pass.</span>
+<span class="kw">w1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">D_in</span>, <span class="kw">H</span>, device=<span class="kw">device</span>, requires_grad = <span class="fl">TRUE</span>)
+<span class="kw">w2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">H</span>, <span class="kw">D_out</span>, device=<span class="kw">device</span>, requires_grad = <span class="fl">TRUE</span>)
+
+<span class="kw">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-6</span>
+<span class="co">for</span> (<span class="kw">t</span> <span class="co">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fl">500</span>)) {
+   <span class="co"># Forward pass: compute predicted y using operations on Tensors; these</span>
+   <span class="co"># are exactly the same operations we used to compute the forward pass using</span>
+   <span class="co"># Tensors, but we do not need to keep references to intermediate values since</span>
+   <span class="co"># we are not implementing the backward pass by hand.</span>
+   <span class="kw">y_pred</span> <span class="op">&lt;-</span> <span class="kw">x</span><span class="op">$</span><span class="fu">mm</span>(<span class="kw">w1</span>)<span class="op">$</span><span class="fu">clamp</span>(min=<span class="fl">0</span>)<span class="op">$</span><span class="fu">mm</span>(<span class="kw">w2</span>)
+   
+   <span class="co"># Compute and print loss using operations on Tensors.</span>
+   <span class="co"># Now loss is a Tensor of shape (1,)</span>
+   <span class="kw">loss</span> <span class="op">&lt;-</span> (<span class="kw">y_pred</span> <span class="op">-</span> <span class="kw">y</span>)<span class="op">$</span><span class="fu">pow</span>(<span class="fl">2</span>)<span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span>()
+   <span class="co">if</span> (<span class="kw">t</span> <span class="op">%%</span> <span class="fl">100</span> <span class="op">==</span> <span class="fl">0</span> <span class="op">||</span> <span class="kw">t</span> <span class="op">==</span> <span class="fl">1</span>)
+      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="st">"Step:"</span>, <span class="kw">t</span>, <span class="st">":"</span>, <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="kw">loss</span>), <span class="st">"\n"</span>)
+   
+   <span class="co"># Use autograd to compute the backward pass. This call will compute the</span>
+   <span class="co"># gradient of loss with respect to all Tensors with requires_grad=True.</span>
+   <span class="co"># After this call w1$grad and w2$grad will be Tensors holding the gradient</span>
+   <span class="co"># of the loss with respect to w1 and w2 respectively.</span>
+   <span class="kw">loss</span><span class="op">$</span><span class="fu">backward</span>()
+   
+   <span class="co"># Manually update weights using gradient descent. Wrap in `with_no_grad`</span>
+   <span class="co"># because weights have requires_grad=TRUE, but we don't need to track this</span>
+   <span class="co"># in autograd.</span>
+   <span class="co"># You can also use optim_sgd to achieve this.</span>
+   <span class="fu"><a href="../../reference/with_no_grad.html">with_no_grad</a></span>({
+      
+      <span class="co"># operations suffixed with an `_` operates on in-place on the tensor.</span>
+      <span class="kw">w1</span><span class="op">$</span><span class="fu">sub_</span>(<span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">w1</span><span class="op">$</span><span class="kw">grad</span>)
+      <span class="kw">w2</span><span class="op">$</span><span class="fu">sub_</span>(<span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">w2</span><span class="op">$</span><span class="kw">grad</span>)
+      
+      <span class="co"># Manually zero the gradients after updating weights</span>
+      <span class="kw">w1</span><span class="op">$</span><span class="kw">grad</span><span class="op">$</span><span class="fu">zero_</span>()
+      <span class="kw">w2</span><span class="op">$</span><span class="kw">grad</span><span class="op">$</span><span class="fu">zero_</span>()
+   })
+}
+<span class="co">#&gt; Step: 1 : 27399256 </span>
+<span class="co">#&gt; Step: 100 : 756.7294 </span>
+<span class="co">#&gt; Step: 200 : 9.271971 </span>
+<span class="co">#&gt; Step: 300 : 0.205474 </span>
+<span class="co">#&gt; Step: 400 : 0.00579866 </span>
+<span class="co">#&gt; Step: 500 : 0.0003981641</span>
+</pre></div>
+<p>In the <a href="new-autograd-functions.html">next example</a> we will learn how to create new autograd functions.</p>
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+      <a href="../../articles/getting-started/tensors-and-autograd.html">Tensors and autograd</a>
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+      <a href="../../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
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+      <a href="../../articles/getting-started/what-is-torch.html">What's torch?</a>
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+      <a href="../../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
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+      <a href="../../articles/tensor-creation.html">Creating tensors</a>
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+<!--/.navbar -->
+
+      
+
+      </header><script src="tensors_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header toc-ignore">
+      <h1 data-toc-skip>torch Tensors</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/tensors.Rmd"><code>vignettes/getting-started/tensors.Rmd</code></a></small>
+      <div class="hidden name"><code>tensors.Rmd</code></div>
+
+    </div>
+
+    
+    
+<blockquote>
+<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
+</blockquote>
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
+</pre></div>
+<p>R arrays are great, but they cannot utilize GPUs to accelerate its numerical computations. For modern deep neural networks, GPUs often provide speedups of 50x or greater, so unfortunately pure R won’t be enough for modern deep learning.</p>
+<p>Here we introduce the most fundamental torch concept: the Tensor. A torch Tensor is conceptually similar to an R array: a Tensor is an n-dimensional array, and torch provides many functions for operating on these Tensors. Behind the scenes, Tensors can keep track of a computational graph and gradients, but they’re also useful as a generic tool for scientific computing.</p>
+<p>Also unlike R, torch Tensors can utilize GPUs to accelerate their numeric computations. To run a torch Tensor on GPU, you simply need to cast it to a new datatype.</p>
+<p>Here we use torch Tensors to fit a two-layer network to random data. Like the R before we need to manually implement the forward and backward passes through the network:</p>
+<div class="sourceCode" id="cb2"><pre class="downlit">
+<span class="co">if</span> (<span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span>()) {
+   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cuda"</span>)
+} <span class="co">else</span> {
+   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cpu"</span>)
+}
+   
+<span class="co"># N is batch size; D_in is input dimension;</span>
+<span class="co"># H is hidden dimension; D_out is output dimension.</span>
+<span class="kw">N</span> <span class="op">&lt;-</span> <span class="fl">64</span>
+<span class="kw">D_in</span> <span class="op">&lt;-</span> <span class="fl">1000</span>
+<span class="kw">H</span> <span class="op">&lt;-</span> <span class="fl">100</span>
+<span class="kw">D_out</span> <span class="op">&lt;-</span> <span class="fl">10</span>
+
+<span class="co"># Create random input and output data</span>
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_in</span>, device=<span class="kw">device</span>)
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_out</span>, device=<span class="kw">device</span>)
+
+<span class="co"># Randomly initialize weights</span>
+<span class="kw">w1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">D_in</span>, <span class="kw">H</span>, device=<span class="kw">device</span>)
+<span class="kw">w2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">H</span>, <span class="kw">D_out</span>, device=<span class="kw">device</span>)
+
+<span class="kw">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-6</span>
+<span class="co">for</span> (<span class="kw">t</span> <span class="co">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fl">500</span>)) {
+   <span class="co"># Forward pass: compute predicted y</span>
+   <span class="kw">h</span> <span class="op">&lt;-</span> <span class="kw">x</span><span class="op">$</span><span class="fu">mm</span>(<span class="kw">w1</span>)
+   <span class="kw">h_relu</span> <span class="op">&lt;-</span> <span class="kw">h</span><span class="op">$</span><span class="fu">clamp</span>(min=<span class="fl">0</span>)
+   <span class="kw">y_pred</span> <span class="op">&lt;-</span> <span class="kw">h_relu</span><span class="op">$</span><span class="fu">mm</span>(<span class="kw">w2</span>)
+   
+   <span class="co"># Compute and print loss</span>
+   <span class="kw">loss</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>((<span class="kw">y_pred</span> <span class="op">-</span> <span class="kw">y</span>)<span class="op">$</span><span class="fu">pow</span>(<span class="fl">2</span>)<span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span>())
+   <span class="co">if</span> (<span class="kw">t</span> <span class="op">%%</span> <span class="fl">100</span> <span class="op">==</span> <span class="fl">0</span> <span class="op">||</span> <span class="kw">t</span> <span class="op">==</span> <span class="fl">1</span>)
+      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="st">"Step:"</span>, <span class="kw">t</span>, <span class="st">":"</span>, <span class="kw">loss</span>, <span class="st">"\n"</span>)
+   
+   <span class="co"># Backprop to compute gradients of w1 and w2 with respect to loss</span>
+   <span class="kw">grad_y_pred</span> <span class="op">&lt;-</span> <span class="fl">2.0</span> <span class="op">*</span> (<span class="kw">y_pred</span> <span class="op">-</span> <span class="kw">y</span>)
+   <span class="kw">grad_w2</span> <span class="op">&lt;-</span> <span class="kw">h_relu</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span>()<span class="op">$</span><span class="fu">mm</span>(<span class="kw">grad_y_pred</span>)
+   <span class="kw">grad_h_relu</span> <span class="op">&lt;-</span> <span class="kw">grad_y_pred</span><span class="op">$</span><span class="fu">mm</span>(<span class="kw">w2</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span>())
+   <span class="kw">grad_h</span> <span class="op">&lt;-</span> <span class="kw">grad_h_relu</span><span class="op">$</span><span class="fu">clone</span>()
+   <span class="kw">grad_h</span>[<span class="kw">h</span> <span class="op">&lt;</span> <span class="fl">0</span>] <span class="op">&lt;-</span> <span class="fl">0</span>
+   <span class="kw">grad_w1</span> <span class="op">&lt;-</span> <span class="kw">x</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span>()<span class="op">$</span><span class="fu">mm</span>(<span class="kw">grad_h</span>)
+   
+   <span class="co"># Update weights using gradient descent</span>
+   <span class="kw">w1</span> <span class="op">&lt;-</span> <span class="kw">w1</span> <span class="op">-</span> <span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">grad_w1</span>
+   <span class="kw">w2</span> <span class="op">&lt;-</span> <span class="kw">w2</span> <span class="op">-</span> <span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">grad_w2</span>
+}
+<span class="co">#&gt; Step: 1 : 31418640 </span>
+<span class="co">#&gt; Step: 100 : 959.8534 </span>
+<span class="co">#&gt; Step: 200 : 34.41588 </span>
+<span class="co">#&gt; Step: 300 : 1.953276 </span>
+<span class="co">#&gt; Step: 400 : 0.1213879 </span>
+<span class="co">#&gt; Step: 500 : 0.008213471</span>
+</pre></div>
+<p>In the <a href="tensors-and-autograd.html">next example</a> we will use autograd instead of computing the gradients manually.</p>
+  </div>
+
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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new file mode 100644
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--- /dev/null
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@@ -0,0 +1,15 @@
+// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
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diff --git a/static/docs/articles/getting-started/warmup.html b/static/docs/articles/getting-started/warmup.html
new file mode 100644
index 000000000..f85e2bf06
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+<!DOCTYPE html>
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+        <a class="navbar-link" href="../../index.html">torch</a>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.3</span>
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+<li class="dropdown-header">Beginers guide</li>
+    <li>
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+      <a href="../../articles/getting-started/tensors.html">Tensors</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/tensors-and-autograd.html">Tensors and autograd</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
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+      <a href="../../articles/getting-started/optim.html">optim: optimizers in torch</a>
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+      <a href="../../articles/getting-started/custom-nn.html">Custom nn modules</a>
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+      <a href="../../articles/getting-started/control-flow-and-weight-sharing.html">Control flow &amp; Weight sharing</a>
+    </li>
+    <li class="dropdown-header">Torch Mechanics</li>
+    <li>
+      <a href="../../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
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+      <a href="../../articles/getting-started/neural-networks.html">Neural networks</a>
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+      <a href="../../articles/tensor-creation.html">Creating tensors</a>
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+
+      
+
+      </header><script src="warmup_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header toc-ignore">
+      <h1 data-toc-skip>Warm-up</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/warmup.Rmd"><code>vignettes/getting-started/warmup.Rmd</code></a></small>
+      <div class="hidden name"><code>warmup.Rmd</code></div>
+
+    </div>
+
+    
+    
+<blockquote>
+<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
+</blockquote>
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
+</pre></div>
+<p>A fully-connected ReLU network with one hidden layer and no biases, trained to predict y from x using Euclidean error.</p>
+<p>This implementation uses pure R to manually compute the forward pass, loss, and backward pass.</p>
+<p>An R array is a generic n-dimensional array; it does not know anything about deep learning or gradients or computational graphs, and is just a way to perform generic numeric computations.</p>
+<div class="sourceCode" id="cb2"><pre class="downlit">
+<span class="co"># N is batch size; D_in is input dimension;</span>
+<span class="co"># H is hidden dimension; D_out is output dimension.</span>
+<span class="kw">N</span> <span class="op">&lt;-</span> <span class="fl">64</span>
+<span class="kw">D_in</span> <span class="op">&lt;-</span> <span class="fl">1000</span>
+<span class="kw">H</span> <span class="op">&lt;-</span> <span class="fl">100</span>
+<span class="kw">D_out</span> <span class="op">&lt;-</span> <span class="fl">10</span>
+
+<span class="co"># Create random input and output data</span>
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/array.html">array</a></span>(<span class="fu"><a href="https://rdrr.io/r/stats/Normal.html">rnorm</a></span>(<span class="kw">N</span><span class="op">*</span><span class="kw">D_in</span>), dim = <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="kw">N</span>, <span class="kw">D_in</span>))
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/array.html">array</a></span>(<span class="fu"><a href="https://rdrr.io/r/stats/Normal.html">rnorm</a></span>(<span class="kw">N</span><span class="op">*</span><span class="kw">D_out</span>), dim = <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="kw">N</span>, <span class="kw">D_out</span>))
+
+<span class="co"># Randomly initialize weights</span>
+<span class="kw">w1</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/array.html">array</a></span>(<span class="fu"><a href="https://rdrr.io/r/stats/Normal.html">rnorm</a></span>(<span class="kw">D_in</span><span class="op">*</span><span class="kw">H</span>), dim = <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="kw">D_in</span>, <span class="kw">H</span>))
+<span class="kw">w2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/array.html">array</a></span>(<span class="fu"><a href="https://rdrr.io/r/stats/Normal.html">rnorm</a></span>(<span class="kw">H</span><span class="op">*</span><span class="kw">D_out</span>), dim = <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="kw">H</span>, <span class="kw">D_out</span>))
+
+<span class="kw">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-6</span>
+<span class="co">for</span> (<span class="kw">t</span> <span class="co">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fl">500</span>)) {
+   <span class="co"># Forward pass: compute predicted y</span>
+   <span class="kw">h</span> <span class="op">&lt;-</span> <span class="kw">x</span> <span class="op">%*%</span> <span class="kw">w1</span>
+   <span class="kw">h_relu</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span>(<span class="kw">h</span> <span class="op">&lt;</span> <span class="fl">0</span>, <span class="fl">0</span>, <span class="kw">h</span>)
+   <span class="kw">y_pred</span> <span class="op">&lt;-</span> <span class="kw">h_relu</span> <span class="op">%*%</span> <span class="kw">w2</span>
+   
+   <span class="co"># Compute and print loss</span>
+   <span class="kw">loss</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span>((<span class="kw">y_pred</span> <span class="op">-</span> <span class="kw">y</span>)<span class="op">^</span><span class="fl">2</span>)
+   <span class="co">if</span> (<span class="kw">t</span> <span class="op">%%</span> <span class="fl">100</span> <span class="op">==</span> <span class="fl">0</span> <span class="op">||</span> <span class="kw">t</span> <span class="op">==</span> <span class="fl">1</span>)
+      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="st">"Step:"</span>, <span class="kw">t</span>, <span class="st">":"</span>, <span class="kw">loss</span>, <span class="st">"\n"</span>)
+   
+   <span class="co"># Backprop to compute gradients of w1 and w2 with respect to loss</span>
+   <span class="kw">grad_y_pred</span> <span class="op">&lt;-</span> <span class="fl">2</span> <span class="op">*</span> (<span class="kw">y_pred</span> <span class="op">-</span> <span class="kw">y</span>)
+   <span class="kw">grad_w2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span>(<span class="kw">h_relu</span>) <span class="op">%*%</span> <span class="kw">grad_y_pred</span>
+   <span class="kw">grad_h_relu</span> <span class="op">&lt;-</span> <span class="kw">grad_y_pred</span> <span class="op">%*%</span> <span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span>(<span class="kw">w2</span>)
+   <span class="kw">grad_h</span> <span class="op">&lt;-</span> <span class="kw">grad_h_relu</span>
+   <span class="kw">grad_h</span>[<span class="kw">h</span> <span class="op">&lt;</span> <span class="fl">0</span>] <span class="op">&lt;-</span> <span class="fl">0</span>
+   <span class="kw">grad_w1</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span>(<span class="kw">x</span>) <span class="op">%*%</span> <span class="kw">grad_h</span>
+   
+   <span class="co"># Update weights</span>
+   <span class="kw">w1</span> <span class="op">&lt;-</span> <span class="kw">w1</span> <span class="op">-</span> <span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">grad_w1</span>
+   <span class="kw">w2</span> <span class="op">&lt;-</span> <span class="kw">w2</span> <span class="op">-</span> <span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">grad_w2</span>
+}
+<span class="co">#&gt; Step: 1 : 28115720 </span>
+<span class="co">#&gt; Step: 100 : 536.8496 </span>
+<span class="co">#&gt; Step: 200 : 2.748443 </span>
+<span class="co">#&gt; Step: 300 : 0.01913319 </span>
+<span class="co">#&gt; Step: 400 : 0.0001405911 </span>
+<span class="co">#&gt; Step: 500 : 1.056453e-06</span>
+</pre></div>
+<p>In the <a href="tensors.html">next example</a> we will replace the R array for a torch Tensor.</p>
+  </div>
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new file mode 100644
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@@ -0,0 +1,15 @@
+// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
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+// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
+
+document.addEventListener('DOMContentLoaded', function() {
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diff --git a/static/docs/articles/getting-started/what-is-torch.html b/static/docs/articles/getting-started/what-is-torch.html
new file mode 100644
index 000000000..cf83c6ada
--- /dev/null
+++ b/static/docs/articles/getting-started/what-is-torch.html
@@ -0,0 +1,411 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
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+        <a class="navbar-link" href="../../index.html">torch</a>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.3</span>
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+<li class="dropdown-header">Beginers guide</li>
+    <li>
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+      <a href="../../articles/getting-started/tensors.html">Tensors</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/tensors-and-autograd.html">Tensors and autograd</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/optim.html">optim: optimizers in torch</a>
+    </li>
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+      <a href="../../articles/getting-started/custom-nn.html">Custom nn modules</a>
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+    <li>
+      <a href="../../articles/getting-started/control-flow-and-weight-sharing.html">Control flow &amp; Weight sharing</a>
+    </li>
+    <li class="dropdown-header">Torch Mechanics</li>
+    <li>
+      <a href="../../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
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+      <a href="../../articles/getting-started/neural-networks.html">Neural networks</a>
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+<li class="dropdown-header">Tensors</li>
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+      <a href="../../articles/tensor-creation.html">Creating tensors</a>
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+
+      
+
+      </header><script src="what-is-torch_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header toc-ignore">
+      <h1 data-toc-skip>What is torch?</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/what-is-torch.Rmd"><code>vignettes/getting-started/what-is-torch.Rmd</code></a></small>
+      <div class="hidden name"><code>what-is-torch.Rmd</code></div>
+
+    </div>
+
+    
+    
+<blockquote>
+<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/blitz/tensor_tutorial.html#sphx-glr-beginner-blitz-tensor-tutorial-py">here</a>. All credits goes to <a href="http://soumith.ch/">Soumith Chintala</a>.</p>
+</blockquote>
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
+</pre></div>
+<p>It’s a scientific computing package targeted at two sets of audiences:</p>
+<ul>
+<li>An array library to use the power of GPUs</li>
+<li>a deep learning research platform that provides maximum flexibility and speed</li>
+</ul>
+<div id="getting-started" class="section level2">
+<h2 class="hasAnchor">
+<a href="#getting-started" class="anchor"></a>Getting started</h2>
+<div id="tensors" class="section level3">
+<h3 class="hasAnchor">
+<a href="#tensors" class="anchor"></a>Tensors</h3>
+<p>Tensors are similar to R arrays, with the addition being that Tensors can also be used on a GPU to accelerate computing.</p>
+<blockquote>
+<p>Note: An uninitialized matrix is declared, but does not contain definite known values before it is used. When an uninitialized matrix is created, whatever values were in the allocated memory at the time will appear as the initial values.</p>
+</blockquote>
+<p>Construct a 5x3 matrix, uninitialized:</p>
+<div class="sourceCode" id="cb2"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_empty.html">torch_empty</a></span>(<span class="fl">5</span>, <span class="fl">3</span>)
+<span class="kw">x</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  0.0000e+00  1.5846e+29  1.7045e+23</span>
+<span class="co">#&gt; -1.5849e+29  7.0065e-45  0.0000e+00</span>
+<span class="co">#&gt;  0.0000e+00  0.0000e+00  0.0000e+00</span>
+<span class="co">#&gt;  0.0000e+00  0.0000e+00  0.0000e+00</span>
+<span class="co">#&gt;  0.0000e+00  0.0000e+00  0.0000e+00</span>
+<span class="co">#&gt; [ CPUFloatType{5,3} ]</span>
+</pre></div>
+<p>Construct a randomly initialized matrix:</p>
+<div class="sourceCode" id="cb3"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">5</span>, <span class="fl">3</span>)
+<span class="kw">x</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  0.2655  0.4783  0.8396</span>
+<span class="co">#&gt;  0.2444  0.6980  0.7983</span>
+<span class="co">#&gt;  0.6614  0.9127  0.8649</span>
+<span class="co">#&gt;  0.8563  0.3856  0.0944</span>
+<span class="co">#&gt;  0.8246  0.9818  0.8342</span>
+<span class="co">#&gt; [ CPUFloatType{5,3} ]</span>
+</pre></div>
+<p>Construct a matrix filled zeros and of dtype long:</p>
+<div class="sourceCode" id="cb4"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">5</span>, <span class="fl">3</span>, dtype = <span class="fu"><a href="../../reference/torch_dtype.html">torch_long</a></span>())
+<span class="kw">x</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  0  0  0</span>
+<span class="co">#&gt;  0  0  0</span>
+<span class="co">#&gt;  0  0  0</span>
+<span class="co">#&gt;  0  0  0</span>
+<span class="co">#&gt;  0  0  0</span>
+<span class="co">#&gt; [ CPULongType{5,3} ]</span>
+</pre></div>
+<p>Construct a tensor directly from data:</p>
+<div class="sourceCode" id="cb5"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">5.5</span>, <span class="fl">3</span>))
+<span class="kw">x</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  5.5000</span>
+<span class="co">#&gt;  3.0000</span>
+<span class="co">#&gt; [ CPUFloatType{2} ]</span>
+</pre></div>
+<p>or create a tensor based on an existing tensor. These methods will reuse properties of the input tensor, e.g. dtype, unless new values are provided by user</p>
+<div class="sourceCode" id="cb6"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn_like.html">torch_randn_like</a></span>(<span class="kw">x</span>, dtype = <span class="fu"><a href="../../reference/torch_dtype.html">torch_float</a></span>()) <span class="co"># override dtype!</span>
+<span class="kw">x</span>                                               <span class="co"># result has the same size</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  0.7172</span>
+<span class="co">#&gt;  0.9112</span>
+<span class="co">#&gt; [ CPUFloatType{2} ]</span>
+</pre></div>
+<p>Get its size:</p>
+<div class="sourceCode" id="cb7"><pre class="downlit">
+<span class="kw">x</span><span class="op">$</span><span class="fu">size</span>()
+<span class="co">#&gt; [1] 2</span>
+</pre></div>
+</div>
+<div id="operations" class="section level3">
+<h3 class="hasAnchor">
+<a href="#operations" class="anchor"></a>Operations</h3>
+<p>There are multiple syntaxes for operations. In the following example, we will take a look at the addition operation.</p>
+<p>Addition: syntax 1</p>
+<div class="sourceCode" id="cb8"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">5</span>, <span class="fl">3</span>)
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">5</span>, <span class="fl">3</span>)
+<span class="kw">x</span> <span class="op">+</span> <span class="kw">y</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  0.7737  0.8053  0.3150</span>
+<span class="co">#&gt;  1.3053  1.0479  0.5301</span>
+<span class="co">#&gt;  1.6027  0.7272  0.9115</span>
+<span class="co">#&gt;  1.3239  0.8749  0.7270</span>
+<span class="co">#&gt;  1.1298  1.3922  0.7527</span>
+<span class="co">#&gt; [ CPUFloatType{5,3} ]</span>
+</pre></div>
+<p>Addition: syntax 2</p>
+<div class="sourceCode" id="cb9"><pre class="downlit">
+<span class="fu"><a href="../../reference/torch_add.html">torch_add</a></span>(<span class="kw">x</span>, <span class="kw">y</span>)
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  0.7737  0.8053  0.3150</span>
+<span class="co">#&gt;  1.3053  1.0479  0.5301</span>
+<span class="co">#&gt;  1.6027  0.7272  0.9115</span>
+<span class="co">#&gt;  1.3239  0.8749  0.7270</span>
+<span class="co">#&gt;  1.1298  1.3922  0.7527</span>
+<span class="co">#&gt; [ CPUFloatType{5,3} ]</span>
+</pre></div>
+<p>Addition: in-place</p>
+<div class="sourceCode" id="cb10"><pre class="downlit">
+<span class="kw">y</span><span class="op">$</span><span class="fu">add_</span>(<span class="kw">x</span>)
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  0.7737  0.8053  0.3150</span>
+<span class="co">#&gt;  1.3053  1.0479  0.5301</span>
+<span class="co">#&gt;  1.6027  0.7272  0.9115</span>
+<span class="co">#&gt;  1.3239  0.8749  0.7270</span>
+<span class="co">#&gt;  1.1298  1.3922  0.7527</span>
+<span class="co">#&gt; [ CPUFloatType{5,3} ]</span>
+<span class="kw">y</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  0.7737  0.8053  0.3150</span>
+<span class="co">#&gt;  1.3053  1.0479  0.5301</span>
+<span class="co">#&gt;  1.6027  0.7272  0.9115</span>
+<span class="co">#&gt;  1.3239  0.8749  0.7270</span>
+<span class="co">#&gt;  1.1298  1.3922  0.7527</span>
+<span class="co">#&gt; [ CPUFloatType{5,3} ]</span>
+</pre></div>
+<blockquote>
+<p>Note: Any operation that mutates a tensor in-place is post-fixed with an <code>_</code>. For example: <code>x$copy_(y)</code>, <code>x$t_()</code>, will change x.</p>
+</blockquote>
+<p>You can use standard R-like indexing with all bells and whistles! See more about indexing with <code><a href="../../articles/indexing.html">vignette("indexing")</a></code>.</p>
+<div class="sourceCode" id="cb11"><pre class="downlit">
+<span class="kw">x</span>[, <span class="fl">1</span>]
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  0.4454</span>
+<span class="co">#&gt;  0.5480</span>
+<span class="co">#&gt;  0.7439</span>
+<span class="co">#&gt;  0.7984</span>
+<span class="co">#&gt;  0.9449</span>
+<span class="co">#&gt; [ CPUFloatType{5} ]</span>
+</pre></div>
+<p>Resizing: If you want to resize/reshape tensor, you can use <code>torch_view</code>:</p>
+<div class="sourceCode" id="cb12"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">4</span>, <span class="fl">4</span>)
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="kw">x</span><span class="op">$</span><span class="fu">view</span>(<span class="fl">16</span>)
+<span class="kw">z</span> <span class="op">&lt;-</span> <span class="kw">x</span><span class="op">$</span><span class="fu">view</span>(size = <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="op">-</span><span class="fl">1</span>, <span class="fl">8</span>))  <span class="co"># the size -1 is inferred from other dimensions</span>
+<span class="kw">x</span><span class="op">$</span><span class="fu">size</span>()
+<span class="co">#&gt; [1] 4 4</span>
+<span class="kw">y</span><span class="op">$</span><span class="fu">size</span>()
+<span class="co">#&gt; [1] 16</span>
+<span class="kw">z</span><span class="op">$</span><span class="fu">size</span>()
+<span class="co">#&gt; [1] 2 8</span>
+</pre></div>
+<p>If you have a one element tensor, use <code>$item()</code> to get the value as an R number</p>
+<div class="sourceCode" id="cb13"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">1</span>)
+<span class="kw">x</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  1.1464</span>
+<span class="co">#&gt; [ CPUFloatType{1} ]</span>
+<span class="kw">x</span><span class="op">$</span><span class="fu">item</span>()
+<span class="co">#&gt; [1] 1.146403</span>
+</pre></div>
+<p>You can find a complete list of operations in the reference page.</p>
+</div>
+</div>
+<div id="r-bridge" class="section level2">
+<h2 class="hasAnchor">
+<a href="#r-bridge" class="anchor"></a>R bridge</h2>
+<p>Converting a Torch Tensor to an R array and vice versa is a breeze.</p>
+<div id="converting-a-torch-tensor-into-an-r-array" class="section level3">
+<h3 class="hasAnchor">
+<a href="#converting-a-torch-tensor-into-an-r-array" class="anchor"></a>Converting a torch tensor into an R array</h3>
+<div class="sourceCode" id="cb14"><pre class="downlit">
+<span class="kw">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span>(<span class="fl">5</span>)
+<span class="kw">a</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt; [ CPUFloatType{5} ]</span>
+</pre></div>
+<div class="sourceCode" id="cb15"><pre class="downlit">
+<span class="kw">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/as_array.html">as_array</a></span>(<span class="kw">a</span>)
+<span class="kw">b</span>
+<span class="co">#&gt; [1] 1 1 1 1 1</span>
+</pre></div>
+</div>
+<div id="converting-r-arrays-to-torch-tensors" class="section level3">
+<h3 class="hasAnchor">
+<a href="#converting-r-arrays-to-torch-tensors" class="anchor"></a>Converting R arrays to torch tensors</h3>
+<div class="sourceCode" id="cb16"><pre class="downlit">
+<span class="kw">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html">rep</a></span>(<span class="fl">1</span>, <span class="fl">5</span>)
+<span class="kw">a</span>
+<span class="co">#&gt; [1] 1 1 1 1 1</span>
+<span class="kw">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="kw">a</span>)
+<span class="kw">b</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt; [ CPUFloatType{5} ]</span>
+</pre></div>
+<p>Currently supported types are numerics and boolean types.</p>
+</div>
+</div>
+<div id="cuda-tensors" class="section level2">
+<h2 class="hasAnchor">
+<a href="#cuda-tensors" class="anchor"></a>CUDA tensors</h2>
+<p>Tensors can be moved onto any device using the <code>$to</code> method.</p>
+<div class="sourceCode" id="cb17"><pre class="downlit">
+<span class="co">if</span> (<span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span>()) {
+  <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cuda"</span>)
+  <span class="kw">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones_like.html">torch_ones_like</a></span>(<span class="kw">x</span>, device = <span class="kw">device</span>)  <span class="co"># directly create a tensor on GPU</span>
+  <span class="kw">x</span> <span class="op">&lt;-</span> <span class="kw">x</span><span class="op">$</span><span class="fu">to</span>(<span class="kw">device</span>)                       <span class="co"># or just use strings ``.to("cuda")``</span>
+  <span class="kw">z</span> <span class="op">&lt;-</span> <span class="kw">x</span> <span class="op">+</span> <span class="kw">y</span>
+  <span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="kw">z</span>)
+  <span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="kw">z</span><span class="op">$</span><span class="fu">to</span>(device = <span class="st">"cpu"</span>, <span class="fu"><a href="../../reference/torch_dtype.html">torch_double</a></span>())) <span class="co"># `$to` can also change dtype together!</span>
+}
+</pre></div>
+</div>
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+      <h1 data-toc-skip>Indexing tensors</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/indexing.Rmd"><code>vignettes/indexing.Rmd</code></a></small>
+      <div class="hidden name"><code>indexing.Rmd</code></div>
+
+    </div>
+
+    
+    
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
+</pre></div>
+<p>In this article we describe the indexing operator for torch tensors and how it compares to the R indexing operator for arrays.</p>
+<p>Torch’s indexing semantics are closer to numpy’s semantics than R’s. You will find a lot of similarities between this article and the <code>numpy</code> indexing article available <a href="https://docs.scipy.org/doc/numpy-1.10.0/user/basics.indexing.html">here</a>.</p>
+<div id="single-element-indexing" class="section level2">
+<h2 class="hasAnchor">
+<a href="#single-element-indexing" class="anchor"></a>Single element indexing</h2>
+<p>Single element indexing for a 1-D tensors works mostly as expected. Like R, it is 1-based. Unlike R though, it accepts negative indices for indexing from the end of the array. (In R, negative indices are used to remove elements.)</p>
+<div class="sourceCode" id="cb2"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">10</span>)
+<span class="kw">x</span>[<span class="fl">1</span>]
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt; 1</span>
+<span class="co">#&gt; [ CPULongType{} ]</span>
+<span class="kw">x</span>[<span class="op">-</span><span class="fl">1</span>]
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt; 10</span>
+<span class="co">#&gt; [ CPULongType{} ]</span>
+</pre></div>
+<p>You can also subset matrices and higher dimensions arrays using the same syntax:</p>
+<div class="sourceCode" id="cb3"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="kw">x</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/stats/reshape.html">reshape</a></span>(shape = <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>,<span class="fl">5</span>))
+<span class="kw">x</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;   1   2   3   4   5</span>
+<span class="co">#&gt;   6   7   8   9  10</span>
+<span class="co">#&gt; [ CPULongType{2,5} ]</span>
+<span class="kw">x</span>[<span class="fl">1</span>,<span class="fl">3</span>]
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt; 3</span>
+<span class="co">#&gt; [ CPULongType{} ]</span>
+<span class="kw">x</span>[<span class="fl">1</span>,<span class="op">-</span><span class="fl">1</span>]
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt; 5</span>
+<span class="co">#&gt; [ CPULongType{} ]</span>
+</pre></div>
+<p>Note that if one indexes a multidimensional tensor with fewer indices than dimensions, one gets an error, unlike in R that would flatten the array. For example:</p>
+<div class="sourceCode" id="cb4"><pre class="downlit">
+<span class="kw">x</span>[<span class="fl">1</span>]
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  2</span>
+<span class="co">#&gt;  3</span>
+<span class="co">#&gt;  4</span>
+<span class="co">#&gt;  5</span>
+<span class="co">#&gt; [ CPULongType{5} ]</span>
+</pre></div>
+</div>
+<div id="slicing-and-striding" class="section level2">
+<h2 class="hasAnchor">
+<a href="#slicing-and-striding" class="anchor"></a>Slicing and striding</h2>
+<p>It is possible to slice and stride arrays to extract sub-arrays of the same number of dimensions, but of different sizes than the original. This is best illustrated by a few examples:</p>
+<div class="sourceCode" id="cb5"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">10</span>)
+<span class="kw">x</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;   1</span>
+<span class="co">#&gt;   2</span>
+<span class="co">#&gt;   3</span>
+<span class="co">#&gt;   4</span>
+<span class="co">#&gt;   5</span>
+<span class="co">#&gt;   6</span>
+<span class="co">#&gt;   7</span>
+<span class="co">#&gt;   8</span>
+<span class="co">#&gt;   9</span>
+<span class="co">#&gt;  10</span>
+<span class="co">#&gt; [ CPULongType{10} ]</span>
+<span class="kw">x</span>[<span class="fl">2</span><span class="op">:</span><span class="fl">5</span>]
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  2</span>
+<span class="co">#&gt;  3</span>
+<span class="co">#&gt;  4</span>
+<span class="co">#&gt;  5</span>
+<span class="co">#&gt; [ CPULongType{4} ]</span>
+<span class="kw">x</span>[<span class="fl">1</span><span class="op">:</span>(<span class="op">-</span><span class="fl">7</span>)]
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  2</span>
+<span class="co">#&gt;  3</span>
+<span class="co">#&gt;  4</span>
+<span class="co">#&gt; [ CPULongType{4} ]</span>
+</pre></div>
+<p>You can also use the <code>1:10:2</code> syntax which means: In the range from 1 to 10, take every second item. For example:</p>
+<div class="sourceCode" id="cb6"><pre class="downlit">
+<span class="kw">x</span>[<span class="fl">1</span><span class="op">:</span><span class="fl">5</span><span class="op">:</span><span class="fl">2</span>]
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  3</span>
+<span class="co">#&gt;  5</span>
+<span class="co">#&gt; [ CPULongType{3} ]</span>
+</pre></div>
+<p>Another special syntax is the <code>N</code>, meaning the size of the specified dimension.</p>
+<div class="sourceCode" id="cb7"><pre class="downlit">
+<span class="kw">x</span>[<span class="fl">5</span><span class="op">:</span><span class="kw">N</span>]
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;   5</span>
+<span class="co">#&gt;   6</span>
+<span class="co">#&gt;   7</span>
+<span class="co">#&gt;   8</span>
+<span class="co">#&gt;   9</span>
+<span class="co">#&gt;  10</span>
+<span class="co">#&gt; [ CPULongType{6} ]</span>
+</pre></div>
+</div>
+<div id="getting-the-complete-dimension" class="section level2">
+<h2 class="hasAnchor">
+<a href="#getting-the-complete-dimension" class="anchor"></a>Getting the complete dimension</h2>
+<p>Like in R, you can take all elements in a dimension by leaving an index empty.</p>
+<p>Consider a matrix:</p>
+<div class="sourceCode" id="cb8"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">2</span>, <span class="fl">3</span>)
+<span class="kw">x</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  1.4158  0.9219 -0.1461</span>
+<span class="co">#&gt;  0.9801  0.7556  0.2140</span>
+<span class="co">#&gt; [ CPUFloatType{2,3} ]</span>
+</pre></div>
+<p>The following syntax will give you the first row:</p>
+<div class="sourceCode" id="cb9"><pre class="downlit">
+<span class="kw">x</span>[<span class="fl">1</span>,]
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  1.4158</span>
+<span class="co">#&gt;  0.9219</span>
+<span class="co">#&gt; -0.1461</span>
+<span class="co">#&gt; [ CPUFloatType{3} ]</span>
+</pre></div>
+<p>And this would give you the first 2 columns:</p>
+<div class="sourceCode" id="cb10"><pre class="downlit">
+<span class="kw">x</span>[,<span class="fl">1</span><span class="op">:</span><span class="fl">2</span>]
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  1.4158  0.9219</span>
+<span class="co">#&gt;  0.9801  0.7556</span>
+<span class="co">#&gt; [ CPUFloatType{2,2} ]</span>
+</pre></div>
+</div>
+<div id="dropping-dimensions" class="section level2">
+<h2 class="hasAnchor">
+<a href="#dropping-dimensions" class="anchor"></a>Dropping dimensions</h2>
+<p>By default, when indexing by a single integer, this dimension will be dropped to avoid the singleton dimension:</p>
+<div class="sourceCode" id="cb11"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">2</span>, <span class="fl">3</span>)
+<span class="kw">x</span>[<span class="fl">1</span>,]<span class="op">$</span><span class="kw">shape</span>
+<span class="co">#&gt; [1] 3</span>
+</pre></div>
+<p>You can optionally use the <code>drop = FALSE</code> argument to avoid dropping the dimension.</p>
+<div class="sourceCode" id="cb12"><pre class="downlit">
+<span class="kw">x</span>[<span class="fl">1</span>,,drop = <span class="fl">FALSE</span>]<span class="op">$</span><span class="kw">shape</span>
+<span class="co">#&gt; [1] 1 3</span>
+</pre></div>
+</div>
+<div id="adding-a-new-dimension" class="section level2">
+<h2 class="hasAnchor">
+<a href="#adding-a-new-dimension" class="anchor"></a>Adding a new dimension</h2>
+<p>It’s possible to add a new dimension to a tensor using index-like syntax:</p>
+<div class="sourceCode" id="cb13"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">10</span>))
+<span class="kw">x</span><span class="op">$</span><span class="kw">shape</span>
+<span class="co">#&gt; [1] 1</span>
+<span class="kw">x</span>[, <span class="kw">newaxis</span>]<span class="op">$</span><span class="kw">shape</span>
+<span class="co">#&gt; [1] 1 1</span>
+<span class="kw">x</span>[, <span class="kw">newaxis</span>, <span class="kw">newaxis</span>]<span class="op">$</span><span class="kw">shape</span>
+<span class="co">#&gt; [1] 1 1 1</span>
+</pre></div>
+<p>You can also use <code>NULL</code> instead of <code>newaxis</code>:</p>
+<div class="sourceCode" id="cb14"><pre class="downlit">
+<span class="kw">x</span>[,<span class="kw">NULL</span>]<span class="op">$</span><span class="kw">shape</span>
+<span class="co">#&gt; [1] 1 1</span>
+</pre></div>
+</div>
+<div id="dealing-with-variable-number-of-indices" class="section level2">
+<h2 class="hasAnchor">
+<a href="#dealing-with-variable-number-of-indices" class="anchor"></a>Dealing with variable number of indices</h2>
+<p>Sometimes we don’t know how many dimensions a tensor has, but we do know what to do with the last available dimension, or the first one. To subsume all others, we can use <code>..</code>:</p>
+<div class="sourceCode" id="cb15"><pre class="downlit">
+<span class="kw">z</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">125</span>)<span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/stats/reshape.html">reshape</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">5</span>,<span class="fl">5</span>,<span class="fl">5</span>))
+<span class="kw">z</span>[<span class="fl">1</span>,<span class="kw">..</span>]
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;   1   2   3   4   5</span>
+<span class="co">#&gt;   6   7   8   9  10</span>
+<span class="co">#&gt;  11  12  13  14  15</span>
+<span class="co">#&gt;  16  17  18  19  20</span>
+<span class="co">#&gt;  21  22  23  24  25</span>
+<span class="co">#&gt; [ CPULongType{5,5} ]</span>
+<span class="kw">z</span>[<span class="kw">..</span>,<span class="fl">1</span>]
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;    1    6   11   16   21</span>
+<span class="co">#&gt;   26   31   36   41   46</span>
+<span class="co">#&gt;   51   56   61   66   71</span>
+<span class="co">#&gt;   76   81   86   91   96</span>
+<span class="co">#&gt;  101  106  111  116  121</span>
+<span class="co">#&gt; [ CPULongType{5,5} ]</span>
+</pre></div>
+</div>
+  </div>
+
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new file mode 100644
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+// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
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+document.addEventListener('DOMContentLoaded', function() {
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diff --git a/static/docs/articles/loading-data.html b/static/docs/articles/loading-data.html
new file mode 100644
index 000000000..a00e41ea3
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+++ b/static/docs/articles/loading-data.html
@@ -0,0 +1,390 @@
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+      <h1 data-toc-skip>Loading data</h1>
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+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/loading-data.Rmd"><code>vignettes/loading-data.Rmd</code></a></small>
+      <div class="hidden name"><code>loading-data.Rmd</code></div>
+
+    </div>
+
+    
+    
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
+</pre></div>
+<div id="datasets-and-data-loaders" class="section level2">
+<h2 class="hasAnchor">
+<a href="#datasets-and-data-loaders" class="anchor"></a>Datasets and data loaders</h2>
+<p>Central to data ingestion and preprocessing are datasets and data loaders.</p>
+<p><code>torch</code> comes equipped with a bag of datasets related to, mostly, image recognition and natural language processing (e.g., <code>mnist_dataset()</code>), which can be iterated over by means of <code>dataloader</code>s:</p>
+<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1"></a><span class="co"># ...</span></span>
+<span id="cb2-2"><a href="#cb2-2"></a>ds &lt;-<span class="st"> </span><span class="kw">mnist_dataset</span>(</span>
+<span id="cb2-3"><a href="#cb2-3"></a>  dir, </span>
+<span id="cb2-4"><a href="#cb2-4"></a>  <span class="dt">download =</span> <span class="ot">TRUE</span>, </span>
+<span id="cb2-5"><a href="#cb2-5"></a>  <span class="dt">transform =</span> <span class="cf">function</span>(x) {</span>
+<span id="cb2-6"><a href="#cb2-6"></a>    x &lt;-<span class="st"> </span>x<span class="op">$</span><span class="kw">to</span>(<span class="dt">dtype =</span> <span class="kw">torch_float</span>())<span class="op">/</span><span class="dv">256</span></span>
+<span id="cb2-7"><a href="#cb2-7"></a>    x[newaxis,..]</span>
+<span id="cb2-8"><a href="#cb2-8"></a>  }</span>
+<span id="cb2-9"><a href="#cb2-9"></a>)</span>
+<span id="cb2-10"><a href="#cb2-10"></a></span>
+<span id="cb2-11"><a href="#cb2-11"></a>dl &lt;-<span class="st"> </span><span class="kw">dataloader</span>(ds, <span class="dt">batch_size =</span> <span class="dv">32</span>, <span class="dt">shuffle =</span> <span class="ot">TRUE</span>)</span>
+<span id="cb2-12"><a href="#cb2-12"></a></span>
+<span id="cb2-13"><a href="#cb2-13"></a><span class="cf">for</span> (b <span class="cf">in</span> <span class="kw">enumerate</span>(dl)) {</span>
+<span id="cb2-14"><a href="#cb2-14"></a>  <span class="co"># ...</span></span></code></pre></div>
+<p>Cf. <code>vignettes/examples/mnist-cnn.R</code> for a complete example.</p>
+<p>What if you want to train on a different dataset? In these cases, you subclass <code>Dataset</code>, an abstract container that needs to know how to iterate over the given data. To that purpose, your subclass needs to implement <code>.getitem()</code>, and say what should be returned when the data loader is asking for the next batch.</p>
+<p>In <code>.getitem()</code>, you can implement whatever preprocessing you require. Additionally, you should implement <code>.length()</code>, so users can find out how many items there are in the dataset.</p>
+<p>While this may sound complicated, it is not at all. The base logic is straightforward – complexity will, naturally, correlate with how involved your preprocessing is. To provide you with a simple but functional prototype, here we show how to create your own <code>dataset</code> to train on <a href="https://github.com/allisonhorst/palmerpenguins">Allison Horst's penguins</a>.</p>
+</div>
+<div id="a-custom-dataset" class="section level2">
+<h2 class="hasAnchor">
+<a href="#a-custom-dataset" class="anchor"></a>A custom dataset</h2>
+<div class="sourceCode" id="cb3"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://allisonhorst.github.io/palmerpenguins">palmerpenguins</a></span>)
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw">magrittr</span>)
+
+<span class="kw">penguins</span>
+<span class="co">#&gt; # A tibble: 344 x 8</span>
+<span class="co">#&gt;    species island bill_length_mm bill_depth_mm flipper_length_… body_mass_g</span>
+<span class="co">#&gt;    &lt;fct&gt;   &lt;fct&gt;           &lt;dbl&gt;         &lt;dbl&gt;            &lt;int&gt;       &lt;int&gt;</span>
+<span class="co">#&gt;  1 Adelie  Torge…           39.1          18.7              181        3750</span>
+<span class="co">#&gt;  2 Adelie  Torge…           39.5          17.4              186        3800</span>
+<span class="co">#&gt;  3 Adelie  Torge…           40.3          18                195        3250</span>
+<span class="co">#&gt;  4 Adelie  Torge…           NA            NA                 NA          NA</span>
+<span class="co">#&gt;  5 Adelie  Torge…           36.7          19.3              193        3450</span>
+<span class="co">#&gt;  6 Adelie  Torge…           39.3          20.6              190        3650</span>
+<span class="co">#&gt;  7 Adelie  Torge…           38.9          17.8              181        3625</span>
+<span class="co">#&gt;  8 Adelie  Torge…           39.2          19.6              195        4675</span>
+<span class="co">#&gt;  9 Adelie  Torge…           34.1          18.1              193        3475</span>
+<span class="co">#&gt; 10 Adelie  Torge…           42            20.2              190        4250</span>
+<span class="co">#&gt; # … with 334 more rows, and 2 more variables: sex &lt;fct&gt;, year &lt;int&gt;</span>
+</pre></div>
+<p><code>Dataset</code>s are R6 classes created using the <code><a href="../reference/dataset.html">dataset()</a></code> constructor. You can pass a <code>name</code> and various member functions. Among those should be <code>initialize()</code>, to create instance variables, <code>.getitem()</code>, to indicate how the data should be returned, and <code>.length()</code>, to say how many items we have.</p>
+<p>In addition, any number of helper functions can be defined.</p>
+<p>Here, we assume the <code>penguins</code> have already been loaded, and all preprocessing consists in removing lines with <code>NA</code> values, transforming <code>factor</code>s to numbers starting from 0, and converting from R data types to <code>torch</code> tensors.</p>
+<p>In <code>.getitem</code>, we essentially decide how this data is going to be used: All variables besides <code>species</code> go into <code>x</code>, the predictor, and <code>species</code> will constitute <code>y</code>, the target. Predictor and target are returned in a list, to be accessed as <code>batch[[1]]</code> and <code>batch[[2]]</code> during training.</p>
+<div class="sourceCode" id="cb4"><pre class="downlit">
+<span class="kw">penguins_dataset</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/dataset.html">dataset</a></span>(
+  
+  name = <span class="st">"penguins_dataset"</span>,
+  
+  initialize = <span class="fu">function</span>() {
+    <span class="kw">self</span><span class="op">$</span><span class="kw">data</span> <span class="op">&lt;-</span> <span class="kw">self</span><span class="op">$</span><span class="fu">prepare_penguin_data</span>()
+  },
+  
+  .getitem = <span class="fu">function</span>(<span class="kw">index</span>) {
+    
+    <span class="kw">x</span> <span class="op">&lt;-</span> <span class="kw">self</span><span class="op">$</span><span class="kw">data</span>[<span class="kw">index</span>, <span class="fl">2</span><span class="op">:</span><span class="op">-</span><span class="fl">1</span>]
+    <span class="kw">y</span> <span class="op">&lt;-</span> <span class="kw">self</span><span class="op">$</span><span class="kw">data</span>[<span class="kw">index</span>, <span class="fl">1</span>]<span class="op">$</span><span class="fu">to</span>(<span class="fu"><a href="../reference/torch_dtype.html">torch_long</a></span>())
+    
+    <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="kw">x</span>, <span class="kw">y</span>)
+  },
+  
+  .length = <span class="fu">function</span>() {
+    <span class="kw">self</span><span class="op">$</span><span class="kw">data</span><span class="op">$</span><span class="fu">size</span>()[[<span class="fl">1</span>]]
+  },
+  
+  prepare_penguin_data = <span class="fu">function</span>() {
+    
+    <span class="kw">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/na.fail.html">na.omit</a></span>(<span class="kw">penguins</span>) 
+    <span class="co"># conveniently, the categorical data are already factors</span>
+    <span class="kw">input</span><span class="op">$</span><span class="kw">species</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="kw">input</span><span class="op">$</span><span class="kw">species</span>)
+    <span class="kw">input</span><span class="op">$</span><span class="kw">island</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="kw">input</span><span class="op">$</span><span class="kw">island</span>)
+    <span class="kw">input</span><span class="op">$</span><span class="kw">sex</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="kw">input</span><span class="op">$</span><span class="kw">sex</span>)
+    
+    <span class="kw">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html">as.matrix</a></span>(<span class="kw">input</span>)
+    <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="kw">input</span>)
+  }
+)
+</pre></div>
+<p>Let’s create the dataset , query for it’s length, and look at its first item:</p>
+<div class="sourceCode" id="cb5"><pre class="downlit">
+<span class="kw">tuxes</span> <span class="op">&lt;-</span> <span class="fu">penguins_dataset</span>()
+<span class="kw">tuxes</span><span class="op">$</span><span class="fu">.length</span>()
+<span class="co">#&gt; [1] 333</span>
+<span class="kw">tuxes</span><span class="op">$</span><span class="fu">.getitem</span>(<span class="fl">1</span>)
+<span class="co">#&gt; [[1]]</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;     3.0000</span>
+<span class="co">#&gt;    39.1000</span>
+<span class="co">#&gt;    18.7000</span>
+<span class="co">#&gt;   181.0000</span>
+<span class="co">#&gt;  3750.0000</span>
+<span class="co">#&gt;     2.0000</span>
+<span class="co">#&gt;  2007.0000</span>
+<span class="co">#&gt; [ CPUFloatType{7} ]</span>
+<span class="co">#&gt; </span>
+<span class="co">#&gt; [[2]]</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt; 1</span>
+<span class="co">#&gt; [ CPULongType{} ]</span>
+</pre></div>
+<p>To be able to iterate over <code>tuxes</code>, we need a data loader (we override the default batch size of <code>1</code>):</p>
+<div class="sourceCode" id="cb6"><pre class="downlit">
+<span class="kw">dl</span> <span class="op">&lt;-</span><span class="kw">tuxes</span> <span class="op">%&gt;%</span> <span class="fu"><a href="../reference/dataloader.html">dataloader</a></span>(batch_size = <span class="fl">8</span>)
+</pre></div>
+<p>Calling <code>.length()</code> on a data loader (as opposed to a dataset) will return the number of <code>batches</code> we have:</p>
+<div class="sourceCode" id="cb7"><pre class="downlit">
+<span class="kw">dl</span><span class="op">$</span><span class="fu">.length</span>()
+<span class="co">#&gt; [1] 42</span>
+</pre></div>
+<p>And we can create an iterator to inspect the first batch:</p>
+<div class="sourceCode" id="cb8"><pre class="downlit">
+<span class="kw">iter</span> <span class="op">&lt;-</span> <span class="kw">dl</span><span class="op">$</span><span class="fu">.iter</span>()
+<span class="kw">b</span> <span class="op">&lt;-</span> <span class="kw">iter</span><span class="op">$</span><span class="fu">.next</span>()
+<span class="kw">b</span>
+<span class="co">#&gt; [[1]]</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;     3.0000    39.1000    18.7000   181.0000  3750.0000     2.0000  2007.0000</span>
+<span class="co">#&gt;     3.0000    39.5000    17.4000   186.0000  3800.0000     1.0000  2007.0000</span>
+<span class="co">#&gt;     3.0000    40.3000    18.0000   195.0000  3250.0000     1.0000  2007.0000</span>
+<span class="co">#&gt;     3.0000    36.7000    19.3000   193.0000  3450.0000     1.0000  2007.0000</span>
+<span class="co">#&gt;     3.0000    39.3000    20.6000   190.0000  3650.0000     2.0000  2007.0000</span>
+<span class="co">#&gt;     3.0000    38.9000    17.8000   181.0000  3625.0000     1.0000  2007.0000</span>
+<span class="co">#&gt;     3.0000    39.2000    19.6000   195.0000  4675.0000     2.0000  2007.0000</span>
+<span class="co">#&gt;     3.0000    41.1000    17.6000   182.0000  3200.0000     1.0000  2007.0000</span>
+<span class="co">#&gt; [ CPUFloatType{8,7} ]</span>
+<span class="co">#&gt; </span>
+<span class="co">#&gt; [[2]]</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt; [ CPULongType{8} ]</span>
+</pre></div>
+<p>To train a network, we can use <code>enumerate</code> to iterate over batches.</p>
+</div>
+<div id="training-with-data-loaders" class="section level2">
+<h2 class="hasAnchor">
+<a href="#training-with-data-loaders" class="anchor"></a>Training with data loaders</h2>
+<p>Our example network is very simple. (In reality, we would want to treat <code>island</code> as the categorical variable it is, and either one-hot-encode or embed it.)</p>
+<div class="sourceCode" id="cb9"><pre class="downlit">
+<span class="kw">net</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/nn_module.html">nn_module</a></span>(
+  <span class="st">"PenguinNet"</span>,
+  initialize = <span class="fu">function</span>() {
+    <span class="kw">self</span><span class="op">$</span><span class="kw">fc1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/nn_linear.html">nn_linear</a></span>(<span class="fl">7</span>, <span class="fl">32</span>)
+    <span class="kw">self</span><span class="op">$</span><span class="kw">fc2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/nn_linear.html">nn_linear</a></span>(<span class="fl">32</span>, <span class="fl">3</span>)
+  },
+  forward = <span class="fu">function</span>(<span class="kw">x</span>) {
+    <span class="kw">x</span> <span class="op">%&gt;%</span> 
+      <span class="kw">self</span><span class="op">$</span><span class="fu">fc1</span>() <span class="op">%&gt;%</span> 
+      <span class="fu"><a href="../reference/nnf_relu.html">nnf_relu</a></span>() <span class="op">%&gt;%</span> 
+      <span class="kw">self</span><span class="op">$</span><span class="fu">fc2</span>() <span class="op">%&gt;%</span> 
+      <span class="fu"><a href="../reference/nnf_log_softmax.html">nnf_log_softmax</a></span>(dim = <span class="fl">1</span>)
+  }
+)
+
+<span class="kw">model</span> <span class="op">&lt;-</span> <span class="fu">net</span>()
+</pre></div>
+<p>We still need an optimizer:</p>
+<div class="sourceCode" id="cb10"><pre class="downlit">
+<span class="kw">optimizer</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/optim_sgd.html">optim_sgd</a></span>(<span class="kw">model</span><span class="op">$</span><span class="kw">parameters</span>, lr = <span class="fl">0.01</span>)
+</pre></div>
+<p>And we’re ready to train:</p>
+<div class="sourceCode" id="cb11"><pre class="downlit">
+<span class="co">for</span> (<span class="kw">epoch</span> <span class="co">in</span> <span class="fl">1</span><span class="op">:</span><span class="fl">10</span>) {
+  
+  <span class="kw">l</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>()
+  
+  <span class="co">for</span> (<span class="kw">b</span> <span class="co">in</span> <span class="fu"><a href="../reference/enumerate.html">enumerate</a></span>(<span class="kw">dl</span>)) {
+    <span class="kw">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span>()
+    <span class="kw">output</span> <span class="op">&lt;-</span> <span class="fu">model</span>(<span class="kw">b</span>[[<span class="fl">1</span>]])
+    <span class="kw">loss</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/nnf_nll_loss.html">nnf_nll_loss</a></span>(<span class="kw">output</span>, <span class="kw">b</span>[[<span class="fl">2</span>]])
+    <span class="kw">loss</span><span class="op">$</span><span class="fu">backward</span>()
+    <span class="kw">optimizer</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/stats/step.html">step</a></span>()
+    <span class="kw">l</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="kw">l</span>, <span class="kw">loss</span><span class="op">$</span><span class="fu">item</span>())
+  }
+  
+  <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/sprintf.html">sprintf</a></span>(<span class="st">"Loss at epoch %d: %3f\n"</span>, <span class="kw">epoch</span>, <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>(<span class="kw">l</span>)))
+}
+<span class="co">#&gt; Loss at epoch 1: 51.747068</span>
+<span class="co">#&gt; Loss at epoch 2: 2.068251</span>
+<span class="co">#&gt; Loss at epoch 3: 2.068251</span>
+<span class="co">#&gt; Loss at epoch 4: 2.068251</span>
+<span class="co">#&gt; Loss at epoch 5: 2.068251</span>
+<span class="co">#&gt; Loss at epoch 6: 2.068251</span>
+<span class="co">#&gt; Loss at epoch 7: 2.068251</span>
+<span class="co">#&gt; Loss at epoch 8: 2.068251</span>
+<span class="co">#&gt; Loss at epoch 9: 2.068251</span>
+<span class="co">#&gt; Loss at epoch 10: 2.068251</span>
+</pre></div>
+</div>
+  </div>
+
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diff --git a/static/docs/articles/tensor-creation.html b/static/docs/articles/tensor-creation.html
new file mode 100644
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+  <div class="col-md-9 contents">
+    <div class="page-header toc-ignore">
+      <h1 data-toc-skip>Creating tensors</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/tensor-creation.Rmd"><code>vignettes/tensor-creation.Rmd</code></a></small>
+      <div class="hidden name"><code>tensor-creation.Rmd</code></div>
+
+    </div>
+
+    
+    
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
+</pre></div>
+<p>In this article we describe various ways of creating <code>torch</code> tensors in R.</p>
+<div id="from-r-objects" class="section level2">
+<h2 class="hasAnchor">
+<a href="#from-r-objects" class="anchor"></a>From R objects</h2>
+<p>You can create tensors from R objects using the <code>torch_tensor</code> function. The <code>torch_tensor</code> function takes an R vector, matrix or array and creates an equivalent <code>torch_tensor</code>.</p>
+<p>You can see a few examples below:</p>
+<div class="sourceCode" id="cb2"><pre class="downlit">
+<span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1</span>,<span class="fl">2</span>,<span class="fl">3</span>))
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  2</span>
+<span class="co">#&gt;  3</span>
+<span class="co">#&gt; [ CPUFloatType{3} ]</span>
+
+<span class="co"># conform to row-major indexing used in torch</span>
+<span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">10</span>, ncol = <span class="fl">5</span>, nrow = <span class="fl">2</span>, byrow = <span class="fl">TRUE</span>))
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;   1   2   3   4   5</span>
+<span class="co">#&gt;   6   7   8   9  10</span>
+<span class="co">#&gt; [ CPULongType{2,5} ]</span>
+<span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/array.html">array</a></span>(<span class="fu"><a href="https://rdrr.io/r/stats/Uniform.html">runif</a></span>(<span class="fl">12</span>), dim = <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>, <span class="fl">2</span>, <span class="fl">3</span>)))
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt; (1,.,.) = </span>
+<span class="co">#&gt;   0.5612  0.4325  0.6571</span>
+<span class="co">#&gt;   0.4899  0.4636  0.9910</span>
+<span class="co">#&gt; </span>
+<span class="co">#&gt; (2,.,.) = </span>
+<span class="co">#&gt;   0.7073  0.8791  0.3117</span>
+<span class="co">#&gt;   0.9112  0.6383  0.1045</span>
+<span class="co">#&gt; [ CPUFloatType{2,2,3} ]</span>
+</pre></div>
+<p>By default, we will create tensors in the <code>cpu</code> device, converting their R datatype to the corresponding torch <code>dtype</code>.</p>
+<blockquote>
+<p><strong>Note</strong> currently, only numeric and boolean types are supported.</p>
+</blockquote>
+<p>You can always modify <code>dtype</code> and <code>device</code> when converting an R object to a torch tensor. For example:</p>
+<div class="sourceCode" id="cb3"><pre class="downlit">
+<span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1</span>, dtype = <span class="fu"><a href="../reference/torch_dtype.html">torch_long</a></span>())
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt; [ CPULongType{1} ]</span>
+<span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1</span>, device = <span class="st">"cpu"</span>, dtype = <span class="fu"><a href="../reference/torch_dtype.html">torch_float64</a></span>())
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt; [ CPUDoubleType{1} ]</span>
+</pre></div>
+<p>Other options available when creating a tensor are:</p>
+<ul>
+<li>
+<code>requires_grad</code>: boolean indicating if you want <code>autograd</code> to record operations on them for automatic differentiation.</li>
+<li>
+<code>pin_memory</code>: – If set, the tensor returned would be allocated in pinned memory. Works only for CPU tensors.</li>
+</ul>
+<p>These options are available for all functions that can be used to create new tensors, including the factory functions listed in the next section.</p>
+</div>
+<div id="using-creation-functions" class="section level2">
+<h2 class="hasAnchor">
+<a href="#using-creation-functions" class="anchor"></a>Using creation functions</h2>
+<p>You can also use the <code>torch_*</code> functions listed below to create torch tensors using some algorithm.</p>
+<p>For example, the <code>torch_randn</code> function will create tensors using the normal distribution with mean 0 and standard deviation 1. You can use the <code>...</code> argument to pass the size of the dimensions. For example, the code below will create a normally distributed tensor with shape 5x3.</p>
+<div class="sourceCode" id="cb4"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">5</span>, <span class="fl">3</span>)
+<span class="kw">x</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt; -1.5887 -0.0033  1.0389</span>
+<span class="co">#&gt;  0.0472 -1.0173 -1.5143</span>
+<span class="co">#&gt;  1.9183 -0.6090 -0.9197</span>
+<span class="co">#&gt;  1.7162 -1.8687  0.8053</span>
+<span class="co">#&gt;  1.0018  0.6406 -0.5853</span>
+<span class="co">#&gt; [ CPUFloatType{5,3} ]</span>
+</pre></div>
+<p>Another example is <code>torch_ones</code>, which creates a tensor filled with ones.</p>
+<div class="sourceCode" id="cb5"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_ones.html">torch_ones</a></span>(<span class="fl">2</span>, <span class="fl">4</span>, dtype = <span class="fu"><a href="../reference/torch_dtype.html">torch_int64</a></span>(), device = <span class="st">"cpu"</span>)
+<span class="kw">x</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  1  1  1  1</span>
+<span class="co">#&gt;  1  1  1  1</span>
+<span class="co">#&gt; [ CPULongType{2,4} ]</span>
+</pre></div>
+<p>Here is the full list of functions that can be used to bulk-create tensors in torch:</p>
+<ul>
+<li>
+<code>torch_arange</code>: Returns a tensor with a sequence of integers,</li>
+<li>
+<code>torch_empty</code>: Returns a tensor with uninitialized values,</li>
+<li>
+<code>torch_eye</code>: Returns an identity matrix,</li>
+<li>
+<code>torch_full</code>: Returns a tensor filled with a single value,</li>
+<li>
+<code>torch_linspace</code>: Returns a tensor with values linearly spaced in some interval,</li>
+<li>
+<code>torch_logspace</code>: Returns a tensor with values logarithmically spaced in some interval,</li>
+<li>
+<code>torch_ones</code>: Returns a tensor filled with all ones,</li>
+<li>
+<code>torch_rand</code>: Returns a tensor filled with values drawn from a uniform distribution on [0, 1).</li>
+<li>
+<code>torch_randint</code>: Returns a tensor with integers randomly drawn from an interval,</li>
+<li>
+<code>torch_randn</code>: Returns a tensor filled with values drawn from a unit normal distribution,</li>
+<li>
+<code>torch_randperm</code>: Returns a tensor filled with a random permutation of integers in some interval,</li>
+<li>
+<code>torch_zeros</code>: Returns a tensor filled with all zeros.</li>
+</ul>
+</div>
+<div id="conversion" class="section level2">
+<h2 class="hasAnchor">
+<a href="#conversion" class="anchor"></a>Conversion</h2>
+<p>Once a tensor exists you can convert between <code>dtype</code>s and move to a different device with <code>to</code> method. For example:</p>
+<div class="sourceCode" id="cb6"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1</span>)
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="kw">x</span><span class="op">$</span><span class="fu">to</span>(dtype = <span class="fu"><a href="../reference/torch_dtype.html">torch_int32</a></span>())
+<span class="kw">x</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt; [ CPUFloatType{1} ]</span>
+<span class="kw">y</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt; [ CPUIntType{1} ]</span>
+</pre></div>
+<p>You can also copy a tensor to the GPU using:</p>
+<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a>x &lt;-<span class="st"> </span><span class="kw">torch_tensor</span>(<span class="dv">1</span>)</span>
+<span id="cb7-2"><a href="#cb7-2"></a>y &lt;-<span class="st"> </span>x<span class="op">$</span><span class="kw">cuda</span>()<span class="er">)</span></span></code></pre></div>
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+      <h1 data-toc-skip>Tensor objects</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/tensor/index.Rmd"><code>vignettes/tensor/index.Rmd</code></a></small>
+      <div class="hidden name"><code>index.Rmd</code></div>
+
+    </div>
+
+    
+    
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
+</pre></div>
+<p>Central to torch is the <code>torch_tensor</code> objects. <code>torch_tensor</code>’s are R objects very similar to R6 instances. Tensors have a large amount of methods that can be called using the <code><a href="https://rdrr.io/r/base/Extract.html">$</a></code> operator.</p>
+<p>Following is a list of all methods that can be called by tensor objects and their documentation. You can also look at <a href="https://pytorch.org/docs/stable/tensors.html">PyTorch’s documentation</a> for additional details.</p>
+<div id="t" class="section level2">
+<h2 class="hasAnchor">
+<a href="#t" class="anchor"></a>T</h2>
+<p>Is this Tensor with its dimensions reversed.</p>
+<p>If <code>n</code> is the number of dimensions in <code>x</code>, <code>x$T</code> is equivalent to <code>x$permute(n-1, n-2, ..., 0)</code>.</p>
+</div>
+<div id="abs" class="section level2">
+<h2 class="hasAnchor">
+<a href="#abs" class="anchor"></a>abs</h2>
+<p>abs() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_abs.html">?torch_abs</a></code></p>
+</div>
+<div id="abs_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#abs_" class="anchor"></a>abs_</h2>
+<p>abs_() -&gt; Tensor</p>
+<p>In-place version of <code>$abs</code></p>
+</div>
+<div id="absolute" class="section level2">
+<h2 class="hasAnchor">
+<a href="#absolute" class="anchor"></a>absolute</h2>
+<p>absolute() -&gt; Tensor</p>
+<p>Alias for [$abs()]</p>
+</div>
+<div id="absolute_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#absolute_" class="anchor"></a>absolute_</h2>
+<p>absolute_() -&gt; Tensor</p>
+<p>In-place version of <code>$absolute</code> Alias for [$abs_()]</p>
+</div>
+<div id="acos" class="section level2">
+<h2 class="hasAnchor">
+<a href="#acos" class="anchor"></a>acos</h2>
+<p>acos() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_acos.html">?torch_acos</a></code></p>
+</div>
+<div id="acos_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#acos_" class="anchor"></a>acos_</h2>
+<p>acos_() -&gt; Tensor</p>
+<p>In-place version of <code>$acos</code></p>
+</div>
+<div id="acosh" class="section level2">
+<h2 class="hasAnchor">
+<a href="#acosh" class="anchor"></a>acosh</h2>
+<p>acosh() -&gt; Tensor</p>
+<p>See <code>?torch_acosh</code></p>
+</div>
+<div id="acosh_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#acosh_" class="anchor"></a>acosh_</h2>
+<p>acosh_() -&gt; Tensor</p>
+<p>In-place version of <code>$acosh</code></p>
+</div>
+<div id="add" class="section level2">
+<h2 class="hasAnchor">
+<a href="#add" class="anchor"></a>add</h2>
+<p>add(other, *, alpha=1) -&gt; Tensor</p>
+<p>Add a scalar or tensor to <code>self</code> tensor. If both <code>alpha</code> and <code>other</code> are specified, each element of <code>other</code> is scaled by <code>alpha</code> before being used.</p>
+<p>When <code>other</code> is a tensor, the shape of <code>other</code> must be broadcastable with the shape of the underlying tensor</p>
+<p>See <code><a href="../../reference/torch_add.html">?torch_add</a></code></p>
+</div>
+<div id="add_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#add_" class="anchor"></a>add_</h2>
+<p>add_(other, *, alpha=1) -&gt; Tensor</p>
+<p>In-place version of <code>$add</code></p>
+</div>
+<div id="addbmm" class="section level2">
+<h2 class="hasAnchor">
+<a href="#addbmm" class="anchor"></a>addbmm</h2>
+<p>addbmm(batch1, batch2, *, beta=1, alpha=1) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_addbmm.html">?torch_addbmm</a></code></p>
+</div>
+<div id="addbmm_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#addbmm_" class="anchor"></a>addbmm_</h2>
+<p>addbmm_(batch1, batch2, *, beta=1, alpha=1) -&gt; Tensor</p>
+<p>In-place version of <code>$addbmm</code></p>
+</div>
+<div id="addcdiv" class="section level2">
+<h2 class="hasAnchor">
+<a href="#addcdiv" class="anchor"></a>addcdiv</h2>
+<p>addcdiv(tensor1, tensor2, *, value=1) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_addcdiv.html">?torch_addcdiv</a></code></p>
+</div>
+<div id="addcdiv_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#addcdiv_" class="anchor"></a>addcdiv_</h2>
+<p>addcdiv_(tensor1, tensor2, *, value=1) -&gt; Tensor</p>
+<p>In-place version of <code>$addcdiv</code></p>
+</div>
+<div id="addcmul" class="section level2">
+<h2 class="hasAnchor">
+<a href="#addcmul" class="anchor"></a>addcmul</h2>
+<p>addcmul(tensor1, tensor2, *, value=1) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_addcmul.html">?torch_addcmul</a></code></p>
+</div>
+<div id="addcmul_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#addcmul_" class="anchor"></a>addcmul_</h2>
+<p>addcmul_(tensor1, tensor2, *, value=1) -&gt; Tensor</p>
+<p>In-place version of <code>$addcmul</code></p>
+</div>
+<div id="addmm" class="section level2">
+<h2 class="hasAnchor">
+<a href="#addmm" class="anchor"></a>addmm</h2>
+<p>addmm(mat1, mat2, *, beta=1, alpha=1) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_addmm.html">?torch_addmm</a></code></p>
+</div>
+<div id="addmm_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#addmm_" class="anchor"></a>addmm_</h2>
+<p>addmm_(mat1, mat2, *, beta=1, alpha=1) -&gt; Tensor</p>
+<p>In-place version of <code>$addmm</code></p>
+</div>
+<div id="addmv" class="section level2">
+<h2 class="hasAnchor">
+<a href="#addmv" class="anchor"></a>addmv</h2>
+<p>addmv(mat, vec, *, beta=1, alpha=1) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_addmv.html">?torch_addmv</a></code></p>
+</div>
+<div id="addmv_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#addmv_" class="anchor"></a>addmv_</h2>
+<p>addmv_(mat, vec, *, beta=1, alpha=1) -&gt; Tensor</p>
+<p>In-place version of <code>$addmv</code></p>
+</div>
+<div id="addr" class="section level2">
+<h2 class="hasAnchor">
+<a href="#addr" class="anchor"></a>addr</h2>
+<p>addr(vec1, vec2, *, beta=1, alpha=1) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_addr.html">?torch_addr</a></code></p>
+</div>
+<div id="addr_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#addr_" class="anchor"></a>addr_</h2>
+<p>addr_(vec1, vec2, *, beta=1, alpha=1) -&gt; Tensor</p>
+<p>In-place version of <code>$addr</code></p>
+</div>
+<div id="align_as" class="section level2">
+<h2 class="hasAnchor">
+<a href="#align_as" class="anchor"></a>align_as</h2>
+<p>align_as(other) -&gt; Tensor</p>
+<p>Permutes the dimensions of the <code>self</code> tensor to match the dimension order in the <code>other</code> tensor, adding size-one dims for any new names.</p>
+<p>This operation is useful for explicit broadcasting by names (see examples).</p>
+<p>All of the dims of <code>self</code> must be named in order to use this method. The resulting tensor is a view on the original tensor.</p>
+<p>All dimension names of <code>self</code> must be present in <code>other$names</code>. <code>other</code> may contain named dimensions that are not in <code>self$names</code>; the output tensor has a size-one dimension for each of those new names.</p>
+<p>To align a tensor to a specific order, use <code>$align_to</code>.</p>
+<div id="examples" class="section level3">
+<h3 class="hasAnchor">
+<a href="#examples" class="anchor"></a>Examples:</h3>
+<div class="sourceCode" id="cb2"><pre class="downlit">
+<span class="co"># Example 1: Applying a mask</span>
+<span class="kw">mask</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randint.html">torch_randint</a></span>(low = <span class="fl">0</span>, high = <span class="fl">2</span>, size = <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">127</span>, <span class="fl">128</span>), dtype=<span class="fu"><a href="../../reference/torch_dtype.html">torch_bool</a></span>())<span class="op">$</span><span class="fu">refine_names</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">'W'</span>, <span class="st">'H'</span>))
+<span class="kw">imgs</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">32</span>, <span class="fl">128</span>, <span class="fl">127</span>, <span class="fl">3</span>, names=<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">'N'</span>, <span class="st">'H'</span>, <span class="st">'W'</span>, <span class="st">'C'</span>))
+<span class="kw">imgs</span><span class="op">$</span><span class="fu">masked_fill_</span>(<span class="kw">mask</span><span class="op">$</span><span class="fu">align_as</span>(<span class="kw">imgs</span>), <span class="fl">0</span>)
+
+<span class="co"># Example 2: Applying a per-channel-scale</span>
+<span class="kw">scale_channels</span> <span class="op">&lt;-</span> <span class="fu">function</span>(<span class="kw">input</span>, <span class="kw">scale</span>) {
+  <span class="kw">scale</span> <span class="op">&lt;-</span> <span class="kw">scale</span><span class="op">$</span><span class="fu">refine_names</span>(<span class="st">"C"</span>)
+  <span class="kw">input</span> <span class="op">*</span> <span class="kw">scale</span><span class="op">$</span><span class="fu">align_as</span>(<span class="kw">input</span>)
+}
+
+<span class="kw">num_channels</span> <span class="op">&lt;-</span> <span class="fl">3</span>
+<span class="kw">scale</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">num_channels</span>, names=<span class="st">'C'</span>)
+<span class="kw">imgs</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">32</span>, <span class="fl">128</span>, <span class="fl">128</span>, <span class="kw">num_channels</span>, names=<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">'N'</span>, <span class="st">'H'</span>, <span class="st">'W'</span>, <span class="st">'C'</span>))
+<span class="kw">more_imgs</span> <span class="op">=</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">32</span>, <span class="kw">num_channels</span>, <span class="fl">128</span>, <span class="fl">128</span>, names=<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">'N'</span>, <span class="st">'C'</span>, <span class="st">'H'</span>, <span class="st">'W'</span>))
+<span class="kw">videos</span> <span class="op">=</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">3</span>, <span class="kw">num_channels</span>, <span class="fl">128</span>, <span class="fl">128</span>, <span class="fl">128</span>, names=<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">'N'</span>, <span class="st">'C'</span>, <span class="st">'H'</span>, <span class="st">'W'</span>, <span class="st">'D'</span>))
+
+<span class="co"># scale_channels is agnostic to the dimension order of the input</span>
+<span class="fu">scale_channels</span>(<span class="kw">imgs</span>, <span class="kw">scale</span>)
+<span class="fu">scale_channels</span>(<span class="kw">more_imgs</span>, <span class="kw">scale</span>)
+<span class="fu">scale_channels</span>(<span class="kw">videos</span>, <span class="kw">scale</span>)
+</pre></div>
+</div>
+<div id="warning" class="section level3">
+<h3 class="hasAnchor">
+<a href="#warning" class="anchor"></a>Warning:</h3>
+<p>The named tensor API is experimental and subject to change.</p>
+</div>
+</div>
+<div id="align_to" class="section level2">
+<h2 class="hasAnchor">
+<a href="#align_to" class="anchor"></a>align_to</h2>
+<p>Permutes the dimensions of the <code>self</code> tensor to match the order specified in <code>names</code>, adding size-one dims for any new names.</p>
+<p>All of the dims of <code>self</code> must be named in order to use this method. The resulting tensor is a view on the original tensor.</p>
+<p>All dimension names of <code>self</code> must be present in <code>names</code>. <code>names</code> may contain additional names that are not in <code>self$names</code>; the output tensor has a size-one dimension for each of those new names.</p>
+<div id="arguments" class="section level3">
+<h3 class="hasAnchor">
+<a href="#arguments" class="anchor"></a>Arguments:</h3>
+<ul>
+<li>names (iterable of str): The desired dimension ordering of the output tensor. May contain up to one Ellipsis that is expanded to all unmentioned dim names of <code>self</code>.</li>
+</ul>
+</div>
+<div id="examples-1" class="section level3">
+<h3 class="hasAnchor">
+<a href="#examples-1" class="anchor"></a>Examples:</h3>
+<div id="warning-1" class="section level4">
+<h4 class="hasAnchor">
+<a href="#warning-1" class="anchor"></a>Warning:</h4>
+<p>The named tensor API is experimental and subject to change.</p>
+</div>
+</div>
+</div>
+<div id="all" class="section level2">
+<h2 class="hasAnchor">
+<a href="#all" class="anchor"></a>all</h2>
+<p>all() -&gt; bool</p>
+<p>Returns TRUE if all elements in the tensor are TRUE, FALSE otherwise.</p>
+<div id="examples-2" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-2" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb3"><pre class="downlit">
+<span class="kw">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">1</span>, <span class="fl">2</span>)<span class="op">$</span><span class="fu">to</span>(dtype = <span class="fu"><a href="../../reference/torch_dtype.html">torch_bool</a></span>())
+<span class="kw">a</span>
+<span class="kw">a</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/all.html">all</a></span>()
+</pre></div>
+<p>all(dim, keepdim=FALSE, out=NULL) -&gt; Tensor</p>
+<p>Returns TRUE if all elements in each row of the tensor in the given dimension <code>dim</code> are TRUE, FALSE otherwise.</p>
+<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size as <code>input</code> except in the dimension <code>dim</code> where it is of size 1. Otherwise, <code>dim</code> is squeezed (see <code>?torch_squeeze()),</code> resulting in the output tensor having 1 fewer dimension than <code>input</code>.</p>
+</div>
+<div id="arguments-1" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-1" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>dim (int): the dimension to reduce</li>
+<li>keepdim (bool): whether the output tensor has <code>dim</code> retained or not</li>
+<li>out (Tensor, optional): the output tensor</li>
+</ul>
+</div>
+<div id="examples-3" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-3" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb4"><pre class="downlit">
+<span class="kw">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">4</span>, <span class="fl">2</span>)<span class="op">$</span><span class="fu">to</span>(dtype = <span class="fu"><a href="../../reference/torch_dtype.html">torch_bool</a></span>())
+<span class="kw">a</span>
+<span class="kw">a</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/all.html">all</a></span>(dim=<span class="fl">2</span>)
+<span class="kw">a</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/all.html">all</a></span>(dim=<span class="fl">1</span>)
+</pre></div>
+</div>
+</div>
+<div id="allclose" class="section level2">
+<h2 class="hasAnchor">
+<a href="#allclose" class="anchor"></a>allclose</h2>
+<p>allclose(other, rtol=1e-05, atol=1e-08, equal_nan=FALSE) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_allclose.html">?torch_allclose</a></code></p>
+</div>
+<div id="angle" class="section level2">
+<h2 class="hasAnchor">
+<a href="#angle" class="anchor"></a>angle</h2>
+<p>angle() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_angle.html">?torch_angle</a></code></p>
+</div>
+<div id="any" class="section level2">
+<h2 class="hasAnchor">
+<a href="#any" class="anchor"></a>any</h2>
+<p>any() -&gt; bool</p>
+<p>Returns TRUE if any elements in the tensor are TRUE, FALSE otherwise.</p>
+<div id="examples-4" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-4" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb5"><pre class="downlit">
+<span class="kw">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">1</span>, <span class="fl">2</span>)<span class="op">$</span><span class="fu">to</span>(dtype = <span class="fu"><a href="../../reference/torch_dtype.html">torch_bool</a></span>())
+<span class="kw">a</span>
+<span class="kw">a</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/any.html">any</a></span>()
+</pre></div>
+<p>any(dim, keepdim=FALSE, out=NULL) -&gt; Tensor</p>
+<p>Returns TRUE if any elements in each row of the tensor in the given dimension <code>dim</code> are TRUE, FALSE otherwise.</p>
+<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size as <code>input</code> except in the dimension <code>dim</code> where it is of size 1. Otherwise, <code>dim</code> is squeezed (see <code>?torch_squeeze()),</code> resulting in the output tensor having 1 fewer dimension than <code>input</code>.</p>
+</div>
+<div id="arguments-2" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-2" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>dim (int): the dimension to reduce</li>
+<li>keepdim (bool): whether the output tensor has <code>dim</code> retained or not</li>
+<li>out (Tensor, optional): the output tensor</li>
+</ul>
+</div>
+<div id="examples-5" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-5" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb6"><pre class="downlit">
+<span class="kw">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">4</span>, <span class="fl">2</span>) <span class="op">&lt;</span> <span class="fl">0</span>
+<span class="kw">a</span>
+<span class="kw">a</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/any.html">any</a></span>(<span class="fl">2</span>)
+<span class="kw">a</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/any.html">any</a></span>(<span class="fl">1</span>)
+</pre></div>
+</div>
+</div>
+<div id="apply_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#apply_" class="anchor"></a>apply_</h2>
+<p>apply_(callable) -&gt; Tensor</p>
+<p>Applies the function <code>callable</code> to each element in the tensor, replacing each element with the value returned by <code>callable</code>.</p>
+<div id="note" class="section level4">
+<h4 class="hasAnchor">
+<a href="#note" class="anchor"></a>Note:</h4>
+<p>This function only works with CPU tensors and should not be used in code sections that require high performance.</p>
+</div>
+</div>
+<div id="argmax" class="section level2">
+<h2 class="hasAnchor">
+<a href="#argmax" class="anchor"></a>argmax</h2>
+<p>argmax(dim=NULL, keepdim=FALSE) -&gt; LongTensor</p>
+<p>See <code><a href="../../reference/torch_argmax.html">?torch_argmax</a></code></p>
+</div>
+<div id="argmin" class="section level2">
+<h2 class="hasAnchor">
+<a href="#argmin" class="anchor"></a>argmin</h2>
+<p>argmin(dim=NULL, keepdim=FALSE) -&gt; LongTensor</p>
+<p>See <code><a href="../../reference/torch_argmin.html">?torch_argmin</a></code></p>
+</div>
+<div id="argsort" class="section level2">
+<h2 class="hasAnchor">
+<a href="#argsort" class="anchor"></a>argsort</h2>
+<p>argsort(dim=-1, descending=FALSE) -&gt; LongTensor</p>
+<p>See <code><a href="../../reference/torch_argsort.html">?torch_argsort</a></code></p>
+</div>
+<div id="as_strided" class="section level2">
+<h2 class="hasAnchor">
+<a href="#as_strided" class="anchor"></a>as_strided</h2>
+<p>as_strided(size, stride, storage_offset=0) -&gt; Tensor</p>
+<p>See [torch_as_strided()]</p>
+</div>
+<div id="as_subclass" class="section level2">
+<h2 class="hasAnchor">
+<a href="#as_subclass" class="anchor"></a>as_subclass</h2>
+<p>as_subclass(cls) -&gt; Tensor</p>
+<p>Makes a <code>cls</code> instance with the same data pointer as <code>self</code>. Changes in the output mirror changes in <code>self</code>, and the output stays attached to the autograd graph. <code>cls</code> must be a subclass of <code>Tensor</code>.</p>
+</div>
+<div id="asin" class="section level2">
+<h2 class="hasAnchor">
+<a href="#asin" class="anchor"></a>asin</h2>
+<p>asin() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_asin.html">?torch_asin</a></code></p>
+</div>
+<div id="asin_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#asin_" class="anchor"></a>asin_</h2>
+<p>asin_() -&gt; Tensor</p>
+<p>In-place version of <code>$asin</code></p>
+</div>
+<div id="asinh" class="section level2">
+<h2 class="hasAnchor">
+<a href="#asinh" class="anchor"></a>asinh</h2>
+<p>asinh() -&gt; Tensor</p>
+<p>See <code>?torch_asinh</code></p>
+</div>
+<div id="asinh_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#asinh_" class="anchor"></a>asinh_</h2>
+<p>asinh_() -&gt; Tensor</p>
+<p>In-place version of <code>$asinh</code></p>
+</div>
+<div id="atan" class="section level2">
+<h2 class="hasAnchor">
+<a href="#atan" class="anchor"></a>atan</h2>
+<p>atan() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_atan.html">?torch_atan</a></code></p>
+</div>
+<div id="atan2" class="section level2">
+<h2 class="hasAnchor">
+<a href="#atan2" class="anchor"></a>atan2</h2>
+<p>atan2(other) -&gt; Tensor</p>
+<p>See [torch_atan2()]</p>
+</div>
+<div id="atan2_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#atan2_" class="anchor"></a>atan2_</h2>
+<p>atan2_(other) -&gt; Tensor</p>
+<p>In-place version of <code>$atan2</code></p>
+</div>
+<div id="atan_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#atan_" class="anchor"></a>atan_</h2>
+<p>atan_() -&gt; Tensor</p>
+<p>In-place version of <code>$atan</code></p>
+</div>
+<div id="atanh" class="section level2">
+<h2 class="hasAnchor">
+<a href="#atanh" class="anchor"></a>atanh</h2>
+<p>atanh() -&gt; Tensor</p>
+<p>See <code>?torch_atanh</code></p>
+</div>
+<div id="atanh_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#atanh_" class="anchor"></a>atanh_</h2>
+<p>In-place version of <code>$atanh</code></p>
+</div>
+<div id="backward" class="section level2">
+<h2 class="hasAnchor">
+<a href="#backward" class="anchor"></a>backward</h2>
+<p>Computes the gradient of current tensor w.r.t. graph leaves.</p>
+<p>The graph is differentiated using the chain rule. If the tensor is non-scalar (i.e. its data has more than one element) and requires gradient, the function additionally requires specifying <code>gradient</code>. It should be a tensor of matching type and location, that contains the gradient of the differentiated function w.r.t. <code>self</code>.</p>
+<p>This function accumulates gradients in the leaves - you might need to zero <code>$grad</code> attributes or set them to <code>NULL</code> before calling it. See <code>Default gradient layouts&lt;default-grad-layouts&gt;</code> for details on the memory layout of accumulated gradients.</p>
+<div id="arguments-3" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-3" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>gradient (Tensor or NULL): Gradient w.r.t. the tensor. If it is a tensor, it will be automatically converted to a Tensor that does not require grad unless <code>create_graph</code> is TRUE. NULL values can be specified for scalar Tensors or ones that don’t require grad. If a NULL value would be acceptable then this argument is optional.</li>
+<li>retain_graph (bool, optional): If <code>FALSE</code>, the graph used to compute the grads will be freed. Note that in nearly all cases setting this option to TRUE is not needed and often can be worked around in a much more efficient way. Defaults to the value of <code>create_graph</code>.</li>
+<li>create_graph (bool, optional): If <code>TRUE</code>, graph of the derivative will be constructed, allowing to compute higher order derivative products. Defaults to <code>FALSE</code>.</li>
+</ul>
+</div>
+</div>
+<div id="baddbmm" class="section level2">
+<h2 class="hasAnchor">
+<a href="#baddbmm" class="anchor"></a>baddbmm</h2>
+<p>baddbmm(batch1, batch2, *, beta=1, alpha=1) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_baddbmm.html">?torch_baddbmm</a></code></p>
+</div>
+<div id="baddbmm_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#baddbmm_" class="anchor"></a>baddbmm_</h2>
+<p>baddbmm_(batch1, batch2, *, beta=1, alpha=1) -&gt; Tensor</p>
+<p>In-place version of <code>$baddbmm</code></p>
+</div>
+<div id="bernoulli" class="section level2">
+<h2 class="hasAnchor">
+<a href="#bernoulli" class="anchor"></a>bernoulli</h2>
+<p>bernoulli(*, generator=NULL) -&gt; Tensor</p>
+<p>Returns a result tensor where each <span class="math inline">\(\texttt{result[i]}\)</span> is independently sampled from <span class="math inline">\(\text{Bernoulli}(\texttt{self[i]})\)</span>. <code>self</code> must have floating point <code>dtype</code>, and the result will have the same <code>dtype</code>.</p>
+<p>See <code><a href="../../reference/torch_bernoulli.html">?torch_bernoulli</a></code></p>
+</div>
+<div id="bernoulli_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#bernoulli_" class="anchor"></a>bernoulli_</h2>
+<p>bernoulli_(p=0.5, *, generator=NULL) -&gt; Tensor</p>
+<p>Fills each location of <code>self</code> with an independent sample from <span class="math inline">\(\text{Bernoulli}(\texttt{p})\)</span>. <code>self</code> can have integral <code>dtype</code>.</p>
+<p>bernoulli_(p_tensor, *, generator=NULL) -&gt; Tensor</p>
+<p><code>p_tensor</code> should be a tensor containing probabilities to be used for drawing the binary random number.</p>
+<p>The <span class="math inline">\(\text{i}^{th}\)</span> element of <code>self</code> tensor will be set to a value sampled from <span class="math inline">\(\text{Bernoulli}(\texttt{p\_tensor[i]})\)</span>.</p>
+<p><code>self</code> can have integral <code>dtype</code>, but <code>p_tensor</code> must have floating point <code>dtype</code>.</p>
+<p>See also <code>$bernoulli</code> and <code><a href="../../reference/torch_bernoulli.html">?torch_bernoulli</a></code></p>
+</div>
+<div id="bfloat16" class="section level2">
+<h2 class="hasAnchor">
+<a href="#bfloat16" class="anchor"></a>bfloat16</h2>
+<p>bfloat16(memory_format=torch_preserve_format) -&gt; Tensor <code>self$bfloat16()</code> is equivalent to <code>self$to(torch_bfloat16)</code>. See [to()].</p>
+<div id="arguments-4" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-4" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
+<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
+</ul>
+</div>
+</div>
+<div id="bincount" class="section level2">
+<h2 class="hasAnchor">
+<a href="#bincount" class="anchor"></a>bincount</h2>
+<p>bincount(weights=NULL, minlength=0) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_bincount.html">?torch_bincount</a></code></p>
+</div>
+<div id="bitwise_and" class="section level2">
+<h2 class="hasAnchor">
+<a href="#bitwise_and" class="anchor"></a>bitwise_and</h2>
+<p>bitwise_and() -&gt; Tensor</p>
+<p>See [torch_bitwise_and()]</p>
+</div>
+<div id="bitwise_and_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#bitwise_and_" class="anchor"></a>bitwise_and_</h2>
+<p>bitwise_and_() -&gt; Tensor</p>
+<p>In-place version of <code>$bitwise_and</code></p>
+</div>
+<div id="bitwise_not" class="section level2">
+<h2 class="hasAnchor">
+<a href="#bitwise_not" class="anchor"></a>bitwise_not</h2>
+<p>bitwise_not() -&gt; Tensor</p>
+<p>See [torch_bitwise_not()]</p>
+</div>
+<div id="bitwise_not_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#bitwise_not_" class="anchor"></a>bitwise_not_</h2>
+<p>bitwise_not_() -&gt; Tensor</p>
+<p>In-place version of <code>$bitwise_not</code></p>
+</div>
+<div id="bitwise_or" class="section level2">
+<h2 class="hasAnchor">
+<a href="#bitwise_or" class="anchor"></a>bitwise_or</h2>
+<p>bitwise_or() -&gt; Tensor</p>
+<p>See [torch_bitwise_or()]</p>
+</div>
+<div id="bitwise_or_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#bitwise_or_" class="anchor"></a>bitwise_or_</h2>
+<p>bitwise_or_() -&gt; Tensor</p>
+<p>In-place version of <code>$bitwise_or</code></p>
+</div>
+<div id="bitwise_xor" class="section level2">
+<h2 class="hasAnchor">
+<a href="#bitwise_xor" class="anchor"></a>bitwise_xor</h2>
+<p>bitwise_xor() -&gt; Tensor</p>
+<p>See [torch_bitwise_xor()]</p>
+</div>
+<div id="bitwise_xor_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#bitwise_xor_" class="anchor"></a>bitwise_xor_</h2>
+<p>bitwise_xor_() -&gt; Tensor</p>
+<p>In-place version of <code>$bitwise_xor</code></p>
+</div>
+<div id="bmm" class="section level2">
+<h2 class="hasAnchor">
+<a href="#bmm" class="anchor"></a>bmm</h2>
+<p>bmm(batch2) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_bmm.html">?torch_bmm</a></code></p>
+</div>
+<div id="bool" class="section level2">
+<h2 class="hasAnchor">
+<a href="#bool" class="anchor"></a>bool</h2>
+<p>bool(memory_format=torch_preserve_format) -&gt; Tensor</p>
+<p><code>self$bool()</code> is equivalent to <code>self$to(torch_bool)</code>. See [to()].</p>
+<div id="arguments-5" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-5" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
+<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
+</ul>
+</div>
+</div>
+<div id="byte" class="section level2">
+<h2 class="hasAnchor">
+<a href="#byte" class="anchor"></a>byte</h2>
+<p>byte(memory_format=torch_preserve_format) -&gt; Tensor</p>
+<p><code>self$byte()</code> is equivalent to <code>self$to(torch_uint8)</code>. See [to()].</p>
+<div id="arguments-6" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-6" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
+<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
+</ul>
+</div>
+</div>
+<div id="cauchy_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#cauchy_" class="anchor"></a>cauchy_</h2>
+<p>cauchy_(median=0, sigma=1, *, generator=NULL) -&gt; Tensor</p>
+<p>Fills the tensor with numbers drawn from the Cauchy distribution:</p>
+<p><span class="math display">\[
+f(x) = \dfrac{1}{\pi} \dfrac{\sigma}{(x - \text{median})^2 + \sigma^2}
+\]</span></p>
+</div>
+<div id="ceil" class="section level2">
+<h2 class="hasAnchor">
+<a href="#ceil" class="anchor"></a>ceil</h2>
+<p>ceil() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_ceil.html">?torch_ceil</a></code></p>
+</div>
+<div id="ceil_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#ceil_" class="anchor"></a>ceil_</h2>
+<p>ceil_() -&gt; Tensor</p>
+<p>In-place version of <code>$ceil</code></p>
+</div>
+<div id="char" class="section level2">
+<h2 class="hasAnchor">
+<a href="#char" class="anchor"></a>char</h2>
+<p>char(memory_format=torch_preserve_format) -&gt; Tensor</p>
+<p><code>self$char()</code> is equivalent to <code>self$to(torch_int8)</code>. See [to()].</p>
+<div id="arguments-7" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-7" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
+<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
+</ul>
+</div>
+</div>
+<div id="cholesky" class="section level2">
+<h2 class="hasAnchor">
+<a href="#cholesky" class="anchor"></a>cholesky</h2>
+<p>cholesky(upper=FALSE) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_cholesky.html">?torch_cholesky</a></code></p>
+</div>
+<div id="cholesky_inverse" class="section level2">
+<h2 class="hasAnchor">
+<a href="#cholesky_inverse" class="anchor"></a>cholesky_inverse</h2>
+<p>cholesky_inverse(upper=FALSE) -&gt; Tensor</p>
+<p>See [torch_cholesky_inverse()]</p>
+</div>
+<div id="cholesky_solve" class="section level2">
+<h2 class="hasAnchor">
+<a href="#cholesky_solve" class="anchor"></a>cholesky_solve</h2>
+<p>cholesky_solve(input2, upper=FALSE) -&gt; Tensor</p>
+<p>See [torch_cholesky_solve()]</p>
+</div>
+<div id="chunk" class="section level2">
+<h2 class="hasAnchor">
+<a href="#chunk" class="anchor"></a>chunk</h2>
+<p>chunk(chunks, dim=0) -&gt; List of Tensors</p>
+<p>See <code><a href="../../reference/torch_chunk.html">?torch_chunk</a></code></p>
+</div>
+<div id="clamp" class="section level2">
+<h2 class="hasAnchor">
+<a href="#clamp" class="anchor"></a>clamp</h2>
+<p>clamp(min, max) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_clamp.html">?torch_clamp</a></code></p>
+</div>
+<div id="clamp_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#clamp_" class="anchor"></a>clamp_</h2>
+<p>clamp_(min, max) -&gt; Tensor</p>
+<p>In-place version of <code>$clamp</code></p>
+</div>
+<div id="clone" class="section level2">
+<h2 class="hasAnchor">
+<a href="#clone" class="anchor"></a>clone</h2>
+<p>clone(memory_format=torch_preserve_format) -&gt; Tensor</p>
+<p>Returns a copy of the <code>self</code> tensor. The copy has the same size and data type as <code>self</code>.</p>
+<div id="note-1" class="section level4">
+<h4 class="hasAnchor">
+<a href="#note-1" class="anchor"></a>Note:</h4>
+<p>Unlike <code>copy_()</code>, this function is recorded in the computation graph. Gradients propagating to the cloned tensor will propagate to the original tensor.</p>
+</div>
+<div id="arguments-8" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-8" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
+<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
+</ul>
+</div>
+</div>
+<div id="conj" class="section level2">
+<h2 class="hasAnchor">
+<a href="#conj" class="anchor"></a>conj</h2>
+<p>conj() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_conj.html">?torch_conj</a></code></p>
+</div>
+<div id="contiguous" class="section level2">
+<h2 class="hasAnchor">
+<a href="#contiguous" class="anchor"></a>contiguous</h2>
+<p>contiguous(memory_format=torch_contiguous_format) -&gt; Tensor</p>
+<p>Returns a contiguous in memory tensor containing the same data as <code>self</code> tensor. If <code>self</code> tensor is already in the specified memory format, this function returns the <code>self</code> tensor.</p>
+<div id="arguments-9" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-9" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
+<li>returned Tensor. Default: <code>torch_contiguous_format</code>.</li>
+</ul>
+</div>
+</div>
+<div id="copy_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#copy_" class="anchor"></a>copy_</h2>
+<p>copy_(src, non_blocking=FALSE) -&gt; Tensor</p>
+<p>Copies the elements from <code>src</code> into <code>self</code> tensor and returns <code>self</code>.</p>
+<p>The <code>src</code> tensor must be :ref:<code>broadcastable &lt;broadcasting-semantics&gt;</code> with the <code>self</code> tensor. It may be of a different data type or reside on a different device.</p>
+<div id="arguments-10" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-10" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>src (Tensor): the source tensor to copy from</li>
+<li>non_blocking (bool): if <code>TRUE</code> and this copy is between CPU and GPU,</li>
+<li>the copy may occur asynchronously with respect to the host. For other</li>
+<li>cases, this argument has no effect.</li>
+</ul>
+</div>
+</div>
+<div id="cos" class="section level2">
+<h2 class="hasAnchor">
+<a href="#cos" class="anchor"></a>cos</h2>
+<p>cos() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_cos.html">?torch_cos</a></code></p>
+</div>
+<div id="cos_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#cos_" class="anchor"></a>cos_</h2>
+<p>cos_() -&gt; Tensor</p>
+<p>In-place version of <code>$cos</code></p>
+</div>
+<div id="cosh" class="section level2">
+<h2 class="hasAnchor">
+<a href="#cosh" class="anchor"></a>cosh</h2>
+<p>cosh() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_cosh.html">?torch_cosh</a></code></p>
+</div>
+<div id="cosh_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#cosh_" class="anchor"></a>cosh_</h2>
+<p>cosh_() -&gt; Tensor</p>
+<p>In-place version of <code>$cosh</code></p>
+</div>
+<div id="cpu" class="section level2">
+<h2 class="hasAnchor">
+<a href="#cpu" class="anchor"></a>cpu</h2>
+<p>cpu(memory_format=torch_preserve_format) -&gt; Tensor</p>
+<p>Returns a copy of this object in CPU memory.</p>
+<p>If this object is already in CPU memory and on the correct device, then no copy is performed and the original object is returned.</p>
+<div id="arguments-11" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-11" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
+<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
+</ul>
+</div>
+</div>
+<div id="cross" class="section level2">
+<h2 class="hasAnchor">
+<a href="#cross" class="anchor"></a>cross</h2>
+<p>cross(other, dim=-1) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_cross.html">?torch_cross</a></code></p>
+</div>
+<div id="cuda" class="section level2">
+<h2 class="hasAnchor">
+<a href="#cuda" class="anchor"></a>cuda</h2>
+<p>cuda(device=NULL, non_blocking=FALSE, memory_format=torch_preserve_format) -&gt; Tensor</p>
+<p>Returns a copy of this object in CUDA memory.</p>
+<p>If this object is already in CUDA memory and on the correct device, then no copy is performed and the original object is returned.</p>
+<div id="arguments-12" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-12" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>device (<code>torch_device</code>): The destination GPU device. Defaults to the current CUDA device.</li>
+<li>non_blocking (bool): If <code>TRUE</code> and the source is in pinned memory, the copy will be asynchronous with respect to the host. Otherwise, the argument has no effect. Default: <code>FALSE</code>.</li>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
+<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
+</ul>
+</div>
+</div>
+<div id="cummax" class="section level2">
+<h2 class="hasAnchor">
+<a href="#cummax" class="anchor"></a>cummax</h2>
+<p>cummax(dim) -&gt; (Tensor, Tensor)</p>
+<p>See <code><a href="../../reference/torch_cummax.html">?torch_cummax</a></code></p>
+</div>
+<div id="cummin" class="section level2">
+<h2 class="hasAnchor">
+<a href="#cummin" class="anchor"></a>cummin</h2>
+<p>cummin(dim) -&gt; (Tensor, Tensor)</p>
+<p>See <code><a href="../../reference/torch_cummin.html">?torch_cummin</a></code></p>
+</div>
+<div id="cumprod" class="section level2">
+<h2 class="hasAnchor">
+<a href="#cumprod" class="anchor"></a>cumprod</h2>
+<p>cumprod(dim, dtype=NULL) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_cumprod.html">?torch_cumprod</a></code></p>
+</div>
+<div id="cumsum" class="section level2">
+<h2 class="hasAnchor">
+<a href="#cumsum" class="anchor"></a>cumsum</h2>
+<p>cumsum(dim, dtype=NULL) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_cumsum.html">?torch_cumsum</a></code></p>
+</div>
+<div id="data_ptr" class="section level2">
+<h2 class="hasAnchor">
+<a href="#data_ptr" class="anchor"></a>data_ptr</h2>
+<p>data_ptr() -&gt; int</p>
+<p>Returns the address of the first element of <code>self</code> tensor.</p>
+</div>
+<div id="deg2rad" class="section level2">
+<h2 class="hasAnchor">
+<a href="#deg2rad" class="anchor"></a>deg2rad</h2>
+<p>deg2rad() -&gt; Tensor</p>
+<p>See [torch_deg2rad()]</p>
+</div>
+<div id="deg2rad_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#deg2rad_" class="anchor"></a>deg2rad_</h2>
+<p>deg2rad_() -&gt; Tensor</p>
+<p>In-place version of <code>$deg2rad</code></p>
+</div>
+<div id="dense_dim" class="section level2">
+<h2 class="hasAnchor">
+<a href="#dense_dim" class="anchor"></a>dense_dim</h2>
+<p>dense_dim() -&gt; int</p>
+<p>If <code>self</code> is a sparse COO tensor (i.e., with <code>torch_sparse_coo</code> layout), this returns the number of dense dimensions. Otherwise, this throws an error.</p>
+<p>See also <code>$sparse_dim</code>.</p>
+</div>
+<div id="dequantize" class="section level2">
+<h2 class="hasAnchor">
+<a href="#dequantize" class="anchor"></a>dequantize</h2>
+<p>dequantize() -&gt; Tensor</p>
+<p>Given a quantized Tensor, dequantize it and return the dequantized float Tensor.</p>
+</div>
+<div id="det" class="section level2">
+<h2 class="hasAnchor">
+<a href="#det" class="anchor"></a>det</h2>
+<p>det() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_det.html">?torch_det</a></code></p>
+</div>
+<div id="detach" class="section level2">
+<h2 class="hasAnchor">
+<a href="#detach" class="anchor"></a>detach</h2>
+<p>Returns a new Tensor, detached from the current graph.</p>
+<p>The result will never require gradient.</p>
+<div id="note-2" class="section level4">
+<h4 class="hasAnchor">
+<a href="#note-2" class="anchor"></a>Note:</h4>
+<p>Returned Tensor shares the same storage with the original one. In-place modifications on either of them will be seen, and may trigger errors in correctness checks. IMPORTANT NOTE: Previously, in-place size / stride / storage changes (such as <code>resize_</code> / <code>resize_as_</code> / <code>set_</code> / <code>transpose_</code>) to the returned tensor also update the original tensor. Now, these in-place changes will not update the original tensor anymore, and will instead trigger an error. For sparse tensors: In-place indices / values changes (such as <code>zero_</code> / <code>copy_</code> / <code>add_</code>) to the returned tensor will not update the original tensor anymore, and will instead trigger an error.</p>
+</div>
+</div>
+<div id="detach_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#detach_" class="anchor"></a>detach_</h2>
+<p>Detaches the Tensor from the graph that created it, making it a leaf. Views cannot be detached in-place.</p>
+</div>
+<div id="device" class="section level2">
+<h2 class="hasAnchor">
+<a href="#device" class="anchor"></a>device</h2>
+<p>Is the <code>torch_device</code> where this Tensor is.</p>
+</div>
+<div id="diag" class="section level2">
+<h2 class="hasAnchor">
+<a href="#diag" class="anchor"></a>diag</h2>
+<p>diag(diagonal=0) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_diag.html">?torch_diag</a></code></p>
+</div>
+<div id="diag_embed" class="section level2">
+<h2 class="hasAnchor">
+<a href="#diag_embed" class="anchor"></a>diag_embed</h2>
+<p>diag_embed(offset=0, dim1=-2, dim2=-1) -&gt; Tensor</p>
+<p>See [torch_diag_embed()]</p>
+</div>
+<div id="diagflat" class="section level2">
+<h2 class="hasAnchor">
+<a href="#diagflat" class="anchor"></a>diagflat</h2>
+<p>diagflat(offset=0) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_diagflat.html">?torch_diagflat</a></code></p>
+</div>
+<div id="diagonal" class="section level2">
+<h2 class="hasAnchor">
+<a href="#diagonal" class="anchor"></a>diagonal</h2>
+<p>diagonal(offset=0, dim1=0, dim2=1) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_diagonal.html">?torch_diagonal</a></code></p>
+</div>
+<div id="digamma" class="section level2">
+<h2 class="hasAnchor">
+<a href="#digamma" class="anchor"></a>digamma</h2>
+<p>digamma() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_digamma.html">?torch_digamma</a></code></p>
+</div>
+<div id="digamma_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#digamma_" class="anchor"></a>digamma_</h2>
+<p>digamma_() -&gt; Tensor</p>
+<p>In-place version of <code>$digamma</code></p>
+</div>
+<div id="dim" class="section level2">
+<h2 class="hasAnchor">
+<a href="#dim" class="anchor"></a>dim</h2>
+<p>dim() -&gt; int</p>
+<p>Returns the number of dimensions of <code>self</code> tensor.</p>
+</div>
+<div id="dist" class="section level2">
+<h2 class="hasAnchor">
+<a href="#dist" class="anchor"></a>dist</h2>
+<p>dist(other, p=2) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_dist.html">?torch_dist</a></code></p>
+</div>
+<div id="div" class="section level2">
+<h2 class="hasAnchor">
+<a href="#div" class="anchor"></a>div</h2>
+<p>div(value) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_div.html">?torch_div</a></code></p>
+</div>
+<div id="div_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#div_" class="anchor"></a>div_</h2>
+<p>div_(value) -&gt; Tensor</p>
+<p>In-place version of <code>$div</code></p>
+</div>
+<div id="dot" class="section level2">
+<h2 class="hasAnchor">
+<a href="#dot" class="anchor"></a>dot</h2>
+<p>dot(tensor2) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_dot.html">?torch_dot</a></code></p>
+</div>
+<div id="double" class="section level2">
+<h2 class="hasAnchor">
+<a href="#double" class="anchor"></a>double</h2>
+<p>double(memory_format=torch_preserve_format) -&gt; Tensor</p>
+<p><code>self$double()</code> is equivalent to <code>self$to(torch_float64)</code>. See [to()].</p>
+<div id="arguments-13" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-13" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
+<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
+</ul>
+</div>
+</div>
+<div id="eig" class="section level2">
+<h2 class="hasAnchor">
+<a href="#eig" class="anchor"></a>eig</h2>
+<p>eig(eigenvectors=FALSE) -&gt; (Tensor, Tensor)</p>
+<p>See <code><a href="../../reference/torch_eig.html">?torch_eig</a></code></p>
+</div>
+<div id="element_size" class="section level2">
+<h2 class="hasAnchor">
+<a href="#element_size" class="anchor"></a>element_size</h2>
+<p>element_size() -&gt; int</p>
+<p>Returns the size in bytes of an individual element.</p>
+<div id="examples-6" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-6" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb7"><pre class="downlit">
+<span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1</span>))<span class="op">$</span><span class="fu">element_size</span>()
+</pre></div>
+</div>
+</div>
+<div id="eq" class="section level2">
+<h2 class="hasAnchor">
+<a href="#eq" class="anchor"></a>eq</h2>
+<p>eq(other) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_eq.html">?torch_eq</a></code></p>
+</div>
+<div id="eq_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#eq_" class="anchor"></a>eq_</h2>
+<p>eq_(other) -&gt; Tensor</p>
+<p>In-place version of <code>$eq</code></p>
+</div>
+<div id="equal" class="section level2">
+<h2 class="hasAnchor">
+<a href="#equal" class="anchor"></a>equal</h2>
+<p>equal(other) -&gt; bool</p>
+<p>See <code><a href="../../reference/torch_equal.html">?torch_equal</a></code></p>
+</div>
+<div id="erf" class="section level2">
+<h2 class="hasAnchor">
+<a href="#erf" class="anchor"></a>erf</h2>
+<p>erf() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_erf.html">?torch_erf</a></code></p>
+</div>
+<div id="erf_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#erf_" class="anchor"></a>erf_</h2>
+<p>erf_() -&gt; Tensor</p>
+<p>In-place version of <code>$erf</code></p>
+</div>
+<div id="erfc" class="section level2">
+<h2 class="hasAnchor">
+<a href="#erfc" class="anchor"></a>erfc</h2>
+<p>erfc() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_erfc.html">?torch_erfc</a></code></p>
+</div>
+<div id="erfc_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#erfc_" class="anchor"></a>erfc_</h2>
+<p>erfc_() -&gt; Tensor</p>
+<p>In-place version of <code>$erfc</code></p>
+</div>
+<div id="erfinv" class="section level2">
+<h2 class="hasAnchor">
+<a href="#erfinv" class="anchor"></a>erfinv</h2>
+<p>erfinv() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_erfinv.html">?torch_erfinv</a></code></p>
+</div>
+<div id="erfinv_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#erfinv_" class="anchor"></a>erfinv_</h2>
+<p>erfinv_() -&gt; Tensor</p>
+<p>In-place version of <code>$erfinv</code></p>
+</div>
+<div id="exp" class="section level2">
+<h2 class="hasAnchor">
+<a href="#exp" class="anchor"></a>exp</h2>
+<p>exp() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_exp.html">?torch_exp</a></code></p>
+</div>
+<div id="exp_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#exp_" class="anchor"></a>exp_</h2>
+<p>exp_() -&gt; Tensor</p>
+<p>In-place version of <code>$exp</code></p>
+</div>
+<div id="expand" class="section level2">
+<h2 class="hasAnchor">
+<a href="#expand" class="anchor"></a>expand</h2>
+<p>expand(*sizes) -&gt; Tensor</p>
+<p>Returns a new view of the <code>self</code> tensor with singleton dimensions expanded to a larger size.</p>
+<p>Passing -1 as the size for a dimension means not changing the size of that dimension.</p>
+<p>Tensor can be also expanded to a larger number of dimensions, and the new ones will be appended at the front. For the new dimensions, the size cannot be set to -1.</p>
+<p>Expanding a tensor does not allocate new memory, but only creates a new view on the existing tensor where a dimension of size one is expanded to a larger size by setting the <code>stride</code> to 0. Any dimension of size 1 can be expanded to an arbitrary value without allocating new memory.</p>
+<div id="arguments-14" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-14" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>sizes (torch_Size or int…): the desired expanded size</li>
+</ul>
+</div>
+<div id="warning-2" class="section level4">
+<h4 class="hasAnchor">
+<a href="#warning-2" class="anchor"></a>Warning:</h4>
+<p>More than one element of an expanded tensor may refer to a single memory location. As a result, in-place operations (especially ones that are vectorized) may result in incorrect behavior. If you need to write to the tensors, please clone them first.</p>
+</div>
+<div id="examples-7" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-7" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb8"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1</span>,<span class="fl">2</span>,<span class="fl">3</span>), ncol = <span class="fl">1</span>))
+<span class="kw">x</span><span class="op">$</span><span class="fu">size</span>()
+<span class="kw">x</span><span class="op">$</span><span class="fu">expand</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">3</span>, <span class="fl">4</span>))
+<span class="kw">x</span><span class="op">$</span><span class="fu">expand</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="op">-</span><span class="fl">1</span>, <span class="fl">4</span>))  <span class="co"># -1 means not changing the size of that dimension</span>
+</pre></div>
+</div>
+</div>
+<div id="expand_as" class="section level2">
+<h2 class="hasAnchor">
+<a href="#expand_as" class="anchor"></a>expand_as</h2>
+<p>expand_as(other) -&gt; Tensor</p>
+<p>Expand this tensor to the same size as <code>other</code>. <code>self$expand_as(other)</code> is equivalent to <code>self$expand(other.size())</code>.</p>
+<p>Please see <code>$expand</code> for more information about <code>expand</code>.</p>
+<div id="arguments-15" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-15" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>other (`$): The result tensor has the same size</li>
+<li>as <code>other</code>.</li>
+</ul>
+</div>
+</div>
+<div id="expm1" class="section level2">
+<h2 class="hasAnchor">
+<a href="#expm1" class="anchor"></a>expm1</h2>
+<p>expm1() -&gt; Tensor</p>
+<p>See [torch_expm1()]</p>
+</div>
+<div id="expm1_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#expm1_" class="anchor"></a>expm1_</h2>
+<p>expm1_() -&gt; Tensor</p>
+<p>In-place version of <code>$expm1</code></p>
+</div>
+<div id="exponential_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#exponential_" class="anchor"></a>exponential_</h2>
+<p>exponential_(lambd=1, *, generator=NULL) -&gt; Tensor</p>
+<p>Fills <code>self</code> tensor with elements drawn from the exponential distribution:</p>
+<p><span class="math display">\[
+f(x) = \lambda e^{-\lambda x}
+\]</span></p>
+</div>
+<div id="fft" class="section level2">
+<h2 class="hasAnchor">
+<a href="#fft" class="anchor"></a>fft</h2>
+<p>fft(signal_ndim, normalized=FALSE) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_fft.html">?torch_fft</a></code></p>
+</div>
+<div id="fill_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#fill_" class="anchor"></a>fill_</h2>
+<p>fill_(value) -&gt; Tensor</p>
+<p>Fills <code>self</code> tensor with the specified value.</p>
+</div>
+<div id="fill_diagonal_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#fill_diagonal_" class="anchor"></a>fill_diagonal_</h2>
+<p>fill_diagonal_(fill_value, wrap=FALSE) -&gt; Tensor</p>
+<p>Fill the main diagonal of a tensor that has at least 2-dimensions. When dims&gt;2, all dimensions of input must be of equal length. This function modifies the input tensor in-place, and returns the input tensor.</p>
+<div id="arguments-16" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-16" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>fill_value (Scalar): the fill value</li>
+<li>wrap (bool): the diagonal ‘wrapped’ after N columns for tall matrices.</li>
+</ul>
+</div>
+<div id="examples-8" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-8" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb9"><pre class="downlit">
+<span class="kw">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">3</span>, <span class="fl">3</span>)
+<span class="kw">a</span><span class="op">$</span><span class="fu">fill_diagonal_</span>(<span class="fl">5</span>)
+<span class="kw">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">7</span>, <span class="fl">3</span>)
+<span class="kw">b</span><span class="op">$</span><span class="fu">fill_diagonal_</span>(<span class="fl">5</span>)
+<span class="kw">c</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">7</span>, <span class="fl">3</span>)
+<span class="kw">c</span><span class="op">$</span><span class="fu">fill_diagonal_</span>(<span class="fl">5</span>, wrap=<span class="fl">TRUE</span>)
+</pre></div>
+</div>
+</div>
+<div id="flatten" class="section level2">
+<h2 class="hasAnchor">
+<a href="#flatten" class="anchor"></a>flatten</h2>
+<p>flatten(input, start_dim=0, end_dim=-1) -&gt; Tensor</p>
+<p>see <code><a href="../../reference/torch_flatten.html">?torch_flatten</a></code></p>
+</div>
+<div id="flip" class="section level2">
+<h2 class="hasAnchor">
+<a href="#flip" class="anchor"></a>flip</h2>
+<p>flip(dims) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_flip.html">?torch_flip</a></code></p>
+</div>
+<div id="fliplr" class="section level2">
+<h2 class="hasAnchor">
+<a href="#fliplr" class="anchor"></a>fliplr</h2>
+<p>fliplr() -&gt; Tensor</p>
+<p>See <code>?torch_fliplr</code></p>
+</div>
+<div id="flipud" class="section level2">
+<h2 class="hasAnchor">
+<a href="#flipud" class="anchor"></a>flipud</h2>
+<p>flipud() -&gt; Tensor</p>
+<p>See <code>?torch_flipud</code></p>
+</div>
+<div id="float" class="section level2">
+<h2 class="hasAnchor">
+<a href="#float" class="anchor"></a>float</h2>
+<p>float(memory_format=torch_preserve_format) -&gt; Tensor</p>
+<p><code>self$float()</code> is equivalent to <code>self$to(torch_float32)</code>. See [to()].</p>
+<div id="arguments-17" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-17" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
+<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
+</ul>
+</div>
+</div>
+<div id="floor" class="section level2">
+<h2 class="hasAnchor">
+<a href="#floor" class="anchor"></a>floor</h2>
+<p>floor() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_floor.html">?torch_floor</a></code></p>
+</div>
+<div id="floor_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#floor_" class="anchor"></a>floor_</h2>
+<p>floor_() -&gt; Tensor</p>
+<p>In-place version of <code>$floor</code></p>
+</div>
+<div id="floor_divide" class="section level2">
+<h2 class="hasAnchor">
+<a href="#floor_divide" class="anchor"></a>floor_divide</h2>
+<p>floor_divide(value) -&gt; Tensor</p>
+<p>See [torch_floor_divide()]</p>
+</div>
+<div id="floor_divide_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#floor_divide_" class="anchor"></a>floor_divide_</h2>
+<p>floor_divide_(value) -&gt; Tensor</p>
+<p>In-place version of <code>$floor_divide</code></p>
+</div>
+<div id="fmod" class="section level2">
+<h2 class="hasAnchor">
+<a href="#fmod" class="anchor"></a>fmod</h2>
+<p>fmod(divisor) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_fmod.html">?torch_fmod</a></code></p>
+</div>
+<div id="fmod_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#fmod_" class="anchor"></a>fmod_</h2>
+<p>fmod_(divisor) -&gt; Tensor</p>
+<p>In-place version of <code>$fmod</code></p>
+</div>
+<div id="frac" class="section level2">
+<h2 class="hasAnchor">
+<a href="#frac" class="anchor"></a>frac</h2>
+<p>frac() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_frac.html">?torch_frac</a></code></p>
+</div>
+<div id="frac_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#frac_" class="anchor"></a>frac_</h2>
+<p>frac_() -&gt; Tensor</p>
+<p>In-place version of <code>$frac</code></p>
+</div>
+<div id="gather" class="section level2">
+<h2 class="hasAnchor">
+<a href="#gather" class="anchor"></a>gather</h2>
+<p>gather(dim, index) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_gather.html">?torch_gather</a></code></p>
+</div>
+<div id="ge" class="section level2">
+<h2 class="hasAnchor">
+<a href="#ge" class="anchor"></a>ge</h2>
+<p>ge(other) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_ge.html">?torch_ge</a></code></p>
+</div>
+<div id="ge_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#ge_" class="anchor"></a>ge_</h2>
+<p>ge_(other) -&gt; Tensor</p>
+<p>In-place version of <code>$ge</code></p>
+</div>
+<div id="geometric_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#geometric_" class="anchor"></a>geometric_</h2>
+<p>geometric_(p, *, generator=NULL) -&gt; Tensor</p>
+<p>Fills <code>self</code> tensor with elements drawn from the geometric distribution:</p>
+<p><span class="math display">\[
+f(X=k) = p^{k - 1} (1 - p)
+\]</span></p>
+</div>
+<div id="geqrf" class="section level2">
+<h2 class="hasAnchor">
+<a href="#geqrf" class="anchor"></a>geqrf</h2>
+<p>geqrf() -&gt; (Tensor, Tensor)</p>
+<p>See <code><a href="../../reference/torch_geqrf.html">?torch_geqrf</a></code></p>
+</div>
+<div id="ger" class="section level2">
+<h2 class="hasAnchor">
+<a href="#ger" class="anchor"></a>ger</h2>
+<p>ger(vec2) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_ger.html">?torch_ger</a></code></p>
+</div>
+<div id="get_device" class="section level2">
+<h2 class="hasAnchor">
+<a href="#get_device" class="anchor"></a>get_device</h2>
+<p>get_device() -&gt; Device ordinal (Integer)</p>
+<p>For CUDA tensors, this function returns the device ordinal of the GPU on which the tensor resides. For CPU tensors, an error is thrown.</p>
+<div id="examples-9" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-9" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb10"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">3</span>, <span class="fl">4</span>, <span class="fl">5</span>, device=<span class="st">'cuda:0'</span>)
+<span class="kw">x</span><span class="op">$</span><span class="fu">get_device</span>()
+<span class="kw">x</span><span class="op">$</span><span class="fu">cpu</span>()<span class="op">$</span><span class="fu">get_device</span>()  <span class="co"># RuntimeError: get_device is not implemented for type torch_FloatTensor</span>
+</pre></div>
+</div>
+</div>
+<div id="grad" class="section level2">
+<h2 class="hasAnchor">
+<a href="#grad" class="anchor"></a>grad</h2>
+<p>This attribute is <code>NULL</code> by default and becomes a Tensor the first time a call to <code>backward</code> computes gradients for <code>self</code>. The attribute will then contain the gradients computed and future calls to [backward()] will accumulate (add) gradients into it.</p>
+</div>
+<div id="gt" class="section level2">
+<h2 class="hasAnchor">
+<a href="#gt" class="anchor"></a>gt</h2>
+<p>gt(other) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_gt.html">?torch_gt</a></code></p>
+</div>
+<div id="gt_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#gt_" class="anchor"></a>gt_</h2>
+<p>gt_(other) -&gt; Tensor</p>
+<p>In-place version of <code>$gt</code></p>
+</div>
+<div id="half" class="section level2">
+<h2 class="hasAnchor">
+<a href="#half" class="anchor"></a>half</h2>
+<p>half(memory_format=torch_preserve_format) -&gt; Tensor</p>
+<p><code>self$half()</code> is equivalent to <code>self$to(torch_float16)</code>. See [to()].</p>
+<div id="arguments-18" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-18" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
+<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
+</ul>
+</div>
+</div>
+<div id="hardshrink" class="section level2">
+<h2 class="hasAnchor">
+<a href="#hardshrink" class="anchor"></a>hardshrink</h2>
+<p>hardshrink(lambd=0.5) -&gt; Tensor</p>
+<p>See [torch_nn.functional.hardshrink()]</p>
+</div>
+<div id="has_names" class="section level2">
+<h2 class="hasAnchor">
+<a href="#has_names" class="anchor"></a>has_names</h2>
+<p>Is <code>TRUE</code> if any of this tensor’s dimensions are named. Otherwise, is <code>FALSE</code>.</p>
+</div>
+<div id="histc" class="section level2">
+<h2 class="hasAnchor">
+<a href="#histc" class="anchor"></a>histc</h2>
+<p>histc(bins=100, min=0, max=0) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_histc.html">?torch_histc</a></code></p>
+</div>
+<div id="ifft" class="section level2">
+<h2 class="hasAnchor">
+<a href="#ifft" class="anchor"></a>ifft</h2>
+<p>ifft(signal_ndim, normalized=FALSE) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_ifft.html">?torch_ifft</a></code></p>
+</div>
+<div id="imag" class="section level2">
+<h2 class="hasAnchor">
+<a href="#imag" class="anchor"></a>imag</h2>
+<p>Returns a new tensor containing imaginary values of the <code>self</code> tensor. The returned tensor and <code>self</code> share the same underlying storage.</p>
+<div id="warning-3" class="section level4">
+<h4 class="hasAnchor">
+<a href="#warning-3" class="anchor"></a>Warning:</h4>
+<p>[imag()] is only supported for tensors with complex dtypes.</p>
+</div>
+<div id="examples-10" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-10" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb11"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">4</span>, dtype=<span class="fu">torch_cfloat</span>())
+<span class="kw">x</span>
+<span class="kw">x</span><span class="op">$</span><span class="kw">imag</span>
+</pre></div>
+</div>
+</div>
+<div id="index_add" class="section level2">
+<h2 class="hasAnchor">
+<a href="#index_add" class="anchor"></a>index_add</h2>
+<p>index_add(tensor1, dim, index, tensor2) -&gt; Tensor</p>
+<p>Out-of-place version of <code>$index_add_</code>. <code>tensor1</code> corresponds to <code>self</code> in <code>$index_add_</code>.</p>
+</div>
+<div id="index_add_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#index_add_" class="anchor"></a>index_add_</h2>
+<p>index_add_(dim, index, tensor) -&gt; Tensor</p>
+<p>Accumulate the elements of <code>tensor</code> into the <code>self</code> tensor by adding to the indices in the order given in <code>index</code>. For example, if <code>dim == 0</code> and <code>index[i] == j</code>, then the <code>i</code> th row of <code>tensor</code> is added to the <code>j</code> th row of <code>self</code>.</p>
+<p>The <code>dim</code> th dimension of <code>tensor</code> must have the same size as the length of <code>index</code> (which must be a vector), and all other dimensions must match <code>self</code>, or an error will be raised.</p>
+<div id="note-3" class="section level4">
+<h4 class="hasAnchor">
+<a href="#note-3" class="anchor"></a>Note:</h4>
+<p>In some circumstances when using the CUDA backend with CuDNN, this operator may select a nondeterministic algorithm to increase performance. If this is undesirable, you can try to make the operation deterministic (potentially at a performance cost) by setting <code>torch_backends.cudnn.deterministic = TRUE</code>.</p>
+</div>
+<div id="arguments-19" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-19" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>dim (int): dimension along which to index</li>
+<li>index (LongTensor): indices of <code>tensor</code> to select from</li>
+<li>tensor (Tensor): the tensor containing values to add</li>
+</ul>
+</div>
+<div id="examples-11" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-11" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb12"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span>(<span class="fl">5</span>, <span class="fl">3</span>)
+<span class="kw">t</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">9</span>, ncol = <span class="fl">3</span>), dtype=<span class="fu"><a href="../../reference/torch_dtype.html">torch_float</a></span>())
+<span class="kw">index</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1L</span>, <span class="fl">4L</span>, <span class="fl">3L</span>))
+<span class="kw">x</span><span class="op">$</span><span class="fu">index_add_</span>(<span class="fl">1</span>, <span class="kw">index</span>, <span class="kw">t</span>)
+</pre></div>
+</div>
+</div>
+<div id="index_copy" class="section level2">
+<h2 class="hasAnchor">
+<a href="#index_copy" class="anchor"></a>index_copy</h2>
+<p>index_copy(tensor1, dim, index, tensor2) -&gt; Tensor</p>
+<p>Out-of-place version of <code>$index_copy_</code>. <code>tensor1</code> corresponds to <code>self</code> in <code>$index_copy_</code>.</p>
+</div>
+<div id="index_copy_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#index_copy_" class="anchor"></a>index_copy_</h2>
+<p>index_copy_(dim, index, tensor) -&gt; Tensor</p>
+<p>Copies the elements of <code>tensor</code> into the <code>self</code> tensor by selecting the indices in the order given in <code>index</code>. For example, if <code>dim == 0</code> and <code>index[i] == j</code>, then the <code>i</code> th row of <code>tensor</code> is copied to the <code>j</code> th row of <code>self</code>.</p>
+<p>The <code>dim</code> th dimension of <code>tensor</code> must have the same size as the length of <code>index</code> (which must be a vector), and all other dimensions must match <code>self</code>, or an error will be raised.</p>
+<div id="arguments-20" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-20" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>dim (int): dimension along which to index</li>
+<li>index (LongTensor): indices of <code>tensor</code> to select from</li>
+<li>tensor (Tensor): the tensor containing values to copy</li>
+</ul>
+</div>
+<div id="examples-12" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-12" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb13"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">5</span>, <span class="fl">3</span>)
+<span class="kw">t</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">9</span>, ncol = <span class="fl">3</span>), dtype=<span class="fu"><a href="../../reference/torch_dtype.html">torch_float</a></span>())
+<span class="kw">index</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1</span>, <span class="fl">5</span>, <span class="fl">3</span>))
+<span class="kw">x</span><span class="op">$</span><span class="fu">index_copy_</span>(<span class="fl">1</span>, <span class="kw">index</span>, <span class="kw">t</span>)
+</pre></div>
+</div>
+</div>
+<div id="index_fill" class="section level2">
+<h2 class="hasAnchor">
+<a href="#index_fill" class="anchor"></a>index_fill</h2>
+<p>index_fill(tensor1, dim, index, value) -&gt; Tensor</p>
+<p>Out-of-place version of <code>$index_fill_</code>. <code>tensor1</code> corresponds to <code>self</code> in <code>$index_fill_</code>.</p>
+</div>
+<div id="index_fill_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#index_fill_" class="anchor"></a>index_fill_</h2>
+<p>index_fill_(dim, index, val) -&gt; Tensor</p>
+<p>Fills the elements of the <code>self</code> tensor with value <code>val</code> by selecting the indices in the order given in <code>index</code>.</p>
+<div id="arguments-21" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-21" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>dim (int): dimension along which to index</li>
+<li>index (LongTensor): indices of <code>self</code> tensor to fill in</li>
+<li>val (float): the value to fill with</li>
+</ul>
+</div>
+<div id="examples-13" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-13" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb14"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">9</span>, ncol = <span class="fl">3</span>), dtype=<span class="fu"><a href="../../reference/torch_dtype.html">torch_float</a></span>())
+<span class="kw">index</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1</span>, <span class="fl">3</span>), dtype = <span class="fu"><a href="../../reference/torch_dtype.html">torch_long</a></span>())
+<span class="kw">x</span><span class="op">$</span><span class="fu">index_fill_</span>(<span class="fl">1</span>, <span class="kw">index</span>, <span class="op">-</span><span class="fl">1</span>)
+</pre></div>
+</div>
+</div>
+<div id="index_put" class="section level2">
+<h2 class="hasAnchor">
+<a href="#index_put" class="anchor"></a>index_put</h2>
+<p>index_put(tensor1, indices, value, accumulate=FALSE) -&gt; Tensor</p>
+<p>Out-place version of <code>$index_put_</code>. <code>tensor1</code> corresponds to <code>self</code> in <code>$index_put_</code>.</p>
+</div>
+<div id="index_put_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#index_put_" class="anchor"></a>index_put_</h2>
+<p>index_put_(indices, value, accumulate=FALSE) -&gt; Tensor</p>
+<p>Puts values from the tensor <code>value</code> into the tensor <code>self</code> using the indices specified in <code>indices</code> (which is a tuple of Tensors). The expression <code>tensor.index_put_(indices, value)</code> is equivalent to <code>tensor[indices] = value</code>. Returns <code>self</code>.</p>
+<p>If <code>accumulate</code> is <code>TRUE</code>, the elements in <code>value</code> are added to <code>self</code>. If accumulate is <code>FALSE</code>, the behavior is undefined if indices contain duplicate elements.</p>
+<div id="arguments-22" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-22" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>indices (tuple of LongTensor): tensors used to index into <code>self</code>.</li>
+<li>value (Tensor): tensor of same dtype as <code>self</code>.</li>
+<li>accumulate (bool): whether to accumulate into self</li>
+</ul>
+</div>
+</div>
+<div id="index_select" class="section level2">
+<h2 class="hasAnchor">
+<a href="#index_select" class="anchor"></a>index_select</h2>
+<p>index_select(dim, index) -&gt; Tensor</p>
+<p>See [torch_index_select()]</p>
+</div>
+<div id="indices" class="section level2">
+<h2 class="hasAnchor">
+<a href="#indices" class="anchor"></a>indices</h2>
+<p>indices() -&gt; Tensor</p>
+<p>If <code>self</code> is a sparse COO tensor (i.e., with <code>torch_sparse_coo</code> layout), this returns a view of the contained indices tensor. Otherwise, this throws an error.</p>
+<p>See also <code>Tensor.values</code>.</p>
+<div id="note-4" class="section level4">
+<h4 class="hasAnchor">
+<a href="#note-4" class="anchor"></a>Note:</h4>
+<p>This method can only be called on a coalesced sparse tensor. See <code>Tensor.coalesce</code> for details.</p>
+</div>
+</div>
+<div id="int" class="section level2">
+<h2 class="hasAnchor">
+<a href="#int" class="anchor"></a>int</h2>
+<p>int(memory_format=torch_preserve_format) -&gt; Tensor</p>
+<p><code>self$int()</code> is equivalent to <code>self$to(torch_int32)</code>. See [to()].</p>
+<div id="arguments-23" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-23" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
+<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
+</ul>
+</div>
+</div>
+<div id="int_repr" class="section level2">
+<h2 class="hasAnchor">
+<a href="#int_repr" class="anchor"></a>int_repr</h2>
+<p>int_repr() -&gt; Tensor</p>
+<p>Given a quantized Tensor, <code>self$int_repr()</code> returns a CPU Tensor with uint8_t as data type that stores the underlying uint8_t values of the given Tensor.</p>
+</div>
+<div id="inverse" class="section level2">
+<h2 class="hasAnchor">
+<a href="#inverse" class="anchor"></a>inverse</h2>
+<p>inverse() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_inverse.html">?torch_inverse</a></code></p>
+</div>
+<div id="irfft" class="section level2">
+<h2 class="hasAnchor">
+<a href="#irfft" class="anchor"></a>irfft</h2>
+<p>irfft(signal_ndim, normalized=FALSE, onesided=TRUE, signal_sizes=NULL) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_irfft.html">?torch_irfft</a></code></p>
+</div>
+<div id="is_complex" class="section level2">
+<h2 class="hasAnchor">
+<a href="#is_complex" class="anchor"></a>is_complex</h2>
+<p>is_complex() -&gt; bool</p>
+<p>Returns TRUE if the data type of <code>self</code> is a complex data type.</p>
+</div>
+<div id="is_contiguous" class="section level2">
+<h2 class="hasAnchor">
+<a href="#is_contiguous" class="anchor"></a>is_contiguous</h2>
+<p>is_contiguous(memory_format=torch_contiguous_format) -&gt; bool</p>
+<p>Returns TRUE if <code>self</code> tensor is contiguous in memory in the order specified by memory format.</p>
+<div id="arguments-24" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-24" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>memory_format (<code>torch_memory_format</code>, optional): Specifies memory allocation</li>
+<li>order. Default: <code>torch_contiguous_format</code>.</li>
+</ul>
+</div>
+</div>
+<div id="is_cuda" class="section level2">
+<h2 class="hasAnchor">
+<a href="#is_cuda" class="anchor"></a>is_cuda</h2>
+<p>Is <code>TRUE</code> if the Tensor is stored on the GPU, <code>FALSE</code> otherwise.</p>
+</div>
+<div id="is_floating_point" class="section level2">
+<h2 class="hasAnchor">
+<a href="#is_floating_point" class="anchor"></a>is_floating_point</h2>
+<p>is_floating_point() -&gt; bool</p>
+<p>Returns TRUE if the data type of <code>self</code> is a floating point data type.</p>
+</div>
+<div id="is_leaf" class="section level2">
+<h2 class="hasAnchor">
+<a href="#is_leaf" class="anchor"></a>is_leaf</h2>
+<p>All Tensors that have <code>requires_grad</code> which is <code>FALSE</code> will be leaf Tensors by convention.</p>
+<p>For Tensors that have <code>requires_grad</code> which is <code>TRUE</code>, they will be leaf Tensors if they were created by the user. This means that they are not the result of an operation and so <code>grad_fn</code> is NULL.</p>
+<p>Only leaf Tensors will have their <code>grad</code> populated during a call to [backward()]. To get <code>grad</code> populated for non-leaf Tensors, you can use [retain_grad()].</p>
+<div id="examples-14" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-14" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb15"><pre class="downlit">
+<span class="kw">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">10</span>, requires_grad=<span class="fl">TRUE</span>)
+<span class="kw">a</span><span class="op">$</span><span class="fu">is_leaf</span>()
+
+<span class="co"># b &lt;- torch_rand(10, requires_grad=TRUE)$cuda()</span>
+<span class="co"># b$is_leaf()</span>
+<span class="co"># FALSE</span>
+<span class="co"># b was created by the operation that cast a cpu Tensor into a cuda Tensor</span>
+
+<span class="kw">c</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">10</span>, requires_grad=<span class="fl">TRUE</span>) <span class="op">+</span> <span class="fl">2</span>
+<span class="kw">c</span><span class="op">$</span><span class="fu">is_leaf</span>()
+<span class="co"># c was created by the addition operation</span>
+
+<span class="co"># d &lt;- torch_rand(10)$cuda()</span>
+<span class="co"># d$is_leaf()</span>
+<span class="co"># TRUE</span>
+<span class="co"># d does not require gradients and so has no operation creating it (that is tracked by the autograd engine)</span>
+
+<span class="co"># e &lt;- torch_rand(10)$cuda()$requires_grad_()</span>
+<span class="co"># e$is_leaf()</span>
+<span class="co"># TRUE</span>
+<span class="co"># e requires gradients and has no operations creating it</span>
+
+<span class="co"># f &lt;- torch_rand(10, requires_grad=TRUE, device="cuda")</span>
+<span class="co"># f$is_leaf</span>
+<span class="co"># TRUE</span>
+<span class="co"># f requires grad, has no operation creating it</span>
+</pre></div>
+</div>
+</div>
+<div id="is_meta" class="section level2">
+<h2 class="hasAnchor">
+<a href="#is_meta" class="anchor"></a>is_meta</h2>
+<p>Is <code>TRUE</code> if the Tensor is a meta tensor, <code>FALSE</code> otherwise. Meta tensors are like normal tensors, but they carry no data.</p>
+</div>
+<div id="is_pinned" class="section level2">
+<h2 class="hasAnchor">
+<a href="#is_pinned" class="anchor"></a>is_pinned</h2>
+<p>Returns true if this tensor resides in pinned memory.</p>
+</div>
+<div id="is_quantized" class="section level2">
+<h2 class="hasAnchor">
+<a href="#is_quantized" class="anchor"></a>is_quantized</h2>
+<p>Is <code>TRUE</code> if the Tensor is quantized, <code>FALSE</code> otherwise.</p>
+</div>
+<div id="is_set_to" class="section level2">
+<h2 class="hasAnchor">
+<a href="#is_set_to" class="anchor"></a>is_set_to</h2>
+<p>is_set_to(tensor) -&gt; bool</p>
+<p>Returns TRUE if this object refers to the same <code>THTensor</code> object from the Torch C API as the given tensor.</p>
+</div>
+<div id="is_shared" class="section level2">
+<h2 class="hasAnchor">
+<a href="#is_shared" class="anchor"></a>is_shared</h2>
+<p>Checks if tensor is in shared memory.</p>
+<p>This is always <code>TRUE</code> for CUDA tensors.</p>
+</div>
+<div id="is_signed" class="section level2">
+<h2 class="hasAnchor">
+<a href="#is_signed" class="anchor"></a>is_signed</h2>
+<p>is_signed() -&gt; bool</p>
+<p>Returns TRUE if the data type of <code>self</code> is a signed data type.</p>
+</div>
+<div id="isclose" class="section level2">
+<h2 class="hasAnchor">
+<a href="#isclose" class="anchor"></a>isclose</h2>
+<p>isclose(other, rtol=1e-05, atol=1e-08, equal_nan=FALSE) -&gt; Tensor</p>
+<p>See <code>?torch_isclose</code></p>
+</div>
+<div id="isfinite" class="section level2">
+<h2 class="hasAnchor">
+<a href="#isfinite" class="anchor"></a>isfinite</h2>
+<p>isfinite() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_isfinite.html">?torch_isfinite</a></code></p>
+</div>
+<div id="isinf" class="section level2">
+<h2 class="hasAnchor">
+<a href="#isinf" class="anchor"></a>isinf</h2>
+<p>isinf() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_isinf.html">?torch_isinf</a></code></p>
+</div>
+<div id="isnan" class="section level2">
+<h2 class="hasAnchor">
+<a href="#isnan" class="anchor"></a>isnan</h2>
+<p>isnan() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_isnan.html">?torch_isnan</a></code></p>
+</div>
+<div id="istft" class="section level2">
+<h2 class="hasAnchor">
+<a href="#istft" class="anchor"></a>istft</h2>
+<p>See <code>?torch_istft</code> ## item</p>
+<p>item() -&gt; number</p>
+<p>Returns the value of this tensor as a standard Python number. This only works for tensors with one element. For other cases, see <code>$tolist</code>.</p>
+<p>This operation is not differentiable.</p>
+<div id="examples-15" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-15" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb16"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1.0</span>)
+<span class="kw">x</span><span class="op">$</span><span class="fu">item</span>()
+</pre></div>
+</div>
+</div>
+<div id="kthvalue" class="section level2">
+<h2 class="hasAnchor">
+<a href="#kthvalue" class="anchor"></a>kthvalue</h2>
+<p>kthvalue(k, dim=NULL, keepdim=FALSE) -&gt; (Tensor, LongTensor)</p>
+<p>See <code><a href="../../reference/torch_kthvalue.html">?torch_kthvalue</a></code></p>
+</div>
+<div id="le" class="section level2">
+<h2 class="hasAnchor">
+<a href="#le" class="anchor"></a>le</h2>
+<p>le(other) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_le.html">?torch_le</a></code></p>
+</div>
+<div id="le_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#le_" class="anchor"></a>le_</h2>
+<p>le_(other) -&gt; Tensor</p>
+<p>In-place version of <code>$le</code></p>
+</div>
+<div id="lerp" class="section level2">
+<h2 class="hasAnchor">
+<a href="#lerp" class="anchor"></a>lerp</h2>
+<p>lerp(end, weight) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_lerp.html">?torch_lerp</a></code></p>
+</div>
+<div id="lerp_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#lerp_" class="anchor"></a>lerp_</h2>
+<p>lerp_(end, weight) -&gt; Tensor</p>
+<p>In-place version of <code>$lerp</code></p>
+</div>
+<div id="lgamma" class="section level2">
+<h2 class="hasAnchor">
+<a href="#lgamma" class="anchor"></a>lgamma</h2>
+<p>lgamma() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_lgamma.html">?torch_lgamma</a></code></p>
+</div>
+<div id="lgamma_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#lgamma_" class="anchor"></a>lgamma_</h2>
+<p>lgamma_() -&gt; Tensor</p>
+<p>In-place version of <code>$lgamma</code></p>
+</div>
+<div id="log" class="section level2">
+<h2 class="hasAnchor">
+<a href="#log" class="anchor"></a>log</h2>
+<p>log() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_log.html">?torch_log</a></code></p>
+</div>
+<div id="log10" class="section level2">
+<h2 class="hasAnchor">
+<a href="#log10" class="anchor"></a>log10</h2>
+<p>log10() -&gt; Tensor</p>
+<p>See [torch_log10()]</p>
+</div>
+<div id="log10_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#log10_" class="anchor"></a>log10_</h2>
+<p>log10_() -&gt; Tensor</p>
+<p>In-place version of <code>$log10</code></p>
+</div>
+<div id="log1p" class="section level2">
+<h2 class="hasAnchor">
+<a href="#log1p" class="anchor"></a>log1p</h2>
+<p>log1p() -&gt; Tensor</p>
+<p>See [torch_log1p()]</p>
+</div>
+<div id="log1p_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#log1p_" class="anchor"></a>log1p_</h2>
+<p>log1p_() -&gt; Tensor</p>
+<p>In-place version of <code>$log1p</code></p>
+</div>
+<div id="log2" class="section level2">
+<h2 class="hasAnchor">
+<a href="#log2" class="anchor"></a>log2</h2>
+<p>log2() -&gt; Tensor</p>
+<p>See [torch_log2()]</p>
+</div>
+<div id="log2_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#log2_" class="anchor"></a>log2_</h2>
+<p>log2_() -&gt; Tensor</p>
+<p>In-place version of <code>$log2</code></p>
+</div>
+<div id="log_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#log_" class="anchor"></a>log_</h2>
+<p>log_() -&gt; Tensor</p>
+<p>In-place version of <code>$log</code></p>
+</div>
+<div id="log_normal_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#log_normal_" class="anchor"></a>log_normal_</h2>
+<p>log_normal_(mean=1, std=2, *, generator=NULL)</p>
+<p>Fills <code>self</code> tensor with numbers samples from the log-normal distribution parameterized by the given mean <code>\mu</code> and standard deviation <code>\sigma</code>. Note that <code>mean</code> and <code>std</code> are the mean and standard deviation of the underlying normal distribution, and not of the returned distribution:</p>
+<p><span class="math display">\[
+f(x) = \dfrac{1}{x \sigma \sqrt{2\pi}}\ e^{-\frac{(\ln x - \mu)^2}{2\sigma^2}}
+\]</span></p>
+</div>
+<div id="logaddexp" class="section level2">
+<h2 class="hasAnchor">
+<a href="#logaddexp" class="anchor"></a>logaddexp</h2>
+<p>logaddexp(other) -&gt; Tensor</p>
+<p>See <code>?torch_logaddexp</code></p>
+</div>
+<div id="logaddexp2" class="section level2">
+<h2 class="hasAnchor">
+<a href="#logaddexp2" class="anchor"></a>logaddexp2</h2>
+<p>logaddexp2(other) -&gt; Tensor</p>
+<p>See [torch_logaddexp2()]</p>
+</div>
+<div id="logcumsumexp" class="section level2">
+<h2 class="hasAnchor">
+<a href="#logcumsumexp" class="anchor"></a>logcumsumexp</h2>
+<p>logcumsumexp(dim) -&gt; Tensor</p>
+<p>See <code>?torch_logcumsumexp</code></p>
+</div>
+<div id="logdet" class="section level2">
+<h2 class="hasAnchor">
+<a href="#logdet" class="anchor"></a>logdet</h2>
+<p>logdet() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_logdet.html">?torch_logdet</a></code></p>
+</div>
+<div id="logical_and" class="section level2">
+<h2 class="hasAnchor">
+<a href="#logical_and" class="anchor"></a>logical_and</h2>
+<p>logical_and() -&gt; Tensor</p>
+<p>See [torch_logical_and()]</p>
+</div>
+<div id="logical_and_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#logical_and_" class="anchor"></a>logical_and_</h2>
+<p>logical_and_() -&gt; Tensor</p>
+<p>In-place version of <code>$logical_and</code></p>
+</div>
+<div id="logical_not" class="section level2">
+<h2 class="hasAnchor">
+<a href="#logical_not" class="anchor"></a>logical_not</h2>
+<p>logical_not() -&gt; Tensor</p>
+<p>See [torch_logical_not()]</p>
+</div>
+<div id="logical_not_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#logical_not_" class="anchor"></a>logical_not_</h2>
+<p>logical_not_() -&gt; Tensor</p>
+<p>In-place version of <code>$logical_not</code></p>
+</div>
+<div id="logical_or" class="section level2">
+<h2 class="hasAnchor">
+<a href="#logical_or" class="anchor"></a>logical_or</h2>
+<p>logical_or() -&gt; Tensor</p>
+<p>See [torch_logical_or()]</p>
+</div>
+<div id="logical_or_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#logical_or_" class="anchor"></a>logical_or_</h2>
+<p>logical_or_() -&gt; Tensor</p>
+<p>In-place version of <code>$logical_or</code></p>
+</div>
+<div id="logical_xor" class="section level2">
+<h2 class="hasAnchor">
+<a href="#logical_xor" class="anchor"></a>logical_xor</h2>
+<p>logical_xor() -&gt; Tensor</p>
+<p>See [torch_logical_xor()]</p>
+</div>
+<div id="logical_xor_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#logical_xor_" class="anchor"></a>logical_xor_</h2>
+<p>logical_xor_() -&gt; Tensor</p>
+<p>In-place version of <code>$logical_xor</code></p>
+</div>
+<div id="logsumexp" class="section level2">
+<h2 class="hasAnchor">
+<a href="#logsumexp" class="anchor"></a>logsumexp</h2>
+<p>logsumexp(dim, keepdim=FALSE) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_logsumexp.html">?torch_logsumexp</a></code></p>
+</div>
+<div id="long" class="section level2">
+<h2 class="hasAnchor">
+<a href="#long" class="anchor"></a>long</h2>
+<p>long(memory_format=torch_preserve_format) -&gt; Tensor</p>
+<p><code>self$long()</code> is equivalent to <code>self$to(torch_int64)</code>. See [to()].</p>
+<div id="arguments-25" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-25" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
+<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
+</ul>
+</div>
+</div>
+<div id="lstsq" class="section level2">
+<h2 class="hasAnchor">
+<a href="#lstsq" class="anchor"></a>lstsq</h2>
+<p>lstsq(A) -&gt; (Tensor, Tensor)</p>
+<p>See <code><a href="../../reference/torch_lstsq.html">?torch_lstsq</a></code></p>
+</div>
+<div id="lt" class="section level2">
+<h2 class="hasAnchor">
+<a href="#lt" class="anchor"></a>lt</h2>
+<p>lt(other) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_lt.html">?torch_lt</a></code></p>
+</div>
+<div id="lt_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#lt_" class="anchor"></a>lt_</h2>
+<p>lt_(other) -&gt; Tensor</p>
+<p>In-place version of <code>$lt</code></p>
+</div>
+<div id="lu" class="section level2">
+<h2 class="hasAnchor">
+<a href="#lu" class="anchor"></a>lu</h2>
+<p>See <code><a href="../../reference/torch_lu.html">?torch_lu</a></code> ## lu_solve</p>
+<p>lu_solve(LU_data, LU_pivots) -&gt; Tensor</p>
+<p>See [torch_lu_solve()]</p>
+</div>
+<div id="map_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#map_" class="anchor"></a>map_</h2>
+<p>map_(tensor, callable)</p>
+<p>Applies <code>callable</code> for each element in <code>self</code> tensor and the given <code>tensor</code> and stores the results in <code>self</code> tensor. <code>self</code> tensor and the given <code>tensor</code> must be broadcastable.</p>
+<p>The <code>callable</code> should have the signature:</p>
+<p><code>callable(a, b) -&gt; number</code></p>
+</div>
+<div id="masked_fill" class="section level2">
+<h2 class="hasAnchor">
+<a href="#masked_fill" class="anchor"></a>masked_fill</h2>
+<p>masked_fill(mask, value) -&gt; Tensor</p>
+<p>Out-of-place version of <code>$masked_fill_</code></p>
+</div>
+<div id="masked_fill_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#masked_fill_" class="anchor"></a>masked_fill_</h2>
+<p>masked_fill_(mask, value)</p>
+<p>Fills elements of <code>self</code> tensor with <code>value</code> where <code>mask</code> is TRUE. The shape of <code>mask</code> must be <code>broadcastable &lt;broadcasting-semantics&gt;</code> with the shape of the underlying tensor.</p>
+<div id="arguments-26" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-26" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>mask (BoolTensor): the boolean mask</li>
+<li>value (float): the value to fill in with</li>
+</ul>
+</div>
+</div>
+<div id="masked_scatter" class="section level2">
+<h2 class="hasAnchor">
+<a href="#masked_scatter" class="anchor"></a>masked_scatter</h2>
+<p>masked_scatter(mask, tensor) -&gt; Tensor</p>
+<p>Out-of-place version of <code>$masked_scatter_</code></p>
+</div>
+<div id="masked_scatter_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#masked_scatter_" class="anchor"></a>masked_scatter_</h2>
+<p>masked_scatter_(mask, source)</p>
+<p>Copies elements from <code>source</code> into <code>self</code> tensor at positions where the <code>mask</code> is TRUE. The shape of <code>mask</code> must be :ref:<code>broadcastable &lt;broadcasting-semantics&gt;</code> with the shape of the underlying tensor. The <code>source</code> should have at least as many elements as the number of ones in <code>mask</code></p>
+<div id="arguments-27" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-27" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>mask (BoolTensor): the boolean mask</li>
+<li>source (Tensor): the tensor to copy from</li>
+</ul>
+</div>
+<div id="note-5" class="section level4">
+<h4 class="hasAnchor">
+<a href="#note-5" class="anchor"></a>Note:</h4>
+<p>The <code>mask</code> operates on the <code>self</code> tensor, not on the given <code>source</code> tensor.</p>
+</div>
+</div>
+<div id="masked_select" class="section level2">
+<h2 class="hasAnchor">
+<a href="#masked_select" class="anchor"></a>masked_select</h2>
+<p>masked_select(mask) -&gt; Tensor</p>
+<p>See [torch_masked_select()]</p>
+</div>
+<div id="matmul" class="section level2">
+<h2 class="hasAnchor">
+<a href="#matmul" class="anchor"></a>matmul</h2>
+<p>matmul(tensor2) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_matmul.html">?torch_matmul</a></code></p>
+</div>
+<div id="matrix_power" class="section level2">
+<h2 class="hasAnchor">
+<a href="#matrix_power" class="anchor"></a>matrix_power</h2>
+<p>matrix_power(n) -&gt; Tensor</p>
+<p>See [torch_matrix_power()]</p>
+</div>
+<div id="max" class="section level2">
+<h2 class="hasAnchor">
+<a href="#max" class="anchor"></a>max</h2>
+<p>max(dim=NULL, keepdim=FALSE) -&gt; Tensor or (Tensor, Tensor)</p>
+<p>See <code><a href="../../reference/torch_max.html">?torch_max</a></code></p>
+</div>
+<div id="mean" class="section level2">
+<h2 class="hasAnchor">
+<a href="#mean" class="anchor"></a>mean</h2>
+<p>mean(dim=NULL, keepdim=FALSE) -&gt; Tensor or (Tensor, Tensor)</p>
+<p>See <code><a href="../../reference/torch_mean.html">?torch_mean</a></code></p>
+</div>
+<div id="median" class="section level2">
+<h2 class="hasAnchor">
+<a href="#median" class="anchor"></a>median</h2>
+<p>median(dim=NULL, keepdim=FALSE) -&gt; (Tensor, LongTensor)</p>
+<p>See <code><a href="../../reference/torch_median.html">?torch_median</a></code></p>
+</div>
+<div id="min" class="section level2">
+<h2 class="hasAnchor">
+<a href="#min" class="anchor"></a>min</h2>
+<p>min(dim=NULL, keepdim=FALSE) -&gt; Tensor or (Tensor, Tensor)</p>
+<p>See <code><a href="../../reference/torch_min.html">?torch_min</a></code></p>
+</div>
+<div id="mm" class="section level2">
+<h2 class="hasAnchor">
+<a href="#mm" class="anchor"></a>mm</h2>
+<p>mm(mat2) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_mm.html">?torch_mm</a></code></p>
+</div>
+<div id="mode" class="section level2">
+<h2 class="hasAnchor">
+<a href="#mode" class="anchor"></a>mode</h2>
+<p>mode(dim=NULL, keepdim=FALSE) -&gt; (Tensor, LongTensor)</p>
+<p>See <code><a href="../../reference/torch_mode.html">?torch_mode</a></code></p>
+</div>
+<div id="mul" class="section level2">
+<h2 class="hasAnchor">
+<a href="#mul" class="anchor"></a>mul</h2>
+<p>mul(value) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_mul.html">?torch_mul</a></code></p>
+</div>
+<div id="mul_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#mul_" class="anchor"></a>mul_</h2>
+<p>mul_(value)</p>
+<p>In-place version of <code>$mul</code></p>
+</div>
+<div id="multinomial" class="section level2">
+<h2 class="hasAnchor">
+<a href="#multinomial" class="anchor"></a>multinomial</h2>
+<p>multinomial(num_samples, replacement=FALSE, *, generator=NULL) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_multinomial.html">?torch_multinomial</a></code></p>
+</div>
+<div id="mv" class="section level2">
+<h2 class="hasAnchor">
+<a href="#mv" class="anchor"></a>mv</h2>
+<p>mv(vec) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_mv.html">?torch_mv</a></code></p>
+</div>
+<div id="mvlgamma" class="section level2">
+<h2 class="hasAnchor">
+<a href="#mvlgamma" class="anchor"></a>mvlgamma</h2>
+<p>mvlgamma(p) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_mvlgamma.html">?torch_mvlgamma</a></code></p>
+</div>
+<div id="mvlgamma_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#mvlgamma_" class="anchor"></a>mvlgamma_</h2>
+<p>mvlgamma_(p) -&gt; Tensor</p>
+<p>In-place version of <code>$mvlgamma</code></p>
+</div>
+<div id="names" class="section level2">
+<h2 class="hasAnchor">
+<a href="#names" class="anchor"></a>names</h2>
+<p>Stores names for each of this tensor’s dimensions.</p>
+<p><code>names[idx]</code> corresponds to the name of tensor dimension <code>idx</code>. Names are either a string if the dimension is named or <code>NULL</code> if the dimension is unnamed.</p>
+<p>Dimension names may contain characters or underscore. Furthermore, a dimension name must be a valid Python variable name (i.e., does not start with underscore).</p>
+<p>Tensors may not have two named dimensions with the same name.</p>
+<div id="warning-4" class="section level4">
+<h4 class="hasAnchor">
+<a href="#warning-4" class="anchor"></a>Warning:</h4>
+<p>The named tensor API is experimental and subject to change.</p>
+</div>
+</div>
+<div id="narrow" class="section level2">
+<h2 class="hasAnchor">
+<a href="#narrow" class="anchor"></a>narrow</h2>
+<p>narrow(dimension, start, length) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_narrow.html">?torch_narrow</a></code></p>
+<div id="examples-16" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-16" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb17"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">9</span>, ncol = <span class="fl">3</span>))
+<span class="kw">x</span><span class="op">$</span><span class="fu">narrow</span>(<span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">3</span>)
+<span class="kw">x</span><span class="op">$</span><span class="fu">narrow</span>(<span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">2</span>)
+</pre></div>
+</div>
+</div>
+<div id="narrow_copy" class="section level2">
+<h2 class="hasAnchor">
+<a href="#narrow_copy" class="anchor"></a>narrow_copy</h2>
+<p>narrow_copy(dimension, start, length) -&gt; Tensor</p>
+<p>Same as <code>Tensor.narrow</code> except returning a copy rather than shared storage. This is primarily for sparse tensors, which do not have a shared-storage narrow method. Calling <code>narrow_copy` with</code>dimemsion &gt; self<span class="math inline">\(sparse_dim()`` will return a copy with the relevant dense dimension narrowed, and ``self\)</span>shape`` updated accordingly.</p>
+</div>
+<div id="ndim" class="section level2">
+<h2 class="hasAnchor">
+<a href="#ndim" class="anchor"></a>ndim</h2>
+<p>Alias for <code>$dim()</code></p>
+</div>
+<div id="ndimension" class="section level2">
+<h2 class="hasAnchor">
+<a href="#ndimension" class="anchor"></a>ndimension</h2>
+<p>ndimension() -&gt; int</p>
+<p>Alias for <code>$dim()</code></p>
+</div>
+<div id="ne" class="section level2">
+<h2 class="hasAnchor">
+<a href="#ne" class="anchor"></a>ne</h2>
+<p>ne(other) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_ne.html">?torch_ne</a></code></p>
+</div>
+<div id="ne_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#ne_" class="anchor"></a>ne_</h2>
+<p>ne_(other) -&gt; Tensor</p>
+<p>In-place version of <code>$ne</code></p>
+</div>
+<div id="neg" class="section level2">
+<h2 class="hasAnchor">
+<a href="#neg" class="anchor"></a>neg</h2>
+<p>neg() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_neg.html">?torch_neg</a></code></p>
+</div>
+<div id="neg_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#neg_" class="anchor"></a>neg_</h2>
+<p>neg_() -&gt; Tensor</p>
+<p>In-place version of <code>$neg</code></p>
+</div>
+<div id="nelement" class="section level2">
+<h2 class="hasAnchor">
+<a href="#nelement" class="anchor"></a>nelement</h2>
+<p>nelement() -&gt; int</p>
+<p>Alias for <code>$numel</code></p>
+</div>
+<div id="new_empty" class="section level2">
+<h2 class="hasAnchor">
+<a href="#new_empty" class="anchor"></a>new_empty</h2>
+<p>new_empty(size, dtype=NULL, device=NULL, requires_grad=FALSE) -&gt; Tensor</p>
+<p>Returns a Tensor of size <code>size</code> filled with uninitialized data. By default, the returned Tensor has the same <code>torch_dtype</code> and <code>torch_device</code> as this tensor.</p>
+<div id="arguments-28" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-28" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>dtype (<code>torch_dtype</code>, optional): the desired type of returned tensor. Default: if NULL, same <code>torch_dtype</code> as this tensor.</li>
+<li>device (<code>torch_device</code>, optional): the desired device of returned tensor. Default: if NULL, same <code>torch_device</code> as this tensor.</li>
+<li>requires_grad (bool, optional): If autograd should record operations on the</li>
+<li>returned tensor. Default: <code>FALSE</code>.</li>
+</ul>
+</div>
+<div id="examples-17" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-17" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb18"><pre class="downlit">
+<span class="kw">tensor</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span>(<span class="fl">5</span>)
+<span class="kw">tensor</span><span class="op">$</span><span class="fu">new_empty</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>, <span class="fl">3</span>))
+</pre></div>
+</div>
+</div>
+<div id="new_full" class="section level2">
+<h2 class="hasAnchor">
+<a href="#new_full" class="anchor"></a>new_full</h2>
+<p>new_full(size, fill_value, dtype=NULL, device=NULL, requires_grad=FALSE) -&gt; Tensor</p>
+<p>Returns a Tensor of size <code>size</code> filled with <code>fill_value</code>. By default, the returned Tensor has the same <code>torch_dtype</code> and <code>torch_device</code> as this tensor.</p>
+<div id="arguments-29" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-29" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>fill_value (scalar): the number to fill the output tensor with.</li>
+<li>dtype (<code>torch_dtype</code>, optional): the desired type of returned tensor. Default: if NULL, same <code>torch_dtype</code> as this tensor.</li>
+<li>device (<code>torch_device</code>, optional): the desired device of returned tensor. Default: if NULL, same <code>torch_device</code> as this tensor.</li>
+<li>requires_grad (bool, optional): If autograd should record operations on the</li>
+<li>returned tensor. Default: <code>FALSE</code>.</li>
+</ul>
+</div>
+<div id="examples-18" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-18" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb19"><pre class="downlit">
+<span class="kw">tensor</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>), dtype=<span class="fu"><a href="../../reference/torch_dtype.html">torch_float64</a></span>())
+<span class="kw">tensor</span><span class="op">$</span><span class="fu">new_full</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">3</span>, <span class="fl">4</span>), <span class="fl">3.141592</span>)
+</pre></div>
+</div>
+</div>
+<div id="new_ones" class="section level2">
+<h2 class="hasAnchor">
+<a href="#new_ones" class="anchor"></a>new_ones</h2>
+<p>new_ones(size, dtype=NULL, device=NULL, requires_grad=FALSE) -&gt; Tensor</p>
+<p>Returns a Tensor of size <code>size</code> filled with <code>1</code>. By default, the returned Tensor has the same <code>torch_dtype</code> and <code>torch_device</code> as this tensor.</p>
+<div id="arguments-30" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-30" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>size (int…): a list, tuple, or <code>torch_Size</code> of integers defining the</li>
+<li>shape of the output tensor.</li>
+<li>dtype (<code>torch_dtype</code>, optional): the desired type of returned tensor. Default: if NULL, same <code>torch_dtype</code> as this tensor.</li>
+<li>device (<code>torch_device</code>, optional): the desired device of returned tensor. Default: if NULL, same <code>torch_device</code> as this tensor.</li>
+<li>requires_grad (bool, optional): If autograd should record operations on the</li>
+<li>returned tensor. Default: <code>FALSE</code>.</li>
+</ul>
+</div>
+<div id="examples-19" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-19" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb20"><pre class="downlit">
+<span class="kw">tensor</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>), dtype=<span class="fu"><a href="../../reference/torch_dtype.html">torch_int32</a></span>())
+<span class="kw">tensor</span><span class="op">$</span><span class="fu">new_ones</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>, <span class="fl">3</span>))
+</pre></div>
+</div>
+</div>
+<div id="new_tensor" class="section level2">
+<h2 class="hasAnchor">
+<a href="#new_tensor" class="anchor"></a>new_tensor</h2>
+<p>new_tensor(data, dtype=NULL, device=NULL, requires_grad=FALSE) -&gt; Tensor</p>
+<p>Returns a new Tensor with <code>data</code> as the tensor data. By default, the returned Tensor has the same <code>torch_dtype</code> and <code>torch_device</code> as this tensor.</p>
+<div id="warning-5" class="section level4">
+<h4 class="hasAnchor">
+<a href="#warning-5" class="anchor"></a>Warning:</h4>
+<p><code>new_tensor</code> always copies <code>data(). If you have a Tensor</code>data` and want to avoid a copy, use [$requires_grad_()] or [$detach()]. If you have a numpy array and want to avoid a copy, use [torch_from_numpy()].</p>
+<p>When data is a tensor <code>x</code>, [new_tensor()()] reads out ‘the data’ from whatever it is passed, and constructs a leaf variable. Therefore <code>tensor$new_tensor(x)</code> is equivalent to <code>x$clone()$detach()</code> and <code>tensor$new_tensor(x, requires_grad=TRUE)</code> is equivalent to <code>x$clone()$detach()$requires_grad_(TRUE)</code>. The equivalents using <code>clone()</code> and <code><a href="https://rdrr.io/r/base/detach.html">detach()</a></code> are recommended.</p>
+</div>
+<div id="arguments-31" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-31" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>data (array_like): The returned Tensor copies <code>data</code>.</li>
+<li>dtype (<code>torch_dtype</code>, optional): the desired type of returned tensor. Default: if NULL, same <code>torch_dtype</code> as this tensor.</li>
+<li>device (<code>torch_device</code>, optional): the desired device of returned tensor. Default: if NULL, same <code>torch_device</code> as this tensor.</li>
+<li>requires_grad (bool, optional): If autograd should record operations on the</li>
+<li>returned tensor. Default: <code>FALSE</code>.</li>
+</ul>
+</div>
+<div id="examples-20" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-20" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb21"><pre class="downlit">
+<span class="kw">tensor</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>), dtype=<span class="kw">torch_int8</span>)
+<span class="kw">data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">4</span>, ncol = <span class="fl">2</span>)
+<span class="kw">tensor</span><span class="op">$</span><span class="fu">new_tensor</span>(<span class="kw">data</span>)
+</pre></div>
+</div>
+</div>
+<div id="new_zeros" class="section level2">
+<h2 class="hasAnchor">
+<a href="#new_zeros" class="anchor"></a>new_zeros</h2>
+<p>new_zeros(size, dtype=NULL, device=NULL, requires_grad=FALSE) -&gt; Tensor</p>
+<p>Returns a Tensor of size <code>size</code> filled with <code>0</code>. By default, the returned Tensor has the same <code>torch_dtype</code> and <code>torch_device</code> as this tensor.</p>
+<div id="arguments-32" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-32" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>size (int…): a list, tuple, or <code>torch_Size</code> of integers defining the</li>
+<li>shape of the output tensor.</li>
+<li>dtype (<code>torch_dtype</code>, optional): the desired type of returned tensor. Default: if NULL, same <code>torch_dtype</code> as this tensor.</li>
+<li>device (<code>torch_device</code>, optional): the desired device of returned tensor. Default: if NULL, same <code>torch_device</code> as this tensor.</li>
+<li>requires_grad (bool, optional): If autograd should record operations on the</li>
+<li>returned tensor. Default: <code>FALSE</code>.</li>
+</ul>
+</div>
+<div id="examples-21" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-21" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb22"><pre class="downlit">
+<span class="kw">tensor</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1</span>), dtype=<span class="fu"><a href="../../reference/torch_dtype.html">torch_float64</a></span>())
+<span class="kw">tensor</span><span class="op">$</span><span class="fu">new_zeros</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>, <span class="fl">3</span>))
+</pre></div>
+</div>
+</div>
+<div id="nonzero" class="section level2">
+<h2 class="hasAnchor">
+<a href="#nonzero" class="anchor"></a>nonzero</h2>
+<p>nonzero() -&gt; LongTensor</p>
+<p>See <code><a href="../../reference/torch_nonzero.html">?torch_nonzero</a></code></p>
+</div>
+<div id="norm" class="section level2">
+<h2 class="hasAnchor">
+<a href="#norm" class="anchor"></a>norm</h2>
+<p>See <code><a href="../../reference/torch_norm.html">?torch_norm</a></code> ## normal_</p>
+<p>normal_(mean=0, std=1, *, generator=NULL) -&gt; Tensor</p>
+<p>Fills <code>self</code> tensor with elements samples from the normal distribution parameterized by <code>mean</code> and <code>std</code>.</p>
+</div>
+<div id="numel" class="section level2">
+<h2 class="hasAnchor">
+<a href="#numel" class="anchor"></a>numel</h2>
+<p>numel() -&gt; int</p>
+<p>See <code>?torch_numel</code></p>
+</div>
+<div id="numpy" class="section level2">
+<h2 class="hasAnchor">
+<a href="#numpy" class="anchor"></a>numpy</h2>
+<p>numpy() -&gt; numpy.ndarray</p>
+<p>Returns <code>self</code> tensor as a NumPy :class:<code>ndarray</code>. This tensor and the returned <code>ndarray</code> share the same underlying storage. Changes to <code>self</code> tensor will be reflected in the :class:<code>ndarray</code> and vice versa.</p>
+</div>
+<div id="orgqr" class="section level2">
+<h2 class="hasAnchor">
+<a href="#orgqr" class="anchor"></a>orgqr</h2>
+<p>orgqr(input2) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_orgqr.html">?torch_orgqr</a></code></p>
+</div>
+<div id="ormqr" class="section level2">
+<h2 class="hasAnchor">
+<a href="#ormqr" class="anchor"></a>ormqr</h2>
+<p>ormqr(input2, input3, left=TRUE, transpose=FALSE) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_ormqr.html">?torch_ormqr</a></code></p>
+</div>
+<div id="permute" class="section level2">
+<h2 class="hasAnchor">
+<a href="#permute" class="anchor"></a>permute</h2>
+<p>permute(*dims) -&gt; Tensor</p>
+<p>Returns a view of the original tensor with its dimensions permuted.</p>
+<div id="arguments-33" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-33" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>dims (int…): The desired ordering of dimensions</li>
+</ul>
+</div>
+<div id="examples-22" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-22" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb23"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">5</span>)
+<span class="kw">x</span><span class="op">$</span><span class="fu">size</span>()
+<span class="kw">x</span><span class="op">$</span><span class="fu">permute</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">3</span>, <span class="fl">1</span>, <span class="fl">2</span>))<span class="op">$</span><span class="fu">size</span>()
+</pre></div>
+</div>
+</div>
+<div id="pin_memory" class="section level2">
+<h2 class="hasAnchor">
+<a href="#pin_memory" class="anchor"></a>pin_memory</h2>
+<p>pin_memory() -&gt; Tensor</p>
+<p>Copies the tensor to pinned memory, if it’s not already pinned.</p>
+</div>
+<div id="pinverse" class="section level2">
+<h2 class="hasAnchor">
+<a href="#pinverse" class="anchor"></a>pinverse</h2>
+<p>pinverse() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_pinverse.html">?torch_pinverse</a></code></p>
+</div>
+<div id="polygamma" class="section level2">
+<h2 class="hasAnchor">
+<a href="#polygamma" class="anchor"></a>polygamma</h2>
+<p>polygamma(n) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_polygamma.html">?torch_polygamma</a></code></p>
+</div>
+<div id="polygamma_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#polygamma_" class="anchor"></a>polygamma_</h2>
+<p>polygamma_(n) -&gt; Tensor</p>
+<p>In-place version of <code>$polygamma</code></p>
+</div>
+<div id="pow" class="section level2">
+<h2 class="hasAnchor">
+<a href="#pow" class="anchor"></a>pow</h2>
+<p>pow(exponent) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_pow.html">?torch_pow</a></code></p>
+</div>
+<div id="pow_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#pow_" class="anchor"></a>pow_</h2>
+<p>pow_(exponent) -&gt; Tensor</p>
+<p>In-place version of <code>$pow</code></p>
+</div>
+<div id="prod" class="section level2">
+<h2 class="hasAnchor">
+<a href="#prod" class="anchor"></a>prod</h2>
+<p>prod(dim=NULL, keepdim=FALSE, dtype=NULL) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_prod.html">?torch_prod</a></code></p>
+</div>
+<div id="put_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#put_" class="anchor"></a>put_</h2>
+<p>put_(indices, tensor, accumulate=FALSE) -&gt; Tensor</p>
+<p>Copies the elements from <code>tensor</code> into the positions specified by indices. For the purpose of indexing, the <code>self</code> tensor is treated as if it were a 1-D tensor.</p>
+<p>If <code>accumulate</code> is <code>TRUE</code>, the elements in <code>tensor</code> are added to <code>self</code>. If accumulate is <code>FALSE</code>, the behavior is undefined if indices contain duplicate elements.</p>
+<div id="arguments-34" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-34" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>indices (LongTensor): the indices into self</li>
+<li>tensor (Tensor): the tensor containing values to copy from</li>
+<li>accumulate (bool): whether to accumulate into self</li>
+</ul>
+</div>
+<div id="examples-23" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-23" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb24"><pre class="downlit">
+<span class="kw">src</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span>(<span class="fl">3</span><span class="op">:</span><span class="fl">8</span>, ncol = <span class="fl">3</span>))
+<span class="kw">src</span><span class="op">$</span><span class="fu">put_</span>(<span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">2</span>), <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">9</span><span class="op">:</span><span class="fl">10</span>))
+</pre></div>
+</div>
+</div>
+<div id="q_per_channel_axis" class="section level2">
+<h2 class="hasAnchor">
+<a href="#q_per_channel_axis" class="anchor"></a>q_per_channel_axis</h2>
+<p>q_per_channel_axis() -&gt; int</p>
+<p>Given a Tensor quantized by linear (affine) per-channel quantization, returns the index of dimension on which per-channel quantization is applied.</p>
+</div>
+<div id="q_per_channel_scales" class="section level2">
+<h2 class="hasAnchor">
+<a href="#q_per_channel_scales" class="anchor"></a>q_per_channel_scales</h2>
+<p>q_per_channel_scales() -&gt; Tensor</p>
+<p>Given a Tensor quantized by linear (affine) per-channel quantization, returns a Tensor of scales of the underlying quantizer. It has the number of elements that matches the corresponding dimensions (from q_per_channel_axis) of the tensor.</p>
+</div>
+<div id="q_per_channel_zero_points" class="section level2">
+<h2 class="hasAnchor">
+<a href="#q_per_channel_zero_points" class="anchor"></a>q_per_channel_zero_points</h2>
+<p>q_per_channel_zero_points() -&gt; Tensor</p>
+<p>Given a Tensor quantized by linear (affine) per-channel quantization, returns a tensor of zero_points of the underlying quantizer. It has the number of elements that matches the corresponding dimensions (from q_per_channel_axis) of the tensor.</p>
+</div>
+<div id="q_scale" class="section level2">
+<h2 class="hasAnchor">
+<a href="#q_scale" class="anchor"></a>q_scale</h2>
+<p>q_scale() -&gt; float</p>
+<p>Given a Tensor quantized by linear(affine) quantization, returns the scale of the underlying quantizer().</p>
+</div>
+<div id="q_zero_point" class="section level2">
+<h2 class="hasAnchor">
+<a href="#q_zero_point" class="anchor"></a>q_zero_point</h2>
+<p>q_zero_point() -&gt; int</p>
+<p>Given a Tensor quantized by linear(affine) quantization, returns the zero_point of the underlying quantizer().</p>
+</div>
+<div id="qr" class="section level2">
+<h2 class="hasAnchor">
+<a href="#qr" class="anchor"></a>qr</h2>
+<p>qr(some=TRUE) -&gt; (Tensor, Tensor)</p>
+<p>See <code><a href="../../reference/torch_qr.html">?torch_qr</a></code></p>
+</div>
+<div id="qscheme" class="section level2">
+<h2 class="hasAnchor">
+<a href="#qscheme" class="anchor"></a>qscheme</h2>
+<p>qscheme() -&gt; torch_qscheme</p>
+<p>Returns the quantization scheme of a given QTensor.</p>
+</div>
+<div id="rad2deg" class="section level2">
+<h2 class="hasAnchor">
+<a href="#rad2deg" class="anchor"></a>rad2deg</h2>
+<p>rad2deg() -&gt; Tensor</p>
+<p>See [torch_rad2deg()]</p>
+</div>
+<div id="rad2deg_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#rad2deg_" class="anchor"></a>rad2deg_</h2>
+<p>rad2deg_() -&gt; Tensor</p>
+<p>In-place version of <code>$rad2deg</code></p>
+</div>
+<div id="random_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#random_" class="anchor"></a>random_</h2>
+<p>random_(from=0, to=NULL, *, generator=NULL) -&gt; Tensor</p>
+<p>Fills <code>self</code> tensor with numbers sampled from the discrete uniform distribution over <code>[from, to - 1]</code>. If not specified, the values are usually only bounded by <code>self</code> tensor’s data type. However, for floating point types, if unspecified, range will be <code>[0, 2^mantissa]</code> to ensure that every value is representable. For example, <code>torch_tensor(1, dtype=torch_double).random_()</code> will be uniform in <code>[0, 2^53]</code>.</p>
+</div>
+<div id="real" class="section level2">
+<h2 class="hasAnchor">
+<a href="#real" class="anchor"></a>real</h2>
+<p>Returns a new tensor containing real values of the <code>self</code> tensor. The returned tensor and <code>self</code> share the same underlying storage.</p>
+<div id="warning-6" class="section level4">
+<h4 class="hasAnchor">
+<a href="#warning-6" class="anchor"></a>Warning:</h4>
+<p>[real()] is only supported for tensors with complex dtypes.</p>
+</div>
+<div id="examples-24" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-24" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb25"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">4</span>, dtype=<span class="fu">torch_cfloat</span>())
+<span class="kw">x</span>
+<span class="kw">x</span><span class="op">$</span><span class="kw">real</span>
+</pre></div>
+</div>
+</div>
+<div id="reciprocal" class="section level2">
+<h2 class="hasAnchor">
+<a href="#reciprocal" class="anchor"></a>reciprocal</h2>
+<p>reciprocal() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_reciprocal.html">?torch_reciprocal</a></code></p>
+</div>
+<div id="reciprocal_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#reciprocal_" class="anchor"></a>reciprocal_</h2>
+<p>reciprocal_() -&gt; Tensor</p>
+<p>In-place version of <code>$reciprocal</code></p>
+</div>
+<div id="record_stream" class="section level2">
+<h2 class="hasAnchor">
+<a href="#record_stream" class="anchor"></a>record_stream</h2>
+<p>record_stream(stream)</p>
+<p>Ensures that the tensor memory is not reused for another tensor until all current work queued on <code>stream</code> are complete.</p>
+<div id="note-6" class="section level4">
+<h4 class="hasAnchor">
+<a href="#note-6" class="anchor"></a>Note:</h4>
+<p>The caching allocator is aware of only the stream where a tensor was allocated. Due to the awareness, it already correctly manages the life cycle of tensors on only one stream. But if a tensor is used on a stream different from the stream of origin, the allocator might reuse the memory unexpectedly. Calling this method lets the allocator know which streams have used the tensor.</p>
+</div>
+</div>
+<div id="refine_names" class="section level2">
+<h2 class="hasAnchor">
+<a href="#refine_names" class="anchor"></a>refine_names</h2>
+<p>Refines the dimension names of <code>self</code> according to <code>names</code>.</p>
+<p>Refining is a special case of renaming that “lifts” unnamed dimensions. A <code>NULL</code> dim can be refined to have any name; a named dim can only be refined to have the same name.</p>
+<p>Because named tensors can coexist with unnamed tensors, refining names gives a nice way to write named-tensor-aware code that works with both named and unnamed tensors.</p>
+<p><code>names</code> may contain up to one Ellipsis (<code>...</code>). The Ellipsis is expanded greedily; it is expanded in-place to fill <code>names</code> to the same length as <code>self$dim()</code> using names from the corresponding indices of <code>self$names</code>.</p>
+<div id="arguments-35" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-35" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>names (iterable of str): The desired names of the output tensor. May contain up to one Ellipsis.</li>
+</ul>
+</div>
+<div id="examples-25" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-25" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb26"><pre class="downlit">
+<span class="kw">imgs</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">32</span>, <span class="fl">3</span>, <span class="fl">128</span>, <span class="fl">128</span>)
+<span class="kw">named_imgs</span> <span class="op">&lt;-</span> <span class="kw">imgs</span><span class="op">$</span><span class="fu">refine_names</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">'N'</span>, <span class="st">'C'</span>, <span class="st">'H'</span>, <span class="st">'W'</span>))
+<span class="kw">named_imgs</span><span class="op">$</span><span class="kw">names</span>
+</pre></div>
+</div>
+</div>
+<div id="register_hook" class="section level2">
+<h2 class="hasAnchor">
+<a href="#register_hook" class="anchor"></a>register_hook</h2>
+<p>Registers a backward hook.</p>
+<p>The hook will be called every time a gradient with respect to the Tensor is computed. The hook should have the following signature::</p>
+<p>hook(grad) -&gt; Tensor or NULL</p>
+<p>The hook should not modify its argument, but it can optionally return a new gradient which will be used in place of <code>grad</code>.</p>
+<p>This function returns a handle with a method <code>handle$remove()</code> that removes the hook from the module.</p>
+<div id="example" class="section level4">
+<h4 class="hasAnchor">
+<a href="#example" class="anchor"></a>Example</h4>
+<div class="sourceCode" id="cb27"><pre class="downlit">
+<span class="kw">v</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">0.</span>, <span class="fl">0.</span>, <span class="fl">0.</span>), requires_grad=<span class="fl">TRUE</span>)
+<span class="kw">h</span> <span class="op">&lt;-</span> <span class="kw">v</span><span class="op">$</span><span class="fu">register_hook</span>(<span class="fu">function</span>(<span class="kw">grad</span>) <span class="kw">grad</span> <span class="op">*</span> <span class="fl">2</span>)  <span class="co"># double the gradient</span>
+<span class="kw">v</span><span class="op">$</span><span class="fu">backward</span>(<span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1.</span>, <span class="fl">2.</span>, <span class="fl">3.</span>)))
+<span class="kw">v</span><span class="op">$</span><span class="kw">grad</span>
+<span class="kw">h</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/rm.html">remove</a></span>()
+</pre></div>
+</div>
+</div>
+<div id="remainder" class="section level2">
+<h2 class="hasAnchor">
+<a href="#remainder" class="anchor"></a>remainder</h2>
+<p>remainder(divisor) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_remainder.html">?torch_remainder</a></code></p>
+</div>
+<div id="remainder_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#remainder_" class="anchor"></a>remainder_</h2>
+<p>remainder_(divisor) -&gt; Tensor</p>
+<p>In-place version of <code>$remainder</code></p>
+</div>
+<div id="rename" class="section level2">
+<h2 class="hasAnchor">
+<a href="#rename" class="anchor"></a>rename</h2>
+<p>Renames dimension names of <code>self</code>.</p>
+<p>There are two main usages:</p>
+<p><code>self$rename(**rename_map)</code> returns a view on tensor that has dims renamed as specified in the mapping <code>rename_map</code>.</p>
+<p><code>self$rename(*names)</code> returns a view on tensor, renaming all dimensions positionally using <code>names</code>. Use <code>self$rename(NULL)</code> to drop names on a tensor.</p>
+<p>One cannot specify both positional args <code>names</code> and keyword args <code>rename_map</code>.</p>
+<div id="examples-26" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-26" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb28"><pre class="downlit">
+<span class="kw">imgs</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">5</span>, <span class="fl">7</span>, names=<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">'N'</span>, <span class="st">'C'</span>, <span class="st">'H'</span>, <span class="st">'W'</span>))
+<span class="kw">renamed_imgs</span> <span class="op">&lt;-</span> <span class="kw">imgs</span><span class="op">$</span><span class="fu">rename</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">"Batch"</span>, <span class="st">"Channels"</span>, <span class="st">"Height"</span>, <span class="st">"Width"</span>))
+</pre></div>
+</div>
+</div>
+<div id="rename_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#rename_" class="anchor"></a>rename_</h2>
+<p>In-place version of <code>$rename</code>.</p>
+</div>
+<div id="renorm" class="section level2">
+<h2 class="hasAnchor">
+<a href="#renorm" class="anchor"></a>renorm</h2>
+<p>renorm(p, dim, maxnorm) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_renorm.html">?torch_renorm</a></code></p>
+</div>
+<div id="renorm_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#renorm_" class="anchor"></a>renorm_</h2>
+<p>renorm_(p, dim, maxnorm) -&gt; Tensor</p>
+<p>In-place version of <code>$renorm</code></p>
+</div>
+<div id="repeat" class="section level2">
+<h2 class="hasAnchor">
+<a href="#repeat" class="anchor"></a>repeat</h2>
+<p>repeat(*sizes) -&gt; Tensor</p>
+<p>Repeats this tensor along the specified dimensions.</p>
+<p>Unlike <code>$expand</code>, this function copies the tensor’s data.</p>
+<div id="arguments-36" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-36" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>sizes (torch_Size or int…): The number of times to repeat this tensor along each</li>
+<li>dimension</li>
+</ul>
+</div>
+<div id="examples-27" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-27" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb29"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span>))
+<span class="kw">x</span><span class="op">$</span><span class="fu">`repeat`</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">4</span>, <span class="fl">2</span>))
+<span class="kw">x</span><span class="op">$</span><span class="fu">`repeat`</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">4</span>, <span class="fl">2</span>, <span class="fl">1</span>))<span class="op">$</span><span class="fu">size</span>()
+</pre></div>
+</div>
+</div>
+<div id="repeat_interleave" class="section level2">
+<h2 class="hasAnchor">
+<a href="#repeat_interleave" class="anchor"></a>repeat_interleave</h2>
+<p>repeat_interleave(repeats, dim=NULL) -&gt; Tensor</p>
+<p>See [torch_repeat_interleave()].</p>
+</div>
+<div id="requires_grad" class="section level2">
+<h2 class="hasAnchor">
+<a href="#requires_grad" class="anchor"></a>requires_grad</h2>
+<p>Is <code>TRUE</code> if gradients need to be computed for this Tensor, <code>FALSE</code> otherwise.</p>
+<div id="note-7" class="section level4">
+<h4 class="hasAnchor">
+<a href="#note-7" class="anchor"></a>Note:</h4>
+<p>The fact that gradients need to be computed for a Tensor do not mean that the <code>grad</code> attribute will be populated, see <code>is_leaf</code> for more details.</p>
+</div>
+</div>
+<div id="requires_grad_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#requires_grad_" class="anchor"></a>requires_grad_</h2>
+<p>requires_grad_(requires_grad=TRUE) -&gt; Tensor</p>
+<p>Change if autograd should record operations on this tensor: sets this tensor’s <code>requires_grad</code> attribute in-place. Returns this tensor.</p>
+<p>[requires_grad_()]’s main use case is to tell autograd to begin recording operations on a Tensor <code>tensor</code>. If <code>tensor</code> has <code>requires_grad=FALSE</code> (because it was obtained through a DataLoader, or required preprocessing or initialization), <code>tensor.requires_grad_()</code> makes it so that autograd will begin to record operations on <code>tensor</code>.</p>
+<div id="arguments-37" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-37" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>requires_grad (bool): If autograd should record operations on this tensor. Default: <code>TRUE</code>.</li>
+</ul>
+</div>
+<div id="examples-28" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-28" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb30"><pre class="downlit">
+<span class="co"># Let's say we want to preprocess some saved weights and use</span>
+<span class="co"># the result as new weights.</span>
+<span class="kw">saved_weights</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">0.1</span>, <span class="fl">0.2</span>, <span class="fl">0.3</span>, <span class="fl">0.25</span>)
+<span class="kw">loaded_weights</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="kw">saved_weights</span>)
+<span class="kw">weights</span> <span class="op">&lt;-</span> <span class="fu">preprocess</span>(<span class="kw">loaded_weights</span>)  <span class="co"># some function</span>
+<span class="kw">weights</span>
+
+<span class="co"># Now, start to record operations done to weights</span>
+<span class="kw">weights</span><span class="op">$</span><span class="fu">requires_grad_</span>()
+<span class="kw">out</span> <span class="op">&lt;-</span> <span class="kw">weights</span><span class="op">$</span><span class="fu">pow</span>(<span class="fl">2</span>)<span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span>()
+<span class="kw">out</span><span class="op">$</span><span class="fu">backward</span>()
+<span class="kw">weights</span><span class="op">$</span><span class="kw">grad</span>
+</pre></div>
+</div>
+</div>
+<div id="reshape" class="section level2">
+<h2 class="hasAnchor">
+<a href="#reshape" class="anchor"></a>reshape</h2>
+<p>reshape(*shape) -&gt; Tensor</p>
+<p>Returns a tensor with the same data and number of elements as <code>self</code> but with the specified shape. This method returns a view if <code>shape</code> is compatible with the current shape. See <code>$view</code> on when it is possible to return a view.</p>
+<p>See <code><a href="../../reference/torch_reshape.html">?torch_reshape</a></code></p>
+<div id="arguments-38" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-38" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>shape (tuple of ints or int…): the desired shape</li>
+</ul>
+</div>
+</div>
+<div id="reshape_as" class="section level2">
+<h2 class="hasAnchor">
+<a href="#reshape_as" class="anchor"></a>reshape_as</h2>
+<p>reshape_as(other) -&gt; Tensor</p>
+<p>Returns this tensor as the same shape as <code>other</code>. <code>self$reshape_as(other)</code> is equivalent to <code>self$reshape(other.sizes())</code>. This method returns a view if <code>other.sizes()</code> is compatible with the current shape. See <code>$view</code> on when it is possible to return a view.</p>
+<p>Please see <code>reshape</code> for more information about <code>reshape</code>.</p>
+<div id="arguments-39" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-39" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>other (`$): The result tensor has the same shape</li>
+<li>as <code>other</code>.</li>
+</ul>
+</div>
+</div>
+<div id="resize_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#resize_" class="anchor"></a>resize_</h2>
+<p>resize_(*sizes, memory_format=torch_contiguous_format) -&gt; Tensor</p>
+<p>Resizes <code>self</code> tensor to the specified size. If the number of elements is larger than the current storage size, then the underlying storage is resized to fit the new number of elements. If the number of elements is smaller, the underlying storage is not changed. Existing elements are preserved but any new memory is uninitialized.</p>
+<div id="warning-7" class="section level4">
+<h4 class="hasAnchor">
+<a href="#warning-7" class="anchor"></a>Warning:</h4>
+<p>This is a low-level method. The storage is reinterpreted as C-contiguous, ignoring the current strides (unless the target size equals the current size, in which case the tensor is left unchanged). For most purposes, you will instead want to use <code>$view()</code>, which checks for contiguity, or <code>$reshape()</code>, which copies data if needed. To change the size in-place with custom strides, see <code>$set_()</code>.</p>
+</div>
+<div id="arguments-40" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-40" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>sizes (torch_Size or int…): the desired size</li>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of Tensor. Default: <code>torch_contiguous_format</code>. Note that memory format of <code>self</code> is going to be unaffected if <code>self$size()</code> matches <code>sizes</code>.</li>
+</ul>
+</div>
+<div id="examples-29" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-29" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb31"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">6</span>, ncol = <span class="fl">2</span>))
+<span class="kw">x</span><span class="op">$</span><span class="fu">resize_</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>, <span class="fl">2</span>))
+</pre></div>
+</div>
+</div>
+<div id="resize_as_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#resize_as_" class="anchor"></a>resize_as_</h2>
+<p>resize_as_(tensor, memory_format=torch_contiguous_format) -&gt; Tensor</p>
+<p>Resizes the <code>self</code> tensor to be the same size as the specified <code>tensor</code>. This is equivalent to <code>self$resize_(tensor.size())</code>.</p>
+<div id="arguments-41" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-41" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of Tensor. Default: <code>torch_contiguous_format</code>. Note that memory format of <code>self</code> is going to be unaffected if <code>self$size()</code> matches <code>tensor.size()</code>.</li>
+</ul>
+</div>
+</div>
+<div id="retain_grad" class="section level2">
+<h2 class="hasAnchor">
+<a href="#retain_grad" class="anchor"></a>retain_grad</h2>
+<p>Enables <code>$grad</code> attribute for non-leaf Tensors.</p>
+</div>
+<div id="rfft" class="section level2">
+<h2 class="hasAnchor">
+<a href="#rfft" class="anchor"></a>rfft</h2>
+<p>rfft(signal_ndim, normalized=FALSE, onesided=TRUE) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_rfft.html">?torch_rfft</a></code></p>
+</div>
+<div id="roll" class="section level2">
+<h2 class="hasAnchor">
+<a href="#roll" class="anchor"></a>roll</h2>
+<p>roll(shifts, dims) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_roll.html">?torch_roll</a></code></p>
+</div>
+<div id="rot90" class="section level2">
+<h2 class="hasAnchor">
+<a href="#rot90" class="anchor"></a>rot90</h2>
+<p>rot90(k, dims) -&gt; Tensor</p>
+<p>See [torch_rot90()]</p>
+</div>
+<div id="round" class="section level2">
+<h2 class="hasAnchor">
+<a href="#round" class="anchor"></a>round</h2>
+<p>round() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_round.html">?torch_round</a></code></p>
+</div>
+<div id="round_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#round_" class="anchor"></a>round_</h2>
+<p>round_() -&gt; Tensor</p>
+<p>In-place version of <code>$round</code></p>
+</div>
+<div id="rsqrt" class="section level2">
+<h2 class="hasAnchor">
+<a href="#rsqrt" class="anchor"></a>rsqrt</h2>
+<p>rsqrt() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_rsqrt.html">?torch_rsqrt</a></code></p>
+</div>
+<div id="rsqrt_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#rsqrt_" class="anchor"></a>rsqrt_</h2>
+<p>rsqrt_() -&gt; Tensor</p>
+<p>In-place version of <code>$rsqrt</code></p>
+</div>
+<div id="scatter" class="section level2">
+<h2 class="hasAnchor">
+<a href="#scatter" class="anchor"></a>scatter</h2>
+<p>scatter(dim, index, src) -&gt; Tensor</p>
+<p>Out-of-place version of <code>$scatter_</code></p>
+</div>
+<div id="scatter_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#scatter_" class="anchor"></a>scatter_</h2>
+<p>scatter_(dim, index, src) -&gt; Tensor</p>
+<p>Writes all values from the tensor <code>src</code> into <code>self</code> at the indices specified in the <code>index</code> tensor. For each value in <code>src</code>, its output index is specified by its index in <code>src</code> for <code>dimension != dim</code> and by the corresponding value in <code>index</code> for <code>dimension = dim</code>.</p>
+<p>For a 3-D tensor, <code>self</code> is updated as:</p>
+<pre><code>self[index[i][j][k]][j][k] = src[i][j][k]  # if dim == 0
+self[i][index[i][j][k]][k] = src[i][j][k]  # if dim == 1
+self[i][j][index[i][j][k]] = src[i][j][k]  # if dim == 2</code></pre>
+<p>This is the reverse operation of the manner described in <code>$gather</code>.</p>
+<p><code>self</code>, <code>index</code> and <code>src</code> (if it is a Tensor) should have same number of dimensions. It is also required that <code>index.size(d) &lt;= src.size(d)</code> for all dimensions <code>d</code>, and that <code>index.size(d) &lt;= self$size(d)</code> for all dimensions <code>d != dim</code>.</p>
+<p>Moreover, as for <code>$gather</code>, the values of <code>index</code> must be between <code>0</code> and <code>self$size(dim) - 1</code> inclusive, and all values in a row along the specified dimension <code>dim</code> must be unique.</p>
+<div id="arguments-42" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-42" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>dim (int): the axis along which to index</li>
+<li>index (LongTensor): the indices of elements to scatter,</li>
+<li>can be either empty or the same size of src. When empty, the operation returns identity</li>
+<li>src (Tensor): the source element(s) to scatter,</li>
+<li>incase <code>value</code> is not specified</li>
+<li>value (float): the source element(s) to scatter,</li>
+<li>incase <code>src</code> is not specified</li>
+</ul>
+</div>
+<div id="examples-30" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-30" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb33"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">2</span>, <span class="fl">5</span>)
+<span class="kw">x</span>
+<span class="fu"><a href="../../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">3</span>, <span class="fl">5</span>)<span class="op">$</span><span class="fu">scatter_</span>(
+        <span class="fl">1</span>, 
+        <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/cbind.html">rbind</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">3</span>, <span class="fl">1</span>, <span class="fl">1</span>), <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">3</span>, <span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span>)), <span class="kw">x</span>)
+)
+
+<span class="kw">z</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">2</span>, <span class="fl">4</span>)<span class="op">$</span><span class="fu">scatter_</span>(
+        <span class="fl">2</span>, 
+        <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span>(<span class="fl">3</span><span class="op">:</span><span class="fl">4</span>, ncol = <span class="fl">1</span>)), <span class="fl">1.23</span>
+)
+</pre></div>
+</div>
+</div>
+<div id="scatter_add" class="section level2">
+<h2 class="hasAnchor">
+<a href="#scatter_add" class="anchor"></a>scatter_add</h2>
+<p>scatter_add(dim, index, src) -&gt; Tensor</p>
+<p>Out-of-place version of <code>$scatter_add_</code></p>
+</div>
+<div id="scatter_add_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#scatter_add_" class="anchor"></a>scatter_add_</h2>
+<p>scatter_add_(dim, index, src) -&gt; Tensor</p>
+<p>Adds all values from the tensor <code>other</code> into <code>self</code> at the indices specified in the <code>index</code> tensor in a similar fashion as <code>~$scatter_</code>. For each value in <code>src</code>, it is added to an index in <code>self</code> which is specified by its index in <code>src</code> for <code>dimension != dim</code> and by the corresponding value in <code>index</code> for <code>dimension = dim</code>.</p>
+<p>For a 3-D tensor, <code>self</code> is updated as::</p>
+<pre><code>self[index[i][j][k]][j][k] += src[i][j][k]  # if dim == 0
+self[i][index[i][j][k]][k] += src[i][j][k]  # if dim == 1
+self[i][j][index[i][j][k]] += src[i][j][k]  # if dim == 2</code></pre>
+<p><code>self</code>, <code>index</code> and <code>src</code> should have same number of dimensions. It is also required that <code>index.size(d) &lt;= src.size(d)</code> for all dimensions <code>d</code>, and that <code>index.size(d) &lt;= self$size(d)</code> for all dimensions <code>d != dim</code>.</p>
+<div id="note-8" class="section level4">
+<h4 class="hasAnchor">
+<a href="#note-8" class="anchor"></a>Note:</h4>
+<p>In some circumstances when using the CUDA backend with CuDNN, this operator may select a nondeterministic algorithm to increase performance. If this is undesirable, you can try to make the operation deterministic (potentially at a performance cost) by setting <code>torch_backends.cudnn.deterministic = TRUE</code>.</p>
+</div>
+<div id="arguments-43" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-43" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>dim (int): the axis along which to index</li>
+<li>index (LongTensor): the indices of elements to scatter and add,</li>
+<li>can be either empty or the same size of src. When empty, the operation returns identity.</li>
+<li>src (Tensor): the source elements to scatter and add</li>
+</ul>
+</div>
+<div id="examples-31" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-31" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb35"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">2</span>, <span class="fl">5</span>)
+<span class="kw">x</span>
+<span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span>(<span class="fl">3</span>, <span class="fl">5</span>)<span class="op">$</span><span class="fu">scatter_add_</span>(<span class="fl">1</span>, <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/cbind.html">rbind</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">0</span>, <span class="fl">0</span>), <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>, <span class="fl">0</span>, <span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">2</span>))), <span class="kw">x</span>)
+</pre></div>
+</div>
+</div>
+<div id="select" class="section level2">
+<h2 class="hasAnchor">
+<a href="#select" class="anchor"></a>select</h2>
+<p>select(dim, index) -&gt; Tensor</p>
+<p>Slices the <code>self</code> tensor along the selected dimension at the given index. This function returns a view of the original tensor with the given dimension removed.</p>
+<div id="arguments-44" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-44" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>dim (int): the dimension to slice</li>
+<li>index (int): the index to select with</li>
+</ul>
+</div>
+<div id="note-9" class="section level4">
+<h4 class="hasAnchor">
+<a href="#note-9" class="anchor"></a>Note:</h4>
+<p><code>select</code> is equivalent to slicing. For example, <code>tensor$select(0, index)</code> is equivalent to <code>tensor[index]</code> and <code>tensor$select(2, index)</code> is equivalent to <code>tensor[:,:,index]</code>.</p>
+</div>
+</div>
+<div id="set_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#set_" class="anchor"></a>set_</h2>
+<p>set_(source=NULL, storage_offset=0, size=NULL, stride=NULL) -&gt; Tensor</p>
+<p>Sets the underlying storage, size, and strides. If <code>source</code> is a tensor, <code>self</code> tensor will share the same storage and have the same size and strides as <code>source</code>. Changes to elements in one tensor will be reflected in the other.</p>
+<div id="arguments-45" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-45" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>source (Tensor or Storage): the tensor or storage to use</li>
+<li>storage_offset (int, optional): the offset in the storage</li>
+<li>size (torch_Size, optional): the desired size. Defaults to the size of the source.</li>
+<li>stride (tuple, optional): the desired stride. Defaults to C-contiguous strides.</li>
+</ul>
+</div>
+</div>
+<div id="share_memory_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#share_memory_" class="anchor"></a>share_memory_</h2>
+<p>Moves the underlying storage to shared memory.</p>
+<p>This is a no-op if the underlying storage is already in shared memory and for CUDA tensors. Tensors in shared memory cannot be resized.</p>
+</div>
+<div id="short" class="section level2">
+<h2 class="hasAnchor">
+<a href="#short" class="anchor"></a>short</h2>
+<p>short(memory_format=torch_preserve_format) -&gt; Tensor</p>
+<p><code>self$short()</code> is equivalent to <code>self$to(torch_int16)</code>. See [to()].</p>
+<div id="arguments-46" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-46" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
+<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
+</ul>
+</div>
+</div>
+<div id="sigmoid" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sigmoid" class="anchor"></a>sigmoid</h2>
+<p>sigmoid() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_sigmoid.html">?torch_sigmoid</a></code></p>
+</div>
+<div id="sigmoid_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sigmoid_" class="anchor"></a>sigmoid_</h2>
+<p>sigmoid_() -&gt; Tensor</p>
+<p>In-place version of <code>$sigmoid</code></p>
+</div>
+<div id="sign" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sign" class="anchor"></a>sign</h2>
+<p>sign() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_sign.html">?torch_sign</a></code></p>
+</div>
+<div id="sign_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sign_" class="anchor"></a>sign_</h2>
+<p>sign_() -&gt; Tensor</p>
+<p>In-place version of <code>$sign</code></p>
+</div>
+<div id="sin" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sin" class="anchor"></a>sin</h2>
+<p>sin() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_sin.html">?torch_sin</a></code></p>
+</div>
+<div id="sin_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sin_" class="anchor"></a>sin_</h2>
+<p>sin_() -&gt; Tensor</p>
+<p>In-place version of <code>$sin</code></p>
+</div>
+<div id="sinh" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sinh" class="anchor"></a>sinh</h2>
+<p>sinh() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_sinh.html">?torch_sinh</a></code></p>
+</div>
+<div id="sinh_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sinh_" class="anchor"></a>sinh_</h2>
+<p>sinh_() -&gt; Tensor</p>
+<p>In-place version of <code>$sinh</code></p>
+</div>
+<div id="size" class="section level2">
+<h2 class="hasAnchor">
+<a href="#size" class="anchor"></a>size</h2>
+<p>size() -&gt; torch_Size</p>
+<p>Returns the size of the <code>self</code> tensor. The returned value is a subclass of <code>tuple</code>.</p>
+<div id="examples-32" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-32" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb36"><pre class="downlit">
+<span class="fu"><a href="../../reference/torch_empty.html">torch_empty</a></span>(<span class="fl">3</span>, <span class="fl">4</span>, <span class="fl">5</span>)<span class="op">$</span><span class="fu">size</span>()
+</pre></div>
+</div>
+</div>
+<div id="slogdet" class="section level2">
+<h2 class="hasAnchor">
+<a href="#slogdet" class="anchor"></a>slogdet</h2>
+<p>slogdet() -&gt; (Tensor, Tensor)</p>
+<p>See <code><a href="../../reference/torch_slogdet.html">?torch_slogdet</a></code></p>
+</div>
+<div id="solve" class="section level2">
+<h2 class="hasAnchor">
+<a href="#solve" class="anchor"></a>solve</h2>
+<p>solve(A) -&gt; Tensor, Tensor</p>
+<p>See <code><a href="../../reference/torch_solve.html">?torch_solve</a></code></p>
+</div>
+<div id="sort" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sort" class="anchor"></a>sort</h2>
+<p>sort(dim=-1, descending=FALSE) -&gt; (Tensor, LongTensor)</p>
+<p>See <code><a href="../../reference/torch_sort.html">?torch_sort</a></code></p>
+</div>
+<div id="sparse_dim" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sparse_dim" class="anchor"></a>sparse_dim</h2>
+<p>sparse_dim() -&gt; int</p>
+<p>If <code>self</code> is a sparse COO tensor (i.e., with <code>torch_sparse_coo</code> layout), this returns the number of sparse dimensions. Otherwise, this throws an error.</p>
+<p>See also <code>Tensor.dense_dim</code>.</p>
+</div>
+<div id="sparse_mask" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sparse_mask" class="anchor"></a>sparse_mask</h2>
+<p>sparse_mask(input, mask) -&gt; Tensor</p>
+<p>Returns a new SparseTensor with values from Tensor <code>input</code> filtered by indices of <code>mask</code> and values are ignored. <code>input</code> and <code>mask</code> must have the same shape.</p>
+<div id="arguments-47" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-47" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>input (Tensor): an input Tensor</li>
+<li>mask (SparseTensor): a SparseTensor which we filter <code>input</code> based on its indices</li>
+</ul>
+</div>
+</div>
+<div id="split" class="section level2">
+<h2 class="hasAnchor">
+<a href="#split" class="anchor"></a>split</h2>
+<p>See <code><a href="../../reference/torch_split.html">?torch_split</a></code></p>
+</div>
+<div id="sqrt" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sqrt" class="anchor"></a>sqrt</h2>
+<p>sqrt() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_sqrt.html">?torch_sqrt</a></code></p>
+</div>
+<div id="sqrt_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sqrt_" class="anchor"></a>sqrt_</h2>
+<p>sqrt_() -&gt; Tensor</p>
+<p>In-place version of <code>$sqrt</code></p>
+</div>
+<div id="square" class="section level2">
+<h2 class="hasAnchor">
+<a href="#square" class="anchor"></a>square</h2>
+<p>square() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_square.html">?torch_square</a></code></p>
+</div>
+<div id="square_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#square_" class="anchor"></a>square_</h2>
+<p>square_() -&gt; Tensor</p>
+<p>In-place version of <code>$square</code></p>
+</div>
+<div id="squeeze" class="section level2">
+<h2 class="hasAnchor">
+<a href="#squeeze" class="anchor"></a>squeeze</h2>
+<p>squeeze(dim=NULL) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_squeeze.html">?torch_squeeze</a></code></p>
+</div>
+<div id="squeeze_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#squeeze_" class="anchor"></a>squeeze_</h2>
+<p>squeeze_(dim=NULL) -&gt; Tensor</p>
+<p>In-place version of <code>$squeeze</code></p>
+</div>
+<div id="std" class="section level2">
+<h2 class="hasAnchor">
+<a href="#std" class="anchor"></a>std</h2>
+<p>std(dim=NULL, unbiased=TRUE, keepdim=FALSE) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_std.html">?torch_std</a></code></p>
+</div>
+<div id="stft" class="section level2">
+<h2 class="hasAnchor">
+<a href="#stft" class="anchor"></a>stft</h2>
+<p>See <code><a href="../../reference/torch_stft.html">?torch_stft</a></code></p>
+</div>
+<div id="storage" class="section level2">
+<h2 class="hasAnchor">
+<a href="#storage" class="anchor"></a>storage</h2>
+<p>storage() -&gt; torch_Storage</p>
+<p>Returns the underlying storage.</p>
+</div>
+<div id="storage_offset" class="section level2">
+<h2 class="hasAnchor">
+<a href="#storage_offset" class="anchor"></a>storage_offset</h2>
+<p>storage_offset() -&gt; int</p>
+<p>Returns <code>self</code> tensor’s offset in the underlying storage in terms of number of storage elements (not bytes).</p>
+<div id="examples-33" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-33" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb37"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">4</span>, <span class="fl">5</span>))
+<span class="kw">x</span><span class="op">$</span><span class="fu">storage_offset</span>()
+<span class="kw">x</span>[<span class="fl">3</span><span class="op">:</span><span class="kw">N</span>]<span class="op">$</span><span class="fu">storage_offset</span>()
+</pre></div>
+</div>
+</div>
+<div id="storage_type" class="section level2">
+<h2 class="hasAnchor">
+<a href="#storage_type" class="anchor"></a>storage_type</h2>
+<p>storage_type() -&gt; type</p>
+<p>Returns the type of the underlying storage.</p>
+</div>
+<div id="stride" class="section level2">
+<h2 class="hasAnchor">
+<a href="#stride" class="anchor"></a>stride</h2>
+<p>stride(dim) -&gt; tuple or int</p>
+<p>Returns the stride of <code>self</code> tensor.</p>
+<p>Stride is the jump necessary to go from one element to the next one in the specified dimension <code>dim</code>. A tuple of all strides is returned when no argument is passed in. Otherwise, an integer value is returned as the stride in the particular dimension <code>dim</code>.</p>
+<div id="arguments-48" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-48" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>dim (int, optional): the desired dimension in which stride is required</li>
+</ul>
+</div>
+<div id="examples-34" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-34" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb38"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">10</span>, nrow = <span class="fl">2</span>))
+<span class="kw">x</span><span class="op">$</span><span class="fu">stride</span>()
+<span class="kw">x</span><span class="op">$</span><span class="fu">stride</span>(<span class="fl">1</span>)
+<span class="kw">x</span><span class="op">$</span><span class="fu">stride</span>(<span class="op">-</span><span class="fl">1</span>)
+</pre></div>
+</div>
+</div>
+<div id="sub" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sub" class="anchor"></a>sub</h2>
+<p>sub(other, *, alpha=1) -&gt; Tensor</p>
+<p>Subtracts a scalar or tensor from <code>self</code> tensor. If both <code>alpha</code> and <code>other</code> are specified, each element of <code>other</code> is scaled by <code>alpha</code> before being used.</p>
+<p>When <code>other</code> is a tensor, the shape of <code>other</code> must be <code>broadcastable &lt;broadcasting-semantics&gt;</code> with the shape of the underlying tensor.</p>
+</div>
+<div id="sub_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sub_" class="anchor"></a>sub_</h2>
+<p>sub_(other, *, alpha=1) -&gt; Tensor</p>
+<p>In-place version of <code>$sub</code></p>
+</div>
+<div id="sum" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sum" class="anchor"></a>sum</h2>
+<p>sum(dim=NULL, keepdim=FALSE, dtype=NULL) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_sum.html">?torch_sum</a></code></p>
+</div>
+<div id="sum_to_size" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sum_to_size" class="anchor"></a>sum_to_size</h2>
+<p>sum_to_size(*size) -&gt; Tensor</p>
+<p>Sum <code>this</code> tensor to <code>size</code>. <code>size</code> must be broadcastable to <code>this</code> tensor size.</p>
+<div id="arguments-49" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-49" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>size (int…): a sequence of integers defining the shape of the output tensor.</li>
+</ul>
+</div>
+</div>
+<div id="svd" class="section level2">
+<h2 class="hasAnchor">
+<a href="#svd" class="anchor"></a>svd</h2>
+<p>svd(some=TRUE, compute_uv=TRUE) -&gt; (Tensor, Tensor, Tensor)</p>
+<p>See <code><a href="../../reference/torch_svd.html">?torch_svd</a></code></p>
+</div>
+<div id="symeig" class="section level2">
+<h2 class="hasAnchor">
+<a href="#symeig" class="anchor"></a>symeig</h2>
+<p>symeig(eigenvectors=FALSE, upper=TRUE) -&gt; (Tensor, Tensor)</p>
+<p>See <code><a href="../../reference/torch_symeig.html">?torch_symeig</a></code></p>
+</div>
+<div id="t-1" class="section level2">
+<h2 class="hasAnchor">
+<a href="#t-1" class="anchor"></a>t</h2>
+<p>t() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_t.html">?torch_t</a></code></p>
+</div>
+<div id="t_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#t_" class="anchor"></a>t_</h2>
+<p>t_() -&gt; Tensor</p>
+<p>In-place version of <code>$t</code></p>
+</div>
+<div id="take" class="section level2">
+<h2 class="hasAnchor">
+<a href="#take" class="anchor"></a>take</h2>
+<p>take(indices) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_take.html">?torch_take</a></code></p>
+</div>
+<div id="tan" class="section level2">
+<h2 class="hasAnchor">
+<a href="#tan" class="anchor"></a>tan</h2>
+<p>tan() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_tan.html">?torch_tan</a></code></p>
+</div>
+<div id="tan_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#tan_" class="anchor"></a>tan_</h2>
+<p>tan_() -&gt; Tensor</p>
+<p>In-place version of <code>$tan</code></p>
+</div>
+<div id="tanh" class="section level2">
+<h2 class="hasAnchor">
+<a href="#tanh" class="anchor"></a>tanh</h2>
+<p>tanh() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_tanh.html">?torch_tanh</a></code></p>
+</div>
+<div id="tanh_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#tanh_" class="anchor"></a>tanh_</h2>
+<p>tanh_() -&gt; Tensor</p>
+<p>In-place version of <code>$tanh</code></p>
+</div>
+<div id="to" class="section level2">
+<h2 class="hasAnchor">
+<a href="#to" class="anchor"></a>to</h2>
+<p>to(*args, **kwargs) -&gt; Tensor</p>
+<p>Performs Tensor dtype and/or device conversion. A <code>torch_dtype</code> and :class:<code>torch_device</code> are inferred from the arguments of <code>self$to(*args, **kwargs)</code>.</p>
+<div id="note-10" class="section level4">
+<h4 class="hasAnchor">
+<a href="#note-10" class="anchor"></a>Note:</h4>
+<p>If the <code>self</code> Tensor already has the correct <code>torch_dtype</code> and :class:<code>torch_device</code>, then <code>self</code> is returned. Otherwise, the returned tensor is a copy of <code>self</code> with the desired <code>torch_dtype</code> and :class:<code>torch_device</code>.</p>
+<p>Here are the ways to call <code>to</code>:</p>
+<p>to(dtype, non_blocking=FALSE, copy=FALSE, memory_format=torch_preserve_format) -&gt; Tensor</p>
+<p>Returns a Tensor with the specified <code>dtype</code></p>
+</div>
+<div id="arguments-50" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-50" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of returned Tensor. Default: <code>torch_preserve_format</code>.</li>
+</ul>
+<p>to(device=NULL, dtype=NULL, non_blocking=FALSE, copy=FALSE, memory_format=torch_preserve_format) -&gt; Tensor</p>
+<p>Returns a Tensor with the specified <code>device</code> and (optional) <code>dtype</code>. If <code>dtype</code> is <code>NULL</code> it is inferred to be <code>self$dtype</code>. When <code>non_blocking</code>, tries to convert asynchronously with respect to the host if possible, e.g., converting a CPU Tensor with pinned memory to a CUDA Tensor.</p>
+<p>When <code>copy</code> is set, a new Tensor is created even when the Tensor already matches the desired conversion.</p>
+</div>
+<div id="arguments-51" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-51" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of returned Tensor. Default: <code>torch_preserve_format</code>.</li>
+</ul>
+<p>function:: to(other, non_blocking=FALSE, copy=FALSE) -&gt; Tensor</p>
+<p>Returns a Tensor with same <code>torch_dtype</code> and :class:<code>torch_device</code> as the Tensor <code>other</code>. When <code>non_blocking</code>, tries to convert asynchronously with respect to the host if possible, e.g., converting a CPU Tensor with pinned memory to a CUDA Tensor.</p>
+<p>When <code>copy</code> is set, a new Tensor is created even when the Tensor already matches the desired conversion.</p>
+</div>
+<div id="examples-35" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-35" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb39"><pre class="downlit">
+<span class="kw">tensor</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">2</span>, <span class="fl">2</span>)  <span class="co"># Initially dtype=float32, device=cpu</span>
+<span class="kw">tensor</span><span class="op">$</span><span class="fu">to</span>(dtype = <span class="fu"><a href="../../reference/torch_dtype.html">torch_float64</a></span>())
+
+<span class="kw">other</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">1</span>, dtype=<span class="fu"><a href="../../reference/torch_dtype.html">torch_float64</a></span>())
+<span class="kw">tensor</span><span class="op">$</span><span class="fu">to</span>(other = <span class="kw">other</span>, non_blocking=<span class="fl">TRUE</span>)
+</pre></div>
+</div>
+</div>
+<div id="to_mkldnn" class="section level2">
+<h2 class="hasAnchor">
+<a href="#to_mkldnn" class="anchor"></a>to_mkldnn</h2>
+<p>to_mkldnn() -&gt; Tensor Returns a copy of the tensor in <code>torch_mkldnn</code> layout.</p>
+</div>
+<div id="to_sparse" class="section level2">
+<h2 class="hasAnchor">
+<a href="#to_sparse" class="anchor"></a>to_sparse</h2>
+<p>to_sparse(sparseDims) -&gt; Tensor Returns a sparse copy of the tensor. PyTorch supports sparse tensors in <code>coordinate format &lt;sparse-docs&gt;</code>.</p>
+<div id="arguments-52" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-52" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>sparseDims (int, optional): the number of sparse dimensions to include in the new sparse tensor</li>
+</ul>
+</div>
+</div>
+<div id="tolist" class="section level2">
+<h2 class="hasAnchor">
+<a href="#tolist" class="anchor"></a>tolist</h2>
+<p>tolist() -&gt; list or number</p>
+<p>Returns the tensor as a (nested) list. For scalars, a standard Python number is returned, just like with <code>$item</code>. Tensors are automatically moved to the CPU first if necessary.</p>
+<p>This operation is not differentiable.</p>
+</div>
+<div id="topk" class="section level2">
+<h2 class="hasAnchor">
+<a href="#topk" class="anchor"></a>topk</h2>
+<p>topk(k, dim=NULL, largest=TRUE, sorted=TRUE) -&gt; (Tensor, LongTensor)</p>
+<p>See <code><a href="../../reference/torch_topk.html">?torch_topk</a></code></p>
+</div>
+<div id="trace" class="section level2">
+<h2 class="hasAnchor">
+<a href="#trace" class="anchor"></a>trace</h2>
+<p>trace() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_trace.html">?torch_trace</a></code></p>
+</div>
+<div id="transpose" class="section level2">
+<h2 class="hasAnchor">
+<a href="#transpose" class="anchor"></a>transpose</h2>
+<p>transpose(dim0, dim1) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_transpose.html">?torch_transpose</a></code></p>
+</div>
+<div id="transpose_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#transpose_" class="anchor"></a>transpose_</h2>
+<p>transpose_(dim0, dim1) -&gt; Tensor</p>
+<p>In-place version of <code>$transpose</code></p>
+</div>
+<div id="triangular_solve" class="section level2">
+<h2 class="hasAnchor">
+<a href="#triangular_solve" class="anchor"></a>triangular_solve</h2>
+<p>triangular_solve(A, upper=TRUE, transpose=FALSE, unitriangular=FALSE) -&gt; (Tensor, Tensor)</p>
+<p>See [torch_triangular_solve()]</p>
+</div>
+<div id="tril" class="section level2">
+<h2 class="hasAnchor">
+<a href="#tril" class="anchor"></a>tril</h2>
+<p>tril(k=0) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_tril.html">?torch_tril</a></code></p>
+</div>
+<div id="tril_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#tril_" class="anchor"></a>tril_</h2>
+<p>tril_(k=0) -&gt; Tensor</p>
+<p>In-place version of <code>$tril</code></p>
+</div>
+<div id="triu" class="section level2">
+<h2 class="hasAnchor">
+<a href="#triu" class="anchor"></a>triu</h2>
+<p>triu(k=0) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_triu.html">?torch_triu</a></code></p>
+</div>
+<div id="triu_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#triu_" class="anchor"></a>triu_</h2>
+<p>triu_(k=0) -&gt; Tensor</p>
+<p>In-place version of <code>$triu</code></p>
+</div>
+<div id="true_divide" class="section level2">
+<h2 class="hasAnchor">
+<a href="#true_divide" class="anchor"></a>true_divide</h2>
+<p>true_divide(value) -&gt; Tensor</p>
+<p>See [torch_true_divide()]</p>
+</div>
+<div id="true_divide_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#true_divide_" class="anchor"></a>true_divide_</h2>
+<p>true_divide_(value) -&gt; Tensor</p>
+<p>In-place version of <code>$true_divide_</code></p>
+</div>
+<div id="trunc" class="section level2">
+<h2 class="hasAnchor">
+<a href="#trunc" class="anchor"></a>trunc</h2>
+<p>trunc() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_trunc.html">?torch_trunc</a></code></p>
+</div>
+<div id="trunc_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#trunc_" class="anchor"></a>trunc_</h2>
+<p>trunc_() -&gt; Tensor</p>
+<p>In-place version of <code>$trunc</code></p>
+</div>
+<div id="type" class="section level2">
+<h2 class="hasAnchor">
+<a href="#type" class="anchor"></a>type</h2>
+<p>type(dtype=NULL, non_blocking=FALSE, **kwargs) -&gt; str or Tensor Returns the type if <code>dtype</code> is not provided, else casts this object to the specified type.</p>
+<p>If this is already of the correct type, no copy is performed and the original object is returned.</p>
+<div id="arguments-53" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-53" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>dtype (type or string): The desired type</li>
+<li>non_blocking (bool): If <code>TRUE</code>, and the source is in pinned memory</li>
+<li>and destination is on the GPU or vice versa, the copy is performed</li>
+<li>asynchronously with respect to the host. Otherwise, the argument</li>
+<li>has no effect. **kwargs: For compatibility, may contain the key <code>async</code> in place of</li>
+<li>the <code>non_blocking</code> argument. The <code>async</code> arg is deprecated.</li>
+</ul>
+</div>
+</div>
+<div id="type_as" class="section level2">
+<h2 class="hasAnchor">
+<a href="#type_as" class="anchor"></a>type_as</h2>
+<p>type_as(tensor) -&gt; Tensor</p>
+<p>Returns this tensor cast to the type of the given tensor.</p>
+<p>This is a no-op if the tensor is already of the correct type. This is equivalent to <code>self$type(tensor.type())</code></p>
+<div id="arguments-54" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-54" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>tensor (Tensor): the tensor which has the desired type</li>
+</ul>
+</div>
+</div>
+<div id="unbind" class="section level2">
+<h2 class="hasAnchor">
+<a href="#unbind" class="anchor"></a>unbind</h2>
+<p>unbind(dim=0) -&gt; seq</p>
+<p>See <code><a href="../../reference/torch_unbind.html">?torch_unbind</a></code></p>
+</div>
+<div id="unflatten" class="section level2">
+<h2 class="hasAnchor">
+<a href="#unflatten" class="anchor"></a>unflatten</h2>
+<p>Unflattens the named dimension <code>dim</code>, viewing it in the shape specified by <code>namedshape</code>.</p>
+<div id="arguments-55" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-55" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>namedshape: (iterable of <code>(name, size)</code> tuples).</li>
+</ul>
+</div>
+</div>
+<div id="unfold" class="section level2">
+<h2 class="hasAnchor">
+<a href="#unfold" class="anchor"></a>unfold</h2>
+<p>unfold(dimension, size, step) -&gt; Tensor</p>
+<p>Returns a view of the original tensor which contains all slices of size <code>size</code> from <code>self</code> tensor in the dimension <code>dimension</code>.</p>
+<p>Step between two slices is given by <code>step</code>.</p>
+<p>If <code>sizedim</code> is the size of dimension <code>dimension</code> for <code>self</code>, the size of dimension <code>dimension</code> in the returned tensor will be <code>(sizedim - size) / step + 1</code>.</p>
+<p>An additional dimension of size <code>size</code> is appended in the returned tensor.</p>
+<div id="arguments-56" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-56" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>dimension (int): dimension in which unfolding happens</li>
+<li>size (int): the size of each slice that is unfolded</li>
+<li>step (int): the step between each slice</li>
+</ul>
+</div>
+</div>
+<div id="uniform_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#uniform_" class="anchor"></a>uniform_</h2>
+<p>uniform_(from=0, to=1) -&gt; Tensor</p>
+<p>Fills <code>self</code> tensor with numbers sampled from the continuous uniform distribution:</p>
+<p><span class="math display">\[
+P(x) = \dfrac{1}{\text{to} - \text{from}}
+\]</span></p>
+</div>
+<div id="unique" class="section level2">
+<h2 class="hasAnchor">
+<a href="#unique" class="anchor"></a>unique</h2>
+<p>Returns the unique elements of the input tensor.</p>
+<p>See <code>?torch_unique</code></p>
+</div>
+<div id="unique_consecutive" class="section level2">
+<h2 class="hasAnchor">
+<a href="#unique_consecutive" class="anchor"></a>unique_consecutive</h2>
+<p>Eliminates all but the first element from every consecutive group of equivalent elements.</p>
+<p>See [torch_unique_consecutive()]</p>
+</div>
+<div id="unsqueeze" class="section level2">
+<h2 class="hasAnchor">
+<a href="#unsqueeze" class="anchor"></a>unsqueeze</h2>
+<p>unsqueeze(dim) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_unsqueeze.html">?torch_unsqueeze</a></code></p>
+</div>
+<div id="unsqueeze_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#unsqueeze_" class="anchor"></a>unsqueeze_</h2>
+<p>unsqueeze_(dim) -&gt; Tensor</p>
+<p>In-place version of <code>$unsqueeze</code></p>
+</div>
+<div id="values" class="section level2">
+<h2 class="hasAnchor">
+<a href="#values" class="anchor"></a>values</h2>
+<p>values() -&gt; Tensor</p>
+<p>If <code>self</code> is a sparse COO tensor (i.e., with <code>torch_sparse_coo</code> layout), this returns a view of the contained values tensor. Otherwise, this throws an error.</p>
+<div id="note-11" class="section level4">
+<h4 class="hasAnchor">
+<a href="#note-11" class="anchor"></a>Note:</h4>
+<p>This method can only be called on a coalesced sparse tensor. See <code>Tensor$coalesce</code> for details.</p>
+</div>
+</div>
+<div id="var" class="section level2">
+<h2 class="hasAnchor">
+<a href="#var" class="anchor"></a>var</h2>
+<p>var(dim=NULL, unbiased=TRUE, keepdim=FALSE) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_var.html">?torch_var</a></code></p>
+</div>
+<div id="view" class="section level2">
+<h2 class="hasAnchor">
+<a href="#view" class="anchor"></a>view</h2>
+<p>view(*shape) -&gt; Tensor</p>
+<p>Returns a new tensor with the same data as the <code>self</code> tensor but of a different <code>shape</code>.</p>
+<p>The returned tensor shares the same data and must have the same number of elements, but may have a different size. For a tensor to be viewed, the new view size must be compatible with its original size and stride, i.e., each new view dimension must either be a subspace of an original dimension, or only span across original dimensions <code>d, d+1, \dots, d+k</code> that satisfy the following contiguity-like condition that <code>\forall i = d, \dots, d+k-1</code>,</p>
+<p><span class="math display">\[
+\text{stride}[i] = \text{stride}[i+1] \times \text{size}[i+1]
+\]</span></p>
+<p>Otherwise, it will not be possible to view <code>self</code> tensor as <code>shape</code> without copying it (e.g., via <code>contiguous</code>). When it is unclear whether a <code>view</code> can be performed, it is advisable to use :meth:<code>reshape</code>, which returns a view if the shapes are compatible, and copies (equivalent to calling <code>contiguous</code>) otherwise.</p>
+<div id="arguments-57" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-57" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>shape (torch_Size or int…): the desired size</li>
+</ul>
+</div>
+</div>
+<div id="view_as" class="section level2">
+<h2 class="hasAnchor">
+<a href="#view_as" class="anchor"></a>view_as</h2>
+<p>view_as(other) -&gt; Tensor</p>
+<p>View this tensor as the same size as <code>other</code>. <code>self$view_as(other)</code> is equivalent to <code>self$view(other.size())</code>.</p>
+<p>Please see <code>$view</code> for more information about <code>view</code>.</p>
+<div id="arguments-58" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-58" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>other (`$): The result tensor has the same size</li>
+<li>as <code>other</code>.</li>
+</ul>
+</div>
+</div>
+<div id="where" class="section level2">
+<h2 class="hasAnchor">
+<a href="#where" class="anchor"></a>where</h2>
+<p>where(condition, y) -&gt; Tensor</p>
+<p><code>self$where(condition, y)</code> is equivalent to <code><a href="../../reference/torch_where.html">torch_where(condition, self, y)</a></code>. See <code><a href="../../reference/torch_where.html">?torch_where</a></code></p>
+</div>
+<div id="zero_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#zero_" class="anchor"></a>zero_</h2>
+<p>zero_() -&gt; Tensor</p>
+<p>Fills <code>self</code> tensor with zeros.</p>
+</div>
+  </div>
+
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+
+        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
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+<ul class="nav navbar-nav navbar-right">
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+<!--/.nav-collapse -->
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+<!--/.container -->
+</div>
+<!--/.navbar -->
+
+      
+
+      </header><script src="using-autograd_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header toc-ignore">
+      <h1 data-toc-skip>Using autograd</h1>
+            
+      
+      <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>
+      <div class="hidden name"><code>using-autograd.Rmd</code></div>
+
+    </div>
+
+    
+    
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
+</pre></div>
+<p>So far, all we’ve been using from torch is <em>tensors</em>, but we’ve been performing all calculations ourselves – the computing the predictions, the loss, the gradients (and thus, the necessary updates to the weights), and the new weight values. In this chapter, we’ll make a significant change: Namely, we spare ourselves the cumbersome calculation of gradients, and have torch do it for us.</p>
+<p>Before we see that in action, let’s get some more background.</p>
+<div id="automatic-differentiation-with-autograd" class="section level2">
+<h2 class="hasAnchor">
+<a href="#automatic-differentiation-with-autograd" class="anchor"></a>Automatic differentiation with autograd</h2>
+<p>Torch uses a module called <em>autograd</em> to record operations performed on tensors, and store what has to be done to obtain the respective gradients. These actions are stored as functions, and those functions are applied in order when the gradient of the output (normally, the loss) with respect to those tensors is calculated: starting from the output node and <em>propagating</em> gradients <em>back</em> through the network. This is a form of <em>reverse mode automatic differentiation</em>.</p>
+<p>As users, we can see a bit of this implementation. As a prerequisite for this “recording” to happen, tensors have to be created with <code>requires_grad = TRUE</code>. E.g.</p>
+<div class="sourceCode" id="cb2"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_ones.html">torch_ones</a></span>(<span class="fl">2</span>,<span class="fl">2</span>, requires_grad = <span class="fl">TRUE</span>)
+</pre></div>
+<p>To be clear, this is a tensor <em>with respect to which</em> gradients have to be calculated – normally, a tensor representing a weight or a bias, not the input data <a href="#fn1" class="footnote-ref" id="fnref1"><sup>1</sup></a>. If we now perform some operation on that tensor, assigning the result to <code>y</code></p>
+<div class="sourceCode" id="cb3"><pre class="downlit">
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="kw">x</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>()
+</pre></div>
+<p>we find that <code>y</code> now has a non-empty <code>grad_fn</code> that tells torch how to compute the gradient of <code>y</code> with respect to <code>x</code>:</p>
+<div class="sourceCode" id="cb4"><pre class="downlit">
+<span class="kw">y</span><span class="op">$</span><span class="kw">grad_fn</span>
+<span class="co">#&gt; MeanBackward0</span>
+</pre></div>
+<p>Actual computation of gradients is triggered by calling <code>backward()</code> on the output tensor.</p>
+<div class="sourceCode" id="cb5"><pre class="downlit">
+<span class="kw">y</span><span class="op">$</span><span class="fu">backward</span>()
+</pre></div>
+<p>That executed, <code>x</code> now has a non-empty field <code>grad</code> that stores the gradient of <code>y</code> with respect to <code>x</code>:</p>
+<div class="sourceCode" id="cb6"><pre class="downlit">
+<span class="kw">x</span><span class="op">$</span><span class="kw">grad</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  0.2500  0.2500</span>
+<span class="co">#&gt;  0.2500  0.2500</span>
+<span class="co">#&gt; [ CPUFloatType{2,2} ]</span>
+</pre></div>
+<p>With a longer chain of computations, we can peek at how torch builds up a graph of backward operations.</p>
+<p>Here is a slightly more complex example. We call <code>retain_grad()</code> on <code>y</code> and <code>z</code> just for demonstration purposes; by default, intermediate gradients – while of course they have to be computed – aren’t stored, in order to save memory.</p>
+<div class="sourceCode" id="cb7"><pre class="downlit">
+<span class="kw">x1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_ones.html">torch_ones</a></span>(<span class="fl">2</span>,<span class="fl">2</span>, requires_grad = <span class="fl">TRUE</span>)
+<span class="kw">x2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1.1</span>, requires_grad = <span class="fl">TRUE</span>)
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="kw">x1</span> <span class="op">*</span> (<span class="kw">x2</span> <span class="op">+</span> <span class="fl">2</span>)
+<span class="kw">y</span><span class="op">$</span><span class="fu">retain_grad</span>()
+<span class="kw">z</span> <span class="op">&lt;-</span> <span class="kw">y</span><span class="op">$</span><span class="fu">pow</span>(<span class="fl">2</span>) <span class="op">*</span> <span class="fl">3</span>
+<span class="kw">z</span><span class="op">$</span><span class="fu">retain_grad</span>()
+<span class="kw">out</span> <span class="op">&lt;-</span> <span class="kw">z</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>()
+</pre></div>
+<p>Starting from <code>out$grad_fn</code>, we can follow the graph all back to the leaf nodes:</p>
+<div class="sourceCode" id="cb8"><pre class="downlit">
+<span class="co"># how to compute the gradient for mean, the last operation executed</span>
+<span class="kw">out</span><span class="op">$</span><span class="kw">grad_fn</span>
+<span class="co">#&gt; MeanBackward0</span>
+<span class="co"># how to compute the gradient for the multiplication by 3 in z = y$pow(2) * 3</span>
+<span class="kw">out</span><span class="op">$</span><span class="kw">grad_fn</span><span class="op">$</span><span class="kw">next_functions</span>
+<span class="co">#&gt; [[1]]</span>
+<span class="co">#&gt; MulBackward1</span>
+<span class="co"># how to compute the gradient for pow in z = y.pow(2) * 3</span>
+<span class="kw">out</span><span class="op">$</span><span class="kw">grad_fn</span><span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]<span class="op">$</span><span class="kw">next_functions</span>
+<span class="co">#&gt; [[1]]</span>
+<span class="co">#&gt; PowBackward0</span>
+<span class="co"># how to compute the gradient for the multiplication in y = x * (x + 2)</span>
+<span class="kw">out</span><span class="op">$</span><span class="kw">grad_fn</span><span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]<span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]<span class="op">$</span><span class="kw">next_functions</span>
+<span class="co">#&gt; [[1]]</span>
+<span class="co">#&gt; MulBackward0</span>
+<span class="co"># how to compute the gradient for the two branches of y = x * (x + 2),</span>
+<span class="co"># where the left branch is a leaf node (AccumulateGrad for x1)</span>
+<span class="kw">out</span><span class="op">$</span><span class="kw">grad_fn</span><span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]<span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]<span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]<span class="op">$</span><span class="kw">next_functions</span>
+<span class="co">#&gt; [[1]]</span>
+<span class="co">#&gt; torch::autograd::AccumulateGrad</span>
+<span class="co">#&gt; [[2]]</span>
+<span class="co">#&gt; AddBackward1</span>
+<span class="co"># here we arrive at the other leaf node (AccumulateGrad for x2)</span>
+<span class="kw">out</span><span class="op">$</span><span class="kw">grad_fn</span><span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]<span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]<span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]<span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">2</span>]]<span class="op">$</span><span class="kw">next_functions</span>
+<span class="co">#&gt; [[1]]</span>
+<span class="co">#&gt; torch::autograd::AccumulateGrad</span>
+</pre></div>
+<p>After calling <code>out$backward()</code>, all tensors in the graph will have their respective gradients created. Without our calls to <code>retain_grad</code> above, <code>z$grad</code> and <code>y$grad</code> would be empty:</p>
+<div class="sourceCode" id="cb9"><pre class="downlit">
+<span class="kw">out</span><span class="op">$</span><span class="fu">backward</span>()
+<span class="kw">z</span><span class="op">$</span><span class="kw">grad</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  0.2500  0.2500</span>
+<span class="co">#&gt;  0.2500  0.2500</span>
+<span class="co">#&gt; [ CPUFloatType{2,2} ]</span>
+<span class="kw">y</span><span class="op">$</span><span class="kw">grad</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  4.6500  4.6500</span>
+<span class="co">#&gt;  4.6500  4.6500</span>
+<span class="co">#&gt; [ CPUFloatType{2,2} ]</span>
+<span class="kw">x2</span><span class="op">$</span><span class="kw">grad</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  18.6000</span>
+<span class="co">#&gt; [ CPUFloatType{1} ]</span>
+<span class="kw">x1</span><span class="op">$</span><span class="kw">grad</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  14.4150  14.4150</span>
+<span class="co">#&gt;  14.4150  14.4150</span>
+<span class="co">#&gt; [ CPUFloatType{2,2} ]</span>
+</pre></div>
+<p>Thus acquainted with autograd, we’re ready to modify our example.</p>
+</div>
+<div id="the-simple-network-now-using-autograd" class="section level2">
+<h2 class="hasAnchor">
+<a href="#the-simple-network-now-using-autograd" class="anchor"></a>The simple network, now using autograd</h2>
+<p>For a single new line calling <code>loss$backward()</code>, now a number of lines (that did manual backprop) are gone:</p>
+<div class="sourceCode" id="cb10"><pre class="downlit">
+<span class="co">### generate training data -----------------------------------------------------</span>
+<span class="co"># input dimensionality (number of input features)</span>
+<span class="kw">d_in</span> <span class="op">&lt;-</span> <span class="fl">3</span>
+<span class="co"># output dimensionality (number of predicted features)</span>
+<span class="kw">d_out</span> <span class="op">&lt;-</span> <span class="fl">1</span>
+<span class="co"># number of observations in training set</span>
+<span class="kw">n</span> <span class="op">&lt;-</span> <span class="fl">100</span>
+<span class="co"># create random data</span>
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">n</span>, <span class="kw">d_in</span>)
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="kw">x</span>[,<span class="fl">1</span>]<span class="op">*</span><span class="fl">0.2</span> <span class="op">-</span> <span class="kw">x</span>[<span class="kw">..</span>,<span class="fl">2</span>]<span class="op">*</span><span class="fl">1.3</span> <span class="op">-</span> <span class="kw">x</span>[<span class="kw">..</span>,<span class="fl">3</span>]<span class="op">*</span><span class="fl">0.5</span> <span class="op">+</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">n</span>)
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="kw">y</span><span class="op">$</span><span class="fu">unsqueeze</span>(dim = <span class="fl">1</span>)
+<span class="co">### initialize weights ---------------------------------------------------------</span>
+<span class="co"># dimensionality of hidden layer</span>
+<span class="kw">d_hidden</span> <span class="op">&lt;-</span> <span class="fl">32</span>
+<span class="co"># weights connecting input to hidden layer</span>
+<span class="kw">w1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">d_in</span>, <span class="kw">d_hidden</span>, requires_grad = <span class="fl">TRUE</span>)
+<span class="co"># weights connecting hidden to output layer</span>
+<span class="kw">w2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">d_hidden</span>, <span class="kw">d_out</span>, requires_grad = <span class="fl">TRUE</span>)
+<span class="co"># hidden layer bias</span>
+<span class="kw">b1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">1</span>, <span class="kw">d_hidden</span>, requires_grad = <span class="fl">TRUE</span>)
+<span class="co"># output layer bias</span>
+<span class="kw">b2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">1</span>, <span class="kw">d_out</span>,requires_grad = <span class="fl">TRUE</span>)
+<span class="co">### network parameters ---------------------------------------------------------</span>
+<span class="kw">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-4</span>
+<span class="co">### training loop --------------------------------------------------------------</span>
+<span class="co">for</span> (<span class="kw">t</span> <span class="co">in</span> <span class="fl">1</span><span class="op">:</span><span class="fl">200</span>) {
+
+    <span class="co">### -------- Forward pass -------- </span>
+    <span class="kw">y_pred</span> <span class="op">&lt;-</span> <span class="kw">x</span><span class="op">$</span><span class="fu">mm</span>(<span class="kw">w1</span>)<span class="op">$</span><span class="fu"><a href="https://rdrr.io/pkg/magrittr/man/aliases.html">add</a></span>(<span class="kw">b1</span>)<span class="op">$</span><span class="fu">clamp</span>(min = <span class="fl">0</span>)<span class="op">$</span><span class="fu">mm</span>(<span class="kw">w2</span>)<span class="op">$</span><span class="fu"><a href="https://rdrr.io/pkg/magrittr/man/aliases.html">add</a></span>(<span class="kw">b2</span>)
+    <span class="co">### -------- compute loss -------- </span>
+    <span class="kw">loss</span> <span class="op">&lt;-</span> (<span class="kw">y_pred</span> <span class="op">-</span> <span class="kw">y</span>)<span class="op">$</span><span class="fu">pow</span>(<span class="fl">2</span>)<span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>()
+    <span class="co">if</span> (<span class="kw">t</span> <span class="op">%%</span> <span class="fl">10</span> <span class="op">==</span> <span class="fl">0</span>) <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="kw">t</span>, <span class="fu"><a href="../reference/as_array.html">as_array</a></span>(<span class="kw">loss</span>), <span class="st">"\n"</span>)
+    <span class="co">### -------- Backpropagation -------- </span>
+    <span class="co"># compute the gradient of loss with respect to all tensors with requires_grad = True.</span>
+    <span class="kw">loss</span><span class="op">$</span><span class="fu">backward</span>()
+ 
+    <span class="co">### -------- Update weights -------- </span>
+    
+    <span class="co"># Wrap in torch.no_grad() because this is a part we DON'T want to record for automatic gradient computation</span>
+    <span class="fu"><a href="../reference/with_no_grad.html">with_no_grad</a></span>({
+      
+      <span class="kw">w1</span><span class="op">$</span><span class="fu">sub_</span>(<span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">w1</span><span class="op">$</span><span class="kw">grad</span>)
+      <span class="kw">w2</span><span class="op">$</span><span class="fu">sub_</span>(<span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">w2</span><span class="op">$</span><span class="kw">grad</span>)
+      <span class="kw">b1</span><span class="op">$</span><span class="fu">sub_</span>(<span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">b1</span><span class="op">$</span><span class="kw">grad</span>)
+      <span class="kw">b2</span><span class="op">$</span><span class="fu">sub_</span>(<span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">b2</span><span class="op">$</span><span class="kw">grad</span>)
+      
+      <span class="co"># Zero the gradients after every pass, because they'd accumulate otherwise</span>
+      <span class="kw">w1</span><span class="op">$</span><span class="kw">grad</span><span class="op">$</span><span class="fu">zero_</span>()
+      <span class="kw">w2</span><span class="op">$</span><span class="kw">grad</span><span class="op">$</span><span class="fu">zero_</span>()
+      <span class="kw">b1</span><span class="op">$</span><span class="kw">grad</span><span class="op">$</span><span class="fu">zero_</span>()
+      <span class="kw">b2</span><span class="op">$</span><span class="kw">grad</span><span class="op">$</span><span class="fu">zero_</span>()
+    
+    })
+    
+}
+<span class="co">#&gt; 10 27.60956 </span>
+<span class="co">#&gt; 20 25.39985 </span>
+<span class="co">#&gt; 30 23.42485 </span>
+<span class="co">#&gt; 40 21.65899 </span>
+<span class="co">#&gt; 50 20.07844 </span>
+<span class="co">#&gt; 60 18.66107 </span>
+<span class="co">#&gt; 70 17.38713 </span>
+<span class="co">#&gt; 80 16.24375 </span>
+<span class="co">#&gt; 90 15.21763 </span>
+<span class="co">#&gt; 100 14.29351 </span>
+<span class="co">#&gt; 110 13.45975 </span>
+<span class="co">#&gt; 120 12.70921 </span>
+<span class="co">#&gt; 130 12.03104 </span>
+<span class="co">#&gt; 140 11.41835 </span>
+<span class="co">#&gt; 150 10.86677 </span>
+<span class="co">#&gt; 160 10.36613 </span>
+<span class="co">#&gt; 170 9.911062 </span>
+<span class="co">#&gt; 180 9.496947 </span>
+<span class="co">#&gt; 190 9.121381 </span>
+<span class="co">#&gt; 200 8.778724</span>
+</pre></div>
+<p>We still manually compute the forward pass, and we still manually update the weights. In the last two chapters of this section, we’ll see how these parts of the logic can be made more modular and reusable, as well.</p>
+</div>
+<div class="footnotes">
+<hr>
+<ol>
+<li id="fn1"><p>Unless we <em>want</em> to change the data, as in adversarial example generation<a href="#fnref1" class="footnote-back">↩︎</a></p></li>
+</ol>
+</div>
+  </div>
+
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+
+        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
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+  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
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+
+  </body>
+</html>
diff --git a/static/docs/articles/using-autograd_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/articles/using-autograd_files/accessible-code-block-0.0.1/empty-anchor.js
new file mode 100644
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+++ b/static/docs/articles/using-autograd_files/accessible-code-block-0.0.1/empty-anchor.js
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+// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
+// v0.0.1
+// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
+
+document.addEventListener('DOMContentLoaded', function() {
+  const codeList = document.getElementsByClassName("sourceCode");
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+  }
+});
diff --git a/static/docs/authors.html b/static/docs/authors.html
new file mode 100644
index 000000000..95f59cb12
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@@ -0,0 +1,238 @@
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+      <a href="articles/getting-started/nn.html">nn: neural networks with torch</a>
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+
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+      <li>
+        <p><strong>Daniel Falbel</strong>. Author, maintainer, copyright holder. 
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+        <p><strong>Javier Luraschi</strong>. Author, copyright holder. 
+        </p>
+      </li>
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+        <p><strong>Dmitriy Selivanov</strong>. Contributor. 
+        </p>
+      </li>
+      <li>
+        <p><strong>Athos Damiani</strong>. Contributor. 
+        </p>
+      </li>
+      <li>
+        <p><strong> RStudio</strong>. Copyright holder. 
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+/*!
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diff --git a/static/docs/bootstrap-toc.js b/static/docs/bootstrap-toc.js
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+/*!
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+<!-- Generated by pkgdown: do not edit by hand -->
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+  <meta charset="utf-8">
+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+      <h1>Contributing to torch</h1>
+    </div>
+
+<div id="contributing-to-torch" class="section level1">
+
+<p>This outlines how to propose a change to torch. For more detailed info about contributing to this, and other tidyverse packages, please see the <a href="https://rstd.io/tidy-contrib"><strong>development contributing guide</strong></a>.</p>
+<div id="fixing-typos" class="section level2">
+<h2 class="hasAnchor">
+<a href="#fixing-typos" class="anchor"></a>Fixing typos</h2>
+<p>You can fix typos, spelling mistakes, or grammatical errors in the documentation directly using the GitHub web interface, as long as the changes are made in the <em>source</em> file. This generally means you’ll need to edit <a href="https://roxygen2.r-lib.org/articles/roxygen2.html">roxygen2 comments</a> in an <code>.R</code>, not a <code>.Rd</code> file. You can find the <code>.R</code> file that generates the <code>.Rd</code> by reading the comment in the first line.</p>
+<p>See also the [Documentation] section.</p>
+</div>
+<div id="filing-bugs" class="section level2">
+<h2 class="hasAnchor">
+<a href="#filing-bugs" class="anchor"></a>Filing bugs</h2>
+<p>If you find a bug in <code>torch</code> please open an issue <a href="https://github.com/mlverse/torch/issues">here</a>. Please, provide detailed information on how to reproduce the bug. It would be great to also provide a <a href="https://reprex.tidyverse.org/"><code>reprex</code></a>.</p>
+</div>
+<div id="feature-requests" class="section level2">
+<h2 class="hasAnchor">
+<a href="#feature-requests" class="anchor"></a>Feature requests</h2>
+<p>Feel free to open issues <a href="https://github.com/mlverse/torch/issues">here</a> and add the <code>feature-request</code> tag. Try searching if there’s already an open issue for your <code>feature-request</code>, in this case it’s better to comment or upvote it intead of opening a new one.</p>
+</div>
+<div id="examples" class="section level2">
+<h2 class="hasAnchor">
+<a href="#examples" class="anchor"></a>Examples</h2>
+<p>We welcome contributed examples. feel free to open a PR with new examples. The should be placed in the <code>vignettes/examples</code> folder.</p>
+<p>The examples should be an .R file and a .Rmd file with the same name that just renders the code.</p>
+<p>See <a href="https://github.com/mlverse/torch/blob/master/vignettes/examples/mnist-mlp.R">mnist-mlp.R</a> and <a href="https://github.com/mlverse/torch/blob/master/vignettes/examples/mnist-mlp.Rmd">mnist-mlp.Rmd</a></p>
+<p>One must be able to run the example without manually downloading any dataset/file. You should also add an entry to the <a href="https://github.com/mlverse/torch/blob/master/_pkgdown.yml#L24-L25"><code>_pkgdown.yaml</code> file</a>.</p>
+</div>
+<div id="code-contributions" class="section level2">
+<h2 class="hasAnchor">
+<a href="#code-contributions" class="anchor"></a>Code contributions</h2>
+<p>We have many open issues in the <a href="https://github.com/mlverse/torch/issues">github repo</a> if there’s one item that you want to work on, you can comment on it an ask for directions.</p>
+</div>
+<div id="documentation" class="section level2">
+<h2 class="hasAnchor">
+<a href="#documentation" class="anchor"></a>Documentation</h2>
+<p>We use roxygen2 to generate the documentation. IN order to update the docs, edit the file in the <code>R</code> directory. To regenerate and preview the docs, use the custom <code>tools/document.R</code> script, as we need to patch roxygen2 to avoid running the examples on CRAN.</p>
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+  <div class="col-md-9 contents">
+    <div class="page-header toc-ignore">
+      <h1 data-toc-skip>mnist-cnn</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/examples/mnist-cnn.Rmd"><code>vignettes/examples/mnist-cnn.Rmd</code></a></small>
+      <div class="hidden name"><code>mnist-cnn.Rmd</code></div>
+
+    </div>
+
+    
+    
+<div class="sourceCode" id="cb1"><html><body><pre class="r"><span class="no">dir</span> <span class="kw">&lt;-</span> <span class="st">"~/Downloads/mnist"</span>
+
+<span class="no">ds</span> <span class="kw">&lt;-</span> <span class="fu">mnist_dataset</span>(
+  <span class="no">dir</span>,
+  <span class="kw">download</span> <span class="kw">=</span> <span class="fl">TRUE</span>,
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+    <span class="no">x</span> <span class="kw">&lt;-</span> <span class="no">x</span>$<span class="fu">to</span>(<span class="kw">dtype</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/torch_dtype.html">torch_float</a></span>())/<span class="fl">256</span>
+    <span class="no">x</span>[<span class="no">newaxis</span>,<span class="no">..</span>]
+  }
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+
+<span class="no">net</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nn_module.html">nn_module</a></span>(
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+  },
+  <span class="kw">forward</span> <span class="kw">=</span> <span class="kw">function</span>(<span class="no">x</span>) {
+    <span class="no">x</span> <span class="kw">&lt;-</span> <span class="no">self</span>$<span class="fu">conv1</span>(<span class="no">x</span>)
+    <span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nnf_relu.html">nnf_relu</a></span>(<span class="no">x</span>)
+    <span class="no">x</span> <span class="kw">&lt;-</span> <span class="no">self</span>$<span class="fu">conv2</span>(<span class="no">x</span>)
+    <span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nnf_relu.html">nnf_relu</a></span>(<span class="no">x</span>)
+    <span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nnf_max_pool2d.html">nnf_max_pool2d</a></span>(<span class="no">x</span>, <span class="fl">2</span>)
+    <span class="no">x</span> <span class="kw">&lt;-</span> <span class="no">self</span>$<span class="fu">dropout1</span>(<span class="no">x</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>$<span class="fu">fc1</span>(<span class="no">x</span>)
+    <span class="no">x</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nnf_relu.html">nnf_relu</a></span>(<span class="no">x</span>)
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+    <span class="no">x</span> <span class="kw">&lt;-</span> <span class="no">self</span>$<span class="fu">fc2</span>(<span class="no">x</span>)
+    <span class="no">output</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nnf_log_softmax.html">nnf_log_softmax</a></span>(<span class="no">x</span>, <span class="kw">dim</span><span class="kw">=</span><span class="fl">1</span>)
+    <span class="no">output</span>
+  }
+)
+
+<span class="no">model</span> <span class="kw">&lt;-</span> <span class="fu">net</span>()
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+
+<span class="no">epochs</span> <span class="kw">&lt;-</span> <span class="fl">10</span>
+
+<span class="kw">for</span> (<span class="no">epoch</span> <span class="kw">in</span> <span class="fl">1</span>:<span class="fl">10</span>) {
+
+  <span class="no">pb</span> <span class="kw">&lt;-</span> <span class="kw pkg">progress</span><span class="kw ns">::</span><span class="no">progress_bar</span>$<span class="fu">new</span>(
+    <span class="kw">total</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/length.html">length</a></span>(<span class="no">dl</span>),
+    <span class="kw">format</span> <span class="kw">=</span> <span class="st">"[:bar] :eta Loss: :loss"</span>
+  )
+  <span class="no">l</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>()
+
+  <span class="kw">for</span> (<span class="no">b</span> <span class="kw">in</span> <span class="fu"><a href="../../reference/enumerate.html">enumerate</a></span>(<span class="no">dl</span>)) {
+    <span class="no">optimizer</span>$<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>$<span class="fu">item</span>())
+    <span class="no">pb</span>$<span class="fu">tick</span>(<span class="kw">tokens</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="kw">loss</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>(<span class="no">l</span>)))
+  }
+
+  <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/sprintf.html">sprintf</a></span>(<span class="st">"Loss at epoch %d: %3f\n"</span>, <span class="no">epoch</span>, <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>(<span class="no">l</span>)))
+}</pre></body></html></div>
+  </div>
+
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+
+      </div>
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+</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/static/docs/dev/articles/examples/mnist-cnn_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/dev/articles/examples/mnist-cnn_files/accessible-code-block-0.0.1/empty-anchor.js
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index 000000000..ca349fd6a
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+++ b/static/docs/dev/articles/examples/mnist-cnn_files/accessible-code-block-0.0.1/empty-anchor.js
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+// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
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+// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
+
+document.addEventListener('DOMContentLoaded', function() {
+  const codeList = document.getElementsByClassName("sourceCode");
+  for (var i = 0; i < codeList.length; i++) {
+    var linkList = codeList[i].getElementsByTagName('a');
+    for (var j = 0; j < linkList.length; j++) {
+      if (linkList[j].innerHTML === "") {
+        linkList[j].setAttribute('aria-hidden', 'true');
+      }
+    }
+  }
+});
diff --git a/static/docs/dev/articles/examples/mnist-dcgan.html b/static/docs/dev/articles/examples/mnist-dcgan.html
new file mode 100644
index 000000000..e5ef1e932
--- /dev/null
+++ b/static/docs/dev/articles/examples/mnist-dcgan.html
@@ -0,0 +1,341 @@
+<!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">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>mnist-dcgan • torch</title>
+<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.4.0/united/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous">
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+<link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous">
+<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../../pkgdown.css" rel="stylesheet">
+<script src="../../pkgdown.js"></script><meta property="og:title" content="mnist-dcgan">
+<meta property="og:description" content="torch">
+<meta name="robots" content="noindex">
+<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
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+        <a class="navbar-link" href="../../index.html">torch</a>
+        <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.0.1.9000</span>
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+<li class="dropdown-header">Beginers guide</li>
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+      <a href="../../articles/getting-started/tensors.html">Tensors</a>
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+      <a href="../../articles/getting-started/tensors-and-autograd.html">Tensors and autograd</a>
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+      <a href="../../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
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+      <a href="../../articles/getting-started/nn.html">nn: neural networks with torch</a>
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+      <a href="../../articles/getting-started/optim.html">optim: optimizers in torch</a>
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+      <a href="../../articles/getting-started/custom-nn.html">Custom nn modules</a>
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+      <a href="../../articles/getting-started/control-flow-and-weight-sharing.html">Control flow &amp; Weight sharing</a>
+    </li>
+    <li class="dropdown-header">Torch Mechanics</li>
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+      <a href="../../articles/getting-started/what-is-torch.html">What's torch?</a>
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+      <a href="../../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
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+<li class="dropdown-header">Tensors</li>
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+      <a href="../../articles/using-autograd.html">Using autograd</a>
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+</li>
+<li class="dropdown">
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
+    Examples
+     
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+<li class="dropdown-header">Vision</li>
+    <li>
+      <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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+    <li>
+      <a href="../../articles/examples/mnist-dcgan.html">mnist-dcgan</a>
+    </li>
+  </ul>
+</li>
+<li>
+  <a href="../../reference/index.html">Reference</a>
+</li>
+      </ul>
+<ul class="nav navbar-nav navbar-right">
+<li>
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+    <span class="fab fa fab fa-github fa-lg"></span>
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+</div>
+<!--/.nav-collapse -->
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+<!--/.container -->
+</div>
+<!--/.navbar -->
+
+      
+
+      </header><script src="mnist-dcgan_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header toc-ignore">
+      <h1 data-toc-skip>mnist-dcgan</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/examples/mnist-dcgan.Rmd"><code>vignettes/examples/mnist-dcgan.Rmd</code></a></small>
+      <div class="hidden name"><code>mnist-dcgan.Rmd</code></div>
+
+    </div>
+
+    
+    
+<div class="sourceCode" id="cb1"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="no">torch</span>)
+
+<span class="no">dir</span> <span class="kw">&lt;-</span> <span class="st">"~/Downloads/mnist"</span>
+
+<span class="no">ds</span> <span class="kw">&lt;-</span> <span class="fu">mnist_dataset</span>(
+  <span class="no">dir</span>,
+  <span class="kw">download</span> <span class="kw">=</span> <span class="fl">TRUE</span>,
+  <span class="kw">transform</span> <span class="kw">=</span> <span class="kw">function</span>(<span class="no">x</span>) {
+    <span class="no">x</span> <span class="kw">&lt;-</span> <span class="no">x</span>$<span class="fu">to</span>(<span class="kw">dtype</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/torch_dtype.html">torch_float</a></span>())/<span class="fl">256</span>
+    <span class="no">x</span> <span class="kw">&lt;-</span> <span class="fl">2</span>*(<span class="no">x</span> - <span class="fl">0.5</span>)
+    <span class="no">x</span>[<span class="no">newaxis</span>,<span class="no">..</span>]
+  }
+)
+<span class="no">dl</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/dataloader.html">dataloader</a></span>(<span class="no">ds</span>, <span class="kw">batch_size</span> <span class="kw">=</span> <span class="fl">32</span>, <span class="kw">shuffle</span> <span class="kw">=</span> <span class="fl">TRUE</span>)
+
+<span class="no">generator</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nn_module.html">nn_module</a></span>(
+  <span class="st">"generator"</span>,
+  <span class="kw">initialize</span> <span class="kw">=</span> <span class="kw">function</span>(<span class="no">latent_dim</span>, <span class="no">out_channels</span>) {
+    <span class="no">self</span>$<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_conv_transpose2d.html">nn_conv_transpose2d</a></span>(<span class="no">latent_dim</span>, <span class="fl">512</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">1</span>, <span class="kw">padding</span> <span class="kw">=</span> <span class="fl">0</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">512</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">512</span>, <span class="fl">256</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">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>()
+    )
+  },
+  <span class="kw">forward</span> <span class="kw">=</span> <span class="kw">function</span>(<span class="no">input</span>) {
+    <span class="no">self</span>$<span class="fu">main</span>(<span class="no">input</span>)
+  }
+)
+
+<span class="no">discriminator</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nn_module.html">nn_module</a></span>(
+  <span class="st">"discriminator"</span>,
+  <span class="kw">initialize</span> <span class="kw">=</span> <span class="kw">function</span>(<span class="no">in_channels</span>) {
+    <span class="no">self</span>$<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">progress_bar</span>$<span class="fu">new</span>(
+    <span class="kw">total</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/length.html">length</a></span>(<span class="no">dl</span>),
+    <span class="kw">format</span> <span class="kw">=</span> <span class="st">"[:bar] :eta Loss D: :lossd Loss G: :lossg"</span>
+  )
+  <span class="no">lossg</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>()
+  <span class="no">lossd</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>()
+
+  <span class="kw">for</span> (<span class="no">b</span> <span class="kw">in</span> <span class="fu"><a href="../../reference/enumerate.html">enumerate</a></span>(<span class="no">dl</span>)) {
+
+    <span class="no">y_real</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span>(<span class="fl">32</span>, <span class="kw">device</span> <span class="kw">=</span> <span class="no">device</span>)
+    <span class="no">y_fake</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">32</span>, <span class="kw">device</span> <span class="kw">=</span> <span class="no">device</span>)
+
+    <span class="no">noise</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">32</span>, <span class="fl">100</span>, <span class="fl">1</span>, <span class="fl">1</span>, <span class="kw">device</span> <span class="kw">=</span> <span class="no">device</span>)
+    <span class="no">fake</span> <span class="kw">&lt;-</span> <span class="fu">G</span>(<span class="no">noise</span>)
+
+    <span class="no">img</span> <span class="kw">&lt;-</span> <span class="no">b</span><span class="kw">[[</span><span class="fl">1</span>]]$<span class="fu">to</span>(<span class="kw">device</span> <span class="kw">=</span> <span class="no">device</span>)
+
+    <span class="co"># train the discriminator ---</span>
+    <span class="no">D_loss</span> <span class="kw">&lt;-</span> <span class="fu">loss</span>(<span class="fu"><a href="https://rdrr.io/r/stats/deriv.html">D</a></span>(<span class="no">img</span>), <span class="no">y_real</span>) + <span class="fu">loss</span>(<span class="fu"><a href="https://rdrr.io/r/stats/deriv.html">D</a></span>(<span class="no">fake</span>$<span class="fu"><a href="https://rdrr.io/r/base/detach.html">detach</a></span>()), <span class="no">y_fake</span>)
+
+    <span class="no">D_optimizer</span>$<span class="fu">zero_grad</span>()
+    <span class="no">D_loss</span>$<span class="fu">backward</span>()
+    <span class="no">D_optimizer</span>$<span class="fu"><a href="https://rdrr.io/r/stats/step.html">step</a></span>()
+
+    <span class="co"># train the generator ---</span>
+
+    <span class="no">G_loss</span> <span class="kw">&lt;-</span> <span class="fu">loss</span>(<span class="fu"><a href="https://rdrr.io/r/stats/deriv.html">D</a></span>(<span class="no">fake</span>), <span class="no">y_real</span>)
+
+    <span class="no">G_optimizer</span>$<span class="fu">zero_grad</span>()
+    <span class="no">G_loss</span>$<span class="fu">backward</span>()
+    <span class="no">G_optimizer</span>$<span class="fu"><a href="https://rdrr.io/r/stats/step.html">step</a></span>()
+
+    <span class="no">lossd</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="no">lossd</span>, <span class="no">D_loss</span>$<span class="fu">item</span>())
+    <span class="no">lossg</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="no">lossg</span>, <span class="no">G_loss</span>$<span class="fu">item</span>())
+    <span class="no">pb</span>$<span class="fu">tick</span>(<span class="kw">tokens</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="kw">lossd</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>(<span class="no">lossd</span>), <span class="kw">lossg</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>(<span class="no">lossg</span>)))
+  }
+  <span class="fu">plot_gen</span>(<span class="no">fixed_noise</span>)
+
+  <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/sprintf.html">sprintf</a></span>(<span class="st">"Epoch %d - Loss D: %3f Loss G: %3f\n"</span>, <span class="no">epoch</span>, <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>(<span class="no">lossd</span>), <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>(<span class="no">lossg</span>)))
+}</pre></body></html></div>
+  </div>
+
+  <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>
+
+  
+
+
+  </body>
+</html>
diff --git a/static/docs/dev/articles/examples/mnist-dcgan_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/dev/articles/examples/mnist-dcgan_files/accessible-code-block-0.0.1/empty-anchor.js
new file mode 100644
index 000000000..ca349fd6a
--- /dev/null
+++ b/static/docs/dev/articles/examples/mnist-dcgan_files/accessible-code-block-0.0.1/empty-anchor.js
@@ -0,0 +1,15 @@
+// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
+// v0.0.1
+// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
+
+document.addEventListener('DOMContentLoaded', function() {
+  const codeList = document.getElementsByClassName("sourceCode");
+  for (var i = 0; i < codeList.length; i++) {
+    var linkList = codeList[i].getElementsByTagName('a');
+    for (var j = 0; j < linkList.length; j++) {
+      if (linkList[j].innerHTML === "") {
+        linkList[j].setAttribute('aria-hidden', 'true');
+      }
+    }
+  }
+});
diff --git a/static/docs/dev/articles/examples/mnist-mlp.html b/static/docs/dev/articles/examples/mnist-mlp.html
new file mode 100644
index 000000000..b13de836a
--- /dev/null
+++ b/static/docs/dev/articles/examples/mnist-mlp.html
@@ -0,0 +1,248 @@
+<!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">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>mnist-mlp • torch</title>
+<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.4.0/united/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous">
+<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../../bootstrap-toc.css">
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+      <a href="../../articles/getting-started/what-is-torch.html">What's torch?</a>
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+  <div class="col-md-9 contents">
+    <div class="page-header toc-ignore">
+      <h1 data-toc-skip>mnist-mlp</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/examples/mnist-mlp.Rmd"><code>vignettes/examples/mnist-mlp.Rmd</code></a></small>
+      <div class="hidden name"><code>mnist-mlp.Rmd</code></div>
+
+    </div>
+
+    
+    
+<div class="sourceCode" id="cb1"><html><body><pre class="r"><span class="no">dir</span> <span class="kw">&lt;-</span> <span class="st">"~/Downloads/mnist"</span>
+
+<span class="no">ds</span> <span class="kw">&lt;-</span> <span class="fu">mnist_dataset</span>(
+  <span class="no">dir</span>,
+  <span class="kw">download</span> <span class="kw">=</span> <span class="fl">TRUE</span>,
+  <span class="kw">transform</span> <span class="kw">=</span> <span class="kw">function</span>(<span class="no">x</span>) {
+    <span class="no">x</span>$<span class="fu">to</span>(<span class="kw">dtype</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/torch_dtype.html">torch_float</a></span>())/<span class="fl">256</span>
+  }
+)
+<span class="no">dl</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/dataloader.html">dataloader</a></span>(<span class="no">ds</span>, <span class="kw">batch_size</span> <span class="kw">=</span> <span class="fl">32</span>, <span class="kw">shuffle</span> <span class="kw">=</span> <span class="fl">TRUE</span>)
+
+<span class="no">net</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nn_module.html">nn_module</a></span>(
+  <span class="st">"Net"</span>,
+  <span class="kw">initialize</span> <span class="kw">=</span> <span class="kw">function</span>() {
+    <span class="no">self</span>$<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">784</span>, <span class="fl">128</span>)
+    <span class="no">self</span>$<span class="no">fc2</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="fl">128</span>, <span class="fl">10</span>)
+  },
+  <span class="kw">forward</span> <span class="kw">=</span> <span class="kw">function</span>(<span class="no">x</span>) {
+    <span class="no">x</span> <span class="kw">%&gt;%</span>
+      <span class="fu"><a href="../../reference/torch_flatten.html">torch_flatten</a></span>(<span class="kw">start_dim</span> <span class="kw">=</span> <span class="fl">2</span>) <span class="kw">%&gt;%</span>
+      <span class="no">self</span>$<span class="fu">fc1</span>() <span class="kw">%&gt;%</span>
+      <span class="fu"><a href="../../reference/nnf_relu.html">nnf_relu</a></span>() <span class="kw">%&gt;%</span>
+      <span class="no">self</span>$<span class="fu">fc2</span>() <span class="kw">%&gt;%</span>
+      <span class="fu"><a href="../../reference/nnf_log_softmax.html">nnf_log_softmax</a></span>(<span class="kw">dim</span> <span class="kw">=</span> <span class="fl">1</span>)
+  }
+)
+
+<span class="no">model</span> <span class="kw">&lt;-</span> <span class="fu">net</span>()
+<span class="no">optimizer</span> <span class="kw">&lt;-</span> <span class="fu"><a href="../../reference/optim_sgd.html">optim_sgd</a></span>(<span class="no">model</span>$<span class="no">parameters</span>, <span class="kw">lr</span> <span class="kw">=</span> <span class="fl">0.01</span>)
+
+<span class="no">epochs</span> <span class="kw">&lt;-</span> <span class="fl">10</span>
+
+<span class="kw">for</span> (<span class="no">epoch</span> <span class="kw">in</span> <span class="fl">1</span>:<span class="fl">10</span>) {
+
+  <span class="no">pb</span> <span class="kw">&lt;-</span> <span class="kw pkg">progress</span><span class="kw ns">::</span><span class="no">progress_bar</span>$<span class="fu">new</span>(
+    <span class="kw">total</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/length.html">length</a></span>(<span class="no">dl</span>),
+    <span class="kw">format</span> <span class="kw">=</span> <span class="st">"[:bar] :eta Loss: :loss"</span>
+  )
+  <span class="no">l</span> <span class="kw">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>()
+
+  <span class="kw">for</span> (<span class="no">b</span> <span class="kw">in</span> <span class="fu"><a href="../../reference/enumerate.html">enumerate</a></span>(<span class="no">dl</span>)) {
+    <span class="no">optimizer</span>$<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>$<span class="fu">item</span>())
+    <span class="no">pb</span>$<span class="fu">tick</span>(<span class="kw">tokens</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="kw">loss</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>(<span class="no">l</span>)))
+  }
+
+  <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/sprintf.html">sprintf</a></span>(<span class="st">"Loss at epoch %d: %3f\n"</span>, <span class="no">epoch</span>, <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>(<span class="no">l</span>)))
+}</pre></body></html></div>
+  </div>
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diff --git a/static/docs/dev/articles/examples/mnist-mlp_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/dev/articles/examples/mnist-mlp_files/accessible-code-block-0.0.1/empty-anchor.js
new file mode 100644
index 000000000..ca349fd6a
--- /dev/null
+++ b/static/docs/dev/articles/examples/mnist-mlp_files/accessible-code-block-0.0.1/empty-anchor.js
@@ -0,0 +1,15 @@
+// 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");
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+      if (linkList[j].innerHTML === "") {
+        linkList[j].setAttribute('aria-hidden', 'true');
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+});
diff --git a/static/docs/dev/articles/extending-autograd.html b/static/docs/dev/articles/extending-autograd.html
new file mode 100644
index 000000000..45a744410
--- /dev/null
+++ b/static/docs/dev/articles/extending-autograd.html
@@ -0,0 +1,266 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
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+<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet">
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+<li class="dropdown-header">Beginers guide</li>
+    <li>
+      <a href="../articles/getting-started/warmup.html">Warm-up</a>
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+      <a href="../articles/getting-started/tensors.html">Tensors</a>
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+      <a href="../articles/getting-started/tensors-and-autograd.html">Tensors and autograd</a>
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+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
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+      <a href="../articles/getting-started/optim.html">optim: optimizers in torch</a>
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+      <a href="../articles/getting-started/custom-nn.html">Custom nn modules</a>
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+    <li>
+      <a href="../articles/getting-started/control-flow-and-weight-sharing.html">Control flow &amp; Weight sharing</a>
+    </li>
+    <li class="dropdown-header">Torch Mechanics</li>
+    <li>
+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
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+      <a href="../articles/getting-started/neural-networks.html">Neural networks</a>
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+      <a href="../articles/extending-autograd.html">Extending autograd</a>
+    </li>
+  </ul>
+</li>
+<li class="dropdown-header">Examples</li>
+<li>
+  <a href="../reference/index.html">Reference</a>
+</li>
+<li>
+  <a href="../news/index.html">Changelog</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><script src="extending-autograd_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
+  <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>
+
+    </div>
+
+    
+    
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
+</pre></div>
+<p>Adding operations to autograd requires implementing a new <code>autograd_function</code> for each operation. Recall that <code>autograd_functions</code>s are what <code>autograd</code> uses to compute the results and gradients, and encode the operation history. Every new function requires you to implement 2 methods:</p>
+<ul>
+<li><p><code>forward()</code> - the code that performs the operation. It can take as many arguments as you want, with some of them being optional, if you specify the default values. All kinds of R objects are accepted here. Tensor arguments that track history (i.e., with <code>requires_grad=TRUE</code>) will be converted to ones that don’t track history before the call, and their use will be registered in the graph. Note that this logic won’t traverse lists or any other data structures and will only consider Tensor’s that are direct arguments to the call. You can return either a single Tensor output, or a list of <code>Tensor</code>s if there are multiple outputs. Also, please refer to the docs of <code>autograd_function</code> to find descriptions of useful methods that can be called only from <code>forward()</code>.</p></li>
+<li><p><code>backward()</code> - gradient formula. It will be given as many Tensor arguments as there were outputs, with each of them representing gradient w.r.t. that output. It should return as many <code>Tensor</code>s as there were <code>Tensor's</code> that required gradients in <code>forward</code>, with each of them containing the gradient w.r.t. its corresponding input.</p></li>
+</ul>
+<div id="note" class="section level2">
+<h2 class="hasAnchor">
+<a href="#note" class="anchor"></a>Note</h2>
+<p>It’s the user’s responsibility to use the special functions in the forward’s <code>ctx</code> properly in order to ensure that the new <code>autograd_function</code> works properly with the autograd engine.</p>
+<ul>
+<li><p><code>save_for_backward()</code> must be used when saving input or ouput of the forward to be used later in the backward.</p></li>
+<li><p><code>mark_dirty()</code> must be used to mark any input that is modified inplace by the forward function.</p></li>
+<li><p><code>mark_non_differentiable()</code> must be used to tell the engine if an output is not differentiable.</p></li>
+</ul>
+</div>
+<div id="examples" class="section level2">
+<h2 class="hasAnchor">
+<a href="#examples" class="anchor"></a>Examples</h2>
+<p>Below you can find code for a linear function:</p>
+<div class="sourceCode" id="cb2"><pre class="downlit">
+<span class="kw">linear</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/autograd_function.html">autograd_function</a></span>(
+  forward = <span class="fu">function</span>(<span class="kw">ctx</span>, <span class="kw">input</span>, <span class="kw">weight</span>, <span class="kw">bias</span> = <span class="kw">NULL</span>) {
+    <span class="kw">ctx</span><span class="op">$</span><span class="fu">save_for_backward</span>(input = <span class="kw">input</span>, weight = <span class="kw">weight</span>, bias = <span class="kw">bias</span>)
+    <span class="kw">output</span> <span class="op">&lt;-</span> <span class="kw">input</span><span class="op">$</span><span class="fu">mm</span>(<span class="kw">weight</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span>())
+    <span class="co">if</span> (<span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/NULL.html">is.null</a></span>(<span class="kw">bias</span>))
+      <span class="kw">output</span> <span class="op">&lt;-</span> <span class="kw">output</span> <span class="op">+</span> <span class="kw">bias</span><span class="op">$</span><span class="fu">unsqueeze</span>(<span class="fl">0</span>)<span class="op">$</span><span class="fu">expand_as</span>(<span class="kw">output</span>)
+    
+    <span class="kw">output</span>
+  },
+  backward = <span class="fu">function</span>(<span class="kw">ctx</span>, <span class="kw">grad_output</span>) {
+    
+    <span class="kw">s</span> <span class="op">&lt;-</span> <span class="kw">ctx</span><span class="op">$</span><span class="kw">saved_variables</span>
+    
+    <span class="kw">grads</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(
+      input = <span class="kw">NULL</span>,
+      weight = <span class="kw">NULL</span>,
+      bias = <span class="kw">NULL</span>
+    )
+    
+    <span class="co">if</span> (<span class="kw">ctx</span><span class="op">$</span><span class="kw">needs_input_grad</span><span class="op">$</span><span class="kw">input</span>)
+      <span class="kw">grads</span><span class="op">$</span><span class="kw">input</span> <span class="op">&lt;-</span> <span class="kw">grad_output</span><span class="op">$</span><span class="fu">mm</span>(<span class="kw">s</span><span class="op">$</span><span class="kw">weight</span>)
+    
+    <span class="co">if</span> (<span class="kw">ctx</span><span class="op">$</span><span class="kw">needs_input_grad</span><span class="op">$</span><span class="kw">weight</span>)
+      <span class="kw">grads</span><span class="op">$</span><span class="kw">weight</span> <span class="op">&lt;-</span> <span class="kw">grad_output</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span>()<span class="op">$</span><span class="fu">mm</span>(<span class="kw">s</span><span class="op">$</span><span class="kw">input</span>)
+    
+    <span class="co">if</span> (<span class="op">!</span><span class="fu"><a href="https://rdrr.io/r/base/NULL.html">is.null</a></span>(<span class="kw">s</span><span class="op">$</span><span class="kw">bias</span>) <span class="op">&amp;&amp;</span> <span class="kw">ctx</span><span class="op">$</span><span class="kw">needs_input_grad</span><span class="op">$</span><span class="kw">bias</span>)
+      <span class="kw">grads</span><span class="op">$</span><span class="kw">bias</span> <span class="op">&lt;-</span> <span class="kw">grad_output</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span>(dim = <span class="fl">0</span>)
+    
+    <span class="kw">grads</span>
+  }
+)
+</pre></div>
+<p>Here, we give an additional example of a function that is parametrized by non-Tensor arguments:</p>
+<div class="sourceCode" id="cb3"><pre class="downlit">
+<span class="kw">mul_constant</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/autograd_function.html">autograd_function</a></span>(
+  forward = <span class="fu">function</span>(<span class="kw">ctx</span>, <span class="kw">tensor</span>, <span class="kw">constant</span>) {
+    <span class="kw">ctx</span><span class="op">$</span><span class="fu">save_for_backward</span>(constant = <span class="kw">constant</span>)
+    <span class="kw">tensor</span> <span class="op">*</span> <span class="kw">constant</span>
+  },
+  backward = <span class="fu">function</span>(<span class="kw">ctx</span>, <span class="kw">grad_output</span>) {
+    <span class="kw">v</span> <span class="op">&lt;-</span> <span class="kw">ctx</span><span class="op">$</span><span class="kw">saved_variables</span>
+    <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(
+      tensor = <span class="kw">grad_output</span> <span class="op">*</span> <span class="kw">v</span><span class="op">$</span><span class="kw">constant</span>
+    )
+  }
+)
+</pre></div>
+<div class="sourceCode" id="cb4"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1</span>, requires_grad = <span class="fl">TRUE</span>)
+<span class="kw">o</span> <span class="op">&lt;-</span> <span class="fu">mul_constant</span>(<span class="kw">x</span>, <span class="fl">2</span>)
+<span class="kw">o</span><span class="op">$</span><span class="fu">backward</span>()
+<span class="kw">x</span><span class="op">$</span><span class="kw">grad</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  2</span>
+<span class="co">#&gt; [ CPUFloatType{1} ]</span>
+</pre></div>
+</div>
+  </div>
+
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+
+        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
+    </nav>
+</div>
+
+</div>
+
+
+
+      <footer><div class="copyright">
+  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+</div>
+
+<div class="pkgdown">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+</div>
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+    <div class="page-header toc-ignore">
+      <h1 data-toc-skip>Autograd: automatic differentiation</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/autograd.Rmd"><code>vignettes/getting-started/autograd.Rmd</code></a></small>
+      <div class="hidden name"><code>autograd.Rmd</code></div>
+
+    </div>
+
+    
+    
+<blockquote>
+<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/blitz/tensor_tutorial.html#sphx-glr-beginner-blitz-tensor-tutorial-py">here</a>. All credits goes to <a href="http://soumith.ch/">Soumith Chintala</a>.</p>
+</blockquote>
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
+</pre></div>
+<p>Central to all neural networks in torch is the autograd functionality. Let’s first briefly visit this, and we will then go to training our first neural network.</p>
+<p>Autograd provides automatic differentiation for all operations on Tensors. It is a define-by-run framework, which means that your backprop is defined by how your code is run, and that every single iteration can be different.</p>
+<p>Let us see this in more simple terms with some examples.</p>
+<div id="tensor" class="section level2">
+<h2 class="hasAnchor">
+<a href="#tensor" class="anchor"></a>Tensor</h2>
+<p><code>torch_tensor</code> is the central class of the package. If you set its attribute <code>$requires_grad</code> as <code>TRUE</code>, it starts to track all operations on it. When you finish your computation you can call <code>$backward()</code> and have all the gradients computed automatically. The gradient for this tensor will be accumulated into <code>$grad</code> attribute.</p>
+<p>To stop a tensor from tracking history, you can call <code>$detach()</code> to detach it from the computation history, and to prevent future computation from being tracked.</p>
+<p>To prevent tracking history (and using memory), you can also wrap the code block in <code>with_no_grad({&lt;code&gt;})</code>. This can be particularly helpful when evaluating a model because the model may have trainable parameters with <code>requires_grad=TRUE</code>, but for which we don’t need the gradients.</p>
+<p>There’s one more class which is very important for autograd implementation - a <code>autograd_function</code>.</p>
+<p>Tensor and Function are interconnected and build up an acyclic graph, that encodes a complete history of computation. Each tensor has a <code>$grad_fn</code> attribute that references an <code>autograd_function</code> that has created the Tensor (except for Tensors created by the user - their <code>grad_fn</code> is <code>NULL</code>).</p>
+<p>If you want to compute the derivatives, you can call <code>$backward()</code> on a Tensor. If Tensor is a scalar (i.e. it holds a one element data), you don’t need to specify any arguments to <code>backward()</code>, however if it has more elements, you need to specify a gradient argument that is a tensor of matching shape.</p>
+<p>Create a tensor and set <code>requires_grad=TRUE</code> to track computation with it:</p>
+<div class="sourceCode" id="cb2"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span>(<span class="fl">2</span>, <span class="fl">2</span>, requires_grad = <span class="fl">TRUE</span>)
+<span class="kw">x</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  1  1</span>
+<span class="co">#&gt;  1  1</span>
+<span class="co">#&gt; [ CPUFloatType{2,2} ]</span>
+</pre></div>
+<p>Do a tensor operation:</p>
+<div class="sourceCode" id="cb3"><pre class="downlit">
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="kw">x</span> <span class="op">+</span> <span class="fl">2</span>
+<span class="kw">y</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  3  3</span>
+<span class="co">#&gt;  3  3</span>
+<span class="co">#&gt; [ CPUFloatType{2,2} ]</span>
+</pre></div>
+<p><code>y</code> was created as a result of an operation, so it has a <code>grad_fn</code>.</p>
+<div class="sourceCode" id="cb4"><pre class="downlit">
+<span class="kw">y</span><span class="op">$</span><span class="kw">grad_fn</span>
+<span class="co">#&gt; AddBackward1</span>
+</pre></div>
+<p>Do more operations on <code>y</code></p>
+<div class="sourceCode" id="cb5"><pre class="downlit">
+<span class="kw">z</span> <span class="op">&lt;-</span> <span class="kw">y</span> <span class="op">*</span> <span class="kw">y</span> <span class="op">*</span> <span class="fl">3</span>
+<span class="kw">z</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  27  27</span>
+<span class="co">#&gt;  27  27</span>
+<span class="co">#&gt; [ CPUFloatType{2,2} ]</span>
+<span class="kw">out</span> <span class="op">&lt;-</span> <span class="kw">z</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>()
+<span class="kw">out</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt; 27</span>
+<span class="co">#&gt; [ CPUFloatType{} ]</span>
+</pre></div>
+<p><code>$requires_grad_( ... )</code> changes an existing Tensor’s <code>requires_grad</code> flag in-place. The input flag defaults to <code>FALSE</code> if not given.</p>
+<div class="sourceCode" id="cb6"><pre class="downlit">
+<span class="kw">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">2</span>, <span class="fl">2</span>)
+<span class="kw">a</span> <span class="op">&lt;-</span> (<span class="kw">a</span> <span class="op">*</span> <span class="fl">3</span>) <span class="op">/</span> (<span class="kw">a</span> <span class="op">-</span> <span class="fl">1</span>)
+<span class="kw">a</span><span class="op">$</span><span class="kw">requires_grad</span>
+<span class="co">#&gt; [1] FALSE</span>
+<span class="kw">a</span><span class="op">$</span><span class="fu">requires_grad_</span>(<span class="fl">TRUE</span>)
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt; -13.0655   2.0776</span>
+<span class="co">#&gt;   0.0652  -0.4122</span>
+<span class="co">#&gt; [ CPUFloatType{2,2} ]</span>
+<span class="kw">a</span><span class="op">$</span><span class="kw">requires_grad</span>
+<span class="co">#&gt; [1] TRUE</span>
+<span class="kw">b</span> <span class="op">&lt;-</span> (<span class="kw">a</span> <span class="op">*</span> <span class="kw">a</span>)<span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span>()
+<span class="kw">b</span><span class="op">$</span><span class="kw">grad_fn</span>
+<span class="co">#&gt; SumBackward0</span>
+</pre></div>
+</div>
+<div id="gradients" class="section level2">
+<h2 class="hasAnchor">
+<a href="#gradients" class="anchor"></a>Gradients</h2>
+<p>Let’s backprop now. Because out contains a single scalar, <code>out$backward()</code> is equivalent to <code>out$backward(torch.tensor(1.))</code>.</p>
+<div class="sourceCode" id="cb7"><pre class="downlit">
+<span class="kw">out</span><span class="op">$</span><span class="fu">backward</span>()
+</pre></div>
+<p>Print gradients d(out)/dx</p>
+<div class="sourceCode" id="cb8"><pre class="downlit">
+<span class="kw">x</span><span class="op">$</span><span class="kw">grad</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  4.5000  4.5000</span>
+<span class="co">#&gt;  4.5000  4.5000</span>
+<span class="co">#&gt; [ CPUFloatType{2,2} ]</span>
+</pre></div>
+<p>You should have got a matrix of <code>4.5</code>. Let’s call the <code>out</code> <em>Tensor</em> <span class="math inline">\(o\)</span>.</p>
+<p>We have that <span class="math inline">\(o = \frac{1}{4}\sum_i z_i\)</span>, <span class="math inline">\(z_i = 3(x_i+2)^2\)</span> and <span class="math inline">\(z_i\bigr\rvert_{x_i=1} = 27\)</span>. Therefore, <span class="math inline">\(\frac{\partial o}{\partial x_i} = \frac{3}{2}(x_i+2)\)</span>, hence <span class="math inline">\(\frac{\partial o}{\partial x_i}\bigr\rvert_{x_i=1} = \frac{9}{2} = 4.5\)</span>.</p>
+<p>Mathematically, if you have a vector valued function <span class="math inline">\(\vec{y}=f(\vec{x})\)</span>, then the gradient of <span class="math inline">\(\vec{y}\)</span> with respect to <span class="math inline">\(\vec{x}\)</span> is a Jacobian matrix:</p>
+<p><span class="math display">\[
+  J=\left(\begin{array}{ccc}
+   \frac{\partial y_{1}}{\partial x_{1}} &amp; \cdots &amp; \frac{\partial y_{1}}{\partial x_{n}}\\
+   \vdots &amp; \ddots &amp; \vdots\\
+   \frac{\partial y_{m}}{\partial x_{1}} &amp; \cdots &amp; \frac{\partial y_{m}}{\partial x_{n}}
+   \end{array}\right)
+\]</span></p>
+<p>Generally speaking, <code>autograd</code> is an engine for computing vector-Jacobian product. That is, given any vector <span class="math inline">\(v=\left(\begin{array}{cccc} v_{1} &amp; v_{2} &amp; \cdots &amp; v_{m}\end{array}\right)^{T}\)</span>, compute the product <span class="math inline">\(v^{T}\cdot J\)</span>. If <span class="math inline">\(v\)</span> happens to be the gradient of a scalar function <span class="math inline">\(l=g\left(\vec{y}\right)\)</span>, that is, <span class="math inline">\(v=\left(\begin{array}{ccc}\frac{\partial l}{\partial y_{1}} &amp; \cdots &amp; \frac{\partial l}{\partial y_{m}}\end{array}\right)^{T}\)</span>, then by the chain rule, the vector-Jacobian product would be the gradient of <span class="math inline">\(l\)</span> with respect to <span class="math inline">\(\vec{x}\)</span>:</p>
+<p><span class="math display">\[
+  J^{T}\cdot v=\left(\begin{array}{ccc}
+   \frac{\partial y_{1}}{\partial x_{1}} &amp; \cdots &amp; \frac{\partial y_{m}}{\partial x_{1}}\\
+   \vdots &amp; \ddots &amp; \vdots\\
+   \frac{\partial y_{1}}{\partial x_{n}} &amp; \cdots &amp; \frac{\partial y_{m}}{\partial x_{n}}
+   \end{array}\right)\left(\begin{array}{c}
+   \frac{\partial l}{\partial y_{1}}\\
+   \vdots\\
+   \frac{\partial l}{\partial y_{m}}
+   \end{array}\right)=\left(\begin{array}{c}
+   \frac{\partial l}{\partial x_{1}}\\
+   \vdots\\
+   \frac{\partial l}{\partial x_{n}}
+   \end{array}\right)
+\]</span></p>
+<p>(Note that <span class="math inline">\(v^{T}\cdot J\)</span> gives a row vector which can be treated as a column vector by taking <span class="math inline">\(J^{T}\cdot v\)</span>.)</p>
+<p>This characteristic of vector-Jacobian product makes it very convenient to feed external gradients into a model that has non-scalar output.</p>
+<p>Now let’s take a look at an example of vector-Jacobian product:</p>
+<div class="sourceCode" id="cb9"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">3</span>, requires_grad=<span class="fl">TRUE</span>)
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="fl">100</span> <span class="op">*</span> <span class="kw">x</span>
+<span class="kw">y</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  81.4641</span>
+<span class="co">#&gt; -29.4692</span>
+<span class="co">#&gt;  15.0851</span>
+<span class="co">#&gt; [ CPUFloatType{3} ]</span>
+</pre></div>
+<p>Now in this case y is no longer a scalar. <code>autograd</code> could not compute the full Jacobian directly, but if we just want the vector-Jacobian product, simply pass the vector to backward as argument:</p>
+<div class="sourceCode" id="cb10"><pre class="downlit">
+<span class="kw">v</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">0.1</span>, <span class="fl">1.0</span>, <span class="fl">0.0001</span>))
+<span class="kw">y</span><span class="op">$</span><span class="fu">backward</span>(<span class="kw">v</span>)
+
+<span class="kw">x</span><span class="op">$</span><span class="kw">grad</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  1.0000e+01</span>
+<span class="co">#&gt;  1.0000e+02</span>
+<span class="co">#&gt;  1.0000e-02</span>
+<span class="co">#&gt; [ CPUFloatType{3} ]</span>
+</pre></div>
+<p>You can also stop autograd from tracking history on Tensors with <code>$requires_grad=TRUE</code> either by wrapping the code block in with <code><a href="../../reference/with_no_grad.html">with_no_grad()</a></code>:</p>
+<div class="sourceCode" id="cb11"><pre class="downlit">
+<span class="kw">x</span><span class="op">$</span><span class="kw">requires_grad</span>
+<span class="co">#&gt; [1] TRUE</span>
+(<span class="kw">x</span> <span class="op">**</span> <span class="fl">2</span>)<span class="op">$</span><span class="kw">requires_grad</span>
+<span class="co">#&gt; [1] TRUE</span>
+
+<span class="fu"><a href="../../reference/with_no_grad.html">with_no_grad</a></span>({
+  <span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>((<span class="kw">x</span> <span class="op">**</span> <span class="fl">2</span>)<span class="op">$</span><span class="kw">requires_grad</span>)
+})
+<span class="co">#&gt; [1] FALSE</span>
+</pre></div>
+<div class="sourceCode" id="cb12"><pre class="downlit">
+<span class="kw">x</span><span class="op">$</span><span class="kw">requires_grad</span>
+<span class="co">#&gt; [1] TRUE</span>
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="kw">x</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/detach.html">detach</a></span>()
+<span class="kw">y</span><span class="op">$</span><span class="kw">requires_grad</span>
+<span class="co">#&gt; [1] FALSE</span>
+<span class="kw">x</span><span class="op">$</span><span class="fu">eq</span>(<span class="kw">y</span>)<span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/all.html">all</a></span>()
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt; 1</span>
+<span class="co">#&gt; [ CPUBoolType{} ]</span>
+</pre></div>
+<p>Read Later:</p>
+<p>Document about <code><a href="../../reference/autograd_function.html">help(autograd_function)</a></code>, <code><a href="../../articles/using-autograd.html">vignette("using-autograd")</a></code>, <code><a href="../../articles/extending-autograd.html">vignette("extending-autograd")</a></code>.</p>
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@@ -0,0 +1,15 @@
+// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
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+<!DOCTYPE html>
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+      </header><script src="control-flow-and-weight-sharing_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header toc-ignore">
+      <h1 data-toc-skip>Control flow &amp; Weight sharing</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/control-flow-and-weight-sharing.Rmd"><code>vignettes/getting-started/control-flow-and-weight-sharing.Rmd</code></a></small>
+      <div class="hidden name"><code>control-flow-and-weight-sharing.Rmd</code></div>
+
+    </div>
+
+    
+    
+<blockquote>
+<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
+</blockquote>
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
+</pre></div>
+<p>As an example of dynamic graphs and weight sharing, we implement a very strange model: a fully-connected ReLU network that on each forward pass chooses a random number between 1 and 4 and uses that many hidden layers, reusing the same weights multiple times to compute the innermost hidden layers.</p>
+<p>For this model we can use normal R flow control to implement the loop, and we can implement weight sharing among the innermost layers by simply reusing the same Module multiple times when defining the forward pass.</p>
+<p>We can easily implement this model using <code>nn_module</code>:</p>
+<div class="sourceCode" id="cb2"><pre class="downlit">
+<span class="kw">dynamic_net</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_module.html">nn_module</a></span>(
+   <span class="st">"dynamic_net"</span>,
+   <span class="co"># In the constructor we construct three nn_linear instances that we will use</span>
+   <span class="co"># in the forward pass.</span>
+   initialize = <span class="fu">function</span>(<span class="kw">D_in</span>, <span class="kw">H</span>, <span class="kw">D_out</span>) {
+      <span class="kw">self</span><span class="op">$</span><span class="kw">input_linear</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="kw">D_in</span>, <span class="kw">H</span>)
+      <span class="kw">self</span><span class="op">$</span><span class="kw">middle_linear</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="kw">H</span>, <span class="kw">H</span>)
+      <span class="kw">self</span><span class="op">$</span><span class="kw">output_linear</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="kw">H</span>, <span class="kw">D_out</span>)
+   },
+   <span class="co"># For the forward pass of the model, we randomly choose either 0, 1, 2, or 3</span>
+   <span class="co"># and reuse the middle_linear Module that many times to compute hidden layer</span>
+   <span class="co"># representations.</span>
+   <span class="co"># </span>
+   <span class="co"># Since each forward pass builds a dynamic computation graph, we can use normal</span>
+   <span class="co"># R control-flow operators like loops or conditional statements when</span>
+   <span class="co"># defining the forward pass of the model.</span>
+   <span class="co"># </span>
+   <span class="co"># Here we also see that it is perfectly safe to reuse the same Module many</span>
+   <span class="co"># times when defining a computational graph. This is a big improvement from Lua</span>
+   <span class="co"># Torch, where each Module could be used only once.</span>
+   forward = <span class="fu">function</span>(<span class="kw">x</span>) {
+      <span class="kw">h_relu</span> <span class="op">&lt;-</span> <span class="kw">self</span><span class="op">$</span><span class="fu">input_linear</span>(<span class="kw">x</span>)<span class="op">$</span><span class="fu">clamp</span>(min = <span class="fl">0</span>)
+      <span class="co">for</span> (<span class="kw">i</span> <span class="co">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/sample.html">sample.int</a></span>(<span class="fl">4</span>, size = <span class="fl">1</span>))) {
+         <span class="kw">h_relu</span> <span class="op">&lt;-</span> <span class="kw">self</span><span class="op">$</span><span class="fu">middle_linear</span>(<span class="kw">h_relu</span>)<span class="op">$</span><span class="fu">clamp</span>(min=<span class="fl">0</span>)
+      }
+      <span class="kw">y_pred</span> <span class="op">&lt;-</span> <span class="kw">self</span><span class="op">$</span><span class="fu">output_linear</span>(<span class="kw">h_relu</span>)
+      <span class="kw">y_pred</span>
+   }
+)
+
+
+<span class="co">if</span> (<span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span>()) {
+   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cuda"</span>)
+} <span class="co">else</span> {
+   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cpu"</span>)
+}
+   
+<span class="co"># N is batch size; D_in is input dimension;</span>
+<span class="co"># H is hidden dimension; D_out is output dimension.</span>
+<span class="kw">N</span> <span class="op">&lt;-</span> <span class="fl">64</span>
+<span class="kw">D_in</span> <span class="op">&lt;-</span> <span class="fl">1000</span>
+<span class="kw">H</span> <span class="op">&lt;-</span> <span class="fl">100</span>
+<span class="kw">D_out</span> <span class="op">&lt;-</span> <span class="fl">10</span>
+
+<span class="co"># Create random input and output data</span>
+<span class="co"># Setting requires_grad=FALSE (the default) indicates that we do not need to </span>
+<span class="co"># compute gradients with respect to these Tensors during the backward pass.</span>
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_in</span>, device=<span class="kw">device</span>)
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_out</span>, device=<span class="kw">device</span>)
+
+<span class="co"># Construct our model by instantiating the class defined above</span>
+<span class="kw">model</span> <span class="op">&lt;-</span> <span class="fu">dynamic_net</span>(<span class="kw">D_in</span>, <span class="kw">H</span>, <span class="kw">D_out</span>)
+
+<span class="co"># The nn package also contains definitions of popular loss functions; in this</span>
+<span class="co"># case we will use Mean Squared Error (MSE) as our loss function.</span>
+<span class="kw">loss_fn</span> <span class="op">&lt;-</span> <span class="kw">nnf_mse_loss</span>
+
+<span class="co"># Use the optim package to define an Optimizer that will update the weights of</span>
+<span class="co"># the model for us. Here we will use Adam; the optim package contains many other</span>
+<span class="co"># optimization algorithms. The first argument to the Adam constructor tells the</span>
+<span class="co"># optimizer which Tensors it should update.</span>
+<span class="kw">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-4</span>
+<span class="kw">optimizer</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/optim_sgd.html">optim_sgd</a></span>(<span class="kw">model</span><span class="op">$</span><span class="kw">parameters</span>, lr=<span class="kw">learning_rate</span>, momentum = <span class="fl">0.9</span>)
+
+<span class="co">for</span> (<span class="kw">t</span> <span class="co">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fl">500</span>)) {
+   <span class="co"># Forward pass: compute predicted y by passing x to the model. Module objects</span>
+   <span class="co"># can be called like functions. When doing so you pass a Tensor of input</span>
+   <span class="co"># data to the Module and it produces a Tensor of output data.</span>
+   <span class="kw">y_pred</span> <span class="op">&lt;-</span> <span class="fu">model</span>(<span class="kw">x</span>)
+   
+   <span class="co"># Compute and print loss. We pass Tensors containing the predicted and true</span>
+   <span class="co"># values of y, and the loss function returns a Tensor containing the</span>
+   <span class="co"># loss.</span>
+   <span class="kw">loss</span> <span class="op">&lt;-</span> <span class="fu">loss_fn</span>(<span class="kw">y_pred</span>, <span class="kw">y</span>)
+   <span class="co">if</span> (<span class="kw">t</span> <span class="op">%%</span> <span class="fl">100</span> <span class="op">==</span> <span class="fl">0</span> <span class="op">||</span> <span class="kw">t</span> <span class="op">==</span> <span class="fl">1</span>)
+      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="st">"Step:"</span>, <span class="kw">t</span>, <span class="st">":"</span>, <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="kw">loss</span>), <span class="st">"\n"</span>)
+   
+   <span class="co"># Before the backward pass, use the optimizer object to zero all of the</span>
+   <span class="co"># gradients for the variables it will update (which are the learnable</span>
+   <span class="co"># weights of the model). This is because by default, gradients are</span>
+   <span class="co"># accumulated in buffers( i.e, not overwritten) whenever $backward()</span>
+   <span class="co"># is called. Checkout docs of `autograd_backward` for more details.</span>
+   <span class="kw">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span>()
+
+   <span class="co"># Backward pass: compute gradient of the loss with respect to model</span>
+   <span class="co"># parameters</span>
+   <span class="kw">loss</span><span class="op">$</span><span class="fu">backward</span>()
+
+   <span class="co"># Calling the step function on an Optimizer makes an update to its</span>
+   <span class="co"># parameters</span>
+   <span class="kw">optimizer</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/stats/step.html">step</a></span>()
+}
+<span class="co">#&gt; Step: 1 : 0.9501783 </span>
+<span class="co">#&gt; Step: 100 : 0.948805 </span>
+<span class="co">#&gt; Step: 200 : 0.9428347 </span>
+<span class="co">#&gt; Step: 300 : 0.9459909 </span>
+<span class="co">#&gt; Step: 400 : 0.9362943 </span>
+<span class="co">#&gt; Step: 500 : 0.9435167</span>
+</pre></div>
+  </div>
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@@ -0,0 +1,15 @@
+// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
+// v0.0.1
+// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
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+document.addEventListener('DOMContentLoaded', function() {
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diff --git a/static/docs/dev/articles/getting-started/custom-nn.html b/static/docs/dev/articles/getting-started/custom-nn.html
new file mode 100644
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--- /dev/null
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+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
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+<li class="dropdown-header">Beginers guide</li>
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+      <a href="../../articles/getting-started/tensors.html">Tensors</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/tensors-and-autograd.html">Tensors and autograd</a>
+    </li>
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+      <a href="../../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
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+      <a href="../../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/optim.html">optim: optimizers in torch</a>
+    </li>
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+      <a href="../../articles/getting-started/custom-nn.html">Custom nn modules</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/control-flow-and-weight-sharing.html">Control flow &amp; Weight sharing</a>
+    </li>
+    <li class="dropdown-header">Torch Mechanics</li>
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+  <div class="col-md-9 contents">
+    <div class="page-header toc-ignore">
+      <h1 data-toc-skip>Custom nn modules</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/custom-nn.Rmd"><code>vignettes/getting-started/custom-nn.Rmd</code></a></small>
+      <div class="hidden name"><code>custom-nn.Rmd</code></div>
+
+    </div>
+
+    
+    
+<blockquote>
+<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
+</blockquote>
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
+</pre></div>
+<p>Sometimes you will want to specify models that are more complex than a sequence of existing Modules; for these cases you can define your own Modules by using <code>nn_module</code> function and defining a forward which receives input Tensors and produces output Tensors using other modules or other autograd operations on Tensors.</p>
+<p>In this example we implement our two-layer network as a custom Module subclass:</p>
+<div class="sourceCode" id="cb2"><pre class="downlit">
+<span class="kw">two_layer_net</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_module.html">nn_module</a></span>(
+   <span class="st">"two_layer_net"</span>,
+   initialize = <span class="fu">function</span>(<span class="kw">D_in</span>, <span class="kw">H</span>, <span class="kw">D_out</span>) {
+      <span class="kw">self</span><span class="op">$</span><span class="kw">linear1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="kw">D_in</span>, <span class="kw">H</span>)
+      <span class="kw">self</span><span class="op">$</span><span class="kw">linear2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="kw">H</span>, <span class="kw">D_out</span>)
+   },
+   forward = <span class="fu">function</span>(<span class="kw">x</span>) {
+      <span class="kw">x</span> <span class="op">%&gt;%</span> 
+         <span class="kw">self</span><span class="op">$</span><span class="fu">linear1</span>() <span class="op">%&gt;%</span> 
+         <span class="fu"><a href="../../reference/nnf_relu.html">nnf_relu</a></span>() <span class="op">%&gt;%</span> 
+         <span class="kw">self</span><span class="op">$</span><span class="fu">linear2</span>()
+   }
+)
+
+
+<span class="co">if</span> (<span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span>()) {
+   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cuda"</span>)
+} <span class="co">else</span> {
+   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cpu"</span>)
+}
+   
+<span class="co"># N is batch size; D_in is input dimension;</span>
+<span class="co"># H is hidden dimension; D_out is output dimension.</span>
+<span class="kw">N</span> <span class="op">&lt;-</span> <span class="fl">64</span>
+<span class="kw">D_in</span> <span class="op">&lt;-</span> <span class="fl">1000</span>
+<span class="kw">H</span> <span class="op">&lt;-</span> <span class="fl">100</span>
+<span class="kw">D_out</span> <span class="op">&lt;-</span> <span class="fl">10</span>
+
+<span class="co"># Create random input and output data</span>
+<span class="co"># Setting requires_grad=FALSE (the default) indicates that we do not need to </span>
+<span class="co"># compute gradients with respect to these Tensors during the backward pass.</span>
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_in</span>, device=<span class="kw">device</span>)
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_out</span>, device=<span class="kw">device</span>)
+
+<span class="co"># Construct our model by instantiating the class defined above</span>
+<span class="kw">model</span> <span class="op">&lt;-</span> <span class="fu">two_layer_net</span>(<span class="kw">D_in</span>, <span class="kw">H</span>, <span class="kw">D_out</span>)
+
+<span class="co"># The nn package also contains definitions of popular loss functions; in this</span>
+<span class="co"># case we will use Mean Squared Error (MSE) as our loss function.</span>
+<span class="kw">loss_fn</span> <span class="op">&lt;-</span> <span class="kw">nnf_mse_loss</span>
+
+<span class="co"># Use the optim package to define an Optimizer that will update the weights of</span>
+<span class="co"># the model for us. Here we will use Adam; the optim package contains many other</span>
+<span class="co"># optimization algorithms. The first argument to the Adam constructor tells the</span>
+<span class="co"># optimizer which Tensors it should update.</span>
+<span class="kw">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-4</span>
+<span class="kw">optimizer</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/optim_sgd.html">optim_sgd</a></span>(<span class="kw">model</span><span class="op">$</span><span class="kw">parameters</span>, lr=<span class="kw">learning_rate</span>)
+
+<span class="co">for</span> (<span class="kw">t</span> <span class="co">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fl">500</span>)) {
+   <span class="co"># Forward pass: compute predicted y by passing x to the model. Module objects</span>
+   <span class="co"># can be called like functions. When doing so you pass a Tensor of input</span>
+   <span class="co"># data to the Module and it produces a Tensor of output data.</span>
+   <span class="kw">y_pred</span> <span class="op">&lt;-</span> <span class="fu">model</span>(<span class="kw">x</span>)
+   
+   <span class="co"># Compute and print loss. We pass Tensors containing the predicted and true</span>
+   <span class="co"># values of y, and the loss function returns a Tensor containing the</span>
+   <span class="co"># loss.</span>
+   <span class="kw">loss</span> <span class="op">&lt;-</span> <span class="fu">loss_fn</span>(<span class="kw">y_pred</span>, <span class="kw">y</span>)
+   <span class="co">if</span> (<span class="kw">t</span> <span class="op">%%</span> <span class="fl">100</span> <span class="op">==</span> <span class="fl">0</span> <span class="op">||</span> <span class="kw">t</span> <span class="op">==</span> <span class="fl">1</span>)
+      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="st">"Step:"</span>, <span class="kw">t</span>, <span class="st">":"</span>, <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="kw">loss</span>), <span class="st">"\n"</span>)
+   
+   <span class="co"># Before the backward pass, use the optimizer object to zero all of the</span>
+   <span class="co"># gradients for the variables it will update (which are the learnable</span>
+   <span class="co"># weights of the model). This is because by default, gradients are</span>
+   <span class="co"># accumulated in buffers( i.e, not overwritten) whenever $backward()</span>
+   <span class="co"># is called. Checkout docs of `autograd_backward` for more details.</span>
+   <span class="kw">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span>()
+
+   <span class="co"># Backward pass: compute gradient of the loss with respect to model</span>
+   <span class="co"># parameters</span>
+   <span class="kw">loss</span><span class="op">$</span><span class="fu">backward</span>()
+
+   <span class="co"># Calling the step function on an Optimizer makes an update to its</span>
+   <span class="co"># parameters</span>
+   <span class="kw">optimizer</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/stats/step.html">step</a></span>()
+}
+<span class="co">#&gt; Step: 1 : 1.066273 </span>
+<span class="co">#&gt; Step: 100 : 1.052879 </span>
+<span class="co">#&gt; Step: 200 : 1.039649 </span>
+<span class="co">#&gt; Step: 300 : 1.02675 </span>
+<span class="co">#&gt; Step: 400 : 1.014158 </span>
+<span class="co">#&gt; Step: 500 : 1.001865</span>
+</pre></div>
+<p>In the <a href="control-flow-and-weight-sharing.html">next example</a> we will about dynamic graphs in torch.</p>
+  </div>
+
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diff --git a/static/docs/dev/articles/getting-started/custom-nn_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/dev/articles/getting-started/custom-nn_files/accessible-code-block-0.0.1/empty-anchor.js
new file mode 100644
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--- /dev/null
+++ b/static/docs/dev/articles/getting-started/custom-nn_files/accessible-code-block-0.0.1/empty-anchor.js
@@ -0,0 +1,15 @@
+// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
+// v0.0.1
+// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
+
+document.addEventListener('DOMContentLoaded', function() {
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diff --git a/static/docs/dev/articles/getting-started/neural-networks.html b/static/docs/dev/articles/getting-started/neural-networks.html
new file mode 100644
index 000000000..e4d919f3f
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@@ -0,0 +1,410 @@
+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
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+<li class="dropdown-header">Beginers guide</li>
+    <li>
+      <a href="../../articles/getting-started/warmup.html">Warm-up</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/tensors.html">Tensors</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/tensors-and-autograd.html">Tensors and autograd</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/optim.html">optim: optimizers in torch</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/custom-nn.html">Custom nn modules</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/control-flow-and-weight-sharing.html">Control flow &amp; Weight sharing</a>
+    </li>
+    <li class="dropdown-header">Torch Mechanics</li>
+    <li>
+      <a href="../../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/neural-networks.html">Neural networks</a>
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+<li class="dropdown-header">Tensors</li>
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+      <a href="../../articles/tensor-creation.html">Creating tensors</a>
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+      
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+      </header><script src="neural-networks_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header toc-ignore">
+      <h1 data-toc-skip>Neural networks</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/neural-networks.Rmd"><code>vignettes/getting-started/neural-networks.Rmd</code></a></small>
+      <div class="hidden name"><code>neural-networks.Rmd</code></div>
+
+    </div>
+
+    
+    
+<blockquote>
+<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/blitz/tensor_tutorial.html#sphx-glr-beginner-blitz-tensor-tutorial-py">here</a>. All credits goes to <a href="http://soumith.ch/">Soumith Chintala</a>.</p>
+</blockquote>
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
+</pre></div>
+<p>Neural networks can be constructed using the <code>nn</code> functionality.</p>
+<p>Now that you had a glimpse of <code>autograd</code>, <code>nn</code> depends on <code>autograd</code> to define models and differentiate them. An nn.Module contains layers, and a method <code>forward(input)</code> that returns the output.</p>
+<p>For example, look at this network that classifies digit images:</p>
+<div class="figure">
+<img src="assets/mnist.png" alt=""><p class="caption">Convnet for mnist classification</p>
+</div>
+<p>It is a simple feed-forward network. It takes the input, feeds it through several layers one after the other, and then finally gives the output.</p>
+<p>A typical training procedure for a neural network is as follows:</p>
+<ul>
+<li>Define the neural network that has some learnable parameters (or weights)</li>
+<li>Iterate over a dataset of inputs</li>
+<li>Process input through the network</li>
+<li>Compute the loss (how far is the output from being correct)</li>
+<li>Propagate gradients back into the network’s parameters</li>
+<li>Update the weights of the network, typically using a simple update rule: <code>weight = weight - learning_rate * gradient</code>.</li>
+</ul>
+<div id="define-the-network" class="section level2">
+<h2 class="hasAnchor">
+<a href="#define-the-network" class="anchor"></a>Define the network</h2>
+<p>Let’s define this network:</p>
+<div class="sourceCode" id="cb2"><pre class="downlit">
+<span class="kw">Net</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_module.html">nn_module</a></span>(
+  initialize = <span class="fu">function</span>() {
+    <span class="kw">self</span><span class="op">$</span><span class="kw">conv1</span> <span class="op">=</span> <span class="fu"><a href="../../reference/nn_conv2d.html">nn_conv2d</a></span>(<span class="fl">1</span>, <span class="fl">6</span>, <span class="fl">3</span>)
+    <span class="kw">self</span><span class="op">$</span><span class="kw">conv2</span> <span class="op">=</span> <span class="fu"><a href="../../reference/nn_conv2d.html">nn_conv2d</a></span>(<span class="fl">6</span>, <span class="fl">16</span>, <span class="fl">3</span>)
+    <span class="co"># an affine operation: y = Wx + b</span>
+    <span class="kw">self</span><span class="op">$</span><span class="kw">fc1</span> <span class="op">=</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="fl">16</span> <span class="op">*</span> <span class="fl">6</span> <span class="op">*</span> <span class="fl">6</span>, <span class="fl">120</span>)  <span class="co"># 6*6 from image dimension</span>
+    <span class="kw">self</span><span class="op">$</span><span class="kw">fc2</span> <span class="op">=</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="fl">120</span>, <span class="fl">84</span>)
+    <span class="kw">self</span><span class="op">$</span><span class="kw">fc3</span> <span class="op">=</span> <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="fl">84</span>, <span class="fl">10</span>)
+  },
+  forward = <span class="fu">function</span>(<span class="kw">x</span>) {
+    <span class="kw">x</span> <span class="op">%&gt;%</span> 
+      
+      <span class="kw">self</span><span class="op">$</span><span class="fu">conv1</span>() <span class="op">%&gt;%</span> 
+      <span class="fu"><a href="../../reference/nnf_relu.html">nnf_relu</a></span>() <span class="op">%&gt;%</span> 
+      <span class="fu"><a href="../../reference/nnf_max_pool2d.html">nnf_max_pool2d</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>,<span class="fl">2</span>)) <span class="op">%&gt;%</span> 
+      
+      <span class="kw">self</span><span class="op">$</span><span class="fu">conv2</span>() <span class="op">%&gt;%</span> 
+      <span class="fu"><a href="../../reference/nnf_relu.html">nnf_relu</a></span>() <span class="op">%&gt;%</span> 
+      <span class="fu"><a href="../../reference/nnf_max_pool2d.html">nnf_max_pool2d</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>,<span class="fl">2</span>)) <span class="op">%&gt;%</span> 
+      
+      <span class="fu"><a href="../../reference/torch_flatten.html">torch_flatten</a></span>(start_dim = <span class="fl">2</span>) <span class="op">%&gt;%</span> 
+      
+      <span class="kw">self</span><span class="op">$</span><span class="fu">fc1</span>() <span class="op">%&gt;%</span> 
+      <span class="fu"><a href="../../reference/nnf_relu.html">nnf_relu</a></span>() <span class="op">%&gt;%</span> 
+      
+      <span class="kw">self</span><span class="op">$</span><span class="fu">fc2</span>() <span class="op">%&gt;%</span> 
+      <span class="fu"><a href="../../reference/nnf_relu.html">nnf_relu</a></span>() <span class="op">%&gt;%</span> 
+      
+      <span class="kw">self</span><span class="op">$</span><span class="fu">fc3</span>()
+  }
+)
+
+<span class="kw">net</span> <span class="op">&lt;-</span> <span class="fu">Net</span>()
+</pre></div>
+<p>You just have to define the <code>forward</code> function, and the <code>backward</code> function (where gradients are computed) is automatically defined for you using <code>autograd.</code> You can use any of the Tensor operations in the <code>forward</code> function.</p>
+<p>The learnable parameters of a model are returned by <code>net$parameters</code>.</p>
+<div class="sourceCode" id="cb3"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/utils/str.html">str</a></span>(<span class="kw">net</span><span class="op">$</span><span class="kw">parameters</span>)
+<span class="co">#&gt; List of 10</span>
+<span class="co">#&gt;  $ conv1.weight:Float [1:6, 1:1, 1:3, 1:3]</span>
+<span class="co">#&gt;  $ conv1.bias  :Float [1:6]</span>
+<span class="co">#&gt;  $ conv2.weight:Float [1:16, 1:6, 1:3, 1:3]</span>
+<span class="co">#&gt;  $ conv2.bias  :Float [1:16]</span>
+<span class="co">#&gt;  $ fc1.weight  :Float [1:120, 1:576]</span>
+<span class="co">#&gt;  $ fc1.bias    :Float [1:120]</span>
+<span class="co">#&gt;  $ fc2.weight  :Float [1:84, 1:120]</span>
+<span class="co">#&gt;  $ fc2.bias    :Float [1:84]</span>
+<span class="co">#&gt;  $ fc3.weight  :Float [1:10, 1:84]</span>
+<span class="co">#&gt;  $ fc3.bias    :Float [1:10]</span>
+</pre></div>
+<p>Let’s try a random 32x32 input. Note: expected input size of this net (LeNet) is 32x32. To use this net on the MNIST dataset, please resize the images from the dataset to 32x32.</p>
+<div class="sourceCode" id="cb4"><pre class="downlit">
+<span class="kw">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">32</span>, <span class="fl">32</span>)
+<span class="kw">out</span> <span class="op">&lt;-</span> <span class="fu">net</span>(<span class="kw">input</span>)
+<span class="kw">out</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt; -0.0352  0.1035  0.0462 -0.0992 -0.0517 -0.0676  0.1250 -0.0572  0.0229  0.0145</span>
+<span class="co">#&gt; [ CPUFloatType{1,10} ]</span>
+</pre></div>
+<p>Zero the gradient buffers of all parameters and backprops with random gradients:</p>
+<div class="sourceCode" id="cb5"><pre class="downlit">
+<span class="kw">net</span><span class="op">$</span><span class="fu">zero_grad</span>()
+<span class="kw">out</span><span class="op">$</span><span class="fu">backward</span>(<span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">1</span>, <span class="fl">10</span>))
+</pre></div>
+<blockquote>
+<p><strong>Note</strong>: <code>nn</code> only supports mini-batches. The entire torch.nn package only supports inputs that are a mini-batch of samples, and not a single sample. For example, <code>nn_conv2d</code> will take in a 4D Tensor of nSamples x nChannels x Height x Width. If you have a single sample, just use <code>input$unsqueeze(1)</code> to add a fake batch dimension.</p>
+</blockquote>
+<p>Before proceeding further, let’s recap all the classes you’ve seen so far.</p>
+<div id="recap" class="section level3">
+<h3 class="hasAnchor">
+<a href="#recap" class="anchor"></a>Recap</h3>
+<ul>
+<li><p><code>torch_tensor</code> - A multi-dimensional array with support for autograd operations like <code>backward()</code>. Also holds the gradient w.r.t. the tensor.</p></li>
+<li><p><code>nn_module</code> - Neural network module. Convenient way of encapsulating parameters, with helpers for moving them to GPU, exporting, loading, etc.</p></li>
+<li><p><code>nn_parameter</code> - A kind of Tensor, that is automatically registered as a parameter when assigned as an attribute to a Module.</p></li>
+<li><p><code>autograd_function</code> - Implements forward and backward definitions of an autograd operation. Every Tensor operation creates at least a single Function node that connects to functions that created a Tensor and encodes its history.</p></li>
+</ul>
+</div>
+<div id="at-this-point-we-covered" class="section level3">
+<h3 class="hasAnchor">
+<a href="#at-this-point-we-covered" class="anchor"></a>At this point, we covered</h3>
+<ul>
+<li>Defining a neural network</li>
+<li>Processing inputs and calling backward</li>
+</ul>
+</div>
+<div id="still-left" class="section level3">
+<h3 class="hasAnchor">
+<a href="#still-left" class="anchor"></a>Still left</h3>
+<ul>
+<li>Computing the loss</li>
+<li>Updating the weights of the network</li>
+</ul>
+</div>
+</div>
+<div id="loss-function" class="section level2">
+<h2 class="hasAnchor">
+<a href="#loss-function" class="anchor"></a>Loss function</h2>
+<p>A loss function takes the (output, target) pair of inputs, and computes a value that estimates how far away the output is from the target.</p>
+<p>There are several different loss functions under the nn package . A simple loss is: <code>nnf_mse_loss</code> which computes the mean-squared error between the input and the target.</p>
+<p>For example:</p>
+<div class="sourceCode" id="cb6"><pre class="downlit">
+<span class="kw">output</span> <span class="op">&lt;-</span> <span class="fu">net</span>(<span class="kw">input</span>)
+<span class="kw">target</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">10</span>)  <span class="co"># a dummy target, for example</span>
+<span class="kw">target</span> <span class="op">&lt;-</span> <span class="kw">target</span><span class="op">$</span><span class="fu">view</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1</span>, <span class="op">-</span><span class="fl">1</span>))  <span class="co"># make it the same shape as output</span>
+
+<span class="kw">loss</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nnf_mse_loss.html">nnf_mse_loss</a></span>(<span class="kw">output</span>, <span class="kw">target</span>)
+<span class="kw">loss</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt; 0.799604</span>
+<span class="co">#&gt; [ CPUFloatType{} ]</span>
+</pre></div>
+<p>Now, if you follow loss in the backward direction, using its <code>$grad_fn</code> attribute, you will see a graph of computations that looks like this:</p>
+<pre><code>input -&gt; conv2d -&gt; relu -&gt; maxpool2d -&gt; conv2d -&gt; relu -&gt; maxpool2d
+      -&gt; view -&gt; linear -&gt; relu -&gt; linear -&gt; relu -&gt; linear
+      -&gt; MSELoss
+      -&gt; loss</code></pre>
+<p>So, when we call <code>loss$backward()</code>, the whole graph is differentiated w.r.t. the loss, and all Tensors in the graph that has requires_grad=True will have their <code>#grad</code> Tensor accumulated with the gradient.</p>
+<p>For illustration, let us follow a few steps backward:</p>
+<div class="sourceCode" id="cb8"><pre class="downlit">
+<span class="kw">loss</span><span class="op">$</span><span class="kw">grad_fn</span>
+<span class="co">#&gt; MseLossBackward</span>
+<span class="kw">loss</span><span class="op">$</span><span class="kw">grad_fn</span><span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]
+<span class="co">#&gt; AddmmBackward</span>
+<span class="kw">loss</span><span class="op">$</span><span class="kw">grad_fn</span><span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]<span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]
+<span class="co">#&gt; torch::autograd::AccumulateGrad</span>
+</pre></div>
+</div>
+<div id="backprop" class="section level2">
+<h2 class="hasAnchor">
+<a href="#backprop" class="anchor"></a>Backprop</h2>
+<p>To backpropagate the error all we have to do is to <code>loss$backward()</code>. You need to clear the existing gradients though, else gradients will be accumulated to existing gradients.</p>
+<p>Now we shall call <code>loss$backward()</code>, and have a look at conv1’s bias gradients before and after the backward.</p>
+<div class="sourceCode" id="cb9"><pre class="downlit">
+<span class="kw">net</span><span class="op">$</span><span class="fu">zero_grad</span>()     <span class="co"># zeroes the gradient buffers of all parameters</span>
+
+<span class="co"># conv1.bias.grad before backward</span>
+<span class="kw">net</span><span class="op">$</span><span class="kw">conv1</span><span class="op">$</span><span class="kw">bias</span><span class="op">$</span><span class="kw">grad</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  0</span>
+<span class="co">#&gt;  0</span>
+<span class="co">#&gt;  0</span>
+<span class="co">#&gt;  0</span>
+<span class="co">#&gt;  0</span>
+<span class="co">#&gt;  0</span>
+<span class="co">#&gt; [ CPUFloatType{6} ]</span>
+
+<span class="kw">loss</span><span class="op">$</span><span class="fu">backward</span>()
+
+<span class="co"># conv1.bias.grad after backward</span>
+<span class="kw">net</span><span class="op">$</span><span class="kw">conv1</span><span class="op">$</span><span class="kw">bias</span><span class="op">$</span><span class="kw">grad</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt; 0.001 *</span>
+<span class="co">#&gt; -3.9505</span>
+<span class="co">#&gt; -0.0917</span>
+<span class="co">#&gt; -4.1386</span>
+<span class="co">#&gt; -7.6969</span>
+<span class="co">#&gt;  1.5123</span>
+<span class="co">#&gt; -5.0254</span>
+<span class="co">#&gt; [ CPUFloatType{6} ]</span>
+</pre></div>
+<p>Now, we have seen how to use loss functions.</p>
+</div>
+<div id="update-the-weights" class="section level2">
+<h2 class="hasAnchor">
+<a href="#update-the-weights" class="anchor"></a>Update the weights</h2>
+<p>The simplest update rule used in practice is the Stochastic Gradient Descent (SGD):</p>
+<p><span class="math display">\[weight = weight - learning_rate * gradient\]</span></p>
+<p>We can implement this using simple R code:</p>
+<div class="sourceCode" id="cb10"><pre class="downlit">
+<span class="kw">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">0.01</span>
+<span class="co">for</span> (<span class="kw">f</span> <span class="co">in</span> <span class="kw">net</span><span class="op">$</span><span class="kw">parameters</span>) {
+  <span class="fu"><a href="../../reference/with_no_grad.html">with_no_grad</a></span>({
+    <span class="kw">f</span><span class="op">$</span><span class="fu">sub_</span>(<span class="kw">f</span><span class="op">$</span><span class="kw">grad</span> <span class="op">*</span> <span class="kw">learning_rate</span>)
+  })
+}
+</pre></div>
+<blockquote>
+<p><strong>Note:</strong> Weight updates here is wraped around <code>with_no_grad</code> as we don’t the updates to be tracked by the autograd engine.</p>
+</blockquote>
+<p>However, as you use neural networks, you want to use various different update rules such as SGD, Nesterov-SGD, Adam, RMSProp, etc.</p>
+<div class="sourceCode" id="cb11"><pre class="downlit">
+<span class="co"># create your optimizer</span>
+<span class="kw">optimizer</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/optim_sgd.html">optim_sgd</a></span>(<span class="kw">net</span><span class="op">$</span><span class="kw">parameters</span>, lr = <span class="fl">0.01</span>)
+
+<span class="co"># in your training loop:</span>
+<span class="kw">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span>()   <span class="co"># zero the gradient buffers</span>
+<span class="kw">output</span> <span class="op">&lt;-</span> <span class="fu">net</span>(<span class="kw">input</span>)
+<span class="kw">loss</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nnf_mse_loss.html">nnf_mse_loss</a></span>(<span class="kw">output</span>, <span class="kw">target</span>)
+<span class="kw">loss</span><span class="op">$</span><span class="fu">backward</span>()
+<span class="kw">optimizer</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/stats/step.html">step</a></span>()    <span class="co"># Does the update</span>
+<span class="co">#&gt; NULL</span>
+</pre></div>
+<blockquote>
+<p><strong>Note:</strong> Observe how gradient buffers had to be manually set to zero using <code>optimizer$zero_grad()</code>. This is because gradients are accumulated as explained in the Backprop section.</p>
+</blockquote>
+</div>
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+      <h1 data-toc-skip>Defining new autograd functions</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/new-autograd-functions.Rmd"><code>vignettes/getting-started/new-autograd-functions.Rmd</code></a></small>
+      <div class="hidden name"><code>new-autograd-functions.Rmd</code></div>
+
+    </div>
+
+    
+    
+<blockquote>
+<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
+</blockquote>
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
+</pre></div>
+<p>Under the hood, each primitive autograd operator is really two functions that operate on Tensors. The forward function computes output Tensors from input Tensors. The backward function receives the gradient of the output Tensors with respect to some scalar value, and computes the gradient of the input Tensors with respect to that same scalar value.</p>
+<p>In torch we can easily define our own autograd operator by defining a subclass of <code>autograd_function</code> and implementing the forward and backward functions. We can then use our new autograd operator by constructing an instance and calling it like a function, passing Tensors containing input data.</p>
+<p>In this example we define our own custom autograd function for performing the ReLU nonlinearity, and use it to implement our two-layer network:</p>
+<div class="sourceCode" id="cb2"><pre class="downlit">
+<span class="co"># We can implement our own custom autograd Functions by subclassing</span>
+<span class="co"># autograd_functioon and implementing the forward and backward passes</span>
+<span class="co"># which operate on Tensors.</span>
+<span class="kw">my_relu</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/autograd_function.html">autograd_function</a></span>(
+   <span class="co"># In the forward pass we receive a Tensor containing the input and return</span>
+   <span class="co"># a Tensor containing the output. ctx is a context object that can be used</span>
+   <span class="co"># to stash information for backward computation. You can cache arbitrary</span>
+   <span class="co"># objects for use in the backward pass using the ctx$save_for_backward method.</span>
+   forward = <span class="fu">function</span>(<span class="kw">ctx</span>, <span class="kw">input</span>) {
+      <span class="kw">ctx</span><span class="op">$</span><span class="fu">save_for_backward</span>(input = <span class="kw">input</span>)
+      <span class="kw">input</span><span class="op">$</span><span class="fu">clamp</span>(min = <span class="fl">0</span>)
+   },
+   <span class="co"># In the backward pass we receive a Tensor containing the gradient of the loss</span>
+   <span class="co"># with respect to the output, and we need to compute the gradient of the loss</span>
+   <span class="co"># with respect to the input.</span>
+   backward = <span class="fu">function</span>(<span class="kw">ctx</span>, <span class="kw">grad_output</span>) {
+      <span class="kw">v</span> <span class="op">&lt;-</span> <span class="kw">ctx</span><span class="op">$</span><span class="kw">saved_variables</span>
+      <span class="kw">grad_input</span> <span class="op">&lt;-</span> <span class="kw">grad_output</span><span class="op">$</span><span class="fu">clone</span>()
+      <span class="kw">grad_input</span>[<span class="kw">v</span><span class="op">$</span><span class="kw">input</span> <span class="op">&lt;</span> <span class="fl">0</span>] <span class="op">&lt;-</span> <span class="fl">0</span>
+      <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(input = <span class="kw">grad_input</span>)
+   }
+)
+
+<span class="co">if</span> (<span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span>()) {
+   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cuda"</span>)
+} <span class="co">else</span> {
+   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cpu"</span>)
+}
+   
+<span class="co"># N is batch size; D_in is input dimension;</span>
+<span class="co"># H is hidden dimension; D_out is output dimension.</span>
+<span class="kw">N</span> <span class="op">&lt;-</span> <span class="fl">64</span>
+<span class="kw">D_in</span> <span class="op">&lt;-</span> <span class="fl">1000</span>
+<span class="kw">H</span> <span class="op">&lt;-</span> <span class="fl">100</span>
+<span class="kw">D_out</span> <span class="op">&lt;-</span> <span class="fl">10</span>
+
+<span class="co"># Create random input and output data</span>
+<span class="co"># Setting requires_grad=FALSE (the default) indicates that we do not need to </span>
+<span class="co"># compute gradients with respect to these Tensors during the backward pass.</span>
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_in</span>, device=<span class="kw">device</span>)
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_out</span>, device=<span class="kw">device</span>)
+
+<span class="co"># Randomly initialize weights</span>
+<span class="co"># Setting requires_grad=TRUE indicates that we want to compute gradients with</span>
+<span class="co"># respect to these Tensors during the backward pass.</span>
+<span class="kw">w1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">D_in</span>, <span class="kw">H</span>, device=<span class="kw">device</span>, requires_grad = <span class="fl">TRUE</span>)
+<span class="kw">w2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">H</span>, <span class="kw">D_out</span>, device=<span class="kw">device</span>, requires_grad = <span class="fl">TRUE</span>)
+
+<span class="kw">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-6</span>
+<span class="co">for</span> (<span class="kw">t</span> <span class="co">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fl">500</span>)) {
+   <span class="co"># Forward pass: compute predicted y using operations on Tensors; these</span>
+   <span class="co"># are exactly the same operations we used to compute the forward pass using</span>
+   <span class="co"># Tensors, but we do not need to keep references to intermediate values since</span>
+   <span class="co"># we are not implementing the backward pass by hand.</span>
+   <span class="kw">y_pred</span> <span class="op">&lt;-</span> <span class="fu">my_relu</span>(<span class="kw">x</span><span class="op">$</span><span class="fu">mm</span>(<span class="kw">w1</span>))<span class="op">$</span><span class="fu">mm</span>(<span class="kw">w2</span>)
+   
+   <span class="co"># Compute and print loss using operations on Tensors.</span>
+   <span class="co"># Now loss is a Tensor of shape (1,)</span>
+   <span class="kw">loss</span> <span class="op">&lt;-</span> (<span class="kw">y_pred</span> <span class="op">-</span> <span class="kw">y</span>)<span class="op">$</span><span class="fu">pow</span>(<span class="fl">2</span>)<span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span>()
+   <span class="co">if</span> (<span class="kw">t</span> <span class="op">%%</span> <span class="fl">100</span> <span class="op">==</span> <span class="fl">0</span> <span class="op">||</span> <span class="kw">t</span> <span class="op">==</span> <span class="fl">1</span>)
+      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="st">"Step:"</span>, <span class="kw">t</span>, <span class="st">":"</span>, <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="kw">loss</span>), <span class="st">"\n"</span>)
+   
+   <span class="co"># Use autograd to compute the backward pass. This call will compute the</span>
+   <span class="co"># gradient of loss with respect to all Tensors with requires_grad=True.</span>
+   <span class="co"># After this call w1$grad and w2$grad will be Tensors holding the gradient</span>
+   <span class="co"># of the loss with respect to w1 and w2 respectively.</span>
+   <span class="kw">loss</span><span class="op">$</span><span class="fu">backward</span>()
+   
+   <span class="co"># Manually update weights using gradient descent. Wrap in `with_no_grad`</span>
+   <span class="co"># because weights have requires_grad=TRUE, but we don't need to track this</span>
+   <span class="co"># in autograd.</span>
+   <span class="co"># You can also use optim_sgd to achieve this.</span>
+   <span class="fu"><a href="../../reference/with_no_grad.html">with_no_grad</a></span>({
+      
+      <span class="co"># operations suffixed with an `_` operates on in-place on the tensor.</span>
+      <span class="kw">w1</span><span class="op">$</span><span class="fu">sub_</span>(<span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">w1</span><span class="op">$</span><span class="kw">grad</span>)
+      <span class="kw">w2</span><span class="op">$</span><span class="fu">sub_</span>(<span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">w2</span><span class="op">$</span><span class="kw">grad</span>)
+      
+      <span class="co"># Manually zero the gradients after updating weights</span>
+      <span class="kw">w1</span><span class="op">$</span><span class="kw">grad</span><span class="op">$</span><span class="fu">zero_</span>()
+      <span class="kw">w2</span><span class="op">$</span><span class="kw">grad</span><span class="op">$</span><span class="fu">zero_</span>()
+   })
+}
+<span class="co">#&gt; Step: 1 : 39805352 </span>
+<span class="co">#&gt; Step: 100 : 334.5283 </span>
+<span class="co">#&gt; Step: 200 : 0.9720595 </span>
+<span class="co">#&gt; Step: 300 : 0.005285224 </span>
+<span class="co">#&gt; Step: 400 : 0.0001543093 </span>
+<span class="co">#&gt; Step: 500 : 3.083485e-05</span>
+</pre></div>
+<p>In the <a href="nn.html">next example</a> we will learn how to use the neural networks abstractions in torch.</p>
+  </div>
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+  <div class="col-md-9 contents">
+    <div class="page-header toc-ignore">
+      <h1 data-toc-skip>nn: neural networks with torch</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/nn.Rmd"><code>vignettes/getting-started/nn.Rmd</code></a></small>
+      <div class="hidden name"><code>nn.Rmd</code></div>
+
+    </div>
+
+    
+    
+<blockquote>
+<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
+</blockquote>
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
+</pre></div>
+<p>Computational graphs and autograd are a very powerful paradigm for defining complex operators and automatically taking derivatives; however for large neural networks raw autograd can be a bit too low-level.</p>
+<p>When building neural networks we frequently think of arranging the computation into layers, some of which have learnable parameters which will be optimized during learning.</p>
+<p>In TensorFlow, packages like Keras, TensorFlow-Slim, and TFLearn provide higher-level abstractions over raw computational graphs that are useful for building neural networks.</p>
+<p>In torch, the nn functionality serves this same purpose. The nn feature defines a set of Modules, which are roughly equivalent to neural network layers. A Module receives input Tensors and computes output Tensors, but may also hold internal state such as Tensors containing learnable parameters. The nn collection also defines a set of useful loss functions that are commonly used when training neural networks.</p>
+<p>In this example we use nn to implement our two-layer network:</p>
+<div class="sourceCode" id="cb2"><pre class="downlit">
+<span class="co">if</span> (<span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span>()) {
+   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cuda"</span>)
+} <span class="co">else</span> {
+   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cpu"</span>)
+}
+   
+<span class="co"># N is batch size; D_in is input dimension;</span>
+<span class="co"># H is hidden dimension; D_out is output dimension.</span>
+<span class="kw">N</span> <span class="op">&lt;-</span> <span class="fl">64</span>
+<span class="kw">D_in</span> <span class="op">&lt;-</span> <span class="fl">1000</span>
+<span class="kw">H</span> <span class="op">&lt;-</span> <span class="fl">100</span>
+<span class="kw">D_out</span> <span class="op">&lt;-</span> <span class="fl">10</span>
+
+<span class="co"># Create random input and output data</span>
+<span class="co"># Setting requires_grad=FALSE (the default) indicates that we do not need to </span>
+<span class="co"># compute gradients with respect to these Tensors during the backward pass.</span>
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_in</span>, device=<span class="kw">device</span>)
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_out</span>, device=<span class="kw">device</span>)
+
+<span class="co"># Use the nn package to define our model as a sequence of layers. nn_sequential</span>
+<span class="co"># is a Module which contains other Modules, and applies them in sequence to</span>
+<span class="co"># produce its output. Each Linear Module computes output from input using a</span>
+<span class="co"># linear function, and holds internal Tensors for its weight and bias.</span>
+<span class="kw">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_sequential.html">nn_sequential</a></span>(
+    <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="kw">D_in</span>, <span class="kw">H</span>),
+    <span class="fu"><a href="../../reference/nn_relu.html">nn_relu</a></span>(),
+    <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="kw">H</span>, <span class="kw">D_out</span>)
+)
+
+<span class="co"># The nn package also contains definitions of popular loss functions; in this</span>
+<span class="co"># case we will use Mean Squared Error (MSE) as our loss function.</span>
+<span class="kw">loss_fn</span> <span class="op">&lt;-</span> <span class="kw">nnf_mse_loss</span>
+
+<span class="kw">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-6</span>
+<span class="co">for</span> (<span class="kw">t</span> <span class="co">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fl">500</span>)) {
+   <span class="co"># Forward pass: compute predicted y by passing x to the model. Module objects</span>
+   <span class="co"># can be called like functions. When doing so you pass a Tensor of input</span>
+   <span class="co"># data to the Module and it produces a Tensor of output data.</span>
+   <span class="kw">y_pred</span> <span class="op">&lt;-</span> <span class="fu">model</span>(<span class="kw">x</span>)
+   
+   <span class="co"># Compute and print loss. We pass Tensors containing the predicted and true</span>
+   <span class="co"># values of y, and the loss function returns a Tensor containing the</span>
+   <span class="co"># loss.</span>
+   <span class="kw">loss</span> <span class="op">&lt;-</span> <span class="fu">loss_fn</span>(<span class="kw">y_pred</span>, <span class="kw">y</span>)
+   <span class="co">if</span> (<span class="kw">t</span> <span class="op">%%</span> <span class="fl">100</span> <span class="op">==</span> <span class="fl">0</span> <span class="op">||</span> <span class="kw">t</span> <span class="op">==</span> <span class="fl">1</span>)
+      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="st">"Step:"</span>, <span class="kw">t</span>, <span class="st">":"</span>, <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="kw">loss</span>), <span class="st">"\n"</span>)
+   
+   <span class="co"># Zero the gradients before running the backward pass.</span>
+   <span class="kw">model</span><span class="op">$</span><span class="fu">zero_grad</span>()
+
+   <span class="co"># Backward pass: compute gradient of the loss with respect to all the learnable</span>
+   <span class="co"># parameters of the model. Internally, the parameters of each Module are stored</span>
+   <span class="co"># in Tensors with requires_grad=TRUE, so this call will compute gradients for</span>
+   <span class="co"># all learnable parameters in the model.</span>
+   <span class="kw">loss</span><span class="op">$</span><span class="fu">backward</span>()
+   
+   <span class="co"># Update the weights using gradient descent. Each parameter is a Tensor, so</span>
+   <span class="co"># we can access its gradients like we did before.</span>
+   <span class="fu"><a href="../../reference/with_no_grad.html">with_no_grad</a></span>({
+      <span class="co">for</span> (<span class="kw">param</span> <span class="co">in</span> <span class="kw">model</span><span class="op">$</span><span class="kw">parameters</span>) {
+         <span class="kw">param</span><span class="op">$</span><span class="fu">sub_</span>(<span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">param</span><span class="op">$</span><span class="kw">grad</span>)
+      }
+   })
+}
+<span class="co">#&gt; Step: 1 : 1.083556 </span>
+<span class="co">#&gt; Step: 100 : 1.083417 </span>
+<span class="co">#&gt; Step: 200 : 1.083277 </span>
+<span class="co">#&gt; Step: 300 : 1.083137 </span>
+<span class="co">#&gt; Step: 400 : 1.082997 </span>
+<span class="co">#&gt; Step: 500 : 1.082856</span>
+</pre></div>
+<p>In the <a href="optim.html">next example</a> we will learn how to use optimizers implemented in torch.</p>
+  </div>
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+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
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+// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
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diff --git a/static/docs/dev/articles/getting-started/optim.html b/static/docs/dev/articles/getting-started/optim.html
new file mode 100644
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+<!DOCTYPE html>
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+  <div class="col-md-9 contents">
+    <div class="page-header toc-ignore">
+      <h1 data-toc-skip>optim: optimizers in torch</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/optim.Rmd"><code>vignettes/getting-started/optim.Rmd</code></a></small>
+      <div class="hidden name"><code>optim.Rmd</code></div>
+
+    </div>
+
+    
+    
+<blockquote>
+<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
+</blockquote>
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
+</pre></div>
+<p>Up to this point we have updated the weights of our models by manually mutating the Tensors holding learnable parameters (with <code>with_no_grad</code> to avoid tracking history in autograd). This is not a huge burden for simple optimization algorithms like stochastic gradient descent, but in practice we often train neural networks using more sophisticated optimizers like AdaGrad, RMSProp, Adam, etc.</p>
+<p>The optim package in torch abstracts the idea of an optimization algorithm and provides implementations of commonly used optimization algorithms.</p>
+<p>In this example we will use the nn package to define our model as before, but we will optimize the model using the Adam algorithm provided by <code>optim</code>:</p>
+<div class="sourceCode" id="cb2"><pre class="downlit">
+<span class="co">if</span> (<span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span>()) {
+   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cuda"</span>)
+} <span class="co">else</span> {
+   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cpu"</span>)
+}
+   
+<span class="co"># N is batch size; D_in is input dimension;</span>
+<span class="co"># H is hidden dimension; D_out is output dimension.</span>
+<span class="kw">N</span> <span class="op">&lt;-</span> <span class="fl">64</span>
+<span class="kw">D_in</span> <span class="op">&lt;-</span> <span class="fl">1000</span>
+<span class="kw">H</span> <span class="op">&lt;-</span> <span class="fl">100</span>
+<span class="kw">D_out</span> <span class="op">&lt;-</span> <span class="fl">10</span>
+
+<span class="co"># Create random input and output data</span>
+<span class="co"># Setting requires_grad=FALSE (the default) indicates that we do not need to </span>
+<span class="co"># compute gradients with respect to these Tensors during the backward pass.</span>
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_in</span>, device=<span class="kw">device</span>)
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_out</span>, device=<span class="kw">device</span>)
+
+<span class="co"># Use the nn package to define our model as a sequence of layers. nn_sequential</span>
+<span class="co"># is a Module which contains other Modules, and applies them in sequence to</span>
+<span class="co"># produce its output. Each Linear Module computes output from input using a</span>
+<span class="co"># linear function, and holds internal Tensors for its weight and bias.</span>
+<span class="kw">model</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/nn_sequential.html">nn_sequential</a></span>(
+    <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="kw">D_in</span>, <span class="kw">H</span>),
+    <span class="fu"><a href="../../reference/nn_relu.html">nn_relu</a></span>(),
+    <span class="fu"><a href="../../reference/nn_linear.html">nn_linear</a></span>(<span class="kw">H</span>, <span class="kw">D_out</span>)
+)
+
+<span class="co"># The nn package also contains definitions of popular loss functions; in this</span>
+<span class="co"># case we will use Mean Squared Error (MSE) as our loss function.</span>
+<span class="kw">loss_fn</span> <span class="op">&lt;-</span> <span class="kw">nnf_mse_loss</span>
+
+<span class="co"># Use the optim package to define an Optimizer that will update the weights of</span>
+<span class="co"># the model for us. Here we will use Adam; the optim package contains many other</span>
+<span class="co"># optimization algorithms. The first argument to the Adam constructor tells the</span>
+<span class="co"># optimizer which Tensors it should update.</span>
+<span class="kw">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-4</span>
+<span class="kw">optimizer</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/optim_adam.html">optim_adam</a></span>(<span class="kw">model</span><span class="op">$</span><span class="kw">parameters</span>, lr=<span class="kw">learning_rate</span>)
+
+<span class="co">for</span> (<span class="kw">t</span> <span class="co">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fl">500</span>)) {
+   <span class="co"># Forward pass: compute predicted y by passing x to the model. Module objects</span>
+   <span class="co"># can be called like functions. When doing so you pass a Tensor of input</span>
+   <span class="co"># data to the Module and it produces a Tensor of output data.</span>
+   <span class="kw">y_pred</span> <span class="op">&lt;-</span> <span class="fu">model</span>(<span class="kw">x</span>)
+   
+   <span class="co"># Compute and print loss. We pass Tensors containing the predicted and true</span>
+   <span class="co"># values of y, and the loss function returns a Tensor containing the</span>
+   <span class="co"># loss.</span>
+   <span class="kw">loss</span> <span class="op">&lt;-</span> <span class="fu">loss_fn</span>(<span class="kw">y_pred</span>, <span class="kw">y</span>)
+   <span class="co">if</span> (<span class="kw">t</span> <span class="op">%%</span> <span class="fl">100</span> <span class="op">==</span> <span class="fl">0</span> <span class="op">||</span> <span class="kw">t</span> <span class="op">==</span> <span class="fl">1</span>)
+      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="st">"Step:"</span>, <span class="kw">t</span>, <span class="st">":"</span>, <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="kw">loss</span>), <span class="st">"\n"</span>)
+   
+   <span class="co"># Before the backward pass, use the optimizer object to zero all of the</span>
+   <span class="co"># gradients for the variables it will update (which are the learnable</span>
+   <span class="co"># weights of the model). This is because by default, gradients are</span>
+   <span class="co"># accumulated in buffers( i.e, not overwritten) whenever $backward()</span>
+   <span class="co"># is called. Checkout docs of `autograd_backward` for more details.</span>
+   <span class="kw">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span>()
+
+   <span class="co"># Backward pass: compute gradient of the loss with respect to model</span>
+   <span class="co"># parameters</span>
+   <span class="kw">loss</span><span class="op">$</span><span class="fu">backward</span>()
+
+   <span class="co"># Calling the step function on an Optimizer makes an update to its</span>
+   <span class="co"># parameters</span>
+   <span class="kw">optimizer</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/stats/step.html">step</a></span>()
+}
+<span class="co">#&gt; Step: 1 : 1.080712 </span>
+<span class="co">#&gt; Step: 100 : 0.07540431 </span>
+<span class="co">#&gt; Step: 200 : 0.001296245 </span>
+<span class="co">#&gt; Step: 300 : 1.112598e-05 </span>
+<span class="co">#&gt; Step: 400 : 7.013001e-08 </span>
+<span class="co">#&gt; Step: 500 : 1.750831e-10</span>
+</pre></div>
+<p>In the <a href="custom-nn.html">next example</a> we will learn how to create custom <code>nn_modules</code>.</p>
+  </div>
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diff --git a/static/docs/dev/articles/getting-started/optim_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/dev/articles/getting-started/optim_files/accessible-code-block-0.0.1/empty-anchor.js
new file mode 100644
index 000000000..ca349fd6a
--- /dev/null
+++ b/static/docs/dev/articles/getting-started/optim_files/accessible-code-block-0.0.1/empty-anchor.js
@@ -0,0 +1,15 @@
+// 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");
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diff --git a/static/docs/dev/articles/getting-started/tensors-and-autograd.html b/static/docs/dev/articles/getting-started/tensors-and-autograd.html
new file mode 100644
index 000000000..0615d9aee
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+<!DOCTYPE html>
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+      <a href="../../articles/tensor-creation.html">Creating tensors</a>
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+      </header><script src="tensors-and-autograd_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header toc-ignore">
+      <h1 data-toc-skip>Tensors and autograd</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/tensors-and-autograd.Rmd"><code>vignettes/getting-started/tensors-and-autograd.Rmd</code></a></small>
+      <div class="hidden name"><code>tensors-and-autograd.Rmd</code></div>
+
+    </div>
+
+    
+    
+<blockquote>
+<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
+</blockquote>
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
+</pre></div>
+<p>In the previous examples, we had to manually implement both the forward and backward passes of our neural network. Manually implementing the backward pass is not a big deal for a small two-layer network, but can quickly get very hairy for large complex networks.</p>
+<p>Thankfully, we can use automatic differentiation to automate the computation of backward passes in neural networks. The autograd feature in torch provides exactly this functionality. When using autograd, the forward pass of your network will define a computational graph; nodes in the graph will be Tensors, and edges will be functions that produce output Tensors from input Tensors. Backpropagating through this graph then allows you to easily compute gradients.</p>
+<p>This sounds complicated, it’s pretty simple to use in practice. Each Tensor represents a node in a computational graph. If x is a Tensor that has <code>x$requires_grad=TRUE</code> then <code>x$grad</code> is another Tensor holding the gradient of x with respect to some scalar value.</p>
+<p>Here we use torch Tensors and autograd to implement our two-layer network; now we no longer need to manually implement the backward pass through the network:</p>
+<div class="sourceCode" id="cb2"><pre class="downlit">
+<span class="co">if</span> (<span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span>()) {
+   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cuda"</span>)
+} <span class="co">else</span> {
+   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cpu"</span>)
+}
+   
+<span class="co"># N is batch size; D_in is input dimension;</span>
+<span class="co"># H is hidden dimension; D_out is output dimension.</span>
+<span class="kw">N</span> <span class="op">&lt;-</span> <span class="fl">64</span>
+<span class="kw">D_in</span> <span class="op">&lt;-</span> <span class="fl">1000</span>
+<span class="kw">H</span> <span class="op">&lt;-</span> <span class="fl">100</span>
+<span class="kw">D_out</span> <span class="op">&lt;-</span> <span class="fl">10</span>
+
+<span class="co"># Create random input and output data</span>
+<span class="co"># Setting requires_grad=FALSE (the default) indicates that we do not need to </span>
+<span class="co"># compute gradients with respect to these Tensors during the backward pass.</span>
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_in</span>, device=<span class="kw">device</span>)
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_out</span>, device=<span class="kw">device</span>)
+
+<span class="co"># Randomly initialize weights</span>
+<span class="co"># Setting requires_grad=TRUE indicates that we want to compute gradients with</span>
+<span class="co"># respect to these Tensors during the backward pass.</span>
+<span class="kw">w1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">D_in</span>, <span class="kw">H</span>, device=<span class="kw">device</span>, requires_grad = <span class="fl">TRUE</span>)
+<span class="kw">w2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">H</span>, <span class="kw">D_out</span>, device=<span class="kw">device</span>, requires_grad = <span class="fl">TRUE</span>)
+
+<span class="kw">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-6</span>
+<span class="co">for</span> (<span class="kw">t</span> <span class="co">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fl">500</span>)) {
+   <span class="co"># Forward pass: compute predicted y using operations on Tensors; these</span>
+   <span class="co"># are exactly the same operations we used to compute the forward pass using</span>
+   <span class="co"># Tensors, but we do not need to keep references to intermediate values since</span>
+   <span class="co"># we are not implementing the backward pass by hand.</span>
+   <span class="kw">y_pred</span> <span class="op">&lt;-</span> <span class="kw">x</span><span class="op">$</span><span class="fu">mm</span>(<span class="kw">w1</span>)<span class="op">$</span><span class="fu">clamp</span>(min=<span class="fl">0</span>)<span class="op">$</span><span class="fu">mm</span>(<span class="kw">w2</span>)
+   
+   <span class="co"># Compute and print loss using operations on Tensors.</span>
+   <span class="co"># Now loss is a Tensor of shape (1,)</span>
+   <span class="kw">loss</span> <span class="op">&lt;-</span> (<span class="kw">y_pred</span> <span class="op">-</span> <span class="kw">y</span>)<span class="op">$</span><span class="fu">pow</span>(<span class="fl">2</span>)<span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span>()
+   <span class="co">if</span> (<span class="kw">t</span> <span class="op">%%</span> <span class="fl">100</span> <span class="op">==</span> <span class="fl">0</span> <span class="op">||</span> <span class="kw">t</span> <span class="op">==</span> <span class="fl">1</span>)
+      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="st">"Step:"</span>, <span class="kw">t</span>, <span class="st">":"</span>, <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="kw">loss</span>), <span class="st">"\n"</span>)
+   
+   <span class="co"># Use autograd to compute the backward pass. This call will compute the</span>
+   <span class="co"># gradient of loss with respect to all Tensors with requires_grad=True.</span>
+   <span class="co"># After this call w1$grad and w2$grad will be Tensors holding the gradient</span>
+   <span class="co"># of the loss with respect to w1 and w2 respectively.</span>
+   <span class="kw">loss</span><span class="op">$</span><span class="fu">backward</span>()
+   
+   <span class="co"># Manually update weights using gradient descent. Wrap in `with_no_grad`</span>
+   <span class="co"># because weights have requires_grad=TRUE, but we don't need to track this</span>
+   <span class="co"># in autograd.</span>
+   <span class="co"># You can also use optim_sgd to achieve this.</span>
+   <span class="fu"><a href="../../reference/with_no_grad.html">with_no_grad</a></span>({
+      
+      <span class="co"># operations suffixed with an `_` operates on in-place on the tensor.</span>
+      <span class="kw">w1</span><span class="op">$</span><span class="fu">sub_</span>(<span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">w1</span><span class="op">$</span><span class="kw">grad</span>)
+      <span class="kw">w2</span><span class="op">$</span><span class="fu">sub_</span>(<span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">w2</span><span class="op">$</span><span class="kw">grad</span>)
+      
+      <span class="co"># Manually zero the gradients after updating weights</span>
+      <span class="kw">w1</span><span class="op">$</span><span class="kw">grad</span><span class="op">$</span><span class="fu">zero_</span>()
+      <span class="kw">w2</span><span class="op">$</span><span class="kw">grad</span><span class="op">$</span><span class="fu">zero_</span>()
+   })
+}
+<span class="co">#&gt; Step: 1 : 34836468 </span>
+<span class="co">#&gt; Step: 100 : 390.6113 </span>
+<span class="co">#&gt; Step: 200 : 1.657834 </span>
+<span class="co">#&gt; Step: 300 : 0.0120327 </span>
+<span class="co">#&gt; Step: 400 : 0.0002646378 </span>
+<span class="co">#&gt; Step: 500 : 4.226738e-05</span>
+</pre></div>
+<p>In the <a href="new-autograd-functions.html">next example</a> we will learn how to create new autograd functions.</p>
+  </div>
+
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+</div>
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diff --git a/static/docs/dev/articles/getting-started/tensors-and-autograd_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/dev/articles/getting-started/tensors-and-autograd_files/accessible-code-block-0.0.1/empty-anchor.js
new file mode 100644
index 000000000..ca349fd6a
--- /dev/null
+++ b/static/docs/dev/articles/getting-started/tensors-and-autograd_files/accessible-code-block-0.0.1/empty-anchor.js
@@ -0,0 +1,15 @@
+// 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');
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+      if (linkList[j].innerHTML === "") {
+        linkList[j].setAttribute('aria-hidden', 'true');
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+    }
+  }
+});
diff --git a/static/docs/dev/articles/getting-started/tensors.html b/static/docs/dev/articles/getting-started/tensors.html
new file mode 100644
index 000000000..1834ffa8f
--- /dev/null
+++ b/static/docs/dev/articles/getting-started/tensors.html
@@ -0,0 +1,248 @@
+<!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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+      <h1 data-toc-skip>torch Tensors</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/tensors.Rmd"><code>vignettes/getting-started/tensors.Rmd</code></a></small>
+      <div class="hidden name"><code>tensors.Rmd</code></div>
+
+    </div>
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+    
+    
+<blockquote>
+<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
+</blockquote>
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
+</pre></div>
+<p>R arrays are great, but they cannot utilize GPUs to accelerate its numerical computations. For modern deep neural networks, GPUs often provide speedups of 50x or greater, so unfortunately pure R won’t be enough for modern deep learning.</p>
+<p>Here we introduce the most fundamental torch concept: the Tensor. A torch Tensor is conceptually similar to an R array: a Tensor is an n-dimensional array, and torch provides many functions for operating on these Tensors. Behind the scenes, Tensors can keep track of a computational graph and gradients, but they’re also useful as a generic tool for scientific computing.</p>
+<p>Also unlike R, torch Tensors can utilize GPUs to accelerate their numeric computations. To run a torch Tensor on GPU, you simply need to cast it to a new datatype.</p>
+<p>Here we use torch Tensors to fit a two-layer network to random data. Like the R before we need to manually implement the forward and backward passes through the network:</p>
+<div class="sourceCode" id="cb2"><pre class="downlit">
+<span class="co">if</span> (<span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span>()) {
+   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cuda"</span>)
+} <span class="co">else</span> {
+   <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cpu"</span>)
+}
+   
+<span class="co"># N is batch size; D_in is input dimension;</span>
+<span class="co"># H is hidden dimension; D_out is output dimension.</span>
+<span class="kw">N</span> <span class="op">&lt;-</span> <span class="fl">64</span>
+<span class="kw">D_in</span> <span class="op">&lt;-</span> <span class="fl">1000</span>
+<span class="kw">H</span> <span class="op">&lt;-</span> <span class="fl">100</span>
+<span class="kw">D_out</span> <span class="op">&lt;-</span> <span class="fl">10</span>
+
+<span class="co"># Create random input and output data</span>
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_in</span>, device=<span class="kw">device</span>)
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">N</span>, <span class="kw">D_out</span>, device=<span class="kw">device</span>)
+
+<span class="co"># Randomly initialize weights</span>
+<span class="kw">w1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">D_in</span>, <span class="kw">H</span>, device=<span class="kw">device</span>)
+<span class="kw">w2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">H</span>, <span class="kw">D_out</span>, device=<span class="kw">device</span>)
+
+<span class="kw">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-6</span>
+<span class="co">for</span> (<span class="kw">t</span> <span class="co">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fl">500</span>)) {
+   <span class="co"># Forward pass: compute predicted y</span>
+   <span class="kw">h</span> <span class="op">&lt;-</span> <span class="kw">x</span><span class="op">$</span><span class="fu">mm</span>(<span class="kw">w1</span>)
+   <span class="kw">h_relu</span> <span class="op">&lt;-</span> <span class="kw">h</span><span class="op">$</span><span class="fu">clamp</span>(min=<span class="fl">0</span>)
+   <span class="kw">y_pred</span> <span class="op">&lt;-</span> <span class="kw">h_relu</span><span class="op">$</span><span class="fu">mm</span>(<span class="kw">w2</span>)
+   
+   <span class="co"># Compute and print loss</span>
+   <span class="kw">loss</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>((<span class="kw">y_pred</span> <span class="op">-</span> <span class="kw">y</span>)<span class="op">$</span><span class="fu">pow</span>(<span class="fl">2</span>)<span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span>())
+   <span class="co">if</span> (<span class="kw">t</span> <span class="op">%%</span> <span class="fl">100</span> <span class="op">==</span> <span class="fl">0</span> <span class="op">||</span> <span class="kw">t</span> <span class="op">==</span> <span class="fl">1</span>)
+      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="st">"Step:"</span>, <span class="kw">t</span>, <span class="st">":"</span>, <span class="kw">loss</span>, <span class="st">"\n"</span>)
+   
+   <span class="co"># Backprop to compute gradients of w1 and w2 with respect to loss</span>
+   <span class="kw">grad_y_pred</span> <span class="op">&lt;-</span> <span class="fl">2.0</span> <span class="op">*</span> (<span class="kw">y_pred</span> <span class="op">-</span> <span class="kw">y</span>)
+   <span class="kw">grad_w2</span> <span class="op">&lt;-</span> <span class="kw">h_relu</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span>()<span class="op">$</span><span class="fu">mm</span>(<span class="kw">grad_y_pred</span>)
+   <span class="kw">grad_h_relu</span> <span class="op">&lt;-</span> <span class="kw">grad_y_pred</span><span class="op">$</span><span class="fu">mm</span>(<span class="kw">w2</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span>())
+   <span class="kw">grad_h</span> <span class="op">&lt;-</span> <span class="kw">grad_h_relu</span><span class="op">$</span><span class="fu">clone</span>()
+   <span class="kw">grad_h</span>[<span class="kw">h</span> <span class="op">&lt;</span> <span class="fl">0</span>] <span class="op">&lt;-</span> <span class="fl">0</span>
+   <span class="kw">grad_w1</span> <span class="op">&lt;-</span> <span class="kw">x</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span>()<span class="op">$</span><span class="fu">mm</span>(<span class="kw">grad_h</span>)
+   
+   <span class="co"># Update weights using gradient descent</span>
+   <span class="kw">w1</span> <span class="op">&lt;-</span> <span class="kw">w1</span> <span class="op">-</span> <span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">grad_w1</span>
+   <span class="kw">w2</span> <span class="op">&lt;-</span> <span class="kw">w2</span> <span class="op">-</span> <span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">grad_w2</span>
+}
+<span class="co">#&gt; Step: 1 : 39707928 </span>
+<span class="co">#&gt; Step: 100 : 498.2322 </span>
+<span class="co">#&gt; Step: 200 : 2.06651 </span>
+<span class="co">#&gt; Step: 300 : 0.01554536 </span>
+<span class="co">#&gt; Step: 400 : 0.0003302942 </span>
+<span class="co">#&gt; Step: 500 : 5.156323e-05</span>
+</pre></div>
+<p>In the <a href="tensors-and-autograd.html">next example</a> we will use autograd instead of computing the gradients manually.</p>
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+      <a href="../../articles/getting-started/tensors.html">Tensors</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/tensors-and-autograd.html">Tensors and autograd</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/optim.html">optim: optimizers in torch</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/custom-nn.html">Custom nn modules</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/control-flow-and-weight-sharing.html">Control flow &amp; Weight sharing</a>
+    </li>
+    <li class="dropdown-header">Torch Mechanics</li>
+    <li>
+      <a href="../../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/neural-networks.html">Neural networks</a>
+    </li>
+  </ul>
+</li>
+<li class="dropdown">
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+    Articles
+     
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+<li class="dropdown-header">Tensors</li>
+    <li>
+      <a href="../../articles/tensor-creation.html">Creating tensors</a>
+    </li>
+    <li>
+      <a href="../../articles/indexing.html">Indexing</a>
+    </li>
+    <li>
+      <a href="../../articles/tensor/index.html">Tensor class</a>
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+    <li class="dropdown-header">Datasets</li>
+    <li>
+      <a href="../../articles/loading-data.html">Loading Data</a>
+    </li>
+    <li class="dropdown-header">Autograd</li>
+    <li>
+      <a href="../../articles/using-autograd.html">Using autograd</a>
+    </li>
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+      <a href="../../articles/extending-autograd.html">Extending autograd</a>
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+  <a href="../../news/index.html">Changelog</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><script src="warmup_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header toc-ignore">
+      <h1 data-toc-skip>Warm-up</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/warmup.Rmd"><code>vignettes/getting-started/warmup.Rmd</code></a></small>
+      <div class="hidden name"><code>warmup.Rmd</code></div>
+
+    </div>
+
+    
+    
+<blockquote>
+<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/examples_tensor/two_layer_net_numpy.html#sphx-glr-beginner-examples-tensor-two-layer-net-numpy-py">here</a>. All credits goes to <a href="https://github.com/jcjohnson/pytorch-examples">Justin Johnson</a>.</p>
+</blockquote>
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
+</pre></div>
+<p>A fully-connected ReLU network with one hidden layer and no biases, trained to predict y from x using Euclidean error.</p>
+<p>This implementation uses pure R to manually compute the forward pass, loss, and backward pass.</p>
+<p>An R array is a generic n-dimensional array; it does not know anything about deep learning or gradients or computational graphs, and is just a way to perform generic numeric computations.</p>
+<div class="sourceCode" id="cb2"><pre class="downlit">
+<span class="co"># N is batch size; D_in is input dimension;</span>
+<span class="co"># H is hidden dimension; D_out is output dimension.</span>
+<span class="kw">N</span> <span class="op">&lt;-</span> <span class="fl">64</span>
+<span class="kw">D_in</span> <span class="op">&lt;-</span> <span class="fl">1000</span>
+<span class="kw">H</span> <span class="op">&lt;-</span> <span class="fl">100</span>
+<span class="kw">D_out</span> <span class="op">&lt;-</span> <span class="fl">10</span>
+
+<span class="co"># Create random input and output data</span>
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/array.html">array</a></span>(<span class="fu"><a href="https://rdrr.io/r/stats/Normal.html">rnorm</a></span>(<span class="kw">N</span><span class="op">*</span><span class="kw">D_in</span>), dim = <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="kw">N</span>, <span class="kw">D_in</span>))
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/array.html">array</a></span>(<span class="fu"><a href="https://rdrr.io/r/stats/Normal.html">rnorm</a></span>(<span class="kw">N</span><span class="op">*</span><span class="kw">D_out</span>), dim = <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="kw">N</span>, <span class="kw">D_out</span>))
+
+<span class="co"># Randomly initialize weights</span>
+<span class="kw">w1</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/array.html">array</a></span>(<span class="fu"><a href="https://rdrr.io/r/stats/Normal.html">rnorm</a></span>(<span class="kw">D_in</span><span class="op">*</span><span class="kw">H</span>), dim = <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="kw">D_in</span>, <span class="kw">H</span>))
+<span class="kw">w2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/array.html">array</a></span>(<span class="fu"><a href="https://rdrr.io/r/stats/Normal.html">rnorm</a></span>(<span class="kw">H</span><span class="op">*</span><span class="kw">D_out</span>), dim = <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="kw">H</span>, <span class="kw">D_out</span>))
+
+<span class="kw">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-6</span>
+<span class="co">for</span> (<span class="kw">t</span> <span class="co">in</span> <span class="fu"><a href="https://rdrr.io/r/base/seq.html">seq_len</a></span>(<span class="fl">500</span>)) {
+   <span class="co"># Forward pass: compute predicted y</span>
+   <span class="kw">h</span> <span class="op">&lt;-</span> <span class="kw">x</span> <span class="op">%*%</span> <span class="kw">w1</span>
+   <span class="kw">h_relu</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/ifelse.html">ifelse</a></span>(<span class="kw">h</span> <span class="op">&lt;</span> <span class="fl">0</span>, <span class="fl">0</span>, <span class="kw">h</span>)
+   <span class="kw">y_pred</span> <span class="op">&lt;-</span> <span class="kw">h_relu</span> <span class="op">%*%</span> <span class="kw">w2</span>
+   
+   <span class="co"># Compute and print loss</span>
+   <span class="kw">loss</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span>((<span class="kw">y_pred</span> <span class="op">-</span> <span class="kw">y</span>)<span class="op">^</span><span class="fl">2</span>)
+   <span class="co">if</span> (<span class="kw">t</span> <span class="op">%%</span> <span class="fl">100</span> <span class="op">==</span> <span class="fl">0</span> <span class="op">||</span> <span class="kw">t</span> <span class="op">==</span> <span class="fl">1</span>)
+      <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="st">"Step:"</span>, <span class="kw">t</span>, <span class="st">":"</span>, <span class="kw">loss</span>, <span class="st">"\n"</span>)
+   
+   <span class="co"># Backprop to compute gradients of w1 and w2 with respect to loss</span>
+   <span class="kw">grad_y_pred</span> <span class="op">&lt;-</span> <span class="fl">2</span> <span class="op">*</span> (<span class="kw">y_pred</span> <span class="op">-</span> <span class="kw">y</span>)
+   <span class="kw">grad_w2</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span>(<span class="kw">h_relu</span>) <span class="op">%*%</span> <span class="kw">grad_y_pred</span>
+   <span class="kw">grad_h_relu</span> <span class="op">&lt;-</span> <span class="kw">grad_y_pred</span> <span class="op">%*%</span> <span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span>(<span class="kw">w2</span>)
+   <span class="kw">grad_h</span> <span class="op">&lt;-</span> <span class="kw">grad_h_relu</span>
+   <span class="kw">grad_h</span>[<span class="kw">h</span> <span class="op">&lt;</span> <span class="fl">0</span>] <span class="op">&lt;-</span> <span class="fl">0</span>
+   <span class="kw">grad_w1</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/t.html">t</a></span>(<span class="kw">x</span>) <span class="op">%*%</span> <span class="kw">grad_h</span>
+   
+   <span class="co"># Update weights</span>
+   <span class="kw">w1</span> <span class="op">&lt;-</span> <span class="kw">w1</span> <span class="op">-</span> <span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">grad_w1</span>
+   <span class="kw">w2</span> <span class="op">&lt;-</span> <span class="kw">w2</span> <span class="op">-</span> <span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">grad_w2</span>
+}
+<span class="co">#&gt; Step: 1 : 45068030 </span>
+<span class="co">#&gt; Step: 100 : 1143.562 </span>
+<span class="co">#&gt; Step: 200 : 10.35666 </span>
+<span class="co">#&gt; Step: 300 : 0.1330576 </span>
+<span class="co">#&gt; Step: 400 : 0.001955063 </span>
+<span class="co">#&gt; Step: 500 : 3.09119e-05</span>
+</pre></div>
+<p>In the <a href="tensors.html">next example</a> we will replace the R array for a torch Tensor.</p>
+  </div>
+
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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@@ -0,0 +1,15 @@
+// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
+// v0.0.1
+// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
+
+document.addEventListener('DOMContentLoaded', function() {
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diff --git a/static/docs/dev/articles/getting-started/what-is-torch.html b/static/docs/dev/articles/getting-started/what-is-torch.html
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+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
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+<li class="dropdown-header">Beginers guide</li>
+    <li>
+      <a href="../../articles/getting-started/warmup.html">Warm-up</a>
+    </li>
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+      <a href="../../articles/getting-started/tensors.html">Tensors</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/tensors-and-autograd.html">Tensors and autograd</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/optim.html">optim: optimizers in torch</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/custom-nn.html">Custom nn modules</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/control-flow-and-weight-sharing.html">Control flow &amp; Weight sharing</a>
+    </li>
+    <li class="dropdown-header">Torch Mechanics</li>
+    <li>
+      <a href="../../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
+    <li>
+      <a href="../../articles/getting-started/neural-networks.html">Neural networks</a>
+    </li>
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+<li class="dropdown-header">Tensors</li>
+    <li>
+      <a href="../../articles/tensor-creation.html">Creating tensors</a>
+    </li>
+    <li>
+      <a href="../../articles/indexing.html">Indexing</a>
+    </li>
+    <li>
+      <a href="../../articles/tensor/index.html">Tensor class</a>
+    </li>
+    <li class="dropdown-header">Datasets</li>
+    <li>
+      <a href="../../articles/loading-data.html">Loading Data</a>
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+    <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-header">Examples</li>
+<li>
+  <a href="../../reference/index.html">Reference</a>
+</li>
+<li>
+  <a href="../../news/index.html">Changelog</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><script src="what-is-torch_files/accessible-code-block-0.0.1/empty-anchor.js"></script><div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header toc-ignore">
+      <h1 data-toc-skip>What is torch?</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/getting-started/what-is-torch.Rmd"><code>vignettes/getting-started/what-is-torch.Rmd</code></a></small>
+      <div class="hidden name"><code>what-is-torch.Rmd</code></div>
+
+    </div>
+
+    
+    
+<blockquote>
+<p>Note: This is an R port of the official tutorial available <a href="https://pytorch.org/tutorials/beginner/blitz/tensor_tutorial.html#sphx-glr-beginner-blitz-tensor-tutorial-py">here</a>. All credits goes to <a href="http://soumith.ch/">Soumith Chintala</a>.</p>
+</blockquote>
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
+</pre></div>
+<p>It’s a scientific computing package targeted at two sets of audiences:</p>
+<ul>
+<li>An array library to use the power of GPUs</li>
+<li>a deep learning research platform that provides maximum flexibility and speed</li>
+</ul>
+<div id="getting-started" class="section level2">
+<h2 class="hasAnchor">
+<a href="#getting-started" class="anchor"></a>Getting started</h2>
+<div id="tensors" class="section level3">
+<h3 class="hasAnchor">
+<a href="#tensors" class="anchor"></a>Tensors</h3>
+<p>Tensors are similar to R arrays, with the addition being that Tensors can also be used on a GPU to accelerate computing.</p>
+<blockquote>
+<p>Note: An uninitialized matrix is declared, but does not contain definite known values before it is used. When an uninitialized matrix is created, whatever values were in the allocated memory at the time will appear as the initial values.</p>
+</blockquote>
+<p>Construct a 5x3 matrix, uninitialized:</p>
+<div class="sourceCode" id="cb2"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_empty.html">torch_empty</a></span>(<span class="fl">5</span>, <span class="fl">3</span>)
+<span class="kw">x</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  0  0  0</span>
+<span class="co">#&gt;  0  0  0</span>
+<span class="co">#&gt;  0  0  0</span>
+<span class="co">#&gt;  0  0  0</span>
+<span class="co">#&gt;  0  0  0</span>
+<span class="co">#&gt; [ CPUFloatType{5,3} ]</span>
+</pre></div>
+<p>Construct a randomly initialized matrix:</p>
+<div class="sourceCode" id="cb3"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">5</span>, <span class="fl">3</span>)
+<span class="kw">x</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  0.9781  0.5050  0.6961</span>
+<span class="co">#&gt;  0.2296  0.9370  0.3338</span>
+<span class="co">#&gt;  0.0232  0.7163  0.3911</span>
+<span class="co">#&gt;  0.2576  0.0083  0.4875</span>
+<span class="co">#&gt;  0.1455  0.5404  0.8133</span>
+<span class="co">#&gt; [ CPUFloatType{5,3} ]</span>
+</pre></div>
+<p>Construct a matrix filled zeros and of dtype long:</p>
+<div class="sourceCode" id="cb4"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">5</span>, <span class="fl">3</span>, dtype = <span class="fu"><a href="../../reference/torch_dtype.html">torch_long</a></span>())
+<span class="kw">x</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  0  0  0</span>
+<span class="co">#&gt;  0  0  0</span>
+<span class="co">#&gt;  0  0  0</span>
+<span class="co">#&gt;  0  0  0</span>
+<span class="co">#&gt;  0  0  0</span>
+<span class="co">#&gt; [ CPULongType{5,3} ]</span>
+</pre></div>
+<p>Construct a tensor directly from data:</p>
+<div class="sourceCode" id="cb5"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">5.5</span>, <span class="fl">3</span>))
+<span class="kw">x</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  5.5000</span>
+<span class="co">#&gt;  3.0000</span>
+<span class="co">#&gt; [ CPUFloatType{2} ]</span>
+</pre></div>
+<p>or create a tensor based on an existing tensor. These methods will reuse properties of the input tensor, e.g. dtype, unless new values are provided by user</p>
+<div class="sourceCode" id="cb6"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn_like.html">torch_randn_like</a></span>(<span class="kw">x</span>, dtype = <span class="fu"><a href="../../reference/torch_dtype.html">torch_float</a></span>()) <span class="co"># override dtype!</span>
+<span class="kw">x</span>                                               <span class="co"># result has the same size</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt; 0.01 *</span>
+<span class="co">#&gt; -5.4223</span>
+<span class="co">#&gt; -20.1941</span>
+<span class="co">#&gt; [ CPUFloatType{2} ]</span>
+</pre></div>
+<p>Get its size:</p>
+<div class="sourceCode" id="cb7"><pre class="downlit">
+<span class="kw">x</span><span class="op">$</span><span class="fu">size</span>()
+<span class="co">#&gt; [1] 2</span>
+</pre></div>
+</div>
+<div id="operations" class="section level3">
+<h3 class="hasAnchor">
+<a href="#operations" class="anchor"></a>Operations</h3>
+<p>There are multiple syntaxes for operations. In the following example, we will take a look at the addition operation.</p>
+<p>Addition: syntax 1</p>
+<div class="sourceCode" id="cb8"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">5</span>, <span class="fl">3</span>)
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">5</span>, <span class="fl">3</span>)
+<span class="kw">x</span> <span class="op">+</span> <span class="kw">y</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  0.7164  0.8170  1.3400</span>
+<span class="co">#&gt;  0.2271  0.6043  1.1158</span>
+<span class="co">#&gt;  1.3897  1.6707  0.6946</span>
+<span class="co">#&gt;  1.7146  0.9900  0.8561</span>
+<span class="co">#&gt;  0.5293  0.8795  1.0980</span>
+<span class="co">#&gt; [ CPUFloatType{5,3} ]</span>
+</pre></div>
+<p>Addition: syntax 2</p>
+<div class="sourceCode" id="cb9"><pre class="downlit">
+<span class="fu"><a href="../../reference/torch_add.html">torch_add</a></span>(<span class="kw">x</span>, <span class="kw">y</span>)
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  0.7164  0.8170  1.3400</span>
+<span class="co">#&gt;  0.2271  0.6043  1.1158</span>
+<span class="co">#&gt;  1.3897  1.6707  0.6946</span>
+<span class="co">#&gt;  1.7146  0.9900  0.8561</span>
+<span class="co">#&gt;  0.5293  0.8795  1.0980</span>
+<span class="co">#&gt; [ CPUFloatType{5,3} ]</span>
+</pre></div>
+<p>Addition: in-place</p>
+<div class="sourceCode" id="cb10"><pre class="downlit">
+<span class="kw">y</span><span class="op">$</span><span class="fu">add_</span>(<span class="kw">x</span>)
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  0.7164  0.8170  1.3400</span>
+<span class="co">#&gt;  0.2271  0.6043  1.1158</span>
+<span class="co">#&gt;  1.3897  1.6707  0.6946</span>
+<span class="co">#&gt;  1.7146  0.9900  0.8561</span>
+<span class="co">#&gt;  0.5293  0.8795  1.0980</span>
+<span class="co">#&gt; [ CPUFloatType{5,3} ]</span>
+<span class="kw">y</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  0.7164  0.8170  1.3400</span>
+<span class="co">#&gt;  0.2271  0.6043  1.1158</span>
+<span class="co">#&gt;  1.3897  1.6707  0.6946</span>
+<span class="co">#&gt;  1.7146  0.9900  0.8561</span>
+<span class="co">#&gt;  0.5293  0.8795  1.0980</span>
+<span class="co">#&gt; [ CPUFloatType{5,3} ]</span>
+</pre></div>
+<blockquote>
+<p>Note: Any operation that mutates a tensor in-place is post-fixed with an <code>_</code>. For example: <code>x$copy_(y)</code>, <code>x$t_()</code>, will change x.</p>
+</blockquote>
+<p>You can use standard R-like indexing with all bells and whistles! See more about indexing with <code><a href="../../articles/indexing.html">vignette("indexing")</a></code>.</p>
+<div class="sourceCode" id="cb11"><pre class="downlit">
+<span class="kw">x</span>[, <span class="fl">1</span>]
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  0.0192</span>
+<span class="co">#&gt;  0.0535</span>
+<span class="co">#&gt;  0.7355</span>
+<span class="co">#&gt;  0.8610</span>
+<span class="co">#&gt;  0.2385</span>
+<span class="co">#&gt; [ CPUFloatType{5} ]</span>
+</pre></div>
+<p>Resizing: If you want to resize/reshape tensor, you can use <code>torch_view</code>:</p>
+<div class="sourceCode" id="cb12"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">4</span>, <span class="fl">4</span>)
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="kw">x</span><span class="op">$</span><span class="fu">view</span>(<span class="fl">16</span>)
+<span class="kw">z</span> <span class="op">&lt;-</span> <span class="kw">x</span><span class="op">$</span><span class="fu">view</span>(size = <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="op">-</span><span class="fl">1</span>, <span class="fl">8</span>))  <span class="co"># the size -1 is inferred from other dimensions</span>
+<span class="kw">x</span><span class="op">$</span><span class="fu">size</span>()
+<span class="co">#&gt; [1] 4 4</span>
+<span class="kw">y</span><span class="op">$</span><span class="fu">size</span>()
+<span class="co">#&gt; [1] 16</span>
+<span class="kw">z</span><span class="op">$</span><span class="fu">size</span>()
+<span class="co">#&gt; [1] 2 8</span>
+</pre></div>
+<p>If you have a one element tensor, use <code>$item()</code> to get the value as an R number</p>
+<div class="sourceCode" id="cb13"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">1</span>)
+<span class="kw">x</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  0.2725</span>
+<span class="co">#&gt; [ CPUFloatType{1} ]</span>
+<span class="kw">x</span><span class="op">$</span><span class="fu">item</span>()
+<span class="co">#&gt; [1] 0.2725014</span>
+</pre></div>
+<p>You can find a complete list of operations in the reference page.</p>
+</div>
+</div>
+<div id="r-bridge" class="section level2">
+<h2 class="hasAnchor">
+<a href="#r-bridge" class="anchor"></a>R bridge</h2>
+<p>Converting a Torch Tensor to an R array and vice versa is a breeze.</p>
+<div id="converting-a-torch-tensor-into-an-r-array" class="section level3">
+<h3 class="hasAnchor">
+<a href="#converting-a-torch-tensor-into-an-r-array" class="anchor"></a>Converting a torch tensor into an R array</h3>
+<div class="sourceCode" id="cb14"><pre class="downlit">
+<span class="kw">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span>(<span class="fl">5</span>)
+<span class="kw">a</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt; [ CPUFloatType{5} ]</span>
+</pre></div>
+<div class="sourceCode" id="cb15"><pre class="downlit">
+<span class="kw">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/as_array.html">as_array</a></span>(<span class="kw">a</span>)
+<span class="kw">b</span>
+<span class="co">#&gt; [1] 1 1 1 1 1</span>
+</pre></div>
+</div>
+<div id="converting-r-arrays-to-torch-tensors" class="section level3">
+<h3 class="hasAnchor">
+<a href="#converting-r-arrays-to-torch-tensors" class="anchor"></a>Converting R arrays to torch tensors</h3>
+<div class="sourceCode" id="cb16"><pre class="downlit">
+<span class="kw">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/rep.html">rep</a></span>(<span class="fl">1</span>, <span class="fl">5</span>)
+<span class="kw">a</span>
+<span class="co">#&gt; [1] 1 1 1 1 1</span>
+<span class="kw">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="kw">a</span>)
+<span class="kw">b</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt; [ CPUFloatType{5} ]</span>
+</pre></div>
+<p>Currently supported types are numerics and boolean types.</p>
+</div>
+</div>
+<div id="cuda-tensors" class="section level2">
+<h2 class="hasAnchor">
+<a href="#cuda-tensors" class="anchor"></a>CUDA tensors</h2>
+<p>Tensors can be moved onto any device using the <code>$to</code> method.</p>
+<div class="sourceCode" id="cb17"><pre class="downlit">
+<span class="co">if</span> (<span class="fu"><a href="../../reference/cuda_is_available.html">cuda_is_available</a></span>()) {
+  <span class="kw">device</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_device.html">torch_device</a></span>(<span class="st">"cuda"</span>)
+  <span class="kw">y</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones_like.html">torch_ones_like</a></span>(<span class="kw">x</span>, device = <span class="kw">device</span>)  <span class="co"># directly create a tensor on GPU</span>
+  <span class="kw">x</span> <span class="op">&lt;-</span> <span class="kw">x</span><span class="op">$</span><span class="fu">to</span>(<span class="kw">device</span>)                       <span class="co"># or just use strings ``.to("cuda")``</span>
+  <span class="kw">z</span> <span class="op">&lt;-</span> <span class="kw">x</span> <span class="op">+</span> <span class="kw">y</span>
+  <span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="kw">z</span>)
+  <span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="kw">z</span><span class="op">$</span><span class="fu">to</span>(device = <span class="st">"cpu"</span>, <span class="fu"><a href="../../reference/torch_dtype.html">torch_double</a></span>())) <span class="co"># `$to` can also change dtype together!</span>
+}
+</pre></div>
+</div>
+  </div>
+
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/articles/getting-started/what-is-torch_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/dev/articles/getting-started/what-is-torch_files/accessible-code-block-0.0.1/empty-anchor.js
new file mode 100644
index 000000000..ca349fd6a
--- /dev/null
+++ b/static/docs/dev/articles/getting-started/what-is-torch_files/accessible-code-block-0.0.1/empty-anchor.js
@@ -0,0 +1,15 @@
+// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
+// v0.0.1
+// Written by JooYoung Seo (jooyoung@psu.edu) and Atsushi Yasumoto on June 1st, 2020.
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+document.addEventListener('DOMContentLoaded', function() {
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+  <div class="col-md-9 contents">
+    <div class="page-header toc-ignore">
+      <h1 data-toc-skip>Indexing tensors</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/indexing.Rmd"><code>vignettes/indexing.Rmd</code></a></small>
+      <div class="hidden name"><code>indexing.Rmd</code></div>
+
+    </div>
+
+    
+    
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
+</pre></div>
+<p>In this article we describe the indexing operator for torch tensors and how it compares to the R indexing operator for arrays.</p>
+<p>Torch’s indexing semantics are closer to numpy’s semantics than R’s. You will find a lot of similarities between this article and the <code>numpy</code> indexing article available <a href="https://docs.scipy.org/doc/numpy-1.10.0/user/basics.indexing.html">here</a>.</p>
+<div id="single-element-indexing" class="section level2">
+<h2 class="hasAnchor">
+<a href="#single-element-indexing" class="anchor"></a>Single element indexing</h2>
+<p>Single element indexing for a 1-D tensors works mostly as expected. Like R, it is 1-based. Unlike R though, it accepts negative indices for indexing from the end of the array. (In R, negative indices are used to remove elements.)</p>
+<div class="sourceCode" id="cb2"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">10</span>)
+<span class="kw">x</span>[<span class="fl">1</span>]
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt; 1</span>
+<span class="co">#&gt; [ CPULongType{} ]</span>
+<span class="kw">x</span>[<span class="op">-</span><span class="fl">1</span>]
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt; 10</span>
+<span class="co">#&gt; [ CPULongType{} ]</span>
+</pre></div>
+<p>You can also subset matrices and higher dimensions arrays using the same syntax:</p>
+<div class="sourceCode" id="cb3"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="kw">x</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/stats/reshape.html">reshape</a></span>(shape = <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>,<span class="fl">5</span>))
+<span class="kw">x</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;   1   2   3   4   5</span>
+<span class="co">#&gt;   6   7   8   9  10</span>
+<span class="co">#&gt; [ CPULongType{2,5} ]</span>
+<span class="kw">x</span>[<span class="fl">1</span>,<span class="fl">3</span>]
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt; 3</span>
+<span class="co">#&gt; [ CPULongType{} ]</span>
+<span class="kw">x</span>[<span class="fl">1</span>,<span class="op">-</span><span class="fl">1</span>]
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt; 5</span>
+<span class="co">#&gt; [ CPULongType{} ]</span>
+</pre></div>
+<p>Note that if one indexes a multidimensional tensor with fewer indices than dimensions, one gets an error, unlike in R that would flatten the array. For example:</p>
+<div class="sourceCode" id="cb4"><pre class="downlit">
+<span class="kw">x</span>[<span class="fl">1</span>]
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  2</span>
+<span class="co">#&gt;  3</span>
+<span class="co">#&gt;  4</span>
+<span class="co">#&gt;  5</span>
+<span class="co">#&gt; [ CPULongType{5} ]</span>
+</pre></div>
+</div>
+<div id="slicing-and-striding" class="section level2">
+<h2 class="hasAnchor">
+<a href="#slicing-and-striding" class="anchor"></a>Slicing and striding</h2>
+<p>It is possible to slice and stride arrays to extract sub-arrays of the same number of dimensions, but of different sizes than the original. This is best illustrated by a few examples:</p>
+<div class="sourceCode" id="cb5"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">10</span>)
+<span class="kw">x</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;   1</span>
+<span class="co">#&gt;   2</span>
+<span class="co">#&gt;   3</span>
+<span class="co">#&gt;   4</span>
+<span class="co">#&gt;   5</span>
+<span class="co">#&gt;   6</span>
+<span class="co">#&gt;   7</span>
+<span class="co">#&gt;   8</span>
+<span class="co">#&gt;   9</span>
+<span class="co">#&gt;  10</span>
+<span class="co">#&gt; [ CPULongType{10} ]</span>
+<span class="kw">x</span>[<span class="fl">2</span><span class="op">:</span><span class="fl">5</span>]
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  2</span>
+<span class="co">#&gt;  3</span>
+<span class="co">#&gt;  4</span>
+<span class="co">#&gt;  5</span>
+<span class="co">#&gt; [ CPULongType{4} ]</span>
+<span class="kw">x</span>[<span class="fl">1</span><span class="op">:</span>(<span class="op">-</span><span class="fl">7</span>)]
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  2</span>
+<span class="co">#&gt;  3</span>
+<span class="co">#&gt;  4</span>
+<span class="co">#&gt; [ CPULongType{4} ]</span>
+</pre></div>
+<p>You can also use the <code>1:10:2</code> syntax which means: In the range from 1 to 10, take every second item. For example:</p>
+<div class="sourceCode" id="cb6"><pre class="downlit">
+<span class="kw">x</span>[<span class="fl">1</span><span class="op">:</span><span class="fl">5</span><span class="op">:</span><span class="fl">2</span>]
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  3</span>
+<span class="co">#&gt;  5</span>
+<span class="co">#&gt; [ CPULongType{3} ]</span>
+</pre></div>
+<p>Another special syntax is the <code>N</code>, meaning the size of the specified dimension.</p>
+<div class="sourceCode" id="cb7"><pre class="downlit">
+<span class="kw">x</span>[<span class="fl">5</span><span class="op">:</span><span class="kw">N</span>]
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;   5</span>
+<span class="co">#&gt;   6</span>
+<span class="co">#&gt;   7</span>
+<span class="co">#&gt;   8</span>
+<span class="co">#&gt;   9</span>
+<span class="co">#&gt;  10</span>
+<span class="co">#&gt; [ CPULongType{6} ]</span>
+</pre></div>
+</div>
+<div id="getting-the-complete-dimension" class="section level2">
+<h2 class="hasAnchor">
+<a href="#getting-the-complete-dimension" class="anchor"></a>Getting the complete dimension</h2>
+<p>Like in R, you can take all elements in a dimension by leaving an index empty.</p>
+<p>Consider a matrix:</p>
+<div class="sourceCode" id="cb8"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">2</span>, <span class="fl">3</span>)
+<span class="kw">x</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt; -0.8220  1.2598  0.9492</span>
+<span class="co">#&gt; -1.2370  1.2724 -0.1865</span>
+<span class="co">#&gt; [ CPUFloatType{2,3} ]</span>
+</pre></div>
+<p>The following syntax will give you the first row:</p>
+<div class="sourceCode" id="cb9"><pre class="downlit">
+<span class="kw">x</span>[<span class="fl">1</span>,]
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt; -0.8220</span>
+<span class="co">#&gt;  1.2598</span>
+<span class="co">#&gt;  0.9492</span>
+<span class="co">#&gt; [ CPUFloatType{3} ]</span>
+</pre></div>
+<p>And this would give you the first 2 columns:</p>
+<div class="sourceCode" id="cb10"><pre class="downlit">
+<span class="kw">x</span>[,<span class="fl">1</span><span class="op">:</span><span class="fl">2</span>]
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt; -0.8220  1.2598</span>
+<span class="co">#&gt; -1.2370  1.2724</span>
+<span class="co">#&gt; [ CPUFloatType{2,2} ]</span>
+</pre></div>
+</div>
+<div id="dropping-dimensions" class="section level2">
+<h2 class="hasAnchor">
+<a href="#dropping-dimensions" class="anchor"></a>Dropping dimensions</h2>
+<p>By default, when indexing by a single integer, this dimension will be dropped to avoid the singleton dimension:</p>
+<div class="sourceCode" id="cb11"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">2</span>, <span class="fl">3</span>)
+<span class="kw">x</span>[<span class="fl">1</span>,]<span class="op">$</span><span class="kw">shape</span>
+<span class="co">#&gt; [1] 3</span>
+</pre></div>
+<p>You can optionally use the <code>drop = FALSE</code> argument to avoid dropping the dimension.</p>
+<div class="sourceCode" id="cb12"><pre class="downlit">
+<span class="kw">x</span>[<span class="fl">1</span>,,drop = <span class="fl">FALSE</span>]<span class="op">$</span><span class="kw">shape</span>
+<span class="co">#&gt; [1] 1 3</span>
+</pre></div>
+</div>
+<div id="adding-a-new-dimension" class="section level2">
+<h2 class="hasAnchor">
+<a href="#adding-a-new-dimension" class="anchor"></a>Adding a new dimension</h2>
+<p>It’s possible to add a new dimension to a tensor using index-like syntax:</p>
+<div class="sourceCode" id="cb13"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">10</span>))
+<span class="kw">x</span><span class="op">$</span><span class="kw">shape</span>
+<span class="co">#&gt; [1] 1</span>
+<span class="kw">x</span>[, <span class="kw">newaxis</span>]<span class="op">$</span><span class="kw">shape</span>
+<span class="co">#&gt; [1] 1 1</span>
+<span class="kw">x</span>[, <span class="kw">newaxis</span>, <span class="kw">newaxis</span>]<span class="op">$</span><span class="kw">shape</span>
+<span class="co">#&gt; [1] 1 1 1</span>
+</pre></div>
+<p>You can also use <code>NULL</code> instead of <code>newaxis</code>:</p>
+<div class="sourceCode" id="cb14"><pre class="downlit">
+<span class="kw">x</span>[,<span class="kw">NULL</span>]<span class="op">$</span><span class="kw">shape</span>
+<span class="co">#&gt; [1] 1 1</span>
+</pre></div>
+</div>
+<div id="dealing-with-variable-number-of-indices" class="section level2">
+<h2 class="hasAnchor">
+<a href="#dealing-with-variable-number-of-indices" class="anchor"></a>Dealing with variable number of indices</h2>
+<p>Sometimes we don’t know how many dimensions a tensor has, but we do know what to do with the last available dimension, or the first one. To subsume all others, we can use <code>..</code>:</p>
+<div class="sourceCode" id="cb15"><pre class="downlit">
+<span class="kw">z</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">125</span>)<span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/stats/reshape.html">reshape</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">5</span>,<span class="fl">5</span>,<span class="fl">5</span>))
+<span class="kw">z</span>[<span class="fl">1</span>,<span class="kw">..</span>]
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;   1   2   3   4   5</span>
+<span class="co">#&gt;   6   7   8   9  10</span>
+<span class="co">#&gt;  11  12  13  14  15</span>
+<span class="co">#&gt;  16  17  18  19  20</span>
+<span class="co">#&gt;  21  22  23  24  25</span>
+<span class="co">#&gt; [ CPULongType{5,5} ]</span>
+<span class="kw">z</span>[<span class="kw">..</span>,<span class="fl">1</span>]
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;    1    6   11   16   21</span>
+<span class="co">#&gt;   26   31   36   41   46</span>
+<span class="co">#&gt;   51   56   61   66   71</span>
+<span class="co">#&gt;   76   81   86   91   96</span>
+<span class="co">#&gt;  101  106  111  116  121</span>
+<span class="co">#&gt; [ CPULongType{5,5} ]</span>
+</pre></div>
+</div>
+  </div>
+
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@@ -0,0 +1,15 @@
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diff --git a/static/docs/dev/articles/loading-data.html b/static/docs/dev/articles/loading-data.html
new file mode 100644
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+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
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+  <div class="col-md-9 contents">
+    <div class="page-header toc-ignore">
+      <h1 data-toc-skip>Loading data</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/loading-data.Rmd"><code>vignettes/loading-data.Rmd</code></a></small>
+      <div class="hidden name"><code>loading-data.Rmd</code></div>
+
+    </div>
+
+    
+    
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
+</pre></div>
+<div id="datasets-and-data-loaders" class="section level2">
+<h2 class="hasAnchor">
+<a href="#datasets-and-data-loaders" class="anchor"></a>Datasets and data loaders</h2>
+<p>Central to data ingestion and preprocessing are datasets and data loaders.</p>
+<p><code>torch</code> comes equipped with a bag of datasets related to, mostly, image recognition and natural language processing (e.g., <code>mnist_dataset()</code>), which can be iterated over by means of <code>dataloader</code>s:</p>
+<div class="sourceCode" id="cb2"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1"></a><span class="co"># ...</span></span>
+<span id="cb2-2"><a href="#cb2-2"></a>ds &lt;-<span class="st"> </span><span class="kw">mnist_dataset</span>(</span>
+<span id="cb2-3"><a href="#cb2-3"></a>  dir, </span>
+<span id="cb2-4"><a href="#cb2-4"></a>  <span class="dt">download =</span> <span class="ot">TRUE</span>, </span>
+<span id="cb2-5"><a href="#cb2-5"></a>  <span class="dt">transform =</span> <span class="cf">function</span>(x) {</span>
+<span id="cb2-6"><a href="#cb2-6"></a>    x &lt;-<span class="st"> </span>x<span class="op">$</span><span class="kw">to</span>(<span class="dt">dtype =</span> <span class="kw">torch_float</span>())<span class="op">/</span><span class="dv">256</span></span>
+<span id="cb2-7"><a href="#cb2-7"></a>    x[newaxis,..]</span>
+<span id="cb2-8"><a href="#cb2-8"></a>  }</span>
+<span id="cb2-9"><a href="#cb2-9"></a>)</span>
+<span id="cb2-10"><a href="#cb2-10"></a></span>
+<span id="cb2-11"><a href="#cb2-11"></a>dl &lt;-<span class="st"> </span><span class="kw">dataloader</span>(ds, <span class="dt">batch_size =</span> <span class="dv">32</span>, <span class="dt">shuffle =</span> <span class="ot">TRUE</span>)</span>
+<span id="cb2-12"><a href="#cb2-12"></a></span>
+<span id="cb2-13"><a href="#cb2-13"></a><span class="cf">for</span> (b <span class="cf">in</span> <span class="kw">enumerate</span>(dl)) {</span>
+<span id="cb2-14"><a href="#cb2-14"></a>  <span class="co"># ...</span></span></code></pre></div>
+<p>Cf. <code>vignettes/examples/mnist-cnn.R</code> for a complete example.</p>
+<p>What if you want to train on a different dataset? In these cases, you subclass <code>Dataset</code>, an abstract container that needs to know how to iterate over the given data. To that purpose, your subclass needs to implement <code>.getitem()</code>, and say what should be returned when the data loader is asking for the next batch.</p>
+<p>In <code>.getitem()</code>, you can implement whatever preprocessing you require. Additionally, you should implement <code>.length()</code>, so users can find out how many items there are in the dataset.</p>
+<p>While this may sound complicated, it is not at all. The base logic is straightforward – complexity will, naturally, correlate with how involved your preprocessing is. To provide you with a simple but functional prototype, here we show how to create your own <code>dataset</code> to train on <a href="https://github.com/allisonhorst/palmerpenguins">Allison Horst's penguins</a>.</p>
+</div>
+<div id="a-custom-dataset" class="section level2">
+<h2 class="hasAnchor">
+<a href="#a-custom-dataset" class="anchor"></a>A custom dataset</h2>
+<div class="sourceCode" id="cb3"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://allisonhorst.github.io/palmerpenguins">palmerpenguins</a></span>)
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw">magrittr</span>)
+
+<span class="kw">penguins</span>
+<span class="co">#&gt; # A tibble: 344 x 8</span>
+<span class="co">#&gt;    species island bill_length_mm bill_depth_mm flipper_length_… body_mass_g</span>
+<span class="co">#&gt;    &lt;fct&gt;   &lt;fct&gt;           &lt;dbl&gt;         &lt;dbl&gt;            &lt;int&gt;       &lt;int&gt;</span>
+<span class="co">#&gt;  1 Adelie  Torge…           39.1          18.7              181        3750</span>
+<span class="co">#&gt;  2 Adelie  Torge…           39.5          17.4              186        3800</span>
+<span class="co">#&gt;  3 Adelie  Torge…           40.3          18                195        3250</span>
+<span class="co">#&gt;  4 Adelie  Torge…           NA            NA                 NA          NA</span>
+<span class="co">#&gt;  5 Adelie  Torge…           36.7          19.3              193        3450</span>
+<span class="co">#&gt;  6 Adelie  Torge…           39.3          20.6              190        3650</span>
+<span class="co">#&gt;  7 Adelie  Torge…           38.9          17.8              181        3625</span>
+<span class="co">#&gt;  8 Adelie  Torge…           39.2          19.6              195        4675</span>
+<span class="co">#&gt;  9 Adelie  Torge…           34.1          18.1              193        3475</span>
+<span class="co">#&gt; 10 Adelie  Torge…           42            20.2              190        4250</span>
+<span class="co">#&gt; # … with 334 more rows, and 2 more variables: sex &lt;fct&gt;, year &lt;int&gt;</span>
+</pre></div>
+<p><code>Dataset</code>s are R6 classes created using the <code><a href="../reference/dataset.html">dataset()</a></code> constructor. You can pass a <code>name</code> and various member functions. Among those should be <code>initialize()</code>, to create instance variables, <code>.getitem()</code>, to indicate how the data should be returned, and <code>.length()</code>, to say how many items we have.</p>
+<p>In addition, any number of helper functions can be defined.</p>
+<p>Here, we assume the <code>penguins</code> have already been loaded, and all preprocessing consists in removing lines with <code>NA</code> values, transforming <code>factor</code>s to numbers starting from 0, and converting from R data types to <code>torch</code> tensors.</p>
+<p>In <code>.getitem</code>, we essentially decide how this data is going to be used: All variables besides <code>species</code> go into <code>x</code>, the predictor, and <code>species</code> will constitute <code>y</code>, the target. Predictor and target are returned in a list, to be accessed as <code>batch[[1]]</code> and <code>batch[[2]]</code> during training.</p>
+<div class="sourceCode" id="cb4"><pre class="downlit">
+<span class="kw">penguins_dataset</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/dataset.html">dataset</a></span>(
+  
+  name = <span class="st">"penguins_dataset"</span>,
+  
+  initialize = <span class="fu">function</span>() {
+    <span class="kw">self</span><span class="op">$</span><span class="kw">data</span> <span class="op">&lt;-</span> <span class="kw">self</span><span class="op">$</span><span class="fu">prepare_penguin_data</span>()
+  },
+  
+  .getitem = <span class="fu">function</span>(<span class="kw">index</span>) {
+    
+    <span class="kw">x</span> <span class="op">&lt;-</span> <span class="kw">self</span><span class="op">$</span><span class="kw">data</span>[<span class="kw">index</span>, <span class="fl">2</span><span class="op">:</span><span class="op">-</span><span class="fl">1</span>]
+    <span class="kw">y</span> <span class="op">&lt;-</span> <span class="kw">self</span><span class="op">$</span><span class="kw">data</span>[<span class="kw">index</span>, <span class="fl">1</span>]<span class="op">$</span><span class="fu">to</span>(<span class="fu"><a href="../reference/torch_dtype.html">torch_long</a></span>())
+    
+    <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="kw">x</span>, <span class="kw">y</span>)
+  },
+  
+  .length = <span class="fu">function</span>() {
+    <span class="kw">self</span><span class="op">$</span><span class="kw">data</span><span class="op">$</span><span class="fu">size</span>()[[<span class="fl">1</span>]]
+  },
+  
+  prepare_penguin_data = <span class="fu">function</span>() {
+    
+    <span class="kw">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/stats/na.fail.html">na.omit</a></span>(<span class="kw">penguins</span>) 
+    <span class="co"># conveniently, the categorical data are already factors</span>
+    <span class="kw">input</span><span class="op">$</span><span class="kw">species</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="kw">input</span><span class="op">$</span><span class="kw">species</span>)
+    <span class="kw">input</span><span class="op">$</span><span class="kw">island</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="kw">input</span><span class="op">$</span><span class="kw">island</span>)
+    <span class="kw">input</span><span class="op">$</span><span class="kw">sex</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/numeric.html">as.numeric</a></span>(<span class="kw">input</span><span class="op">$</span><span class="kw">sex</span>)
+    
+    <span class="kw">input</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html">as.matrix</a></span>(<span class="kw">input</span>)
+    <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="kw">input</span>)
+  }
+)
+</pre></div>
+<p>Let’s create the dataset , query for it’s length, and look at its first item:</p>
+<div class="sourceCode" id="cb5"><pre class="downlit">
+<span class="kw">tuxes</span> <span class="op">&lt;-</span> <span class="fu">penguins_dataset</span>()
+<span class="kw">tuxes</span><span class="op">$</span><span class="fu">.length</span>()
+<span class="co">#&gt; [1] 333</span>
+<span class="kw">tuxes</span><span class="op">$</span><span class="fu">.getitem</span>(<span class="fl">1</span>)
+<span class="co">#&gt; [[1]]</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;     3.0000</span>
+<span class="co">#&gt;    39.1000</span>
+<span class="co">#&gt;    18.7000</span>
+<span class="co">#&gt;   181.0000</span>
+<span class="co">#&gt;  3750.0000</span>
+<span class="co">#&gt;     2.0000</span>
+<span class="co">#&gt;  2007.0000</span>
+<span class="co">#&gt; [ CPUFloatType{7} ]</span>
+<span class="co">#&gt; </span>
+<span class="co">#&gt; [[2]]</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt; 1</span>
+<span class="co">#&gt; [ CPULongType{} ]</span>
+</pre></div>
+<p>To be able to iterate over <code>tuxes</code>, we need a data loader (we override the default batch size of <code>1</code>):</p>
+<div class="sourceCode" id="cb6"><pre class="downlit">
+<span class="kw">dl</span> <span class="op">&lt;-</span><span class="kw">tuxes</span> <span class="op">%&gt;%</span> <span class="fu"><a href="../reference/dataloader.html">dataloader</a></span>(batch_size = <span class="fl">8</span>)
+</pre></div>
+<p>Calling <code>.length()</code> on a data loader (as opposed to a dataset) will return the number of <code>batches</code> we have:</p>
+<div class="sourceCode" id="cb7"><pre class="downlit">
+<span class="kw">dl</span><span class="op">$</span><span class="fu">.length</span>()
+<span class="co">#&gt; [1] 42</span>
+</pre></div>
+<p>And we can create an iterator to inspect the first batch:</p>
+<div class="sourceCode" id="cb8"><pre class="downlit">
+<span class="kw">iter</span> <span class="op">&lt;-</span> <span class="kw">dl</span><span class="op">$</span><span class="fu">.iter</span>()
+<span class="kw">b</span> <span class="op">&lt;-</span> <span class="kw">iter</span><span class="op">$</span><span class="fu">.next</span>()
+<span class="kw">b</span>
+<span class="co">#&gt; [[1]]</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;     3.0000    39.1000    18.7000   181.0000  3750.0000     2.0000  2007.0000</span>
+<span class="co">#&gt;     3.0000    39.5000    17.4000   186.0000  3800.0000     1.0000  2007.0000</span>
+<span class="co">#&gt;     3.0000    40.3000    18.0000   195.0000  3250.0000     1.0000  2007.0000</span>
+<span class="co">#&gt;     3.0000    36.7000    19.3000   193.0000  3450.0000     1.0000  2007.0000</span>
+<span class="co">#&gt;     3.0000    39.3000    20.6000   190.0000  3650.0000     2.0000  2007.0000</span>
+<span class="co">#&gt;     3.0000    38.9000    17.8000   181.0000  3625.0000     1.0000  2007.0000</span>
+<span class="co">#&gt;     3.0000    39.2000    19.6000   195.0000  4675.0000     2.0000  2007.0000</span>
+<span class="co">#&gt;     3.0000    41.1000    17.6000   182.0000  3200.0000     1.0000  2007.0000</span>
+<span class="co">#&gt; [ CPUFloatType{8,7} ]</span>
+<span class="co">#&gt; </span>
+<span class="co">#&gt; [[2]]</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt; [ CPULongType{8} ]</span>
+</pre></div>
+<p>To train a network, we can use <code>enumerate</code> to iterate over batches.</p>
+</div>
+<div id="training-with-data-loaders" class="section level2">
+<h2 class="hasAnchor">
+<a href="#training-with-data-loaders" class="anchor"></a>Training with data loaders</h2>
+<p>Our example network is very simple. (In reality, we would want to treat <code>island</code> as the categorical variable it is, and either one-hot-encode or embed it.)</p>
+<div class="sourceCode" id="cb9"><pre class="downlit">
+<span class="kw">net</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/nn_module.html">nn_module</a></span>(
+  <span class="st">"PenguinNet"</span>,
+  initialize = <span class="fu">function</span>() {
+    <span class="kw">self</span><span class="op">$</span><span class="kw">fc1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/nn_linear.html">nn_linear</a></span>(<span class="fl">7</span>, <span class="fl">32</span>)
+    <span class="kw">self</span><span class="op">$</span><span class="kw">fc2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/nn_linear.html">nn_linear</a></span>(<span class="fl">32</span>, <span class="fl">3</span>)
+  },
+  forward = <span class="fu">function</span>(<span class="kw">x</span>) {
+    <span class="kw">x</span> <span class="op">%&gt;%</span> 
+      <span class="kw">self</span><span class="op">$</span><span class="fu">fc1</span>() <span class="op">%&gt;%</span> 
+      <span class="fu"><a href="../reference/nnf_relu.html">nnf_relu</a></span>() <span class="op">%&gt;%</span> 
+      <span class="kw">self</span><span class="op">$</span><span class="fu">fc2</span>() <span class="op">%&gt;%</span> 
+      <span class="fu"><a href="../reference/nnf_log_softmax.html">nnf_log_softmax</a></span>(dim = <span class="fl">1</span>)
+  }
+)
+
+<span class="kw">model</span> <span class="op">&lt;-</span> <span class="fu">net</span>()
+</pre></div>
+<p>We still need an optimizer:</p>
+<div class="sourceCode" id="cb10"><pre class="downlit">
+<span class="kw">optimizer</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/optim_sgd.html">optim_sgd</a></span>(<span class="kw">model</span><span class="op">$</span><span class="kw">parameters</span>, lr = <span class="fl">0.01</span>)
+</pre></div>
+<p>And we’re ready to train:</p>
+<div class="sourceCode" id="cb11"><pre class="downlit">
+<span class="co">for</span> (<span class="kw">epoch</span> <span class="co">in</span> <span class="fl">1</span><span class="op">:</span><span class="fl">10</span>) {
+  
+  <span class="kw">l</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>()
+  
+  <span class="co">for</span> (<span class="kw">b</span> <span class="co">in</span> <span class="fu"><a href="../reference/enumerate.html">enumerate</a></span>(<span class="kw">dl</span>)) {
+    <span class="kw">optimizer</span><span class="op">$</span><span class="fu">zero_grad</span>()
+    <span class="kw">output</span> <span class="op">&lt;-</span> <span class="fu">model</span>(<span class="kw">b</span>[[<span class="fl">1</span>]])
+    <span class="kw">loss</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/nnf_nll_loss.html">nnf_nll_loss</a></span>(<span class="kw">output</span>, <span class="kw">b</span>[[<span class="fl">2</span>]])
+    <span class="kw">loss</span><span class="op">$</span><span class="fu">backward</span>()
+    <span class="kw">optimizer</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/stats/step.html">step</a></span>()
+    <span class="kw">l</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="kw">l</span>, <span class="kw">loss</span><span class="op">$</span><span class="fu">item</span>())
+  }
+  
+  <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/sprintf.html">sprintf</a></span>(<span class="st">"Loss at epoch %d: %3f\n"</span>, <span class="kw">epoch</span>, <span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>(<span class="kw">l</span>)))
+}
+<span class="co">#&gt; Loss at epoch 1: 51.747068</span>
+<span class="co">#&gt; Loss at epoch 2: 2.068251</span>
+<span class="co">#&gt; Loss at epoch 3: 2.068251</span>
+<span class="co">#&gt; Loss at epoch 4: 2.068251</span>
+<span class="co">#&gt; Loss at epoch 5: 2.068251</span>
+<span class="co">#&gt; Loss at epoch 6: 2.068251</span>
+<span class="co">#&gt; Loss at epoch 7: 2.068251</span>
+<span class="co">#&gt; Loss at epoch 8: 2.068251</span>
+<span class="co">#&gt; Loss at epoch 9: 2.068251</span>
+<span class="co">#&gt; Loss at epoch 10: 2.068251</span>
+</pre></div>
+</div>
+  </div>
+
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/articles/loading-data_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/dev/articles/loading-data_files/accessible-code-block-0.0.1/empty-anchor.js
new file mode 100644
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@@ -0,0 +1,15 @@
+// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
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+document.addEventListener('DOMContentLoaded', function() {
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diff --git a/static/docs/dev/articles/tensor-creation.html b/static/docs/dev/articles/tensor-creation.html
new file mode 100644
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+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
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+  <div class="col-md-9 contents">
+    <div class="page-header toc-ignore">
+      <h1 data-toc-skip>Creating tensors</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/tensor-creation.Rmd"><code>vignettes/tensor-creation.Rmd</code></a></small>
+      <div class="hidden name"><code>tensor-creation.Rmd</code></div>
+
+    </div>
+
+    
+    
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
+</pre></div>
+<p>In this article we describe various ways of creating <code>torch</code> tensors in R.</p>
+<div id="from-r-objects" class="section level2">
+<h2 class="hasAnchor">
+<a href="#from-r-objects" class="anchor"></a>From R objects</h2>
+<p>You can create tensors from R objects using the <code>torch_tensor</code> function. The <code>torch_tensor</code> function takes an R vector, matrix or array and creates an equivalent <code>torch_tensor</code>.</p>
+<p>You can see a few examples below:</p>
+<div class="sourceCode" id="cb2"><pre class="downlit">
+<span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1</span>,<span class="fl">2</span>,<span class="fl">3</span>))
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt;  2</span>
+<span class="co">#&gt;  3</span>
+<span class="co">#&gt; [ CPUFloatType{3} ]</span>
+
+<span class="co"># conform to row-major indexing used in torch</span>
+<span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">10</span>, ncol = <span class="fl">5</span>, nrow = <span class="fl">2</span>, byrow = <span class="fl">TRUE</span>))
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;   1   2   3   4   5</span>
+<span class="co">#&gt;   6   7   8   9  10</span>
+<span class="co">#&gt; [ CPULongType{2,5} ]</span>
+<span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/array.html">array</a></span>(<span class="fu"><a href="https://rdrr.io/r/stats/Uniform.html">runif</a></span>(<span class="fl">12</span>), dim = <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>, <span class="fl">2</span>, <span class="fl">3</span>)))
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt; (1,.,.) = </span>
+<span class="co">#&gt;   0.2270  0.0942  0.5878</span>
+<span class="co">#&gt;   0.1103  0.5365  0.2416</span>
+<span class="co">#&gt; </span>
+<span class="co">#&gt; (2,.,.) = </span>
+<span class="co">#&gt;   0.9161  0.4202  0.3500</span>
+<span class="co">#&gt;   0.3538  0.9176  0.5131</span>
+<span class="co">#&gt; [ CPUFloatType{2,2,3} ]</span>
+</pre></div>
+<p>By default, we will create tensors in the <code>cpu</code> device, converting their R datatype to the corresponding torch <code>dtype</code>.</p>
+<blockquote>
+<p><strong>Note</strong> currently, only numeric and boolean types are supported.</p>
+</blockquote>
+<p>You can always modify <code>dtype</code> and <code>device</code> when converting an R object to a torch tensor. For example:</p>
+<div class="sourceCode" id="cb3"><pre class="downlit">
+<span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1</span>, dtype = <span class="fu"><a href="../reference/torch_dtype.html">torch_long</a></span>())
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt; [ CPULongType{1} ]</span>
+<span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1</span>, device = <span class="st">"cpu"</span>, dtype = <span class="fu"><a href="../reference/torch_dtype.html">torch_float64</a></span>())
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt; [ CPUDoubleType{1} ]</span>
+</pre></div>
+<p>Other options available when creating a tensor are:</p>
+<ul>
+<li>
+<code>requires_grad</code>: boolean indicating if you want <code>autograd</code> to record operations on them for automatic differentiation.</li>
+<li>
+<code>pin_memory</code>: – If set, the tensor returned would be allocated in pinned memory. Works only for CPU tensors.</li>
+</ul>
+<p>These options are available for all functions that can be used to create new tensors, including the factory functions listed in the next section.</p>
+</div>
+<div id="using-creation-functions" class="section level2">
+<h2 class="hasAnchor">
+<a href="#using-creation-functions" class="anchor"></a>Using creation functions</h2>
+<p>You can also use the <code>torch_*</code> functions listed below to create torch tensors using some algorithm.</p>
+<p>For example, the <code>torch_randn</code> function will create tensors using the normal distribution with mean 0 and standard deviation 1. You can use the <code>...</code> argument to pass the size of the dimensions. For example, the code below will create a normally distributed tensor with shape 5x3.</p>
+<div class="sourceCode" id="cb4"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">5</span>, <span class="fl">3</span>)
+<span class="kw">x</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt; -0.0891 -1.7818 -0.3516</span>
+<span class="co">#&gt;  0.4988 -1.3215  1.0235</span>
+<span class="co">#&gt; -0.2183  1.8560 -0.4986</span>
+<span class="co">#&gt;  1.1953 -0.5551 -1.1393</span>
+<span class="co">#&gt; -0.4159  0.4346  1.6532</span>
+<span class="co">#&gt; [ CPUFloatType{5,3} ]</span>
+</pre></div>
+<p>Another example is <code>torch_ones</code>, which creates a tensor filled with ones.</p>
+<div class="sourceCode" id="cb5"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_ones.html">torch_ones</a></span>(<span class="fl">2</span>, <span class="fl">4</span>, dtype = <span class="fu"><a href="../reference/torch_dtype.html">torch_int64</a></span>(), device = <span class="st">"cpu"</span>)
+<span class="kw">x</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  1  1  1  1</span>
+<span class="co">#&gt;  1  1  1  1</span>
+<span class="co">#&gt; [ CPULongType{2,4} ]</span>
+</pre></div>
+<p>Here is the full list of functions that can be used to bulk-create tensors in torch:</p>
+<ul>
+<li>
+<code>torch_arange</code>: Returns a tensor with a sequence of integers,</li>
+<li>
+<code>torch_empty</code>: Returns a tensor with uninitialized values,</li>
+<li>
+<code>torch_eye</code>: Returns an identity matrix,</li>
+<li>
+<code>torch_full</code>: Returns a tensor filled with a single value,</li>
+<li>
+<code>torch_linspace</code>: Returns a tensor with values linearly spaced in some interval,</li>
+<li>
+<code>torch_logspace</code>: Returns a tensor with values logarithmically spaced in some interval,</li>
+<li>
+<code>torch_ones</code>: Returns a tensor filled with all ones,</li>
+<li>
+<code>torch_rand</code>: Returns a tensor filled with values drawn from a uniform distribution on [0, 1).</li>
+<li>
+<code>torch_randint</code>: Returns a tensor with integers randomly drawn from an interval,</li>
+<li>
+<code>torch_randn</code>: Returns a tensor filled with values drawn from a unit normal distribution,</li>
+<li>
+<code>torch_randperm</code>: Returns a tensor filled with a random permutation of integers in some interval,</li>
+<li>
+<code>torch_zeros</code>: Returns a tensor filled with all zeros.</li>
+</ul>
+</div>
+<div id="conversion" class="section level2">
+<h2 class="hasAnchor">
+<a href="#conversion" class="anchor"></a>Conversion</h2>
+<p>Once a tensor exists you can convert between <code>dtype</code>s and move to a different device with <code>to</code> method. For example:</p>
+<div class="sourceCode" id="cb6"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1</span>)
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="kw">x</span><span class="op">$</span><span class="fu">to</span>(dtype = <span class="fu"><a href="../reference/torch_dtype.html">torch_int32</a></span>())
+<span class="kw">x</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt; [ CPUFloatType{1} ]</span>
+<span class="kw">y</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  1</span>
+<span class="co">#&gt; [ CPUIntType{1} ]</span>
+</pre></div>
+<p>You can also copy a tensor to the GPU using:</p>
+<div class="sourceCode" id="cb7"><pre class="sourceCode r"><code class="sourceCode r"><span id="cb7-1"><a href="#cb7-1"></a>x &lt;-<span class="st"> </span><span class="kw">torch_tensor</span>(<span class="dv">1</span>)</span>
+<span id="cb7-2"><a href="#cb7-2"></a>y &lt;-<span class="st"> </span>x<span class="op">$</span><span class="kw">cuda</span>()<span class="er">)</span></span></code></pre></div>
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+      <h1 data-toc-skip>Tensor objects</h1>
+            
+      
+      <small class="dont-index">Source: <a href="https://github.com/mlverse/torch/blob/master/vignettes/tensor/index.Rmd"><code>vignettes/tensor/index.Rmd</code></a></small>
+      <div class="hidden name"><code>index.Rmd</code></div>
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+    
+    
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
+</pre></div>
+<p>Central to torch is the <code>torch_tensor</code> objects. <code>torch_tensor</code>’s are R objects very similar to R6 instances. Tensors have a large amount of methods that can be called using the <code><a href="https://rdrr.io/r/base/Extract.html">$</a></code> operator.</p>
+<p>Following is a list of all methods that can be called by tensor objects and their documentation. You can also look at <a href="https://pytorch.org/docs/stable/tensors.html">PyTorch’s documentation</a> for additional details.</p>
+<div id="t" class="section level2">
+<h2 class="hasAnchor">
+<a href="#t" class="anchor"></a>T</h2>
+<p>Is this Tensor with its dimensions reversed.</p>
+<p>If <code>n</code> is the number of dimensions in <code>x</code>, <code>x$T</code> is equivalent to <code>x$permute(n-1, n-2, ..., 0)</code>.</p>
+</div>
+<div id="abs" class="section level2">
+<h2 class="hasAnchor">
+<a href="#abs" class="anchor"></a>abs</h2>
+<p>abs() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_abs.html">?torch_abs</a></code></p>
+</div>
+<div id="abs_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#abs_" class="anchor"></a>abs_</h2>
+<p>abs_() -&gt; Tensor</p>
+<p>In-place version of <code>$abs</code></p>
+</div>
+<div id="absolute" class="section level2">
+<h2 class="hasAnchor">
+<a href="#absolute" class="anchor"></a>absolute</h2>
+<p>absolute() -&gt; Tensor</p>
+<p>Alias for [$abs()]</p>
+</div>
+<div id="absolute_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#absolute_" class="anchor"></a>absolute_</h2>
+<p>absolute_() -&gt; Tensor</p>
+<p>In-place version of <code>$absolute</code> Alias for [$abs_()]</p>
+</div>
+<div id="acos" class="section level2">
+<h2 class="hasAnchor">
+<a href="#acos" class="anchor"></a>acos</h2>
+<p>acos() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_acos.html">?torch_acos</a></code></p>
+</div>
+<div id="acos_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#acos_" class="anchor"></a>acos_</h2>
+<p>acos_() -&gt; Tensor</p>
+<p>In-place version of <code>$acos</code></p>
+</div>
+<div id="acosh" class="section level2">
+<h2 class="hasAnchor">
+<a href="#acosh" class="anchor"></a>acosh</h2>
+<p>acosh() -&gt; Tensor</p>
+<p>See <code>?torch_acosh</code></p>
+</div>
+<div id="acosh_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#acosh_" class="anchor"></a>acosh_</h2>
+<p>acosh_() -&gt; Tensor</p>
+<p>In-place version of <code>$acosh</code></p>
+</div>
+<div id="add" class="section level2">
+<h2 class="hasAnchor">
+<a href="#add" class="anchor"></a>add</h2>
+<p>add(other, *, alpha=1) -&gt; Tensor</p>
+<p>Add a scalar or tensor to <code>self</code> tensor. If both <code>alpha</code> and <code>other</code> are specified, each element of <code>other</code> is scaled by <code>alpha</code> before being used.</p>
+<p>When <code>other</code> is a tensor, the shape of <code>other</code> must be broadcastable with the shape of the underlying tensor</p>
+<p>See <code><a href="../../reference/torch_add.html">?torch_add</a></code></p>
+</div>
+<div id="add_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#add_" class="anchor"></a>add_</h2>
+<p>add_(other, *, alpha=1) -&gt; Tensor</p>
+<p>In-place version of <code>$add</code></p>
+</div>
+<div id="addbmm" class="section level2">
+<h2 class="hasAnchor">
+<a href="#addbmm" class="anchor"></a>addbmm</h2>
+<p>addbmm(batch1, batch2, *, beta=1, alpha=1) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_addbmm.html">?torch_addbmm</a></code></p>
+</div>
+<div id="addbmm_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#addbmm_" class="anchor"></a>addbmm_</h2>
+<p>addbmm_(batch1, batch2, *, beta=1, alpha=1) -&gt; Tensor</p>
+<p>In-place version of <code>$addbmm</code></p>
+</div>
+<div id="addcdiv" class="section level2">
+<h2 class="hasAnchor">
+<a href="#addcdiv" class="anchor"></a>addcdiv</h2>
+<p>addcdiv(tensor1, tensor2, *, value=1) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_addcdiv.html">?torch_addcdiv</a></code></p>
+</div>
+<div id="addcdiv_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#addcdiv_" class="anchor"></a>addcdiv_</h2>
+<p>addcdiv_(tensor1, tensor2, *, value=1) -&gt; Tensor</p>
+<p>In-place version of <code>$addcdiv</code></p>
+</div>
+<div id="addcmul" class="section level2">
+<h2 class="hasAnchor">
+<a href="#addcmul" class="anchor"></a>addcmul</h2>
+<p>addcmul(tensor1, tensor2, *, value=1) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_addcmul.html">?torch_addcmul</a></code></p>
+</div>
+<div id="addcmul_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#addcmul_" class="anchor"></a>addcmul_</h2>
+<p>addcmul_(tensor1, tensor2, *, value=1) -&gt; Tensor</p>
+<p>In-place version of <code>$addcmul</code></p>
+</div>
+<div id="addmm" class="section level2">
+<h2 class="hasAnchor">
+<a href="#addmm" class="anchor"></a>addmm</h2>
+<p>addmm(mat1, mat2, *, beta=1, alpha=1) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_addmm.html">?torch_addmm</a></code></p>
+</div>
+<div id="addmm_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#addmm_" class="anchor"></a>addmm_</h2>
+<p>addmm_(mat1, mat2, *, beta=1, alpha=1) -&gt; Tensor</p>
+<p>In-place version of <code>$addmm</code></p>
+</div>
+<div id="addmv" class="section level2">
+<h2 class="hasAnchor">
+<a href="#addmv" class="anchor"></a>addmv</h2>
+<p>addmv(mat, vec, *, beta=1, alpha=1) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_addmv.html">?torch_addmv</a></code></p>
+</div>
+<div id="addmv_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#addmv_" class="anchor"></a>addmv_</h2>
+<p>addmv_(mat, vec, *, beta=1, alpha=1) -&gt; Tensor</p>
+<p>In-place version of <code>$addmv</code></p>
+</div>
+<div id="addr" class="section level2">
+<h2 class="hasAnchor">
+<a href="#addr" class="anchor"></a>addr</h2>
+<p>addr(vec1, vec2, *, beta=1, alpha=1) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_addr.html">?torch_addr</a></code></p>
+</div>
+<div id="addr_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#addr_" class="anchor"></a>addr_</h2>
+<p>addr_(vec1, vec2, *, beta=1, alpha=1) -&gt; Tensor</p>
+<p>In-place version of <code>$addr</code></p>
+</div>
+<div id="align_as" class="section level2">
+<h2 class="hasAnchor">
+<a href="#align_as" class="anchor"></a>align_as</h2>
+<p>align_as(other) -&gt; Tensor</p>
+<p>Permutes the dimensions of the <code>self</code> tensor to match the dimension order in the <code>other</code> tensor, adding size-one dims for any new names.</p>
+<p>This operation is useful for explicit broadcasting by names (see examples).</p>
+<p>All of the dims of <code>self</code> must be named in order to use this method. The resulting tensor is a view on the original tensor.</p>
+<p>All dimension names of <code>self</code> must be present in <code>other$names</code>. <code>other</code> may contain named dimensions that are not in <code>self$names</code>; the output tensor has a size-one dimension for each of those new names.</p>
+<p>To align a tensor to a specific order, use <code>$align_to</code>.</p>
+<div id="examples" class="section level3">
+<h3 class="hasAnchor">
+<a href="#examples" class="anchor"></a>Examples:</h3>
+<div class="sourceCode" id="cb2"><pre class="downlit">
+<span class="co"># Example 1: Applying a mask</span>
+<span class="kw">mask</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randint.html">torch_randint</a></span>(low = <span class="fl">0</span>, high = <span class="fl">2</span>, size = <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">127</span>, <span class="fl">128</span>), dtype=<span class="fu"><a href="../../reference/torch_dtype.html">torch_bool</a></span>())<span class="op">$</span><span class="fu">refine_names</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">'W'</span>, <span class="st">'H'</span>))
+<span class="kw">imgs</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">32</span>, <span class="fl">128</span>, <span class="fl">127</span>, <span class="fl">3</span>, names=<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">'N'</span>, <span class="st">'H'</span>, <span class="st">'W'</span>, <span class="st">'C'</span>))
+<span class="kw">imgs</span><span class="op">$</span><span class="fu">masked_fill_</span>(<span class="kw">mask</span><span class="op">$</span><span class="fu">align_as</span>(<span class="kw">imgs</span>), <span class="fl">0</span>)
+
+<span class="co"># Example 2: Applying a per-channel-scale</span>
+<span class="kw">scale_channels</span> <span class="op">&lt;-</span> <span class="fu">function</span>(<span class="kw">input</span>, <span class="kw">scale</span>) {
+  <span class="kw">scale</span> <span class="op">&lt;-</span> <span class="kw">scale</span><span class="op">$</span><span class="fu">refine_names</span>(<span class="st">"C"</span>)
+  <span class="kw">input</span> <span class="op">*</span> <span class="kw">scale</span><span class="op">$</span><span class="fu">align_as</span>(<span class="kw">input</span>)
+}
+
+<span class="kw">num_channels</span> <span class="op">&lt;-</span> <span class="fl">3</span>
+<span class="kw">scale</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">num_channels</span>, names=<span class="st">'C'</span>)
+<span class="kw">imgs</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">32</span>, <span class="fl">128</span>, <span class="fl">128</span>, <span class="kw">num_channels</span>, names=<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">'N'</span>, <span class="st">'H'</span>, <span class="st">'W'</span>, <span class="st">'C'</span>))
+<span class="kw">more_imgs</span> <span class="op">=</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">32</span>, <span class="kw">num_channels</span>, <span class="fl">128</span>, <span class="fl">128</span>, names=<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">'N'</span>, <span class="st">'C'</span>, <span class="st">'H'</span>, <span class="st">'W'</span>))
+<span class="kw">videos</span> <span class="op">=</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">3</span>, <span class="kw">num_channels</span>, <span class="fl">128</span>, <span class="fl">128</span>, <span class="fl">128</span>, names=<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">'N'</span>, <span class="st">'C'</span>, <span class="st">'H'</span>, <span class="st">'W'</span>, <span class="st">'D'</span>))
+
+<span class="co"># scale_channels is agnostic to the dimension order of the input</span>
+<span class="fu">scale_channels</span>(<span class="kw">imgs</span>, <span class="kw">scale</span>)
+<span class="fu">scale_channels</span>(<span class="kw">more_imgs</span>, <span class="kw">scale</span>)
+<span class="fu">scale_channels</span>(<span class="kw">videos</span>, <span class="kw">scale</span>)
+</pre></div>
+</div>
+<div id="warning" class="section level3">
+<h3 class="hasAnchor">
+<a href="#warning" class="anchor"></a>Warning:</h3>
+<p>The named tensor API is experimental and subject to change.</p>
+</div>
+</div>
+<div id="align_to" class="section level2">
+<h2 class="hasAnchor">
+<a href="#align_to" class="anchor"></a>align_to</h2>
+<p>Permutes the dimensions of the <code>self</code> tensor to match the order specified in <code>names</code>, adding size-one dims for any new names.</p>
+<p>All of the dims of <code>self</code> must be named in order to use this method. The resulting tensor is a view on the original tensor.</p>
+<p>All dimension names of <code>self</code> must be present in <code>names</code>. <code>names</code> may contain additional names that are not in <code>self$names</code>; the output tensor has a size-one dimension for each of those new names.</p>
+<div id="arguments" class="section level3">
+<h3 class="hasAnchor">
+<a href="#arguments" class="anchor"></a>Arguments:</h3>
+<ul>
+<li>names (iterable of str): The desired dimension ordering of the output tensor. May contain up to one Ellipsis that is expanded to all unmentioned dim names of <code>self</code>.</li>
+</ul>
+</div>
+<div id="examples-1" class="section level3">
+<h3 class="hasAnchor">
+<a href="#examples-1" class="anchor"></a>Examples:</h3>
+<div id="warning-1" class="section level4">
+<h4 class="hasAnchor">
+<a href="#warning-1" class="anchor"></a>Warning:</h4>
+<p>The named tensor API is experimental and subject to change.</p>
+</div>
+</div>
+</div>
+<div id="all" class="section level2">
+<h2 class="hasAnchor">
+<a href="#all" class="anchor"></a>all</h2>
+<p>all() -&gt; bool</p>
+<p>Returns TRUE if all elements in the tensor are TRUE, FALSE otherwise.</p>
+<div id="examples-2" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-2" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb3"><pre class="downlit">
+<span class="kw">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">1</span>, <span class="fl">2</span>)<span class="op">$</span><span class="fu">to</span>(dtype = <span class="fu"><a href="../../reference/torch_dtype.html">torch_bool</a></span>())
+<span class="kw">a</span>
+<span class="kw">a</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/all.html">all</a></span>()
+</pre></div>
+<p>all(dim, keepdim=FALSE, out=NULL) -&gt; Tensor</p>
+<p>Returns TRUE if all elements in each row of the tensor in the given dimension <code>dim</code> are TRUE, FALSE otherwise.</p>
+<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size as <code>input</code> except in the dimension <code>dim</code> where it is of size 1. Otherwise, <code>dim</code> is squeezed (see <code>?torch_squeeze()),</code> resulting in the output tensor having 1 fewer dimension than <code>input</code>.</p>
+</div>
+<div id="arguments-1" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-1" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>dim (int): the dimension to reduce</li>
+<li>keepdim (bool): whether the output tensor has <code>dim</code> retained or not</li>
+<li>out (Tensor, optional): the output tensor</li>
+</ul>
+</div>
+<div id="examples-3" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-3" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb4"><pre class="downlit">
+<span class="kw">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">4</span>, <span class="fl">2</span>)<span class="op">$</span><span class="fu">to</span>(dtype = <span class="fu"><a href="../../reference/torch_dtype.html">torch_bool</a></span>())
+<span class="kw">a</span>
+<span class="kw">a</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/all.html">all</a></span>(dim=<span class="fl">2</span>)
+<span class="kw">a</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/all.html">all</a></span>(dim=<span class="fl">1</span>)
+</pre></div>
+</div>
+</div>
+<div id="allclose" class="section level2">
+<h2 class="hasAnchor">
+<a href="#allclose" class="anchor"></a>allclose</h2>
+<p>allclose(other, rtol=1e-05, atol=1e-08, equal_nan=FALSE) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_allclose.html">?torch_allclose</a></code></p>
+</div>
+<div id="angle" class="section level2">
+<h2 class="hasAnchor">
+<a href="#angle" class="anchor"></a>angle</h2>
+<p>angle() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_angle.html">?torch_angle</a></code></p>
+</div>
+<div id="any" class="section level2">
+<h2 class="hasAnchor">
+<a href="#any" class="anchor"></a>any</h2>
+<p>any() -&gt; bool</p>
+<p>Returns TRUE if any elements in the tensor are TRUE, FALSE otherwise.</p>
+<div id="examples-4" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-4" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb5"><pre class="downlit">
+<span class="kw">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">1</span>, <span class="fl">2</span>)<span class="op">$</span><span class="fu">to</span>(dtype = <span class="fu"><a href="../../reference/torch_dtype.html">torch_bool</a></span>())
+<span class="kw">a</span>
+<span class="kw">a</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/any.html">any</a></span>()
+</pre></div>
+<p>any(dim, keepdim=FALSE, out=NULL) -&gt; Tensor</p>
+<p>Returns TRUE if any elements in each row of the tensor in the given dimension <code>dim</code> are TRUE, FALSE otherwise.</p>
+<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size as <code>input</code> except in the dimension <code>dim</code> where it is of size 1. Otherwise, <code>dim</code> is squeezed (see <code>?torch_squeeze()),</code> resulting in the output tensor having 1 fewer dimension than <code>input</code>.</p>
+</div>
+<div id="arguments-2" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-2" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>dim (int): the dimension to reduce</li>
+<li>keepdim (bool): whether the output tensor has <code>dim</code> retained or not</li>
+<li>out (Tensor, optional): the output tensor</li>
+</ul>
+</div>
+<div id="examples-5" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-5" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb6"><pre class="downlit">
+<span class="kw">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">4</span>, <span class="fl">2</span>) <span class="op">&lt;</span> <span class="fl">0</span>
+<span class="kw">a</span>
+<span class="kw">a</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/any.html">any</a></span>(<span class="fl">2</span>)
+<span class="kw">a</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/any.html">any</a></span>(<span class="fl">1</span>)
+</pre></div>
+</div>
+</div>
+<div id="apply_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#apply_" class="anchor"></a>apply_</h2>
+<p>apply_(callable) -&gt; Tensor</p>
+<p>Applies the function <code>callable</code> to each element in the tensor, replacing each element with the value returned by <code>callable</code>.</p>
+<div id="note" class="section level4">
+<h4 class="hasAnchor">
+<a href="#note" class="anchor"></a>Note:</h4>
+<p>This function only works with CPU tensors and should not be used in code sections that require high performance.</p>
+</div>
+</div>
+<div id="argmax" class="section level2">
+<h2 class="hasAnchor">
+<a href="#argmax" class="anchor"></a>argmax</h2>
+<p>argmax(dim=NULL, keepdim=FALSE) -&gt; LongTensor</p>
+<p>See <code><a href="../../reference/torch_argmax.html">?torch_argmax</a></code></p>
+</div>
+<div id="argmin" class="section level2">
+<h2 class="hasAnchor">
+<a href="#argmin" class="anchor"></a>argmin</h2>
+<p>argmin(dim=NULL, keepdim=FALSE) -&gt; LongTensor</p>
+<p>See <code><a href="../../reference/torch_argmin.html">?torch_argmin</a></code></p>
+</div>
+<div id="argsort" class="section level2">
+<h2 class="hasAnchor">
+<a href="#argsort" class="anchor"></a>argsort</h2>
+<p>argsort(dim=-1, descending=FALSE) -&gt; LongTensor</p>
+<p>See <code><a href="../../reference/torch_argsort.html">?torch_argsort</a></code></p>
+</div>
+<div id="as_strided" class="section level2">
+<h2 class="hasAnchor">
+<a href="#as_strided" class="anchor"></a>as_strided</h2>
+<p>as_strided(size, stride, storage_offset=0) -&gt; Tensor</p>
+<p>See [torch_as_strided()]</p>
+</div>
+<div id="as_subclass" class="section level2">
+<h2 class="hasAnchor">
+<a href="#as_subclass" class="anchor"></a>as_subclass</h2>
+<p>as_subclass(cls) -&gt; Tensor</p>
+<p>Makes a <code>cls</code> instance with the same data pointer as <code>self</code>. Changes in the output mirror changes in <code>self</code>, and the output stays attached to the autograd graph. <code>cls</code> must be a subclass of <code>Tensor</code>.</p>
+</div>
+<div id="asin" class="section level2">
+<h2 class="hasAnchor">
+<a href="#asin" class="anchor"></a>asin</h2>
+<p>asin() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_asin.html">?torch_asin</a></code></p>
+</div>
+<div id="asin_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#asin_" class="anchor"></a>asin_</h2>
+<p>asin_() -&gt; Tensor</p>
+<p>In-place version of <code>$asin</code></p>
+</div>
+<div id="asinh" class="section level2">
+<h2 class="hasAnchor">
+<a href="#asinh" class="anchor"></a>asinh</h2>
+<p>asinh() -&gt; Tensor</p>
+<p>See <code>?torch_asinh</code></p>
+</div>
+<div id="asinh_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#asinh_" class="anchor"></a>asinh_</h2>
+<p>asinh_() -&gt; Tensor</p>
+<p>In-place version of <code>$asinh</code></p>
+</div>
+<div id="atan" class="section level2">
+<h2 class="hasAnchor">
+<a href="#atan" class="anchor"></a>atan</h2>
+<p>atan() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_atan.html">?torch_atan</a></code></p>
+</div>
+<div id="atan2" class="section level2">
+<h2 class="hasAnchor">
+<a href="#atan2" class="anchor"></a>atan2</h2>
+<p>atan2(other) -&gt; Tensor</p>
+<p>See [torch_atan2()]</p>
+</div>
+<div id="atan2_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#atan2_" class="anchor"></a>atan2_</h2>
+<p>atan2_(other) -&gt; Tensor</p>
+<p>In-place version of <code>$atan2</code></p>
+</div>
+<div id="atan_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#atan_" class="anchor"></a>atan_</h2>
+<p>atan_() -&gt; Tensor</p>
+<p>In-place version of <code>$atan</code></p>
+</div>
+<div id="atanh" class="section level2">
+<h2 class="hasAnchor">
+<a href="#atanh" class="anchor"></a>atanh</h2>
+<p>atanh() -&gt; Tensor</p>
+<p>See <code>?torch_atanh</code></p>
+</div>
+<div id="atanh_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#atanh_" class="anchor"></a>atanh_</h2>
+<p>In-place version of <code>$atanh</code></p>
+</div>
+<div id="backward" class="section level2">
+<h2 class="hasAnchor">
+<a href="#backward" class="anchor"></a>backward</h2>
+<p>Computes the gradient of current tensor w.r.t. graph leaves.</p>
+<p>The graph is differentiated using the chain rule. If the tensor is non-scalar (i.e. its data has more than one element) and requires gradient, the function additionally requires specifying <code>gradient</code>. It should be a tensor of matching type and location, that contains the gradient of the differentiated function w.r.t. <code>self</code>.</p>
+<p>This function accumulates gradients in the leaves - you might need to zero <code>$grad</code> attributes or set them to <code>NULL</code> before calling it. See <code>Default gradient layouts&lt;default-grad-layouts&gt;</code> for details on the memory layout of accumulated gradients.</p>
+<div id="arguments-3" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-3" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>gradient (Tensor or NULL): Gradient w.r.t. the tensor. If it is a tensor, it will be automatically converted to a Tensor that does not require grad unless <code>create_graph</code> is TRUE. NULL values can be specified for scalar Tensors or ones that don’t require grad. If a NULL value would be acceptable then this argument is optional.</li>
+<li>retain_graph (bool, optional): If <code>FALSE</code>, the graph used to compute the grads will be freed. Note that in nearly all cases setting this option to TRUE is not needed and often can be worked around in a much more efficient way. Defaults to the value of <code>create_graph</code>.</li>
+<li>create_graph (bool, optional): If <code>TRUE</code>, graph of the derivative will be constructed, allowing to compute higher order derivative products. Defaults to <code>FALSE</code>.</li>
+</ul>
+</div>
+</div>
+<div id="baddbmm" class="section level2">
+<h2 class="hasAnchor">
+<a href="#baddbmm" class="anchor"></a>baddbmm</h2>
+<p>baddbmm(batch1, batch2, *, beta=1, alpha=1) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_baddbmm.html">?torch_baddbmm</a></code></p>
+</div>
+<div id="baddbmm_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#baddbmm_" class="anchor"></a>baddbmm_</h2>
+<p>baddbmm_(batch1, batch2, *, beta=1, alpha=1) -&gt; Tensor</p>
+<p>In-place version of <code>$baddbmm</code></p>
+</div>
+<div id="bernoulli" class="section level2">
+<h2 class="hasAnchor">
+<a href="#bernoulli" class="anchor"></a>bernoulli</h2>
+<p>bernoulli(*, generator=NULL) -&gt; Tensor</p>
+<p>Returns a result tensor where each <span class="math inline">\(\texttt{result[i]}\)</span> is independently sampled from <span class="math inline">\(\text{Bernoulli}(\texttt{self[i]})\)</span>. <code>self</code> must have floating point <code>dtype</code>, and the result will have the same <code>dtype</code>.</p>
+<p>See <code><a href="../../reference/torch_bernoulli.html">?torch_bernoulli</a></code></p>
+</div>
+<div id="bernoulli_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#bernoulli_" class="anchor"></a>bernoulli_</h2>
+<p>bernoulli_(p=0.5, *, generator=NULL) -&gt; Tensor</p>
+<p>Fills each location of <code>self</code> with an independent sample from <span class="math inline">\(\text{Bernoulli}(\texttt{p})\)</span>. <code>self</code> can have integral <code>dtype</code>.</p>
+<p>bernoulli_(p_tensor, *, generator=NULL) -&gt; Tensor</p>
+<p><code>p_tensor</code> should be a tensor containing probabilities to be used for drawing the binary random number.</p>
+<p>The <span class="math inline">\(\text{i}^{th}\)</span> element of <code>self</code> tensor will be set to a value sampled from <span class="math inline">\(\text{Bernoulli}(\texttt{p\_tensor[i]})\)</span>.</p>
+<p><code>self</code> can have integral <code>dtype</code>, but <code>p_tensor</code> must have floating point <code>dtype</code>.</p>
+<p>See also <code>$bernoulli</code> and <code><a href="../../reference/torch_bernoulli.html">?torch_bernoulli</a></code></p>
+</div>
+<div id="bfloat16" class="section level2">
+<h2 class="hasAnchor">
+<a href="#bfloat16" class="anchor"></a>bfloat16</h2>
+<p>bfloat16(memory_format=torch_preserve_format) -&gt; Tensor <code>self$bfloat16()</code> is equivalent to <code>self$to(torch_bfloat16)</code>. See [to()].</p>
+<div id="arguments-4" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-4" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
+<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
+</ul>
+</div>
+</div>
+<div id="bincount" class="section level2">
+<h2 class="hasAnchor">
+<a href="#bincount" class="anchor"></a>bincount</h2>
+<p>bincount(weights=NULL, minlength=0) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_bincount.html">?torch_bincount</a></code></p>
+</div>
+<div id="bitwise_and" class="section level2">
+<h2 class="hasAnchor">
+<a href="#bitwise_and" class="anchor"></a>bitwise_and</h2>
+<p>bitwise_and() -&gt; Tensor</p>
+<p>See [torch_bitwise_and()]</p>
+</div>
+<div id="bitwise_and_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#bitwise_and_" class="anchor"></a>bitwise_and_</h2>
+<p>bitwise_and_() -&gt; Tensor</p>
+<p>In-place version of <code>$bitwise_and</code></p>
+</div>
+<div id="bitwise_not" class="section level2">
+<h2 class="hasAnchor">
+<a href="#bitwise_not" class="anchor"></a>bitwise_not</h2>
+<p>bitwise_not() -&gt; Tensor</p>
+<p>See [torch_bitwise_not()]</p>
+</div>
+<div id="bitwise_not_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#bitwise_not_" class="anchor"></a>bitwise_not_</h2>
+<p>bitwise_not_() -&gt; Tensor</p>
+<p>In-place version of <code>$bitwise_not</code></p>
+</div>
+<div id="bitwise_or" class="section level2">
+<h2 class="hasAnchor">
+<a href="#bitwise_or" class="anchor"></a>bitwise_or</h2>
+<p>bitwise_or() -&gt; Tensor</p>
+<p>See [torch_bitwise_or()]</p>
+</div>
+<div id="bitwise_or_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#bitwise_or_" class="anchor"></a>bitwise_or_</h2>
+<p>bitwise_or_() -&gt; Tensor</p>
+<p>In-place version of <code>$bitwise_or</code></p>
+</div>
+<div id="bitwise_xor" class="section level2">
+<h2 class="hasAnchor">
+<a href="#bitwise_xor" class="anchor"></a>bitwise_xor</h2>
+<p>bitwise_xor() -&gt; Tensor</p>
+<p>See [torch_bitwise_xor()]</p>
+</div>
+<div id="bitwise_xor_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#bitwise_xor_" class="anchor"></a>bitwise_xor_</h2>
+<p>bitwise_xor_() -&gt; Tensor</p>
+<p>In-place version of <code>$bitwise_xor</code></p>
+</div>
+<div id="bmm" class="section level2">
+<h2 class="hasAnchor">
+<a href="#bmm" class="anchor"></a>bmm</h2>
+<p>bmm(batch2) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_bmm.html">?torch_bmm</a></code></p>
+</div>
+<div id="bool" class="section level2">
+<h2 class="hasAnchor">
+<a href="#bool" class="anchor"></a>bool</h2>
+<p>bool(memory_format=torch_preserve_format) -&gt; Tensor</p>
+<p><code>self$bool()</code> is equivalent to <code>self$to(torch_bool)</code>. See [to()].</p>
+<div id="arguments-5" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-5" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
+<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
+</ul>
+</div>
+</div>
+<div id="byte" class="section level2">
+<h2 class="hasAnchor">
+<a href="#byte" class="anchor"></a>byte</h2>
+<p>byte(memory_format=torch_preserve_format) -&gt; Tensor</p>
+<p><code>self$byte()</code> is equivalent to <code>self$to(torch_uint8)</code>. See [to()].</p>
+<div id="arguments-6" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-6" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
+<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
+</ul>
+</div>
+</div>
+<div id="cauchy_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#cauchy_" class="anchor"></a>cauchy_</h2>
+<p>cauchy_(median=0, sigma=1, *, generator=NULL) -&gt; Tensor</p>
+<p>Fills the tensor with numbers drawn from the Cauchy distribution:</p>
+<p><span class="math display">\[
+f(x) = \dfrac{1}{\pi} \dfrac{\sigma}{(x - \text{median})^2 + \sigma^2}
+\]</span></p>
+</div>
+<div id="ceil" class="section level2">
+<h2 class="hasAnchor">
+<a href="#ceil" class="anchor"></a>ceil</h2>
+<p>ceil() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_ceil.html">?torch_ceil</a></code></p>
+</div>
+<div id="ceil_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#ceil_" class="anchor"></a>ceil_</h2>
+<p>ceil_() -&gt; Tensor</p>
+<p>In-place version of <code>$ceil</code></p>
+</div>
+<div id="char" class="section level2">
+<h2 class="hasAnchor">
+<a href="#char" class="anchor"></a>char</h2>
+<p>char(memory_format=torch_preserve_format) -&gt; Tensor</p>
+<p><code>self$char()</code> is equivalent to <code>self$to(torch_int8)</code>. See [to()].</p>
+<div id="arguments-7" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-7" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
+<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
+</ul>
+</div>
+</div>
+<div id="cholesky" class="section level2">
+<h2 class="hasAnchor">
+<a href="#cholesky" class="anchor"></a>cholesky</h2>
+<p>cholesky(upper=FALSE) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_cholesky.html">?torch_cholesky</a></code></p>
+</div>
+<div id="cholesky_inverse" class="section level2">
+<h2 class="hasAnchor">
+<a href="#cholesky_inverse" class="anchor"></a>cholesky_inverse</h2>
+<p>cholesky_inverse(upper=FALSE) -&gt; Tensor</p>
+<p>See [torch_cholesky_inverse()]</p>
+</div>
+<div id="cholesky_solve" class="section level2">
+<h2 class="hasAnchor">
+<a href="#cholesky_solve" class="anchor"></a>cholesky_solve</h2>
+<p>cholesky_solve(input2, upper=FALSE) -&gt; Tensor</p>
+<p>See [torch_cholesky_solve()]</p>
+</div>
+<div id="chunk" class="section level2">
+<h2 class="hasAnchor">
+<a href="#chunk" class="anchor"></a>chunk</h2>
+<p>chunk(chunks, dim=0) -&gt; List of Tensors</p>
+<p>See <code><a href="../../reference/torch_chunk.html">?torch_chunk</a></code></p>
+</div>
+<div id="clamp" class="section level2">
+<h2 class="hasAnchor">
+<a href="#clamp" class="anchor"></a>clamp</h2>
+<p>clamp(min, max) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_clamp.html">?torch_clamp</a></code></p>
+</div>
+<div id="clamp_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#clamp_" class="anchor"></a>clamp_</h2>
+<p>clamp_(min, max) -&gt; Tensor</p>
+<p>In-place version of <code>$clamp</code></p>
+</div>
+<div id="clone" class="section level2">
+<h2 class="hasAnchor">
+<a href="#clone" class="anchor"></a>clone</h2>
+<p>clone(memory_format=torch_preserve_format) -&gt; Tensor</p>
+<p>Returns a copy of the <code>self</code> tensor. The copy has the same size and data type as <code>self</code>.</p>
+<div id="note-1" class="section level4">
+<h4 class="hasAnchor">
+<a href="#note-1" class="anchor"></a>Note:</h4>
+<p>Unlike <code>copy_()</code>, this function is recorded in the computation graph. Gradients propagating to the cloned tensor will propagate to the original tensor.</p>
+</div>
+<div id="arguments-8" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-8" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
+<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
+</ul>
+</div>
+</div>
+<div id="conj" class="section level2">
+<h2 class="hasAnchor">
+<a href="#conj" class="anchor"></a>conj</h2>
+<p>conj() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_conj.html">?torch_conj</a></code></p>
+</div>
+<div id="contiguous" class="section level2">
+<h2 class="hasAnchor">
+<a href="#contiguous" class="anchor"></a>contiguous</h2>
+<p>contiguous(memory_format=torch_contiguous_format) -&gt; Tensor</p>
+<p>Returns a contiguous in memory tensor containing the same data as <code>self</code> tensor. If <code>self</code> tensor is already in the specified memory format, this function returns the <code>self</code> tensor.</p>
+<div id="arguments-9" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-9" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
+<li>returned Tensor. Default: <code>torch_contiguous_format</code>.</li>
+</ul>
+</div>
+</div>
+<div id="copy_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#copy_" class="anchor"></a>copy_</h2>
+<p>copy_(src, non_blocking=FALSE) -&gt; Tensor</p>
+<p>Copies the elements from <code>src</code> into <code>self</code> tensor and returns <code>self</code>.</p>
+<p>The <code>src</code> tensor must be :ref:<code>broadcastable &lt;broadcasting-semantics&gt;</code> with the <code>self</code> tensor. It may be of a different data type or reside on a different device.</p>
+<div id="arguments-10" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-10" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>src (Tensor): the source tensor to copy from</li>
+<li>non_blocking (bool): if <code>TRUE</code> and this copy is between CPU and GPU,</li>
+<li>the copy may occur asynchronously with respect to the host. For other</li>
+<li>cases, this argument has no effect.</li>
+</ul>
+</div>
+</div>
+<div id="cos" class="section level2">
+<h2 class="hasAnchor">
+<a href="#cos" class="anchor"></a>cos</h2>
+<p>cos() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_cos.html">?torch_cos</a></code></p>
+</div>
+<div id="cos_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#cos_" class="anchor"></a>cos_</h2>
+<p>cos_() -&gt; Tensor</p>
+<p>In-place version of <code>$cos</code></p>
+</div>
+<div id="cosh" class="section level2">
+<h2 class="hasAnchor">
+<a href="#cosh" class="anchor"></a>cosh</h2>
+<p>cosh() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_cosh.html">?torch_cosh</a></code></p>
+</div>
+<div id="cosh_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#cosh_" class="anchor"></a>cosh_</h2>
+<p>cosh_() -&gt; Tensor</p>
+<p>In-place version of <code>$cosh</code></p>
+</div>
+<div id="cpu" class="section level2">
+<h2 class="hasAnchor">
+<a href="#cpu" class="anchor"></a>cpu</h2>
+<p>cpu(memory_format=torch_preserve_format) -&gt; Tensor</p>
+<p>Returns a copy of this object in CPU memory.</p>
+<p>If this object is already in CPU memory and on the correct device, then no copy is performed and the original object is returned.</p>
+<div id="arguments-11" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-11" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
+<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
+</ul>
+</div>
+</div>
+<div id="cross" class="section level2">
+<h2 class="hasAnchor">
+<a href="#cross" class="anchor"></a>cross</h2>
+<p>cross(other, dim=-1) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_cross.html">?torch_cross</a></code></p>
+</div>
+<div id="cuda" class="section level2">
+<h2 class="hasAnchor">
+<a href="#cuda" class="anchor"></a>cuda</h2>
+<p>cuda(device=NULL, non_blocking=FALSE, memory_format=torch_preserve_format) -&gt; Tensor</p>
+<p>Returns a copy of this object in CUDA memory.</p>
+<p>If this object is already in CUDA memory and on the correct device, then no copy is performed and the original object is returned.</p>
+<div id="arguments-12" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-12" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>device (<code>torch_device</code>): The destination GPU device. Defaults to the current CUDA device.</li>
+<li>non_blocking (bool): If <code>TRUE</code> and the source is in pinned memory, the copy will be asynchronous with respect to the host. Otherwise, the argument has no effect. Default: <code>FALSE</code>.</li>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
+<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
+</ul>
+</div>
+</div>
+<div id="cummax" class="section level2">
+<h2 class="hasAnchor">
+<a href="#cummax" class="anchor"></a>cummax</h2>
+<p>cummax(dim) -&gt; (Tensor, Tensor)</p>
+<p>See <code><a href="../../reference/torch_cummax.html">?torch_cummax</a></code></p>
+</div>
+<div id="cummin" class="section level2">
+<h2 class="hasAnchor">
+<a href="#cummin" class="anchor"></a>cummin</h2>
+<p>cummin(dim) -&gt; (Tensor, Tensor)</p>
+<p>See <code><a href="../../reference/torch_cummin.html">?torch_cummin</a></code></p>
+</div>
+<div id="cumprod" class="section level2">
+<h2 class="hasAnchor">
+<a href="#cumprod" class="anchor"></a>cumprod</h2>
+<p>cumprod(dim, dtype=NULL) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_cumprod.html">?torch_cumprod</a></code></p>
+</div>
+<div id="cumsum" class="section level2">
+<h2 class="hasAnchor">
+<a href="#cumsum" class="anchor"></a>cumsum</h2>
+<p>cumsum(dim, dtype=NULL) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_cumsum.html">?torch_cumsum</a></code></p>
+</div>
+<div id="data_ptr" class="section level2">
+<h2 class="hasAnchor">
+<a href="#data_ptr" class="anchor"></a>data_ptr</h2>
+<p>data_ptr() -&gt; int</p>
+<p>Returns the address of the first element of <code>self</code> tensor.</p>
+</div>
+<div id="deg2rad" class="section level2">
+<h2 class="hasAnchor">
+<a href="#deg2rad" class="anchor"></a>deg2rad</h2>
+<p>deg2rad() -&gt; Tensor</p>
+<p>See [torch_deg2rad()]</p>
+</div>
+<div id="deg2rad_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#deg2rad_" class="anchor"></a>deg2rad_</h2>
+<p>deg2rad_() -&gt; Tensor</p>
+<p>In-place version of <code>$deg2rad</code></p>
+</div>
+<div id="dense_dim" class="section level2">
+<h2 class="hasAnchor">
+<a href="#dense_dim" class="anchor"></a>dense_dim</h2>
+<p>dense_dim() -&gt; int</p>
+<p>If <code>self</code> is a sparse COO tensor (i.e., with <code>torch_sparse_coo</code> layout), this returns the number of dense dimensions. Otherwise, this throws an error.</p>
+<p>See also <code>$sparse_dim</code>.</p>
+</div>
+<div id="dequantize" class="section level2">
+<h2 class="hasAnchor">
+<a href="#dequantize" class="anchor"></a>dequantize</h2>
+<p>dequantize() -&gt; Tensor</p>
+<p>Given a quantized Tensor, dequantize it and return the dequantized float Tensor.</p>
+</div>
+<div id="det" class="section level2">
+<h2 class="hasAnchor">
+<a href="#det" class="anchor"></a>det</h2>
+<p>det() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_det.html">?torch_det</a></code></p>
+</div>
+<div id="detach" class="section level2">
+<h2 class="hasAnchor">
+<a href="#detach" class="anchor"></a>detach</h2>
+<p>Returns a new Tensor, detached from the current graph.</p>
+<p>The result will never require gradient.</p>
+<div id="note-2" class="section level4">
+<h4 class="hasAnchor">
+<a href="#note-2" class="anchor"></a>Note:</h4>
+<p>Returned Tensor shares the same storage with the original one. In-place modifications on either of them will be seen, and may trigger errors in correctness checks. IMPORTANT NOTE: Previously, in-place size / stride / storage changes (such as <code>resize_</code> / <code>resize_as_</code> / <code>set_</code> / <code>transpose_</code>) to the returned tensor also update the original tensor. Now, these in-place changes will not update the original tensor anymore, and will instead trigger an error. For sparse tensors: In-place indices / values changes (such as <code>zero_</code> / <code>copy_</code> / <code>add_</code>) to the returned tensor will not update the original tensor anymore, and will instead trigger an error.</p>
+</div>
+</div>
+<div id="detach_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#detach_" class="anchor"></a>detach_</h2>
+<p>Detaches the Tensor from the graph that created it, making it a leaf. Views cannot be detached in-place.</p>
+</div>
+<div id="device" class="section level2">
+<h2 class="hasAnchor">
+<a href="#device" class="anchor"></a>device</h2>
+<p>Is the <code>torch_device</code> where this Tensor is.</p>
+</div>
+<div id="diag" class="section level2">
+<h2 class="hasAnchor">
+<a href="#diag" class="anchor"></a>diag</h2>
+<p>diag(diagonal=0) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_diag.html">?torch_diag</a></code></p>
+</div>
+<div id="diag_embed" class="section level2">
+<h2 class="hasAnchor">
+<a href="#diag_embed" class="anchor"></a>diag_embed</h2>
+<p>diag_embed(offset=0, dim1=-2, dim2=-1) -&gt; Tensor</p>
+<p>See [torch_diag_embed()]</p>
+</div>
+<div id="diagflat" class="section level2">
+<h2 class="hasAnchor">
+<a href="#diagflat" class="anchor"></a>diagflat</h2>
+<p>diagflat(offset=0) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_diagflat.html">?torch_diagflat</a></code></p>
+</div>
+<div id="diagonal" class="section level2">
+<h2 class="hasAnchor">
+<a href="#diagonal" class="anchor"></a>diagonal</h2>
+<p>diagonal(offset=0, dim1=0, dim2=1) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_diagonal.html">?torch_diagonal</a></code></p>
+</div>
+<div id="digamma" class="section level2">
+<h2 class="hasAnchor">
+<a href="#digamma" class="anchor"></a>digamma</h2>
+<p>digamma() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_digamma.html">?torch_digamma</a></code></p>
+</div>
+<div id="digamma_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#digamma_" class="anchor"></a>digamma_</h2>
+<p>digamma_() -&gt; Tensor</p>
+<p>In-place version of <code>$digamma</code></p>
+</div>
+<div id="dim" class="section level2">
+<h2 class="hasAnchor">
+<a href="#dim" class="anchor"></a>dim</h2>
+<p>dim() -&gt; int</p>
+<p>Returns the number of dimensions of <code>self</code> tensor.</p>
+</div>
+<div id="dist" class="section level2">
+<h2 class="hasAnchor">
+<a href="#dist" class="anchor"></a>dist</h2>
+<p>dist(other, p=2) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_dist.html">?torch_dist</a></code></p>
+</div>
+<div id="div" class="section level2">
+<h2 class="hasAnchor">
+<a href="#div" class="anchor"></a>div</h2>
+<p>div(value) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_div.html">?torch_div</a></code></p>
+</div>
+<div id="div_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#div_" class="anchor"></a>div_</h2>
+<p>div_(value) -&gt; Tensor</p>
+<p>In-place version of <code>$div</code></p>
+</div>
+<div id="dot" class="section level2">
+<h2 class="hasAnchor">
+<a href="#dot" class="anchor"></a>dot</h2>
+<p>dot(tensor2) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_dot.html">?torch_dot</a></code></p>
+</div>
+<div id="double" class="section level2">
+<h2 class="hasAnchor">
+<a href="#double" class="anchor"></a>double</h2>
+<p>double(memory_format=torch_preserve_format) -&gt; Tensor</p>
+<p><code>self$double()</code> is equivalent to <code>self$to(torch_float64)</code>. See [to()].</p>
+<div id="arguments-13" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-13" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
+<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
+</ul>
+</div>
+</div>
+<div id="eig" class="section level2">
+<h2 class="hasAnchor">
+<a href="#eig" class="anchor"></a>eig</h2>
+<p>eig(eigenvectors=FALSE) -&gt; (Tensor, Tensor)</p>
+<p>See <code><a href="../../reference/torch_eig.html">?torch_eig</a></code></p>
+</div>
+<div id="element_size" class="section level2">
+<h2 class="hasAnchor">
+<a href="#element_size" class="anchor"></a>element_size</h2>
+<p>element_size() -&gt; int</p>
+<p>Returns the size in bytes of an individual element.</p>
+<div id="examples-6" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-6" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb7"><pre class="downlit">
+<span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1</span>))<span class="op">$</span><span class="fu">element_size</span>()
+</pre></div>
+</div>
+</div>
+<div id="eq" class="section level2">
+<h2 class="hasAnchor">
+<a href="#eq" class="anchor"></a>eq</h2>
+<p>eq(other) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_eq.html">?torch_eq</a></code></p>
+</div>
+<div id="eq_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#eq_" class="anchor"></a>eq_</h2>
+<p>eq_(other) -&gt; Tensor</p>
+<p>In-place version of <code>$eq</code></p>
+</div>
+<div id="equal" class="section level2">
+<h2 class="hasAnchor">
+<a href="#equal" class="anchor"></a>equal</h2>
+<p>equal(other) -&gt; bool</p>
+<p>See <code><a href="../../reference/torch_equal.html">?torch_equal</a></code></p>
+</div>
+<div id="erf" class="section level2">
+<h2 class="hasAnchor">
+<a href="#erf" class="anchor"></a>erf</h2>
+<p>erf() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_erf.html">?torch_erf</a></code></p>
+</div>
+<div id="erf_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#erf_" class="anchor"></a>erf_</h2>
+<p>erf_() -&gt; Tensor</p>
+<p>In-place version of <code>$erf</code></p>
+</div>
+<div id="erfc" class="section level2">
+<h2 class="hasAnchor">
+<a href="#erfc" class="anchor"></a>erfc</h2>
+<p>erfc() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_erfc.html">?torch_erfc</a></code></p>
+</div>
+<div id="erfc_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#erfc_" class="anchor"></a>erfc_</h2>
+<p>erfc_() -&gt; Tensor</p>
+<p>In-place version of <code>$erfc</code></p>
+</div>
+<div id="erfinv" class="section level2">
+<h2 class="hasAnchor">
+<a href="#erfinv" class="anchor"></a>erfinv</h2>
+<p>erfinv() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_erfinv.html">?torch_erfinv</a></code></p>
+</div>
+<div id="erfinv_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#erfinv_" class="anchor"></a>erfinv_</h2>
+<p>erfinv_() -&gt; Tensor</p>
+<p>In-place version of <code>$erfinv</code></p>
+</div>
+<div id="exp" class="section level2">
+<h2 class="hasAnchor">
+<a href="#exp" class="anchor"></a>exp</h2>
+<p>exp() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_exp.html">?torch_exp</a></code></p>
+</div>
+<div id="exp_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#exp_" class="anchor"></a>exp_</h2>
+<p>exp_() -&gt; Tensor</p>
+<p>In-place version of <code>$exp</code></p>
+</div>
+<div id="expand" class="section level2">
+<h2 class="hasAnchor">
+<a href="#expand" class="anchor"></a>expand</h2>
+<p>expand(*sizes) -&gt; Tensor</p>
+<p>Returns a new view of the <code>self</code> tensor with singleton dimensions expanded to a larger size.</p>
+<p>Passing -1 as the size for a dimension means not changing the size of that dimension.</p>
+<p>Tensor can be also expanded to a larger number of dimensions, and the new ones will be appended at the front. For the new dimensions, the size cannot be set to -1.</p>
+<p>Expanding a tensor does not allocate new memory, but only creates a new view on the existing tensor where a dimension of size one is expanded to a larger size by setting the <code>stride</code> to 0. Any dimension of size 1 can be expanded to an arbitrary value without allocating new memory.</p>
+<div id="arguments-14" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-14" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>sizes (torch_Size or int…): the desired expanded size</li>
+</ul>
+</div>
+<div id="warning-2" class="section level4">
+<h4 class="hasAnchor">
+<a href="#warning-2" class="anchor"></a>Warning:</h4>
+<p>More than one element of an expanded tensor may refer to a single memory location. As a result, in-place operations (especially ones that are vectorized) may result in incorrect behavior. If you need to write to the tensors, please clone them first.</p>
+</div>
+<div id="examples-7" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-7" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb8"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1</span>,<span class="fl">2</span>,<span class="fl">3</span>), ncol = <span class="fl">1</span>))
+<span class="kw">x</span><span class="op">$</span><span class="fu">size</span>()
+<span class="kw">x</span><span class="op">$</span><span class="fu">expand</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">3</span>, <span class="fl">4</span>))
+<span class="kw">x</span><span class="op">$</span><span class="fu">expand</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="op">-</span><span class="fl">1</span>, <span class="fl">4</span>))  <span class="co"># -1 means not changing the size of that dimension</span>
+</pre></div>
+</div>
+</div>
+<div id="expand_as" class="section level2">
+<h2 class="hasAnchor">
+<a href="#expand_as" class="anchor"></a>expand_as</h2>
+<p>expand_as(other) -&gt; Tensor</p>
+<p>Expand this tensor to the same size as <code>other</code>. <code>self$expand_as(other)</code> is equivalent to <code>self$expand(other.size())</code>.</p>
+<p>Please see <code>$expand</code> for more information about <code>expand</code>.</p>
+<div id="arguments-15" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-15" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>other (`$): The result tensor has the same size</li>
+<li>as <code>other</code>.</li>
+</ul>
+</div>
+</div>
+<div id="expm1" class="section level2">
+<h2 class="hasAnchor">
+<a href="#expm1" class="anchor"></a>expm1</h2>
+<p>expm1() -&gt; Tensor</p>
+<p>See [torch_expm1()]</p>
+</div>
+<div id="expm1_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#expm1_" class="anchor"></a>expm1_</h2>
+<p>expm1_() -&gt; Tensor</p>
+<p>In-place version of <code>$expm1</code></p>
+</div>
+<div id="exponential_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#exponential_" class="anchor"></a>exponential_</h2>
+<p>exponential_(lambd=1, *, generator=NULL) -&gt; Tensor</p>
+<p>Fills <code>self</code> tensor with elements drawn from the exponential distribution:</p>
+<p><span class="math display">\[
+f(x) = \lambda e^{-\lambda x}
+\]</span></p>
+</div>
+<div id="fft" class="section level2">
+<h2 class="hasAnchor">
+<a href="#fft" class="anchor"></a>fft</h2>
+<p>fft(signal_ndim, normalized=FALSE) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_fft.html">?torch_fft</a></code></p>
+</div>
+<div id="fill_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#fill_" class="anchor"></a>fill_</h2>
+<p>fill_(value) -&gt; Tensor</p>
+<p>Fills <code>self</code> tensor with the specified value.</p>
+</div>
+<div id="fill_diagonal_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#fill_diagonal_" class="anchor"></a>fill_diagonal_</h2>
+<p>fill_diagonal_(fill_value, wrap=FALSE) -&gt; Tensor</p>
+<p>Fill the main diagonal of a tensor that has at least 2-dimensions. When dims&gt;2, all dimensions of input must be of equal length. This function modifies the input tensor in-place, and returns the input tensor.</p>
+<div id="arguments-16" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-16" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>fill_value (Scalar): the fill value</li>
+<li>wrap (bool): the diagonal ‘wrapped’ after N columns for tall matrices.</li>
+</ul>
+</div>
+<div id="examples-8" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-8" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb9"><pre class="downlit">
+<span class="kw">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">3</span>, <span class="fl">3</span>)
+<span class="kw">a</span><span class="op">$</span><span class="fu">fill_diagonal_</span>(<span class="fl">5</span>)
+<span class="kw">b</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">7</span>, <span class="fl">3</span>)
+<span class="kw">b</span><span class="op">$</span><span class="fu">fill_diagonal_</span>(<span class="fl">5</span>)
+<span class="kw">c</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">7</span>, <span class="fl">3</span>)
+<span class="kw">c</span><span class="op">$</span><span class="fu">fill_diagonal_</span>(<span class="fl">5</span>, wrap=<span class="fl">TRUE</span>)
+</pre></div>
+</div>
+</div>
+<div id="flatten" class="section level2">
+<h2 class="hasAnchor">
+<a href="#flatten" class="anchor"></a>flatten</h2>
+<p>flatten(input, start_dim=0, end_dim=-1) -&gt; Tensor</p>
+<p>see <code><a href="../../reference/torch_flatten.html">?torch_flatten</a></code></p>
+</div>
+<div id="flip" class="section level2">
+<h2 class="hasAnchor">
+<a href="#flip" class="anchor"></a>flip</h2>
+<p>flip(dims) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_flip.html">?torch_flip</a></code></p>
+</div>
+<div id="fliplr" class="section level2">
+<h2 class="hasAnchor">
+<a href="#fliplr" class="anchor"></a>fliplr</h2>
+<p>fliplr() -&gt; Tensor</p>
+<p>See <code>?torch_fliplr</code></p>
+</div>
+<div id="flipud" class="section level2">
+<h2 class="hasAnchor">
+<a href="#flipud" class="anchor"></a>flipud</h2>
+<p>flipud() -&gt; Tensor</p>
+<p>See <code>?torch_flipud</code></p>
+</div>
+<div id="float" class="section level2">
+<h2 class="hasAnchor">
+<a href="#float" class="anchor"></a>float</h2>
+<p>float(memory_format=torch_preserve_format) -&gt; Tensor</p>
+<p><code>self$float()</code> is equivalent to <code>self$to(torch_float32)</code>. See [to()].</p>
+<div id="arguments-17" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-17" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
+<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
+</ul>
+</div>
+</div>
+<div id="floor" class="section level2">
+<h2 class="hasAnchor">
+<a href="#floor" class="anchor"></a>floor</h2>
+<p>floor() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_floor.html">?torch_floor</a></code></p>
+</div>
+<div id="floor_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#floor_" class="anchor"></a>floor_</h2>
+<p>floor_() -&gt; Tensor</p>
+<p>In-place version of <code>$floor</code></p>
+</div>
+<div id="floor_divide" class="section level2">
+<h2 class="hasAnchor">
+<a href="#floor_divide" class="anchor"></a>floor_divide</h2>
+<p>floor_divide(value) -&gt; Tensor</p>
+<p>See [torch_floor_divide()]</p>
+</div>
+<div id="floor_divide_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#floor_divide_" class="anchor"></a>floor_divide_</h2>
+<p>floor_divide_(value) -&gt; Tensor</p>
+<p>In-place version of <code>$floor_divide</code></p>
+</div>
+<div id="fmod" class="section level2">
+<h2 class="hasAnchor">
+<a href="#fmod" class="anchor"></a>fmod</h2>
+<p>fmod(divisor) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_fmod.html">?torch_fmod</a></code></p>
+</div>
+<div id="fmod_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#fmod_" class="anchor"></a>fmod_</h2>
+<p>fmod_(divisor) -&gt; Tensor</p>
+<p>In-place version of <code>$fmod</code></p>
+</div>
+<div id="frac" class="section level2">
+<h2 class="hasAnchor">
+<a href="#frac" class="anchor"></a>frac</h2>
+<p>frac() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_frac.html">?torch_frac</a></code></p>
+</div>
+<div id="frac_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#frac_" class="anchor"></a>frac_</h2>
+<p>frac_() -&gt; Tensor</p>
+<p>In-place version of <code>$frac</code></p>
+</div>
+<div id="gather" class="section level2">
+<h2 class="hasAnchor">
+<a href="#gather" class="anchor"></a>gather</h2>
+<p>gather(dim, index) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_gather.html">?torch_gather</a></code></p>
+</div>
+<div id="ge" class="section level2">
+<h2 class="hasAnchor">
+<a href="#ge" class="anchor"></a>ge</h2>
+<p>ge(other) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_ge.html">?torch_ge</a></code></p>
+</div>
+<div id="ge_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#ge_" class="anchor"></a>ge_</h2>
+<p>ge_(other) -&gt; Tensor</p>
+<p>In-place version of <code>$ge</code></p>
+</div>
+<div id="geometric_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#geometric_" class="anchor"></a>geometric_</h2>
+<p>geometric_(p, *, generator=NULL) -&gt; Tensor</p>
+<p>Fills <code>self</code> tensor with elements drawn from the geometric distribution:</p>
+<p><span class="math display">\[
+f(X=k) = p^{k - 1} (1 - p)
+\]</span></p>
+</div>
+<div id="geqrf" class="section level2">
+<h2 class="hasAnchor">
+<a href="#geqrf" class="anchor"></a>geqrf</h2>
+<p>geqrf() -&gt; (Tensor, Tensor)</p>
+<p>See <code><a href="../../reference/torch_geqrf.html">?torch_geqrf</a></code></p>
+</div>
+<div id="ger" class="section level2">
+<h2 class="hasAnchor">
+<a href="#ger" class="anchor"></a>ger</h2>
+<p>ger(vec2) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_ger.html">?torch_ger</a></code></p>
+</div>
+<div id="get_device" class="section level2">
+<h2 class="hasAnchor">
+<a href="#get_device" class="anchor"></a>get_device</h2>
+<p>get_device() -&gt; Device ordinal (Integer)</p>
+<p>For CUDA tensors, this function returns the device ordinal of the GPU on which the tensor resides. For CPU tensors, an error is thrown.</p>
+<div id="examples-9" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-9" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb10"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">3</span>, <span class="fl">4</span>, <span class="fl">5</span>, device=<span class="st">'cuda:0'</span>)
+<span class="kw">x</span><span class="op">$</span><span class="fu">get_device</span>()
+<span class="kw">x</span><span class="op">$</span><span class="fu">cpu</span>()<span class="op">$</span><span class="fu">get_device</span>()  <span class="co"># RuntimeError: get_device is not implemented for type torch_FloatTensor</span>
+</pre></div>
+</div>
+</div>
+<div id="grad" class="section level2">
+<h2 class="hasAnchor">
+<a href="#grad" class="anchor"></a>grad</h2>
+<p>This attribute is <code>NULL</code> by default and becomes a Tensor the first time a call to <code>backward</code> computes gradients for <code>self</code>. The attribute will then contain the gradients computed and future calls to [backward()] will accumulate (add) gradients into it.</p>
+</div>
+<div id="gt" class="section level2">
+<h2 class="hasAnchor">
+<a href="#gt" class="anchor"></a>gt</h2>
+<p>gt(other) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_gt.html">?torch_gt</a></code></p>
+</div>
+<div id="gt_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#gt_" class="anchor"></a>gt_</h2>
+<p>gt_(other) -&gt; Tensor</p>
+<p>In-place version of <code>$gt</code></p>
+</div>
+<div id="half" class="section level2">
+<h2 class="hasAnchor">
+<a href="#half" class="anchor"></a>half</h2>
+<p>half(memory_format=torch_preserve_format) -&gt; Tensor</p>
+<p><code>self$half()</code> is equivalent to <code>self$to(torch_float16)</code>. See [to()].</p>
+<div id="arguments-18" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-18" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
+<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
+</ul>
+</div>
+</div>
+<div id="hardshrink" class="section level2">
+<h2 class="hasAnchor">
+<a href="#hardshrink" class="anchor"></a>hardshrink</h2>
+<p>hardshrink(lambd=0.5) -&gt; Tensor</p>
+<p>See [torch_nn.functional.hardshrink()]</p>
+</div>
+<div id="has_names" class="section level2">
+<h2 class="hasAnchor">
+<a href="#has_names" class="anchor"></a>has_names</h2>
+<p>Is <code>TRUE</code> if any of this tensor’s dimensions are named. Otherwise, is <code>FALSE</code>.</p>
+</div>
+<div id="histc" class="section level2">
+<h2 class="hasAnchor">
+<a href="#histc" class="anchor"></a>histc</h2>
+<p>histc(bins=100, min=0, max=0) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_histc.html">?torch_histc</a></code></p>
+</div>
+<div id="ifft" class="section level2">
+<h2 class="hasAnchor">
+<a href="#ifft" class="anchor"></a>ifft</h2>
+<p>ifft(signal_ndim, normalized=FALSE) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_ifft.html">?torch_ifft</a></code></p>
+</div>
+<div id="imag" class="section level2">
+<h2 class="hasAnchor">
+<a href="#imag" class="anchor"></a>imag</h2>
+<p>Returns a new tensor containing imaginary values of the <code>self</code> tensor. The returned tensor and <code>self</code> share the same underlying storage.</p>
+<div id="warning-3" class="section level4">
+<h4 class="hasAnchor">
+<a href="#warning-3" class="anchor"></a>Warning:</h4>
+<p>[imag()] is only supported for tensors with complex dtypes.</p>
+</div>
+<div id="examples-10" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-10" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb11"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">4</span>, dtype=<span class="fu">torch_cfloat</span>())
+<span class="kw">x</span>
+<span class="kw">x</span><span class="op">$</span><span class="kw">imag</span>
+</pre></div>
+</div>
+</div>
+<div id="index_add" class="section level2">
+<h2 class="hasAnchor">
+<a href="#index_add" class="anchor"></a>index_add</h2>
+<p>index_add(tensor1, dim, index, tensor2) -&gt; Tensor</p>
+<p>Out-of-place version of <code>$index_add_</code>. <code>tensor1</code> corresponds to <code>self</code> in <code>$index_add_</code>.</p>
+</div>
+<div id="index_add_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#index_add_" class="anchor"></a>index_add_</h2>
+<p>index_add_(dim, index, tensor) -&gt; Tensor</p>
+<p>Accumulate the elements of <code>tensor</code> into the <code>self</code> tensor by adding to the indices in the order given in <code>index</code>. For example, if <code>dim == 0</code> and <code>index[i] == j</code>, then the <code>i</code> th row of <code>tensor</code> is added to the <code>j</code> th row of <code>self</code>.</p>
+<p>The <code>dim</code> th dimension of <code>tensor</code> must have the same size as the length of <code>index</code> (which must be a vector), and all other dimensions must match <code>self</code>, or an error will be raised.</p>
+<div id="note-3" class="section level4">
+<h4 class="hasAnchor">
+<a href="#note-3" class="anchor"></a>Note:</h4>
+<p>In some circumstances when using the CUDA backend with CuDNN, this operator may select a nondeterministic algorithm to increase performance. If this is undesirable, you can try to make the operation deterministic (potentially at a performance cost) by setting <code>torch_backends.cudnn.deterministic = TRUE</code>.</p>
+</div>
+<div id="arguments-19" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-19" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>dim (int): dimension along which to index</li>
+<li>index (LongTensor): indices of <code>tensor</code> to select from</li>
+<li>tensor (Tensor): the tensor containing values to add</li>
+</ul>
+</div>
+<div id="examples-11" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-11" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb12"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span>(<span class="fl">5</span>, <span class="fl">3</span>)
+<span class="kw">t</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">9</span>, ncol = <span class="fl">3</span>), dtype=<span class="fu"><a href="../../reference/torch_dtype.html">torch_float</a></span>())
+<span class="kw">index</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1L</span>, <span class="fl">4L</span>, <span class="fl">3L</span>))
+<span class="kw">x</span><span class="op">$</span><span class="fu">index_add_</span>(<span class="fl">1</span>, <span class="kw">index</span>, <span class="kw">t</span>)
+</pre></div>
+</div>
+</div>
+<div id="index_copy" class="section level2">
+<h2 class="hasAnchor">
+<a href="#index_copy" class="anchor"></a>index_copy</h2>
+<p>index_copy(tensor1, dim, index, tensor2) -&gt; Tensor</p>
+<p>Out-of-place version of <code>$index_copy_</code>. <code>tensor1</code> corresponds to <code>self</code> in <code>$index_copy_</code>.</p>
+</div>
+<div id="index_copy_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#index_copy_" class="anchor"></a>index_copy_</h2>
+<p>index_copy_(dim, index, tensor) -&gt; Tensor</p>
+<p>Copies the elements of <code>tensor</code> into the <code>self</code> tensor by selecting the indices in the order given in <code>index</code>. For example, if <code>dim == 0</code> and <code>index[i] == j</code>, then the <code>i</code> th row of <code>tensor</code> is copied to the <code>j</code> th row of <code>self</code>.</p>
+<p>The <code>dim</code> th dimension of <code>tensor</code> must have the same size as the length of <code>index</code> (which must be a vector), and all other dimensions must match <code>self</code>, or an error will be raised.</p>
+<div id="arguments-20" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-20" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>dim (int): dimension along which to index</li>
+<li>index (LongTensor): indices of <code>tensor</code> to select from</li>
+<li>tensor (Tensor): the tensor containing values to copy</li>
+</ul>
+</div>
+<div id="examples-12" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-12" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb13"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">5</span>, <span class="fl">3</span>)
+<span class="kw">t</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">9</span>, ncol = <span class="fl">3</span>), dtype=<span class="fu"><a href="../../reference/torch_dtype.html">torch_float</a></span>())
+<span class="kw">index</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1</span>, <span class="fl">5</span>, <span class="fl">3</span>))
+<span class="kw">x</span><span class="op">$</span><span class="fu">index_copy_</span>(<span class="fl">1</span>, <span class="kw">index</span>, <span class="kw">t</span>)
+</pre></div>
+</div>
+</div>
+<div id="index_fill" class="section level2">
+<h2 class="hasAnchor">
+<a href="#index_fill" class="anchor"></a>index_fill</h2>
+<p>index_fill(tensor1, dim, index, value) -&gt; Tensor</p>
+<p>Out-of-place version of <code>$index_fill_</code>. <code>tensor1</code> corresponds to <code>self</code> in <code>$index_fill_</code>.</p>
+</div>
+<div id="index_fill_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#index_fill_" class="anchor"></a>index_fill_</h2>
+<p>index_fill_(dim, index, val) -&gt; Tensor</p>
+<p>Fills the elements of the <code>self</code> tensor with value <code>val</code> by selecting the indices in the order given in <code>index</code>.</p>
+<div id="arguments-21" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-21" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>dim (int): dimension along which to index</li>
+<li>index (LongTensor): indices of <code>self</code> tensor to fill in</li>
+<li>val (float): the value to fill with</li>
+</ul>
+</div>
+<div id="examples-13" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-13" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb14"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">9</span>, ncol = <span class="fl">3</span>), dtype=<span class="fu"><a href="../../reference/torch_dtype.html">torch_float</a></span>())
+<span class="kw">index</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1</span>, <span class="fl">3</span>), dtype = <span class="fu"><a href="../../reference/torch_dtype.html">torch_long</a></span>())
+<span class="kw">x</span><span class="op">$</span><span class="fu">index_fill_</span>(<span class="fl">1</span>, <span class="kw">index</span>, <span class="op">-</span><span class="fl">1</span>)
+</pre></div>
+</div>
+</div>
+<div id="index_put" class="section level2">
+<h2 class="hasAnchor">
+<a href="#index_put" class="anchor"></a>index_put</h2>
+<p>index_put(tensor1, indices, value, accumulate=FALSE) -&gt; Tensor</p>
+<p>Out-place version of <code>$index_put_</code>. <code>tensor1</code> corresponds to <code>self</code> in <code>$index_put_</code>.</p>
+</div>
+<div id="index_put_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#index_put_" class="anchor"></a>index_put_</h2>
+<p>index_put_(indices, value, accumulate=FALSE) -&gt; Tensor</p>
+<p>Puts values from the tensor <code>value</code> into the tensor <code>self</code> using the indices specified in <code>indices</code> (which is a tuple of Tensors). The expression <code>tensor.index_put_(indices, value)</code> is equivalent to <code>tensor[indices] = value</code>. Returns <code>self</code>.</p>
+<p>If <code>accumulate</code> is <code>TRUE</code>, the elements in <code>value</code> are added to <code>self</code>. If accumulate is <code>FALSE</code>, the behavior is undefined if indices contain duplicate elements.</p>
+<div id="arguments-22" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-22" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>indices (tuple of LongTensor): tensors used to index into <code>self</code>.</li>
+<li>value (Tensor): tensor of same dtype as <code>self</code>.</li>
+<li>accumulate (bool): whether to accumulate into self</li>
+</ul>
+</div>
+</div>
+<div id="index_select" class="section level2">
+<h2 class="hasAnchor">
+<a href="#index_select" class="anchor"></a>index_select</h2>
+<p>index_select(dim, index) -&gt; Tensor</p>
+<p>See [torch_index_select()]</p>
+</div>
+<div id="indices" class="section level2">
+<h2 class="hasAnchor">
+<a href="#indices" class="anchor"></a>indices</h2>
+<p>indices() -&gt; Tensor</p>
+<p>If <code>self</code> is a sparse COO tensor (i.e., with <code>torch_sparse_coo</code> layout), this returns a view of the contained indices tensor. Otherwise, this throws an error.</p>
+<p>See also <code>Tensor.values</code>.</p>
+<div id="note-4" class="section level4">
+<h4 class="hasAnchor">
+<a href="#note-4" class="anchor"></a>Note:</h4>
+<p>This method can only be called on a coalesced sparse tensor. See <code>Tensor.coalesce</code> for details.</p>
+</div>
+</div>
+<div id="int" class="section level2">
+<h2 class="hasAnchor">
+<a href="#int" class="anchor"></a>int</h2>
+<p>int(memory_format=torch_preserve_format) -&gt; Tensor</p>
+<p><code>self$int()</code> is equivalent to <code>self$to(torch_int32)</code>. See [to()].</p>
+<div id="arguments-23" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-23" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
+<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
+</ul>
+</div>
+</div>
+<div id="int_repr" class="section level2">
+<h2 class="hasAnchor">
+<a href="#int_repr" class="anchor"></a>int_repr</h2>
+<p>int_repr() -&gt; Tensor</p>
+<p>Given a quantized Tensor, <code>self$int_repr()</code> returns a CPU Tensor with uint8_t as data type that stores the underlying uint8_t values of the given Tensor.</p>
+</div>
+<div id="inverse" class="section level2">
+<h2 class="hasAnchor">
+<a href="#inverse" class="anchor"></a>inverse</h2>
+<p>inverse() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_inverse.html">?torch_inverse</a></code></p>
+</div>
+<div id="irfft" class="section level2">
+<h2 class="hasAnchor">
+<a href="#irfft" class="anchor"></a>irfft</h2>
+<p>irfft(signal_ndim, normalized=FALSE, onesided=TRUE, signal_sizes=NULL) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_irfft.html">?torch_irfft</a></code></p>
+</div>
+<div id="is_complex" class="section level2">
+<h2 class="hasAnchor">
+<a href="#is_complex" class="anchor"></a>is_complex</h2>
+<p>is_complex() -&gt; bool</p>
+<p>Returns TRUE if the data type of <code>self</code> is a complex data type.</p>
+</div>
+<div id="is_contiguous" class="section level2">
+<h2 class="hasAnchor">
+<a href="#is_contiguous" class="anchor"></a>is_contiguous</h2>
+<p>is_contiguous(memory_format=torch_contiguous_format) -&gt; bool</p>
+<p>Returns TRUE if <code>self</code> tensor is contiguous in memory in the order specified by memory format.</p>
+<div id="arguments-24" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-24" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>memory_format (<code>torch_memory_format</code>, optional): Specifies memory allocation</li>
+<li>order. Default: <code>torch_contiguous_format</code>.</li>
+</ul>
+</div>
+</div>
+<div id="is_cuda" class="section level2">
+<h2 class="hasAnchor">
+<a href="#is_cuda" class="anchor"></a>is_cuda</h2>
+<p>Is <code>TRUE</code> if the Tensor is stored on the GPU, <code>FALSE</code> otherwise.</p>
+</div>
+<div id="is_floating_point" class="section level2">
+<h2 class="hasAnchor">
+<a href="#is_floating_point" class="anchor"></a>is_floating_point</h2>
+<p>is_floating_point() -&gt; bool</p>
+<p>Returns TRUE if the data type of <code>self</code> is a floating point data type.</p>
+</div>
+<div id="is_leaf" class="section level2">
+<h2 class="hasAnchor">
+<a href="#is_leaf" class="anchor"></a>is_leaf</h2>
+<p>All Tensors that have <code>requires_grad</code> which is <code>FALSE</code> will be leaf Tensors by convention.</p>
+<p>For Tensors that have <code>requires_grad</code> which is <code>TRUE</code>, they will be leaf Tensors if they were created by the user. This means that they are not the result of an operation and so <code>grad_fn</code> is NULL.</p>
+<p>Only leaf Tensors will have their <code>grad</code> populated during a call to [backward()]. To get <code>grad</code> populated for non-leaf Tensors, you can use [retain_grad()].</p>
+<div id="examples-14" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-14" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb15"><pre class="downlit">
+<span class="kw">a</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">10</span>, requires_grad=<span class="fl">TRUE</span>)
+<span class="kw">a</span><span class="op">$</span><span class="fu">is_leaf</span>()
+
+<span class="co"># b &lt;- torch_rand(10, requires_grad=TRUE)$cuda()</span>
+<span class="co"># b$is_leaf()</span>
+<span class="co"># FALSE</span>
+<span class="co"># b was created by the operation that cast a cpu Tensor into a cuda Tensor</span>
+
+<span class="kw">c</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">10</span>, requires_grad=<span class="fl">TRUE</span>) <span class="op">+</span> <span class="fl">2</span>
+<span class="kw">c</span><span class="op">$</span><span class="fu">is_leaf</span>()
+<span class="co"># c was created by the addition operation</span>
+
+<span class="co"># d &lt;- torch_rand(10)$cuda()</span>
+<span class="co"># d$is_leaf()</span>
+<span class="co"># TRUE</span>
+<span class="co"># d does not require gradients and so has no operation creating it (that is tracked by the autograd engine)</span>
+
+<span class="co"># e &lt;- torch_rand(10)$cuda()$requires_grad_()</span>
+<span class="co"># e$is_leaf()</span>
+<span class="co"># TRUE</span>
+<span class="co"># e requires gradients and has no operations creating it</span>
+
+<span class="co"># f &lt;- torch_rand(10, requires_grad=TRUE, device="cuda")</span>
+<span class="co"># f$is_leaf</span>
+<span class="co"># TRUE</span>
+<span class="co"># f requires grad, has no operation creating it</span>
+</pre></div>
+</div>
+</div>
+<div id="is_meta" class="section level2">
+<h2 class="hasAnchor">
+<a href="#is_meta" class="anchor"></a>is_meta</h2>
+<p>Is <code>TRUE</code> if the Tensor is a meta tensor, <code>FALSE</code> otherwise. Meta tensors are like normal tensors, but they carry no data.</p>
+</div>
+<div id="is_pinned" class="section level2">
+<h2 class="hasAnchor">
+<a href="#is_pinned" class="anchor"></a>is_pinned</h2>
+<p>Returns true if this tensor resides in pinned memory.</p>
+</div>
+<div id="is_quantized" class="section level2">
+<h2 class="hasAnchor">
+<a href="#is_quantized" class="anchor"></a>is_quantized</h2>
+<p>Is <code>TRUE</code> if the Tensor is quantized, <code>FALSE</code> otherwise.</p>
+</div>
+<div id="is_set_to" class="section level2">
+<h2 class="hasAnchor">
+<a href="#is_set_to" class="anchor"></a>is_set_to</h2>
+<p>is_set_to(tensor) -&gt; bool</p>
+<p>Returns TRUE if this object refers to the same <code>THTensor</code> object from the Torch C API as the given tensor.</p>
+</div>
+<div id="is_shared" class="section level2">
+<h2 class="hasAnchor">
+<a href="#is_shared" class="anchor"></a>is_shared</h2>
+<p>Checks if tensor is in shared memory.</p>
+<p>This is always <code>TRUE</code> for CUDA tensors.</p>
+</div>
+<div id="is_signed" class="section level2">
+<h2 class="hasAnchor">
+<a href="#is_signed" class="anchor"></a>is_signed</h2>
+<p>is_signed() -&gt; bool</p>
+<p>Returns TRUE if the data type of <code>self</code> is a signed data type.</p>
+</div>
+<div id="isclose" class="section level2">
+<h2 class="hasAnchor">
+<a href="#isclose" class="anchor"></a>isclose</h2>
+<p>isclose(other, rtol=1e-05, atol=1e-08, equal_nan=FALSE) -&gt; Tensor</p>
+<p>See <code>?torch_isclose</code></p>
+</div>
+<div id="isfinite" class="section level2">
+<h2 class="hasAnchor">
+<a href="#isfinite" class="anchor"></a>isfinite</h2>
+<p>isfinite() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_isfinite.html">?torch_isfinite</a></code></p>
+</div>
+<div id="isinf" class="section level2">
+<h2 class="hasAnchor">
+<a href="#isinf" class="anchor"></a>isinf</h2>
+<p>isinf() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_isinf.html">?torch_isinf</a></code></p>
+</div>
+<div id="isnan" class="section level2">
+<h2 class="hasAnchor">
+<a href="#isnan" class="anchor"></a>isnan</h2>
+<p>isnan() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_isnan.html">?torch_isnan</a></code></p>
+</div>
+<div id="istft" class="section level2">
+<h2 class="hasAnchor">
+<a href="#istft" class="anchor"></a>istft</h2>
+<p>See <code>?torch_istft</code> ## item</p>
+<p>item() -&gt; number</p>
+<p>Returns the value of this tensor as a standard Python number. This only works for tensors with one element. For other cases, see <code>$tolist</code>.</p>
+<p>This operation is not differentiable.</p>
+<div id="examples-15" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-15" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb16"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1.0</span>)
+<span class="kw">x</span><span class="op">$</span><span class="fu">item</span>()
+</pre></div>
+</div>
+</div>
+<div id="kthvalue" class="section level2">
+<h2 class="hasAnchor">
+<a href="#kthvalue" class="anchor"></a>kthvalue</h2>
+<p>kthvalue(k, dim=NULL, keepdim=FALSE) -&gt; (Tensor, LongTensor)</p>
+<p>See <code><a href="../../reference/torch_kthvalue.html">?torch_kthvalue</a></code></p>
+</div>
+<div id="le" class="section level2">
+<h2 class="hasAnchor">
+<a href="#le" class="anchor"></a>le</h2>
+<p>le(other) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_le.html">?torch_le</a></code></p>
+</div>
+<div id="le_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#le_" class="anchor"></a>le_</h2>
+<p>le_(other) -&gt; Tensor</p>
+<p>In-place version of <code>$le</code></p>
+</div>
+<div id="lerp" class="section level2">
+<h2 class="hasAnchor">
+<a href="#lerp" class="anchor"></a>lerp</h2>
+<p>lerp(end, weight) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_lerp.html">?torch_lerp</a></code></p>
+</div>
+<div id="lerp_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#lerp_" class="anchor"></a>lerp_</h2>
+<p>lerp_(end, weight) -&gt; Tensor</p>
+<p>In-place version of <code>$lerp</code></p>
+</div>
+<div id="lgamma" class="section level2">
+<h2 class="hasAnchor">
+<a href="#lgamma" class="anchor"></a>lgamma</h2>
+<p>lgamma() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_lgamma.html">?torch_lgamma</a></code></p>
+</div>
+<div id="lgamma_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#lgamma_" class="anchor"></a>lgamma_</h2>
+<p>lgamma_() -&gt; Tensor</p>
+<p>In-place version of <code>$lgamma</code></p>
+</div>
+<div id="log" class="section level2">
+<h2 class="hasAnchor">
+<a href="#log" class="anchor"></a>log</h2>
+<p>log() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_log.html">?torch_log</a></code></p>
+</div>
+<div id="log10" class="section level2">
+<h2 class="hasAnchor">
+<a href="#log10" class="anchor"></a>log10</h2>
+<p>log10() -&gt; Tensor</p>
+<p>See [torch_log10()]</p>
+</div>
+<div id="log10_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#log10_" class="anchor"></a>log10_</h2>
+<p>log10_() -&gt; Tensor</p>
+<p>In-place version of <code>$log10</code></p>
+</div>
+<div id="log1p" class="section level2">
+<h2 class="hasAnchor">
+<a href="#log1p" class="anchor"></a>log1p</h2>
+<p>log1p() -&gt; Tensor</p>
+<p>See [torch_log1p()]</p>
+</div>
+<div id="log1p_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#log1p_" class="anchor"></a>log1p_</h2>
+<p>log1p_() -&gt; Tensor</p>
+<p>In-place version of <code>$log1p</code></p>
+</div>
+<div id="log2" class="section level2">
+<h2 class="hasAnchor">
+<a href="#log2" class="anchor"></a>log2</h2>
+<p>log2() -&gt; Tensor</p>
+<p>See [torch_log2()]</p>
+</div>
+<div id="log2_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#log2_" class="anchor"></a>log2_</h2>
+<p>log2_() -&gt; Tensor</p>
+<p>In-place version of <code>$log2</code></p>
+</div>
+<div id="log_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#log_" class="anchor"></a>log_</h2>
+<p>log_() -&gt; Tensor</p>
+<p>In-place version of <code>$log</code></p>
+</div>
+<div id="log_normal_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#log_normal_" class="anchor"></a>log_normal_</h2>
+<p>log_normal_(mean=1, std=2, *, generator=NULL)</p>
+<p>Fills <code>self</code> tensor with numbers samples from the log-normal distribution parameterized by the given mean <code>\mu</code> and standard deviation <code>\sigma</code>. Note that <code>mean</code> and <code>std</code> are the mean and standard deviation of the underlying normal distribution, and not of the returned distribution:</p>
+<p><span class="math display">\[
+f(x) = \dfrac{1}{x \sigma \sqrt{2\pi}}\ e^{-\frac{(\ln x - \mu)^2}{2\sigma^2}}
+\]</span></p>
+</div>
+<div id="logaddexp" class="section level2">
+<h2 class="hasAnchor">
+<a href="#logaddexp" class="anchor"></a>logaddexp</h2>
+<p>logaddexp(other) -&gt; Tensor</p>
+<p>See <code>?torch_logaddexp</code></p>
+</div>
+<div id="logaddexp2" class="section level2">
+<h2 class="hasAnchor">
+<a href="#logaddexp2" class="anchor"></a>logaddexp2</h2>
+<p>logaddexp2(other) -&gt; Tensor</p>
+<p>See [torch_logaddexp2()]</p>
+</div>
+<div id="logcumsumexp" class="section level2">
+<h2 class="hasAnchor">
+<a href="#logcumsumexp" class="anchor"></a>logcumsumexp</h2>
+<p>logcumsumexp(dim) -&gt; Tensor</p>
+<p>See <code>?torch_logcumsumexp</code></p>
+</div>
+<div id="logdet" class="section level2">
+<h2 class="hasAnchor">
+<a href="#logdet" class="anchor"></a>logdet</h2>
+<p>logdet() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_logdet.html">?torch_logdet</a></code></p>
+</div>
+<div id="logical_and" class="section level2">
+<h2 class="hasAnchor">
+<a href="#logical_and" class="anchor"></a>logical_and</h2>
+<p>logical_and() -&gt; Tensor</p>
+<p>See [torch_logical_and()]</p>
+</div>
+<div id="logical_and_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#logical_and_" class="anchor"></a>logical_and_</h2>
+<p>logical_and_() -&gt; Tensor</p>
+<p>In-place version of <code>$logical_and</code></p>
+</div>
+<div id="logical_not" class="section level2">
+<h2 class="hasAnchor">
+<a href="#logical_not" class="anchor"></a>logical_not</h2>
+<p>logical_not() -&gt; Tensor</p>
+<p>See [torch_logical_not()]</p>
+</div>
+<div id="logical_not_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#logical_not_" class="anchor"></a>logical_not_</h2>
+<p>logical_not_() -&gt; Tensor</p>
+<p>In-place version of <code>$logical_not</code></p>
+</div>
+<div id="logical_or" class="section level2">
+<h2 class="hasAnchor">
+<a href="#logical_or" class="anchor"></a>logical_or</h2>
+<p>logical_or() -&gt; Tensor</p>
+<p>See [torch_logical_or()]</p>
+</div>
+<div id="logical_or_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#logical_or_" class="anchor"></a>logical_or_</h2>
+<p>logical_or_() -&gt; Tensor</p>
+<p>In-place version of <code>$logical_or</code></p>
+</div>
+<div id="logical_xor" class="section level2">
+<h2 class="hasAnchor">
+<a href="#logical_xor" class="anchor"></a>logical_xor</h2>
+<p>logical_xor() -&gt; Tensor</p>
+<p>See [torch_logical_xor()]</p>
+</div>
+<div id="logical_xor_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#logical_xor_" class="anchor"></a>logical_xor_</h2>
+<p>logical_xor_() -&gt; Tensor</p>
+<p>In-place version of <code>$logical_xor</code></p>
+</div>
+<div id="logsumexp" class="section level2">
+<h2 class="hasAnchor">
+<a href="#logsumexp" class="anchor"></a>logsumexp</h2>
+<p>logsumexp(dim, keepdim=FALSE) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_logsumexp.html">?torch_logsumexp</a></code></p>
+</div>
+<div id="long" class="section level2">
+<h2 class="hasAnchor">
+<a href="#long" class="anchor"></a>long</h2>
+<p>long(memory_format=torch_preserve_format) -&gt; Tensor</p>
+<p><code>self$long()</code> is equivalent to <code>self$to(torch_int64)</code>. See [to()].</p>
+<div id="arguments-25" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-25" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
+<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
+</ul>
+</div>
+</div>
+<div id="lstsq" class="section level2">
+<h2 class="hasAnchor">
+<a href="#lstsq" class="anchor"></a>lstsq</h2>
+<p>lstsq(A) -&gt; (Tensor, Tensor)</p>
+<p>See <code><a href="../../reference/torch_lstsq.html">?torch_lstsq</a></code></p>
+</div>
+<div id="lt" class="section level2">
+<h2 class="hasAnchor">
+<a href="#lt" class="anchor"></a>lt</h2>
+<p>lt(other) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_lt.html">?torch_lt</a></code></p>
+</div>
+<div id="lt_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#lt_" class="anchor"></a>lt_</h2>
+<p>lt_(other) -&gt; Tensor</p>
+<p>In-place version of <code>$lt</code></p>
+</div>
+<div id="lu" class="section level2">
+<h2 class="hasAnchor">
+<a href="#lu" class="anchor"></a>lu</h2>
+<p>See <code><a href="../../reference/torch_lu.html">?torch_lu</a></code> ## lu_solve</p>
+<p>lu_solve(LU_data, LU_pivots) -&gt; Tensor</p>
+<p>See [torch_lu_solve()]</p>
+</div>
+<div id="map_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#map_" class="anchor"></a>map_</h2>
+<p>map_(tensor, callable)</p>
+<p>Applies <code>callable</code> for each element in <code>self</code> tensor and the given <code>tensor</code> and stores the results in <code>self</code> tensor. <code>self</code> tensor and the given <code>tensor</code> must be broadcastable.</p>
+<p>The <code>callable</code> should have the signature:</p>
+<p><code>callable(a, b) -&gt; number</code></p>
+</div>
+<div id="masked_fill" class="section level2">
+<h2 class="hasAnchor">
+<a href="#masked_fill" class="anchor"></a>masked_fill</h2>
+<p>masked_fill(mask, value) -&gt; Tensor</p>
+<p>Out-of-place version of <code>$masked_fill_</code></p>
+</div>
+<div id="masked_fill_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#masked_fill_" class="anchor"></a>masked_fill_</h2>
+<p>masked_fill_(mask, value)</p>
+<p>Fills elements of <code>self</code> tensor with <code>value</code> where <code>mask</code> is TRUE. The shape of <code>mask</code> must be <code>broadcastable &lt;broadcasting-semantics&gt;</code> with the shape of the underlying tensor.</p>
+<div id="arguments-26" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-26" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>mask (BoolTensor): the boolean mask</li>
+<li>value (float): the value to fill in with</li>
+</ul>
+</div>
+</div>
+<div id="masked_scatter" class="section level2">
+<h2 class="hasAnchor">
+<a href="#masked_scatter" class="anchor"></a>masked_scatter</h2>
+<p>masked_scatter(mask, tensor) -&gt; Tensor</p>
+<p>Out-of-place version of <code>$masked_scatter_</code></p>
+</div>
+<div id="masked_scatter_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#masked_scatter_" class="anchor"></a>masked_scatter_</h2>
+<p>masked_scatter_(mask, source)</p>
+<p>Copies elements from <code>source</code> into <code>self</code> tensor at positions where the <code>mask</code> is TRUE. The shape of <code>mask</code> must be :ref:<code>broadcastable &lt;broadcasting-semantics&gt;</code> with the shape of the underlying tensor. The <code>source</code> should have at least as many elements as the number of ones in <code>mask</code></p>
+<div id="arguments-27" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-27" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>mask (BoolTensor): the boolean mask</li>
+<li>source (Tensor): the tensor to copy from</li>
+</ul>
+</div>
+<div id="note-5" class="section level4">
+<h4 class="hasAnchor">
+<a href="#note-5" class="anchor"></a>Note:</h4>
+<p>The <code>mask</code> operates on the <code>self</code> tensor, not on the given <code>source</code> tensor.</p>
+</div>
+</div>
+<div id="masked_select" class="section level2">
+<h2 class="hasAnchor">
+<a href="#masked_select" class="anchor"></a>masked_select</h2>
+<p>masked_select(mask) -&gt; Tensor</p>
+<p>See [torch_masked_select()]</p>
+</div>
+<div id="matmul" class="section level2">
+<h2 class="hasAnchor">
+<a href="#matmul" class="anchor"></a>matmul</h2>
+<p>matmul(tensor2) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_matmul.html">?torch_matmul</a></code></p>
+</div>
+<div id="matrix_power" class="section level2">
+<h2 class="hasAnchor">
+<a href="#matrix_power" class="anchor"></a>matrix_power</h2>
+<p>matrix_power(n) -&gt; Tensor</p>
+<p>See [torch_matrix_power()]</p>
+</div>
+<div id="max" class="section level2">
+<h2 class="hasAnchor">
+<a href="#max" class="anchor"></a>max</h2>
+<p>max(dim=NULL, keepdim=FALSE) -&gt; Tensor or (Tensor, Tensor)</p>
+<p>See <code><a href="../../reference/torch_max.html">?torch_max</a></code></p>
+</div>
+<div id="mean" class="section level2">
+<h2 class="hasAnchor">
+<a href="#mean" class="anchor"></a>mean</h2>
+<p>mean(dim=NULL, keepdim=FALSE) -&gt; Tensor or (Tensor, Tensor)</p>
+<p>See <code><a href="../../reference/torch_mean.html">?torch_mean</a></code></p>
+</div>
+<div id="median" class="section level2">
+<h2 class="hasAnchor">
+<a href="#median" class="anchor"></a>median</h2>
+<p>median(dim=NULL, keepdim=FALSE) -&gt; (Tensor, LongTensor)</p>
+<p>See <code><a href="../../reference/torch_median.html">?torch_median</a></code></p>
+</div>
+<div id="min" class="section level2">
+<h2 class="hasAnchor">
+<a href="#min" class="anchor"></a>min</h2>
+<p>min(dim=NULL, keepdim=FALSE) -&gt; Tensor or (Tensor, Tensor)</p>
+<p>See <code><a href="../../reference/torch_min.html">?torch_min</a></code></p>
+</div>
+<div id="mm" class="section level2">
+<h2 class="hasAnchor">
+<a href="#mm" class="anchor"></a>mm</h2>
+<p>mm(mat2) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_mm.html">?torch_mm</a></code></p>
+</div>
+<div id="mode" class="section level2">
+<h2 class="hasAnchor">
+<a href="#mode" class="anchor"></a>mode</h2>
+<p>mode(dim=NULL, keepdim=FALSE) -&gt; (Tensor, LongTensor)</p>
+<p>See <code><a href="../../reference/torch_mode.html">?torch_mode</a></code></p>
+</div>
+<div id="mul" class="section level2">
+<h2 class="hasAnchor">
+<a href="#mul" class="anchor"></a>mul</h2>
+<p>mul(value) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_mul.html">?torch_mul</a></code></p>
+</div>
+<div id="mul_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#mul_" class="anchor"></a>mul_</h2>
+<p>mul_(value)</p>
+<p>In-place version of <code>$mul</code></p>
+</div>
+<div id="multinomial" class="section level2">
+<h2 class="hasAnchor">
+<a href="#multinomial" class="anchor"></a>multinomial</h2>
+<p>multinomial(num_samples, replacement=FALSE, *, generator=NULL) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_multinomial.html">?torch_multinomial</a></code></p>
+</div>
+<div id="mv" class="section level2">
+<h2 class="hasAnchor">
+<a href="#mv" class="anchor"></a>mv</h2>
+<p>mv(vec) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_mv.html">?torch_mv</a></code></p>
+</div>
+<div id="mvlgamma" class="section level2">
+<h2 class="hasAnchor">
+<a href="#mvlgamma" class="anchor"></a>mvlgamma</h2>
+<p>mvlgamma(p) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_mvlgamma.html">?torch_mvlgamma</a></code></p>
+</div>
+<div id="mvlgamma_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#mvlgamma_" class="anchor"></a>mvlgamma_</h2>
+<p>mvlgamma_(p) -&gt; Tensor</p>
+<p>In-place version of <code>$mvlgamma</code></p>
+</div>
+<div id="names" class="section level2">
+<h2 class="hasAnchor">
+<a href="#names" class="anchor"></a>names</h2>
+<p>Stores names for each of this tensor’s dimensions.</p>
+<p><code>names[idx]</code> corresponds to the name of tensor dimension <code>idx</code>. Names are either a string if the dimension is named or <code>NULL</code> if the dimension is unnamed.</p>
+<p>Dimension names may contain characters or underscore. Furthermore, a dimension name must be a valid Python variable name (i.e., does not start with underscore).</p>
+<p>Tensors may not have two named dimensions with the same name.</p>
+<div id="warning-4" class="section level4">
+<h4 class="hasAnchor">
+<a href="#warning-4" class="anchor"></a>Warning:</h4>
+<p>The named tensor API is experimental and subject to change.</p>
+</div>
+</div>
+<div id="narrow" class="section level2">
+<h2 class="hasAnchor">
+<a href="#narrow" class="anchor"></a>narrow</h2>
+<p>narrow(dimension, start, length) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_narrow.html">?torch_narrow</a></code></p>
+<div id="examples-16" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-16" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb17"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">9</span>, ncol = <span class="fl">3</span>))
+<span class="kw">x</span><span class="op">$</span><span class="fu">narrow</span>(<span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">3</span>)
+<span class="kw">x</span><span class="op">$</span><span class="fu">narrow</span>(<span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">2</span>)
+</pre></div>
+</div>
+</div>
+<div id="narrow_copy" class="section level2">
+<h2 class="hasAnchor">
+<a href="#narrow_copy" class="anchor"></a>narrow_copy</h2>
+<p>narrow_copy(dimension, start, length) -&gt; Tensor</p>
+<p>Same as <code>Tensor.narrow</code> except returning a copy rather than shared storage. This is primarily for sparse tensors, which do not have a shared-storage narrow method. Calling <code>narrow_copy` with</code>dimemsion &gt; self<span class="math inline">\(sparse_dim()`` will return a copy with the relevant dense dimension narrowed, and ``self\)</span>shape`` updated accordingly.</p>
+</div>
+<div id="ndim" class="section level2">
+<h2 class="hasAnchor">
+<a href="#ndim" class="anchor"></a>ndim</h2>
+<p>Alias for <code>$dim()</code></p>
+</div>
+<div id="ndimension" class="section level2">
+<h2 class="hasAnchor">
+<a href="#ndimension" class="anchor"></a>ndimension</h2>
+<p>ndimension() -&gt; int</p>
+<p>Alias for <code>$dim()</code></p>
+</div>
+<div id="ne" class="section level2">
+<h2 class="hasAnchor">
+<a href="#ne" class="anchor"></a>ne</h2>
+<p>ne(other) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_ne.html">?torch_ne</a></code></p>
+</div>
+<div id="ne_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#ne_" class="anchor"></a>ne_</h2>
+<p>ne_(other) -&gt; Tensor</p>
+<p>In-place version of <code>$ne</code></p>
+</div>
+<div id="neg" class="section level2">
+<h2 class="hasAnchor">
+<a href="#neg" class="anchor"></a>neg</h2>
+<p>neg() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_neg.html">?torch_neg</a></code></p>
+</div>
+<div id="neg_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#neg_" class="anchor"></a>neg_</h2>
+<p>neg_() -&gt; Tensor</p>
+<p>In-place version of <code>$neg</code></p>
+</div>
+<div id="nelement" class="section level2">
+<h2 class="hasAnchor">
+<a href="#nelement" class="anchor"></a>nelement</h2>
+<p>nelement() -&gt; int</p>
+<p>Alias for <code>$numel</code></p>
+</div>
+<div id="new_empty" class="section level2">
+<h2 class="hasAnchor">
+<a href="#new_empty" class="anchor"></a>new_empty</h2>
+<p>new_empty(size, dtype=NULL, device=NULL, requires_grad=FALSE) -&gt; Tensor</p>
+<p>Returns a Tensor of size <code>size</code> filled with uninitialized data. By default, the returned Tensor has the same <code>torch_dtype</code> and <code>torch_device</code> as this tensor.</p>
+<div id="arguments-28" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-28" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>dtype (<code>torch_dtype</code>, optional): the desired type of returned tensor. Default: if NULL, same <code>torch_dtype</code> as this tensor.</li>
+<li>device (<code>torch_device</code>, optional): the desired device of returned tensor. Default: if NULL, same <code>torch_device</code> as this tensor.</li>
+<li>requires_grad (bool, optional): If autograd should record operations on the</li>
+<li>returned tensor. Default: <code>FALSE</code>.</li>
+</ul>
+</div>
+<div id="examples-17" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-17" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb18"><pre class="downlit">
+<span class="kw">tensor</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span>(<span class="fl">5</span>)
+<span class="kw">tensor</span><span class="op">$</span><span class="fu">new_empty</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>, <span class="fl">3</span>))
+</pre></div>
+</div>
+</div>
+<div id="new_full" class="section level2">
+<h2 class="hasAnchor">
+<a href="#new_full" class="anchor"></a>new_full</h2>
+<p>new_full(size, fill_value, dtype=NULL, device=NULL, requires_grad=FALSE) -&gt; Tensor</p>
+<p>Returns a Tensor of size <code>size</code> filled with <code>fill_value</code>. By default, the returned Tensor has the same <code>torch_dtype</code> and <code>torch_device</code> as this tensor.</p>
+<div id="arguments-29" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-29" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>fill_value (scalar): the number to fill the output tensor with.</li>
+<li>dtype (<code>torch_dtype</code>, optional): the desired type of returned tensor. Default: if NULL, same <code>torch_dtype</code> as this tensor.</li>
+<li>device (<code>torch_device</code>, optional): the desired device of returned tensor. Default: if NULL, same <code>torch_device</code> as this tensor.</li>
+<li>requires_grad (bool, optional): If autograd should record operations on the</li>
+<li>returned tensor. Default: <code>FALSE</code>.</li>
+</ul>
+</div>
+<div id="examples-18" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-18" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb19"><pre class="downlit">
+<span class="kw">tensor</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>), dtype=<span class="fu"><a href="../../reference/torch_dtype.html">torch_float64</a></span>())
+<span class="kw">tensor</span><span class="op">$</span><span class="fu">new_full</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">3</span>, <span class="fl">4</span>), <span class="fl">3.141592</span>)
+</pre></div>
+</div>
+</div>
+<div id="new_ones" class="section level2">
+<h2 class="hasAnchor">
+<a href="#new_ones" class="anchor"></a>new_ones</h2>
+<p>new_ones(size, dtype=NULL, device=NULL, requires_grad=FALSE) -&gt; Tensor</p>
+<p>Returns a Tensor of size <code>size</code> filled with <code>1</code>. By default, the returned Tensor has the same <code>torch_dtype</code> and <code>torch_device</code> as this tensor.</p>
+<div id="arguments-30" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-30" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>size (int…): a list, tuple, or <code>torch_Size</code> of integers defining the</li>
+<li>shape of the output tensor.</li>
+<li>dtype (<code>torch_dtype</code>, optional): the desired type of returned tensor. Default: if NULL, same <code>torch_dtype</code> as this tensor.</li>
+<li>device (<code>torch_device</code>, optional): the desired device of returned tensor. Default: if NULL, same <code>torch_device</code> as this tensor.</li>
+<li>requires_grad (bool, optional): If autograd should record operations on the</li>
+<li>returned tensor. Default: <code>FALSE</code>.</li>
+</ul>
+</div>
+<div id="examples-19" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-19" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb20"><pre class="downlit">
+<span class="kw">tensor</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>), dtype=<span class="fu"><a href="../../reference/torch_dtype.html">torch_int32</a></span>())
+<span class="kw">tensor</span><span class="op">$</span><span class="fu">new_ones</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>, <span class="fl">3</span>))
+</pre></div>
+</div>
+</div>
+<div id="new_tensor" class="section level2">
+<h2 class="hasAnchor">
+<a href="#new_tensor" class="anchor"></a>new_tensor</h2>
+<p>new_tensor(data, dtype=NULL, device=NULL, requires_grad=FALSE) -&gt; Tensor</p>
+<p>Returns a new Tensor with <code>data</code> as the tensor data. By default, the returned Tensor has the same <code>torch_dtype</code> and <code>torch_device</code> as this tensor.</p>
+<div id="warning-5" class="section level4">
+<h4 class="hasAnchor">
+<a href="#warning-5" class="anchor"></a>Warning:</h4>
+<p><code>new_tensor</code> always copies <code>data(). If you have a Tensor</code>data` and want to avoid a copy, use [$requires_grad_()] or [$detach()]. If you have a numpy array and want to avoid a copy, use [torch_from_numpy()].</p>
+<p>When data is a tensor <code>x</code>, [new_tensor()()] reads out ‘the data’ from whatever it is passed, and constructs a leaf variable. Therefore <code>tensor$new_tensor(x)</code> is equivalent to <code>x$clone()$detach()</code> and <code>tensor$new_tensor(x, requires_grad=TRUE)</code> is equivalent to <code>x$clone()$detach()$requires_grad_(TRUE)</code>. The equivalents using <code>clone()</code> and <code><a href="https://rdrr.io/r/base/detach.html">detach()</a></code> are recommended.</p>
+</div>
+<div id="arguments-31" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-31" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>data (array_like): The returned Tensor copies <code>data</code>.</li>
+<li>dtype (<code>torch_dtype</code>, optional): the desired type of returned tensor. Default: if NULL, same <code>torch_dtype</code> as this tensor.</li>
+<li>device (<code>torch_device</code>, optional): the desired device of returned tensor. Default: if NULL, same <code>torch_device</code> as this tensor.</li>
+<li>requires_grad (bool, optional): If autograd should record operations on the</li>
+<li>returned tensor. Default: <code>FALSE</code>.</li>
+</ul>
+</div>
+<div id="examples-20" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-20" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb21"><pre class="downlit">
+<span class="kw">tensor</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>), dtype=<span class="kw">torch_int8</span>)
+<span class="kw">data</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">4</span>, ncol = <span class="fl">2</span>)
+<span class="kw">tensor</span><span class="op">$</span><span class="fu">new_tensor</span>(<span class="kw">data</span>)
+</pre></div>
+</div>
+</div>
+<div id="new_zeros" class="section level2">
+<h2 class="hasAnchor">
+<a href="#new_zeros" class="anchor"></a>new_zeros</h2>
+<p>new_zeros(size, dtype=NULL, device=NULL, requires_grad=FALSE) -&gt; Tensor</p>
+<p>Returns a Tensor of size <code>size</code> filled with <code>0</code>. By default, the returned Tensor has the same <code>torch_dtype</code> and <code>torch_device</code> as this tensor.</p>
+<div id="arguments-32" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-32" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>size (int…): a list, tuple, or <code>torch_Size</code> of integers defining the</li>
+<li>shape of the output tensor.</li>
+<li>dtype (<code>torch_dtype</code>, optional): the desired type of returned tensor. Default: if NULL, same <code>torch_dtype</code> as this tensor.</li>
+<li>device (<code>torch_device</code>, optional): the desired device of returned tensor. Default: if NULL, same <code>torch_device</code> as this tensor.</li>
+<li>requires_grad (bool, optional): If autograd should record operations on the</li>
+<li>returned tensor. Default: <code>FALSE</code>.</li>
+</ul>
+</div>
+<div id="examples-21" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-21" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb22"><pre class="downlit">
+<span class="kw">tensor</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1</span>), dtype=<span class="fu"><a href="../../reference/torch_dtype.html">torch_float64</a></span>())
+<span class="kw">tensor</span><span class="op">$</span><span class="fu">new_zeros</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>, <span class="fl">3</span>))
+</pre></div>
+</div>
+</div>
+<div id="nonzero" class="section level2">
+<h2 class="hasAnchor">
+<a href="#nonzero" class="anchor"></a>nonzero</h2>
+<p>nonzero() -&gt; LongTensor</p>
+<p>See <code><a href="../../reference/torch_nonzero.html">?torch_nonzero</a></code></p>
+</div>
+<div id="norm" class="section level2">
+<h2 class="hasAnchor">
+<a href="#norm" class="anchor"></a>norm</h2>
+<p>See <code><a href="../../reference/torch_norm.html">?torch_norm</a></code> ## normal_</p>
+<p>normal_(mean=0, std=1, *, generator=NULL) -&gt; Tensor</p>
+<p>Fills <code>self</code> tensor with elements samples from the normal distribution parameterized by <code>mean</code> and <code>std</code>.</p>
+</div>
+<div id="numel" class="section level2">
+<h2 class="hasAnchor">
+<a href="#numel" class="anchor"></a>numel</h2>
+<p>numel() -&gt; int</p>
+<p>See <code>?torch_numel</code></p>
+</div>
+<div id="numpy" class="section level2">
+<h2 class="hasAnchor">
+<a href="#numpy" class="anchor"></a>numpy</h2>
+<p>numpy() -&gt; numpy.ndarray</p>
+<p>Returns <code>self</code> tensor as a NumPy :class:<code>ndarray</code>. This tensor and the returned <code>ndarray</code> share the same underlying storage. Changes to <code>self</code> tensor will be reflected in the :class:<code>ndarray</code> and vice versa.</p>
+</div>
+<div id="orgqr" class="section level2">
+<h2 class="hasAnchor">
+<a href="#orgqr" class="anchor"></a>orgqr</h2>
+<p>orgqr(input2) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_orgqr.html">?torch_orgqr</a></code></p>
+</div>
+<div id="ormqr" class="section level2">
+<h2 class="hasAnchor">
+<a href="#ormqr" class="anchor"></a>ormqr</h2>
+<p>ormqr(input2, input3, left=TRUE, transpose=FALSE) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_ormqr.html">?torch_ormqr</a></code></p>
+</div>
+<div id="permute" class="section level2">
+<h2 class="hasAnchor">
+<a href="#permute" class="anchor"></a>permute</h2>
+<p>permute(*dims) -&gt; Tensor</p>
+<p>Returns a view of the original tensor with its dimensions permuted.</p>
+<div id="arguments-33" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-33" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>dims (int…): The desired ordering of dimensions</li>
+</ul>
+</div>
+<div id="examples-22" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-22" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb23"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">5</span>)
+<span class="kw">x</span><span class="op">$</span><span class="fu">size</span>()
+<span class="kw">x</span><span class="op">$</span><span class="fu">permute</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">3</span>, <span class="fl">1</span>, <span class="fl">2</span>))<span class="op">$</span><span class="fu">size</span>()
+</pre></div>
+</div>
+</div>
+<div id="pin_memory" class="section level2">
+<h2 class="hasAnchor">
+<a href="#pin_memory" class="anchor"></a>pin_memory</h2>
+<p>pin_memory() -&gt; Tensor</p>
+<p>Copies the tensor to pinned memory, if it’s not already pinned.</p>
+</div>
+<div id="pinverse" class="section level2">
+<h2 class="hasAnchor">
+<a href="#pinverse" class="anchor"></a>pinverse</h2>
+<p>pinverse() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_pinverse.html">?torch_pinverse</a></code></p>
+</div>
+<div id="polygamma" class="section level2">
+<h2 class="hasAnchor">
+<a href="#polygamma" class="anchor"></a>polygamma</h2>
+<p>polygamma(n) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_polygamma.html">?torch_polygamma</a></code></p>
+</div>
+<div id="polygamma_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#polygamma_" class="anchor"></a>polygamma_</h2>
+<p>polygamma_(n) -&gt; Tensor</p>
+<p>In-place version of <code>$polygamma</code></p>
+</div>
+<div id="pow" class="section level2">
+<h2 class="hasAnchor">
+<a href="#pow" class="anchor"></a>pow</h2>
+<p>pow(exponent) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_pow.html">?torch_pow</a></code></p>
+</div>
+<div id="pow_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#pow_" class="anchor"></a>pow_</h2>
+<p>pow_(exponent) -&gt; Tensor</p>
+<p>In-place version of <code>$pow</code></p>
+</div>
+<div id="prod" class="section level2">
+<h2 class="hasAnchor">
+<a href="#prod" class="anchor"></a>prod</h2>
+<p>prod(dim=NULL, keepdim=FALSE, dtype=NULL) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_prod.html">?torch_prod</a></code></p>
+</div>
+<div id="put_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#put_" class="anchor"></a>put_</h2>
+<p>put_(indices, tensor, accumulate=FALSE) -&gt; Tensor</p>
+<p>Copies the elements from <code>tensor</code> into the positions specified by indices. For the purpose of indexing, the <code>self</code> tensor is treated as if it were a 1-D tensor.</p>
+<p>If <code>accumulate</code> is <code>TRUE</code>, the elements in <code>tensor</code> are added to <code>self</code>. If accumulate is <code>FALSE</code>, the behavior is undefined if indices contain duplicate elements.</p>
+<div id="arguments-34" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-34" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>indices (LongTensor): the indices into self</li>
+<li>tensor (Tensor): the tensor containing values to copy from</li>
+<li>accumulate (bool): whether to accumulate into self</li>
+</ul>
+</div>
+<div id="examples-23" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-23" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb24"><pre class="downlit">
+<span class="kw">src</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span>(<span class="fl">3</span><span class="op">:</span><span class="fl">8</span>, ncol = <span class="fl">3</span>))
+<span class="kw">src</span><span class="op">$</span><span class="fu">put_</span>(<span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">2</span>), <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">9</span><span class="op">:</span><span class="fl">10</span>))
+</pre></div>
+</div>
+</div>
+<div id="q_per_channel_axis" class="section level2">
+<h2 class="hasAnchor">
+<a href="#q_per_channel_axis" class="anchor"></a>q_per_channel_axis</h2>
+<p>q_per_channel_axis() -&gt; int</p>
+<p>Given a Tensor quantized by linear (affine) per-channel quantization, returns the index of dimension on which per-channel quantization is applied.</p>
+</div>
+<div id="q_per_channel_scales" class="section level2">
+<h2 class="hasAnchor">
+<a href="#q_per_channel_scales" class="anchor"></a>q_per_channel_scales</h2>
+<p>q_per_channel_scales() -&gt; Tensor</p>
+<p>Given a Tensor quantized by linear (affine) per-channel quantization, returns a Tensor of scales of the underlying quantizer. It has the number of elements that matches the corresponding dimensions (from q_per_channel_axis) of the tensor.</p>
+</div>
+<div id="q_per_channel_zero_points" class="section level2">
+<h2 class="hasAnchor">
+<a href="#q_per_channel_zero_points" class="anchor"></a>q_per_channel_zero_points</h2>
+<p>q_per_channel_zero_points() -&gt; Tensor</p>
+<p>Given a Tensor quantized by linear (affine) per-channel quantization, returns a tensor of zero_points of the underlying quantizer. It has the number of elements that matches the corresponding dimensions (from q_per_channel_axis) of the tensor.</p>
+</div>
+<div id="q_scale" class="section level2">
+<h2 class="hasAnchor">
+<a href="#q_scale" class="anchor"></a>q_scale</h2>
+<p>q_scale() -&gt; float</p>
+<p>Given a Tensor quantized by linear(affine) quantization, returns the scale of the underlying quantizer().</p>
+</div>
+<div id="q_zero_point" class="section level2">
+<h2 class="hasAnchor">
+<a href="#q_zero_point" class="anchor"></a>q_zero_point</h2>
+<p>q_zero_point() -&gt; int</p>
+<p>Given a Tensor quantized by linear(affine) quantization, returns the zero_point of the underlying quantizer().</p>
+</div>
+<div id="qr" class="section level2">
+<h2 class="hasAnchor">
+<a href="#qr" class="anchor"></a>qr</h2>
+<p>qr(some=TRUE) -&gt; (Tensor, Tensor)</p>
+<p>See <code><a href="../../reference/torch_qr.html">?torch_qr</a></code></p>
+</div>
+<div id="qscheme" class="section level2">
+<h2 class="hasAnchor">
+<a href="#qscheme" class="anchor"></a>qscheme</h2>
+<p>qscheme() -&gt; torch_qscheme</p>
+<p>Returns the quantization scheme of a given QTensor.</p>
+</div>
+<div id="rad2deg" class="section level2">
+<h2 class="hasAnchor">
+<a href="#rad2deg" class="anchor"></a>rad2deg</h2>
+<p>rad2deg() -&gt; Tensor</p>
+<p>See [torch_rad2deg()]</p>
+</div>
+<div id="rad2deg_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#rad2deg_" class="anchor"></a>rad2deg_</h2>
+<p>rad2deg_() -&gt; Tensor</p>
+<p>In-place version of <code>$rad2deg</code></p>
+</div>
+<div id="random_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#random_" class="anchor"></a>random_</h2>
+<p>random_(from=0, to=NULL, *, generator=NULL) -&gt; Tensor</p>
+<p>Fills <code>self</code> tensor with numbers sampled from the discrete uniform distribution over <code>[from, to - 1]</code>. If not specified, the values are usually only bounded by <code>self</code> tensor’s data type. However, for floating point types, if unspecified, range will be <code>[0, 2^mantissa]</code> to ensure that every value is representable. For example, <code>torch_tensor(1, dtype=torch_double).random_()</code> will be uniform in <code>[0, 2^53]</code>.</p>
+</div>
+<div id="real" class="section level2">
+<h2 class="hasAnchor">
+<a href="#real" class="anchor"></a>real</h2>
+<p>Returns a new tensor containing real values of the <code>self</code> tensor. The returned tensor and <code>self</code> share the same underlying storage.</p>
+<div id="warning-6" class="section level4">
+<h4 class="hasAnchor">
+<a href="#warning-6" class="anchor"></a>Warning:</h4>
+<p>[real()] is only supported for tensors with complex dtypes.</p>
+</div>
+<div id="examples-24" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-24" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb25"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">4</span>, dtype=<span class="fu">torch_cfloat</span>())
+<span class="kw">x</span>
+<span class="kw">x</span><span class="op">$</span><span class="kw">real</span>
+</pre></div>
+</div>
+</div>
+<div id="reciprocal" class="section level2">
+<h2 class="hasAnchor">
+<a href="#reciprocal" class="anchor"></a>reciprocal</h2>
+<p>reciprocal() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_reciprocal.html">?torch_reciprocal</a></code></p>
+</div>
+<div id="reciprocal_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#reciprocal_" class="anchor"></a>reciprocal_</h2>
+<p>reciprocal_() -&gt; Tensor</p>
+<p>In-place version of <code>$reciprocal</code></p>
+</div>
+<div id="record_stream" class="section level2">
+<h2 class="hasAnchor">
+<a href="#record_stream" class="anchor"></a>record_stream</h2>
+<p>record_stream(stream)</p>
+<p>Ensures that the tensor memory is not reused for another tensor until all current work queued on <code>stream</code> are complete.</p>
+<div id="note-6" class="section level4">
+<h4 class="hasAnchor">
+<a href="#note-6" class="anchor"></a>Note:</h4>
+<p>The caching allocator is aware of only the stream where a tensor was allocated. Due to the awareness, it already correctly manages the life cycle of tensors on only one stream. But if a tensor is used on a stream different from the stream of origin, the allocator might reuse the memory unexpectedly. Calling this method lets the allocator know which streams have used the tensor.</p>
+</div>
+</div>
+<div id="refine_names" class="section level2">
+<h2 class="hasAnchor">
+<a href="#refine_names" class="anchor"></a>refine_names</h2>
+<p>Refines the dimension names of <code>self</code> according to <code>names</code>.</p>
+<p>Refining is a special case of renaming that “lifts” unnamed dimensions. A <code>NULL</code> dim can be refined to have any name; a named dim can only be refined to have the same name.</p>
+<p>Because named tensors can coexist with unnamed tensors, refining names gives a nice way to write named-tensor-aware code that works with both named and unnamed tensors.</p>
+<p><code>names</code> may contain up to one Ellipsis (<code>...</code>). The Ellipsis is expanded greedily; it is expanded in-place to fill <code>names</code> to the same length as <code>self$dim()</code> using names from the corresponding indices of <code>self$names</code>.</p>
+<div id="arguments-35" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-35" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>names (iterable of str): The desired names of the output tensor. May contain up to one Ellipsis.</li>
+</ul>
+</div>
+<div id="examples-25" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-25" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb26"><pre class="downlit">
+<span class="kw">imgs</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">32</span>, <span class="fl">3</span>, <span class="fl">128</span>, <span class="fl">128</span>)
+<span class="kw">named_imgs</span> <span class="op">&lt;-</span> <span class="kw">imgs</span><span class="op">$</span><span class="fu">refine_names</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">'N'</span>, <span class="st">'C'</span>, <span class="st">'H'</span>, <span class="st">'W'</span>))
+<span class="kw">named_imgs</span><span class="op">$</span><span class="kw">names</span>
+</pre></div>
+</div>
+</div>
+<div id="register_hook" class="section level2">
+<h2 class="hasAnchor">
+<a href="#register_hook" class="anchor"></a>register_hook</h2>
+<p>Registers a backward hook.</p>
+<p>The hook will be called every time a gradient with respect to the Tensor is computed. The hook should have the following signature::</p>
+<p>hook(grad) -&gt; Tensor or NULL</p>
+<p>The hook should not modify its argument, but it can optionally return a new gradient which will be used in place of <code>grad</code>.</p>
+<p>This function returns a handle with a method <code>handle$remove()</code> that removes the hook from the module.</p>
+<div id="example" class="section level4">
+<h4 class="hasAnchor">
+<a href="#example" class="anchor"></a>Example</h4>
+<div class="sourceCode" id="cb27"><pre class="downlit">
+<span class="kw">v</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">0.</span>, <span class="fl">0.</span>, <span class="fl">0.</span>), requires_grad=<span class="fl">TRUE</span>)
+<span class="kw">h</span> <span class="op">&lt;-</span> <span class="kw">v</span><span class="op">$</span><span class="fu">register_hook</span>(<span class="fu">function</span>(<span class="kw">grad</span>) <span class="kw">grad</span> <span class="op">*</span> <span class="fl">2</span>)  <span class="co"># double the gradient</span>
+<span class="kw">v</span><span class="op">$</span><span class="fu">backward</span>(<span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1.</span>, <span class="fl">2.</span>, <span class="fl">3.</span>)))
+<span class="kw">v</span><span class="op">$</span><span class="kw">grad</span>
+<span class="kw">h</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/rm.html">remove</a></span>()
+</pre></div>
+</div>
+</div>
+<div id="remainder" class="section level2">
+<h2 class="hasAnchor">
+<a href="#remainder" class="anchor"></a>remainder</h2>
+<p>remainder(divisor) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_remainder.html">?torch_remainder</a></code></p>
+</div>
+<div id="remainder_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#remainder_" class="anchor"></a>remainder_</h2>
+<p>remainder_(divisor) -&gt; Tensor</p>
+<p>In-place version of <code>$remainder</code></p>
+</div>
+<div id="rename" class="section level2">
+<h2 class="hasAnchor">
+<a href="#rename" class="anchor"></a>rename</h2>
+<p>Renames dimension names of <code>self</code>.</p>
+<p>There are two main usages:</p>
+<p><code>self$rename(**rename_map)</code> returns a view on tensor that has dims renamed as specified in the mapping <code>rename_map</code>.</p>
+<p><code>self$rename(*names)</code> returns a view on tensor, renaming all dimensions positionally using <code>names</code>. Use <code>self$rename(NULL)</code> to drop names on a tensor.</p>
+<p>One cannot specify both positional args <code>names</code> and keyword args <code>rename_map</code>.</p>
+<div id="examples-26" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-26" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb28"><pre class="downlit">
+<span class="kw">imgs</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">5</span>, <span class="fl">7</span>, names=<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">'N'</span>, <span class="st">'C'</span>, <span class="st">'H'</span>, <span class="st">'W'</span>))
+<span class="kw">renamed_imgs</span> <span class="op">&lt;-</span> <span class="kw">imgs</span><span class="op">$</span><span class="fu">rename</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">"Batch"</span>, <span class="st">"Channels"</span>, <span class="st">"Height"</span>, <span class="st">"Width"</span>))
+</pre></div>
+</div>
+</div>
+<div id="rename_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#rename_" class="anchor"></a>rename_</h2>
+<p>In-place version of <code>$rename</code>.</p>
+</div>
+<div id="renorm" class="section level2">
+<h2 class="hasAnchor">
+<a href="#renorm" class="anchor"></a>renorm</h2>
+<p>renorm(p, dim, maxnorm) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_renorm.html">?torch_renorm</a></code></p>
+</div>
+<div id="renorm_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#renorm_" class="anchor"></a>renorm_</h2>
+<p>renorm_(p, dim, maxnorm) -&gt; Tensor</p>
+<p>In-place version of <code>$renorm</code></p>
+</div>
+<div id="repeat" class="section level2">
+<h2 class="hasAnchor">
+<a href="#repeat" class="anchor"></a>repeat</h2>
+<p>repeat(*sizes) -&gt; Tensor</p>
+<p>Repeats this tensor along the specified dimensions.</p>
+<p>Unlike <code>$expand</code>, this function copies the tensor’s data.</p>
+<div id="arguments-36" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-36" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>sizes (torch_Size or int…): The number of times to repeat this tensor along each</li>
+<li>dimension</li>
+</ul>
+</div>
+<div id="examples-27" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-27" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb29"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span>))
+<span class="kw">x</span><span class="op">$</span><span class="fu">`repeat`</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">4</span>, <span class="fl">2</span>))
+<span class="kw">x</span><span class="op">$</span><span class="fu">`repeat`</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">4</span>, <span class="fl">2</span>, <span class="fl">1</span>))<span class="op">$</span><span class="fu">size</span>()
+</pre></div>
+</div>
+</div>
+<div id="repeat_interleave" class="section level2">
+<h2 class="hasAnchor">
+<a href="#repeat_interleave" class="anchor"></a>repeat_interleave</h2>
+<p>repeat_interleave(repeats, dim=NULL) -&gt; Tensor</p>
+<p>See [torch_repeat_interleave()].</p>
+</div>
+<div id="requires_grad" class="section level2">
+<h2 class="hasAnchor">
+<a href="#requires_grad" class="anchor"></a>requires_grad</h2>
+<p>Is <code>TRUE</code> if gradients need to be computed for this Tensor, <code>FALSE</code> otherwise.</p>
+<div id="note-7" class="section level4">
+<h4 class="hasAnchor">
+<a href="#note-7" class="anchor"></a>Note:</h4>
+<p>The fact that gradients need to be computed for a Tensor do not mean that the <code>grad</code> attribute will be populated, see <code>is_leaf</code> for more details.</p>
+</div>
+</div>
+<div id="requires_grad_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#requires_grad_" class="anchor"></a>requires_grad_</h2>
+<p>requires_grad_(requires_grad=TRUE) -&gt; Tensor</p>
+<p>Change if autograd should record operations on this tensor: sets this tensor’s <code>requires_grad</code> attribute in-place. Returns this tensor.</p>
+<p>[requires_grad_()]’s main use case is to tell autograd to begin recording operations on a Tensor <code>tensor</code>. If <code>tensor</code> has <code>requires_grad=FALSE</code> (because it was obtained through a DataLoader, or required preprocessing or initialization), <code>tensor.requires_grad_()</code> makes it so that autograd will begin to record operations on <code>tensor</code>.</p>
+<div id="arguments-37" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-37" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>requires_grad (bool): If autograd should record operations on this tensor. Default: <code>TRUE</code>.</li>
+</ul>
+</div>
+<div id="examples-28" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-28" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb30"><pre class="downlit">
+<span class="co"># Let's say we want to preprocess some saved weights and use</span>
+<span class="co"># the result as new weights.</span>
+<span class="kw">saved_weights</span> <span class="op">&lt;-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">0.1</span>, <span class="fl">0.2</span>, <span class="fl">0.3</span>, <span class="fl">0.25</span>)
+<span class="kw">loaded_weights</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="kw">saved_weights</span>)
+<span class="kw">weights</span> <span class="op">&lt;-</span> <span class="fu">preprocess</span>(<span class="kw">loaded_weights</span>)  <span class="co"># some function</span>
+<span class="kw">weights</span>
+
+<span class="co"># Now, start to record operations done to weights</span>
+<span class="kw">weights</span><span class="op">$</span><span class="fu">requires_grad_</span>()
+<span class="kw">out</span> <span class="op">&lt;-</span> <span class="kw">weights</span><span class="op">$</span><span class="fu">pow</span>(<span class="fl">2</span>)<span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/sum.html">sum</a></span>()
+<span class="kw">out</span><span class="op">$</span><span class="fu">backward</span>()
+<span class="kw">weights</span><span class="op">$</span><span class="kw">grad</span>
+</pre></div>
+</div>
+</div>
+<div id="reshape" class="section level2">
+<h2 class="hasAnchor">
+<a href="#reshape" class="anchor"></a>reshape</h2>
+<p>reshape(*shape) -&gt; Tensor</p>
+<p>Returns a tensor with the same data and number of elements as <code>self</code> but with the specified shape. This method returns a view if <code>shape</code> is compatible with the current shape. See <code>$view</code> on when it is possible to return a view.</p>
+<p>See <code><a href="../../reference/torch_reshape.html">?torch_reshape</a></code></p>
+<div id="arguments-38" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-38" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>shape (tuple of ints or int…): the desired shape</li>
+</ul>
+</div>
+</div>
+<div id="reshape_as" class="section level2">
+<h2 class="hasAnchor">
+<a href="#reshape_as" class="anchor"></a>reshape_as</h2>
+<p>reshape_as(other) -&gt; Tensor</p>
+<p>Returns this tensor as the same shape as <code>other</code>. <code>self$reshape_as(other)</code> is equivalent to <code>self$reshape(other.sizes())</code>. This method returns a view if <code>other.sizes()</code> is compatible with the current shape. See <code>$view</code> on when it is possible to return a view.</p>
+<p>Please see <code>reshape</code> for more information about <code>reshape</code>.</p>
+<div id="arguments-39" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-39" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>other (`$): The result tensor has the same shape</li>
+<li>as <code>other</code>.</li>
+</ul>
+</div>
+</div>
+<div id="resize_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#resize_" class="anchor"></a>resize_</h2>
+<p>resize_(*sizes, memory_format=torch_contiguous_format) -&gt; Tensor</p>
+<p>Resizes <code>self</code> tensor to the specified size. If the number of elements is larger than the current storage size, then the underlying storage is resized to fit the new number of elements. If the number of elements is smaller, the underlying storage is not changed. Existing elements are preserved but any new memory is uninitialized.</p>
+<div id="warning-7" class="section level4">
+<h4 class="hasAnchor">
+<a href="#warning-7" class="anchor"></a>Warning:</h4>
+<p>This is a low-level method. The storage is reinterpreted as C-contiguous, ignoring the current strides (unless the target size equals the current size, in which case the tensor is left unchanged). For most purposes, you will instead want to use <code>$view()</code>, which checks for contiguity, or <code>$reshape()</code>, which copies data if needed. To change the size in-place with custom strides, see <code>$set_()</code>.</p>
+</div>
+<div id="arguments-40" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-40" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>sizes (torch_Size or int…): the desired size</li>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of Tensor. Default: <code>torch_contiguous_format</code>. Note that memory format of <code>self</code> is going to be unaffected if <code>self$size()</code> matches <code>sizes</code>.</li>
+</ul>
+</div>
+<div id="examples-29" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-29" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb31"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">6</span>, ncol = <span class="fl">2</span>))
+<span class="kw">x</span><span class="op">$</span><span class="fu">resize_</span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>, <span class="fl">2</span>))
+</pre></div>
+</div>
+</div>
+<div id="resize_as_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#resize_as_" class="anchor"></a>resize_as_</h2>
+<p>resize_as_(tensor, memory_format=torch_contiguous_format) -&gt; Tensor</p>
+<p>Resizes the <code>self</code> tensor to be the same size as the specified <code>tensor</code>. This is equivalent to <code>self$resize_(tensor.size())</code>.</p>
+<div id="arguments-41" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-41" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of Tensor. Default: <code>torch_contiguous_format</code>. Note that memory format of <code>self</code> is going to be unaffected if <code>self$size()</code> matches <code>tensor.size()</code>.</li>
+</ul>
+</div>
+</div>
+<div id="retain_grad" class="section level2">
+<h2 class="hasAnchor">
+<a href="#retain_grad" class="anchor"></a>retain_grad</h2>
+<p>Enables <code>$grad</code> attribute for non-leaf Tensors.</p>
+</div>
+<div id="rfft" class="section level2">
+<h2 class="hasAnchor">
+<a href="#rfft" class="anchor"></a>rfft</h2>
+<p>rfft(signal_ndim, normalized=FALSE, onesided=TRUE) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_rfft.html">?torch_rfft</a></code></p>
+</div>
+<div id="roll" class="section level2">
+<h2 class="hasAnchor">
+<a href="#roll" class="anchor"></a>roll</h2>
+<p>roll(shifts, dims) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_roll.html">?torch_roll</a></code></p>
+</div>
+<div id="rot90" class="section level2">
+<h2 class="hasAnchor">
+<a href="#rot90" class="anchor"></a>rot90</h2>
+<p>rot90(k, dims) -&gt; Tensor</p>
+<p>See [torch_rot90()]</p>
+</div>
+<div id="round" class="section level2">
+<h2 class="hasAnchor">
+<a href="#round" class="anchor"></a>round</h2>
+<p>round() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_round.html">?torch_round</a></code></p>
+</div>
+<div id="round_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#round_" class="anchor"></a>round_</h2>
+<p>round_() -&gt; Tensor</p>
+<p>In-place version of <code>$round</code></p>
+</div>
+<div id="rsqrt" class="section level2">
+<h2 class="hasAnchor">
+<a href="#rsqrt" class="anchor"></a>rsqrt</h2>
+<p>rsqrt() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_rsqrt.html">?torch_rsqrt</a></code></p>
+</div>
+<div id="rsqrt_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#rsqrt_" class="anchor"></a>rsqrt_</h2>
+<p>rsqrt_() -&gt; Tensor</p>
+<p>In-place version of <code>$rsqrt</code></p>
+</div>
+<div id="scatter" class="section level2">
+<h2 class="hasAnchor">
+<a href="#scatter" class="anchor"></a>scatter</h2>
+<p>scatter(dim, index, src) -&gt; Tensor</p>
+<p>Out-of-place version of <code>$scatter_</code></p>
+</div>
+<div id="scatter_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#scatter_" class="anchor"></a>scatter_</h2>
+<p>scatter_(dim, index, src) -&gt; Tensor</p>
+<p>Writes all values from the tensor <code>src</code> into <code>self</code> at the indices specified in the <code>index</code> tensor. For each value in <code>src</code>, its output index is specified by its index in <code>src</code> for <code>dimension != dim</code> and by the corresponding value in <code>index</code> for <code>dimension = dim</code>.</p>
+<p>For a 3-D tensor, <code>self</code> is updated as:</p>
+<pre><code>self[index[i][j][k]][j][k] = src[i][j][k]  # if dim == 0
+self[i][index[i][j][k]][k] = src[i][j][k]  # if dim == 1
+self[i][j][index[i][j][k]] = src[i][j][k]  # if dim == 2</code></pre>
+<p>This is the reverse operation of the manner described in <code>$gather</code>.</p>
+<p><code>self</code>, <code>index</code> and <code>src</code> (if it is a Tensor) should have same number of dimensions. It is also required that <code>index.size(d) &lt;= src.size(d)</code> for all dimensions <code>d</code>, and that <code>index.size(d) &lt;= self$size(d)</code> for all dimensions <code>d != dim</code>.</p>
+<p>Moreover, as for <code>$gather</code>, the values of <code>index</code> must be between <code>0</code> and <code>self$size(dim) - 1</code> inclusive, and all values in a row along the specified dimension <code>dim</code> must be unique.</p>
+<div id="arguments-42" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-42" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>dim (int): the axis along which to index</li>
+<li>index (LongTensor): the indices of elements to scatter,</li>
+<li>can be either empty or the same size of src. When empty, the operation returns identity</li>
+<li>src (Tensor): the source element(s) to scatter,</li>
+<li>incase <code>value</code> is not specified</li>
+<li>value (float): the source element(s) to scatter,</li>
+<li>incase <code>src</code> is not specified</li>
+</ul>
+</div>
+<div id="examples-30" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-30" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb33"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">2</span>, <span class="fl">5</span>)
+<span class="kw">x</span>
+<span class="fu"><a href="../../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">3</span>, <span class="fl">5</span>)<span class="op">$</span><span class="fu">scatter_</span>(
+        <span class="fl">1</span>, 
+        <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/cbind.html">rbind</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">3</span>, <span class="fl">1</span>, <span class="fl">1</span>), <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">3</span>, <span class="fl">1</span>, <span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span>)), <span class="kw">x</span>)
+)
+
+<span class="kw">z</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">2</span>, <span class="fl">4</span>)<span class="op">$</span><span class="fu">scatter_</span>(
+        <span class="fl">2</span>, 
+        <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span>(<span class="fl">3</span><span class="op">:</span><span class="fl">4</span>, ncol = <span class="fl">1</span>)), <span class="fl">1.23</span>
+)
+</pre></div>
+</div>
+</div>
+<div id="scatter_add" class="section level2">
+<h2 class="hasAnchor">
+<a href="#scatter_add" class="anchor"></a>scatter_add</h2>
+<p>scatter_add(dim, index, src) -&gt; Tensor</p>
+<p>Out-of-place version of <code>$scatter_add_</code></p>
+</div>
+<div id="scatter_add_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#scatter_add_" class="anchor"></a>scatter_add_</h2>
+<p>scatter_add_(dim, index, src) -&gt; Tensor</p>
+<p>Adds all values from the tensor <code>other</code> into <code>self</code> at the indices specified in the <code>index</code> tensor in a similar fashion as <code>~$scatter_</code>. For each value in <code>src</code>, it is added to an index in <code>self</code> which is specified by its index in <code>src</code> for <code>dimension != dim</code> and by the corresponding value in <code>index</code> for <code>dimension = dim</code>.</p>
+<p>For a 3-D tensor, <code>self</code> is updated as::</p>
+<pre><code>self[index[i][j][k]][j][k] += src[i][j][k]  # if dim == 0
+self[i][index[i][j][k]][k] += src[i][j][k]  # if dim == 1
+self[i][j][index[i][j][k]] += src[i][j][k]  # if dim == 2</code></pre>
+<p><code>self</code>, <code>index</code> and <code>src</code> should have same number of dimensions. It is also required that <code>index.size(d) &lt;= src.size(d)</code> for all dimensions <code>d</code>, and that <code>index.size(d) &lt;= self$size(d)</code> for all dimensions <code>d != dim</code>.</p>
+<div id="note-8" class="section level4">
+<h4 class="hasAnchor">
+<a href="#note-8" class="anchor"></a>Note:</h4>
+<p>In some circumstances when using the CUDA backend with CuDNN, this operator may select a nondeterministic algorithm to increase performance. If this is undesirable, you can try to make the operation deterministic (potentially at a performance cost) by setting <code>torch_backends.cudnn.deterministic = TRUE</code>.</p>
+</div>
+<div id="arguments-43" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-43" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>dim (int): the axis along which to index</li>
+<li>index (LongTensor): the indices of elements to scatter and add,</li>
+<li>can be either empty or the same size of src. When empty, the operation returns identity.</li>
+<li>src (Tensor): the source elements to scatter and add</li>
+</ul>
+</div>
+<div id="examples-31" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-31" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb35"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_rand.html">torch_rand</a></span>(<span class="fl">2</span>, <span class="fl">5</span>)
+<span class="kw">x</span>
+<span class="fu"><a href="../../reference/torch_ones.html">torch_ones</a></span>(<span class="fl">3</span>, <span class="fl">5</span>)<span class="op">$</span><span class="fu">scatter_add_</span>(<span class="fl">1</span>, <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/cbind.html">rbind</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">0</span>, <span class="fl">0</span>), <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">2</span>, <span class="fl">0</span>, <span class="fl">0</span>, <span class="fl">1</span>, <span class="fl">2</span>))), <span class="kw">x</span>)
+</pre></div>
+</div>
+</div>
+<div id="select" class="section level2">
+<h2 class="hasAnchor">
+<a href="#select" class="anchor"></a>select</h2>
+<p>select(dim, index) -&gt; Tensor</p>
+<p>Slices the <code>self</code> tensor along the selected dimension at the given index. This function returns a view of the original tensor with the given dimension removed.</p>
+<div id="arguments-44" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-44" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>dim (int): the dimension to slice</li>
+<li>index (int): the index to select with</li>
+</ul>
+</div>
+<div id="note-9" class="section level4">
+<h4 class="hasAnchor">
+<a href="#note-9" class="anchor"></a>Note:</h4>
+<p><code>select</code> is equivalent to slicing. For example, <code>tensor$select(0, index)</code> is equivalent to <code>tensor[index]</code> and <code>tensor$select(2, index)</code> is equivalent to <code>tensor[:,:,index]</code>.</p>
+</div>
+</div>
+<div id="set_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#set_" class="anchor"></a>set_</h2>
+<p>set_(source=NULL, storage_offset=0, size=NULL, stride=NULL) -&gt; Tensor</p>
+<p>Sets the underlying storage, size, and strides. If <code>source</code> is a tensor, <code>self</code> tensor will share the same storage and have the same size and strides as <code>source</code>. Changes to elements in one tensor will be reflected in the other.</p>
+<div id="arguments-45" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-45" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>source (Tensor or Storage): the tensor or storage to use</li>
+<li>storage_offset (int, optional): the offset in the storage</li>
+<li>size (torch_Size, optional): the desired size. Defaults to the size of the source.</li>
+<li>stride (tuple, optional): the desired stride. Defaults to C-contiguous strides.</li>
+</ul>
+</div>
+</div>
+<div id="share_memory_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#share_memory_" class="anchor"></a>share_memory_</h2>
+<p>Moves the underlying storage to shared memory.</p>
+<p>This is a no-op if the underlying storage is already in shared memory and for CUDA tensors. Tensors in shared memory cannot be resized.</p>
+</div>
+<div id="short" class="section level2">
+<h2 class="hasAnchor">
+<a href="#short" class="anchor"></a>short</h2>
+<p>short(memory_format=torch_preserve_format) -&gt; Tensor</p>
+<p><code>self$short()</code> is equivalent to <code>self$to(torch_int16)</code>. See [to()].</p>
+<div id="arguments-46" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-46" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of</li>
+<li>returned Tensor. Default: <code>torch_preserve_format</code>.</li>
+</ul>
+</div>
+</div>
+<div id="sigmoid" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sigmoid" class="anchor"></a>sigmoid</h2>
+<p>sigmoid() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_sigmoid.html">?torch_sigmoid</a></code></p>
+</div>
+<div id="sigmoid_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sigmoid_" class="anchor"></a>sigmoid_</h2>
+<p>sigmoid_() -&gt; Tensor</p>
+<p>In-place version of <code>$sigmoid</code></p>
+</div>
+<div id="sign" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sign" class="anchor"></a>sign</h2>
+<p>sign() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_sign.html">?torch_sign</a></code></p>
+</div>
+<div id="sign_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sign_" class="anchor"></a>sign_</h2>
+<p>sign_() -&gt; Tensor</p>
+<p>In-place version of <code>$sign</code></p>
+</div>
+<div id="sin" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sin" class="anchor"></a>sin</h2>
+<p>sin() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_sin.html">?torch_sin</a></code></p>
+</div>
+<div id="sin_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sin_" class="anchor"></a>sin_</h2>
+<p>sin_() -&gt; Tensor</p>
+<p>In-place version of <code>$sin</code></p>
+</div>
+<div id="sinh" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sinh" class="anchor"></a>sinh</h2>
+<p>sinh() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_sinh.html">?torch_sinh</a></code></p>
+</div>
+<div id="sinh_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sinh_" class="anchor"></a>sinh_</h2>
+<p>sinh_() -&gt; Tensor</p>
+<p>In-place version of <code>$sinh</code></p>
+</div>
+<div id="size" class="section level2">
+<h2 class="hasAnchor">
+<a href="#size" class="anchor"></a>size</h2>
+<p>size() -&gt; torch_Size</p>
+<p>Returns the size of the <code>self</code> tensor. The returned value is a subclass of <code>tuple</code>.</p>
+<div id="examples-32" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-32" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb36"><pre class="downlit">
+<span class="fu"><a href="../../reference/torch_empty.html">torch_empty</a></span>(<span class="fl">3</span>, <span class="fl">4</span>, <span class="fl">5</span>)<span class="op">$</span><span class="fu">size</span>()
+</pre></div>
+</div>
+</div>
+<div id="slogdet" class="section level2">
+<h2 class="hasAnchor">
+<a href="#slogdet" class="anchor"></a>slogdet</h2>
+<p>slogdet() -&gt; (Tensor, Tensor)</p>
+<p>See <code><a href="../../reference/torch_slogdet.html">?torch_slogdet</a></code></p>
+</div>
+<div id="solve" class="section level2">
+<h2 class="hasAnchor">
+<a href="#solve" class="anchor"></a>solve</h2>
+<p>solve(A) -&gt; Tensor, Tensor</p>
+<p>See <code><a href="../../reference/torch_solve.html">?torch_solve</a></code></p>
+</div>
+<div id="sort" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sort" class="anchor"></a>sort</h2>
+<p>sort(dim=-1, descending=FALSE) -&gt; (Tensor, LongTensor)</p>
+<p>See <code><a href="../../reference/torch_sort.html">?torch_sort</a></code></p>
+</div>
+<div id="sparse_dim" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sparse_dim" class="anchor"></a>sparse_dim</h2>
+<p>sparse_dim() -&gt; int</p>
+<p>If <code>self</code> is a sparse COO tensor (i.e., with <code>torch_sparse_coo</code> layout), this returns the number of sparse dimensions. Otherwise, this throws an error.</p>
+<p>See also <code>Tensor.dense_dim</code>.</p>
+</div>
+<div id="sparse_mask" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sparse_mask" class="anchor"></a>sparse_mask</h2>
+<p>sparse_mask(input, mask) -&gt; Tensor</p>
+<p>Returns a new SparseTensor with values from Tensor <code>input</code> filtered by indices of <code>mask</code> and values are ignored. <code>input</code> and <code>mask</code> must have the same shape.</p>
+<div id="arguments-47" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-47" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>input (Tensor): an input Tensor</li>
+<li>mask (SparseTensor): a SparseTensor which we filter <code>input</code> based on its indices</li>
+</ul>
+</div>
+</div>
+<div id="split" class="section level2">
+<h2 class="hasAnchor">
+<a href="#split" class="anchor"></a>split</h2>
+<p>See <code><a href="../../reference/torch_split.html">?torch_split</a></code></p>
+</div>
+<div id="sqrt" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sqrt" class="anchor"></a>sqrt</h2>
+<p>sqrt() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_sqrt.html">?torch_sqrt</a></code></p>
+</div>
+<div id="sqrt_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sqrt_" class="anchor"></a>sqrt_</h2>
+<p>sqrt_() -&gt; Tensor</p>
+<p>In-place version of <code>$sqrt</code></p>
+</div>
+<div id="square" class="section level2">
+<h2 class="hasAnchor">
+<a href="#square" class="anchor"></a>square</h2>
+<p>square() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_square.html">?torch_square</a></code></p>
+</div>
+<div id="square_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#square_" class="anchor"></a>square_</h2>
+<p>square_() -&gt; Tensor</p>
+<p>In-place version of <code>$square</code></p>
+</div>
+<div id="squeeze" class="section level2">
+<h2 class="hasAnchor">
+<a href="#squeeze" class="anchor"></a>squeeze</h2>
+<p>squeeze(dim=NULL) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_squeeze.html">?torch_squeeze</a></code></p>
+</div>
+<div id="squeeze_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#squeeze_" class="anchor"></a>squeeze_</h2>
+<p>squeeze_(dim=NULL) -&gt; Tensor</p>
+<p>In-place version of <code>$squeeze</code></p>
+</div>
+<div id="std" class="section level2">
+<h2 class="hasAnchor">
+<a href="#std" class="anchor"></a>std</h2>
+<p>std(dim=NULL, unbiased=TRUE, keepdim=FALSE) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_std.html">?torch_std</a></code></p>
+</div>
+<div id="stft" class="section level2">
+<h2 class="hasAnchor">
+<a href="#stft" class="anchor"></a>stft</h2>
+<p>See <code><a href="../../reference/torch_stft.html">?torch_stft</a></code></p>
+</div>
+<div id="storage" class="section level2">
+<h2 class="hasAnchor">
+<a href="#storage" class="anchor"></a>storage</h2>
+<p>storage() -&gt; torch_Storage</p>
+<p>Returns the underlying storage.</p>
+</div>
+<div id="storage_offset" class="section level2">
+<h2 class="hasAnchor">
+<a href="#storage_offset" class="anchor"></a>storage_offset</h2>
+<p>storage_offset() -&gt; int</p>
+<p>Returns <code>self</code> tensor’s offset in the underlying storage in terms of number of storage elements (not bytes).</p>
+<div id="examples-33" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-33" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb37"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="fl">1</span>, <span class="fl">2</span>, <span class="fl">3</span>, <span class="fl">4</span>, <span class="fl">5</span>))
+<span class="kw">x</span><span class="op">$</span><span class="fu">storage_offset</span>()
+<span class="kw">x</span>[<span class="fl">3</span><span class="op">:</span><span class="kw">N</span>]<span class="op">$</span><span class="fu">storage_offset</span>()
+</pre></div>
+</div>
+</div>
+<div id="storage_type" class="section level2">
+<h2 class="hasAnchor">
+<a href="#storage_type" class="anchor"></a>storage_type</h2>
+<p>storage_type() -&gt; type</p>
+<p>Returns the type of the underlying storage.</p>
+</div>
+<div id="stride" class="section level2">
+<h2 class="hasAnchor">
+<a href="#stride" class="anchor"></a>stride</h2>
+<p>stride(dim) -&gt; tuple or int</p>
+<p>Returns the stride of <code>self</code> tensor.</p>
+<p>Stride is the jump necessary to go from one element to the next one in the specified dimension <code>dim</code>. A tuple of all strides is returned when no argument is passed in. Otherwise, an integer value is returned as the stride in the particular dimension <code>dim</code>.</p>
+<div id="arguments-48" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-48" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>dim (int, optional): the desired dimension in which stride is required</li>
+</ul>
+</div>
+<div id="examples-34" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-34" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb38"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fu"><a href="https://rdrr.io/r/base/matrix.html">matrix</a></span>(<span class="fl">1</span><span class="op">:</span><span class="fl">10</span>, nrow = <span class="fl">2</span>))
+<span class="kw">x</span><span class="op">$</span><span class="fu">stride</span>()
+<span class="kw">x</span><span class="op">$</span><span class="fu">stride</span>(<span class="fl">1</span>)
+<span class="kw">x</span><span class="op">$</span><span class="fu">stride</span>(<span class="op">-</span><span class="fl">1</span>)
+</pre></div>
+</div>
+</div>
+<div id="sub" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sub" class="anchor"></a>sub</h2>
+<p>sub(other, *, alpha=1) -&gt; Tensor</p>
+<p>Subtracts a scalar or tensor from <code>self</code> tensor. If both <code>alpha</code> and <code>other</code> are specified, each element of <code>other</code> is scaled by <code>alpha</code> before being used.</p>
+<p>When <code>other</code> is a tensor, the shape of <code>other</code> must be <code>broadcastable &lt;broadcasting-semantics&gt;</code> with the shape of the underlying tensor.</p>
+</div>
+<div id="sub_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sub_" class="anchor"></a>sub_</h2>
+<p>sub_(other, *, alpha=1) -&gt; Tensor</p>
+<p>In-place version of <code>$sub</code></p>
+</div>
+<div id="sum" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sum" class="anchor"></a>sum</h2>
+<p>sum(dim=NULL, keepdim=FALSE, dtype=NULL) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_sum.html">?torch_sum</a></code></p>
+</div>
+<div id="sum_to_size" class="section level2">
+<h2 class="hasAnchor">
+<a href="#sum_to_size" class="anchor"></a>sum_to_size</h2>
+<p>sum_to_size(*size) -&gt; Tensor</p>
+<p>Sum <code>this</code> tensor to <code>size</code>. <code>size</code> must be broadcastable to <code>this</code> tensor size.</p>
+<div id="arguments-49" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-49" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>size (int…): a sequence of integers defining the shape of the output tensor.</li>
+</ul>
+</div>
+</div>
+<div id="svd" class="section level2">
+<h2 class="hasAnchor">
+<a href="#svd" class="anchor"></a>svd</h2>
+<p>svd(some=TRUE, compute_uv=TRUE) -&gt; (Tensor, Tensor, Tensor)</p>
+<p>See <code><a href="../../reference/torch_svd.html">?torch_svd</a></code></p>
+</div>
+<div id="symeig" class="section level2">
+<h2 class="hasAnchor">
+<a href="#symeig" class="anchor"></a>symeig</h2>
+<p>symeig(eigenvectors=FALSE, upper=TRUE) -&gt; (Tensor, Tensor)</p>
+<p>See <code><a href="../../reference/torch_symeig.html">?torch_symeig</a></code></p>
+</div>
+<div id="t-1" class="section level2">
+<h2 class="hasAnchor">
+<a href="#t-1" class="anchor"></a>t</h2>
+<p>t() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_t.html">?torch_t</a></code></p>
+</div>
+<div id="t_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#t_" class="anchor"></a>t_</h2>
+<p>t_() -&gt; Tensor</p>
+<p>In-place version of <code>$t</code></p>
+</div>
+<div id="take" class="section level2">
+<h2 class="hasAnchor">
+<a href="#take" class="anchor"></a>take</h2>
+<p>take(indices) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_take.html">?torch_take</a></code></p>
+</div>
+<div id="tan" class="section level2">
+<h2 class="hasAnchor">
+<a href="#tan" class="anchor"></a>tan</h2>
+<p>tan() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_tan.html">?torch_tan</a></code></p>
+</div>
+<div id="tan_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#tan_" class="anchor"></a>tan_</h2>
+<p>tan_() -&gt; Tensor</p>
+<p>In-place version of <code>$tan</code></p>
+</div>
+<div id="tanh" class="section level2">
+<h2 class="hasAnchor">
+<a href="#tanh" class="anchor"></a>tanh</h2>
+<p>tanh() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_tanh.html">?torch_tanh</a></code></p>
+</div>
+<div id="tanh_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#tanh_" class="anchor"></a>tanh_</h2>
+<p>tanh_() -&gt; Tensor</p>
+<p>In-place version of <code>$tanh</code></p>
+</div>
+<div id="to" class="section level2">
+<h2 class="hasAnchor">
+<a href="#to" class="anchor"></a>to</h2>
+<p>to(*args, **kwargs) -&gt; Tensor</p>
+<p>Performs Tensor dtype and/or device conversion. A <code>torch_dtype</code> and :class:<code>torch_device</code> are inferred from the arguments of <code>self$to(*args, **kwargs)</code>.</p>
+<div id="note-10" class="section level4">
+<h4 class="hasAnchor">
+<a href="#note-10" class="anchor"></a>Note:</h4>
+<p>If the <code>self</code> Tensor already has the correct <code>torch_dtype</code> and :class:<code>torch_device</code>, then <code>self</code> is returned. Otherwise, the returned tensor is a copy of <code>self</code> with the desired <code>torch_dtype</code> and :class:<code>torch_device</code>.</p>
+<p>Here are the ways to call <code>to</code>:</p>
+<p>to(dtype, non_blocking=FALSE, copy=FALSE, memory_format=torch_preserve_format) -&gt; Tensor</p>
+<p>Returns a Tensor with the specified <code>dtype</code></p>
+</div>
+<div id="arguments-50" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-50" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of returned Tensor. Default: <code>torch_preserve_format</code>.</li>
+</ul>
+<p>to(device=NULL, dtype=NULL, non_blocking=FALSE, copy=FALSE, memory_format=torch_preserve_format) -&gt; Tensor</p>
+<p>Returns a Tensor with the specified <code>device</code> and (optional) <code>dtype</code>. If <code>dtype</code> is <code>NULL</code> it is inferred to be <code>self$dtype</code>. When <code>non_blocking</code>, tries to convert asynchronously with respect to the host if possible, e.g., converting a CPU Tensor with pinned memory to a CUDA Tensor.</p>
+<p>When <code>copy</code> is set, a new Tensor is created even when the Tensor already matches the desired conversion.</p>
+</div>
+<div id="arguments-51" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-51" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>memory_format (<code>torch_memory_format</code>, optional): the desired memory format of returned Tensor. Default: <code>torch_preserve_format</code>.</li>
+</ul>
+<p>function:: to(other, non_blocking=FALSE, copy=FALSE) -&gt; Tensor</p>
+<p>Returns a Tensor with same <code>torch_dtype</code> and :class:<code>torch_device</code> as the Tensor <code>other</code>. When <code>non_blocking</code>, tries to convert asynchronously with respect to the host if possible, e.g., converting a CPU Tensor with pinned memory to a CUDA Tensor.</p>
+<p>When <code>copy</code> is set, a new Tensor is created even when the Tensor already matches the desired conversion.</p>
+</div>
+<div id="examples-35" class="section level4">
+<h4 class="hasAnchor">
+<a href="#examples-35" class="anchor"></a>Examples:</h4>
+<div class="sourceCode" id="cb39"><pre class="downlit">
+<span class="kw">tensor</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">2</span>, <span class="fl">2</span>)  <span class="co"># Initially dtype=float32, device=cpu</span>
+<span class="kw">tensor</span><span class="op">$</span><span class="fu">to</span>(dtype = <span class="fu"><a href="../../reference/torch_dtype.html">torch_float64</a></span>())
+
+<span class="kw">other</span> <span class="op">&lt;-</span> <span class="fu"><a href="../../reference/torch_randn.html">torch_randn</a></span>(<span class="fl">1</span>, dtype=<span class="fu"><a href="../../reference/torch_dtype.html">torch_float64</a></span>())
+<span class="kw">tensor</span><span class="op">$</span><span class="fu">to</span>(other = <span class="kw">other</span>, non_blocking=<span class="fl">TRUE</span>)
+</pre></div>
+</div>
+</div>
+<div id="to_mkldnn" class="section level2">
+<h2 class="hasAnchor">
+<a href="#to_mkldnn" class="anchor"></a>to_mkldnn</h2>
+<p>to_mkldnn() -&gt; Tensor Returns a copy of the tensor in <code>torch_mkldnn</code> layout.</p>
+</div>
+<div id="to_sparse" class="section level2">
+<h2 class="hasAnchor">
+<a href="#to_sparse" class="anchor"></a>to_sparse</h2>
+<p>to_sparse(sparseDims) -&gt; Tensor Returns a sparse copy of the tensor. PyTorch supports sparse tensors in <code>coordinate format &lt;sparse-docs&gt;</code>.</p>
+<div id="arguments-52" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-52" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>sparseDims (int, optional): the number of sparse dimensions to include in the new sparse tensor</li>
+</ul>
+</div>
+</div>
+<div id="tolist" class="section level2">
+<h2 class="hasAnchor">
+<a href="#tolist" class="anchor"></a>tolist</h2>
+<p>tolist() -&gt; list or number</p>
+<p>Returns the tensor as a (nested) list. For scalars, a standard Python number is returned, just like with <code>$item</code>. Tensors are automatically moved to the CPU first if necessary.</p>
+<p>This operation is not differentiable.</p>
+</div>
+<div id="topk" class="section level2">
+<h2 class="hasAnchor">
+<a href="#topk" class="anchor"></a>topk</h2>
+<p>topk(k, dim=NULL, largest=TRUE, sorted=TRUE) -&gt; (Tensor, LongTensor)</p>
+<p>See <code><a href="../../reference/torch_topk.html">?torch_topk</a></code></p>
+</div>
+<div id="trace" class="section level2">
+<h2 class="hasAnchor">
+<a href="#trace" class="anchor"></a>trace</h2>
+<p>trace() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_trace.html">?torch_trace</a></code></p>
+</div>
+<div id="transpose" class="section level2">
+<h2 class="hasAnchor">
+<a href="#transpose" class="anchor"></a>transpose</h2>
+<p>transpose(dim0, dim1) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_transpose.html">?torch_transpose</a></code></p>
+</div>
+<div id="transpose_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#transpose_" class="anchor"></a>transpose_</h2>
+<p>transpose_(dim0, dim1) -&gt; Tensor</p>
+<p>In-place version of <code>$transpose</code></p>
+</div>
+<div id="triangular_solve" class="section level2">
+<h2 class="hasAnchor">
+<a href="#triangular_solve" class="anchor"></a>triangular_solve</h2>
+<p>triangular_solve(A, upper=TRUE, transpose=FALSE, unitriangular=FALSE) -&gt; (Tensor, Tensor)</p>
+<p>See [torch_triangular_solve()]</p>
+</div>
+<div id="tril" class="section level2">
+<h2 class="hasAnchor">
+<a href="#tril" class="anchor"></a>tril</h2>
+<p>tril(k=0) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_tril.html">?torch_tril</a></code></p>
+</div>
+<div id="tril_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#tril_" class="anchor"></a>tril_</h2>
+<p>tril_(k=0) -&gt; Tensor</p>
+<p>In-place version of <code>$tril</code></p>
+</div>
+<div id="triu" class="section level2">
+<h2 class="hasAnchor">
+<a href="#triu" class="anchor"></a>triu</h2>
+<p>triu(k=0) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_triu.html">?torch_triu</a></code></p>
+</div>
+<div id="triu_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#triu_" class="anchor"></a>triu_</h2>
+<p>triu_(k=0) -&gt; Tensor</p>
+<p>In-place version of <code>$triu</code></p>
+</div>
+<div id="true_divide" class="section level2">
+<h2 class="hasAnchor">
+<a href="#true_divide" class="anchor"></a>true_divide</h2>
+<p>true_divide(value) -&gt; Tensor</p>
+<p>See [torch_true_divide()]</p>
+</div>
+<div id="true_divide_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#true_divide_" class="anchor"></a>true_divide_</h2>
+<p>true_divide_(value) -&gt; Tensor</p>
+<p>In-place version of <code>$true_divide_</code></p>
+</div>
+<div id="trunc" class="section level2">
+<h2 class="hasAnchor">
+<a href="#trunc" class="anchor"></a>trunc</h2>
+<p>trunc() -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_trunc.html">?torch_trunc</a></code></p>
+</div>
+<div id="trunc_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#trunc_" class="anchor"></a>trunc_</h2>
+<p>trunc_() -&gt; Tensor</p>
+<p>In-place version of <code>$trunc</code></p>
+</div>
+<div id="type" class="section level2">
+<h2 class="hasAnchor">
+<a href="#type" class="anchor"></a>type</h2>
+<p>type(dtype=NULL, non_blocking=FALSE, **kwargs) -&gt; str or Tensor Returns the type if <code>dtype</code> is not provided, else casts this object to the specified type.</p>
+<p>If this is already of the correct type, no copy is performed and the original object is returned.</p>
+<div id="arguments-53" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-53" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>dtype (type or string): The desired type</li>
+<li>non_blocking (bool): If <code>TRUE</code>, and the source is in pinned memory</li>
+<li>and destination is on the GPU or vice versa, the copy is performed</li>
+<li>asynchronously with respect to the host. Otherwise, the argument</li>
+<li>has no effect. **kwargs: For compatibility, may contain the key <code>async</code> in place of</li>
+<li>the <code>non_blocking</code> argument. The <code>async</code> arg is deprecated.</li>
+</ul>
+</div>
+</div>
+<div id="type_as" class="section level2">
+<h2 class="hasAnchor">
+<a href="#type_as" class="anchor"></a>type_as</h2>
+<p>type_as(tensor) -&gt; Tensor</p>
+<p>Returns this tensor cast to the type of the given tensor.</p>
+<p>This is a no-op if the tensor is already of the correct type. This is equivalent to <code>self$type(tensor.type())</code></p>
+<div id="arguments-54" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-54" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>tensor (Tensor): the tensor which has the desired type</li>
+</ul>
+</div>
+</div>
+<div id="unbind" class="section level2">
+<h2 class="hasAnchor">
+<a href="#unbind" class="anchor"></a>unbind</h2>
+<p>unbind(dim=0) -&gt; seq</p>
+<p>See <code><a href="../../reference/torch_unbind.html">?torch_unbind</a></code></p>
+</div>
+<div id="unflatten" class="section level2">
+<h2 class="hasAnchor">
+<a href="#unflatten" class="anchor"></a>unflatten</h2>
+<p>Unflattens the named dimension <code>dim</code>, viewing it in the shape specified by <code>namedshape</code>.</p>
+<div id="arguments-55" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-55" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>namedshape: (iterable of <code>(name, size)</code> tuples).</li>
+</ul>
+</div>
+</div>
+<div id="unfold" class="section level2">
+<h2 class="hasAnchor">
+<a href="#unfold" class="anchor"></a>unfold</h2>
+<p>unfold(dimension, size, step) -&gt; Tensor</p>
+<p>Returns a view of the original tensor which contains all slices of size <code>size</code> from <code>self</code> tensor in the dimension <code>dimension</code>.</p>
+<p>Step between two slices is given by <code>step</code>.</p>
+<p>If <code>sizedim</code> is the size of dimension <code>dimension</code> for <code>self</code>, the size of dimension <code>dimension</code> in the returned tensor will be <code>(sizedim - size) / step + 1</code>.</p>
+<p>An additional dimension of size <code>size</code> is appended in the returned tensor.</p>
+<div id="arguments-56" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-56" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>dimension (int): dimension in which unfolding happens</li>
+<li>size (int): the size of each slice that is unfolded</li>
+<li>step (int): the step between each slice</li>
+</ul>
+</div>
+</div>
+<div id="uniform_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#uniform_" class="anchor"></a>uniform_</h2>
+<p>uniform_(from=0, to=1) -&gt; Tensor</p>
+<p>Fills <code>self</code> tensor with numbers sampled from the continuous uniform distribution:</p>
+<p><span class="math display">\[
+P(x) = \dfrac{1}{\text{to} - \text{from}}
+\]</span></p>
+</div>
+<div id="unique" class="section level2">
+<h2 class="hasAnchor">
+<a href="#unique" class="anchor"></a>unique</h2>
+<p>Returns the unique elements of the input tensor.</p>
+<p>See <code>?torch_unique</code></p>
+</div>
+<div id="unique_consecutive" class="section level2">
+<h2 class="hasAnchor">
+<a href="#unique_consecutive" class="anchor"></a>unique_consecutive</h2>
+<p>Eliminates all but the first element from every consecutive group of equivalent elements.</p>
+<p>See [torch_unique_consecutive()]</p>
+</div>
+<div id="unsqueeze" class="section level2">
+<h2 class="hasAnchor">
+<a href="#unsqueeze" class="anchor"></a>unsqueeze</h2>
+<p>unsqueeze(dim) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_unsqueeze.html">?torch_unsqueeze</a></code></p>
+</div>
+<div id="unsqueeze_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#unsqueeze_" class="anchor"></a>unsqueeze_</h2>
+<p>unsqueeze_(dim) -&gt; Tensor</p>
+<p>In-place version of <code>$unsqueeze</code></p>
+</div>
+<div id="values" class="section level2">
+<h2 class="hasAnchor">
+<a href="#values" class="anchor"></a>values</h2>
+<p>values() -&gt; Tensor</p>
+<p>If <code>self</code> is a sparse COO tensor (i.e., with <code>torch_sparse_coo</code> layout), this returns a view of the contained values tensor. Otherwise, this throws an error.</p>
+<div id="note-11" class="section level4">
+<h4 class="hasAnchor">
+<a href="#note-11" class="anchor"></a>Note:</h4>
+<p>This method can only be called on a coalesced sparse tensor. See <code>Tensor$coalesce</code> for details.</p>
+</div>
+</div>
+<div id="var" class="section level2">
+<h2 class="hasAnchor">
+<a href="#var" class="anchor"></a>var</h2>
+<p>var(dim=NULL, unbiased=TRUE, keepdim=FALSE) -&gt; Tensor</p>
+<p>See <code><a href="../../reference/torch_var.html">?torch_var</a></code></p>
+</div>
+<div id="view" class="section level2">
+<h2 class="hasAnchor">
+<a href="#view" class="anchor"></a>view</h2>
+<p>view(*shape) -&gt; Tensor</p>
+<p>Returns a new tensor with the same data as the <code>self</code> tensor but of a different <code>shape</code>.</p>
+<p>The returned tensor shares the same data and must have the same number of elements, but may have a different size. For a tensor to be viewed, the new view size must be compatible with its original size and stride, i.e., each new view dimension must either be a subspace of an original dimension, or only span across original dimensions <code>d, d+1, \dots, d+k</code> that satisfy the following contiguity-like condition that <code>\forall i = d, \dots, d+k-1</code>,</p>
+<p><span class="math display">\[
+\text{stride}[i] = \text{stride}[i+1] \times \text{size}[i+1]
+\]</span></p>
+<p>Otherwise, it will not be possible to view <code>self</code> tensor as <code>shape</code> without copying it (e.g., via <code>contiguous</code>). When it is unclear whether a <code>view</code> can be performed, it is advisable to use :meth:<code>reshape</code>, which returns a view if the shapes are compatible, and copies (equivalent to calling <code>contiguous</code>) otherwise.</p>
+<div id="arguments-57" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-57" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>shape (torch_Size or int…): the desired size</li>
+</ul>
+</div>
+</div>
+<div id="view_as" class="section level2">
+<h2 class="hasAnchor">
+<a href="#view_as" class="anchor"></a>view_as</h2>
+<p>view_as(other) -&gt; Tensor</p>
+<p>View this tensor as the same size as <code>other</code>. <code>self$view_as(other)</code> is equivalent to <code>self$view(other.size())</code>.</p>
+<p>Please see <code>$view</code> for more information about <code>view</code>.</p>
+<div id="arguments-58" class="section level4">
+<h4 class="hasAnchor">
+<a href="#arguments-58" class="anchor"></a>Arguments:</h4>
+<ul>
+<li>other (`$): The result tensor has the same size</li>
+<li>as <code>other</code>.</li>
+</ul>
+</div>
+</div>
+<div id="where" class="section level2">
+<h2 class="hasAnchor">
+<a href="#where" class="anchor"></a>where</h2>
+<p>where(condition, y) -&gt; Tensor</p>
+<p><code>self$where(condition, y)</code> is equivalent to <code><a href="../../reference/torch_where.html">torch_where(condition, self, y)</a></code>. See <code><a href="../../reference/torch_where.html">?torch_where</a></code></p>
+</div>
+<div id="zero_" class="section level2">
+<h2 class="hasAnchor">
+<a href="#zero_" class="anchor"></a>zero_</h2>
+<p>zero_() -&gt; Tensor</p>
+<p>Fills <code>self</code> tensor with zeros.</p>
+</div>
+  </div>
+
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+
+        <nav id="toc" data-toggle="toc"><h2 data-toc-skip>Contents</h2>
+    </nav>
+</div>
+
+</div>
+
+
+
+      <footer><div class="copyright">
+  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+</div>
+
+<div class="pkgdown">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+</div>
+
+      </footer>
+</div>
+
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+</html>
diff --git a/static/docs/dev/articles/tensor/index_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/dev/articles/tensor/index_files/accessible-code-block-0.0.1/empty-anchor.js
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+// 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");
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diff --git a/static/docs/dev/articles/using-autograd.html b/static/docs/dev/articles/using-autograd.html
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+  <div class="col-md-9 contents">
+    <div class="page-header toc-ignore">
+      <h1 data-toc-skip>Using autograd</h1>
+            
+      
+      <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>
+      <div class="hidden name"><code>using-autograd.Rmd</code></div>
+
+    </div>
+
+    
+    
+<div class="sourceCode" id="cb1"><pre class="downlit">
+<span class="fu"><a href="https://rdrr.io/r/base/library.html">library</a></span>(<span class="kw"><a href="https://torch.mlverse.org/docs">torch</a></span>)
+</pre></div>
+<p>So far, all we’ve been using from torch is <em>tensors</em>, but we’ve been performing all calculations ourselves – the computing the predictions, the loss, the gradients (and thus, the necessary updates to the weights), and the new weight values. In this chapter, we’ll make a significant change: Namely, we spare ourselves the cumbersome calculation of gradients, and have torch do it for us.</p>
+<p>Before we see that in action, let’s get some more background.</p>
+<div id="automatic-differentiation-with-autograd" class="section level2">
+<h2 class="hasAnchor">
+<a href="#automatic-differentiation-with-autograd" class="anchor"></a>Automatic differentiation with autograd</h2>
+<p>Torch uses a module called <em>autograd</em> to record operations performed on tensors, and store what has to be done to obtain the respective gradients. These actions are stored as functions, and those functions are applied in order when the gradient of the output (normally, the loss) with respect to those tensors is calculated: starting from the output node and <em>propagating</em> gradients <em>back</em> through the network. This is a form of <em>reverse mode automatic differentiation</em>.</p>
+<p>As users, we can see a bit of this implementation. As a prerequisite for this “recording” to happen, tensors have to be created with <code>requires_grad = TRUE</code>. E.g.</p>
+<div class="sourceCode" id="cb2"><pre class="downlit">
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_ones.html">torch_ones</a></span>(<span class="fl">2</span>,<span class="fl">2</span>, requires_grad = <span class="fl">TRUE</span>)
+</pre></div>
+<p>To be clear, this is a tensor <em>with respect to which</em> gradients have to be calculated – normally, a tensor representing a weight or a bias, not the input data <a href="#fn1" class="footnote-ref" id="fnref1"><sup>1</sup></a>. If we now perform some operation on that tensor, assigning the result to <code>y</code></p>
+<div class="sourceCode" id="cb3"><pre class="downlit">
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="kw">x</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>()
+</pre></div>
+<p>we find that <code>y</code> now has a non-empty <code>grad_fn</code> that tells torch how to compute the gradient of <code>y</code> with respect to <code>x</code>:</p>
+<div class="sourceCode" id="cb4"><pre class="downlit">
+<span class="kw">y</span><span class="op">$</span><span class="kw">grad_fn</span>
+<span class="co">#&gt; MeanBackward0</span>
+</pre></div>
+<p>Actual computation of gradients is triggered by calling <code>backward()</code> on the output tensor.</p>
+<div class="sourceCode" id="cb5"><pre class="downlit">
+<span class="kw">y</span><span class="op">$</span><span class="fu">backward</span>()
+</pre></div>
+<p>That executed, <code>x</code> now has a non-empty field <code>grad</code> that stores the gradient of <code>y</code> with respect to <code>x</code>:</p>
+<div class="sourceCode" id="cb6"><pre class="downlit">
+<span class="kw">x</span><span class="op">$</span><span class="kw">grad</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  0.2500  0.2500</span>
+<span class="co">#&gt;  0.2500  0.2500</span>
+<span class="co">#&gt; [ CPUFloatType{2,2} ]</span>
+</pre></div>
+<p>With a longer chain of computations, we can peek at how torch builds up a graph of backward operations.</p>
+<p>Here is a slightly more complex example. We call <code>retain_grad()</code> on <code>y</code> and <code>z</code> just for demonstration purposes; by default, intermediate gradients – while of course they have to be computed – aren’t stored, in order to save memory.</p>
+<div class="sourceCode" id="cb7"><pre class="downlit">
+<span class="kw">x1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_ones.html">torch_ones</a></span>(<span class="fl">2</span>,<span class="fl">2</span>, requires_grad = <span class="fl">TRUE</span>)
+<span class="kw">x2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_tensor.html">torch_tensor</a></span>(<span class="fl">1.1</span>, requires_grad = <span class="fl">TRUE</span>)
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="kw">x1</span> <span class="op">*</span> (<span class="kw">x2</span> <span class="op">+</span> <span class="fl">2</span>)
+<span class="kw">y</span><span class="op">$</span><span class="fu">retain_grad</span>()
+<span class="kw">z</span> <span class="op">&lt;-</span> <span class="kw">y</span><span class="op">$</span><span class="fu">pow</span>(<span class="fl">2</span>) <span class="op">*</span> <span class="fl">3</span>
+<span class="kw">z</span><span class="op">$</span><span class="fu">retain_grad</span>()
+<span class="kw">out</span> <span class="op">&lt;-</span> <span class="kw">z</span><span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>()
+</pre></div>
+<p>Starting from <code>out$grad_fn</code>, we can follow the graph all back to the leaf nodes:</p>
+<div class="sourceCode" id="cb8"><pre class="downlit">
+<span class="co"># how to compute the gradient for mean, the last operation executed</span>
+<span class="kw">out</span><span class="op">$</span><span class="kw">grad_fn</span>
+<span class="co">#&gt; MeanBackward0</span>
+<span class="co"># how to compute the gradient for the multiplication by 3 in z = y$pow(2) * 3</span>
+<span class="kw">out</span><span class="op">$</span><span class="kw">grad_fn</span><span class="op">$</span><span class="kw">next_functions</span>
+<span class="co">#&gt; [[1]]</span>
+<span class="co">#&gt; MulBackward1</span>
+<span class="co"># how to compute the gradient for pow in z = y.pow(2) * 3</span>
+<span class="kw">out</span><span class="op">$</span><span class="kw">grad_fn</span><span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]<span class="op">$</span><span class="kw">next_functions</span>
+<span class="co">#&gt; [[1]]</span>
+<span class="co">#&gt; PowBackward0</span>
+<span class="co"># how to compute the gradient for the multiplication in y = x * (x + 2)</span>
+<span class="kw">out</span><span class="op">$</span><span class="kw">grad_fn</span><span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]<span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]<span class="op">$</span><span class="kw">next_functions</span>
+<span class="co">#&gt; [[1]]</span>
+<span class="co">#&gt; MulBackward0</span>
+<span class="co"># how to compute the gradient for the two branches of y = x * (x + 2),</span>
+<span class="co"># where the left branch is a leaf node (AccumulateGrad for x1)</span>
+<span class="kw">out</span><span class="op">$</span><span class="kw">grad_fn</span><span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]<span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]<span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]<span class="op">$</span><span class="kw">next_functions</span>
+<span class="co">#&gt; [[1]]</span>
+<span class="co">#&gt; torch::autograd::AccumulateGrad</span>
+<span class="co">#&gt; [[2]]</span>
+<span class="co">#&gt; AddBackward1</span>
+<span class="co"># here we arrive at the other leaf node (AccumulateGrad for x2)</span>
+<span class="kw">out</span><span class="op">$</span><span class="kw">grad_fn</span><span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]<span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]<span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">1</span>]]<span class="op">$</span><span class="kw">next_functions</span>[[<span class="fl">2</span>]]<span class="op">$</span><span class="kw">next_functions</span>
+<span class="co">#&gt; [[1]]</span>
+<span class="co">#&gt; torch::autograd::AccumulateGrad</span>
+</pre></div>
+<p>After calling <code>out$backward()</code>, all tensors in the graph will have their respective gradients created. Without our calls to <code>retain_grad</code> above, <code>z$grad</code> and <code>y$grad</code> would be empty:</p>
+<div class="sourceCode" id="cb9"><pre class="downlit">
+<span class="kw">out</span><span class="op">$</span><span class="fu">backward</span>()
+<span class="kw">z</span><span class="op">$</span><span class="kw">grad</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  0.2500  0.2500</span>
+<span class="co">#&gt;  0.2500  0.2500</span>
+<span class="co">#&gt; [ CPUFloatType{2,2} ]</span>
+<span class="kw">y</span><span class="op">$</span><span class="kw">grad</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  4.6500  4.6500</span>
+<span class="co">#&gt;  4.6500  4.6500</span>
+<span class="co">#&gt; [ CPUFloatType{2,2} ]</span>
+<span class="kw">x2</span><span class="op">$</span><span class="kw">grad</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  18.6000</span>
+<span class="co">#&gt; [ CPUFloatType{1} ]</span>
+<span class="kw">x1</span><span class="op">$</span><span class="kw">grad</span>
+<span class="co">#&gt; torch_tensor </span>
+<span class="co">#&gt;  14.4150  14.4150</span>
+<span class="co">#&gt;  14.4150  14.4150</span>
+<span class="co">#&gt; [ CPUFloatType{2,2} ]</span>
+</pre></div>
+<p>Thus acquainted with autograd, we’re ready to modify our example.</p>
+</div>
+<div id="the-simple-network-now-using-autograd" class="section level2">
+<h2 class="hasAnchor">
+<a href="#the-simple-network-now-using-autograd" class="anchor"></a>The simple network, now using autograd</h2>
+<p>For a single new line calling <code>loss$backward()</code>, now a number of lines (that did manual backprop) are gone:</p>
+<div class="sourceCode" id="cb10"><pre class="downlit">
+<span class="co">### generate training data -----------------------------------------------------</span>
+<span class="co"># input dimensionality (number of input features)</span>
+<span class="kw">d_in</span> <span class="op">&lt;-</span> <span class="fl">3</span>
+<span class="co"># output dimensionality (number of predicted features)</span>
+<span class="kw">d_out</span> <span class="op">&lt;-</span> <span class="fl">1</span>
+<span class="co"># number of observations in training set</span>
+<span class="kw">n</span> <span class="op">&lt;-</span> <span class="fl">100</span>
+<span class="co"># create random data</span>
+<span class="kw">x</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">n</span>, <span class="kw">d_in</span>)
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="kw">x</span>[,<span class="fl">1</span>]<span class="op">*</span><span class="fl">0.2</span> <span class="op">-</span> <span class="kw">x</span>[<span class="kw">..</span>,<span class="fl">2</span>]<span class="op">*</span><span class="fl">1.3</span> <span class="op">-</span> <span class="kw">x</span>[<span class="kw">..</span>,<span class="fl">3</span>]<span class="op">*</span><span class="fl">0.5</span> <span class="op">+</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">n</span>)
+<span class="kw">y</span> <span class="op">&lt;-</span> <span class="kw">y</span><span class="op">$</span><span class="fu">unsqueeze</span>(dim = <span class="fl">1</span>)
+<span class="co">### initialize weights ---------------------------------------------------------</span>
+<span class="co"># dimensionality of hidden layer</span>
+<span class="kw">d_hidden</span> <span class="op">&lt;-</span> <span class="fl">32</span>
+<span class="co"># weights connecting input to hidden layer</span>
+<span class="kw">w1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">d_in</span>, <span class="kw">d_hidden</span>, requires_grad = <span class="fl">TRUE</span>)
+<span class="co"># weights connecting hidden to output layer</span>
+<span class="kw">w2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_randn.html">torch_randn</a></span>(<span class="kw">d_hidden</span>, <span class="kw">d_out</span>, requires_grad = <span class="fl">TRUE</span>)
+<span class="co"># hidden layer bias</span>
+<span class="kw">b1</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">1</span>, <span class="kw">d_hidden</span>, requires_grad = <span class="fl">TRUE</span>)
+<span class="co"># output layer bias</span>
+<span class="kw">b2</span> <span class="op">&lt;-</span> <span class="fu"><a href="../reference/torch_zeros.html">torch_zeros</a></span>(<span class="fl">1</span>, <span class="kw">d_out</span>,requires_grad = <span class="fl">TRUE</span>)
+<span class="co">### network parameters ---------------------------------------------------------</span>
+<span class="kw">learning_rate</span> <span class="op">&lt;-</span> <span class="fl">1e-4</span>
+<span class="co">### training loop --------------------------------------------------------------</span>
+<span class="co">for</span> (<span class="kw">t</span> <span class="co">in</span> <span class="fl">1</span><span class="op">:</span><span class="fl">200</span>) {
+
+    <span class="co">### -------- Forward pass -------- </span>
+    <span class="kw">y_pred</span> <span class="op">&lt;-</span> <span class="kw">x</span><span class="op">$</span><span class="fu">mm</span>(<span class="kw">w1</span>)<span class="op">$</span><span class="fu"><a href="https://rdrr.io/pkg/magrittr/man/aliases.html">add</a></span>(<span class="kw">b1</span>)<span class="op">$</span><span class="fu">clamp</span>(min = <span class="fl">0</span>)<span class="op">$</span><span class="fu">mm</span>(<span class="kw">w2</span>)<span class="op">$</span><span class="fu"><a href="https://rdrr.io/pkg/magrittr/man/aliases.html">add</a></span>(<span class="kw">b2</span>)
+    <span class="co">### -------- compute loss -------- </span>
+    <span class="kw">loss</span> <span class="op">&lt;-</span> (<span class="kw">y_pred</span> <span class="op">-</span> <span class="kw">y</span>)<span class="op">$</span><span class="fu">pow</span>(<span class="fl">2</span>)<span class="op">$</span><span class="fu"><a href="https://rdrr.io/r/base/mean.html">mean</a></span>()
+    <span class="co">if</span> (<span class="kw">t</span> <span class="op">%%</span> <span class="fl">10</span> <span class="op">==</span> <span class="fl">0</span>) <span class="fu"><a href="https://rdrr.io/r/base/cat.html">cat</a></span>(<span class="kw">t</span>, <span class="fu"><a href="../reference/as_array.html">as_array</a></span>(<span class="kw">loss</span>), <span class="st">"\n"</span>)
+    <span class="co">### -------- Backpropagation -------- </span>
+    <span class="co"># compute the gradient of loss with respect to all tensors with requires_grad = True.</span>
+    <span class="kw">loss</span><span class="op">$</span><span class="fu">backward</span>()
+ 
+    <span class="co">### -------- Update weights -------- </span>
+    
+    <span class="co"># Wrap in torch.no_grad() because this is a part we DON'T want to record for automatic gradient computation</span>
+    <span class="fu"><a href="../reference/with_no_grad.html">with_no_grad</a></span>({
+      
+      <span class="kw">w1</span><span class="op">$</span><span class="fu">sub_</span>(<span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">w1</span><span class="op">$</span><span class="kw">grad</span>)
+      <span class="kw">w2</span><span class="op">$</span><span class="fu">sub_</span>(<span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">w2</span><span class="op">$</span><span class="kw">grad</span>)
+      <span class="kw">b1</span><span class="op">$</span><span class="fu">sub_</span>(<span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">b1</span><span class="op">$</span><span class="kw">grad</span>)
+      <span class="kw">b2</span><span class="op">$</span><span class="fu">sub_</span>(<span class="kw">learning_rate</span> <span class="op">*</span> <span class="kw">b2</span><span class="op">$</span><span class="kw">grad</span>)
+      
+      <span class="co"># Zero the gradients after every pass, because they'd accumulate otherwise</span>
+      <span class="kw">w1</span><span class="op">$</span><span class="kw">grad</span><span class="op">$</span><span class="fu">zero_</span>()
+      <span class="kw">w2</span><span class="op">$</span><span class="kw">grad</span><span class="op">$</span><span class="fu">zero_</span>()
+      <span class="kw">b1</span><span class="op">$</span><span class="kw">grad</span><span class="op">$</span><span class="fu">zero_</span>()
+      <span class="kw">b2</span><span class="op">$</span><span class="kw">grad</span><span class="op">$</span><span class="fu">zero_</span>()
+    
+    })
+    
+}
+<span class="co">#&gt; 10 28.23399 </span>
+<span class="co">#&gt; 20 25.57913 </span>
+<span class="co">#&gt; 30 23.29141 </span>
+<span class="co">#&gt; 40 21.32386 </span>
+<span class="co">#&gt; 50 19.62816 </span>
+<span class="co">#&gt; 60 18.16152 </span>
+<span class="co">#&gt; 70 16.88838 </span>
+<span class="co">#&gt; 80 15.77924 </span>
+<span class="co">#&gt; 90 14.80951 </span>
+<span class="co">#&gt; 100 13.95841 </span>
+<span class="co">#&gt; 110 13.20845 </span>
+<span class="co">#&gt; 120 12.54546 </span>
+<span class="co">#&gt; 130 11.95689 </span>
+<span class="co">#&gt; 140 11.43233 </span>
+<span class="co">#&gt; 150 10.96239 </span>
+<span class="co">#&gt; 160 10.54092 </span>
+<span class="co">#&gt; 170 10.16278 </span>
+<span class="co">#&gt; 180 9.821123 </span>
+<span class="co">#&gt; 190 9.51112 </span>
+<span class="co">#&gt; 200 9.228704</span>
+</pre></div>
+<p>We still manually compute the forward pass, and we still manually update the weights. In the last two chapters of this section, we’ll see how these parts of the logic can be made more modular and reusable, as well.</p>
+</div>
+<div class="footnotes">
+<hr>
+<ol>
+<li id="fn1"><p>Unless we <em>want</em> to change the data, as in adversarial example generation<a href="#fnref1" class="footnote-back">↩︎</a></p></li>
+</ol>
+</div>
+  </div>
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diff --git a/static/docs/dev/articles/using-autograd_files/accessible-code-block-0.0.1/empty-anchor.js b/static/docs/dev/articles/using-autograd_files/accessible-code-block-0.0.1/empty-anchor.js
new file mode 100644
index 000000000..ca349fd6a
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@@ -0,0 +1,15 @@
+// Hide empty <a> tag within highlighted CodeBlock for screen reader accessibility (see https://github.com/jgm/pandoc/issues/6352#issuecomment-626106786) -->
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+        $headings.each(function(i, el) {
+          var $newNav = helpers.generateNavItem(el);
+          var navLevel = helpers.getNavLevel(el);
+
+          // determine the proper $context
+          if (navLevel === topLevel) {
+            // use top level
+            $context = $topContext;
+          } else if ($prevNav && $context === $topContext) {
+            // create a new level of the tree and switch to it
+            $context = helpers.createChildNavList($prevNav);
+          } // else use the current $context
+
+          $context.append($newNav);
+
+          $prevNav = $newNav;
+        });
+      },
+
+      parseOps: function(arg) {
+        var opts;
+        if (arg.jquery) {
+          opts = {
+            $nav: arg
+          };
+        } else {
+          opts = arg;
+        }
+        opts.$scope = opts.$scope || $(document.body);
+        return opts;
+      }
+    },
+
+    // accepts a jQuery object, or an options object
+    init: function(opts) {
+      opts = this.helpers.parseOps(opts);
+
+      // ensure that the data attribute is in place for styling
+      opts.$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/static/docs/dev/docsearch.css b/static/docs/dev/docsearch.css
new file mode 100644
index 000000000..e5f1fe1df
--- /dev/null
+++ b/static/docs/dev/docsearch.css
@@ -0,0 +1,148 @@
+/* Docsearch -------------------------------------------------------------- */
+/*
+  Source: https://github.com/algolia/docsearch/
+  License: MIT
+*/
+
+.algolia-autocomplete {
+  display: block;
+  -webkit-box-flex: 1;
+  -ms-flex: 1;
+  flex: 1
+}
+
+.algolia-autocomplete .ds-dropdown-menu {
+  width: 100%;
+  min-width: none;
+  max-width: none;
+  padding: .75rem 0;
+  background-color: #fff;
+  background-clip: padding-box;
+  border: 1px solid rgba(0, 0, 0, .1);
+  box-shadow: 0 .5rem 1rem rgba(0, 0, 0, .175);
+}
+
+@media (min-width:768px) {
+  .algolia-autocomplete .ds-dropdown-menu {
+      width: 175%
+  }
+}
+
+.algolia-autocomplete .ds-dropdown-menu::before {
+  display: none
+}
+
+.algolia-autocomplete .ds-dropdown-menu [class^=ds-dataset-] {
+  padding: 0;
+  background-color: rgb(255,255,255);
+  border: 0;
+  max-height: 80vh;
+}
+
+.algolia-autocomplete .ds-dropdown-menu .ds-suggestions {
+  margin-top: 0
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion {
+  padding: 0;
+  overflow: visible
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion--category-header {
+  padding: .125rem 1rem;
+  margin-top: 0;
+  font-size: 1.3em;
+  font-weight: 500;
+  color: #00008B;
+  border-bottom: 0
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion--wrapper {
+    float: none;
+    padding-top: 0
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion--subcategory-column {
+  float: none;
+  width: auto;
+  padding: 0;
+  text-align: left
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion--content {
+  float: none;
+  width: auto;
+  padding: 0
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion--content::before {
+  display: none
+}
+
+.algolia-autocomplete .ds-suggestion:not(:first-child) .algolia-docsearch-suggestion--category-header {
+  padding-top: .75rem;
+  margin-top: .75rem;
+  border-top: 1px solid rgba(0, 0, 0, .1)
+}
+
+.algolia-autocomplete .ds-suggestion .algolia-docsearch-suggestion--subcategory-column {
+  display: block;
+  padding: .1rem 1rem;
+  margin-bottom: 0.1;
+  font-size: 1.0em;
+  font-weight: 400
+  /* display: none */
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion--title {
+  display: block;
+  padding: .25rem 1rem;
+  margin-bottom: 0;
+  font-size: 0.9em;
+  font-weight: 400
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion--text {
+  padding: 0 1rem .5rem;
+  margin-top: -.25rem;
+  font-size: 0.8em;
+  font-weight: 400;
+  line-height: 1.25
+}
+
+.algolia-autocomplete .algolia-docsearch-footer {
+  width: 110px;
+  height: 20px;
+  z-index: 3;
+  margin-top: 10.66667px;
+  float: right;
+  font-size: 0;
+  line-height: 0;
+}
+
+.algolia-autocomplete .algolia-docsearch-footer--logo {
+  background-image: url("data:image/svg+xml;utf8,<svg viewBox='0 0 130 18' xmlns='http://www.w3.org/2000/svg'><defs><linearGradient x1='-36.868%' y1='134.936%' x2='129.432%' y2='-27.7%' id='a'><stop stop-color='%2300AEFF' offset='0%'/><stop stop-color='%233369E7' offset='100%'/></linearGradient></defs><g fill='none' fill-rule='evenodd'><path d='M59.399.022h13.299a2.372 2.372 0 0 1 2.377 2.364V15.62a2.372 2.372 0 0 1-2.377 2.364H59.399a2.372 2.372 0 0 1-2.377-2.364V2.381A2.368 2.368 0 0 1 59.399.022z' fill='url(%23a)'/><path d='M66.257 4.56c-2.815 0-5.1 2.272-5.1 5.078 0 2.806 2.284 5.072 5.1 5.072 2.815 0 5.1-2.272 5.1-5.078 0-2.806-2.279-5.072-5.1-5.072zm0 8.652c-1.983 0-3.593-1.602-3.593-3.574 0-1.972 1.61-3.574 3.593-3.574 1.983 0 3.593 1.602 3.593 3.574a3.582 3.582 0 0 1-3.593 3.574zm0-6.418v2.664c0 .076.082.131.153.093l2.377-1.226c.055-.027.071-.093.044-.147a2.96 2.96 0 0 0-2.465-1.487c-.055 0-.11.044-.11.104l.001-.001zm-3.33-1.956l-.312-.311a.783.783 0 0 0-1.106 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+  background-repeat: no-repeat;
+  background-position: 50%;
+  background-size: 100%;
+  overflow: hidden;
+  text-indent: -9000px;
+  width: 100%;
+  height: 100%;
+  display: block;
+  transform: translate(-8px);
+}
+
+.algolia-autocomplete .algolia-docsearch-suggestion--highlight {
+  color: #FF8C00;
+  background: rgba(232, 189, 54, 0.1)
+}
+
+
+.algolia-autocomplete .algolia-docsearch-suggestion--text .algolia-docsearch-suggestion--highlight {
+  box-shadow: inset 0 -2px 0 0 rgba(105, 105, 105, .5)
+}
+
+.algolia-autocomplete .ds-suggestion.ds-cursor .algolia-docsearch-suggestion--content {
+  background-color: rgba(192, 192, 192, .15)
+}
diff --git a/static/docs/dev/docsearch.js b/static/docs/dev/docsearch.js
new file mode 100644
index 000000000..b35504cd3
--- /dev/null
+++ b/static/docs/dev/docsearch.js
@@ -0,0 +1,85 @@
+$(function() {
+
+  // register a handler to move the focus to the search bar
+  // upon pressing shift + "/" (i.e. "?")
+  $(document).on('keydown', function(e) {
+    if (e.shiftKey && e.keyCode == 191) {
+      e.preventDefault();
+      $("#search-input").focus();
+    }
+  });
+
+  $(document).ready(function() {
+    // do keyword highlighting
+    /* modified from https://jsfiddle.net/julmot/bL6bb5oo/ */
+    var mark = function() {
+
+      var referrer = document.URL ;
+      var paramKey = "q" ;
+
+      if (referrer.indexOf("?") !== -1) {
+        var qs = referrer.substr(referrer.indexOf('?') + 1);
+        var qs_noanchor = qs.split('#')[0];
+        var qsa = qs_noanchor.split('&');
+        var keyword = "";
+
+        for (var i = 0; i < qsa.length; i++) {
+          var currentParam = qsa[i].split('=');
+
+          if (currentParam.length !== 2) {
+            continue;
+          }
+
+          if (currentParam[0] == paramKey) {
+            keyword = decodeURIComponent(currentParam[1].replace(/\+/g, "%20"));
+          }
+        }
+
+        if (keyword !== "") {
+          $(".contents").unmark({
+            done: function() {
+              $(".contents").mark(keyword);
+            }
+          });
+        }
+      }
+    };
+
+    mark();
+  });
+});
+
+/* Search term highlighting ------------------------------*/
+
+function matchedWords(hit) {
+  var words = [];
+
+  var hierarchy = hit._highlightResult.hierarchy;
+  // loop to fetch from lvl0, lvl1, etc.
+  for (var idx in hierarchy) {
+    words = words.concat(hierarchy[idx].matchedWords);
+  }
+
+  var content = hit._highlightResult.content;
+  if (content) {
+    words = words.concat(content.matchedWords);
+  }
+
+  // return unique words
+  var words_uniq = [...new Set(words)];
+  return words_uniq;
+}
+
+function updateHitURL(hit) {
+
+  var words = matchedWords(hit);
+  var url = "";
+
+  if (hit.anchor) {
+    url = hit.url_without_anchor + '?q=' + escape(words.join(" ")) + '#' + hit.anchor;
+  } else {
+    url = hit.url + '?q=' + escape(words.join(" "));
+  }
+
+  return url;
+}
diff --git a/static/docs/dev/index.html b/static/docs/dev/index.html
new file mode 100644
index 000000000..a14897600
--- /dev/null
+++ b/static/docs/dev/index.html
@@ -0,0 +1,312 @@
+<!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">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>Tensors and Neural Networks with GPU Acceleration • torch</title>
+<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.4.0/united/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous">
+<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="bootstrap-toc.css">
+<script src="bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
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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://torch.mlverse.org"><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="sourceCode R"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1"></a>remotes<span class="op">::</span><span class="kw">install_github</span>(<span class="st">"mlverse/torch"</span>)</span></code></pre></div>
+<p>At the first package load additional software will be installed.</p>
+</div>
+<div id="example" class="section level2">
+<h2 class="hasAnchor">
+<a href="#example" class="anchor"></a>Example</h2>
+<p>Currently this package is only a proof of concept and you can only create a Torch Tensor from an R object. And then convert back from a torch Tensor to an R object.</p>
+<div class="sourceCode" id="cb2"><pre class="sourceCode R"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1"></a><span class="kw">library</span>(torch)</span>
+<span id="cb2-2"><a href="#cb2-2"></a>x &lt;-<span class="st"> </span><span class="kw">array</span>(<span class="kw">runif</span>(<span class="dv">8</span>), <span class="dt">dim =</span> <span class="kw">c</span>(<span class="dv">2</span>, <span class="dv">2</span>, <span class="dv">2</span>))</span>
+<span id="cb2-3"><a href="#cb2-3"></a>y &lt;-<span class="st"> </span><span class="kw">torch_tensor</span>(x, <span class="dt">dtype =</span> <span class="kw">torch_float64</span>())</span>
+<span id="cb2-4"><a href="#cb2-4"></a>y</span>
+<span id="cb2-5"><a href="#cb2-5"></a><span class="co">#&gt; torch_tensor </span></span>
+<span id="cb2-6"><a href="#cb2-6"></a><span class="co">#&gt; (1,.,.) = </span></span>
+<span id="cb2-7"><a href="#cb2-7"></a><span class="co">#&gt;   0.5406  0.8648</span></span>
+<span id="cb2-8"><a href="#cb2-8"></a><span class="co">#&gt;   0.3097  0.9715</span></span>
+<span id="cb2-9"><a href="#cb2-9"></a><span class="co">#&gt; </span></span>
+<span id="cb2-10"><a href="#cb2-10"></a><span class="co">#&gt; (2,.,.) = </span></span>
+<span id="cb2-11"><a href="#cb2-11"></a><span class="co">#&gt;   0.1309  0.8992</span></span>
+<span id="cb2-12"><a href="#cb2-12"></a><span class="co">#&gt;   0.4849  0.1902</span></span>
+<span id="cb2-13"><a href="#cb2-13"></a><span class="co">#&gt; [ CPUDoubleType{2,2,2} ]</span></span>
+<span id="cb2-14"><a href="#cb2-14"></a><span class="kw">identical</span>(x, <span class="kw">as_array</span>(y))</span>
+<span id="cb2-15"><a href="#cb2-15"></a><span class="co">#&gt; [1] TRUE</span></span></code></pre></div>
+<div id="simple-autograd-example" class="section level3">
+<h3 class="hasAnchor">
+<a href="#simple-autograd-example" class="anchor"></a>Simple Autograd Example</h3>
+<p>In the following snippet we let torch, using the autograd feature, calculate the derivatives:</p>
+<div class="sourceCode" id="cb3"><pre class="sourceCode R"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1"></a>x &lt;-<span class="st"> </span><span class="kw">torch_tensor</span>(<span class="dv">1</span>, <span class="dt">requires_grad =</span> <span class="ot">TRUE</span>)</span>
+<span id="cb3-2"><a href="#cb3-2"></a>w &lt;-<span class="st"> </span><span class="kw">torch_tensor</span>(<span class="dv">2</span>, <span class="dt">requires_grad =</span> <span class="ot">TRUE</span>)</span>
+<span id="cb3-3"><a href="#cb3-3"></a>b &lt;-<span class="st"> </span><span class="kw">torch_tensor</span>(<span class="dv">3</span>, <span class="dt">requires_grad =</span> <span class="ot">TRUE</span>)</span>
+<span id="cb3-4"><a href="#cb3-4"></a>y &lt;-<span class="st"> </span>w <span class="op">*</span><span class="st"> </span>x <span class="op">+</span><span class="st"> </span>b</span>
+<span id="cb3-5"><a href="#cb3-5"></a>y<span class="op">$</span><span class="kw">backward</span>()</span>
+<span id="cb3-6"><a href="#cb3-6"></a>x<span class="op">$</span>grad</span>
+<span id="cb3-7"><a href="#cb3-7"></a><span class="co">#&gt; torch_tensor </span></span>
+<span id="cb3-8"><a href="#cb3-8"></a><span class="co">#&gt;  2</span></span>
+<span id="cb3-9"><a href="#cb3-9"></a><span class="co">#&gt; [ CPUFloatType{1} ]</span></span>
+<span id="cb3-10"><a href="#cb3-10"></a>w<span class="op">$</span>grad</span>
+<span id="cb3-11"><a href="#cb3-11"></a><span class="co">#&gt; torch_tensor </span></span>
+<span id="cb3-12"><a href="#cb3-12"></a><span class="co">#&gt;  1</span></span>
+<span id="cb3-13"><a href="#cb3-13"></a><span class="co">#&gt; [ CPUFloatType{1} ]</span></span>
+<span id="cb3-14"><a href="#cb3-14"></a>b<span class="op">$</span>grad</span>
+<span id="cb3-15"><a href="#cb3-15"></a><span class="co">#&gt; torch_tensor </span></span>
+<span id="cb3-16"><a href="#cb3-16"></a><span class="co">#&gt;  1</span></span>
+<span id="cb3-17"><a href="#cb3-17"></a><span class="co">#&gt; [ CPUFloatType{1} ]</span></span></code></pre></div>
+</div>
+<div id="linear-regression" class="section level3">
+<h3 class="hasAnchor">
+<a href="#linear-regression" class="anchor"></a>Linear Regression</h3>
+<p>In the following example we are going to fit a linear regression from scratch using torch’s Autograd.</p>
+<p><strong>Note</strong> all methods that end with <code>_</code> (eg. <code>sub_</code>), will modify the tensors in place.</p>
+<div class="sourceCode" id="cb4"><pre class="sourceCode R"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1"></a>x &lt;-<span class="st"> </span><span class="kw">torch_randn</span>(<span class="dv">100</span>, <span class="dv">2</span>)</span>
+<span id="cb4-2"><a href="#cb4-2"></a>y &lt;-<span class="st"> </span><span class="fl">0.1</span> <span class="op">+</span><span class="st"> </span><span class="fl">0.5</span><span class="op">*</span>x[,<span class="dv">1</span>] <span class="op">-</span><span class="st"> </span><span class="fl">0.7</span><span class="op">*</span>x[,<span class="dv">2</span>]</span>
+<span id="cb4-3"><a href="#cb4-3"></a></span>
+<span id="cb4-4"><a href="#cb4-4"></a>w &lt;-<span class="st"> </span><span class="kw">torch_randn</span>(<span class="dv">2</span>, <span class="dv">1</span>, <span class="dt">requires_grad =</span> <span class="ot">TRUE</span>)</span>
+<span id="cb4-5"><a href="#cb4-5"></a>b &lt;-<span class="st"> </span><span class="kw">torch_zeros</span>(<span class="dv">1</span>, <span class="dt">requires_grad =</span> <span class="ot">TRUE</span>)</span>
+<span id="cb4-6"><a href="#cb4-6"></a></span>
+<span id="cb4-7"><a href="#cb4-7"></a>lr &lt;-<span class="st"> </span><span class="fl">0.5</span></span>
+<span id="cb4-8"><a href="#cb4-8"></a><span class="cf">for</span> (i <span class="cf">in</span> <span class="dv">1</span><span class="op">:</span><span class="dv">100</span>) {</span>
+<span id="cb4-9"><a href="#cb4-9"></a>  y_hat &lt;-<span class="st"> </span><span class="kw">torch_mm</span>(x, w) <span class="op">+</span><span class="st"> </span>b</span>
+<span id="cb4-10"><a href="#cb4-10"></a>  loss &lt;-<span class="st"> </span><span class="kw">torch_mean</span>((y <span class="op">-</span><span class="st"> </span>y_hat<span class="op">$</span><span class="kw">squeeze</span>(<span class="dv">1</span>))<span class="op">^</span><span class="dv">2</span>)</span>
+<span id="cb4-11"><a href="#cb4-11"></a>  </span>
+<span id="cb4-12"><a href="#cb4-12"></a>  loss<span class="op">$</span><span class="kw">backward</span>()</span>
+<span id="cb4-13"><a href="#cb4-13"></a>  </span>
+<span id="cb4-14"><a href="#cb4-14"></a>  <span class="kw">with_no_grad</span>({</span>
+<span id="cb4-15"><a href="#cb4-15"></a>    w<span class="op">$</span><span class="kw">sub_</span>(w<span class="op">$</span>grad<span class="op">*</span>lr)</span>
+<span id="cb4-16"><a href="#cb4-16"></a>    b<span class="op">$</span><span class="kw">sub_</span>(b<span class="op">$</span>grad<span class="op">*</span>lr)   </span>
+<span id="cb4-17"><a href="#cb4-17"></a>    </span>
+<span id="cb4-18"><a href="#cb4-18"></a>    w<span class="op">$</span>grad<span class="op">$</span><span class="kw">zero_</span>()</span>
+<span id="cb4-19"><a href="#cb4-19"></a>    b<span class="op">$</span>grad<span class="op">$</span><span class="kw">zero_</span>()</span>
+<span id="cb4-20"><a href="#cb4-20"></a>  })</span>
+<span id="cb4-21"><a href="#cb4-21"></a>}</span>
+<span id="cb4-22"><a href="#cb4-22"></a><span class="kw">print</span>(w)</span>
+<span id="cb4-23"><a href="#cb4-23"></a><span class="co">#&gt; torch_tensor </span></span>
+<span id="cb4-24"><a href="#cb4-24"></a><span class="co">#&gt; 1e-09 *</span></span>
+<span id="cb4-25"><a href="#cb4-25"></a><span class="co">#&gt;  5.2672</span></span>
+<span id="cb4-26"><a href="#cb4-26"></a><span class="co">#&gt;  -6.7969</span></span>
+<span id="cb4-27"><a href="#cb4-27"></a><span class="co">#&gt; [ CPUFloatType{2,1} ]</span></span>
+<span id="cb4-28"><a href="#cb4-28"></a><span class="kw">print</span>(b) </span>
+<span id="cb4-29"><a href="#cb4-29"></a><span class="co">#&gt; torch_tensor </span></span>
+<span id="cb4-30"><a href="#cb4-30"></a><span class="co">#&gt; 0.01 *</span></span>
+<span id="cb4-31"><a href="#cb4-31"></a><span class="co">#&gt; -9.6802</span></span>
+<span id="cb4-32"><a href="#cb4-32"></a><span class="co">#&gt; [ CPUFloatType{1} ]</span></span></code></pre></div>
+</div>
+</div>
+<div id="contributing" class="section level2">
+<h2 class="hasAnchor">
+<a href="#contributing" class="anchor"></a>Contributing</h2>
+<p>No matter your current skills it’s possible to contribute to <code>torch</code> development. See the contributing guide for more information.</p>
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+
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+
+  <body data-spy="scroll" data-target="#toc">
+    <div class="container template-news">
+      <header>
+      <div class="navbar navbar-default navbar-fixed-top" role="navigation">
+  <div class="container">
+    <div class="navbar-header">
+      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
+        <span class="sr-only">Toggle navigation</span>
+        <span class="icon-bar"></span>
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+        <span class="icon-bar"></span>
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+      <span class="navbar-brand">
+        <a class="navbar-link" href="../index.html">torch</a>
+        <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.0.3.9000</span>
+      </span>
+    </div>
+
+    <div id="navbar" class="navbar-collapse collapse">
+      <ul class="nav navbar-nav">
+        <li>
+  <a href="../index.html">
+    <span class="fas fa fas fa-home fa-lg"></span>
+     
+  </a>
+</li>
+<li class="dropdown">
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
+    Getting started
+     
+    <span class="caret"></span>
+  </a>
+  <ul class="dropdown-menu" role="menu">
+    <li class="dropdown-header">Beginers guide</li>
+    <li>
+      <a href="../articles/getting-started/warmup.html">Warm-up</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/tensors.html">Tensors</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/tensors-and-autograd.html">Tensors and autograd</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/optim.html">optim: optimizers in torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/custom-nn.html">Custom nn modules</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/control-flow-and-weight-sharing.html">Control flow &amp; Weight sharing</a>
+    </li>
+    <li class="dropdown-header">Torch Mechanics</li>
+    <li>
+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/neural-networks.html">Neural networks</a>
+    </li>
+  </ul>
+</li>
+<li class="dropdown">
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
+    Articles
+     
+    <span class="caret"></span>
+  </a>
+  <ul class="dropdown-menu" role="menu">
+    <li class="dropdown-header">Tensors</li>
+    <li>
+      <a href="../articles/tensor-creation.html">Creating tensors</a>
+    </li>
+    <li>
+      <a href="../articles/indexing.html">Indexing</a>
+    </li>
+    <li>
+      <a href="../articles/tensor/index.html">Tensor class</a>
+    </li>
+    <li class="dropdown-header">Datasets</li>
+    <li>
+      <a href="../articles/loading-data.html">Loading Data</a>
+    </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-header">Examples</li>
+<li>
+  <a href="../reference/index.html">Reference</a>
+</li>
+<li>
+  <a href="../news/index.html">Changelog</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 data-toc-skip>Changelog <small></small></h1>
+      <small>Source: <a href='https://github.com/mlverse/torch/blob/master/NEWS.md'><code>NEWS.md</code></a></small>
+    </div>
+
+    <div id="torch-development-version" class="section level1">
+<h1 class="page-header" data-toc-text="development version">
+<a href="#torch-development-version" class="anchor"></a>torch (development version)<small> Unreleased </small>
+</h1>
+</div>
+    <div id="torch-002" class="section level1">
+<h1 class="page-header" data-toc-text="0.0.2">
+<a href="#torch-002" class="anchor"></a>torch 0.0.2<small> 2020-08-31 </small>
+</h1>
+<ul>
+<li>Added a <code>NEWS.md</code> file to track changes to the package.</li>
+<li>Auto install when loading the package for the first time.</li>
+</ul>
+</div>
+  </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> 1.6.1.</p>
+</div>
+
+      </footer>
+   </div>
+
+  
+
+
+  </body>
+</html>
+
+
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+ * .Site -> body > .container
+ * .Site-content -> body > .container .row
+ * .footer -> footer
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+
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diff --git a/static/docs/dev/pkgdown.yml b/static/docs/dev/pkgdown.yml
new file mode 100644
index 000000000..5f7fa0b3a
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+pandoc: 2.7.3
+pkgdown: 1.6.1
+pkgdown_sha: ~
+articles:
+  extending-autograd: extending-autograd.html
+  getting-started/autograd: autograd.html
+  getting-started/control-flow-and-weight-sharing: control-flow-and-weight-sharing.html
+  getting-started/custom-nn: custom-nn.html
+  getting-started/neural-networks: neural-networks.html
+  getting-started/new-autograd-functions: new-autograd-functions.html
+  getting-started/nn: nn.html
+  getting-started/optim: optim.html
+  getting-started/tensors-and-autograd: tensors-and-autograd.html
+  getting-started/tensors: tensors.html
+  getting-started/warmup: warmup.html
+  getting-started/what-is-torch: what-is-torch.html
+  indexing: indexing.html
+  loading-data: loading-data.html
+  tensor/index: index.html
+  tensor-creation: tensor-creation.html
+  using-autograd: using-autograd.html
+last_built: 2020-09-21T17:45Z
+
diff --git a/static/docs/dev/reference/AutogradContext.html b/static/docs/dev/reference/AutogradContext.html
new file mode 100644
index 000000000..c1d76a6f9
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+++ b/static/docs/dev/reference/AutogradContext.html
@@ -0,0 +1,338 @@
+<!-- Generated by pkgdown: do not edit by hand -->
+<!DOCTYPE html>
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+        <a class="navbar-link" href="../index.html">torch</a>
+        <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.0.3.9000</span>
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+    <li class="dropdown-header">Beginers guide</li>
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+    </li>
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+      <a href="../articles/getting-started/tensors-and-autograd.html">Tensors and autograd</a>
+    </li>
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+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/optim.html">optim: optimizers in torch</a>
+    </li>
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+      <a href="../articles/getting-started/custom-nn.html">Custom nn modules</a>
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+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
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+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Class representing the context.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/autograd.R'><code>R/autograd.R</code></a></small>
+    <div class="hidden name"><code>AutogradContext.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Class representing the context.</p>
+<p>Class representing the context.</p>
+    </div>
+
+
+
+    <h2 class="hasAnchor" id="public-fields"><a class="anchor" href="#public-fields"></a>Public fields</h2>
+
+    <p><div class="r6-fields"></p><dl>
+<dt><code>ptr</code></dt><dd><p>(Dev related) pointer to the context c++ object.</p></dd>
+
+</dl><p></div></p>
+    <h2 class="hasAnchor" id="active-bindings"><a class="anchor" href="#active-bindings"></a>Active bindings</h2>
+
+    <p><div class="r6-active-bindings"></p><dl>
+<dt><code>needs_input_grad</code></dt><dd><p>boolean listing arguments of <code>forward</code> and whether they require_grad.</p></dd>
+
+<dt><code>saved_variables</code></dt><dd><p>list of objects that were saved for backward via <code>save_for_backward</code>.</p></dd>
+
+</dl><p></div></p>
+    <h2 class="hasAnchor" id="methods"><a class="anchor" href="#methods"></a>Methods</h2>
+
+    
+<h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Public methods</h3>
+
+<ul>
+<li><p><a href='#method-new'><code>AutogradContext$new()</code></a></p></li>
+<li><p><a href='#method-save_for_backward'><code>AutogradContext$save_for_backward()</code></a></p></li>
+<li><p><a href='#method-mark_non_differentiable'><code>AutogradContext$mark_non_differentiable()</code></a></p></li>
+<li><p><a href='#method-mark_dirty'><code>AutogradContext$mark_dirty()</code></a></p></li>
+<li><p><a href='#method-clone'><code>AutogradContext$clone()</code></a></p></li>
+</ul>
+<p><hr>
+<a id="method-new"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code>new()</code></h3>
+<p>(Dev related) Initializes the context. Not user related.</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
+<p><div class="r"></p><pre><span class='kw'>AutogradContext</span><span class='op'>$</span><span class='fu'>new</span>(
+  <span class='kw'>ptr</span>,
+  <span class='kw'>env</span>,
+  argument_names = <span class='kw'>NULL</span>,
+  argument_needs_grad = <span class='kw'>NULL</span>
+)</pre><p></div></p>
+
+<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Arguments</h4>
+<p><div class="arguments"></p><dl>
+<dt><code>ptr</code></dt><dd><p>pointer to the c++ object</p></dd>
+
+<dt><code>env</code></dt><dd><p>environment that encloses both forward and backward</p></dd>
+
+<dt><code>argument_names</code></dt><dd><p>names of forward arguments</p></dd>
+
+<dt><code>argument_needs_grad</code></dt><dd><p>whether each argument in forward needs grad.</p></dd>
+
+</dl><p></div></p>
+<p><hr>
+<a id="method-save_for_backward"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code>save_for_backward()</code></h3>
+<p>Saves given objects for a future call to backward().</p>
+<p>This should be called at most once, and only from inside the <code>forward()</code>
+method.</p>
+<p>Later, saved objects can be accessed through the <code>saved_variables</code> attribute.
+Before returning them to the user, a check is made to ensure they weren’t used
+in any in-place operation that modified their content.</p>
+<p>Arguments can also be any kind of R object.</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
+<p><div class="r"></p><pre><span class='kw'>AutogradContext</span><span class='op'>$</span><span class='fu'>save_for_backward</span>(<span class='kw'>...</span>)</pre><p></div></p>
+
+<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Arguments</h4>
+<p><div class="arguments"></p><dl>
+<dt><code>...</code></dt><dd><p>any kind of R object that will be saved for the backward pass.
+It's common to pass named arguments.</p></dd>
+
+</dl><p></div></p>
+<p><hr>
+<a id="method-mark_non_differentiable"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code>mark_non_differentiable()</code></h3>
+<p>Marks outputs as non-differentiable.</p>
+<p>This should be called at most once, only from inside the <code>forward()</code> method,
+and all arguments should be outputs.</p>
+<p>This will mark outputs as not requiring gradients, increasing the efficiency
+of backward computation. You still need to accept a gradient for each output
+in <code>backward()</code>, but it’s always going to be a zero tensor with the same
+shape as the shape of a corresponding output.</p>
+<p>This is used e.g. for indices returned from a max Function.</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
+<p><div class="r"></p><pre><span class='kw'>AutogradContext</span><span class='op'>$</span><span class='fu'>mark_non_differentiable</span>(<span class='kw'>...</span>)</pre><p></div></p>
+
+<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Arguments</h4>
+<p><div class="arguments"></p><dl>
+<dt><code>...</code></dt><dd><p>non-differentiable outputs.</p></dd>
+
+</dl><p></div></p>
+<p><hr>
+<a id="method-mark_dirty"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code>mark_dirty()</code></h3>
+<p>Marks given tensors as modified in an in-place operation.</p>
+<p>This should be called at most once, only from inside the <code>forward()</code> method,
+and all arguments should be inputs.</p>
+<p>Every tensor that’s been modified in-place in a call to <code>forward()</code> should
+be given to this function, to ensure correctness of our checks. It doesn’t
+matter whether the function is called before or after modification.</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
+<p><div class="r"></p><pre><span class='kw'>AutogradContext</span><span class='op'>$</span><span class='fu'>mark_dirty</span>(<span class='kw'>...</span>)</pre><p></div></p>
+
+<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Arguments</h4>
+<p><div class="arguments"></p><dl>
+<dt><code>...</code></dt><dd><p>tensors that are modified in-place.</p></dd>
+
+</dl><p></div></p>
+<p><hr>
+<a id="method-clone"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code>clone()</code></h3>
+<p>The objects of this class are cloneable with this method.</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
+<p><div class="r"></p><pre><span class='kw'>AutogradContext</span><span class='op'>$</span><span class='fu'>clone</span>(deep = <span class='fl'>FALSE</span>)</pre><p></div></p>
+
+<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Arguments</h4>
+<p><div class="arguments"></p><dl>
+<dt><code>deep</code></dt><dd><p>Whether to make a deep clone.</p></dd>
+
+</dl><p></div></p>
+
+
+
+  </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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+  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
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+      <a href="../articles/using-autograd.html">Using autograd</a>
+    </li>
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+      <a href="../articles/extending-autograd.html">Extending autograd</a>
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+  <a href="../reference/index.html">Reference</a>
+</li>
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Converts to array</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/tensor.R'><code>R/tensor.R</code></a></small>
+    <div class="hidden name"><code>as_array.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Converts to array</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>as_array</span>(<span class='kw'>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>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/autograd_backward.html b/static/docs/dev/reference/autograd_backward.html
new file mode 100644
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+<meta property="og:description" content="The graph is differentiated using the chain rule. If any of tensors are
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+additionally requires specifying grad_tensors. It should be a sequence of
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+      <a href="../articles/using-autograd.html">Using autograd</a>
+    </li>
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+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Computes the sum of gradients of given tensors w.r.t. graph leaves.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/autograd.R'><code>R/autograd.R</code></a></small>
+    <div class="hidden name"><code>autograd_backward.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>The graph is differentiated using the chain rule. If any of tensors are
+non-scalar (i.e. their data has more than one element) and require gradient,
+then the Jacobian-vector product would be computed, in this case the function
+additionally requires specifying <code>grad_tensors</code>. It should be a sequence of
+matching length, that contains the “vector” in the Jacobian-vector product,
+usually the gradient of the differentiated function w.r.t. corresponding
+tensors (None is an acceptable value for all tensors that don’t need gradient
+tensors).</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>autograd_backward</span>(
+  <span class='kw'>tensors</span>,
+  grad_tensors = <span class='kw'>NULL</span>,
+  retain_graph = <span class='kw'>create_graph</span>,
+  create_graph = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensors</th>
+      <td><p>(list of Tensor) – Tensors of which the derivative will
+be computed.</p></td>
+    </tr>
+    <tr>
+      <th>grad_tensors</th>
+      <td><p>(list of (Tensor or <code>NULL)) – The “vector” in the  Jacobian-vector product, usually gradients w.r.t. each element of  corresponding tensors. </code>NULL<code>values can be specified for scalar Tensors or  ones that don’t require grad. If a</code>NULL` value would be acceptable for all
+grad_tensors, then this argument is optional.</p></td>
+    </tr>
+    <tr>
+      <th>retain_graph</th>
+      <td><p>(bool, optional) – If <code>FALSE</code>, the graph used to compute
+the grad will be freed. Note that in nearly all cases setting this option to
+<code>TRUE</code> is not needed and often can be worked around in a much more efficient
+way. Defaults to the value of <code>create_graph</code>.</p></td>
+    </tr>
+    <tr>
+      <th>create_graph</th>
+      <td><p>(bool, optional) – If <code>TRUE</code>, graph of the derivative will
+be constructed, allowing to compute higher order derivative products.
+Defaults to <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>This function accumulates gradients in the leaves - you might need to zero
+them before calling it.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fl'>1</span>, requires_grad = <span class='fl'>TRUE</span>)
+<span class='kw'>y</span> <span class='op'>&lt;-</span> <span class='fl'>2</span> <span class='op'>*</span> <span class='kw'>x</span>
+
+<span class='kw'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fl'>1</span>, requires_grad = <span class='fl'>TRUE</span>)
+<span class='kw'>b</span> <span class='op'>&lt;-</span> <span class='fl'>3</span> <span class='op'>*</span> <span class='kw'>a</span>
+
+<span class='fu'>autograd_backward</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>y</span>, <span class='kw'>b</span>))
+
+}
+</div></pre>
+  </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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+
+
diff --git a/static/docs/dev/reference/autograd_function.html b/static/docs/dev/reference/autograd_function.html
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--- /dev/null
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+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Records operation history and defines formulas for differentiating ops.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/autograd.R'><code>R/autograd.R</code></a></small>
+    <div class="hidden name"><code>autograd_function.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Every operation performed on Tensor's creates a new function object, that
+performs the computation, and records that it happened. The history is
+retained in the form of a DAG of functions, with edges denoting data
+dependencies (input &lt;- output). Then, when backward is called, the graph is
+processed in the topological ordering, by calling <code>backward()</code> methods of each
+Function object, and passing returned gradients on to next Function's.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>autograd_function</span>(<span class='kw'>forward</span>, <span class='kw'>backward</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>forward</th>
+      <td><p>Performs the operation. It must accept a context <code>ctx</code> as the first argument,
+followed by any number of arguments (tensors or other types). The context can be
+used to store tensors that can be then retrieved during the backward pass.
+See <a href='AutogradContext.html'>AutogradContext</a> for more information about context methods.</p></td>
+    </tr>
+    <tr>
+      <th>backward</th>
+      <td><p>Defines a formula for differentiating the operation. It must accept
+a context <code>ctx</code> as the first argument, followed by as many outputs did <code>forward()</code>
+return, and it should return a named list. Each argument is the gradient w.r.t
+the given output, and each element in the returned list should be the gradient
+w.r.t. the corresponding input. The context can be used to retrieve tensors saved
+during the forward pass. It also has an attribute <code>ctx$needs_input_grad</code> as a
+named list of booleans representing whether each input needs gradient.
+E.g., <code>backward()</code> will have <code>ctx$needs_input_grad$input = TRUE</code> if the <code>input</code>
+argument to <code>forward()</code> needs gradient computated w.r.t. the output.
+See <a href='AutogradContext.html'>AutogradContext</a> for more information about context methods.</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>exp2</span> <span class='op'>&lt;-</span> <span class='fu'>autograd_function</span>(
+  forward = <span class='fu'>function</span>(<span class='kw'>ctx</span>, <span class='kw'>i</span>) {
+    <span class='kw'>result</span> <span class='op'>&lt;-</span> <span class='kw'>i</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/Log.html'>exp</a></span>()
+    <span class='kw'>ctx</span><span class='op'>$</span><span class='fu'>save_for_backward</span>(result = <span class='kw'>result</span>)
+    <span class='kw'>result</span>
+  },
+  backward = <span class='fu'>function</span>(<span class='kw'>ctx</span>, <span class='kw'>grad_output</span>) {
+    <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(i = <span class='kw'>grad_output</span> <span class='op'>*</span> <span class='kw'>ctx</span><span class='op'>$</span><span class='kw'>saved_variable</span><span class='op'>$</span><span class='kw'>result</span>)
+  }
+)
+
+}
+</div></pre>
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+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
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+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
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+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
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+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Computes and returns the sum of gradients of outputs w.r.t. the inputs.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/autograd.R'><code>R/autograd.R</code></a></small>
+    <div class="hidden name"><code>autograd_grad.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p><code>grad_outputs</code> should be a list of length matching output containing the “vector”
+in Jacobian-vector product, usually the pre-computed gradients w.r.t. each of
+the outputs. If an output doesn’t require_grad, then the gradient can be None).</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>autograd_grad</span>(
+  <span class='kw'>outputs</span>,
+  <span class='kw'>inputs</span>,
+  grad_outputs = <span class='kw'>NULL</span>,
+  retain_graph = <span class='kw'>create_graph</span>,
+  create_graph = <span class='fl'>FALSE</span>,
+  allow_unused = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>outputs</th>
+      <td><p>(sequence of Tensor) – outputs of the differentiated function.</p></td>
+    </tr>
+    <tr>
+      <th>inputs</th>
+      <td><p>(sequence of Tensor) – Inputs w.r.t. which the gradient will be
+returned (and not accumulated into .grad).</p></td>
+    </tr>
+    <tr>
+      <th>grad_outputs</th>
+      <td><p>(sequence of Tensor) – The “vector” in the Jacobian-vector
+product. Usually gradients w.r.t. each output. None values can be specified for
+scalar Tensors or ones that don’t require grad. If a None value would be acceptable
+for all <code>grad_tensors</code>, then this argument is optional. Default: None.</p></td>
+    </tr>
+    <tr>
+      <th>retain_graph</th>
+      <td><p>(bool, optional) – If <code>FALSE</code>, the graph used to compute the
+grad will be freed. Note that in nearly all cases setting this option to <code>TRUE</code> is
+not needed and often can be worked around in a much more efficient way.
+Defaults to the value of <code>create_graph</code>.</p></td>
+    </tr>
+    <tr>
+      <th>create_graph</th>
+      <td><p>(bool, optional) – If <code>TRUE, graph of the derivative will  be constructed, allowing to compute higher order derivative products.  Default: </code>FALSE`.</p></td>
+    </tr>
+    <tr>
+      <th>allow_unused</th>
+      <td><p>(bool, optional) – If <code>FALSE</code>, specifying inputs that were
+not used when computing outputs (and therefore their grad is always zero) is an
+error. Defaults to <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>If only_inputs is <code>TRUE</code>, the function will only return a list of gradients w.r.t
+the specified inputs. If it’s <code>FALSE</code>, then gradient w.r.t. all remaining leaves
+will still be computed, and will be accumulated into their <code>.grad</code> attribute.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fl'>0.5</span>, requires_grad = <span class='fl'>TRUE</span>)
+<span class='kw'>b</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fl'>0.9</span>, requires_grad = <span class='fl'>TRUE</span>)
+<span class='kw'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/stats/Uniform.html'>runif</a></span>(<span class='fl'>100</span>))
+<span class='kw'>y</span> <span class='op'>&lt;-</span> <span class='fl'>2</span> <span class='op'>*</span> <span class='kw'>x</span> <span class='op'>+</span> <span class='fl'>1</span>
+<span class='kw'>loss</span> <span class='op'>&lt;-</span> (<span class='kw'>y</span> <span class='op'>-</span> (<span class='kw'>w</span><span class='op'>*</span><span class='kw'>x</span> <span class='op'>+</span> <span class='kw'>b</span>))<span class='op'>^</span><span class='fl'>2</span>
+<span class='kw'>loss</span> <span class='op'>&lt;-</span> <span class='kw'>loss</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/mean.html'>mean</a></span>()
+
+<span class='kw'>o</span> <span class='op'>&lt;-</span> <span class='fu'>autograd_grad</span>(<span class='kw'>loss</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>w</span>, <span class='kw'>b</span>))
+<span class='kw'>o</span>
+ 
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt; -0.9935
+#&gt; [ CPUFloatType{1} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt; -1.6206
+#&gt; [ CPUFloatType{1} ]
+#&gt; </div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/autograd_set_grad_mode.html b/static/docs/dev/reference/autograd_set_grad_mode.html
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+++ b/static/docs/dev/reference/autograd_set_grad_mode.html
@@ -0,0 +1,237 @@
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+    <div class="page-header">
+    <h1>Set grad mode</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_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='kw'>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>
+
+
+  </div>
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diff --git a/static/docs/dev/reference/cuda_current_device.html b/static/docs/dev/reference/cuda_current_device.html
new file mode 100644
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--- /dev/null
+++ b/static/docs/dev/reference/cuda_current_device.html
@@ -0,0 +1,229 @@
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Returns the index of a currently selected device.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/cuda.R'><code>R/cuda.R</code></a></small>
+    <div class="hidden name"><code>cuda_current_device.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Returns the index of a currently selected device.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>cuda_current_device</span>()</pre>
+
+
+
+  </div>
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+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Data loader. Combines a dataset and a sampler, and provides
+single- or multi-process iterators over the dataset.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/utils-data-dataloader.R'><code>R/utils-data-dataloader.R</code></a></small>
+    <div class="hidden name"><code>dataloader.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Data loader. Combines a dataset and a sampler, and provides
+single- or multi-process iterators over the dataset.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>dataloader</span>(
+  <span class='kw'>dataset</span>,
+  batch_size = <span class='fl'>1</span>,
+  shuffle = <span class='fl'>FALSE</span>,
+  sampler = <span class='kw'>NULL</span>,
+  batch_sampler = <span class='kw'>NULL</span>,
+  num_workers = <span class='fl'>0</span>,
+  collate_fn = <span class='kw'>NULL</span>,
+  pin_memory = <span class='fl'>FALSE</span>,
+  drop_last = <span class='fl'>FALSE</span>,
+  timeout = <span class='fl'>0</span>,
+  worker_init_fn = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>dataset</th>
+      <td><p>(Dataset): dataset from which to load the data.</p></td>
+    </tr>
+    <tr>
+      <th>batch_size</th>
+      <td><p>(int, optional): how many samples per batch to load
+(default: <code>1</code>).</p></td>
+    </tr>
+    <tr>
+      <th>shuffle</th>
+      <td><p>(bool, optional): set to <code>TRUE</code> to have the data reshuffled
+at every epoch (default: <code>FALSE</code>).</p></td>
+    </tr>
+    <tr>
+      <th>sampler</th>
+      <td><p>(Sampler, optional): defines the strategy to draw samples from
+the dataset. If specified, <code>shuffle</code> must be False.</p></td>
+    </tr>
+    <tr>
+      <th>batch_sampler</th>
+      <td><p>(Sampler, optional): like sampler, but returns a batch of
+indices at a time. Mutually exclusive with <code>batch_size</code>,
+<code>shuffle</code>, <code>sampler</code>, and <code>drop_last</code>.</p></td>
+    </tr>
+    <tr>
+      <th>num_workers</th>
+      <td><p>(int, optional): how many subprocesses to use for data
+loading. 0 means that the data will be loaded in the main process.
+(default: <code>0</code>)</p></td>
+    </tr>
+    <tr>
+      <th>collate_fn</th>
+      <td><p>(callable, optional): merges a list of samples to form a mini-batch.</p></td>
+    </tr>
+    <tr>
+      <th>pin_memory</th>
+      <td><p>(bool, optional): If <code>TRUE</code>, the data loader will copy tensors
+into CUDA pinned memory before returning them.  If your data elements
+are a custom type, or your <code>collate_fn</code> returns a batch that is a custom type
+see the example below.</p></td>
+    </tr>
+    <tr>
+      <th>drop_last</th>
+      <td><p>(bool, optional): set to <code>TRUE</code> to drop the last incomplete batch,
+if the dataset size is not divisible by the batch size. If <code>FALSE</code> and
+the size of dataset is not divisible by the batch size, then the last batch
+will be smaller. (default: <code>FALSE</code>)</p></td>
+    </tr>
+    <tr>
+      <th>timeout</th>
+      <td><p>(numeric, optional): if positive, the timeout value for collecting a batch
+from workers. Should always be non-negative. (default: <code>0</code>)</p></td>
+    </tr>
+    <tr>
+      <th>worker_init_fn</th>
+      <td><p>(callable, optional): If not <code>NULL</code>, this will be called on each
+worker subprocess with the worker id (an int in <code>[0, num_workers - 1]</code>) as
+input, after seeding and before data loading. (default: <code>NULL</code>)</p></td>
+    </tr>
+    </table>
+
+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Creates an iterator from a DataLoader</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/utils-data-dataloader.R'><code>R/utils-data-dataloader.R</code></a></small>
+    <div class="hidden name"><code>dataloader_make_iter.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Creates an iterator from a DataLoader</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>dataloader_make_iter</span>(<span class='kw'>dataloader</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>dataloader</th>
+      <td><p>a dataloader object.</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/dataloader_next.html b/static/docs/dev/reference/dataloader_next.html
new file mode 100644
index 000000000..b7df18d83
--- /dev/null
+++ b/static/docs/dev/reference/dataloader_next.html
@@ -0,0 +1,237 @@
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
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+
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+    <div class="page-header">
+    <h1>Get the next element of a dataloader iterator</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_next.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Get the next element of a dataloader iterator</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>dataloader_next</span>(<span class='kw'>iter</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>iter</th>
+      <td><p>a DataLoader iter created with <a href='dataloader_make_iter.html'>dataloader_make_iter</a>.</p></td>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/dev/reference/dataset.html b/static/docs/dev/reference/dataset.html
new 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
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+of ~torch.utils.data.DataLoader." />
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+    </li>
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+    </li>
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>An abstract class representing a <code>Dataset</code>.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/utils-data.R'><code>R/utils-data.R</code></a></small>
+    <div class="hidden name"><code>dataset.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>All datasets that represent a map from keys to data samples should subclass
+it. All subclasses should overwrite <code>get_item</code>, supporting fetching a
+data sample for a given key. Subclasses could also optionally overwrite
+<code>lenght</code>, which is expected to return the size of the dataset by many
+<code>~torch.utils.data.Sampler</code> implementations and the default options
+of <code>~torch.utils.data.DataLoader</code>.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>dataset</span>(name = <span class='kw'>NULL</span>, inherit = <span class='kw'>Dataset</span>, <span class='kw'>...</span>, parent_env = <span class='fu'><a href='https://rdrr.io/r/base/sys.parent.html'>parent.frame</a></span>())</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>name</th>
+      <td><p>a name for the dataset. It it's also used as the class
+for it.</p></td>
+    </tr>
+    <tr>
+      <th>inherit</th>
+      <td><p>you can optionally inherit from a dataset when creating a
+new dataset.</p></td>
+    </tr>
+    <tr>
+      <th>...</th>
+      <td><p>public methods for the dataset class</p></td>
+    </tr>
+    <tr>
+      <th>parent_env</th>
+      <td><p>An environment to use as the parent of newly-created
+objects.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p><code>~torch.utils.data.DataLoader</code> by default constructs a index
+sampler that yields integral indices.  To make it work with a map-style
+dataset with non-integral indices/keys, a custom sampler must be provided.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/default_dtype.html b/static/docs/dev/reference/default_dtype.html
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+++ b/static/docs/dev/reference/default_dtype.html
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+
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+    <div class="page-header">
+    <h1>Gets and sets the default floating point dtype.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/dtype.R'><code>R/dtype.R</code></a></small>
+    <div class="hidden name"><code>default_dtype.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Gets and sets the default floating point dtype.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_set_default_dtype</span>(<span class='kw'>d</span>)
+
+<span class='fu'>torch_get_default_dtype</span>()</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>d</th>
+      <td><p>The default floating point dtype to set. Initially set to
+<code><a href='torch_dtype.html'>torch_float()</a></code>.</p></td>
+    </tr>
+    </table>
+
+
+  </div>
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+    <div class="page-header">
+    <h1>Enumerate an iterator</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/utils-data-enum.R'><code>R/utils-data-enum.R</code></a></small>
+    <div class="hidden name"><code>enumerate.dataloader.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Enumerate an iterator</p>
+    </div>
+
+    <pre class="usage"><span class='co'># S3 method for dataloader</span>
+<span class='fu'><a href='enumerate.html'>enumerate</a></span>(<span class='kw'>x</span>, max_len = <span class='fl'>1e+06</span>, <span class='kw'>...</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>x</th>
+      <td><p>the generator to enumerate.</p></td>
+    </tr>
+    <tr>
+      <th>max_len</th>
+      <td><p>maximum number of iterations.</p></td>
+    </tr>
+    <tr>
+      <th>...</th>
+      <td><p>passed to specific methods.</p></td>
+    </tr>
+    </table>
+
+
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+    </li>
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+</li>
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Enumerate an iterator</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/utils-data-enum.R'><code>R/utils-data-enum.R</code></a></small>
+    <div class="hidden name"><code>enumerate.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Enumerate an iterator</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>enumerate</span>(<span class='kw'>x</span>, <span class='kw'>...</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>x</th>
+      <td><p>the generator to enumerate.</p></td>
+    </tr>
+    <tr>
+      <th>...</th>
+      <td><p>passed to specific methods.</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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+  <body data-spy="scroll" data-target="#toc">
+    <div class="container template-reference-index">
+      <header>
+      <div class="navbar navbar-default navbar-fixed-top" role="navigation">
+  <div class="container">
+    <div class="navbar-header">
+      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
+        <span class="sr-only">Toggle navigation</span>
+        <span class="icon-bar"></span>
+        <span class="icon-bar"></span>
+        <span class="icon-bar"></span>
+      </button>
+      <span class="navbar-brand">
+        <a class="navbar-link" href="../index.html">torch</a>
+        <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.0.3.9000</span>
+      </span>
+    </div>
+
+    <div id="navbar" class="navbar-collapse collapse">
+      <ul class="nav navbar-nav">
+        <li>
+  <a href="../index.html">
+    <span class="fas fa fas fa-home fa-lg"></span>
+     
+  </a>
+</li>
+<li class="dropdown">
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
+    Getting started
+     
+    <span class="caret"></span>
+  </a>
+  <ul class="dropdown-menu" role="menu">
+    <li class="dropdown-header">Beginers guide</li>
+    <li>
+      <a href="../articles/getting-started/warmup.html">Warm-up</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/tensors.html">Tensors</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/tensors-and-autograd.html">Tensors and autograd</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/optim.html">optim: optimizers in torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/custom-nn.html">Custom nn modules</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/control-flow-and-weight-sharing.html">Control flow &amp; Weight sharing</a>
+    </li>
+    <li class="dropdown-header">Torch Mechanics</li>
+    <li>
+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/neural-networks.html">Neural networks</a>
+    </li>
+  </ul>
+</li>
+<li class="dropdown">
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
+    Articles
+     
+    <span class="caret"></span>
+  </a>
+  <ul class="dropdown-menu" role="menu">
+    <li class="dropdown-header">Tensors</li>
+    <li>
+      <a href="../articles/tensor-creation.html">Creating tensors</a>
+    </li>
+    <li>
+      <a href="../articles/indexing.html">Indexing</a>
+    </li>
+    <li>
+      <a href="../articles/tensor/index.html">Tensor class</a>
+    </li>
+    <li class="dropdown-header">Datasets</li>
+    <li>
+      <a href="../articles/loading-data.html">Loading Data</a>
+    </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-header">Examples</li>
+<li>
+  <a href="../reference/index.html">Reference</a>
+</li>
+<li>
+  <a href="../news/index.html">Changelog</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="contents col-md-9">
+    <div class="page-header">
+      <h1>Reference</h1>
+    </div>
+
+    <table class="ref-index">
+
+    <colgroup>
+      
+      <col class="alias" />
+      <col class="title" />
+    </colgroup>
+
+    <tbody>
+      <tr>
+        <th colspan="2">
+          <h2 id="section-tensor-creation-utilities" class="hasAnchor"><a href="#section-tensor-creation-utilities" class="anchor"></a>Tensor creation utilities</h2>
+          <p class="section-desc"></p>
+        </th>
+      </tr>
+      
+      
+    </tbody><tbody>
+      
+      
+      <tr>
+        
+        <td>
+          <p><code><a href="torch_empty.html">torch_empty()</a></code> </p>
+        </td>
+        <td><p>Empty</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_arange.html">torch_arange()</a></code> </p>
+        </td>
+        <td><p>Arange</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_eye.html">torch_eye()</a></code> </p>
+        </td>
+        <td><p>Eye</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_full.html">torch_full()</a></code> </p>
+        </td>
+        <td><p>Full</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_linspace.html">torch_linspace()</a></code> </p>
+        </td>
+        <td><p>Linspace</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_logspace.html">torch_logspace()</a></code> </p>
+        </td>
+        <td><p>Logspace</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_ones.html">torch_ones()</a></code> </p>
+        </td>
+        <td><p>Ones</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_rand.html">torch_rand()</a></code> </p>
+        </td>
+        <td><p>Rand</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_randint.html">torch_randint()</a></code> </p>
+        </td>
+        <td><p>Randint</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_randn.html">torch_randn()</a></code> </p>
+        </td>
+        <td><p>Randn</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_randperm.html">torch_randperm()</a></code> </p>
+        </td>
+        <td><p>Randperm</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_zeros.html">torch_zeros()</a></code> </p>
+        </td>
+        <td><p>Zeros</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_empty_like.html">torch_empty_like()</a></code> </p>
+        </td>
+        <td><p>Empty_like</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_full_like.html">torch_full_like()</a></code> </p>
+        </td>
+        <td><p>Full_like</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_ones_like.html">torch_ones_like()</a></code> </p>
+        </td>
+        <td><p>Ones_like</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_rand_like.html">torch_rand_like()</a></code> </p>
+        </td>
+        <td><p>Rand_like</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_randint_like.html">torch_randint_like()</a></code> </p>
+        </td>
+        <td><p>Randint_like</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_randn_like.html">torch_randn_like()</a></code> </p>
+        </td>
+        <td><p>Randn_like</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_zeros_like.html">torch_zeros_like()</a></code> </p>
+        </td>
+        <td><p>Zeros_like</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="as_array.html">as_array()</a></code> </p>
+        </td>
+        <td><p>Converts to array</p></td>
+      </tr>
+    </tbody><tbody>
+      <tr>
+        <th colspan="2">
+          <h2 id="section-tensor-attributes" class="hasAnchor"><a href="#section-tensor-attributes" class="anchor"></a>Tensor attributes</h2>
+          <p class="section-desc"></p>
+        </th>
+      </tr>
+      
+      
+    </tbody><tbody>
+      
+      
+      <tr>
+        
+        <td>
+          <p><code><a href="default_dtype.html">torch_set_default_dtype()</a></code> <code><a href="default_dtype.html">torch_get_default_dtype()</a></code> </p>
+        </td>
+        <td><p>Gets and sets the default floating point dtype.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="is_torch_device.html">is_torch_device()</a></code> </p>
+        </td>
+        <td><p>Checks if object is a device</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="is_torch_dtype.html">is_torch_dtype()</a></code> </p>
+        </td>
+        <td><p>Check if object is a torch data type</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_dtype.html">torch_float32()</a></code> <code><a href="torch_dtype.html">torch_float()</a></code> <code><a href="torch_dtype.html">torch_float64()</a></code> <code><a href="torch_dtype.html">torch_double()</a></code> <code><a href="torch_dtype.html">torch_float16()</a></code> <code><a href="torch_dtype.html">torch_half()</a></code> <code><a href="torch_dtype.html">torch_uint8()</a></code> <code><a href="torch_dtype.html">torch_int8()</a></code> <code><a href="torch_dtype.html">torch_int16()</a></code> <code><a href="torch_dtype.html">torch_short()</a></code> <code><a href="torch_dtype.html">torch_int32()</a></code> <code><a href="torch_dtype.html">torch_int()</a></code> <code><a href="torch_dtype.html">torch_int64()</a></code> <code><a href="torch_dtype.html">torch_long()</a></code> <code><a href="torch_dtype.html">torch_bool()</a></code> <code><a href="torch_dtype.html">torch_quint8()</a></code> <code><a href="torch_dtype.html">torch_qint8()</a></code> <code><a href="torch_dtype.html">torch_qint32()</a></code> </p>
+        </td>
+        <td><p>Torch data types</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_finfo.html">torch_finfo()</a></code> </p>
+        </td>
+        <td><p>Floating point type info</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_iinfo.html">torch_iinfo()</a></code> </p>
+        </td>
+        <td><p>Integer type info</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_qscheme.html">torch_per_channel_affine()</a></code> <code><a href="torch_qscheme.html">torch_per_tensor_affine()</a></code> <code><a href="torch_qscheme.html">torch_per_channel_symmetric()</a></code> <code><a href="torch_qscheme.html">torch_per_tensor_symmetric()</a></code> </p>
+        </td>
+        <td><p>Creates the corresponding Scheme object</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_reduction.html">torch_reduction_sum()</a></code> <code><a href="torch_reduction.html">torch_reduction_mean()</a></code> <code><a href="torch_reduction.html">torch_reduction_none()</a></code> </p>
+        </td>
+        <td><p>Creates the reduction objet</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="is_torch_layout.html">is_torch_layout()</a></code> </p>
+        </td>
+        <td><p>Check if an object is a torch layout.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="is_torch_memory_format.html">is_torch_memory_format()</a></code> </p>
+        </td>
+        <td><p>Check if an object is a memory format</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="is_torch_qscheme.html">is_torch_qscheme()</a></code> </p>
+        </td>
+        <td><p>Checks if an object is a QScheme</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="is_undefined_tensor.html">is_undefined_tensor()</a></code> </p>
+        </td>
+        <td><p>Checks if a tensor is undefined</p></td>
+      </tr>
+    </tbody><tbody>
+      <tr>
+        <th colspan="2">
+          <h2 id="section-serialization" class="hasAnchor"><a href="#section-serialization" class="anchor"></a>Serialization</h2>
+          <p class="section-desc"></p>
+        </th>
+      </tr>
+      
+      
+    </tbody><tbody>
+      
+      
+      <tr>
+        
+        <td>
+          <p><code><a href="load_state_dict.html">load_state_dict()</a></code> </p>
+        </td>
+        <td><p>Load a state dict file</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_load.html">torch_load()</a></code> </p>
+        </td>
+        <td><p>Loads a saved object</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_save.html">torch_save()</a></code> </p>
+        </td>
+        <td><p>Saves an object to a disk file.</p></td>
+      </tr>
+    </tbody><tbody>
+      <tr>
+        <th colspan="2">
+          <h2 id="section-mathematical-operations-on-tensors" class="hasAnchor"><a href="#section-mathematical-operations-on-tensors" class="anchor"></a>Mathematical operations on tensors</h2>
+          <p class="section-desc"></p>
+        </th>
+      </tr>
+      
+      
+    </tbody><tbody>
+      
+      
+      <tr>
+        
+        <td>
+          <p><code><a href="torch_abs.html">torch_abs()</a></code> </p>
+        </td>
+        <td><p>Abs</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_acos.html">torch_acos()</a></code> </p>
+        </td>
+        <td><p>Acos</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_adaptive_avg_pool1d.html">torch_adaptive_avg_pool1d()</a></code> </p>
+        </td>
+        <td><p>Adaptive_avg_pool1d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_add.html">torch_add()</a></code> </p>
+        </td>
+        <td><p>Add</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_addbmm.html">torch_addbmm()</a></code> </p>
+        </td>
+        <td><p>Addbmm</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_addcdiv.html">torch_addcdiv()</a></code> </p>
+        </td>
+        <td><p>Addcdiv</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_addcmul.html">torch_addcmul()</a></code> </p>
+        </td>
+        <td><p>Addcmul</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_addmm.html">torch_addmm()</a></code> </p>
+        </td>
+        <td><p>Addmm</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_addmv.html">torch_addmv()</a></code> </p>
+        </td>
+        <td><p>Addmv</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_addr.html">torch_addr()</a></code> </p>
+        </td>
+        <td><p>Addr</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_allclose.html">torch_allclose()</a></code> </p>
+        </td>
+        <td><p>Allclose</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_angle.html">torch_angle()</a></code> </p>
+        </td>
+        <td><p>Angle</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_argmax.html">torch_argmax()</a></code> </p>
+        </td>
+        <td><p>Argmax</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_argmin.html">torch_argmin()</a></code> </p>
+        </td>
+        <td><p>Argmin</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_argsort.html">torch_argsort()</a></code> </p>
+        </td>
+        <td><p>Argsort</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_as_strided.html">torch_as_strided()</a></code> </p>
+        </td>
+        <td><p>As_strided</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_asin.html">torch_asin()</a></code> </p>
+        </td>
+        <td><p>Asin</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_atan.html">torch_atan()</a></code> </p>
+        </td>
+        <td><p>Atan</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_atan2.html">torch_atan2()</a></code> </p>
+        </td>
+        <td><p>Atan2</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_avg_pool1d.html">torch_avg_pool1d()</a></code> </p>
+        </td>
+        <td><p>Avg_pool1d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_baddbmm.html">torch_baddbmm()</a></code> </p>
+        </td>
+        <td><p>Baddbmm</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_bartlett_window.html">torch_bartlett_window()</a></code> </p>
+        </td>
+        <td><p>Bartlett_window</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_bernoulli.html">torch_bernoulli()</a></code> </p>
+        </td>
+        <td><p>Bernoulli</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_bincount.html">torch_bincount()</a></code> </p>
+        </td>
+        <td><p>Bincount</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_bitwise_and.html">torch_bitwise_and()</a></code> </p>
+        </td>
+        <td><p>Bitwise_and</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_bitwise_not.html">torch_bitwise_not()</a></code> </p>
+        </td>
+        <td><p>Bitwise_not</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_bitwise_or.html">torch_bitwise_or()</a></code> </p>
+        </td>
+        <td><p>Bitwise_or</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_bitwise_xor.html">torch_bitwise_xor()</a></code> </p>
+        </td>
+        <td><p>Bitwise_xor</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_blackman_window.html">torch_blackman_window()</a></code> </p>
+        </td>
+        <td><p>Blackman_window</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_bmm.html">torch_bmm()</a></code> </p>
+        </td>
+        <td><p>Bmm</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_broadcast_tensors.html">torch_broadcast_tensors()</a></code> </p>
+        </td>
+        <td><p>Broadcast_tensors</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_can_cast.html">torch_can_cast()</a></code> </p>
+        </td>
+        <td><p>Can_cast</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_cartesian_prod.html">torch_cartesian_prod()</a></code> </p>
+        </td>
+        <td><p>Cartesian_prod</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_cat.html">torch_cat()</a></code> </p>
+        </td>
+        <td><p>Cat</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_cdist.html">torch_cdist()</a></code> </p>
+        </td>
+        <td><p>Cdist</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_ceil.html">torch_ceil()</a></code> </p>
+        </td>
+        <td><p>Ceil</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_celu.html">torch_celu()</a></code> </p>
+        </td>
+        <td><p>Celu</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_celu_.html">torch_celu_()</a></code> </p>
+        </td>
+        <td><p>Celu_</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_chain_matmul.html">torch_chain_matmul()</a></code> </p>
+        </td>
+        <td><p>Chain_matmul</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_cholesky.html">torch_cholesky()</a></code> </p>
+        </td>
+        <td><p>Cholesky</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_cholesky_inverse.html">torch_cholesky_inverse()</a></code> </p>
+        </td>
+        <td><p>Cholesky_inverse</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_cholesky_solve.html">torch_cholesky_solve()</a></code> </p>
+        </td>
+        <td><p>Cholesky_solve</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_chunk.html">torch_chunk()</a></code> </p>
+        </td>
+        <td><p>Chunk</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_clamp.html">torch_clamp()</a></code> </p>
+        </td>
+        <td><p>Clamp</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_combinations.html">torch_combinations()</a></code> </p>
+        </td>
+        <td><p>Combinations</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_conj.html">torch_conj()</a></code> </p>
+        </td>
+        <td><p>Conj</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_conv1d.html">torch_conv1d()</a></code> </p>
+        </td>
+        <td><p>Conv1d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_conv2d.html">torch_conv2d()</a></code> </p>
+        </td>
+        <td><p>Conv2d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_conv3d.html">torch_conv3d()</a></code> </p>
+        </td>
+        <td><p>Conv3d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_conv_tbc.html">torch_conv_tbc()</a></code> </p>
+        </td>
+        <td><p>Conv_tbc</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_conv_transpose1d.html">torch_conv_transpose1d()</a></code> </p>
+        </td>
+        <td><p>Conv_transpose1d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_conv_transpose2d.html">torch_conv_transpose2d()</a></code> </p>
+        </td>
+        <td><p>Conv_transpose2d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_conv_transpose3d.html">torch_conv_transpose3d()</a></code> </p>
+        </td>
+        <td><p>Conv_transpose3d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_cos.html">torch_cos()</a></code> </p>
+        </td>
+        <td><p>Cos</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_cosh.html">torch_cosh()</a></code> </p>
+        </td>
+        <td><p>Cosh</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_cosine_similarity.html">torch_cosine_similarity()</a></code> </p>
+        </td>
+        <td><p>Cosine_similarity</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_cross.html">torch_cross()</a></code> </p>
+        </td>
+        <td><p>Cross</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_cummax.html">torch_cummax()</a></code> </p>
+        </td>
+        <td><p>Cummax</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_cummin.html">torch_cummin()</a></code> </p>
+        </td>
+        <td><p>Cummin</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_cumprod.html">torch_cumprod()</a></code> </p>
+        </td>
+        <td><p>Cumprod</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_cumsum.html">torch_cumsum()</a></code> </p>
+        </td>
+        <td><p>Cumsum</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_det.html">torch_det()</a></code> </p>
+        </td>
+        <td><p>Det</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_device.html">torch_device()</a></code> </p>
+        </td>
+        <td><p>Create a Device object</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_diag.html">torch_diag()</a></code> </p>
+        </td>
+        <td><p>Diag</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_diag_embed.html">torch_diag_embed()</a></code> </p>
+        </td>
+        <td><p>Diag_embed</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_diagflat.html">torch_diagflat()</a></code> </p>
+        </td>
+        <td><p>Diagflat</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_diagonal.html">torch_diagonal()</a></code> </p>
+        </td>
+        <td><p>Diagonal</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_digamma.html">torch_digamma()</a></code> </p>
+        </td>
+        <td><p>Digamma</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_dist.html">torch_dist()</a></code> </p>
+        </td>
+        <td><p>Dist</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_div.html">torch_div()</a></code> </p>
+        </td>
+        <td><p>Div</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_dot.html">torch_dot()</a></code> </p>
+        </td>
+        <td><p>Dot</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_eig.html">torch_eig()</a></code> </p>
+        </td>
+        <td><p>Eig</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_einsum.html">torch_einsum()</a></code> </p>
+        </td>
+        <td><p>Einsum</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_empty_strided.html">torch_empty_strided()</a></code> </p>
+        </td>
+        <td><p>Empty_strided</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_eq.html">torch_eq()</a></code> </p>
+        </td>
+        <td><p>Eq</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_equal.html">torch_equal()</a></code> </p>
+        </td>
+        <td><p>Equal</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_erf.html">torch_erf()</a></code> </p>
+        </td>
+        <td><p>Erf</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_erfc.html">torch_erfc()</a></code> </p>
+        </td>
+        <td><p>Erfc</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_erfinv.html">torch_erfinv()</a></code> </p>
+        </td>
+        <td><p>Erfinv</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_exp.html">torch_exp()</a></code> </p>
+        </td>
+        <td><p>Exp</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_expm1.html">torch_expm1()</a></code> </p>
+        </td>
+        <td><p>Expm1</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_fft.html">torch_fft()</a></code> </p>
+        </td>
+        <td><p>Fft</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_flatten.html">torch_flatten()</a></code> </p>
+        </td>
+        <td><p>Flatten</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_flip.html">torch_flip()</a></code> </p>
+        </td>
+        <td><p>Flip</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_floor.html">torch_floor()</a></code> </p>
+        </td>
+        <td><p>Floor</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_floor_divide.html">torch_floor_divide()</a></code> </p>
+        </td>
+        <td><p>Floor_divide</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_fmod.html">torch_fmod()</a></code> </p>
+        </td>
+        <td><p>Fmod</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_frac.html">torch_frac()</a></code> </p>
+        </td>
+        <td><p>Frac</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_gather.html">torch_gather()</a></code> </p>
+        </td>
+        <td><p>Gather</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_ge.html">torch_ge()</a></code> </p>
+        </td>
+        <td><p>Ge</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_generator.html">torch_generator()</a></code> </p>
+        </td>
+        <td><p>Create a Generator object</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_geqrf.html">torch_geqrf()</a></code> </p>
+        </td>
+        <td><p>Geqrf</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_ger.html">torch_ger()</a></code> </p>
+        </td>
+        <td><p>Ger</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_gt.html">torch_gt()</a></code> </p>
+        </td>
+        <td><p>Gt</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_hamming_window.html">torch_hamming_window()</a></code> </p>
+        </td>
+        <td><p>Hamming_window</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_hann_window.html">torch_hann_window()</a></code> </p>
+        </td>
+        <td><p>Hann_window</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_histc.html">torch_histc()</a></code> </p>
+        </td>
+        <td><p>Histc</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_ifft.html">torch_ifft()</a></code> </p>
+        </td>
+        <td><p>Ifft</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_imag.html">torch_imag()</a></code> </p>
+        </td>
+        <td><p>Imag</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_index_select.html">torch_index_select()</a></code> </p>
+        </td>
+        <td><p>Index_select</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_inverse.html">torch_inverse()</a></code> </p>
+        </td>
+        <td><p>Inverse</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_irfft.html">torch_irfft()</a></code> </p>
+        </td>
+        <td><p>Irfft</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_is_complex.html">torch_is_complex()</a></code> </p>
+        </td>
+        <td><p>Is_complex</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_is_floating_point.html">torch_is_floating_point()</a></code> </p>
+        </td>
+        <td><p>Is_floating_point</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_is_installed.html">torch_is_installed()</a></code> </p>
+        </td>
+        <td><p>Verifies if torch is installed</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_isfinite.html">torch_isfinite()</a></code> </p>
+        </td>
+        <td><p>Isfinite</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_isinf.html">torch_isinf()</a></code> </p>
+        </td>
+        <td><p>Isinf</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_isnan.html">torch_isnan()</a></code> </p>
+        </td>
+        <td><p>Isnan</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_kthvalue.html">torch_kthvalue()</a></code> </p>
+        </td>
+        <td><p>Kthvalue</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_layout.html">torch_strided()</a></code> <code><a href="torch_layout.html">torch_sparse_coo()</a></code> </p>
+        </td>
+        <td><p>Creates the corresponding layout</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_le.html">torch_le()</a></code> </p>
+        </td>
+        <td><p>Le</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_lerp.html">torch_lerp()</a></code> </p>
+        </td>
+        <td><p>Lerp</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_lgamma.html">torch_lgamma()</a></code> </p>
+        </td>
+        <td><p>Lgamma</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_log.html">torch_log()</a></code> </p>
+        </td>
+        <td><p>Log</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_log10.html">torch_log10()</a></code> </p>
+        </td>
+        <td><p>Log10</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_log1p.html">torch_log1p()</a></code> </p>
+        </td>
+        <td><p>Log1p</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_log2.html">torch_log2()</a></code> </p>
+        </td>
+        <td><p>Log2</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_logdet.html">torch_logdet()</a></code> </p>
+        </td>
+        <td><p>Logdet</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_logical_and.html">torch_logical_and()</a></code> </p>
+        </td>
+        <td><p>Logical_and</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_logical_not.html">torch_logical_not</a></code> </p>
+        </td>
+        <td><p>Logical_not</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_logical_or.html">torch_logical_or()</a></code> </p>
+        </td>
+        <td><p>Logical_or</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_logical_xor.html">torch_logical_xor()</a></code> </p>
+        </td>
+        <td><p>Logical_xor</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_logsumexp.html">torch_logsumexp()</a></code> </p>
+        </td>
+        <td><p>Logsumexp</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_lstsq.html">torch_lstsq()</a></code> </p>
+        </td>
+        <td><p>Lstsq</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_lt.html">torch_lt()</a></code> </p>
+        </td>
+        <td><p>Lt</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_lu.html">torch_lu()</a></code> </p>
+        </td>
+        <td><p>LU</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_lu_solve.html">torch_lu_solve()</a></code> </p>
+        </td>
+        <td><p>Lu_solve</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_manual_seed.html">torch_manual_seed()</a></code> </p>
+        </td>
+        <td><p>Sets the seed for generating random numbers.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_masked_select.html">torch_masked_select()</a></code> </p>
+        </td>
+        <td><p>Masked_select</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_matmul.html">torch_matmul()</a></code> </p>
+        </td>
+        <td><p>Matmul</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_matrix_power.html">torch_matrix_power()</a></code> </p>
+        </td>
+        <td><p>Matrix_power</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_matrix_rank.html">torch_matrix_rank()</a></code> </p>
+        </td>
+        <td><p>Matrix_rank</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_max.html">torch_max</a></code> </p>
+        </td>
+        <td><p>Max</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_mean.html">torch_mean()</a></code> </p>
+        </td>
+        <td><p>Mean</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_median.html">torch_median()</a></code> </p>
+        </td>
+        <td><p>Median</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_memory_format.html">torch_contiguous_format()</a></code> <code><a href="torch_memory_format.html">torch_preserve_format()</a></code> <code><a href="torch_memory_format.html">torch_channels_last_format()</a></code> </p>
+        </td>
+        <td><p>Memory format</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_meshgrid.html">torch_meshgrid()</a></code> </p>
+        </td>
+        <td><p>Meshgrid</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_min.html">torch_min</a></code> </p>
+        </td>
+        <td><p>Min</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_mm.html">torch_mm()</a></code> </p>
+        </td>
+        <td><p>Mm</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_mode.html">torch_mode()</a></code> </p>
+        </td>
+        <td><p>Mode</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_mul.html">torch_mul()</a></code> </p>
+        </td>
+        <td><p>Mul</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_multinomial.html">torch_multinomial()</a></code> </p>
+        </td>
+        <td><p>Multinomial</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_mv.html">torch_mv()</a></code> </p>
+        </td>
+        <td><p>Mv</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_mvlgamma.html">torch_mvlgamma()</a></code> </p>
+        </td>
+        <td><p>Mvlgamma</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_narrow.html">torch_narrow()</a></code> </p>
+        </td>
+        <td><p>Narrow</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_ne.html">torch_ne()</a></code> </p>
+        </td>
+        <td><p>Ne</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_neg.html">torch_neg()</a></code> </p>
+        </td>
+        <td><p>Neg</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_nonzero.html">torch_nonzero()</a></code> </p>
+        </td>
+        <td><p>Nonzero</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_norm.html">torch_norm()</a></code> </p>
+        </td>
+        <td><p>Norm</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_normal.html">torch_normal()</a></code> </p>
+        </td>
+        <td><p>Normal</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_orgqr.html">torch_orgqr()</a></code> </p>
+        </td>
+        <td><p>Orgqr</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_ormqr.html">torch_ormqr()</a></code> </p>
+        </td>
+        <td><p>Ormqr</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_pdist.html">torch_pdist()</a></code> </p>
+        </td>
+        <td><p>Pdist</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_pinverse.html">torch_pinverse()</a></code> </p>
+        </td>
+        <td><p>Pinverse</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_pixel_shuffle.html">torch_pixel_shuffle()</a></code> </p>
+        </td>
+        <td><p>Pixel_shuffle</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_poisson.html">torch_poisson()</a></code> </p>
+        </td>
+        <td><p>Poisson</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_polygamma.html">torch_polygamma()</a></code> </p>
+        </td>
+        <td><p>Polygamma</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_pow.html">torch_pow()</a></code> </p>
+        </td>
+        <td><p>Pow</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_prod.html">torch_prod()</a></code> </p>
+        </td>
+        <td><p>Prod</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_promote_types.html">torch_promote_types()</a></code> </p>
+        </td>
+        <td><p>Promote_types</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_qr.html">torch_qr()</a></code> </p>
+        </td>
+        <td><p>Qr</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_quantize_per_channel.html">torch_quantize_per_channel()</a></code> </p>
+        </td>
+        <td><p>Quantize_per_channel</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_quantize_per_tensor.html">torch_quantize_per_tensor()</a></code> </p>
+        </td>
+        <td><p>Quantize_per_tensor</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_range.html">torch_range()</a></code> </p>
+        </td>
+        <td><p>Range</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_real.html">torch_real()</a></code> </p>
+        </td>
+        <td><p>Real</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_reciprocal.html">torch_reciprocal()</a></code> </p>
+        </td>
+        <td><p>Reciprocal</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_relu.html">torch_relu()</a></code> </p>
+        </td>
+        <td><p>Relu</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_relu_.html">torch_relu_()</a></code> </p>
+        </td>
+        <td><p>Relu_</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_remainder.html">torch_remainder()</a></code> </p>
+        </td>
+        <td><p>Remainder</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_renorm.html">torch_renorm()</a></code> </p>
+        </td>
+        <td><p>Renorm</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_repeat_interleave.html">torch_repeat_interleave()</a></code> </p>
+        </td>
+        <td><p>Repeat_interleave</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_reshape.html">torch_reshape()</a></code> </p>
+        </td>
+        <td><p>Reshape</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_result_type.html">torch_result_type()</a></code> </p>
+        </td>
+        <td><p>Result_type</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_rfft.html">torch_rfft()</a></code> </p>
+        </td>
+        <td><p>Rfft</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_roll.html">torch_roll()</a></code> </p>
+        </td>
+        <td><p>Roll</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_rot90.html">torch_rot90()</a></code> </p>
+        </td>
+        <td><p>Rot90</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_round.html">torch_round()</a></code> </p>
+        </td>
+        <td><p>Round</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_rrelu_.html">torch_rrelu_()</a></code> </p>
+        </td>
+        <td><p>Rrelu_</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_rsqrt.html">torch_rsqrt()</a></code> </p>
+        </td>
+        <td><p>Rsqrt</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_selu.html">torch_selu()</a></code> </p>
+        </td>
+        <td><p>Selu</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_selu_.html">torch_selu_()</a></code> </p>
+        </td>
+        <td><p>Selu_</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_sigmoid.html">torch_sigmoid()</a></code> </p>
+        </td>
+        <td><p>Sigmoid</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_sign.html">torch_sign()</a></code> </p>
+        </td>
+        <td><p>Sign</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_sin.html">torch_sin()</a></code> </p>
+        </td>
+        <td><p>Sin</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_sinh.html">torch_sinh()</a></code> </p>
+        </td>
+        <td><p>Sinh</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_slogdet.html">torch_slogdet()</a></code> </p>
+        </td>
+        <td><p>Slogdet</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_solve.html">torch_solve()</a></code> </p>
+        </td>
+        <td><p>Solve</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_sort.html">torch_sort()</a></code> </p>
+        </td>
+        <td><p>Sort</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_sparse_coo_tensor.html">torch_sparse_coo_tensor()</a></code> </p>
+        </td>
+        <td><p>Sparse_coo_tensor</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_split.html">torch_split()</a></code> </p>
+        </td>
+        <td><p>Split</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_sqrt.html">torch_sqrt()</a></code> </p>
+        </td>
+        <td><p>Sqrt</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_square.html">torch_square()</a></code> </p>
+        </td>
+        <td><p>Square</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_squeeze.html">torch_squeeze()</a></code> </p>
+        </td>
+        <td><p>Squeeze</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_stack.html">torch_stack()</a></code> </p>
+        </td>
+        <td><p>Stack</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_std.html">torch_std()</a></code> </p>
+        </td>
+        <td><p>Std</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_std_mean.html">torch_std_mean()</a></code> </p>
+        </td>
+        <td><p>Std_mean</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_stft.html">torch_stft()</a></code> </p>
+        </td>
+        <td><p>Stft</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_sum.html">torch_sum()</a></code> </p>
+        </td>
+        <td><p>Sum</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_svd.html">torch_svd()</a></code> </p>
+        </td>
+        <td><p>Svd</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_symeig.html">torch_symeig()</a></code> </p>
+        </td>
+        <td><p>Symeig</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_t.html">torch_t()</a></code> </p>
+        </td>
+        <td><p>T</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_take.html">torch_take()</a></code> </p>
+        </td>
+        <td><p>Take</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_tan.html">torch_tan()</a></code> </p>
+        </td>
+        <td><p>Tan</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_tanh.html">torch_tanh()</a></code> </p>
+        </td>
+        <td><p>Tanh</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_tensor.html">torch_tensor()</a></code> </p>
+        </td>
+        <td><p>Converts R objects to a torch tensor</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_tensordot.html">torch_tensordot()</a></code> </p>
+        </td>
+        <td><p>Tensordot</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_threshold_.html">torch_threshold_()</a></code> </p>
+        </td>
+        <td><p>Threshold_</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_topk.html">torch_topk()</a></code> </p>
+        </td>
+        <td><p>Topk</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_trace.html">torch_trace()</a></code> </p>
+        </td>
+        <td><p>Trace</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_transpose.html">torch_transpose()</a></code> </p>
+        </td>
+        <td><p>Transpose</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_trapz.html">torch_trapz()</a></code> </p>
+        </td>
+        <td><p>Trapz</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_triangular_solve.html">torch_triangular_solve()</a></code> </p>
+        </td>
+        <td><p>Triangular_solve</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_tril.html">torch_tril()</a></code> </p>
+        </td>
+        <td><p>Tril</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_tril_indices.html">torch_tril_indices()</a></code> </p>
+        </td>
+        <td><p>Tril_indices</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_triu.html">torch_triu()</a></code> </p>
+        </td>
+        <td><p>Triu</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_triu_indices.html">torch_triu_indices()</a></code> </p>
+        </td>
+        <td><p>Triu_indices</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_true_divide.html">torch_true_divide()</a></code> </p>
+        </td>
+        <td><p>TRUE_divide</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_trunc.html">torch_trunc()</a></code> </p>
+        </td>
+        <td><p>Trunc</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_unbind.html">torch_unbind()</a></code> </p>
+        </td>
+        <td><p>Unbind</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_unique_consecutive.html">torch_unique_consecutive()</a></code> </p>
+        </td>
+        <td><p>Unique_consecutive</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_unsqueeze.html">torch_unsqueeze()</a></code> </p>
+        </td>
+        <td><p>Unsqueeze</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_var.html">torch_var()</a></code> </p>
+        </td>
+        <td><p>Var</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_var_mean.html">torch_var_mean()</a></code> </p>
+        </td>
+        <td><p>Var_mean</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_where.html">torch_where()</a></code> </p>
+        </td>
+        <td><p>Where</p></td>
+      </tr>
+    </tbody><tbody>
+      <tr>
+        <th colspan="2">
+          <h2 id="section-neural-network-modules" class="hasAnchor"><a href="#section-neural-network-modules" class="anchor"></a>Neural network modules</h2>
+          <p class="section-desc"></p>
+        </th>
+      </tr>
+      
+      
+    </tbody><tbody>
+      
+      
+      <tr>
+        
+        <td>
+          <p><code><a href="nn_adaptive_avg_pool1d.html">nn_adaptive_avg_pool1d()</a></code> </p>
+        </td>
+        <td><p>Applies a 1D adaptive average pooling over an input signal composed of several input planes.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_adaptive_avg_pool2d.html">nn_adaptive_avg_pool2d()</a></code> </p>
+        </td>
+        <td><p>Applies a 2D adaptive average pooling over an input signal composed of several input planes.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_adaptive_avg_pool3d.html">nn_adaptive_avg_pool3d()</a></code> </p>
+        </td>
+        <td><p>Applies a 3D adaptive average pooling over an input signal composed of several input planes.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_adaptive_log_softmax_with_loss.html">nn_adaptive_log_softmax_with_loss()</a></code> </p>
+        </td>
+        <td><p>AdaptiveLogSoftmaxWithLoss module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_adaptive_max_pool1d.html">nn_adaptive_max_pool1d()</a></code> </p>
+        </td>
+        <td><p>Applies a 1D adaptive max pooling over an input signal composed of several input planes.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_adaptive_max_pool2d.html">nn_adaptive_max_pool2d()</a></code> </p>
+        </td>
+        <td><p>Applies a 2D adaptive max pooling over an input signal composed of several input planes.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_adaptive_max_pool3d.html">nn_adaptive_max_pool3d()</a></code> </p>
+        </td>
+        <td><p>Applies a 3D adaptive max pooling over an input signal composed of several input planes.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_avg_pool1d.html">nn_avg_pool1d()</a></code> </p>
+        </td>
+        <td><p>Applies a 1D average pooling over an input signal composed of several
+input planes.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_avg_pool2d.html">nn_avg_pool2d()</a></code> </p>
+        </td>
+        <td><p>Applies a 2D average pooling over an input signal composed of several input
+planes.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_avg_pool3d.html">nn_avg_pool3d()</a></code> </p>
+        </td>
+        <td><p>Applies a 3D average pooling over an input signal composed of several input
+planes.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_batch_norm1d.html">nn_batch_norm1d()</a></code> </p>
+        </td>
+        <td><p>BatchNorm1D module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_batch_norm2d.html">nn_batch_norm2d()</a></code> </p>
+        </td>
+        <td><p>BatchNorm2D</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_bce_loss.html">nn_bce_loss()</a></code> </p>
+        </td>
+        <td><p>Binary cross entropy loss</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_bilinear.html">nn_bilinear()</a></code> </p>
+        </td>
+        <td><p>Bilinear module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_celu.html">nn_celu()</a></code> </p>
+        </td>
+        <td><p>CELU module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_conv1d.html">nn_conv1d()</a></code> </p>
+        </td>
+        <td><p>Conv1D module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_conv2d.html">nn_conv2d()</a></code> </p>
+        </td>
+        <td><p>Conv2D module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_conv3d.html">nn_conv3d()</a></code> </p>
+        </td>
+        <td><p>Conv3D module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_conv_transpose1d.html">nn_conv_transpose1d()</a></code> </p>
+        </td>
+        <td><p>ConvTranspose1D</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_conv_transpose2d.html">nn_conv_transpose2d()</a></code> </p>
+        </td>
+        <td><p>ConvTranpose2D module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_conv_transpose3d.html">nn_conv_transpose3d()</a></code> </p>
+        </td>
+        <td><p>ConvTranpose3D module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_cross_entropy_loss.html">nn_cross_entropy_loss()</a></code> </p>
+        </td>
+        <td><p>CrossEntropyLoss module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_dropout.html">nn_dropout()</a></code> </p>
+        </td>
+        <td><p>Dropout module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_dropout2d.html">nn_dropout2d()</a></code> </p>
+        </td>
+        <td><p>Dropout2D module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_dropout3d.html">nn_dropout3d()</a></code> </p>
+        </td>
+        <td><p>Dropout3D module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_elu.html">nn_elu()</a></code> </p>
+        </td>
+        <td><p>ELU module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_embedding.html">nn_embedding()</a></code> </p>
+        </td>
+        <td><p>Embedding module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_fractional_max_pool2d.html">nn_fractional_max_pool2d()</a></code> </p>
+        </td>
+        <td><p>Applies a 2D fractional max pooling over an input signal composed of several input planes.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_fractional_max_pool3d.html">nn_fractional_max_pool3d()</a></code> </p>
+        </td>
+        <td><p>Applies a 3D fractional max pooling over an input signal composed of several input planes.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_gelu.html">nn_gelu()</a></code> </p>
+        </td>
+        <td><p>GELU module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_glu.html">nn_glu()</a></code> </p>
+        </td>
+        <td><p>GLU module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_hardshrink.html">nn_hardshrink()</a></code> </p>
+        </td>
+        <td><p>Hardshwink module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_hardsigmoid.html">nn_hardsigmoid()</a></code> </p>
+        </td>
+        <td><p>Hardsigmoid module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_hardswish.html">nn_hardswish()</a></code> </p>
+        </td>
+        <td><p>Hardswish module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_hardtanh.html">nn_hardtanh()</a></code> </p>
+        </td>
+        <td><p>Hardtanh module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_identity.html">nn_identity()</a></code> </p>
+        </td>
+        <td><p>Identity module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_init_calculate_gain.html">nn_init_calculate_gain()</a></code> </p>
+        </td>
+        <td><p>Calculate gain</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_init_constant_.html">nn_init_constant_()</a></code> </p>
+        </td>
+        <td><p>Constant initialization</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_init_dirac_.html">nn_init_dirac_()</a></code> </p>
+        </td>
+        <td><p>Dirac initialization</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_init_eye_.html">nn_init_eye_()</a></code> </p>
+        </td>
+        <td><p>Eye initialization</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_init_kaiming_normal_.html">nn_init_kaiming_normal_()</a></code> </p>
+        </td>
+        <td><p>Kaiming normal initialization</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_init_kaiming_uniform_.html">nn_init_kaiming_uniform_()</a></code> </p>
+        </td>
+        <td><p>Kaiming uniform initialization</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_init_normal_.html">nn_init_normal_()</a></code> </p>
+        </td>
+        <td><p>Normal initialization</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_init_ones_.html">nn_init_ones_()</a></code> </p>
+        </td>
+        <td><p>Ones initialization</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_init_orthogonal_.html">nn_init_orthogonal_()</a></code> </p>
+        </td>
+        <td><p>Orthogonal initialization</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_init_sparse_.html">nn_init_sparse_()</a></code> </p>
+        </td>
+        <td><p>Sparse initialization</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_init_trunc_normal_.html">nn_init_trunc_normal_()</a></code> </p>
+        </td>
+        <td><p>Truncated normal initialization</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_init_uniform_.html">nn_init_uniform_()</a></code> </p>
+        </td>
+        <td><p>Uniform initialization</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_init_xavier_normal_.html">nn_init_xavier_normal_()</a></code> </p>
+        </td>
+        <td><p>Xavier normal initialization</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_init_xavier_uniform_.html">nn_init_xavier_uniform_()</a></code> </p>
+        </td>
+        <td><p>Xavier uniform initialization</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_init_zeros_.html">nn_init_zeros_()</a></code> </p>
+        </td>
+        <td><p>Zeros initialization</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_leaky_relu.html">nn_leaky_relu()</a></code> </p>
+        </td>
+        <td><p>LeakyReLU module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_linear.html">nn_linear()</a></code> </p>
+        </td>
+        <td><p>Linear module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_log_sigmoid.html">nn_log_sigmoid()</a></code> </p>
+        </td>
+        <td><p>LogSigmoid module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_log_softmax.html">nn_log_softmax()</a></code> </p>
+        </td>
+        <td><p>LogSoftmax module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_lp_pool1d.html">nn_lp_pool1d()</a></code> </p>
+        </td>
+        <td><p>Applies a 1D power-average pooling over an input signal composed of several input
+planes.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_lp_pool2d.html">nn_lp_pool2d()</a></code> </p>
+        </td>
+        <td><p>Applies a 2D power-average pooling over an input signal composed of several input
+planes.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_max_pool1d.html">nn_max_pool1d()</a></code> </p>
+        </td>
+        <td><p>MaxPool1D module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_max_pool2d.html">nn_max_pool2d()</a></code> </p>
+        </td>
+        <td><p>MaxPool2D module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_max_pool3d.html">nn_max_pool3d()</a></code> </p>
+        </td>
+        <td><p>Applies a 3D max pooling over an input signal composed of several input
+planes.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_max_unpool1d.html">nn_max_unpool1d()</a></code> </p>
+        </td>
+        <td><p>Computes a partial inverse of <code>MaxPool1d</code>.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_max_unpool2d.html">nn_max_unpool2d()</a></code> </p>
+        </td>
+        <td><p>Computes a partial inverse of <code>MaxPool2d</code>.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_max_unpool3d.html">nn_max_unpool3d()</a></code> </p>
+        </td>
+        <td><p>Computes a partial inverse of <code>MaxPool3d</code>.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_module.html">nn_module()</a></code> </p>
+        </td>
+        <td><p>Base class for all neural network modules.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_module_list.html">nn_module_list()</a></code> </p>
+        </td>
+        <td><p>Holds submodules in a list.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_multihead_attention.html">nn_multihead_attention()</a></code> </p>
+        </td>
+        <td><p>MultiHead attention</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_prelu.html">nn_prelu()</a></code> </p>
+        </td>
+        <td><p>PReLU module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_relu.html">nn_relu()</a></code> </p>
+        </td>
+        <td><p>ReLU module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_relu6.html">nn_relu6()</a></code> </p>
+        </td>
+        <td><p>ReLu6 module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_rnn.html">nn_rnn()</a></code> </p>
+        </td>
+        <td><p>RNN module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_rrelu.html">nn_rrelu()</a></code> </p>
+        </td>
+        <td><p>RReLU module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_selu.html">nn_selu()</a></code> </p>
+        </td>
+        <td><p>SELU module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_sequential.html">nn_sequential()</a></code> </p>
+        </td>
+        <td><p>A sequential container</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_sigmoid.html">nn_sigmoid()</a></code> </p>
+        </td>
+        <td><p>Sigmoid module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_softmax.html">nn_softmax()</a></code> </p>
+        </td>
+        <td><p>Softmax module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_softmax2d.html">nn_softmax2d()</a></code> </p>
+        </td>
+        <td><p>Softmax2d module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_softmin.html">nn_softmin()</a></code> </p>
+        </td>
+        <td><p>Softmin</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_softplus.html">nn_softplus()</a></code> </p>
+        </td>
+        <td><p>Softplus module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_softshrink.html">nn_softshrink()</a></code> </p>
+        </td>
+        <td><p>Softshrink module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_softsign.html">nn_softsign()</a></code> </p>
+        </td>
+        <td><p>Softsign module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_tanh.html">nn_tanh()</a></code> </p>
+        </td>
+        <td><p>Tanh module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_tanhshrink.html">nn_tanhshrink()</a></code> </p>
+        </td>
+        <td><p>Tanhshrink module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_threshold.html">nn_threshold()</a></code> </p>
+        </td>
+        <td><p>Threshoold module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_utils_rnn_pack_padded_sequence.html">nn_utils_rnn_pack_padded_sequence()</a></code> </p>
+        </td>
+        <td><p>Packs a Tensor containing padded sequences of variable length.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_utils_rnn_pack_sequence.html">nn_utils_rnn_pack_sequence()</a></code> </p>
+        </td>
+        <td><p>Packs a list of variable length Tensors</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_utils_rnn_pad_packed_sequence.html">nn_utils_rnn_pad_packed_sequence()</a></code> </p>
+        </td>
+        <td><p>Pads a packed batch of variable length sequences.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_utils_rnn_pad_sequence.html">nn_utils_rnn_pad_sequence()</a></code> </p>
+        </td>
+        <td><p>Pad a list of variable length Tensors with <code>padding_value</code></p></td>
+      </tr>
+    </tbody><tbody>
+      <tr>
+        <th colspan="2">
+          <h2 id="section-neural-networks-functional-module" class="hasAnchor"><a href="#section-neural-networks-functional-module" class="anchor"></a>Neural networks functional module</h2>
+          <p class="section-desc"></p>
+        </th>
+      </tr>
+      
+      
+    </tbody><tbody>
+      
+      
+      <tr>
+        
+        <td>
+          <p><code><a href="nnf_adaptive_avg_pool1d.html">nnf_adaptive_avg_pool1d()</a></code> </p>
+        </td>
+        <td><p>Adaptive_avg_pool1d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_adaptive_avg_pool2d.html">nnf_adaptive_avg_pool2d()</a></code> </p>
+        </td>
+        <td><p>Adaptive_avg_pool2d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_adaptive_avg_pool3d.html">nnf_adaptive_avg_pool3d()</a></code> </p>
+        </td>
+        <td><p>Adaptive_avg_pool3d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_adaptive_max_pool1d.html">nnf_adaptive_max_pool1d()</a></code> </p>
+        </td>
+        <td><p>Adaptive_max_pool1d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_adaptive_max_pool2d.html">nnf_adaptive_max_pool2d()</a></code> </p>
+        </td>
+        <td><p>Adaptive_max_pool2d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_adaptive_max_pool3d.html">nnf_adaptive_max_pool3d()</a></code> </p>
+        </td>
+        <td><p>Adaptive_max_pool3d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_affine_grid.html">nnf_affine_grid()</a></code> </p>
+        </td>
+        <td><p>Affine_grid</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_alpha_dropout.html">nnf_alpha_dropout()</a></code> </p>
+        </td>
+        <td><p>Alpha_dropout</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_avg_pool1d.html">nnf_avg_pool1d()</a></code> </p>
+        </td>
+        <td><p>Avg_pool1d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_avg_pool2d.html">nnf_avg_pool2d()</a></code> </p>
+        </td>
+        <td><p>Avg_pool2d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_avg_pool3d.html">nnf_avg_pool3d()</a></code> </p>
+        </td>
+        <td><p>Avg_pool3d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_batch_norm.html">nnf_batch_norm()</a></code> </p>
+        </td>
+        <td><p>Batch_norm</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_bilinear.html">nnf_bilinear()</a></code> </p>
+        </td>
+        <td><p>Bilinear</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_binary_cross_entropy.html">nnf_binary_cross_entropy()</a></code> </p>
+        </td>
+        <td><p>Binary_cross_entropy</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_binary_cross_entropy_with_logits.html">nnf_binary_cross_entropy_with_logits()</a></code> </p>
+        </td>
+        <td><p>Binary_cross_entropy_with_logits</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_celu.html">nnf_celu()</a></code> <code><a href="nnf_celu.html">nnf_celu_()</a></code> </p>
+        </td>
+        <td><p>Celu</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_conv1d.html">nnf_conv1d()</a></code> </p>
+        </td>
+        <td><p>Conv1d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_conv2d.html">nnf_conv2d()</a></code> </p>
+        </td>
+        <td><p>Conv2d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_conv3d.html">nnf_conv3d()</a></code> </p>
+        </td>
+        <td><p>Conv3d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_conv_tbc.html">nnf_conv_tbc()</a></code> </p>
+        </td>
+        <td><p>Conv_tbc</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_conv_transpose1d.html">nnf_conv_transpose1d()</a></code> </p>
+        </td>
+        <td><p>Conv_transpose1d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_conv_transpose2d.html">nnf_conv_transpose2d()</a></code> </p>
+        </td>
+        <td><p>Conv_transpose2d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_conv_transpose3d.html">nnf_conv_transpose3d()</a></code> </p>
+        </td>
+        <td><p>Conv_transpose3d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_cosine_embedding_loss.html">nnf_cosine_embedding_loss()</a></code> </p>
+        </td>
+        <td><p>Cosine_embedding_loss</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_cosine_similarity.html">nnf_cosine_similarity()</a></code> </p>
+        </td>
+        <td><p>Cosine_similarity</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_cross_entropy.html">nnf_cross_entropy()</a></code> </p>
+        </td>
+        <td><p>Cross_entropy</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_ctc_loss.html">nnf_ctc_loss()</a></code> </p>
+        </td>
+        <td><p>Ctc_loss</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_dropout.html">nnf_dropout()</a></code> </p>
+        </td>
+        <td><p>Dropout</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_dropout2d.html">nnf_dropout2d()</a></code> </p>
+        </td>
+        <td><p>Dropout2d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_dropout3d.html">nnf_dropout3d()</a></code> </p>
+        </td>
+        <td><p>Dropout3d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_elu.html">nnf_elu()</a></code> <code><a href="nnf_elu.html">nnf_elu_()</a></code> </p>
+        </td>
+        <td><p>Elu</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_embedding.html">nnf_embedding()</a></code> </p>
+        </td>
+        <td><p>Embedding</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_embedding_bag.html">nnf_embedding_bag()</a></code> </p>
+        </td>
+        <td><p>Embedding_bag</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_fold.html">nnf_fold()</a></code> </p>
+        </td>
+        <td><p>Fold</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_fractional_max_pool2d.html">nnf_fractional_max_pool2d()</a></code> </p>
+        </td>
+        <td><p>Fractional_max_pool2d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_fractional_max_pool3d.html">nnf_fractional_max_pool3d()</a></code> </p>
+        </td>
+        <td><p>Fractional_max_pool3d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_gelu.html">nnf_gelu()</a></code> </p>
+        </td>
+        <td><p>Gelu</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_glu.html">nnf_glu()</a></code> </p>
+        </td>
+        <td><p>Glu</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_grid_sample.html">nnf_grid_sample()</a></code> </p>
+        </td>
+        <td><p>Grid_sample</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_group_norm.html">nnf_group_norm()</a></code> </p>
+        </td>
+        <td><p>Group_norm</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_gumbel_softmax.html">nnf_gumbel_softmax()</a></code> </p>
+        </td>
+        <td><p>Gumbel_softmax</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_hardshrink.html">nnf_hardshrink()</a></code> </p>
+        </td>
+        <td><p>Hardshrink</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_hardsigmoid.html">nnf_hardsigmoid()</a></code> </p>
+        </td>
+        <td><p>Hardsigmoid</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_hardswish.html">nnf_hardswish()</a></code> </p>
+        </td>
+        <td><p>Hardswish</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_hardtanh.html">nnf_hardtanh()</a></code> <code><a href="nnf_hardtanh.html">nnf_hardtanh_()</a></code> </p>
+        </td>
+        <td><p>Hardtanh</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_hinge_embedding_loss.html">nnf_hinge_embedding_loss()</a></code> </p>
+        </td>
+        <td><p>Hinge_embedding_loss</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_instance_norm.html">nnf_instance_norm()</a></code> </p>
+        </td>
+        <td><p>Instance_norm</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_interpolate.html">nnf_interpolate()</a></code> </p>
+        </td>
+        <td><p>Interpolate</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_kl_div.html">nnf_kl_div()</a></code> </p>
+        </td>
+        <td><p>Kl_div</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_l1_loss.html">nnf_l1_loss()</a></code> </p>
+        </td>
+        <td><p>L1_loss</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_layer_norm.html">nnf_layer_norm()</a></code> </p>
+        </td>
+        <td><p>Layer_norm</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_leaky_relu.html">nnf_leaky_relu()</a></code> </p>
+        </td>
+        <td><p>Leaky_relu</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_linear.html">nnf_linear()</a></code> </p>
+        </td>
+        <td><p>Linear</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_local_response_norm.html">nnf_local_response_norm()</a></code> </p>
+        </td>
+        <td><p>Local_response_norm</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_log_softmax.html">nnf_log_softmax()</a></code> </p>
+        </td>
+        <td><p>Log_softmax</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_logsigmoid.html">nnf_logsigmoid()</a></code> </p>
+        </td>
+        <td><p>Logsigmoid</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_lp_pool1d.html">nnf_lp_pool1d()</a></code> </p>
+        </td>
+        <td><p>Lp_pool1d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_lp_pool2d.html">nnf_lp_pool2d()</a></code> </p>
+        </td>
+        <td><p>Lp_pool2d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_margin_ranking_loss.html">nnf_margin_ranking_loss()</a></code> </p>
+        </td>
+        <td><p>Margin_ranking_loss</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_max_pool1d.html">nnf_max_pool1d()</a></code> </p>
+        </td>
+        <td><p>Max_pool1d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_max_pool2d.html">nnf_max_pool2d()</a></code> </p>
+        </td>
+        <td><p>Max_pool2d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_max_pool3d.html">nnf_max_pool3d()</a></code> </p>
+        </td>
+        <td><p>Max_pool3d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_max_unpool1d.html">nnf_max_unpool1d()</a></code> </p>
+        </td>
+        <td><p>Max_unpool1d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_max_unpool2d.html">nnf_max_unpool2d()</a></code> </p>
+        </td>
+        <td><p>Max_unpool2d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_max_unpool3d.html">nnf_max_unpool3d()</a></code> </p>
+        </td>
+        <td><p>Max_unpool3d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_mse_loss.html">nnf_mse_loss()</a></code> </p>
+        </td>
+        <td><p>Mse_loss</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_multi_head_attention_forward.html">nnf_multi_head_attention_forward()</a></code> </p>
+        </td>
+        <td><p>Multi head attention forward</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_multi_margin_loss.html">nnf_multi_margin_loss()</a></code> </p>
+        </td>
+        <td><p>Multi_margin_loss</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_multilabel_margin_loss.html">nnf_multilabel_margin_loss()</a></code> </p>
+        </td>
+        <td><p>Multilabel_margin_loss</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_multilabel_soft_margin_loss.html">nnf_multilabel_soft_margin_loss()</a></code> </p>
+        </td>
+        <td><p>Multilabel_soft_margin_loss</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_nll_loss.html">nnf_nll_loss()</a></code> </p>
+        </td>
+        <td><p>Nll_loss</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_normalize.html">nnf_normalize()</a></code> </p>
+        </td>
+        <td><p>Normalize</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_one_hot.html">nnf_one_hot()</a></code> </p>
+        </td>
+        <td><p>One_hot</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_pad.html">nnf_pad()</a></code> </p>
+        </td>
+        <td><p>Pad</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_pairwise_distance.html">nnf_pairwise_distance()</a></code> </p>
+        </td>
+        <td><p>Pairwise_distance</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_pdist.html">nnf_pdist()</a></code> </p>
+        </td>
+        <td><p>Pdist</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_pixel_shuffle.html">nnf_pixel_shuffle()</a></code> </p>
+        </td>
+        <td><p>Pixel_shuffle</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_poisson_nll_loss.html">nnf_poisson_nll_loss()</a></code> </p>
+        </td>
+        <td><p>Poisson_nll_loss</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_prelu.html">nnf_prelu()</a></code> </p>
+        </td>
+        <td><p>Prelu</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_relu.html">nnf_relu()</a></code> <code><a href="nnf_relu.html">nnf_relu_()</a></code> </p>
+        </td>
+        <td><p>Relu</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_relu6.html">nnf_relu6()</a></code> </p>
+        </td>
+        <td><p>Relu6</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_rrelu.html">nnf_rrelu()</a></code> <code><a href="nnf_rrelu.html">nnf_rrelu_()</a></code> </p>
+        </td>
+        <td><p>Rrelu</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_selu.html">nnf_selu()</a></code> <code><a href="nnf_selu.html">nnf_selu_()</a></code> </p>
+        </td>
+        <td><p>Selu</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_sigmoid.html">nnf_sigmoid()</a></code> </p>
+        </td>
+        <td><p>Sigmoid</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_smooth_l1_loss.html">nnf_smooth_l1_loss()</a></code> </p>
+        </td>
+        <td><p>Smooth_l1_loss</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_soft_margin_loss.html">nnf_soft_margin_loss()</a></code> </p>
+        </td>
+        <td><p>Soft_margin_loss</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_softmax.html">nnf_softmax()</a></code> </p>
+        </td>
+        <td><p>Softmax</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_softmin.html">nnf_softmin()</a></code> </p>
+        </td>
+        <td><p>Softmin</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_softplus.html">nnf_softplus()</a></code> </p>
+        </td>
+        <td><p>Softplus</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_softshrink.html">nnf_softshrink()</a></code> </p>
+        </td>
+        <td><p>Softshrink</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_softsign.html">nnf_softsign()</a></code> </p>
+        </td>
+        <td><p>Softsign</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_tanhshrink.html">nnf_tanhshrink()</a></code> </p>
+        </td>
+        <td><p>Tanhshrink</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_threshold.html">nnf_threshold()</a></code> <code><a href="nnf_threshold.html">nnf_threshold_()</a></code> </p>
+        </td>
+        <td><p>Threshold</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_triplet_margin_loss.html">nnf_triplet_margin_loss()</a></code> </p>
+        </td>
+        <td><p>Triplet_margin_loss</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_unfold.html">nnf_unfold()</a></code> </p>
+        </td>
+        <td><p>Unfold</p></td>
+      </tr>
+    </tbody><tbody>
+      <tr>
+        <th colspan="2">
+          <h2 id="section-optimizers" class="hasAnchor"><a href="#section-optimizers" class="anchor"></a>Optimizers</h2>
+          <p class="section-desc"></p>
+        </th>
+      </tr>
+      
+      
+    </tbody><tbody>
+      
+      
+      <tr>
+        
+        <td>
+          <p><code><a href="optim_adam.html">optim_adam()</a></code> </p>
+        </td>
+        <td><p>Implements Adam algorithm.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="optim_required.html">optim_required()</a></code> </p>
+        </td>
+        <td><p>Dummy value indicating a required value.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="optim_sgd.html">optim_sgd()</a></code> </p>
+        </td>
+        <td><p>SGD optimizer</p></td>
+      </tr>
+    </tbody><tbody>
+      <tr>
+        <th colspan="2">
+          <h2 id="section-datasets" class="hasAnchor"><a href="#section-datasets" class="anchor"></a>Datasets</h2>
+          <p class="section-desc"></p>
+        </th>
+      </tr>
+      
+      
+    </tbody><tbody>
+      
+      
+      <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="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="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="tensor_dataset.html">tensor_dataset()</a></code> </p>
+        </td>
+        <td><p>Dataset wrapping tensors.</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>
+    </tbody><tbody>
+      <tr>
+        <th colspan="2">
+          <h2 id="section-autograd" class="hasAnchor"><a href="#section-autograd" class="anchor"></a>Autograd</h2>
+          <p class="section-desc"></p>
+        </th>
+      </tr>
+      
+      
+    </tbody><tbody>
+      
+      
+      <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="with_no_grad.html">with_no_grad()</a></code> </p>
+        </td>
+        <td><p>Temporarily modify gradient recording.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="with_enable_grad.html">with_enable_grad()</a></code> </p>
+        </td>
+        <td><p>Enable grad</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="AutogradContext.html">AutogradContext</a></code> </p>
+        </td>
+        <td><p>Class representing the context.</p></td>
+      </tr>
+    </tbody><tbody>
+      <tr>
+        <th colspan="2">
+          <h2 id="section-cuda-utilities" class="hasAnchor"><a href="#section-cuda-utilities" class="anchor"></a>Cuda utilities</h2>
+          <p class="section-desc"></p>
+        </th>
+      </tr>
+      
+      
+    </tbody><tbody>
+      
+      
+      <tr>
+        
+        <td>
+          <p><code><a href="cuda_current_device.html">cuda_current_device()</a></code> </p>
+        </td>
+        <td><p>Returns the index of a currently selected device.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="cuda_device_count.html">cuda_device_count()</a></code> </p>
+        </td>
+        <td><p>Returns the number of GPUs available.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="cuda_is_available.html">cuda_is_available()</a></code> </p>
+        </td>
+        <td><p>Returns a bool indicating if CUDA is currently available.</p></td>
+      </tr>
+    </tbody><tbody>
+      <tr>
+        <th colspan="2">
+          <h2 id="section-installation" class="hasAnchor"><a href="#section-installation" class="anchor"></a>Installation</h2>
+          <p class="section-desc"></p>
+        </th>
+      </tr>
+      
+      
+    </tbody><tbody>
+      
+      
+      <tr>
+        
+        <td>
+          <p><code><a href="install_torch.html">install_torch()</a></code> </p>
+        </td>
+        <td><p>Install Torch</p></td>
+      </tr>
+    </tbody>
+    </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>
+
+
+      <footer>
+      <div class="copyright">
+  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+</div>
+
+<div class="pkgdown">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
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+        <span class="icon-bar"></span>
+      </button>
+      <span class="navbar-brand">
+        <a class="navbar-link" href="../index.html">torch</a>
+        <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.0.3.9000</span>
+      </span>
+    </div>
+
+    <div id="navbar" class="navbar-collapse collapse">
+      <ul class="nav navbar-nav">
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+     
+  </a>
+</li>
+<li class="dropdown">
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
+    Getting started
+     
+    <span class="caret"></span>
+  </a>
+  <ul class="dropdown-menu" role="menu">
+    <li class="dropdown-header">Beginers guide</li>
+    <li>
+      <a href="../articles/getting-started/warmup.html">Warm-up</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/tensors.html">Tensors</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/tensors-and-autograd.html">Tensors and autograd</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/optim.html">optim: optimizers in torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/custom-nn.html">Custom nn modules</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/control-flow-and-weight-sharing.html">Control flow &amp; Weight sharing</a>
+    </li>
+    <li class="dropdown-header">Torch Mechanics</li>
+    <li>
+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/neural-networks.html">Neural networks</a>
+    </li>
+  </ul>
+</li>
+<li class="dropdown">
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+    Articles
+     
+    <span class="caret"></span>
+  </a>
+  <ul class="dropdown-menu" role="menu">
+    <li class="dropdown-header">Tensors</li>
+    <li>
+      <a href="../articles/tensor-creation.html">Creating tensors</a>
+    </li>
+    <li>
+      <a href="../articles/indexing.html">Indexing</a>
+    </li>
+    <li>
+      <a href="../articles/tensor/index.html">Tensor class</a>
+    </li>
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+    <li>
+      <a href="../articles/loading-data.html">Loading Data</a>
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+      <a href="../articles/using-autograd.html">Using autograd</a>
+    </li>
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+      <a href="../articles/extending-autograd.html">Extending autograd</a>
+    </li>
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+</li>
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <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>(
+  version = <span class='st'>"1.5.0"</span>,
+  type = <span class='fu'>install_type</span>(version = <span class='kw'>version</span>),
+  reinstall = <span class='fl'>FALSE</span>,
+  path = <span class='fu'>install_path</span>(),
+  <span class='kw'>...</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>version</th>
+      <td><p>The Torch version to install.</p></td>
+    </tr>
+    <tr>
+      <th>type</th>
+      <td><p>The installation type for Torch. Valid values are <code>"cpu"</code> or the 'CUDA' version.</p></td>
+    </tr>
+    <tr>
+      <th>reinstall</th>
+      <td><p>Re-install Torch even if its already installed?</p></td>
+    </tr>
+    <tr>
+      <th>path</th>
+      <td><p>Optional path to install or check for an already existing installation.</p></td>
+    </tr>
+    <tr>
+      <th>...</th>
+      <td><p>other optional arguments (like <code>load</code> for manual installation.)</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>When using <code>path</code> to install in a specific location, make sure the <code>TORCH_HOME</code> environment
+variable is set to this same path to reuse this installation. The <code>TORCH_INSTALL</code> environment
+variable can be set to <code>0</code> to prevent auto-installing torch and <code>TORCH_LOAD</code> set to <code>0</code>
+to avoid loading dependencies automatically. These environment variables are meant for advanced use
+cases and troubleshootinng only.</p>
+
+  </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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+  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
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+
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+    <h1>Checks if the object is 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>is_dataloader.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Checks if the object is a dataloader</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>is_dataloader</span>(<span class='kw'>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 check</p></td>
+    </tr>
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+
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diff --git a/static/docs/dev/reference/is_torch_device.html b/static/docs/dev/reference/is_torch_device.html
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+    <div class="page-header">
+    <h1>Checks if object is a device</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/device.R'><code>R/device.R</code></a></small>
+    <div class="hidden name"><code>is_torch_device.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Checks if object is a device</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>is_torch_device</span>(<span class='kw'>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 check</p></td>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/dev/reference/is_torch_dtype.html b/static/docs/dev/reference/is_torch_dtype.html
new file mode 100644
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+++ b/static/docs/dev/reference/is_torch_dtype.html
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+    <h1>Check if object is a torch data type</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/dtype.R'><code>R/dtype.R</code></a></small>
+    <div class="hidden name"><code>is_torch_dtype.Rd</code></div>
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+
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+    <p>Check if object is a torch data type</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>is_torch_dtype</span>(<span class='kw'>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>
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+      <th>x</th>
+      <td><p>object to check.</p></td>
+    </tr>
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+    <h1>Check if an object is a torch layout.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/layout.R'><code>R/layout.R</code></a></small>
+    <div class="hidden name"><code>is_torch_layout.Rd</code></div>
+    </div>
+
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+    <p>Check if an object is a torch layout.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>is_torch_layout</span>(<span class='kw'>x</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
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+    <h1>Check if an object is a memory format</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/memory_format.R'><code>R/memory_format.R</code></a></small>
+    <div class="hidden name"><code>is_torch_memory_format.Rd</code></div>
+    </div>
+
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+    <p>Check if an object is a memory format</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>is_torch_memory_format</span>(<span class='kw'>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 check</p></td>
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+    <h1>Checks if an object is a QScheme</h1>
+    <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>is_torch_qscheme.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Checks if an object is a QScheme</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>is_torch_qscheme</span>(<span class='kw'>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 check</p></td>
+    </tr>
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+
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+    <div class="hidden name"><code>is_undefined_tensor.Rd</code></div>
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+
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+    <p>Checks if a tensor is undefined</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>is_undefined_tensor</span>(<span class='kw'>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>tensor to check</p></td>
+    </tr>
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+
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new file mode 100644
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+    <div class="page-header">
+    <h1>Load a state dict file</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/save.R'><code>R/save.R</code></a></small>
+    <div class="hidden name"><code>load_state_dict.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>This function should only be used to load models saved in python.
+For it to work correctly you need to use <code>torch.save</code> with the flag:
+<code>_use_new_zipfile_serialization=True</code> and also remove all <code>nn.Parameter</code>
+classes from the tensors in the dict.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>load_state_dict</span>(<span class='kw'>path</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>path</th>
+      <td><p>to the state dict file</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+
+    <p>a named list of tensors.</p>
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>The above might change with development of <a href='https://github.com/pytorch/pytorch/issues/37213'>this</a>
+in pytorch's C++ api.</p>
+
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diff --git a/static/docs/dev/reference/nn_adaptive_avg_pool1d.html b/static/docs/dev/reference/nn_adaptive_avg_pool1d.html
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+The number of output features is equal to the number of input planes." />
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Applies a 1D adaptive average pooling over an input signal composed of several input planes.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_adaptive_avg_pool1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>The output size is H, for any input size.
+The number of output features is equal to the number of input planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_adaptive_avg_pool1d</span>(<span class='kw'>output_size</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>output_size</th>
+      <td><p>the target output size H</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'># target output size of 5</span>
+<span class='kw'>m</span> <span class='op'>=</span> <span class='fu'>nn_adaptive_avg_pool1d</span>(<span class='fl'>5</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>8</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+<div class="row">
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+    <div class="page-header">
+    <h1>Applies a 2D adaptive average pooling over an input signal composed of several input planes.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_adaptive_avg_pool2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>The output is of size H x W, for any input size.
+The number of output features is equal to the number of input planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_adaptive_avg_pool2d</span>(<span class='kw'>output_size</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>output_size</th>
+      <td><p>the target output size of the image of the form H x W.
+Can be a tuple (H, W) or a single H for a square image H x H.
+H and W can be either a <code>int</code>, or <code>NULL</code> which means the size will
+be the same as that of the input.</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'># target output size of 5x7</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_avg_pool2d</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>,<span class='fl'>7</span>))
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>8</span>, <span class='fl'>9</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+<span class='co'># target output size of 7x7 (square)</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_avg_pool2d</span>(<span class='fl'>7</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>10</span>, <span class='fl'>9</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </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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+The number of output features is equal to the number of input planes." />
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Applies a 3D adaptive average pooling over an input signal composed of several input planes.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_adaptive_avg_pool3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>The output is of size D x H x W, for any input size.
+The number of output features is equal to the number of input planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_adaptive_avg_pool3d</span>(<span class='kw'>output_size</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>output_size</th>
+      <td><p>the target output size of the form D x H x W.
+Can be a tuple (D, H, W) or a single number D for a cube D x D x D.
+D, H and W can be either a <code>int</code>, or <code>None</code> which means the size will
+be the same as that of the input.</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'># target output size of 5x7x9</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_avg_pool3d</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>,<span class='fl'>7</span>,<span class='fl'>9</span>))
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>8</span>, <span class='fl'>9</span>, <span class='fl'>10</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+<span class='co'># target output size of 7x7x7 (cube)</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_avg_pool3d</span>(<span class='fl'>7</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>10</span>, <span class='fl'>9</span>, <span class='fl'>8</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </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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+Efficient softmax approximation for GPUs by Edouard Grave, Armand Joulin, Moustapha Cissé, David Grangier, and Hervé Jégou" />
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+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>AdaptiveLogSoftmaxWithLoss module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn_adaptive.R'><code>R/nn_adaptive.R</code></a></small>
+    <div class="hidden name"><code>nn_adaptive_log_softmax_with_loss.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Efficient softmax approximation as described in
+<a href='https://arxiv.org/abs/1609.04309'>Efficient softmax approximation for GPUs by Edouard Grave, Armand Joulin, Moustapha Cissé, David Grangier, and Hervé Jégou</a></p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_adaptive_log_softmax_with_loss</span>(
+  <span class='kw'>in_features</span>,
+  <span class='kw'>n_classes</span>,
+  <span class='kw'>cutoffs</span>,
+  div_value = <span class='fl'>4</span>,
+  head_bias = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>in_features</th>
+      <td><p>(int): Number of features in the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>n_classes</th>
+      <td><p>(int): Number of classes in the dataset</p></td>
+    </tr>
+    <tr>
+      <th>cutoffs</th>
+      <td><p>(Sequence): Cutoffs used to assign targets to their buckets</p></td>
+    </tr>
+    <tr>
+      <th>div_value</th>
+      <td><p>(float, optional): value used as an exponent to compute sizes
+of the clusters. Default: 4.0</p></td>
+    </tr>
+    <tr>
+      <th>head_bias</th>
+      <td><p>(bool, optional): If <code>True</code>, adds a bias term to the 'head' of the
+adaptive softmax. Default: <code>False</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+
+    <p><code>NamedTuple</code> with <code>output</code> and <code>loss</code> fields:</p><ul>
+<li><p><strong>output</strong> is a Tensor of size <code>N</code> containing computed target
+log probabilities for each example</p></li>
+<li><p><strong>loss</strong> is a Scalar representing the computed negative
+log likelihood loss</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>Adaptive softmax is an approximate strategy for training models with large
+output spaces. It is most effective when the label distribution is highly
+imbalanced, for example in natural language modelling, where the word
+frequency distribution approximately follows the Zipf's law.</p>
+<p>Adaptive softmax partitions the labels into several clusters, according to
+their frequency. These clusters may contain different number of targets
+each.</p>
+<p>Additionally, clusters containing less frequent labels assign lower
+dimensional embeddings to those labels, which speeds up the computation.
+For each minibatch, only clusters for which at least one target is
+present are evaluated.</p>
+<p>The idea is that the clusters which are accessed frequently
+(like the first one, containing most frequent labels), should also be cheap
+to compute -- that is, contain a small number of assigned labels.
+We highly recommend taking a look at the original paper for more details.</p><ul>
+<li><p><code>cutoffs</code> should be an ordered Sequence of integers sorted
+in the increasing order.
+It controls number of clusters and the partitioning of targets into
+clusters. For example setting <code>cutoffs = c(10, 100, 1000)</code>
+means that first <code>10</code> targets will be assigned
+to the 'head' of the adaptive softmax, targets <code>11, 12, ..., 100</code> will be
+assigned to the first cluster, and targets <code>101, 102, ..., 1000</code> will be
+assigned to the second cluster, while targets
+<code>1001, 1002, ..., n_classes - 1</code> will be assigned
+to the last, third cluster.</p></li>
+<li><p><code>div_value</code> is used to compute the size of each additional cluster,
+which is given as
+\(\left\lfloor\frac{\mbox{in\_features}}{\mbox{div\_value}^{idx}}\right\rfloor\),
+where \(idx\) is the cluster index (with clusters
+for less frequent words having larger indices,
+and indices starting from \(1\)).</p></li>
+<li><p><code>head_bias</code> if set to True, adds a bias term to the 'head' of the
+adaptive softmax. See paper for details. Set to False in the official
+implementation.</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>This module returns a <code>NamedTuple</code> with <code>output</code>
+and <code>loss</code> fields. See further documentation for details.</p>
+<p>To compute log-probabilities for all classes, the <code>log_prob</code>
+method can be used.</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p>Labels passed as inputs to this module should be sorted according to
+their frequency. This means that the most frequent label should be
+represented by the index <code>0</code>, and the least frequent
+label should be represented by the index <code>n_classes - 1</code>.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>input: \((N, \mbox{in\_features})\)</p></li>
+<li><p>target: \((N)\) where each value satisfies \(0 &lt;= \mbox{target[i]} &lt;= \mbox{n\_classes}\)</p></li>
+<li><p>output1: \((N)\)</p></li>
+<li><p>output2: <code>Scalar</code></p></li>
+</ul>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nn_adaptive_max_pool1d.html b/static/docs/dev/reference/nn_adaptive_max_pool1d.html
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index 000000000..4bc5e5a01
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+    <div class="page-header">
+    <h1>Applies a 1D adaptive max pooling over an input signal composed of several input planes.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_adaptive_max_pool1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>The output size is H, for any input size.
+The number of output features is equal to the number of input planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_adaptive_max_pool1d</span>(<span class='kw'>output_size</span>, return_indices = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>output_size</th>
+      <td><p>the target output size H</p></td>
+    </tr>
+    <tr>
+      <th>return_indices</th>
+      <td><p>if <code>TRUE</code>, will return the indices along with the outputs.
+Useful to pass to <code><a href='nn_max_unpool1d.html'>nn_max_unpool1d()</a></code>. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'># target output size of 5</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_max_pool1d</span>(<span class='fl'>5</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>8</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Applies a 2D adaptive max pooling over an input signal composed of several input planes.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_adaptive_max_pool2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>The output is of size H x W, for any input size.
+The number of output features is equal to the number of input planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_adaptive_max_pool2d</span>(<span class='kw'>output_size</span>, return_indices = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>output_size</th>
+      <td><p>the target output size of the image of the form H x W.
+Can be a tuple <code>(H, W)</code> or a single H for a square image H x H.
+H and W can be either a <code>int</code>, or <code>None</code> which means the size will
+be the same as that of the input.</p></td>
+    </tr>
+    <tr>
+      <th>return_indices</th>
+      <td><p>if <code>TRUE</code>, will return the indices along with the outputs.
+Useful to pass to <code><a href='nn_max_unpool2d.html'>nn_max_unpool2d()</a></code>. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'># target output size of 5x7</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_max_pool2d</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>,<span class='fl'>7</span>))
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>8</span>, <span class='fl'>9</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+<span class='co'># target output size of 7x7 (square)</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_max_pool2d</span>(<span class='fl'>7</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>10</span>, <span class='fl'>9</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+      <a href="../articles/using-autograd.html">Using autograd</a>
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+<li class="dropdown-header">Examples</li>
+<li>
+  <a href="../reference/index.html">Reference</a>
+</li>
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Applies a 3D adaptive max pooling over an input signal composed of several input planes.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_adaptive_max_pool3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>The output is of size D x H x W, for any input size.
+The number of output features is equal to the number of input planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_adaptive_max_pool3d</span>(<span class='kw'>output_size</span>, return_indices = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>output_size</th>
+      <td><p>the target output size of the image of the form D x H x W.
+Can be a tuple (D, H, W) or a single D for a cube D x D x D.
+D, H and W can be either a <code>int</code>, or <code>None</code> which means the size will
+be the same as that of the input.</p></td>
+    </tr>
+    <tr>
+      <th>return_indices</th>
+      <td><p>if <code>TRUE</code>, will return the indices along with the outputs.
+Useful to pass to <code><a href='nn_max_unpool3d.html'>nn_max_unpool3d()</a></code>. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'># target output size of 5x7x9</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_max_pool3d</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>,<span class='fl'>7</span>,<span class='fl'>9</span>))
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>8</span>, <span class='fl'>9</span>, <span class='fl'>10</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+<span class='co'># target output size of 7x7x7 (cube)</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_max_pool3d</span>(<span class='fl'>7</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>10</span>, <span class='fl'>9</span>, <span class='fl'>8</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Applies a 1D average pooling over an input signal composed of several
+input planes.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_avg_pool1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>In the simplest case, the output value of the layer with input size \((N, C, L)\),
+output \((N, C, L_{out})\) and <code>kernel_size</code> \(k\)
+can be precisely described as:</p>
+<p>$$
+  \mbox{out}(N_i, C_j, l) = \frac{1}{k} \sum_{m=0}^{k-1}
+\mbox{input}(N_i, C_j, \mbox{stride} \times l + m)
+$$</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_avg_pool1d</span>(
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='kw'>NULL</span>,
+  padding = <span class='fl'>0</span>,
+  ceil_mode = <span class='fl'>FALSE</span>,
+  count_include_pad = <span class='fl'>TRUE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>the size of the window</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit zero padding to be added on both sides</p></td>
+    </tr>
+    <tr>
+      <th>ceil_mode</th>
+      <td><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
+    </tr>
+    <tr>
+      <th>count_include_pad</th>
+      <td><p>when TRUE, will include the zero-padding in the averaging calculation</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on both sides
+for <code>padding</code> number of points.</p>
+<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code> can each be
+an <code>int</code> or a one-element tuple.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C, L_{in})\)</p></li>
+<li><p>Output: \((N, C, L_{out})\), where</p></li>
+</ul>
+
+<p>$$
+  L_{out} = \left\lfloor \frac{L_{in} +
+      2 \times \mbox{padding} - \mbox{kernel\_size}}{\mbox{stride}} + 1\right\rfloor
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+  
+<span class='co'># pool with window of size=3, stride=2</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_avg_pool1d</span>(<span class='fl'>3</span>, stride=<span class='fl'>2</span>)
+<span class='fu'>m</span>(<span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>8</span>))
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;   0.8779 -0.8918 -0.1521
+#&gt; [ CPUFloatType{1,1,3} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+$$
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+    <div class="page-header">
+    <h1>Applies a 2D average pooling over an input signal composed of several input
+planes.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_avg_pool2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>In the simplest case, the output value of the layer with input size \((N, C, H, W)\),
+output \((N, C, H_{out}, W_{out})\) and <code>kernel_size</code> \((kH, kW)\)
+can be precisely described as:</p>
+<p>$$
+  out(N_i, C_j, h, w)  = \frac{1}{kH * kW} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1}
+input(N_i, C_j, stride[0] \times h + m, stride[1] \times w + n)
+$$</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_avg_pool2d</span>(
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='kw'>NULL</span>,
+  padding = <span class='fl'>0</span>,
+  ceil_mode = <span class='fl'>FALSE</span>,
+  count_include_pad = <span class='fl'>TRUE</span>,
+  divisor_override = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>the size of the window</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit zero padding to be added on both sides</p></td>
+    </tr>
+    <tr>
+      <th>ceil_mode</th>
+      <td><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
+    </tr>
+    <tr>
+      <th>count_include_pad</th>
+      <td><p>when TRUE, will include the zero-padding in the averaging calculation</p></td>
+    </tr>
+    <tr>
+      <th>divisor_override</th>
+      <td><p>if specified, it will be used as divisor, otherwise <code>kernel_size</code> will be used</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on both sides
+for <code>padding</code> number of points.</p>
+<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code> can either be:</p><ul>
+<li><p>a single <code>int</code> -- in which case the same value is used for the height and width dimension</p></li>
+<li><p>a <code>tuple</code> of two ints -- in which case, the first <code>int</code> is used for the height dimension,
+and the second <code>int</code> for the width dimension</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C, H_{in}, W_{in})\)</p></li>
+<li><p>Output: \((N, C, H_{out}, W_{out})\), where</p></li>
+</ul>
+
+<p>$$
+  H_{out} = \left\lfloor\frac{H_{in}  + 2 \times \mbox{padding}[0] -
+      \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor
+$$
+$$
+  W_{out} = \left\lfloor\frac{W_{in}  + 2 \times \mbox{padding}[1] -
+      \mbox{kernel\_size}[1]}{\mbox{stride}[1]} + 1\right\rfloor
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+  
+<span class='co'># pool of square window of size=3, stride=2</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_avg_pool2d</span>(<span class='fl'>3</span>, stride=<span class='fl'>2</span>)
+<span class='co'># pool of non-square window</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_avg_pool2d</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>), stride=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>))
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>32</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
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+<meta property="og:description" content="In the simplest case, the output value of the layer with input size \((N, C, D, H, W)\),
+output \((N, C, D_{out}, H_{out}, W_{out})\) and kernel_size \((kD, kH, kW)\)
+can be precisely described as:
+$$
+\begin{array}{ll}
+\mbox{out}(N_i, C_j, d, h, w) = &amp;amp; \sum_{k=0}^{kD-1} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1} \\
+&amp;amp; \frac{\mbox{input}(N_i, C_j, \mbox{stride}[0] \times d + k, \mbox{stride}[1] \times h + m, \mbox{stride}[2] \times w + n)}{kD \times kH \times kW}
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+
+      
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Applies a 3D average pooling over an input signal composed of several input
+planes.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_avg_pool3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>In the simplest case, the output value of the layer with input size \((N, C, D, H, W)\),
+output \((N, C, D_{out}, H_{out}, W_{out})\) and <code>kernel_size</code> \((kD, kH, kW)\)
+can be precisely described as:</p>
+<p>$$
+\begin{array}{ll}
+\mbox{out}(N_i, C_j, d, h, w) = &amp; \sum_{k=0}^{kD-1} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1} \\
+&amp; \frac{\mbox{input}(N_i, C_j, \mbox{stride}[0] \times d + k, \mbox{stride}[1] \times h + m, \mbox{stride}[2] \times w + n)}{kD \times kH \times kW}
+\end{array}
+$$</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_avg_pool3d</span>(
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='kw'>NULL</span>,
+  padding = <span class='fl'>0</span>,
+  ceil_mode = <span class='fl'>FALSE</span>,
+  count_include_pad = <span class='fl'>TRUE</span>,
+  divisor_override = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>the size of the window</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit zero padding to be added on all three sides</p></td>
+    </tr>
+    <tr>
+      <th>ceil_mode</th>
+      <td><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
+    </tr>
+    <tr>
+      <th>count_include_pad</th>
+      <td><p>when TRUE, will include the zero-padding in the averaging calculation</p></td>
+    </tr>
+    <tr>
+      <th>divisor_override</th>
+      <td><p>if specified, it will be used as divisor, otherwise <code>kernel_size</code> will be used</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on all three sides
+for <code>padding</code> number of points.</p>
+<p>The parameters <code>kernel_size</code>, <code>stride</code> can either be:</p><ul>
+<li><p>a single <code>int</code> -- in which case the same value is used for the depth, height and width dimension</p></li>
+<li><p>a <code>tuple</code> of three ints -- in which case, the first <code>int</code> is used for the depth dimension,
+the second <code>int</code> for the height dimension and the third <code>int</code> for the width dimension</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C, D_{in}, H_{in}, W_{in})\)</p></li>
+<li><p>Output: \((N, C, D_{out}, H_{out}, W_{out})\), where</p></li>
+</ul>
+
+<p>$$
+  D_{out} = \left\lfloor\frac{D_{in} + 2 \times \mbox{padding}[0] -
+      \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor
+$$
+$$
+  H_{out} = \left\lfloor\frac{H_{in} + 2 \times \mbox{padding}[1] -
+      \mbox{kernel\_size}[1]}{\mbox{stride}[1]} + 1\right\rfloor
+$$
+$$
+  W_{out} = \left\lfloor\frac{W_{in} + 2 \times \mbox{padding}[2] -
+      \mbox{kernel\_size}[2]}{\mbox{stride}[2]} + 1\right\rfloor
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+  
+<span class='co'># pool of square window of size=3, stride=2</span>
+<span class='kw'>m</span> <span class='op'>=</span> <span class='fu'>nn_avg_pool3d</span>(<span class='fl'>3</span>, stride=<span class='fl'>2</span>)
+<span class='co'># pool of non-square window</span>
+<span class='kw'>m</span> <span class='op'>=</span> <span class='fu'>nn_avg_pool3d</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>, <span class='fl'>2</span>), stride=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>2</span>))
+<span class='kw'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>,<span class='fl'>44</span>, <span class='fl'>31</span>)
+<span class='kw'>output</span> <span class='op'>=</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
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+</div>
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+Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift" />
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+<div class="row">
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+    <div class="page-header">
+    <h1>BatchNorm1D module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-batchnorm.R'><code>R/nn-batchnorm.R</code></a></small>
+    <div class="hidden name"><code>nn_batch_norm1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies Batch Normalization over a 2D or 3D input (a mini-batch of 1D
+inputs with optional additional channel dimension) as described in the paper
+<a href='https://arxiv.org/abs/1502.03167'>Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift</a></p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_batch_norm1d</span>(
+  <span class='kw'>num_features</span>,
+  eps = <span class='fl'>1e-05</span>,
+  momentum = <span class='fl'>0.1</span>,
+  affine = <span class='fl'>TRUE</span>,
+  track_running_stats = <span class='fl'>TRUE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>num_features</th>
+      <td><p>\(C\) from an expected input of size
+\((N, C, L)\) or \(L\) from input of size \((N, L)\)</p></td>
+    </tr>
+    <tr>
+      <th>eps</th>
+      <td><p>a value added to the denominator for numerical stability.
+Default: 1e-5</p></td>
+    </tr>
+    <tr>
+      <th>momentum</th>
+      <td><p>the value used for the running_mean and running_var
+computation. Can be set to <code>NULL</code> for cumulative moving average
+(i.e. simple average). Default: 0.1</p></td>
+    </tr>
+    <tr>
+      <th>affine</th>
+      <td><p>a boolean value that when set to <code>TRUE</code>, this module has
+learnable affine parameters. Default: <code>TRUE</code></p></td>
+    </tr>
+    <tr>
+      <th>track_running_stats</th>
+      <td><p>a boolean value that when set to <code>TRUE</code>, this
+module tracks the running mean and variance, and when set to <code>FALSE</code>,
+this module does not track such statistics and always uses batch
+statistics in both training and eval modes. Default: <code>TRUE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+y = \frac{x - \mathrm{E}[x]}{\sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta
+$$</p>
+<p>The mean and standard-deviation are calculated per-dimension over
+the mini-batches and \(\gamma\) and \(\beta\) are learnable parameter vectors
+of size <code>C</code> (where <code>C</code> is the input size). By default, the elements of \(\gamma\)
+are set to 1 and the elements of \(\beta\) are set to 0.</p>
+<p>Also by default, during training this layer keeps running estimates of its
+computed mean and variance, which are then used for normalization during
+evaluation. The running estimates are kept with a default :attr:<code>momentum</code>
+of 0.1.
+If <code>track_running_stats</code> is set to <code>FALSE</code>, this layer then does not
+keep running estimates, and batch statistics are instead used during
+evaluation time as well.</p>
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+
+
+<p>This <code>momentum</code> argument is different from one used in optimizer
+classes and the conventional notion of momentum. Mathematically, the
+update rule for running statistics here is
+\(\hat{x}_{\mbox{new}} = (1 - \mbox{momentum}) \times \hat{x} + \mbox{momentum} \times x_t\),
+where \(\hat{x}\) is the estimated statistic and \(x_t\) is the
+new observed value.</p>
+<p>Because the Batch Normalization is done over the <code>C</code> dimension, computing statistics
+on <code>(N, L)</code> slices, it's common terminology to call this Temporal Batch Normalization.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C)\) or \((N, C, L)\)</p></li>
+<li><p>Output: \((N, C)\) or \((N, C, L)\) (same shape as input)</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'># With Learnable Parameters</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_batch_norm1d</span>(<span class='fl'>100</span>)
+<span class='co'># Without Learnable Parameters</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_batch_norm1d</span>(<span class='fl'>100</span>, affine = <span class='fl'>FALSE</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>100</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>) 
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/nn_batch_norm2d.html b/static/docs/dev/reference/nn_batch_norm2d.html
new file mode 100644
index 000000000..1d8d25422
--- /dev/null
+++ b/static/docs/dev/reference/nn_batch_norm2d.html
@@ -0,0 +1,319 @@
+<!-- Generated by pkgdown: do not edit by hand -->
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+    <div class="page-header">
+    <h1>BatchNorm2D</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-batchnorm.R'><code>R/nn-batchnorm.R</code></a></small>
+    <div class="hidden name"><code>nn_batch_norm2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies Batch Normalization over a 4D input (a mini-batch of 2D inputs
+additional channel dimension) as described in the paper
+<a href='https://arxiv.org/abs/1502.03167'>Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift</a>.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_batch_norm2d</span>(
+  <span class='kw'>num_features</span>,
+  eps = <span class='fl'>1e-05</span>,
+  momentum = <span class='fl'>0.1</span>,
+  affine = <span class='fl'>TRUE</span>,
+  track_running_stats = <span class='fl'>TRUE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>num_features</th>
+      <td><p>\(C\) from an expected input of size
+\((N, C, H, W)\)</p></td>
+    </tr>
+    <tr>
+      <th>eps</th>
+      <td><p>a value added to the denominator for numerical stability.
+Default: 1e-5</p></td>
+    </tr>
+    <tr>
+      <th>momentum</th>
+      <td><p>the value used for the running_mean and running_var
+computation. Can be set to <code>None</code> for cumulative moving average
+(i.e. simple average). Default: 0.1</p></td>
+    </tr>
+    <tr>
+      <th>affine</th>
+      <td><p>a boolean value that when set to <code>TRUE</code>, this module has
+learnable affine parameters. Default: <code>TRUE</code></p></td>
+    </tr>
+    <tr>
+      <th>track_running_stats</th>
+      <td><p>a boolean value that when set to <code>TRUE</code>, this
+module tracks the running mean and variance, and when set to <code>FALSE</code>,
+this module does not track such statistics and uses batch statistics instead
+in both training and eval modes if the running mean and variance are <code>None</code>.
+Default: <code>TRUE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+  y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta
+$$</p>
+<p>The mean and standard-deviation are calculated per-dimension over
+the mini-batches and \(\gamma\) and \(\beta\) are learnable parameter vectors
+of size <code>C</code> (where <code>C</code> is the input size). By default, the elements of \(\gamma\) are set
+to 1 and the elements of \(\beta\) are set to 0. The standard-deviation is calculated
+via the biased estimator, equivalent to <code><a href='torch_var.html'>torch_var(input, unbiased=FALSE)</a></code>.
+Also by default, during training this layer keeps running estimates of its
+computed mean and variance, which are then used for normalization during
+evaluation. The running estimates are kept with a default <code>momentum</code>
+of 0.1.</p>
+<p>If <code>track_running_stats</code> is set to <code>FALSE</code>, this layer then does not
+keep running estimates, and batch statistics are instead used during
+evaluation time as well.</p>
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>This <code>momentum</code> argument is different from one used in optimizer
+classes and the conventional notion of momentum. Mathematically, the
+update rule for running statistics here is
+\(\hat{x}_{\mbox{new}} = (1 - \mbox{momentum}) \times \hat{x} + \mbox{momentum} \times x_t\),
+where \(\hat{x}\) is the estimated statistic and \(x_t\) is the
+new observed value.
+Because the Batch Normalization is done over the <code>C</code> dimension, computing statistics
+on <code>(N, H, W)</code> slices, it's common terminology to call this Spatial Batch Normalization.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C, H, W)\)</p></li>
+<li><p>Output: \((N, C, H, W)\) (same shape as input)</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'># With Learnable Parameters</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_batch_norm2d</span>(<span class='fl'>100</span>)
+<span class='co'># Without Learnable Parameters</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_batch_norm2d</span>(<span class='fl'>100</span>, affine=<span class='fl'>FALSE</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>100</span>, <span class='fl'>35</span>, <span class='fl'>45</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
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+    <h1>Binary cross entropy loss</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-loss.R'><code>R/nn-loss.R</code></a></small>
+    <div class="hidden name"><code>nn_bce_loss.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Creates a criterion that measures the Binary Cross Entropy
+between the target and the output:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_bce_loss</span>(weight = <span class='kw'>NULL</span>, reduction = <span class='st'>"mean"</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>weight</th>
+      <td><p>(Tensor, optional): a manual rescaling weight given to the loss
+of each batch element. If given, has to be a Tensor of size <code>nbatch</code>.</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional): Specifies the reduction to apply to the output:
+<code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
+<code>'mean'</code>: the sum of the output will be divided by the number of
+elements in the output, <code>'sum'</code>: the output will be summed. Note: <code>size_average</code>
+and <code>reduce</code> are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>The unreduced (i.e. with <code>reduction</code> set to <code>'none'</code>) loss can be described as:
+$$
+  \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
+l_n = - w_n \left[ y_n \cdot \log x_n + (1 - y_n) \cdot \log (1 - x_n) \right]
+$$
+where \(N\) is the batch size. If <code>reduction</code> is not <code>'none'</code>
+(default <code>'mean'</code>), then</p>
+<p>$$
+  \ell(x, y) = \left\{ \begin{array}{ll}
+\mbox{mean}(L), &amp; \mbox{if reduction} = \mbox{'mean';}\\
+\mbox{sum}(L),  &amp; \mbox{if reduction} = \mbox{'sum'.}
+\end{array}
+\right.
+$$</p>
+<p>This is used for measuring the error of a reconstruction in for example
+an auto-encoder. Note that the targets \(y\) should be numbers
+between 0 and 1.</p>
+<p>Notice that if \(x_n\) is either 0 or 1, one of the log terms would be
+mathematically undefined in the above loss equation. PyTorch chooses to set
+\(\log (0) = -\infty\), since \(\lim_{x\to 0} \log (x) = -\infty\).</p>
+<p>However, an infinite term in the loss equation is not desirable for several reasons.
+For one, if either \(y_n = 0\) or \((1 - y_n) = 0\), then we would be
+multiplying 0 with infinity. Secondly, if we have an infinite loss value, then
+we would also have an infinite term in our gradient, since
+\(\lim_{x\to 0} \frac{d}{dx} \log (x) = \infty\).</p>
+<p>This would make BCELoss's backward method nonlinear with respect to \(x_n\),
+and using it for things like linear regression would not be straight-forward.
+Our solution is that BCELoss clamps its log function outputs to be greater than
+or equal to -100. This way, we can always have a finite loss value and a linear
+backward method.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where \(*\) means, any number of additional
+dimensions</p></li>
+<li><p>Target: \((N, *)\), same shape as the input</p></li>
+<li><p>Output: scalar. If <code>reduction</code> is <code>'none'</code>, then \((N, *)\), same
+shape as input.</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_sigmoid.html'>nn_sigmoid</a></span>()
+<span class='kw'>loss</span> <span class='op'>&lt;-</span> <span class='fu'>nn_bce_loss</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>3</span>, requires_grad=<span class='fl'>TRUE</span>)
+<span class='kw'>target</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_rand.html'>torch_rand</a></span>(<span class='fl'>3</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>loss</span>(<span class='fu'>m</span>(<span class='kw'>input</span>), <span class='kw'>target</span>)
+<span class='kw'>output</span><span class='op'>$</span><span class='fu'>backward</span>()
+
+}
+</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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+    <div class="page-header">
+    <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='kw'>in1_features</span>, <span class='kw'>in2_features</span>, <span class='kw'>out_features</span>, bias = <span class='fl'>TRUE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>in1_features</th>
+      <td><p>size of each first input sample</p></td>
+    </tr>
+    <tr>
+      <th>in2_features</th>
+      <td><p>size of each second input sample</p></td>
+    </tr>
+    <tr>
+      <th>out_features</th>
+      <td><p>size of each output sample</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>If set to <code>FALSE</code>, the layer will not learn an additive bias.
+Default: <code>TRUE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input1: \((N, *, H_{in1})\) \(H_{in1}=\mbox{in1\_features}\) and
+\(*\) means any number of additional dimensions. All but the last
+dimension of the inputs should be the same.</p></li>
+<li><p>Input2: \((N, *, H_{in2})\) where \(H_{in2}=\mbox{in2\_features}\).</p></li>
+<li><p>Output: \((N, *, H_{out})\) where \(H_{out}=\mbox{out\_features}\)
+and all but the last dimension are the same shape as the input.</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
+
+    
+
+<ul>
+<li><p>weight: the learnable weights of the module of shape
+\((\mbox{out\_features}, \mbox{in1\_features}, \mbox{in2\_features})\).
+The values are initialized from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\), where
+\(k = \frac{1}{\mbox{in1\_features}}\)</p></li>
+<li><p>bias: the learnable bias of the module of shape \((\mbox{out\_features})\).
+If <code>bias</code> is <code>TRUE</code>, the values are initialized from
+\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\), where
+\(k = \frac{1}{\mbox{in1\_features}}\)</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_bilinear</span>(<span class='fl'>20</span>, <span class='fl'>30</span>, <span class='fl'>50</span>)
+<span class='kw'>input1</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>128</span>, <span class='fl'>20</span>)
+<span class='kw'>input2</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>128</span>, <span class='fl'>30</span>)
+<span class='kw'>output</span> <span class='op'>=</span> <span class='fu'>m</span>(<span class='kw'>input1</span>, <span class='kw'>input2</span>)
+<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='kw'>output</span><span class='op'>$</span><span class='fu'>size</span>()) 
+
+}
+</div><div class='output co'>#&gt; [1] 128  50</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+    <div class="page-header">
+    <h1>CELU module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_celu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the element-wise function:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_celu</span>(alpha = <span class='fl'>1</span>, inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>alpha</th>
+      <td><p>the \(\alpha\) value for the CELU formulation. Default: 1.0</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+  \mbox{CELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x/\alpha) - 1))
+$$</p>
+<p>More details can be found in the paper
+<a href='https://arxiv.org/abs/1704.07483'>Continuously Differentiable Exponential Linear Units</a>.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_celu</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
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+planes.
+In the simplest case, the output value of the layer with input size
+\((N, C_{\mbox{in}}, L)\) and output \((N, C_{\mbox{out}}, L_{\mbox{out}})\) can be
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+<div class="row">
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+    <div class="page-header">
+    <h1>Conv1D module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-conv.R'><code>R/nn-conv.R</code></a></small>
+    <div class="hidden name"><code>nn_conv1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 1D convolution over an input signal composed of several input
+planes.
+In the simplest case, the output value of the layer with input size
+\((N, C_{\mbox{in}}, L)\) and output \((N, C_{\mbox{out}}, L_{\mbox{out}})\) can be
+precisely described as:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_conv1d</span>(
+  <span class='kw'>in_channels</span>,
+  <span class='kw'>out_channels</span>,
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='fl'>1</span>,
+  padding = <span class='fl'>0</span>,
+  dilation = <span class='fl'>1</span>,
+  groups = <span class='fl'>1</span>,
+  bias = <span class='fl'>TRUE</span>,
+  padding_mode = <span class='st'>"zeros"</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>in_channels</th>
+      <td><p>(int): Number of channels in the input image</p></td>
+    </tr>
+    <tr>
+      <th>out_channels</th>
+      <td><p>(int): Number of channels produced by the convolution</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>(int or tuple): Size of the convolving kernel</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>(int or tuple, optional): Zero-padding added to both sides of
+the input. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>(int or tuple, optional): Spacing between kernel
+elements. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>(int, optional): Number of blocked connections from input
+channels to output channels. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>(bool, optional): If <code>TRUE</code>, adds a learnable bias to the
+output. Default: <code>TRUE</code></p></td>
+    </tr>
+    <tr>
+      <th>padding_mode</th>
+      <td><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>,
+<code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+\mbox{out}(N_i, C_{\mbox{out}_j}) = \mbox{bias}(C_{\mbox{out}_j}) +
+  \sum_{k = 0}^{C_{in} - 1} \mbox{weight}(C_{\mbox{out}_j}, k)
+\star \mbox{input}(N_i, k)
+$$</p>
+<p>where \(\star\) is the valid
+<a href='https://en.wikipedia.org/wiki/Cross-correlation'>cross-correlation</a> operator,
+\(N\) is a batch size, \(C\) denotes a number of channels,
+\(L\) is a length of signal sequence.</p><ul>
+<li><p><code>stride</code> controls the stride for the cross-correlation, a single
+number or a one-element tuple.</p></li>
+<li><p><code>padding</code> controls the amount of implicit zero-paddings on both sides
+for <code>padding</code> number of points.</p></li>
+<li><p><code>dilation</code> controls the spacing between the kernel points; also
+known as the à trous algorithm. It is harder to describe, but this
+<a href='https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md'>link</a>
+has a nice visualization of what <code>dilation</code> does.</p></li>
+<li><p><code>groups</code> controls the connections between inputs and outputs.
+<code>in_channels</code> and <code>out_channels</code> must both be divisible by
+<code>groups</code>. For example,</p><ul>
+<li><p>At groups=1, all inputs are convolved to all outputs.</p></li>
+<li><p>At groups=2, the operation becomes equivalent to having two conv
+layers side by side, each seeing half the input channels,
+and producing half the output channels, and both subsequently
+concatenated.</p></li>
+<li><p>At groups= <code>in_channels</code>, each input channel is convolved with
+its own set of filters,
+of size \(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\).</p></li>
+</ul></li>
+</ul>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+
+
+<p>Depending of the size of your kernel, several (of the last)
+columns of the input might be lost, because it is a valid
+<code>cross-correlation</code><em>, and not a full <code>cross-correlation</code></em>.
+It is up to the user to add proper padding.</p>
+<p>When <code>groups == in_channels</code> and <code>out_channels == K * in_channels</code>,
+where <code>K</code> is a positive integer, this operation is also termed in
+literature as depthwise convolution.
+In other words, for an input of size \((N, C_{in}, L_{in})\),
+a depthwise convolution with a depthwise multiplier <code>K</code>, can be constructed by arguments
+\((C_{\mbox{in}}=C_{in}, C_{\mbox{out}}=C_{in} \times K, ..., \mbox{groups}=C_{in})\).</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C_{in}, L_{in})\)</p></li>
+<li><p>Output: \((N, C_{out}, L_{out})\) where</p></li>
+</ul>
+
+<p>$$
+  L_{out} = \left\lfloor\frac{L_{in} + 2 \times \mbox{padding} - \mbox{dilation}
+    \times (\mbox{kernel\_size} - 1) - 1}{\mbox{stride}} + 1\right\rfloor
+$$</p>
+    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
+
+    
+
+<ul>
+<li><p>weight (Tensor): the learnable weights of the module of shape
+\((\mbox{out\_channels}, \frac{\mbox{in\_channels}}{\mbox{groups}}, \mbox{kernel\_size})\).
+The values of these weights are sampled from
+\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
+\(k = \frac{groups}{C_{\mbox{in}} * \mbox{kernel\_size}}\)</p></li>
+<li><p>bias (Tensor): the learnable bias of the module of shape
+(out_channels). If <code>bias</code> is <code>TRUE</code>, then the values of these weights are
+sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
+\(k = \frac{groups}{C_{\mbox{in}} * \mbox{kernel\_size}}\)</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv1d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>3</span>, stride=<span class='fl'>2</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+    <div class="page-header">
+    <h1>Conv2D module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-conv.R'><code>R/nn-conv.R</code></a></small>
+    <div class="hidden name"><code>nn_conv2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 2D convolution over an input signal composed of several input
+planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_conv2d</span>(
+  <span class='kw'>in_channels</span>,
+  <span class='kw'>out_channels</span>,
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='fl'>1</span>,
+  padding = <span class='fl'>0</span>,
+  dilation = <span class='fl'>1</span>,
+  groups = <span class='fl'>1</span>,
+  bias = <span class='fl'>TRUE</span>,
+  padding_mode = <span class='st'>"zeros"</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>in_channels</th>
+      <td><p>(int): Number of channels in the input image</p></td>
+    </tr>
+    <tr>
+      <th>out_channels</th>
+      <td><p>(int): Number of channels produced by the convolution</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>(int or tuple): Size of the convolving kernel</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>(int or tuple, optional): Zero-padding added to both sides of
+the input. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>(int, optional): Number of blocked connections from input
+channels to output channels. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>(bool, optional): If <code>TRUE</code>, adds a learnable bias to the
+output. Default: <code>TRUE</code></p></td>
+    </tr>
+    <tr>
+      <th>padding_mode</th>
+      <td><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>,
+<code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>In the simplest case, the output value of the layer with input size
+\((N, C_{\mbox{in}}, H, W)\) and output \((N, C_{\mbox{out}}, H_{\mbox{out}}, W_{\mbox{out}})\)
+can be precisely described as:</p>
+<p>$$
+\mbox{out}(N_i, C_{\mbox{out}_j}) = \mbox{bias}(C_{\mbox{out}_j}) +
+  \sum_{k = 0}^{C_{\mbox{in}} - 1} \mbox{weight}(C_{\mbox{out}_j}, k) \star \mbox{input}(N_i, k)
+$$</p>
+<p>where \(\star\) is the valid 2D cross-correlation operator,
+\(N\) is a batch size, \(C\) denotes a number of channels,
+\(H\) is a height of input planes in pixels, and \(W\) is
+width in pixels.</p><ul>
+<li><p><code>stride</code> controls the stride for the cross-correlation, a single
+number or a tuple.</p></li>
+<li><p><code>padding</code> controls the amount of implicit zero-paddings on both
+sides for <code>padding</code> number of points for each dimension.</p></li>
+<li><p><code>dilation</code> controls the spacing between the kernel points; also
+known as the à trous algorithm. It is harder to describe, but this <code>link</code>_
+has a nice visualization of what <code>dilation</code> does.</p></li>
+<li><p><code>groups</code> controls the connections between inputs and outputs.
+<code>in_channels</code> and <code>out_channels</code> must both be divisible by
+<code>groups</code>. For example,</p><ul>
+<li><p>At groups=1, all inputs are convolved to all outputs.</p></li>
+<li><p>At groups=2, the operation becomes equivalent to having two conv
+layers side by side, each seeing half the input channels,
+and producing half the output channels, and both subsequently
+concatenated.</p></li>
+<li><p>At groups= <code>in_channels</code>, each input channel is convolved with
+its own set of filters, of size:
+\(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\).</p></li>
+</ul></li>
+</ul>
+
+<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>dilation</code> can either be:</p><ul>
+<li><p>a single <code>int</code> -- in which case the same value is used for the height and
+width dimension</p></li>
+<li><p>a <code>tuple</code> of two ints -- in which case, the first <code>int</code> is used for the height dimension,
+and the second <code>int</code> for the width dimension</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+
+
+<p>Depending of the size of your kernel, several (of the last)
+columns of the input might be lost, because it is a valid cross-correlation,
+and not a full cross-correlation.
+It is up to the user to add proper padding.</p>
+<p>When <code>groups == in_channels</code> and <code>out_channels == K * in_channels</code>,
+where <code>K</code> is a positive integer, this operation is also termed in
+literature as depthwise convolution.
+In other words, for an input of size :math:<code>(N, C_{in}, H_{in}, W_{in})</code>,
+a depthwise convolution with a depthwise multiplier <code>K</code>, can be constructed by arguments
+\((in\_channels=C_{in}, out\_channels=C_{in} \times K, ..., groups=C_{in})\).</p>
+<p>In some circumstances when using the CUDA backend with CuDNN, this operator
+may select a nondeterministic algorithm to increase performance. If this is
+undesirable, you can try to make the operation deterministic (potentially at
+a performance cost) by setting <code>backends_cudnn_deterministic = TRUE</code>.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C_{in}, H_{in}, W_{in})\)</p></li>
+<li><p>Output: \((N, C_{out}, H_{out}, W_{out})\) where
+$$
+  H_{out} = \left\lfloor\frac{H_{in}  + 2 \times \mbox{padding}[0] - \mbox{dilation}[0]
+    \times (\mbox{kernel\_size}[0] - 1) - 1}{\mbox{stride}[0]} + 1\right\rfloor
+$$
+$$
+  W_{out} = \left\lfloor\frac{W_{in}  + 2 \times \mbox{padding}[1] - \mbox{dilation}[1]
+    \times (\mbox{kernel\_size}[1] - 1) - 1}{\mbox{stride}[1]} + 1\right\rfloor
+$$</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
+
+    
+
+<ul>
+<li><p>weight (Tensor): the learnable weights of the module of shape
+\((\mbox{out\_channels}, \frac{\mbox{in\_channels}}{\mbox{groups}}\),
+\(\mbox{kernel\_size[0]}, \mbox{kernel\_size[1]})\).
+The values of these weights are sampled from
+\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
+\(k = \frac{groups}{C_{\mbox{in}} * \prod_{i=0}^{1}\mbox{kernel\_size}[i]}\)</p></li>
+<li><p>bias (Tensor): the learnable bias of the module of shape
+(out_channels). If <code>bias</code> is <code>TRUE</code>,
+then the values of these weights are
+sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
+\(k = \frac{groups}{C_{\mbox{in}} * \prod_{i=0}^{1}\mbox{kernel\_size}[i]}\)</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='co'># With square kernels and equal stride</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv2d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>3</span>, stride = <span class='fl'>2</span>)
+<span class='co'># non-square kernels and unequal stride and with padding</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv2d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>), stride=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>), padding=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>2</span>))
+<span class='co'># non-square kernels and unequal stride and with padding and dilation</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv2d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>), stride=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>), padding=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>2</span>), dilation=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>1</span>))
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>100</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)  
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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+    <h1>Conv3D module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-conv.R'><code>R/nn-conv.R</code></a></small>
+    <div class="hidden name"><code>nn_conv3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 3D convolution over an input signal composed of several input
+planes.
+In the simplest case, the output value of the layer with input size \((N, C_{in}, D, H, W)\)
+and output \((N, C_{out}, D_{out}, H_{out}, W_{out})\) can be precisely described as:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_conv3d</span>(
+  <span class='kw'>in_channels</span>,
+  <span class='kw'>out_channels</span>,
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='fl'>1</span>,
+  padding = <span class='fl'>0</span>,
+  dilation = <span class='fl'>1</span>,
+  groups = <span class='fl'>1</span>,
+  bias = <span class='fl'>TRUE</span>,
+  padding_mode = <span class='st'>"zeros"</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>in_channels</th>
+      <td><p>(int): Number of channels in the input image</p></td>
+    </tr>
+    <tr>
+      <th>out_channels</th>
+      <td><p>(int): Number of channels produced by the convolution</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>(int or tuple): Size of the convolving kernel</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>(int or tuple, optional): Zero-padding added to all three sides of the input. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>(int, optional): Number of blocked connections from input channels to output channels. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>(bool, optional): If <code>TRUE</code>, adds a learnable bias to the output. Default: <code>TRUE</code></p></td>
+    </tr>
+    <tr>
+      <th>padding_mode</th>
+      <td><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>, <code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+  out(N_i, C_{out_j}) = bias(C_{out_j}) +
+  \sum_{k = 0}^{C_{in} - 1} weight(C_{out_j}, k) \star input(N_i, k)
+$$</p>
+<p>where \(\star\) is the valid 3D <code>cross-correlation</code> operator</p><ul>
+<li><p><code>stride</code> controls the stride for the cross-correlation.</p></li>
+<li><p><code>padding</code> controls the amount of implicit zero-paddings on both
+sides for <code>padding</code> number of points for each dimension.</p></li>
+<li><p><code>dilation</code> controls the spacing between the kernel points; also known as the à trous algorithm.
+It is harder to describe, but this <code>link</code>_ has a nice visualization of what <code>dilation</code> does.</p></li>
+<li><p><code>groups</code> controls the connections between inputs and outputs.
+<code>in_channels</code> and <code>out_channels</code> must both be divisible by
+<code>groups</code>. For example,</p></li>
+<li><p>At groups=1, all inputs are convolved to all outputs.</p></li>
+<li><p>At groups=2, the operation becomes equivalent to having two conv
+layers side by side, each seeing half the input channels,
+and producing half the output channels, and both subsequently
+concatenated.</p></li>
+<li><p>At groups= <code>in_channels</code>, each input channel is convolved with
+its own set of filters, of size
+\(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\).</p></li>
+</ul>
+
+<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>dilation</code> can either be:</p><ul>
+<li><p>a single <code>int</code> -- in which case the same value is used for the depth, height and width dimension</p></li>
+<li><p>a <code>tuple</code> of three ints -- in which case, the first <code>int</code> is used for the depth dimension,
+the second <code>int</code> for the height dimension and the third <code>int</code> for the width dimension</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>Depending of the size of your kernel, several (of the last)
+columns of the input might be lost, because it is a valid <code>cross-correlation</code><em>,
+and not a full <code>cross-correlation</code></em>.
+It is up to the user to add proper padding.</p>
+<p>When <code>groups == in_channels</code> and <code>out_channels == K * in_channels</code>,
+where <code>K</code> is a positive integer, this operation is also termed in
+literature as depthwise convolution.
+In other words, for an input of size \((N, C_{in}, D_{in}, H_{in}, W_{in})\),
+a depthwise convolution with a depthwise multiplier <code>K</code>, can be constructed by arguments
+\((in\_channels=C_{in}, out\_channels=C_{in} \times K, ..., groups=C_{in})\).</p>
+<p>In some circumstances when using the CUDA backend with CuDNN, this operator
+may select a nondeterministic algorithm to increase performance. If this is
+undesirable, you can try to make the operation deterministic (potentially at
+a performance cost) by setting <code>torch.backends.cudnn.deterministic = TRUE</code>.
+Please see the notes on :doc:<code>/notes/randomness</code> for background.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C_{in}, D_{in}, H_{in}, W_{in})\)</p></li>
+<li><p>Output: \((N, C_{out}, D_{out}, H_{out}, W_{out})\) where
+$$
+  D_{out} = \left\lfloor\frac{D_{in} + 2 \times \mbox{padding}[0] - \mbox{dilation}[0]
+    \times (\mbox{kernel\_size}[0] - 1) - 1}{\mbox{stride}[0]} + 1\right\rfloor
+ $$
+$$
+  H_{out} = \left\lfloor\frac{H_{in} + 2 \times \mbox{padding}[1] - \mbox{dilation}[1]
+    \times (\mbox{kernel\_size}[1] - 1) - 1}{\mbox{stride}[1]} + 1\right\rfloor
+ $$
+$$
+  W_{out} = \left\lfloor\frac{W_{in} + 2 \times \mbox{padding}[2] - \mbox{dilation}[2]
+    \times (\mbox{kernel\_size}[2] - 1) - 1}{\mbox{stride}[2]} + 1\right\rfloor
+ $$</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
+
+    
+
+<ul>
+<li><p>weight (Tensor): the learnable weights of the module of shape
+\((\mbox{out\_channels}, \frac{\mbox{in\_channels}}{\mbox{groups}},\)
+\(\mbox{kernel\_size[0]}, \mbox{kernel\_size[1]}, \mbox{kernel\_size[2]})\).
+The values of these weights are sampled from
+\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
+\(k = \frac{groups}{C_{\mbox{in}} * \prod_{i=0}^{2}\mbox{kernel\_size}[i]}\)</p></li>
+<li><p>bias (Tensor):   the learnable bias of the module of shape (out_channels). If <code>bias</code> is <code>True</code>,
+then the values of these weights are
+sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
+\(k = \frac{groups}{C_{\mbox{in}} * \prod_{i=0}^{2}\mbox{kernel\_size}[i]}\)</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'># With square kernels and equal stride</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv3d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>3</span>, stride=<span class='fl'>2</span>)
+<span class='co'># non-square kernels and unequal stride and with padding</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv3d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>, <span class='fl'>2</span>), stride=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>1</span>), padding=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>2</span>, <span class='fl'>0</span>))
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>10</span>, <span class='fl'>50</span>, <span class='fl'>100</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>ConvTranspose1D</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-conv.R'><code>R/nn-conv.R</code></a></small>
+    <div class="hidden name"><code>nn_conv_transpose1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 1D transposed convolution operator over an input image
+composed of several input planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_conv_transpose1d</span>(
+  <span class='kw'>in_channels</span>,
+  <span class='kw'>out_channels</span>,
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='fl'>1</span>,
+  padding = <span class='fl'>0</span>,
+  output_padding = <span class='fl'>0</span>,
+  groups = <span class='fl'>1</span>,
+  bias = <span class='fl'>TRUE</span>,
+  dilation = <span class='fl'>1</span>,
+  padding_mode = <span class='st'>"zeros"</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>in_channels</th>
+      <td><p>(int): Number of channels in the input image</p></td>
+    </tr>
+    <tr>
+      <th>out_channels</th>
+      <td><p>(int): Number of channels produced by the convolution</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>(int or tuple): Size of the convolving kernel</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>(int or tuple, optional): <code>dilation * (kernel_size - 1) - padding</code> zero-padding
+will be added to both sides of the input. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>output_padding</th>
+      <td><p>(int or tuple, optional): Additional size added to one side
+of the output shape. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>(int, optional): Number of blocked connections from input channels to output channels. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>(bool, optional): If <code>True</code>, adds a learnable bias to the output. Default: <code>TRUE</code></p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding_mode</th>
+      <td><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>,
+<code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>This module can be seen as the gradient of Conv1d with respect to its input.
+It is also known as a fractionally-strided convolution or
+a deconvolution (although it is not an actual deconvolution operation).</p><ul>
+<li><p><code>stride</code> controls the stride for the cross-correlation.</p></li>
+<li><p><code>padding</code> controls the amount of implicit zero-paddings on both
+sides for <code>dilation * (kernel_size - 1) - padding</code> number of points. See note
+below for details.</p></li>
+<li><p><code>output_padding</code> controls the additional size added to one side
+of the output shape. See note below for details.</p></li>
+<li><p><code>dilation</code> controls the spacing between the kernel points; also known as the
+à trous algorithm. It is harder to describe, but this <a href='https://github.com/vdumoulin/conv_arithmetic'>link</a>
+has a nice visualization of what <code>dilation</code> does.</p></li>
+<li><p><code>groups</code> controls the connections between inputs and outputs.
+<code>in_channels</code> and <code>out_channels</code> must both be divisible by
+<code>groups</code>. For example,</p><ul>
+<li><p>At groups=1, all inputs are convolved to all outputs.</p></li>
+<li><p>At groups=2, the operation becomes equivalent to having two conv
+layers side by side, each seeing half the input channels,
+and producing half the output channels, and both subsequently
+concatenated.</p></li>
+<li><p>At groups= <code>in_channels</code>, each input channel is convolved with
+its own set of filters (of size
+\(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\)).</p></li>
+</ul></li>
+</ul>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>Depending of the size of your kernel, several (of the last)
+columns of the input might be lost, because it is a valid <code>cross-correlation</code><em>,
+and not a full <code>cross-correlation</code></em>.
+It is up to the user to add proper padding.</p>
+<p>The <code>padding</code> argument effectively adds <code>dilation * (kernel_size - 1) - padding</code>
+amount of zero padding to both sizes of the input. This is set so that
+when a <code>~torch.nn.Conv1d</code> and a <code>~torch.nn.ConvTranspose1d</code>
+are initialized with same parameters, they are inverses of each other in
+regard to the input and output shapes. However, when <code>stride &gt; 1</code>,
+<code>~torch.nn.Conv1d</code> maps multiple input shapes to the same output
+shape. <code>output_padding</code> is provided to resolve this ambiguity by
+effectively increasing the calculated output shape on one side. Note
+that <code>output_padding</code> is only used to find output shape, but does
+not actually add zero-padding to output.</p>
+<p>In some circumstances when using the CUDA backend with CuDNN, this operator
+may select a nondeterministic algorithm to increase performance. If this is
+undesirable, you can try to make the operation deterministic (potentially at
+a performance cost) by setting <code>torch.backends.cudnn.deterministic = TRUE</code>.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C_{in}, L_{in})\)</p></li>
+<li><p>Output: \((N, C_{out}, L_{out})\) where
+$$
+  L_{out} = (L_{in} - 1) \times \mbox{stride} - 2 \times \mbox{padding} + \mbox{dilation}
+\times (\mbox{kernel\_size} - 1) + \mbox{output\_padding} + 1
+$$</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
+
+    
+
+<ul>
+<li><p>weight (Tensor): the learnable weights of the module of shape
+\((\mbox{in\_channels}, \frac{\mbox{out\_channels}}{\mbox{groups}},\)
+\(\mbox{kernel\_size})\).
+The values of these weights are sampled from
+\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
+\(k = \frac{groups}{C_{\mbox{out}} * \mbox{kernel\_size}}\)</p></li>
+<li><p>bias (Tensor):   the learnable bias of the module of shape (out_channels).
+If <code>bias</code> is <code>TRUE</code>, then the values of these weights are
+sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
+\(k = \frac{groups}{C_{\mbox{out}} * \mbox{kernel\_size}}\)</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv_transpose1d</span>(<span class='fl'>32</span>, <span class='fl'>16</span>, <span class='fl'>2</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>10</span>, <span class='fl'>32</span>, <span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>ConvTranpose2D module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-conv.R'><code>R/nn-conv.R</code></a></small>
+    <div class="hidden name"><code>nn_conv_transpose2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 2D transposed convolution operator over an input image
+composed of several input planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_conv_transpose2d</span>(
+  <span class='kw'>in_channels</span>,
+  <span class='kw'>out_channels</span>,
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='fl'>1</span>,
+  padding = <span class='fl'>0</span>,
+  output_padding = <span class='fl'>0</span>,
+  groups = <span class='fl'>1</span>,
+  bias = <span class='fl'>TRUE</span>,
+  dilation = <span class='fl'>1</span>,
+  padding_mode = <span class='st'>"zeros"</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>in_channels</th>
+      <td><p>(int): Number of channels in the input image</p></td>
+    </tr>
+    <tr>
+      <th>out_channels</th>
+      <td><p>(int): Number of channels produced by the convolution</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>(int or tuple): Size of the convolving kernel</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>(int or tuple, optional): <code>dilation * (kernel_size - 1) - padding</code> zero-padding
+will be added to both sides of each dimension in the input. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>output_padding</th>
+      <td><p>(int or tuple, optional): Additional size added to one side
+of each dimension in the output shape. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>(int, optional): Number of blocked connections from input channels to output channels. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>(bool, optional): If <code>True</code>, adds a learnable bias to the output. Default: <code>True</code></p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding_mode</th>
+      <td><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>,
+<code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>This module can be seen as the gradient of Conv2d with respect to its input.
+It is also known as a fractionally-strided convolution or
+a deconvolution (although it is not an actual deconvolution operation).</p><ul>
+<li><p><code>stride</code> controls the stride for the cross-correlation.</p></li>
+<li><p><code>padding</code> controls the amount of implicit zero-paddings on both
+sides for <code>dilation * (kernel_size - 1) - padding</code> number of points. See note
+below for details.</p></li>
+<li><p><code>output_padding</code> controls the additional size added to one side
+of the output shape. See note below for details.</p></li>
+<li><p><code>dilation</code> controls the spacing between the kernel points; also known as the à trous algorithm.
+It is harder to describe, but this <code>link</code>_ has a nice visualization of what <code>dilation</code> does.</p></li>
+<li><p><code>groups</code> controls the connections between inputs and outputs.
+<code>in_channels</code> and <code>out_channels</code> must both be divisible by
+<code>groups</code>. For example,</p><ul>
+<li><p>At groups=1, all inputs are convolved to all outputs.</p></li>
+<li><p>At groups=2, the operation becomes equivalent to having two conv
+layers side by side, each seeing half the input channels,
+and producing half the output channels, and both subsequently
+concatenated.</p></li>
+<li><p>At groups= <code>in_channels</code>, each input channel is convolved with
+its own set of filters (of size
+\(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\)).</p></li>
+</ul></li>
+</ul>
+
+<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>output_padding</code>
+can either be:</p><ul>
+<li><p>a single <code>int</code> -- in which case the same value is used for the height and width dimensions</p></li>
+<li><p>a <code>tuple</code> of two ints -- in which case, the first <code>int</code> is used for the height dimension,
+and the second <code>int</code> for the width dimension</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>Depending of the size of your kernel, several (of the last)
+columns of the input might be lost, because it is a valid <code>cross-correlation</code>_,
+and not a full <code>cross-correlation</code>. It is up to the user to add proper padding.</p>
+<p>The <code>padding</code> argument effectively adds <code>dilation * (kernel_size - 1) - padding</code>
+amount of zero padding to both sizes of the input. This is set so that
+when a <a href='nn_conv2d.html'>nn_conv2d</a> and a nn_conv_transpose2d are initialized with same
+parameters, they are inverses of each other in
+regard to the input and output shapes. However, when <code>stride &gt; 1</code>,
+<a href='nn_conv2d.html'>nn_conv2d</a> maps multiple input shapes to the same output
+shape. <code>output_padding</code> is provided to resolve this ambiguity by
+effectively increasing the calculated output shape on one side. Note
+that <code>output_padding</code> is only used to find output shape, but does
+not actually add zero-padding to output.</p>
+<p>In some circumstances when using the CUDA backend with CuDNN, this operator
+may select a nondeterministic algorithm to increase performance. If this is
+undesirable, you can try to make the operation deterministic (potentially at
+a performance cost) by setting <code>torch.backends.cudnn.deterministic = TRUE</code>.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C_{in}, H_{in}, W_{in})\)</p></li>
+<li><p>Output: \((N, C_{out}, H_{out}, W_{out})\) where
+$$
+  H_{out} = (H_{in} - 1) \times \mbox{stride}[0] - 2 \times \mbox{padding}[0] + \mbox{dilation}[0]
+\times (\mbox{kernel\_size}[0] - 1) + \mbox{output\_padding}[0] + 1
+$$
+$$
+  W_{out} = (W_{in} - 1) \times \mbox{stride}[1] - 2 \times \mbox{padding}[1] + \mbox{dilation}[1]
+\times (\mbox{kernel\_size}[1] - 1) + \mbox{output\_padding}[1] + 1
+$$</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
+
+    
+
+<ul>
+<li><p>weight (Tensor): the learnable weights of the module of shape
+\((\mbox{in\_channels}, \frac{\mbox{out\_channels}}{\mbox{groups}},\)
+\(\mbox{kernel\_size[0]}, \mbox{kernel\_size[1]})\).
+The values of these weights are sampled from
+\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
+\(k = \frac{groups}{C_{\mbox{out}} * \prod_{i=0}^{1}\mbox{kernel\_size}[i]}\)</p></li>
+<li><p>bias (Tensor):   the learnable bias of the module of shape (out_channels)
+If <code>bias</code> is <code>True</code>, then the values of these weights are
+sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
+\(k = \frac{groups}{C_{\mbox{out}} * \prod_{i=0}^{1}\mbox{kernel\_size}[i]}\)</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'># With square kernels and equal stride</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv_transpose2d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>3</span>, stride=<span class='fl'>2</span>)
+<span class='co'># non-square kernels and unequal stride and with padding</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv_transpose2d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>), stride=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>), padding=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>2</span>))
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>100</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+<span class='co'># exact output size can be also specified as an argument</span>
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>16</span>, <span class='fl'>12</span>, <span class='fl'>12</span>)
+<span class='kw'>downsample</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_conv2d.html'>nn_conv2d</a></span>(<span class='fl'>16</span>, <span class='fl'>16</span>, <span class='fl'>3</span>, stride=<span class='fl'>2</span>, padding=<span class='fl'>1</span>)
+<span class='kw'>upsample</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv_transpose2d</span>(<span class='fl'>16</span>, <span class='fl'>16</span>, <span class='fl'>3</span>, stride=<span class='fl'>2</span>, padding=<span class='fl'>1</span>)
+<span class='kw'>h</span> <span class='op'>&lt;-</span> <span class='fu'>downsample</span>(<span class='kw'>input</span>)
+<span class='kw'>h</span><span class='op'>$</span><span class='fu'>size</span>()
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>upsample</span>(<span class='kw'>h</span>, output_size=<span class='kw'>input</span><span class='op'>$</span><span class='fu'>size</span>())
+<span class='kw'>output</span><span class='op'>$</span><span class='fu'>size</span>()
+
+}
+</div><div class='output co'>#&gt; [1]  1 16 12 12</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>ConvTranpose3D module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-conv.R'><code>R/nn-conv.R</code></a></small>
+    <div class="hidden name"><code>nn_conv_transpose3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 3D transposed convolution operator over an input image composed of several input
+planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_conv_transpose3d</span>(
+  <span class='kw'>in_channels</span>,
+  <span class='kw'>out_channels</span>,
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='fl'>1</span>,
+  padding = <span class='fl'>0</span>,
+  output_padding = <span class='fl'>0</span>,
+  groups = <span class='fl'>1</span>,
+  bias = <span class='fl'>TRUE</span>,
+  dilation = <span class='fl'>1</span>,
+  padding_mode = <span class='st'>"zeros"</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>in_channels</th>
+      <td><p>(int): Number of channels in the input image</p></td>
+    </tr>
+    <tr>
+      <th>out_channels</th>
+      <td><p>(int): Number of channels produced by the convolution</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>(int or tuple): Size of the convolving kernel</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>(int or tuple, optional): <code>dilation * (kernel_size - 1) - padding</code> zero-padding
+will be added to both sides of each dimension in the input. Default: 0
+output_padding (int or tuple, optional): Additional size added to one side
+of each dimension in the output shape. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>output_padding</th>
+      <td><p>(int or tuple, optional): Additional size added to one side
+of each dimension in the output shape. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>(int, optional): Number of blocked connections from input channels to output channels. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>(bool, optional): If <code>True</code>, adds a learnable bias to the output. Default: <code>True</code></p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding_mode</th>
+      <td><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>, <code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>The transposed convolution operator multiplies each input value element-wise by a learnable kernel,
+and sums over the outputs from all input feature planes.</p>
+<p>This module can be seen as the gradient of Conv3d with respect to its input.
+It is also known as a fractionally-strided convolution or
+a deconvolution (although it is not an actual deconvolution operation).</p><ul>
+<li><p><code>stride</code> controls the stride for the cross-correlation.</p></li>
+<li><p><code>padding</code> controls the amount of implicit zero-paddings on both
+sides for <code>dilation * (kernel_size - 1) - padding</code> number of points. See note
+below for details.</p></li>
+<li><p><code>output_padding</code> controls the additional size added to one side
+of the output shape. See note below for details.</p></li>
+<li><p><code>dilation</code> controls the spacing between the kernel points; also known as the à trous algorithm.
+It is harder to describe, but this <code>link</code>_ has a nice visualization of what <code>dilation</code> does.</p></li>
+<li><p><code>groups</code> controls the connections between inputs and outputs.
+<code>in_channels</code> and <code>out_channels</code> must both be divisible by
+<code>groups</code>. For example,</p><ul>
+<li><p>At groups=1, all inputs are convolved to all outputs.</p></li>
+<li><p>At groups=2, the operation becomes equivalent to having two conv
+layers side by side, each seeing half the input channels,
+and producing half the output channels, and both subsequently
+concatenated.</p></li>
+<li><p>At groups= <code>in_channels</code>, each input channel is convolved with
+its own set of filters (of size
+\(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\)).</p></li>
+</ul></li>
+</ul>
+
+<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>output_padding</code>
+can either be:</p><ul>
+<li><p>a single <code>int</code> -- in which case the same value is used for the depth, height and width dimensions</p></li>
+<li><p>a <code>tuple</code> of three ints -- in which case, the first <code>int</code> is used for the depth dimension,
+the second <code>int</code> for the height dimension and the third <code>int</code> for the width dimension</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>Depending of the size of your kernel, several (of the last)
+columns of the input might be lost, because it is a valid <code>cross-correlation</code><em>,
+and not a full <code>cross-correlation</code></em>.
+It is up to the user to add proper padding.</p>
+<p>The <code>padding</code> argument effectively adds <code>dilation * (kernel_size - 1) - padding</code>
+amount of zero padding to both sizes of the input. This is set so that
+when a <code>~torch.nn.Conv3d</code> and a <code>~torch.nn.ConvTranspose3d</code>
+are initialized with same parameters, they are inverses of each other in
+regard to the input and output shapes. However, when <code>stride &gt; 1</code>,
+<code>~torch.nn.Conv3d</code> maps multiple input shapes to the same output
+shape. <code>output_padding</code> is provided to resolve this ambiguity by
+effectively increasing the calculated output shape on one side. Note
+that <code>output_padding</code> is only used to find output shape, but does
+not actually add zero-padding to output.</p>
+<p>In some circumstances when using the CUDA backend with CuDNN, this operator
+may select a nondeterministic algorithm to increase performance. If this is
+undesirable, you can try to make the operation deterministic (potentially at
+a performance cost) by setting <code>torch.backends.cudnn.deterministic = TRUE</code>.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C_{in}, D_{in}, H_{in}, W_{in})\)</p></li>
+<li><p>Output: \((N, C_{out}, D_{out}, H_{out}, W_{out})\) where
+$$
+  D_{out} = (D_{in} - 1) \times \mbox{stride}[0] - 2 \times \mbox{padding}[0] + \mbox{dilation}[0]
+\times (\mbox{kernel\_size}[0] - 1) + \mbox{output\_padding}[0] + 1
+$$
+$$
+  H_{out} = (H_{in} - 1) \times \mbox{stride}[1] - 2 \times \mbox{padding}[1] + \mbox{dilation}[1]
+\times (\mbox{kernel\_size}[1] - 1) + \mbox{output\_padding}[1] + 1
+$$
+$$
+  W_{out} = (W_{in} - 1) \times \mbox{stride}[2] - 2 \times \mbox{padding}[2] + \mbox{dilation}[2]
+\times (\mbox{kernel\_size}[2] - 1) + \mbox{output\_padding}[2] + 1
+$$</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
+
+    
+
+<ul>
+<li><p>weight (Tensor): the learnable weights of the module of shape
+\((\mbox{in\_channels}, \frac{\mbox{out\_channels}}{\mbox{groups}},\)
+\(\mbox{kernel\_size[0]}, \mbox{kernel\_size[1]}, \mbox{kernel\_size[2]})\).
+The values of these weights are sampled from
+\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
+\(k = \frac{groups}{C_{\mbox{out}} * \prod_{i=0}^{2}\mbox{kernel\_size}[i]}\)</p></li>
+<li><p>bias (Tensor):   the learnable bias of the module of shape (out_channels)
+If <code>bias</code> is <code>True</code>, then the values of these weights are
+sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
+\(k = \frac{groups}{C_{\mbox{out}} * \prod_{i=0}^{2}\mbox{kernel\_size}[i]}\)</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='co'># With square kernels and equal stride</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv_transpose3d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>3</span>, stride=<span class='fl'>2</span>)
+<span class='co'># non-square kernels and unequal stride and with padding</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv_transpose3d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>, <span class='fl'>2</span>), stride=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>1</span>), padding=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>4</span>, <span class='fl'>2</span>))
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>10</span>, <span class='fl'>50</span>, <span class='fl'>100</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+}
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/nn_cross_entropy_loss.html b/static/docs/dev/reference/nn_cross_entropy_loss.html
new file mode 100644
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--- /dev/null
+++ b/static/docs/dev/reference/nn_cross_entropy_loss.html
@@ -0,0 +1,310 @@
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+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>CrossEntropyLoss module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-loss.R'><code>R/nn-loss.R</code></a></small>
+    <div class="hidden name"><code>nn_cross_entropy_loss.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>This criterion combines <code><a href='nn_log_softmax.html'>nn_log_softmax()</a></code> and <code>nn_nll_loss()</code> in one single class.
+It is useful when training a classification problem with <code>C</code> classes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_cross_entropy_loss</span>(weight = <span class='kw'>NULL</span>, ignore_index = <span class='op'>-</span><span class='fl'>100</span>, reduction = <span class='st'>"mean"</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>weight</th>
+      <td><p>(Tensor, optional): a manual rescaling weight given to each class.
+If given, has to be a Tensor of size <code>C</code></p></td>
+    </tr>
+    <tr>
+      <th>ignore_index</th>
+      <td><p>(int, optional): Specifies a target value that is ignored
+and does not contribute to the input gradient. When <code>size_average</code> is
+<code>TRUE</code>, the loss is averaged over non-ignored targets.</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional): Specifies the reduction to apply to the output:
+<code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
+<code>'mean'</code>: the sum of the output will be divided by the number of
+elements in the output, <code>'sum'</code>: the output will be summed. Note: <code>size_average</code>
+and <code>reduce</code> are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>If provided, the optional argument <code>weight</code> should be a 1D <code>Tensor</code>
+assigning weight to each of the classes.</p>
+<p>This is particularly useful when you have an unbalanced training set.
+The <code>input</code> is expected to contain raw, unnormalized scores for each class.
+<code>input</code> has to be a Tensor of size either \((minibatch, C)\) or
+\((minibatch, C, d_1, d_2, ..., d_K)\)
+with \(K \geq 1\) for the <code>K</code>-dimensional case (described later).</p>
+<p>This criterion expects a class index in the range \([0, C-1]\) as the
+<code>target</code> for each value of a 1D tensor of size <code>minibatch</code>; if <code>ignore_index</code>
+is specified, this criterion also accepts this class index (this index may not
+necessarily be in the class range).</p>
+<p>The loss can be described as:
+$$
+  \mbox{loss}(x, class) = -\log\left(\frac{\exp(x[class])}{\sum_j \exp(x[j])}\right)
+= -x[class] + \log\left(\sum_j \exp(x[j])\right)
+$$
+or in the case of the <code>weight</code> argument being specified:
+$$
+  \mbox{loss}(x, class) = weight[class] \left(-x[class] + \log\left(\sum_j \exp(x[j])\right)\right)
+$$</p>
+<p>The losses are averaged across observations for each minibatch.
+Can also be used for higher dimension inputs, such as 2D images, by providing
+an input of size \((minibatch, C, d_1, d_2, ..., d_K)\) with \(K \geq 1\),
+where \(K\) is the number of dimensions, and a target of appropriate shape
+(see below).</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C)\) where <code>C = number of classes</code>, or
+\((N, C, d_1, d_2, ..., d_K)\) with \(K \geq 1\)
+in the case of <code>K</code>-dimensional loss.</p></li>
+<li><p>Target: \((N)\) where each value is \(0 \leq \mbox{targets}[i] \leq C-1\), or
+\((N, d_1, d_2, ..., d_K)\) with \(K \geq 1\) in the case of
+K-dimensional loss.</p></li>
+<li><p>Output: scalar.
+If <code>reduction</code> is <code>'none'</code>, then the same size as the target:
+\((N)\), or
+\((N, d_1, d_2, ..., d_K)\) with \(K \geq 1\) in the case
+of K-dimensional loss.</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>loss</span> <span class='op'>&lt;-</span> <span class='fu'>nn_cross_entropy_loss</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>, requires_grad=<span class='fl'>TRUE</span>)
+<span class='kw'>target</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randint.html'>torch_randint</a></span>(low = <span class='fl'>1</span>, high = <span class='fl'>5</span>, size = <span class='fl'>3</span>, dtype = <span class='fu'><a href='torch_dtype.html'>torch_long</a></span>())
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>loss</span>(<span class='kw'>input</span>, <span class='kw'>target</span>)
+<span class='kw'>output</span><span class='op'>$</span><span class='fu'>backward</span>()
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/nn_dropout.html b/static/docs/dev/reference/nn_dropout.html
new file mode 100644
index 000000000..68a657e2c
--- /dev/null
+++ b/static/docs/dev/reference/nn_dropout.html
@@ -0,0 +1,272 @@
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+    <h1>Dropout module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-dropout.R'><code>R/nn-dropout.R</code></a></small>
+    <div class="hidden name"><code>nn_dropout.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>During training, randomly zeroes some of the elements of the input
+tensor with probability <code>p</code> using samples from a Bernoulli
+distribution. Each channel will be zeroed out independently on every forward
+call.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_dropout</span>(p = <span class='fl'>0.5</span>, inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>p</th>
+      <td><p>probability of an element to be zeroed. Default: 0.5</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>If set to <code>TRUE</code>, will do this operation in-place. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>This has proven to be an effective technique for regularization and
+preventing the co-adaptation of neurons as described in the paper
+<a href='https://arxiv.org/abs/1207.0580'>Improving neural networks by preventing co-adaptation of feature detectors</a>.</p>
+<p>Furthermore, the outputs are scaled by a factor of :math:<code>\frac{1}{1-p}</code> during
+training. This means that during evaluation the module simply computes an
+identity function.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((*)\). Input can be of any shape</p></li>
+<li><p>Output: \((*)\). Output is of the same shape as input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_dropout</span>(p = <span class='fl'>0.2</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+<div class="row">
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+    <div class="page-header">
+    <h1>Dropout2D module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-dropout.R'><code>R/nn-dropout.R</code></a></small>
+    <div class="hidden name"><code>nn_dropout2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Randomly zero out entire channels (a channel is a 2D feature map,
+e.g., the \(j\)-th channel of the \(i\)-th sample in the
+batched input is a 2D tensor \(\mbox{input}[i, j]\)).</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_dropout2d</span>(p = <span class='fl'>0.5</span>, inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>p</th>
+      <td><p>(float, optional): probability of an element to be zero-ed.</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>(bool, optional): If set to <code>TRUE</code>, will do this operation
+in-place</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>Each channel will be zeroed out independently on every forward call with
+probability <code>p</code> using samples from a Bernoulli distribution.
+Usually the input comes from <a href='nn_conv2d.html'>nn_conv2d</a> modules.</p>
+<p>As described in the paper
+<a href='https://arxiv.org/abs/1411.4280'>Efficient Object Localization Using Convolutional Networks</a> ,
+if adjacent pixels within feature maps are strongly correlated
+(as is normally the case in early convolution layers) then i.i.d. dropout
+will not regularize the activations and will otherwise just result
+in an effective learning rate decrease.
+In this case, nn_dropout2d will help promote independence between
+feature maps and should be used instead.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C, H, W)\)</p></li>
+<li><p>Output: \((N, C, H, W)\) (same shape as input)</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_dropout2d</span>(p = <span class='fl'>0.2</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>32</span>, <span class='fl'>32</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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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>(p = <span class='fl'>0.5</span>, inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>p</th>
+      <td><p>(float, optional): probability of an element to be zeroed.</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>(bool, optional): If set to <code>TRUE</code>, will do this operation
+in-place</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>Each channel will be zeroed out independently on every forward call with
+probability <code>p</code> using samples from a Bernoulli distribution.
+Usually the input comes from <a href='nn_conv2d.html'>nn_conv2d</a> modules.</p>
+<p>As described in the paper
+<a href='https://arxiv.org/abs/1411.4280'>Efficient Object Localization Using Convolutional Networks</a> ,
+if adjacent pixels within feature maps are strongly correlated
+(as is normally the case in early convolution layers) then i.i.d. dropout
+will not regularize the activations and will otherwise just result
+in an effective learning rate decrease.</p>
+<p>In this case, nn_dropout3d will help promote independence between
+feature maps and should be used instead.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C, D, H, W)\)</p></li>
+<li><p>Output: \((N, C, D, H, W)\) (same shape as input)</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_dropout3d</span>(p = <span class='fl'>0.2</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>4</span>, <span class='fl'>32</span>, <span class='fl'>32</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
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+<div class="row">
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+    <div class="page-header">
+    <h1>ELU module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_elu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the element-wise function:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_elu</span>(alpha = <span class='fl'>1</span>, inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>alpha</th>
+      <td><p>the \(\alpha\) value for the ELU formulation. Default: 1.0</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+  \mbox{ELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x) - 1))
+$$</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_elu</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span>  <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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new file mode 100644
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+<meta property="og:title" content="Embedding module — nn_embedding" />
+<meta property="og:description" content="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." />
+
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+    <div class="page-header">
+    <h1>Embedding module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-sparse.R'><code>R/nn-sparse.R</code></a></small>
+    <div class="hidden name"><code>nn_embedding.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>A simple lookup table that stores embeddings of a fixed dictionary and size.
+This module is often used to store word embeddings and retrieve them using indices.
+The input to the module is a list of indices, and the output is the corresponding
+word embeddings.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_embedding</span>(
+  <span class='kw'>num_embeddings</span>,
+  <span class='kw'>embedding_dim</span>,
+  padding_idx = <span class='kw'>NULL</span>,
+  max_norm = <span class='kw'>NULL</span>,
+  norm_type = <span class='fl'>2</span>,
+  scale_grad_by_freq = <span class='fl'>FALSE</span>,
+  sparse = <span class='fl'>FALSE</span>,
+  .weight = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>num_embeddings</th>
+      <td><p>(int): size of the dictionary of embeddings</p></td>
+    </tr>
+    <tr>
+      <th>embedding_dim</th>
+      <td><p>(int): the size of each embedding vector</p></td>
+    </tr>
+    <tr>
+      <th>padding_idx</th>
+      <td><p>(int, optional): If given, pads the output with the embedding vector at <code>padding_idx</code>
+(initialized to zeros) whenever it encounters the index.</p></td>
+    </tr>
+    <tr>
+      <th>max_norm</th>
+      <td><p>(float, optional): If given, each embedding vector with norm larger than <code>max_norm</code>
+is renormalized to have norm <code>max_norm</code>.</p></td>
+    </tr>
+    <tr>
+      <th>norm_type</th>
+      <td><p>(float, optional): The p of the p-norm to compute for the <code>max_norm</code> option. Default <code>2</code>.</p></td>
+    </tr>
+    <tr>
+      <th>scale_grad_by_freq</th>
+      <td><p>(boolean, optional): If given, this will scale gradients by the inverse of frequency of
+the words in the mini-batch. Default <code>False</code>.</p></td>
+    </tr>
+    <tr>
+      <th>sparse</th>
+      <td><p>(bool, optional): If <code>True</code>, gradient w.r.t. <code>weight</code> matrix will be a sparse tensor.</p></td>
+    </tr>
+    <tr>
+      <th>.weight</th>
+      <td><p>(Tensor) embeddings weights (in case you want to set it manually)</p>
+<p>See Notes for more details regarding sparse gradients.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>Keep in mind that only a limited number of optimizers support
+sparse gradients: currently it's <code>optim.SGD</code> (<code>CUDA</code> and <code>CPU</code>),
+<code>optim.SparseAdam</code> (<code>CUDA</code> and <code>CPU</code>) and <code>optim.Adagrad</code> (<code>CPU</code>)</p>
+<p>With <code>padding_idx</code> set, the embedding vector at
+<code>padding_idx</code> is initialized to all zeros. However, note that this
+vector can be modified afterwards, e.g., using a customized
+initialization method, and thus changing the vector used to pad the
+output. The gradient for this vector from nn_embedding
+is always zero.</p>
+    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
+
+    
+
+<ul>
+<li><p>weight (Tensor): the learnable weights of the module of shape (num_embeddings, embedding_dim)
+initialized from \(\mathcal{N}(0, 1)\)</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((*)\), LongTensor of arbitrary shape containing the indices to extract</p></li>
+<li><p>Output: \((*, H)\), where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is the input shape and \(H=\mbox{embedding\_dim}\)</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'># an Embedding module containing 10 tensors of size 3</span>
+<span class='kw'>embedding</span> <span class='op'>&lt;-</span> <span class='fu'>nn_embedding</span>(<span class='fl'>10</span>, <span class='fl'>3</span>)
+<span class='co'># a batch of 2 samples of 4 indices each</span>
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>4</span>,<span class='fl'>5</span>),<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>,<span class='fl'>3</span>,<span class='fl'>2</span>,<span class='fl'>9</span>)), dtype = <span class='fu'><a href='torch_dtype.html'>torch_long</a></span>())
+<span class='fu'>embedding</span>(<span class='kw'>input</span>)
+<span class='co'># example with padding_idx</span>
+<span class='kw'>embedding</span> <span class='op'>&lt;-</span> <span class='fu'>nn_embedding</span>(<span class='fl'>10</span>, <span class='fl'>3</span>, padding_idx=<span class='fl'>1</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>3</span>,<span class='fl'>1</span>,<span class='fl'>6</span>), nrow = <span class='fl'>1</span>), dtype = <span class='fu'><a href='torch_dtype.html'>torch_long</a></span>())
+<span class='fu'>embedding</span>(<span class='kw'>input</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;   0.0000  0.0000  0.0000
+#&gt;  -1.2943 -1.0279  0.6483
+#&gt;   0.0000  0.0000  0.0000
+#&gt;   0.4053  0.7866 -0.3922
+#&gt; [ CPUFloatType{1,4,3} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nn_fractional_max_pool2d.html b/static/docs/dev/reference/nn_fractional_max_pool2d.html
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index 000000000..5d5a1432d
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Applies a 2D fractional max pooling over an input signal composed of several input planes.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_fractional_max_pool2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Fractional MaxPooling is described in detail in the paper
+<a href='https://arxiv.org/abs/1412.6071'>Fractional MaxPooling</a> by Ben Graham</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_fractional_max_pool2d</span>(
+  <span class='kw'>kernel_size</span>,
+  output_size = <span class='kw'>NULL</span>,
+  output_ratio = <span class='kw'>NULL</span>,
+  return_indices = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>the size of the window to take a max over.
+Can be a single number k (for a square kernel of k x k) or a tuple <code>(kh, kw)</code></p></td>
+    </tr>
+    <tr>
+      <th>output_size</th>
+      <td><p>the target output size of the image of the form <code>oH x oW</code>.
+Can be a tuple <code>(oH, oW)</code> or a single number oH for a square image <code>oH x oH</code></p></td>
+    </tr>
+    <tr>
+      <th>output_ratio</th>
+      <td><p>If one wants to have an output size as a ratio of the input size, this option can be given.
+This has to be a number or tuple in the range (0, 1)</p></td>
+    </tr>
+    <tr>
+      <th>return_indices</th>
+      <td><p>if <code>TRUE</code>, will return the indices along with the outputs.
+Useful to pass to <code><a href='nn_max_unpool2d.html'>nn_max_unpool2d()</a></code>. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>The max-pooling operation is applied in \(kH \times kW\) regions by a stochastic
+step size determined by the target output size.
+The number of output features is equal to the number of input planes.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'># pool of square window of size=3, and target output size 13x12</span>
+<span class='kw'>m</span> <span class='op'>=</span> <span class='fu'>nn_fractional_max_pool2d</span>(<span class='fl'>3</span>, output_size=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>13</span>, <span class='fl'>12</span>))
+<span class='co'># pool of square window and target output size being half of input image size</span>
+<span class='kw'>m</span> <span class='op'>=</span> <span class='fu'>nn_fractional_max_pool2d</span>(<span class='fl'>3</span>, output_ratio=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0.5</span>, <span class='fl'>0.5</span>))
+<span class='kw'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>32</span>)
+<span class='kw'>output</span> <span class='op'>=</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+<div class="row">
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+    <div class="page-header">
+    <h1>Applies a 3D fractional max pooling over an input signal composed of several input planes.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_fractional_max_pool3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Fractional MaxPooling is described in detail in the paper
+<a href='https://arxiv.org/abs/1412.6071'>Fractional MaxPooling</a> by Ben Graham</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_fractional_max_pool3d</span>(
+  <span class='kw'>kernel_size</span>,
+  output_size = <span class='kw'>NULL</span>,
+  output_ratio = <span class='kw'>NULL</span>,
+  return_indices = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>the size of the window to take a max over.
+Can be a single number k (for a square kernel of k x k x k) or a tuple <code>(kt x kh x kw)</code></p></td>
+    </tr>
+    <tr>
+      <th>output_size</th>
+      <td><p>the target output size of the image of the form <code>oT x oH x oW</code>.
+Can be a tuple <code>(oT, oH, oW)</code> or a single number oH for a square image <code>oH x oH x oH</code></p></td>
+    </tr>
+    <tr>
+      <th>output_ratio</th>
+      <td><p>If one wants to have an output size as a ratio of the input size, this option can be given.
+This has to be a number or tuple in the range (0, 1)</p></td>
+    </tr>
+    <tr>
+      <th>return_indices</th>
+      <td><p>if <code>TRUE</code>, will return the indices along with the outputs.
+Useful to pass to <code><a href='nn_max_unpool3d.html'>nn_max_unpool3d()</a></code>. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>The max-pooling operation is applied in \(kTxkHxkW\) regions by a stochastic
+step size determined by the target output size.
+The number of output features is equal to the number of input planes.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'># pool of cubic window of size=3, and target output size 13x12x11</span>
+<span class='kw'>m</span> <span class='op'>=</span> <span class='fu'>nn_fractional_max_pool3d</span>(<span class='fl'>3</span>, output_size=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>13</span>, <span class='fl'>12</span>, <span class='fl'>11</span>))
+<span class='co'># pool of cubic window and target output size being half of input size</span>
+<span class='kw'>m</span> <span class='op'>=</span> <span class='fu'>nn_fractional_max_pool3d</span>(<span class='fl'>3</span>, output_ratio=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0.5</span>, <span class='fl'>0.5</span>, <span class='fl'>0.5</span>))
+<span class='kw'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>32</span>, <span class='fl'>16</span>)
+<span class='kw'>output</span> <span class='op'>=</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nn_gelu.html b/static/docs/dev/reference/nn_gelu.html
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+$$\mbox{GELU}(x) = x * \Phi(x)$$" />
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+    <div class="page-header">
+    <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><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>=</span> <span class='fu'>nn_gelu</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+<meta property="og:description" content="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." />
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+<div class="row">
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+    <div class="page-header">
+    <h1>GLU module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_glu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the gated linear unit function
+\({GLU}(a, b)= a \otimes \sigma(b)\) where \(a\) is the first half
+of the input matrices and \(b\) is the second half.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_glu</span>(dim = <span class='op'>-</span><span class='fl'>1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>dim</th>
+      <td><p>(int): the dimension on which to split the input. Default: -1</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((\ast_1, N, \ast_2)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((\ast_1, M, \ast_2)\) where \(M=N/2\)</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_glu</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>4</span>, <span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </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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+    <h1>Hardshwink module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_hardshrink.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the hard shrinkage function element-wise:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_hardshrink</span>(lambd = <span class='fl'>0.5</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>lambd</th>
+      <td><p>the \(\lambda\) value for the Hardshrink formulation. Default: 0.5</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+  \mbox{HardShrink}(x) =
+  \left\{ \begin{array}{ll}
+x, &amp; \mbox{ if } x &gt; \lambda \\
+x, &amp; \mbox{ if } x &lt; -\lambda \\
+0, &amp; \mbox{ otherwise }
+\end{array}
+\right.
+$$</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_hardshrink</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
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+    <div class="page-header">
+    <h1>Hardsigmoid module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_hardsigmoid.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the element-wise function:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_hardsigmoid</span>()</pre>
+
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+\mbox{Hardsigmoid}(x) = \left\{ \begin{array}{ll}
+  0 &amp; \mbox{if~} x \le -3, \\
+  1 &amp; \mbox{if~} x \ge +3, \\
+  x / 6 + 1 / 2 &amp; \mbox{otherwise}
+\end{array}
+\right.
+$$</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_hardsigmoid</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <h1>Hardswish module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_hardswish.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the hardswish function, element-wise, as described in the paper:
+<a href='https://arxiv.org/abs/1905.02244'>Searching for MobileNetV3</a></p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_hardswish</span>()</pre>
+
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$ \mbox{Hardswish}(x) = \left\{
+  \begin{array}{ll}
+  0 &amp; \mbox{if } x \le -3, \\
+  x &amp; \mbox{if } x \ge +3, \\
+  x \cdot (x + 3)/6 &amp; \mbox{otherwise}
+  \end{array}
+  \right. $$</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_hardswish</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+}
+
+}
+</div></pre>
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+HardTanh is defined as:" />
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+    <div class="page-header">
+    <h1>Hardtanh module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_hardtanh.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the HardTanh function element-wise
+HardTanh is defined as:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_hardtanh</span>(min_val = <span class='op'>-</span><span class='fl'>1</span>, max_val = <span class='fl'>1</span>, inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>min_val</th>
+      <td><p>minimum value of the linear region range. Default: -1</p></td>
+    </tr>
+    <tr>
+      <th>max_val</th>
+      <td><p>maximum value of the linear region range. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+\mbox{HardTanh}(x) = \left\{ \begin{array}{ll}
+  1 &amp; \mbox{ if } x &gt; 1 \\
+  -1 &amp; \mbox{ if } x &lt; -1 \\
+  x &amp; \mbox{ otherwise } \\
+\end{array}
+\right.
+$$</p>
+<p>The range of the linear region :math:<code>[-1, 1]</code> can be adjusted using
+<code>min_val</code> and <code>max_val</code>.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_hardtanh</span>(<span class='op'>-</span><span class='fl'>2</span>, <span class='fl'>2</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nn_identity.html b/static/docs/dev/reference/nn_identity.html
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+    <h1>Identity module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-linear.R'><code>R/nn-linear.R</code></a></small>
+    <div class="hidden name"><code>nn_identity.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>A placeholder identity operator that is argument-insensitive.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_identity</span>(<span class='kw'>...</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>...</th>
+      <td><p>any arguments (unused)</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_identity</span>(<span class='fl'>54</span>, unused_argument1 = <span class='fl'>0.1</span>, unused_argument2 = <span class='fl'>FALSE</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>128</span>, <span class='fl'>20</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='kw'>output</span><span class='op'>$</span><span class='fu'>size</span>())
+
+}
+</div><div class='output co'>#&gt; [1] 128  20</div></pre>
+  </div>
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diff --git a/static/docs/dev/reference/nn_init_calculate_gain.html b/static/docs/dev/reference/nn_init_calculate_gain.html
new file mode 100644
index 000000000..8ddfa92b8
--- /dev/null
+++ b/static/docs/dev/reference/nn_init_calculate_gain.html
@@ -0,0 +1,241 @@
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+    <div class="page-header">
+    <h1>Calculate gain</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_calculate_gain.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Return the recommended gain value for the given nonlinearity function.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_init_calculate_gain</span>(<span class='kw'>nonlinearity</span>, param = <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>nonlinearity</th>
+      <td><p>the non-linear function</p></td>
+    </tr>
+    <tr>
+      <th>param</th>
+      <td><p>optional parameter for the non-linear function</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
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diff --git a/static/docs/dev/reference/nn_init_constant_.html b/static/docs/dev/reference/nn_init_constant_.html
new file mode 100644
index 000000000..21b0b2f2d
--- /dev/null
+++ b/static/docs/dev/reference/nn_init_constant_.html
@@ -0,0 +1,252 @@
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+    <h1>Constant 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_constant_.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Fills the input Tensor with the value <code>val</code>.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_init_constant_</span>(<span class='kw'>tensor</span>, <span class='kw'>val</span>)</pre>
+
+    <h2 class="hasAnchor" 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>val</th>
+      <td><p>the value to fill the tensor with</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
+<span class='fu'>nn_init_constant_</span>(<span class='kw'>w</span>, <span class='fl'>0.3</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.3000  0.3000  0.3000  0.3000  0.3000
+#&gt;  0.3000  0.3000  0.3000  0.3000  0.3000
+#&gt;  0.3000  0.3000  0.3000  0.3000  0.3000
+#&gt; [ CPUFloatType{3,5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nn_init_dirac_.html b/static/docs/dev/reference/nn_init_dirac_.html
new file mode 100644
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+delta function. Preserves the identity of the inputs in Convolutional
+layers, where as many input channels are preserved as possible. In case
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Dirac initialization</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
+    <div class="hidden name"><code>nn_init_dirac_.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Fills the 3, 4, 5-dimensional input <code>Tensor</code> with the Dirac
+delta function. Preserves the identity of the inputs in <code>Convolutional</code>
+layers, where as many input channels are preserved as possible. In case
+of groups&gt;1, each group of channels preserves identity.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_init_dirac_</span>(<span class='kw'>tensor</span>, groups = <span class='fl'>1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensor</th>
+      <td><p>a 3, 4, 5-dimensional <code>torch.Tensor</code></p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>(optional) number of groups in the conv layer (default: 1)</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>16</span>, <span class='fl'>5</span>, <span class='fl'>5</span>)
+<span class='fu'>nn_init_dirac_</span>(<span class='kw'>w</span>)
+}
+
+}
+</div></pre>
+  </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/static/docs/dev/reference/nn_init_eye_.html b/static/docs/dev/reference/nn_init_eye_.html
new file mode 100644
index 000000000..60af72337
--- /dev/null
+++ b/static/docs/dev/reference/nn_init_eye_.html
@@ -0,0 +1,252 @@
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+
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+    <div class="page-header">
+    <h1>Eye 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_eye_.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Fills the 2-dimensional input <code>Tensor</code> with the identity matrix.
+Preserves the identity of the inputs in <code>Linear</code> layers, where as
+many inputs are preserved as possible.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_init_eye_</span>(<span class='kw'>tensor</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensor</th>
+      <td><p>a 2-dimensional torch tensor.</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
+<span class='fu'>nn_init_eye_</span>(<span class='kw'>w</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1  0  0  0  0
+#&gt;  0  1  0  0  0
+#&gt;  0  0  1  0  0
+#&gt; [ CPUFloatType{3,5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nn_init_kaiming_normal_.html b/static/docs/dev/reference/nn_init_kaiming_normal_.html
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+++ b/static/docs/dev/reference/nn_init_kaiming_normal_.html
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+<meta property="og:description" content="Fills the input Tensor with values according to the method
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+
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+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Kaiming normal initialization</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
+    <div class="hidden name"><code>nn_init_kaiming_normal_.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Fills the input <code>Tensor</code> with values according to the method
+described in <code>Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification</code> - He, K. et al. (2015), using a
+normal distribution.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_init_kaiming_normal_</span>(
+  <span class='kw'>tensor</span>,
+  a = <span class='fl'>0</span>,
+  mode = <span class='st'>"fan_in"</span>,
+  nonlinearity = <span class='st'>"leaky_relu"</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensor</th>
+      <td><p>an n-dimensional <code>torch.Tensor</code></p></td>
+    </tr>
+    <tr>
+      <th>a</th>
+      <td><p>the negative slope of the rectifier used after this layer (only used
+with <code>'leaky_relu'</code>)</p></td>
+    </tr>
+    <tr>
+      <th>mode</th>
+      <td><p>either 'fan_in' (default) or 'fan_out'. Choosing 'fan_in' preserves
+the magnitude of the variance of the weights in the forward pass. Choosing
+'fan_out' preserves the magnitudes in the backwards pass.</p></td>
+    </tr>
+    <tr>
+      <th>nonlinearity</th>
+      <td><p>the non-linear function. recommended to use only with 'relu'
+or 'leaky_relu' (default).</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
+<span class='fu'>nn_init_kaiming_normal_</span>(<span class='kw'>w</span>, mode = <span class='st'>"fan_in"</span>, nonlinearity = <span class='st'>"leaky_relu"</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0.5594  0.2408  0.3946  0.5860 -0.4834
+#&gt; -0.0442  0.7170 -0.3028  0.4015 -0.8906
+#&gt; -0.5157 -0.1763  0.9366  0.4640 -0.5356
+#&gt; [ CPUFloatType{3,5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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diff --git a/static/docs/dev/reference/nn_init_kaiming_uniform_.html b/static/docs/dev/reference/nn_init_kaiming_uniform_.html
new file mode 100644
index 000000000..f76da57af
--- /dev/null
+++ b/static/docs/dev/reference/nn_init_kaiming_uniform_.html
@@ -0,0 +1,273 @@
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+    <div class="page-header">
+    <h1>Kaiming uniform initialization</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
+    <div class="hidden name"><code>nn_init_kaiming_uniform_.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Fills the input <code>Tensor</code> with values according to the method
+described in <code>Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification</code> - He, K. et al. (2015), using a
+uniform distribution.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_init_kaiming_uniform_</span>(
+  <span class='kw'>tensor</span>,
+  a = <span class='fl'>0</span>,
+  mode = <span class='st'>"fan_in"</span>,
+  nonlinearity = <span class='st'>"leaky_relu"</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensor</th>
+      <td><p>an n-dimensional <code>torch.Tensor</code></p></td>
+    </tr>
+    <tr>
+      <th>a</th>
+      <td><p>the negative slope of the rectifier used after this layer (only used
+with <code>'leaky_relu'</code>)</p></td>
+    </tr>
+    <tr>
+      <th>mode</th>
+      <td><p>either 'fan_in' (default) or 'fan_out'. Choosing 'fan_in' preserves
+the magnitude of the variance of the weights in the forward pass. Choosing
+'fan_out' preserves the magnitudes in the backwards pass.</p></td>
+    </tr>
+    <tr>
+      <th>nonlinearity</th>
+      <td><p>the non-linear function. recommended to use only with 'relu'
+or 'leaky_relu' (default).</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
+<span class='fu'>nn_init_kaiming_uniform_</span>(<span class='kw'>w</span>, mode = <span class='st'>"fan_in"</span>, nonlinearity = <span class='st'>"leaky_relu"</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0.7460  0.2070 -0.1066 -0.4344 -0.4666
+#&gt; -0.5351 -0.4524  0.0950 -1.0077 -0.2169
+#&gt; -0.9525  0.8753  0.0070 -0.4553 -0.3445
+#&gt; [ CPUFloatType{3,5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/nn_init_normal_.html b/static/docs/dev/reference/nn_init_normal_.html
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+++ b/static/docs/dev/reference/nn_init_normal_.html
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+    <div class="page-header">
+    <h1>Normal initialization</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
+    <div class="hidden name"><code>nn_init_normal_.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Fills the input Tensor with values drawn from the normal distribution</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_init_normal_</span>(<span class='kw'>tensor</span>, mean = <span class='fl'>0</span>, std = <span class='fl'>1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensor</th>
+      <td><p>an n-dimensional Tensor</p></td>
+    </tr>
+    <tr>
+      <th>mean</th>
+      <td><p>the mean of the normal distribution</p></td>
+    </tr>
+    <tr>
+      <th>std</th>
+      <td><p>the standard deviation of the normal distribution</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
+<span class='fu'>nn_init_normal_</span>(<span class='kw'>w</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -1.0569 -1.0900  1.2740 -1.7728  0.0593
+#&gt; -1.7131 -0.1353  0.8191  0.1481 -0.9940
+#&gt; -0.7544 -1.0298  0.4237  1.4650  0.0575
+#&gt; [ CPUFloatType{3,5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/nn_init_ones_.html b/static/docs/dev/reference/nn_init_ones_.html
new file mode 100644
index 000000000..b86684d78
--- /dev/null
+++ b/static/docs/dev/reference/nn_init_ones_.html
@@ -0,0 +1,248 @@
+<!-- Generated by pkgdown: do not edit by hand -->
+<!DOCTYPE html>
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+<title>Ones initialization — nn_init_ones_ • torch</title>
+
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+    <h1>Ones 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_ones_.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Fills the input Tensor with the scalar value <code>1</code></p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_init_ones_</span>(<span class='kw'>tensor</span>)</pre>
+
+    <h2 class="hasAnchor" 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>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
+<span class='fu'>nn_init_ones_</span>(<span class='kw'>w</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1  1  1  1  1
+#&gt;  1  1  1  1  1
+#&gt;  1  1  1  1  1
+#&gt; [ CPUFloatType{3,5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nn_init_orthogonal_.html b/static/docs/dev/reference/nn_init_orthogonal_.html
new file mode 100644
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+++ b/static/docs/dev/reference/nn_init_orthogonal_.html
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+<meta property="og:description" content="Fills the input Tensor with a (semi) orthogonal matrix, as
+described in Exact solutions to the nonlinear dynamics of learning in deep linear neural networks - Saxe, A. et al. (2013). The input tensor must have
+at least 2 dimensions, and for tensors with more than 2 dimensions the
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Orthogonal initialization</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
+    <div class="hidden name"><code>nn_init_orthogonal_.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Fills the input <code>Tensor</code> with a (semi) orthogonal matrix, as
+described in <code>Exact solutions to the nonlinear dynamics of learning in deep linear neural networks</code> - Saxe, A. et al. (2013). The input tensor must have
+at least 2 dimensions, and for tensors with more than 2 dimensions the
+trailing dimensions are flattened.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_init_orthogonal_</span>(<span class='kw'>tensor</span>, gain = <span class='fl'>1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensor</th>
+      <td><p>an n-dimensional <code>Tensor</code></p></td>
+    </tr>
+    <tr>
+      <th>gain</th>
+      <td><p>optional scaling factor</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>,<span class='fl'>5</span>)
+<span class='fu'>nn_init_orthogonal_</span>(<span class='kw'>w</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.0648  0.4597 -0.1806 -0.8669  0.0179
+#&gt;  0.5648  0.4042  0.5758  0.1281 -0.4119
+#&gt; -0.1093 -0.5109  0.7101 -0.4227  0.2102
+#&gt; [ CPUFloatType{3,5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/nn_init_sparse_.html b/static/docs/dev/reference/nn_init_sparse_.html
new file mode 100644
index 000000000..ef8017aa0
--- /dev/null
+++ b/static/docs/dev/reference/nn_init_sparse_.html
@@ -0,0 +1,258 @@
+<!-- Generated by pkgdown: do not edit by hand -->
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+
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+    <div class="page-header">
+    <h1>Sparse initialization</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
+    <div class="hidden name"><code>nn_init_sparse_.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Fills the 2D input <code>Tensor</code> as a sparse matrix, where the
+non-zero elements will be drawn from the normal distribution
+as described in <code>Deep learning via Hessian-free optimization</code> - Martens, J. (2010).</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_init_sparse_</span>(<span class='kw'>tensor</span>, <span class='kw'>sparsity</span>, std = <span class='fl'>0.01</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensor</th>
+      <td><p>an n-dimensional <code>Tensor</code></p></td>
+    </tr>
+    <tr>
+      <th>sparsity</th>
+      <td><p>The fraction of elements in each column to be set to zero</p></td>
+    </tr>
+    <tr>
+      <th>std</th>
+      <td><p>the standard deviation of the normal distribution used to generate
+the non-zero values</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
+<span class='fu'>nn_init_sparse_</span>(<span class='kw'>w</span>, sparsity = <span class='fl'>0.1</span>)
+}
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nn_init_trunc_normal_.html b/static/docs/dev/reference/nn_init_trunc_normal_.html
new file mode 100644
index 000000000..6b55c98b3
--- /dev/null
+++ b/static/docs/dev/reference/nn_init_trunc_normal_.html
@@ -0,0 +1,266 @@
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+<meta property="og:description" content="Fills the input Tensor with values drawn from a truncated
+normal distribution." />
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Truncated normal initialization</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
+    <div class="hidden name"><code>nn_init_trunc_normal_.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Fills the input Tensor with values drawn from a truncated
+normal distribution.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_init_trunc_normal_</span>(<span class='kw'>tensor</span>, mean = <span class='fl'>0</span>, std = <span class='fl'>1</span>, a = <span class='op'>-</span><span class='fl'>2</span>, b = <span class='op'>-</span><span class='fl'>2</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensor</th>
+      <td><p>an n-dimensional Tensor</p></td>
+    </tr>
+    <tr>
+      <th>mean</th>
+      <td><p>the mean of the normal distribution</p></td>
+    </tr>
+    <tr>
+      <th>std</th>
+      <td><p>the standard deviation of the normal distribution</p></td>
+    </tr>
+    <tr>
+      <th>a</th>
+      <td><p>the minimum cutoff value</p></td>
+    </tr>
+    <tr>
+      <th>b</th>
+      <td><p>the maximum cutoff value</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
+<span class='fu'>nn_init_trunc_normal_</span>(<span class='kw'>w</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -2 -2 -2 -2 -2
+#&gt; -2 -2 -2 -2 -2
+#&gt; -2 -2 -2 -2 -2
+#&gt; [ CPUFloatType{3,5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
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diff --git a/static/docs/dev/reference/nn_init_uniform_.html b/static/docs/dev/reference/nn_init_uniform_.html
new file mode 100644
index 000000000..84adc840c
--- /dev/null
+++ b/static/docs/dev/reference/nn_init_uniform_.html
@@ -0,0 +1,256 @@
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+    <h1>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_uniform_.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Fills the input Tensor with values drawn from the uniform distribution</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_init_uniform_</span>(<span class='kw'>tensor</span>, a = <span class='fl'>0</span>, b = <span class='fl'>1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensor</th>
+      <td><p>an n-dimensional Tensor</p></td>
+    </tr>
+    <tr>
+      <th>a</th>
+      <td><p>the lower bound of the uniform distribution</p></td>
+    </tr>
+    <tr>
+      <th>b</th>
+      <td><p>the upper bound of the uniform distribution</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
+<span class='fu'>nn_init_uniform_</span>(<span class='kw'>w</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.8556  0.9331  0.3515  0.8071  0.4948
+#&gt;  0.6075  0.9042  0.7181  0.7329  0.7563
+#&gt;  0.2584  0.5293  0.9757  0.3030  0.3341
+#&gt; [ CPUFloatType{3,5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nn_init_xavier_normal_.html b/static/docs/dev/reference/nn_init_xavier_normal_.html
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+<meta property="og:description" content="Fills the input Tensor with values according to the method
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Xavier normal initialization</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
+    <div class="hidden name"><code>nn_init_xavier_normal_.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Fills the input <code>Tensor</code> with values according to the method
+described in <code>Understanding the difficulty of training deep feedforward neural networks</code> - Glorot, X. &amp; Bengio, Y. (2010), using a normal
+distribution.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_init_xavier_normal_</span>(<span class='kw'>tensor</span>, gain = <span class='fl'>1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensor</th>
+      <td><p>an n-dimensional <code>Tensor</code></p></td>
+    </tr>
+    <tr>
+      <th>gain</th>
+      <td><p>an optional scaling factor</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
+<span class='fu'>nn_init_xavier_normal_</span>(<span class='kw'>w</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.2535 -0.2197  0.5425 -3.0052 -4.2446
+#&gt; -0.3570 -1.6970 -2.0154 -0.5348  2.7582
+#&gt;  0.8714 -0.8924  0.7675  3.2553 -1.4333
+#&gt; [ CPUFloatType{3,5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/nn_init_xavier_uniform_.html b/static/docs/dev/reference/nn_init_xavier_uniform_.html
new file mode 100644
index 000000000..bcd466989
--- /dev/null
+++ b/static/docs/dev/reference/nn_init_xavier_uniform_.html
@@ -0,0 +1,256 @@
+<!-- Generated by pkgdown: do not edit by hand -->
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+    <div class="page-header">
+    <h1>Xavier 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_xavier_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>Understanding the difficulty of training deep feedforward neural networks</code> - Glorot, X. &amp; Bengio, Y. (2010), using a uniform
+distribution.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_init_xavier_uniform_</span>(<span class='kw'>tensor</span>, gain = <span class='fl'>1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensor</th>
+      <td><p>an n-dimensional <code>Tensor</code></p></td>
+    </tr>
+    <tr>
+      <th>gain</th>
+      <td><p>an optional scaling factor</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
+<span class='fu'>nn_init_xavier_uniform_</span>(<span class='kw'>w</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.3397  1.1040 -3.0453 -1.7935  0.9545
+#&gt; -0.0194 -2.4483  2.9345  2.2750 -2.4048
+#&gt; -0.4406 -2.2409  0.4155 -0.1573  1.9776
+#&gt; [ CPUFloatType{3,5} ]</div></pre>
+  </div>
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diff --git a/static/docs/dev/reference/nn_init_zeros_.html b/static/docs/dev/reference/nn_init_zeros_.html
new 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>Zeros initialization</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
+    <div class="hidden name"><code>nn_init_zeros_.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Fills the input Tensor with the scalar value <code>0</code></p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_init_zeros_</span>(<span class='kw'>tensor</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensor</th>
+      <td><p>an n-dimensional tensor</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
+<span class='fu'>nn_init_zeros_</span>(<span class='kw'>w</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0  0  0  0  0
+#&gt;  0  0  0  0  0
+#&gt;  0  0  0  0  0
+#&gt; [ CPUFloatType{3,5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/nn_leaky_relu.html b/static/docs/dev/reference/nn_leaky_relu.html
new file mode 100644
index 000000000..5c79b5318
--- /dev/null
+++ b/static/docs/dev/reference/nn_leaky_relu.html
@@ -0,0 +1,273 @@
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+    <h1>LeakyReLU 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_leaky_relu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the element-wise function:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_leaky_relu</span>(negative_slope = <span class='fl'>0.01</span>, inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>negative_slope</th>
+      <td><p>Controls the angle of the negative slope. Default: 1e-2</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+  \mbox{LeakyReLU}(x) = \max(0, x) + \mbox{negative\_slope} * \min(0, x)
+$$
+or</p>
+<p>$$
+  \mbox{LeakyRELU}(x) =
+  \left\{ \begin{array}{ll}
+x, &amp; \mbox{ if } x \geq 0 \\
+\mbox{negative\_slope} \times x, &amp; \mbox{ otherwise }
+\end{array}
+\right.
+$$</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_leaky_relu</span>(<span class='fl'>0.1</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
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+    <h1>Linear module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-linear.R'><code>R/nn-linear.R</code></a></small>
+    <div class="hidden name"><code>nn_linear.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a linear transformation to the incoming data: <code>y = xA^T + b</code></p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_linear</span>(<span class='kw'>in_features</span>, <span class='kw'>out_features</span>, bias = <span class='fl'>TRUE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>in_features</th>
+      <td><p>size of each input sample</p></td>
+    </tr>
+    <tr>
+      <th>out_features</th>
+      <td><p>size of each output sample</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>If set to <code>FALSE</code>, the layer will not learn an additive bias.
+Default: <code>TRUE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: <code>(N, *, H_in)</code> where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means any number of
+additional dimensions and <code>H_in = in_features</code>.</p></li>
+<li><p>Output: <code>(N, *, H_out)</code> where all but the last dimension
+are the same shape as the input and :math:<code>H_out = out_features</code>.</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
+
+    
+
+<ul>
+<li><p>weight: the learnable weights of the module of shape
+<code>(out_features, in_features)</code>. The values are
+initialized from \(U(-\sqrt{k}, \sqrt{k})\)s, where
+\(k = \frac{1}{\mbox{in\_features}}\)</p></li>
+<li><p>bias: the learnable bias of the module of shape \((\mbox{out\_features})\).
+If <code>bias</code> is <code>TRUE</code>, the values are initialized from
+\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
+\(k = \frac{1}{\mbox{in\_features}}\)</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_linear</span>(<span class='fl'>20</span>, <span class='fl'>30</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>128</span>, <span class='fl'>20</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='kw'>output</span><span class='op'>$</span><span class='fu'>size</span>())
+
+}
+</div><div class='output co'>#&gt; [1] 128  30</div></pre>
+  </div>
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+<meta property="og:description" content="Applies the element-wise function:
+$$
+  \mbox{LogSigmoid}(x) = \log\left(\frac{ 1 }{ 1 + \exp(-x)}\right)
+ $$" />
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+
+      
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>LogSigmoid module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_log_sigmoid.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the element-wise function:
+$$
+  \mbox{LogSigmoid}(x) = \log\left(\frac{ 1 }{ 1 + \exp(-x)}\right)
+ $$</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_log_sigmoid</span>()</pre>
+
+
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_log_sigmoid</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
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diff --git a/static/docs/dev/reference/nn_log_softmax.html b/static/docs/dev/reference/nn_log_softmax.html
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+<meta property="og:description" content="Applies the \(\log(\mbox{Softmax}(x))\) function to an n-dimensional
+input Tensor. The LogSoftmax formulation can be simplified as:" />
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+
+      
+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>LogSoftmax module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_log_softmax.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the \(\log(\mbox{Softmax}(x))\) function to an n-dimensional
+input Tensor. The LogSoftmax formulation can be simplified as:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_log_softmax</span>(<span class='kw'>dim</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>dim</th>
+      <td><p>(int): A dimension along which LogSoftmax will be computed.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+
+    <p>a Tensor of the same dimension and shape as the input with
+values in the range [-inf, 0)</p>
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+  \mbox{LogSoftmax}(x_{i}) = \log\left(\frac{\exp(x_i) }{ \sum_j \exp(x_j)} \right)
+$$</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((*)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((*)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_log_softmax</span>(<span class='fl'>1</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
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+    <div class="page-header">
+    <h1>Applies a 1D power-average pooling over an input signal composed of several input
+planes.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_lp_pool1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>On each window, the function computed is:</p>
+<p>$$
+  f(X) = \sqrt[p]{\sum_{x \in X} x^{p}}
+$$</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_lp_pool1d</span>(<span class='kw'>norm_type</span>, <span class='kw'>kernel_size</span>, stride = <span class='kw'>NULL</span>, ceil_mode = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>norm_type</th>
+      <td><p>if inf than one gets max pooling if 0 you get sum pooling (
+proportional to the avg pooling)</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>a single int, the size of the window</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>a single int, the stride of the window. Default value is <code>kernel_size</code></p></td>
+    </tr>
+    <tr>
+      <th>ceil_mode</th>
+      <td><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    
+<ul>
+<li><p>At p = \(\infty\), one gets Max Pooling</p></li>
+<li><p>At p = 1, one gets Sum Pooling (which is proportional to Average Pooling)</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>If the sum to the power of <code>p</code> is zero, the gradient of this function is
+not defined. This implementation will set the gradient to zero in this case.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C, L_{in})\)</p></li>
+<li><p>Output: \((N, C, L_{out})\), where</p></li>
+</ul>
+
+<p>$$
+  L_{out} = \left\lfloor\frac{L_{in} - \mbox{kernel\_size}}{\mbox{stride}} + 1\right\rfloor
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'># power-2 pool of window of length 3, with stride 2.</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_lp_pool1d</span>(<span class='fl'>2</span>, <span class='fl'>3</span>, stride=<span class='fl'>2</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Applies a 2D power-average pooling over an input signal composed of several input
+planes.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_lp_pool2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>On each window, the function computed is:</p>
+<p>$$
+  f(X) = \sqrt[p]{\sum_{x \in X} x^{p}}
+$$</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_lp_pool2d</span>(<span class='kw'>norm_type</span>, <span class='kw'>kernel_size</span>, stride = <span class='kw'>NULL</span>, ceil_mode = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>norm_type</th>
+      <td><p>if inf than one gets max pooling if 0 you get sum pooling (
+proportional to the avg pooling)</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>the size of the window</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
+    </tr>
+    <tr>
+      <th>ceil_mode</th>
+      <td><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    
+<ul>
+<li><p>At p = \(\infty\), one gets Max Pooling</p></li>
+<li><p>At p = 1, one gets Sum Pooling (which is proportional to average pooling)</p></li>
+</ul>
+
+<p>The parameters <code>kernel_size</code>, <code>stride</code> can either be:</p><ul>
+<li><p>a single <code>int</code> -- in which case the same value is used for the height and width dimension</p></li>
+<li><p>a <code>tuple</code> of two ints -- in which case, the first <code>int</code> is used for the height dimension,
+and the second <code>int</code> for the width dimension</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>If the sum to the power of <code>p</code> is zero, the gradient of this function is
+not defined. This implementation will set the gradient to zero in this case.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C, H_{in}, W_{in})\)</p></li>
+<li><p>Output: \((N, C, H_{out}, W_{out})\), where</p></li>
+</ul>
+
+<p>$$
+  H_{out} = \left\lfloor\frac{H_{in} - \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor
+$$
+$$
+  W_{out} = \left\lfloor\frac{W_{in} - \mbox{kernel\_size}[1]}{\mbox{stride}[1]} + 1\right\rfloor
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+  
+<span class='co'># power-2 pool of square window of size=3, stride=2</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_lp_pool2d</span>(<span class='fl'>2</span>, <span class='fl'>3</span>, stride=<span class='fl'>2</span>)
+<span class='co'># pool of non-square window of power 1.2</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_lp_pool2d</span>(<span class='fl'>1.2</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>), stride=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>))
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>32</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <h1>MaxPool1D module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_max_pool1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 1D max pooling over an input signal composed of several input
+planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_max_pool1d</span>(
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='kw'>NULL</span>,
+  padding = <span class='fl'>0</span>,
+  dilation = <span class='fl'>1</span>,
+  return_indices = <span class='fl'>FALSE</span>,
+  ceil_mode = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>the size of the window to take a max over</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit zero padding to be added on both sides</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>a parameter that controls the stride of elements in the window</p></td>
+    </tr>
+    <tr>
+      <th>return_indices</th>
+      <td><p>if <code>TRUE</code>, will return the max indices along with the outputs.
+Useful for  <code><a href='nn_max_unpool1d.html'>nn_max_unpool1d()</a></code> later.</p></td>
+    </tr>
+    <tr>
+      <th>ceil_mode</th>
+      <td><p>when <code>TRUE</code>, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>In the simplest case, the output value of the layer with input size \((N, C, L)\)
+and output \((N, C, L_{out})\) can be precisely described as:</p>
+<p>$$
+  out(N_i, C_j, k) = \max_{m=0, \ldots, \mbox{kernel\_size} - 1}
+input(N_i, C_j, stride \times k + m)
+$$</p>
+<p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on both sides
+for <code>padding</code> number of points. <code>dilation</code> controls the spacing between the kernel points.
+It is harder to describe, but this <a href='https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md'>link</a>
+has a nice visualization of what <code>dilation</code> does.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C, L_{in})\)</p></li>
+<li><p>Output: \((N, C, L_{out})\), where</p></li>
+</ul>
+
+<p>$$
+  L_{out} = \left\lfloor \frac{L_{in} + 2 \times \mbox{padding} - \mbox{dilation}
+    \times (\mbox{kernel\_size} - 1) - 1}{\mbox{stride}} + 1\right\rfloor
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'># pool of size=3, stride=2</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_max_pool1d</span>(<span class='fl'>3</span>, stride=<span class='fl'>2</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
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+    <div class="page-header">
+    <h1>MaxPool2D module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_max_pool2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 2D max pooling over an input signal composed of several input
+planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_max_pool2d</span>(
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='kw'>NULL</span>,
+  padding = <span class='fl'>0</span>,
+  dilation = <span class='fl'>1</span>,
+  return_indices = <span class='fl'>FALSE</span>,
+  ceil_mode = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>the size of the window to take a max over</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit zero padding to be added on both sides</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>a parameter that controls the stride of elements in the window</p></td>
+    </tr>
+    <tr>
+      <th>return_indices</th>
+      <td><p>if <code>TRUE</code>, will return the max indices along with the outputs.
+Useful for <code><a href='nn_max_unpool2d.html'>nn_max_unpool2d()</a></code> later.</p></td>
+    </tr>
+    <tr>
+      <th>ceil_mode</th>
+      <td><p>when <code>TRUE</code>, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>In the simplest case, the output value of the layer with input size \((N, C, H, W)\),
+output \((N, C, H_{out}, W_{out})\) and <code>kernel_size</code> \((kH, kW)\)
+can be precisely described as:</p>
+<p>$$
+\begin{array}{ll}
+out(N_i, C_j, h, w) ={} &amp; \max_{m=0, \ldots, kH-1} \max_{n=0, \ldots, kW-1} \\
+&amp; \mbox{input}(N_i, C_j, \mbox{stride[0]} \times h + m,
+               \mbox{stride[1]} \times w + n)
+\end{array}
+$$</p>
+<p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on both sides
+for <code>padding</code> number of points. <code>dilation</code> controls the spacing between the kernel points.
+It is harder to describe, but this <code>link</code> has a nice visualization of what <code>dilation</code> does.</p>
+<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>dilation</code> can either be:</p><ul>
+<li><p>a single <code>int</code> -- in which case the same value is used for the height and width dimension</p></li>
+<li><p>a <code>tuple</code> of two ints -- in which case, the first <code>int</code> is used for the height dimension,
+and the second <code>int</code> for the width dimension</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C, H_{in}, W_{in})\)</p></li>
+<li><p>Output: \((N, C, H_{out}, W_{out})\), where</p></li>
+</ul>
+
+<p>$$
+  H_{out} = \left\lfloor\frac{H_{in} + 2 * \mbox{padding[0]} - \mbox{dilation[0]}
+    \times (\mbox{kernel\_size[0]} - 1) - 1}{\mbox{stride[0]}} + 1\right\rfloor
+$$</p>
+<p>$$
+  W_{out} = \left\lfloor\frac{W_{in} + 2 * \mbox{padding[1]} - \mbox{dilation[1]}
+    \times (\mbox{kernel\_size[1]} - 1) - 1}{\mbox{stride[1]}} + 1\right\rfloor
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'># pool of square window of size=3, stride=2</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_max_pool2d</span>(<span class='fl'>3</span>, stride=<span class='fl'>2</span>)
+<span class='co'># pool of non-square window</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_max_pool2d</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>), stride=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>))
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>32</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Applies a 3D max pooling over an input signal composed of several input
+planes.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_max_pool3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>In the simplest case, the output value of the layer with input size \((N, C, D, H, W)\),
+output \((N, C, D_{out}, H_{out}, W_{out})\) and <code>kernel_size</code> \((kD, kH, kW)\)
+can be precisely described as:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_max_pool3d</span>(
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='kw'>NULL</span>,
+  padding = <span class='fl'>0</span>,
+  dilation = <span class='fl'>1</span>,
+  return_indices = <span class='fl'>FALSE</span>,
+  ceil_mode = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>the size of the window to take a max over</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit zero padding to be added on all three sides</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>a parameter that controls the stride of elements in the window</p></td>
+    </tr>
+    <tr>
+      <th>return_indices</th>
+      <td><p>if <code>TRUE</code>, will return the max indices along with the outputs.
+Useful for <code>torch_nn.MaxUnpool3d</code> later</p></td>
+    </tr>
+    <tr>
+      <th>ceil_mode</th>
+      <td><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+\begin{array}{ll}
+\mbox{out}(N_i, C_j, d, h, w) = &amp; \max_{k=0, \ldots, kD-1} \max_{m=0, \ldots, kH-1} \max_{n=0, \ldots, kW-1} \\
+ &amp; \mbox{input}(N_i, C_j, \mbox{stride[0]} \times d + k, \mbox{stride[1]} \times h + m, \mbox{stride[2]} \times w + n)
+\end{array}
+$$</p>
+<p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on both sides
+for <code>padding</code> number of points. <code>dilation</code> controls the spacing between the kernel points.
+It is harder to describe, but this <code>link</code>_ has a nice visualization of what <code>dilation</code> does.
+The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>dilation</code> can either be:</p><ul>
+<li><p>a single <code>int</code> -- in which case the same value is used for the depth, height and width dimension</p></li>
+<li><p>a <code>tuple</code> of three ints -- in which case, the first <code>int</code> is used for the depth dimension,
+the second <code>int</code> for the height dimension and the third <code>int</code> for the width dimension</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C, D_{in}, H_{in}, W_{in})\)</p></li>
+<li><p>Output: \((N, C, D_{out}, H_{out}, W_{out})\), where
+$$
+  D_{out} = \left\lfloor\frac{D_{in} + 2 \times \mbox{padding}[0] - \mbox{dilation}[0] \times
+    (\mbox{kernel\_size}[0] - 1) - 1}{\mbox{stride}[0]} + 1\right\rfloor
+$$</p></li>
+</ul>
+
+<p>$$
+  H_{out} = \left\lfloor\frac{H_{in} + 2 \times \mbox{padding}[1] - \mbox{dilation}[1] \times
+    (\mbox{kernel\_size}[1] - 1) - 1}{\mbox{stride}[1]} + 1\right\rfloor
+$$</p>
+<p>$$
+  W_{out} = \left\lfloor\frac{W_{in} + 2 \times \mbox{padding}[2] - \mbox{dilation}[2] \times
+    (\mbox{kernel\_size}[2] - 1) - 1}{\mbox{stride}[2]} + 1\right\rfloor
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'># pool of square window of size=3, stride=2</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_max_pool3d</span>(<span class='fl'>3</span>, stride=<span class='fl'>2</span>)
+<span class='co'># pool of non-square window</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_max_pool3d</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>, <span class='fl'>2</span>), stride=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>2</span>))
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>,<span class='fl'>44</span>, <span class='fl'>31</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
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+<meta property="og:description" content="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." />
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+    <div class="page-header">
+    <h1>Computes a partial inverse of <code>MaxPool1d</code>.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_max_unpool1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p><code>MaxPool1d</code> is not fully invertible, since the non-maximal values are lost.
+<code>MaxUnpool1d</code> takes in as input the output of <code>MaxPool1d</code>
+including the indices of the maximal values and computes a partial inverse
+in which all non-maximal values are set to zero.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_max_unpool1d</span>(<span class='kw'>kernel_size</span>, stride = <span class='kw'>NULL</span>, padding = <span class='fl'>0</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>(int or tuple): Size of the max pooling window.</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>(int or tuple): Stride of the max pooling window.
+It is set to <code>kernel_size</code> by default.</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>(int or tuple): Padding that was added to the input</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p><code>MaxPool1d</code> can map several input sizes to the same output
+sizes. Hence, the inversion process can get ambiguous.
+To accommodate this, you can provide the needed output size
+as an additional argument <code>output_size</code> in the forward call.
+See the Inputs and Example below.</p>
+    <h2 class="hasAnchor" id="inputs"><a class="anchor" href="#inputs"></a>Inputs</h2>
+
+    
+
+<ul>
+<li><p><code>input</code>: the input Tensor to invert</p></li>
+<li><p><code>indices</code>: the indices given out by <code><a href='nn_max_pool1d.html'>nn_max_pool1d()</a></code></p></li>
+<li><p><code>output_size</code> (optional): the targeted output size</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C, H_{in})\)</p></li>
+<li><p>Output: \((N, C, H_{out})\), where
+$$
+  H_{out} = (H_{in} - 1) \times \mbox{stride}[0] - 2 \times \mbox{padding}[0] + \mbox{kernel\_size}[0]
+$$
+or as given by <code>output_size</code> in the call operator</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>pool</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_max_pool1d.html'>nn_max_pool1d</a></span>(<span class='fl'>2</span>, stride=<span class='fl'>2</span>, return_indices=<span class='fl'>TRUE</span>)
+<span class='kw'>unpool</span> <span class='op'>&lt;-</span> <span class='fu'>nn_max_unpool1d</span>(<span class='fl'>2</span>, stride=<span class='fl'>2</span>)
+
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/array.html'>array</a></span>(<span class='fl'>1</span><span class='op'>:</span><span class='fl'>8</span><span class='op'>/</span><span class='fl'>1</span>, dim = <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>8</span>)))
+<span class='kw'>out</span> <span class='op'>&lt;-</span> <span class='fu'>pool</span>(<span class='kw'>input</span>)
+<span class='fu'>unpool</span>(<span class='kw'>out</span>[[<span class='fl'>1</span>]], <span class='kw'>out</span>[[<span class='fl'>2</span>]])
+
+<span class='co'># Example showcasing the use of output_size</span>
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/array.html'>array</a></span>(<span class='fl'>1</span><span class='op'>:</span><span class='fl'>8</span><span class='op'>/</span><span class='fl'>1</span>, dim = <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>8</span>)))
+<span class='kw'>out</span> <span class='op'>&lt;-</span> <span class='fu'>pool</span>(<span class='kw'>input</span>)
+<span class='fu'>unpool</span>(<span class='kw'>out</span>[[<span class='fl'>1</span>]], <span class='kw'>out</span>[[<span class='fl'>2</span>]], output_size=<span class='kw'>input</span><span class='op'>$</span><span class='fu'>size</span>())
+<span class='fu'>unpool</span>(<span class='kw'>out</span>[[<span class='fl'>1</span>]], <span class='kw'>out</span>[[<span class='fl'>2</span>]])
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,1,.,.) = 
+#&gt;   0
+#&gt;   2
+#&gt;   0
+#&gt;   4
+#&gt;   0
+#&gt;   6
+#&gt;   0
+#&gt;   8
+#&gt; [ CPUFloatType{1,1,8,1} ]</div></pre>
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+    <div class="page-header">
+    <h1>Computes a partial inverse of <code>MaxPool2d</code>.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_max_unpool2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p><code>MaxPool2d</code> is not fully invertible, since the non-maximal values are lost.
+<code>MaxUnpool2d</code> takes in as input the output of <code>MaxPool2d</code>
+including the indices of the maximal values and computes a partial inverse
+in which all non-maximal values are set to zero.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_max_unpool2d</span>(<span class='kw'>kernel_size</span>, stride = <span class='kw'>NULL</span>, padding = <span class='fl'>0</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>(int or tuple): Size of the max pooling window.</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>(int or tuple): Stride of the max pooling window.
+It is set to <code>kernel_size</code> by default.</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>(int or tuple): Padding that was added to the input</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p><code>MaxPool2d</code> can map several input sizes to the same output
+sizes. Hence, the inversion process can get ambiguous.
+To accommodate this, you can provide the needed output size
+as an additional argument <code>output_size</code> in the forward call.
+See the Inputs and Example below.</p>
+    <h2 class="hasAnchor" id="inputs"><a class="anchor" href="#inputs"></a>Inputs</h2>
+
+    
+
+<ul>
+<li><p><code>input</code>: the input Tensor to invert</p></li>
+<li><p><code>indices</code>: the indices given out by <code><a href='nn_max_pool2d.html'>nn_max_pool2d()</a></code></p></li>
+<li><p><code>output_size</code> (optional): the targeted output size</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C, H_{in}, W_{in})\)</p></li>
+<li><p>Output: \((N, C, H_{out}, W_{out})\), where
+$$
+  H_{out} = (H_{in} - 1) \times \mbox{stride[0]} - 2 \times \mbox{padding[0]} + \mbox{kernel\_size[0]}
+$$
+$$
+  W_{out} = (W_{in} - 1) \times \mbox{stride[1]} - 2 \times \mbox{padding[1]} + \mbox{kernel\_size[1]}
+$$
+or as given by <code>output_size</code> in the call operator</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>pool</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_max_pool2d.html'>nn_max_pool2d</a></span>(<span class='fl'>2</span>, stride=<span class='fl'>2</span>, return_indices=<span class='fl'>TRUE</span>)
+<span class='kw'>unpool</span> <span class='op'>&lt;-</span> <span class='fu'>nn_max_unpool2d</span>(<span class='fl'>2</span>, stride=<span class='fl'>2</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>4</span>,<span class='fl'>4</span>)
+<span class='kw'>out</span> <span class='op'>&lt;-</span> <span class='fu'>pool</span>(<span class='kw'>input</span>)
+<span class='fu'>unpool</span>(<span class='kw'>out</span>[[<span class='fl'>1</span>]], <span class='kw'>out</span>[[<span class='fl'>2</span>]])
+
+<span class='co'># specify a different output size than input size</span>
+<span class='fu'>unpool</span>(<span class='kw'>out</span>[[<span class='fl'>1</span>]], <span class='kw'>out</span>[[<span class='fl'>2</span>]], output_size=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>5</span>, <span class='fl'>5</span>))
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,1,.,.) = 
+#&gt;   0.0000  0.0000  0.0000  0.0000  0.3625
+#&gt;   0.0000  0.0000  0.7802  0.0000  0.0000
+#&gt;   0.0000  0.5085  2.6613  0.0000  0.0000
+#&gt;   0.0000  0.0000  0.0000  0.0000  0.0000
+#&gt;   0.0000  0.0000  0.0000  0.0000  0.0000
+#&gt; [ CPUFloatType{1,1,5,5} ]</div></pre>
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+MaxUnpool3d takes in as input the output of MaxPool3d
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+in which all non-maximal values are set to zero." />
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Computes a partial inverse of <code>MaxPool3d</code>.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_max_unpool3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p><code>MaxPool3d</code> is not fully invertible, since the non-maximal values are lost.
+<code>MaxUnpool3d</code> takes in as input the output of <code>MaxPool3d</code>
+including the indices of the maximal values and computes a partial inverse
+in which all non-maximal values are set to zero.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_max_unpool3d</span>(<span class='kw'>kernel_size</span>, stride = <span class='kw'>NULL</span>, padding = <span class='fl'>0</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>(int or tuple): Size of the max pooling window.</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>(int or tuple): Stride of the max pooling window.
+It is set to <code>kernel_size</code> by default.</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>(int or tuple): Padding that was added to the input</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p><code>MaxPool3d</code> can map several input sizes to the same output
+sizes. Hence, the inversion process can get ambiguous.
+To accommodate this, you can provide the needed output size
+as an additional argument <code>output_size</code> in the forward call.
+See the Inputs section below.</p>
+    <h2 class="hasAnchor" id="inputs"><a class="anchor" href="#inputs"></a>Inputs</h2>
+
+    
+
+<ul>
+<li><p><code>input</code>: the input Tensor to invert</p></li>
+<li><p><code>indices</code>: the indices given out by <code><a href='nn_max_pool3d.html'>nn_max_pool3d()</a></code></p></li>
+<li><p><code>output_size</code> (optional): the targeted output size</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C, D_{in}, H_{in}, W_{in})\)</p></li>
+<li><p>Output: \((N, C, D_{out}, H_{out}, W_{out})\), where</p></li>
+</ul>
+
+<p>$$
+  D_{out} = (D_{in} - 1) \times \mbox{stride[0]} - 2 \times \mbox{padding[0]} + \mbox{kernel\_size[0]}
+$$
+$$
+  H_{out} = (H_{in} - 1) \times \mbox{stride[1]} - 2 \times \mbox{padding[1]} + \mbox{kernel\_size[1]}
+$$
+$$
+  W_{out} = (W_{in} - 1) \times \mbox{stride[2]} - 2 \times \mbox{padding[2]} + \mbox{kernel\_size[2]}
+$$</p>
+<p>or as given by <code>output_size</code> in the call operator</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+  
+<span class='co'># pool of square window of size=3, stride=2</span>
+<span class='kw'>pool</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_max_pool3d.html'>nn_max_pool3d</a></span>(<span class='fl'>3</span>, stride=<span class='fl'>2</span>, return_indices=<span class='fl'>TRUE</span>)
+<span class='kw'>unpool</span> <span class='op'>&lt;-</span> <span class='fu'>nn_max_unpool3d</span>(<span class='fl'>3</span>, stride=<span class='fl'>2</span>)
+<span class='kw'>out</span> <span class='op'>&lt;-</span> <span class='fu'>pool</span>(<span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>51</span>, <span class='fl'>33</span>, <span class='fl'>15</span>))
+<span class='kw'>unpooled_output</span> <span class='op'>&lt;-</span> <span class='fu'>unpool</span>(<span class='kw'>out</span>[[<span class='fl'>1</span>]], <span class='kw'>out</span>[[<span class='fl'>2</span>]])
+<span class='kw'>unpooled_output</span><span class='op'>$</span><span class='fu'>size</span>()
+
+}
+</div><div class='output co'>#&gt; [1] 20 16 51 33 15</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nn_module.html b/static/docs/dev/reference/nn_module.html
new file mode 100644
index 000000000..c9f118eb3
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@@ -0,0 +1,276 @@
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+  <a href="https://github.com/mlverse/torch/">
+    <span class="fab fa fab fa-github fa-lg"></span>
+     
+  </a>
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+
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+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Base class for all neural network modules.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn.R'><code>R/nn.R</code></a></small>
+    <div class="hidden name"><code>nn_module.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Your models should also subclass this class.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_module</span>(
+  classname = <span class='kw'>NULL</span>,
+  inherit = <span class='kw'>nn_Module</span>,
+  <span class='kw'>...</span>,
+  parent_env = <span class='fu'><a href='https://rdrr.io/r/base/sys.parent.html'>parent.frame</a></span>()
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>classname</th>
+      <td><p>an optional name for the module</p></td>
+    </tr>
+    <tr>
+      <th>inherit</th>
+      <td><p>an optional module to inherit from</p></td>
+    </tr>
+    <tr>
+      <th>...</th>
+      <td><p>methods implementation</p></td>
+    </tr>
+    <tr>
+      <th>parent_env</th>
+      <td><p>passed to <code><a href='https://rdrr.io/pkg/R6/man/R6Class.html'>R6::R6Class()</a></code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>Modules can also contain other Modules, allowing to nest them in a tree
+structure. You can assign the submodules as regular attributes.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>model</span> <span class='op'>&lt;-</span> <span class='fu'>nn_module</span>(
+ initialize = <span class='fu'>function</span>() {
+   <span class='kw'>self</span><span class='op'>$</span><span class='kw'>conv1</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_conv2d.html'>nn_conv2d</a></span>(<span class='fl'>1</span>, <span class='fl'>20</span>, <span class='fl'>5</span>)
+   <span class='kw'>self</span><span class='op'>$</span><span class='kw'>conv2</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_conv2d.html'>nn_conv2d</a></span>(<span class='fl'>20</span>, <span class='fl'>20</span>, <span class='fl'>5</span>)
+ },
+ forward = <span class='fu'>function</span>(<span class='kw'>input</span>) {
+   <span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='kw'>self</span><span class='op'>$</span><span class='fu'>conv1</span>(<span class='kw'>input</span>)
+   <span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nnf_relu.html'>nnf_relu</a></span>(<span class='kw'>input</span>)
+   <span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='kw'>self</span><span class='op'>$</span><span class='fu'>conv2</span>(<span class='kw'>input</span>)
+   <span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nnf_relu.html'>nnf_relu</a></span>(<span class='kw'>input</span>)
+   <span class='kw'>input</span>
+ }
+)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/nn_module_list.html b/static/docs/dev/reference/nn_module_list.html
new file mode 100644
index 000000000..7ab7fc21d
--- /dev/null
+++ b/static/docs/dev/reference/nn_module_list.html
@@ -0,0 +1,257 @@
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+    <li class="dropdown-header">Beginers guide</li>
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+      <a href="../articles/getting-started/tensors-and-autograd.html">Tensors and autograd</a>
+    </li>
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+    </li>
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+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
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+      <a href="../articles/getting-started/custom-nn.html">Custom nn modules</a>
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+    <li>
+      <a href="../articles/tensor/index.html">Tensor class</a>
+    </li>
+    <li class="dropdown-header">Datasets</li>
+    <li>
+      <a href="../articles/loading-data.html">Loading Data</a>
+    </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-header">Examples</li>
+<li>
+  <a href="../reference/index.html">Reference</a>
+</li>
+<li>
+  <a href="../news/index.html">Changelog</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>Holds submodules in a list.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn.R'><code>R/nn.R</code></a></small>
+    <div class="hidden name"><code>nn_module_list.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>nn_module_list can be indexed like a regular R list, but
+modules it contains are properly registered, and will be visible by all
+<code>nn_module</code> methods.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_module_list</span>(modules = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>())</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>modules</th>
+      <td><p>a list of modules to add</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>my_module</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_module.html'>nn_module</a></span>(
+ initialize = <span class='fu'>function</span>() {
+   <span class='kw'>self</span><span class='op'>$</span><span class='kw'>linears</span> <span class='op'>&lt;-</span> <span class='fu'>nn_module_list</span>(<span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span>(<span class='fl'>1</span><span class='op'>:</span><span class='fl'>10</span>, <span class='fu'>function</span>(<span class='kw'>x</span>) <span class='fu'><a href='nn_linear.html'>nn_linear</a></span>(<span class='fl'>10</span>, <span class='fl'>10</span>)))
+ },
+ forward = <span class='fu'>function</span>(<span class='kw'>x</span>) {
+  <span class='co'>for</span> (<span class='kw'>i</span> <span class='co'>in</span> <span class='fl'>1</span><span class='op'>:</span><span class='fu'><a href='https://rdrr.io/r/base/length.html'>length</a></span>(<span class='kw'>self</span><span class='op'>$</span><span class='kw'>linears</span>))
+    <span class='kw'>x</span> <span class='op'>&lt;-</span> <span class='kw'>self</span><span class='op'>$</span><span class='kw'>linears</span>[[<span class='kw'>i</span>]](<span class='kw'>x</span>)
+  <span class='kw'>x</span>
+ }
+)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
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diff --git a/static/docs/dev/reference/nn_multihead_attention.html b/static/docs/dev/reference/nn_multihead_attention.html
new file mode 100644
index 000000000..f15be5442
--- /dev/null
+++ b/static/docs/dev/reference/nn_multihead_attention.html
@@ -0,0 +1,330 @@
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+<meta property="og:title" content="MultiHead attention — nn_multihead_attention" />
+<meta property="og:description" content="Allows the model to jointly attend to information
+from different representation subspaces.
+See reference: Attention Is All You Need" />
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+    </li>
+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
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+    </li>
+    <li class="dropdown-header">Torch Mechanics</li>
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+      
+    </div><!--/.nav-collapse -->
+  </div><!--/.container -->
+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <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='kw'>embed_dim</span>,
+  <span class='kw'>num_heads</span>,
+  dropout = <span class='fl'>0</span>,
+  bias = <span class='fl'>TRUE</span>,
+  add_bias_kv = <span class='fl'>FALSE</span>,
+  add_zero_attn = <span class='fl'>FALSE</span>,
+  kdim = <span class='kw'>NULL</span>,
+  vdim = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>embed_dim</th>
+      <td><p>total dimension of the model.</p></td>
+    </tr>
+    <tr>
+      <th>num_heads</th>
+      <td><p>parallel attention heads.</p></td>
+    </tr>
+    <tr>
+      <th>dropout</th>
+      <td><p>a Dropout layer on attn_output_weights. Default: 0.0.</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>add bias as module parameter. Default: True.</p></td>
+    </tr>
+    <tr>
+      <th>add_bias_kv</th>
+      <td><p>add bias to the key and value sequences at dim=0.</p></td>
+    </tr>
+    <tr>
+      <th>add_zero_attn</th>
+      <td><p>add a new batch of zeros to the key and
+value sequences at dim=1.</p></td>
+    </tr>
+    <tr>
+      <th>kdim</th>
+      <td><p>total number of features in key. Default: <code>NULL</code></p></td>
+    </tr>
+    <tr>
+      <th>vdim</th>
+      <td><p>total number of features in value. Default: <code>NULL</code>.
+Note: if kdim and vdim are <code>NULL</code>, they will be set to embed_dim such that
+query, key, and value have the same number of features.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+  \mbox{MultiHead}(Q, K, V) = \mbox{Concat}(head_1,\dots,head_h)W^O
+\mbox{where} head_i = \mbox{Attention}(QW_i^Q, KW_i^K, VW_i^V)
+$$</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+
+<p>Inputs:</p><ul>
+<li><p>query: \((L, N, E)\) where L is the target sequence length, N is the batch size, E is
+the embedding dimension.</p></li>
+<li><p>key: \((S, N, E)\), where S is the source sequence length, N is the batch size, E is
+the embedding dimension.</p></li>
+<li><p>value: \((S, N, E)\) where S is the source sequence length, N is the batch size, E is
+the embedding dimension.</p></li>
+<li><p>key_padding_mask: \((N, S)\) where N is the batch size, S is the source sequence length.
+If a ByteTensor is provided, the non-zero positions will be ignored while the position
+with the zero positions will be unchanged. If a BoolTensor is provided, the positions with the
+value of <code>True</code> will be ignored while the position with the value of <code>False</code> will be unchanged.</p></li>
+<li><p>attn_mask: 2D mask \((L, S)\) where L is the target sequence length, S is the source sequence length.
+3D mask \((N*num_heads, L, S)\) where N is the batch size, L is the target sequence length,
+S is the source sequence length. attn_mask ensure that position i is allowed to attend the unmasked
+positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend
+while the zero positions will be unchanged. If a BoolTensor is provided, positions with <code>True</code>
+is not allowed to attend while <code>False</code> values will be unchanged. If a FloatTensor
+is provided, it will be added to the attention weight.</p></li>
+</ul>
+
+<p>Outputs:</p><ul>
+<li><p>attn_output: \((L, N, E)\) where L is the target sequence length, N is the batch size,
+E is the embedding dimension.</p></li>
+<li><p>attn_output_weights: \((N, L, S)\) where N is the batch size,
+L is the target sequence length, S is the source sequence length.</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='kw'>multihead_attn</span> <span class='op'>=</span> <span class='fu'>nn_multihead_attention</span>(<span class='kw'>embed_dim</span>, <span class='kw'>num_heads</span>)
+<span class='kw'>out</span> <span class='op'>&lt;-</span> <span class='fu'>multihead_attn</span>(<span class='kw'>query</span>, <span class='kw'>key</span>, <span class='kw'>value</span>)
+<span class='kw'>attn_output</span> <span class='op'>&lt;-</span> <span class='kw'>out</span>[[<span class='fl'>1</span>]]
+<span class='kw'>attn_output_weights</span> <span class='op'>&lt;-</span> <span class='kw'>out</span>[[<span class='fl'>2</span>]]
+}
+
+}
+</div></pre>
+  </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>
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+</div>
+
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+
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+
+
diff --git a/static/docs/dev/reference/nn_prelu.html b/static/docs/dev/reference/nn_prelu.html
new file mode 100644
index 000000000..c21bb2d73
--- /dev/null
+++ b/static/docs/dev/reference/nn_prelu.html
@@ -0,0 +1,303 @@
+<!-- Generated by pkgdown: do not edit by hand -->
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+  <meta charset="utf-8">
+<meta http-equiv="X-UA-Compatible" content="IE=edge">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+
+<title>PReLU module — nn_prelu • torch</title>
+
+
+<!-- jquery -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script>
+<!-- Bootstrap -->
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+
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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
+$$
+  \mbox{PReLU}(x) =
+  \left\{ \begin{array}{ll}
+x, &amp;amp; \mbox{ if } x \geq 0 \\
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+    <div class="page-header">
+    <h1>PReLU module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_prelu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the element-wise function:
+$$
+  \mbox{PReLU}(x) = \max(0,x) + a * \min(0,x)
+$$
+or
+$$
+  \mbox{PReLU}(x) =
+  \left\{ \begin{array}{ll}
+x, &amp; \mbox{ if } x \geq 0 \\
+ax, &amp; \mbox{ otherwise }
+\end{array}
+\right.
+$$</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_prelu</span>(num_parameters = <span class='fl'>1</span>, init = <span class='fl'>0.25</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>num_parameters</th>
+      <td><p>(int): number of \(a\) to learn.
+Although it takes an int as input, there is only two values are legitimate:
+1, or the number of channels at input. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>init</th>
+      <td><p>(float): the initial value of \(a\). Default: 0.25</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>Here \(a\) is a learnable parameter. When called without arguments, <code>nn.prelu()</code> uses a single
+parameter \(a\) across all input channels. If called with <code>nn_prelu(nChannels)</code>,
+a separate \(a\) is used for each input channel.</p>
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>weight decay should not be used when learning \(a\) for good performance.</p>
+<p>Channel dim is the 2nd dim of input. When input has dims &lt; 2, then there is
+no channel dim and the number of channels = 1.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
+
+    
+
+<ul>
+<li><p>weight (Tensor): the learnable weights of shape (<code>num_parameters</code>).</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_prelu</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
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+    <div class="page-header">
+    <h1>ReLU module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_relu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the rectified linear unit function element-wise
+$$\mbox{ReLU}(x) = (x)^+ = \max(0, x)$$</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_relu</span>(inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>inplace</th>
+      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_relu</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.0000
+#&gt;  0.2676
+#&gt; [ CPUFloatType{2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/nn_relu6.html b/static/docs/dev/reference/nn_relu6.html
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index 000000000..54e905833
--- /dev/null
+++ b/static/docs/dev/reference/nn_relu6.html
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>ReLu6 module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_relu6.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the element-wise function:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_relu6</span>(inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>inplace</th>
+      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+  \mbox{ReLU6}(x) = \min(\max(0,x), 6)
+$$</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_relu6</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
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+    <div class="page-header">
+    <h1>RNN module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-rnn.R'><code>R/nn-rnn.R</code></a></small>
+    <div class="hidden name"><code>nn_rnn.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a multi-layer Elman RNN with \(\tanh\) or \(\mbox{ReLU}\) non-linearity
+to an input sequence.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_rnn</span>(
+  <span class='kw'>input_size</span>,
+  <span class='kw'>hidden_size</span>,
+  num_layers = <span class='fl'>1</span>,
+  nonlinearity = <span class='kw'>NULL</span>,
+  bias = <span class='fl'>TRUE</span>,
+  batch_first = <span class='fl'>FALSE</span>,
+  dropout = <span class='fl'>0</span>,
+  bidirectional = <span class='fl'>FALSE</span>,
+  <span class='kw'>...</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input_size</th>
+      <td><p>The number of expected features in the input <code>x</code></p></td>
+    </tr>
+    <tr>
+      <th>hidden_size</th>
+      <td><p>The number of features in the hidden state <code>h</code></p></td>
+    </tr>
+    <tr>
+      <th>num_layers</th>
+      <td><p>Number of recurrent layers. E.g., setting <code>num_layers=2</code>
+would mean stacking two RNNs together to form a <code>stacked RNN</code>,
+with the second RNN taking in outputs of the first RNN and
+computing the final results. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>nonlinearity</th>
+      <td><p>The non-linearity to use. Can be either <code>'tanh'</code> or
+<code>'relu'</code>. Default: <code>'tanh'</code></p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>If <code>FALSE</code>, then the layer does not use bias weights <code>b_ih</code> and
+<code>b_hh</code>. Default: <code>TRUE</code></p></td>
+    </tr>
+    <tr>
+      <th>batch_first</th>
+      <td><p>If <code>TRUE</code>, then the input and output tensors are provided
+as <code>(batch, seq, feature)</code>. Default: <code>FALSE</code></p></td>
+    </tr>
+    <tr>
+      <th>dropout</th>
+      <td><p>If non-zero, introduces a <code>Dropout</code> layer on the outputs of each
+RNN layer except the last layer, with dropout probability equal to
+<code>dropout</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>bidirectional</th>
+      <td><p>If <code>TRUE</code>, becomes a bidirectional RNN. Default: <code>FALSE</code></p></td>
+    </tr>
+    <tr>
+      <th>...</th>
+      <td><p>other arguments that can be passed to the super class.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>For each element in the input sequence, each layer computes the following
+function:</p>
+<p>$$
+h_t = \tanh(W_{ih} x_t + b_{ih} + W_{hh} h_{(t-1)} + b_{hh})
+$$</p>
+<p>where \(h_t\) is the hidden state at time <code>t</code>, \(x_t\) is
+the input at time <code>t</code>, and \(h_{(t-1)}\) is the hidden state of the
+previous layer at time <code>t-1</code> or the initial hidden state at time <code>0</code>.
+If <code>nonlinearity</code> is <code>'relu'</code>, then \(\mbox{ReLU}\) is used instead of
+\(\tanh\).</p>
+    <h2 class="hasAnchor" id="inputs"><a class="anchor" href="#inputs"></a>Inputs</h2>
+
+    
+
+<ul>
+<li><p><strong>input</strong> of shape <code>(seq_len, batch, input_size)</code>: tensor containing the features
+of the input sequence. The input can also be a packed variable length
+sequence.</p></li>
+<li><p><strong>h_0</strong> of shape <code>(num_layers * num_directions, batch, hidden_size)</code>: tensor
+containing the initial hidden state for each element in the batch.
+Defaults to zero if not provided. If the RNN is bidirectional,
+num_directions should be 2, else it should be 1.</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="outputs"><a class="anchor" href="#outputs"></a>Outputs</h2>
+
+    
+
+<ul>
+<li><p><strong>output</strong> of shape <code>(seq_len, batch, num_directions * hidden_size)</code>: tensor
+containing the output features (<code>h_t</code>) from the last layer of the RNN,
+for each <code>t</code>.  If a :class:<code>nn_packed_sequence</code> has
+been given as the input, the output will also be a packed sequence.
+For the unpacked case, the directions can be separated
+using <code>output$view(seq_len, batch, num_directions, hidden_size)</code>,
+with forward and backward being direction <code>0</code> and <code>1</code> respectively.
+Similarly, the directions can be separated in the packed case.</p></li>
+<li><p><strong>h_n</strong> of shape <code>(num_layers * num_directions, batch, hidden_size)</code>: tensor
+containing the hidden state for <code>t = seq_len</code>.
+Like <em>output</em>, the layers can be separated using
+<code>h_n$view(num_layers, num_directions, batch, hidden_size)</code>.</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input1: \((L, N, H_{in})\) tensor containing input features where
+\(H_{in}=\mbox{input\_size}\) and <code>L</code> represents a sequence length.</p></li>
+<li><p>Input2: \((S, N, H_{out})\) tensor
+containing the initial hidden state for each element in the batch.
+\(H_{out}=\mbox{hidden\_size}\)
+Defaults to zero if not provided. where \(S=\mbox{num\_layers} * \mbox{num\_directions}\)
+If the RNN is bidirectional, num_directions should be 2, else it should be 1.</p></li>
+<li><p>Output1: \((L, N, H_{all})\) where \(H_{all}=\mbox{num\_directions} * \mbox{hidden\_size}\)</p></li>
+<li><p>Output2: \((S, N, H_{out})\) tensor containing the next hidden state
+for each element in the batch</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
+
+    
+
+<ul>
+<li><p><code>weight_ih_l[k]</code>: the learnable input-hidden weights of the k-th layer,
+of shape <code>(hidden_size, input_size)</code> for <code>k = 0</code>. Otherwise, the shape is
+<code>(hidden_size, num_directions * hidden_size)</code></p></li>
+<li><p><code>weight_hh_l[k]</code>: the learnable hidden-hidden weights of the k-th layer,
+of shape <code>(hidden_size, hidden_size)</code></p></li>
+<li><p><code>bias_ih_l[k]</code>: the learnable input-hidden bias of the k-th layer,
+of shape <code>(hidden_size)</code></p></li>
+<li><p><code>bias_hh_l[k]</code>: the learnable hidden-hidden bias of the k-th layer,
+of shape <code>(hidden_size)</code></p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+
+
+<p>All the weights and biases are initialized from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\)
+where \(k = \frac{1}{\mbox{hidden\_size}}\)</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>rnn</span> <span class='op'>&lt;-</span> <span class='fu'>nn_rnn</span>(<span class='fl'>10</span>, <span class='fl'>20</span>, <span class='fl'>2</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>5</span>, <span class='fl'>3</span>, <span class='fl'>10</span>)
+<span class='kw'>h0</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>20</span>)
+<span class='fu'>rnn</span>(<span class='kw'>input</span>, <span class='kw'>h0</span>)
+
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;  Columns 1 to 9  0.7161  0.2226 -0.0357  0.7649 -0.8778 -0.1407  0.4345 -0.4041  0.4700
+#&gt;  -0.6661  0.5943 -0.2670  0.3763 -0.3201 -0.3885  0.6264 -0.2784  0.6874
+#&gt;   0.7119  0.5669  0.6824  0.6195 -0.4618  0.5758 -0.2675  0.0119 -0.0457
+#&gt; 
+#&gt; Columns 10 to 18  0.6897  0.7546  0.6845 -0.4144  0.7661  0.4690 -0.1977  0.7332  0.4410
+#&gt;  -0.1916  0.3248 -0.2304  0.0221 -0.4793  0.2567  0.0762  0.5599  0.9369
+#&gt;   0.9354  0.4537 -0.0197 -0.1920  0.4417 -0.3814 -0.3907  0.7424  0.6078
+#&gt; 
+#&gt; Columns 19 to 20  0.4127 -0.0569
+#&gt;  -0.3132 -0.0694
+#&gt;   0.6415  0.2514
+#&gt; 
+#&gt; (2,.,.) = 
+#&gt;  Columns 1 to 9 -0.2898  0.0264  0.3182 -0.0434 -0.5073 -0.1787 -0.1681  0.0146 -0.0225
+#&gt;  -0.0181  0.2862  0.3248  0.1155  0.1944 -0.4386 -0.2307  0.3997  0.1378
+#&gt;  -0.4991 -0.0315 -0.0003  0.0188 -0.2259 -0.4118 -0.3810 -0.3168 -0.5106
+#&gt; 
+#&gt; Columns 10 to 18  0.7821 -0.4271 -0.3880  0.0370  0.4379  0.2096 -0.5661  0.8335  0.3392
+#&gt;   0.8353 -0.3238 -0.1885 -0.0234 -0.1032 -0.0077  0.0145  0.2430  0.5450
+#&gt;   0.5930 -0.2843 -0.3047 -0.3095  0.4868  0.5259 -0.6060  0.3336  0.2464
+#&gt; 
+#&gt; Columns 19 to 20  0.3208  0.1020
+#&gt;   0.1593  0.5415
+#&gt;   0.0749  0.3683
+#&gt; 
+#&gt; (3,.,.) = 
+#&gt;  Columns 1 to 9 -0.1481 -0.0683  0.2063  0.6848 -0.5957  0.2993 -0.5960 -0.2625 -0.4177
+#&gt;  -0.1699  0.3487  0.1203  0.6257 -0.2836  0.3066 -0.3963  0.0289 -0.3004
+#&gt;   0.0403  0.1568  0.3575  0.1806 -0.2720 -0.2082 -0.6743 -0.2219 -0.0111
+#&gt; 
+#&gt; Columns 10 to 18  0.4896  0.1430 -0.2018 -0.1651  0.3086  0.0932 -0.3173  0.4461  0.2299
+#&gt;   0.3123 -0.1588 -0.4074 -0.2209  0.3587 -0.0577 -0.2296  0.1526  0.0306
+#&gt;   0.8882  0.4440 -0.3783 -0.1405  0.2539  0.3952 -0.4508  0.5254  0.2067
+#&gt; 
+#&gt; Columns 19 to 20  0.4001  0.0285
+#&gt;   0.0215  0.2779
+#&gt;   0.1267 -0.0228
+#&gt; 
+#&gt; (4,.,.) = 
+#&gt;  Columns 1 to 9  0.3506  0.1406  0.6083 -0.1419 -0.2605  0.0976 -0.5502 -0.2227 -0.3056
+#&gt;  -0.4934  0.2197  0.3039  0.3677 -0.3203 -0.1752 -0.3564 -0.3170 -0.2151
+#&gt;   0.0362  0.3331 -0.0831  0.1896 -0.5873 -0.2126 -0.4437 -0.0855 -0.5144
+#&gt; 
+#&gt; Columns 10 to 18  0.6062 -0.0803  0.2738 -0.0751  0.0101  0.5143 -0.1504  0.4770  0.5594
+#&gt;   0.4784  0.1200 -0.2187 -0.4459  0.3747  0.1906 -0.3137 -0.0697  0.4113
+#&gt;   0.7554  0.3072 -0.2845 -0.2918  0.3419  0.5291 -0.0655  0.5719 -0.0901
+#&gt; 
+#&gt; Columns 19 to 20  0.4132  0.0458
+#&gt;  -0.0708  0.1516
+#&gt;   0.1421  0.0844
+#&gt; 
+#&gt; (5,.,.) = 
+#&gt;  Columns 1 to 9 -0.2136 -0.2048  0.3624  0.1719 -0.1033 -0.1328 -0.4668  0.0815 -0.2587
+#&gt;  -0.6862  0.0822  0.4199  0.7108 -0.6515  0.3491 -0.2806 -0.2946 -0.3405
+#&gt;  -0.5081  0.1604 -0.0631  0.6548 -0.3199  0.1808 -0.3563 -0.1484 -0.4249
+#&gt; 
+#&gt; Columns 10 to 18  0.4771 -0.2079 -0.4903 -0.0846 -0.2072  0.1638  0.0225  0.2321  0.3682
+#&gt;   0.4638 -0.3009 -0.4535 -0.5379  0.0756 -0.2180 -0.0427  0.2593  0.3729
+#&gt;   0.5043  0.1803 -0.4552 -0.4080  0.5145  0.1950  0.1238  0.5248  0.6656
+#&gt; 
+#&gt; Columns 19 to 20 -0.1138 -0.2101
+#&gt;   0.2529  0.1496
+#&gt;   0.1794  0.0149
+#&gt; [ CPUFloatType{5,3,20} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;  Columns 1 to 9 -0.3036 -0.2660 -0.3928  0.1197  0.3798  0.0506 -0.1945  0.3296 -0.2563
+#&gt;  -0.4802 -0.3518 -0.5960 -0.8864  0.3126 -0.5528 -0.3168 -0.0299  0.6901
+#&gt;  -0.3496 -0.3312 -0.6669 -0.2741 -0.0690  0.0672 -0.3587 -0.3930  0.6656
+#&gt; 
+#&gt; Columns 10 to 18 -0.5301  0.1393  0.1400  0.3850 -0.1208 -0.2940  0.0193 -0.2334  0.1071
+#&gt;  -0.6637  0.0099 -0.3535  0.4259 -0.0213 -0.7286  0.6091 -0.1422 -0.6873
+#&gt;  -0.4200 -0.5866  0.3875 -0.0704 -0.2061 -0.0485  0.0553 -0.5008  0.0471
+#&gt; 
+#&gt; Columns 19 to 20 -0.2102  0.4555
+#&gt;   0.3224 -0.4292
+#&gt;   0.5104 -0.1123
+#&gt; 
+#&gt; (2,.,.) = 
+#&gt;  Columns 1 to 9 -0.2136 -0.2048  0.3624  0.1719 -0.1033 -0.1328 -0.4668  0.0815 -0.2587
+#&gt;  -0.6862  0.0822  0.4199  0.7108 -0.6515  0.3491 -0.2806 -0.2946 -0.3405
+#&gt;  -0.5081  0.1604 -0.0631  0.6548 -0.3199  0.1808 -0.3563 -0.1484 -0.4249
+#&gt; 
+#&gt; Columns 10 to 18  0.4771 -0.2079 -0.4903 -0.0846 -0.2072  0.1638  0.0225  0.2321  0.3682
+#&gt;   0.4638 -0.3009 -0.4535 -0.5379  0.0756 -0.2180 -0.0427  0.2593  0.3729
+#&gt;   0.5043  0.1803 -0.4552 -0.4080  0.5145  0.1950  0.1238  0.5248  0.6656
+#&gt; 
+#&gt; Columns 19 to 20 -0.1138 -0.2101
+#&gt;   0.2529  0.1496
+#&gt;   0.1794  0.0149
+#&gt; [ CPUFloatType{2,3,20} ]
+#&gt; </div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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+    <div class="page-header">
+    <h1>RReLU module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_rrelu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the randomized leaky rectified liner unit function, element-wise,
+as described in the paper:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_rrelu</span>(lower = <span class='fl'>1</span><span class='op'>/</span><span class='fl'>8</span>, upper = <span class='fl'>1</span><span class='op'>/</span><span class='fl'>3</span>, inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>lower</th>
+      <td><p>lower bound of the uniform distribution. Default: \(\frac{1}{8}\)</p></td>
+    </tr>
+    <tr>
+      <th>upper</th>
+      <td><p>upper bound of the uniform distribution. Default: \(\frac{1}{3}\)</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p><code>Empirical Evaluation of Rectified Activations in Convolutional Network</code>.</p>
+<p>The function is defined as:</p>
+<p>$$
+\mbox{RReLU}(x) =
+\left\{ \begin{array}{ll}
+x &amp; \mbox{if } x \geq 0 \\
+ax &amp; \mbox{ otherwise }
+\end{array}
+\right.
+$$</p>
+<p>where \(a\) is randomly sampled from uniform distribution
+\(\mathcal{U}(\mbox{lower}, \mbox{upper})\).
+See: https://arxiv.org/pdf/1505.00853.pdf</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_rrelu</span>(<span class='fl'>0.1</span>, <span class='fl'>0.3</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.1319
+#&gt;  1.1798
+#&gt; [ CPUFloatType{2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>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>(inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>inplace</th>
+      <td><p>(bool, optional): can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+  \mbox{SELU}(x) = \mbox{scale} * (\max(0,x) + \min(0, \alpha * (\exp(x) - 1)))
+$$</p>
+<p>with \(\alpha = 1.6732632423543772848170429916717\) and
+\(\mbox{scale} = 1.0507009873554804934193349852946\).</p>
+<p>More details can be found in the paper
+<a href='https://arxiv.org/abs/1706.02515'>Self-Normalizing Neural Networks</a>.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_selu</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
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diff --git a/static/docs/dev/reference/nn_sequential.html b/static/docs/dev/reference/nn_sequential.html
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+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <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='kw'>...</span>, name = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>...</th>
+      <td><p>sequence of modules to be added</p></td>
+    </tr>
+    <tr>
+      <th>name</th>
+      <td><p>optional name for the generated module.</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>model</span> <span class='op'>&lt;-</span> <span class='fu'>nn_sequential</span>(
+  <span class='fu'><a href='nn_conv2d.html'>nn_conv2d</a></span>(<span class='fl'>1</span>, <span class='fl'>20</span>, <span class='fl'>5</span>),
+  <span class='fu'><a href='nn_relu.html'>nn_relu</a></span>(),
+  <span class='fu'><a href='nn_conv2d.html'>nn_conv2d</a></span>(<span class='fl'>20</span>, <span class='fl'>64</span>, <span class='fl'>5</span>),
+  <span class='fu'><a href='nn_relu.html'>nn_relu</a></span>()
+)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>32</span>, <span class='fl'>1</span>, <span class='fl'>28</span>, <span class='fl'>28</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>model</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
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diff --git a/static/docs/dev/reference/nn_sigmoid.html b/static/docs/dev/reference/nn_sigmoid.html
new file mode 100644
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--- /dev/null
+++ b/static/docs/dev/reference/nn_sigmoid.html
@@ -0,0 +1,252 @@
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+      
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+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Sigmoid module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_sigmoid.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the element-wise function:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_sigmoid</span>()</pre>
+
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+  \mbox{Sigmoid}(x) = \sigma(x) = \frac{1}{1 + \exp(-x)}
+$$</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_sigmoid</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/nn_softmax.html b/static/docs/dev/reference/nn_softmax.html
new file mode 100644
index 000000000..5accadffa
--- /dev/null
+++ b/static/docs/dev/reference/nn_softmax.html
@@ -0,0 +1,279 @@
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+<meta property="og:description" content="Applies the Softmax function to an n-dimensional input Tensor
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Softmax module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_softmax.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the Softmax function to an n-dimensional input Tensor
+rescaling them so that the elements of the n-dimensional output Tensor
+lie in the range <code>[0,1]</code> and sum to 1.
+Softmax is defined as:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_softmax</span>(<span class='kw'>dim</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>dim</th>
+      <td><p>(int): A dimension along which Softmax will be computed (so every slice
+along dim will sum to 1).</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+
+    <p>:
+a Tensor of the same dimension and shape as the input with
+values in the range <code>[0, 1]</code></p>
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+  \mbox{Softmax}(x_{i}) = \frac{\exp(x_i)}{\sum_j \exp(x_j)}
+$$</p>
+<p>When the input Tensor is a sparse tensor then the unspecifed
+values are treated as <code>-Inf</code>.</p>
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>This module doesn't work directly with NLLLoss,
+which expects the Log to be computed between the Softmax and itself.
+Use <code>LogSoftmax</code> instead (it's faster and has better numerical properties).</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((*)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((*)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_softmax</span>(<span class='fl'>1</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
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+    <div class="page-header">
+    <h1>Softmax2d module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_softmax2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies SoftMax over features to each spatial location.
+When given an image of <code>Channels x Height x Width</code>, it will
+apply <code>Softmax</code> to each location \((Channels, h_i, w_j)\)</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_softmax2d</span>()</pre>
+
+
+    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+
+    <p>a Tensor of the same dimension and shape as the input with
+values in the range <code>[0, 1]</code></p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C, H, W)\)</p></li>
+<li><p>Output: \((N, C, H, W)\) (same shape as input)</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_softmax2d</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>12</span>, <span class='fl'>13</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
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+    <h1>Softmin</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_softmin.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the Softmin function to an n-dimensional input Tensor
+rescaling them so that the elements of the n-dimensional output Tensor
+lie in the range <code>[0, 1]</code> and sum to 1.
+Softmin is defined as:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_softmin</span>(<span class='kw'>dim</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>dim</th>
+      <td><p>(int): A dimension along which Softmin will be computed (so every slice
+along dim will sum to 1).</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+
+    <p>a Tensor of the same dimension and shape as the input, with
+values in the range <code>[0, 1]</code>.</p>
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+  \mbox{Softmin}(x_{i}) = \frac{\exp(-x_i)}{\sum_j \exp(-x_j)}
+$$</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((*)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((*)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_softmin</span>(dim = <span class='fl'>1</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
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+<meta property="og:description" content="Applies the element-wise function:
+$$
+  \mbox{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x))
+$$" />
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+    <h1>Softplus module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_softplus.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the element-wise function:
+$$
+  \mbox{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x))
+$$</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_softplus</span>(beta = <span class='fl'>1</span>, threshold = <span class='fl'>20</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>beta</th>
+      <td><p>the \(\beta\) value for the Softplus formulation. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>threshold</th>
+      <td><p>values above this revert to a linear function. Default: 20</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>SoftPlus is a smooth approximation to the ReLU function and can be used
+to constrain the output of a machine to always be positive.
+For numerical stability the implementation reverts to the linear function
+when \(input \times \beta &gt; threshold\).</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_softplus</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Softshrink module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_softshrink.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the soft shrinkage function elementwise:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_softshrink</span>(lambd = <span class='fl'>0.5</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>lambd</th>
+      <td><p>the \(\lambda\) (must be no less than zero) value for the Softshrink formulation. Default: 0.5</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+  \mbox{SoftShrinkage}(x) =
+  \left\{ \begin{array}{ll}
+x - \lambda, &amp; \mbox{ if } x &gt; \lambda \\
+x + \lambda, &amp; \mbox{ if } x &lt; -\lambda \\
+0, &amp; \mbox{ otherwise }
+\end{array}
+\right.
+$$</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_softshrink</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+<meta property="og:description" content="Applies the element-wise function:
+$$
+  \mbox{SoftSign}(x) = \frac{x}{ 1 + |x|}
+$$" />
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Softsign 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_softsign.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the element-wise function:
+$$
+  \mbox{SoftSign}(x) = \frac{x}{ 1 + |x|}
+$$</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_softsign</span>()</pre>
+
+
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_softsign</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
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+    <div class="page-header">
+    <h1>Tanh module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_tanh.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the element-wise function:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_tanh</span>()</pre>
+
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+  \mbox{Tanh}(x) = \tanh(x) = \frac{\exp(x) - \exp(-x)} {\exp(x) + \exp(-x)}
+$$</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_tanh</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nn_tanhshrink.html b/static/docs/dev/reference/nn_tanhshrink.html
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+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <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><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_tanhshrink</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nn_threshold.html b/static/docs/dev/reference/nn_threshold.html
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Threshoold module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_threshold.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Thresholds each element of the input Tensor.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_threshold</span>(<span class='kw'>threshold</span>, <span class='kw'>value</span>, inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>threshold</th>
+      <td><p>The value to threshold at</p></td>
+    </tr>
+    <tr>
+      <th>value</th>
+      <td><p>The value to replace with</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>Threshold is defined as:
+$$
+  y = 
+  \left\{ \begin{array}{ll}
+  x, &amp;\mbox{ if } x &gt; \mbox{threshold} \\
+  \mbox{value}, &amp;\mbox{ otherwise }
+  \end{array}
+  \right.
+$$</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_threshold</span>(<span class='fl'>0.1</span>, <span class='fl'>20</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Packs a Tensor containing padded sequences of variable length.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-utils-rnn.R'><code>R/nn-utils-rnn.R</code></a></small>
+    <div class="hidden name"><code>nn_utils_rnn_pack_padded_sequence.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p><code>input</code> can be of size <code>T x B x *</code> where <code>T</code> is the length of the
+longest sequence (equal to <code>lengths[1]</code>), <code>B</code> is the batch size, and
+<code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is any number of dimensions (including 0). If <code>batch_first</code> is
+<code>TRUE</code>, <code>B x T x *</code> <code>input</code> is expected.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_utils_rnn_pack_padded_sequence</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>lengths</span>,
+  batch_first = <span class='fl'>FALSE</span>,
+  enforce_sorted = <span class='fl'>TRUE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(Tensor): padded batch of variable length sequences.</p></td>
+    </tr>
+    <tr>
+      <th>lengths</th>
+      <td><p>(Tensor): list of sequences lengths of each batch element.</p></td>
+    </tr>
+    <tr>
+      <th>batch_first</th>
+      <td><p>(bool, optional): if <code>TRUE</code>, the input is expected in <code>B x T x *</code>
+format.</p></td>
+    </tr>
+    <tr>
+      <th>enforce_sorted</th>
+      <td><p>(bool, optional): if <code>TRUE</code>, the input is expected to
+contain sequences sorted by length in a decreasing order. If
+<code>FALSE</code>, the input will get sorted unconditionally. Default: <code>TRUE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+
+    <p>a <code>PackedSequence</code> object</p>
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>For unsorted sequences, use <code>enforce_sorted = FALSE</code>. If <code>enforce_sorted</code> is
+<code>TRUE</code>, the sequences should be sorted by length in a decreasing order, i.e.
+<code>input[,1]</code> should be the longest sequence, and <code>input[,B]</code> the shortest
+one. <code>enforce_sorted = TRUE</code> is only necessary for ONNX export.</p>
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>This function accepts any input that has at least two dimensions. You
+can apply it to pack the labels, and use the output of the RNN with
+them to compute the loss directly. A Tensor can be retrieved from
+a <code>PackedSequence</code> object by accessing its <code>.data</code> attribute.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+</li>
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+  <a href="../news/index.html">Changelog</a>
+</li>
+      </ul>
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+      
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+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Packs a list of variable length Tensors</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-utils-rnn.R'><code>R/nn-utils-rnn.R</code></a></small>
+    <div class="hidden name"><code>nn_utils_rnn_pack_sequence.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p><code>sequences</code> should be a list of Tensors of size <code>L x *</code>, where <code>L</code> is
+the length of a sequence and <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is any number of trailing dimensions,
+including zero.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_utils_rnn_pack_sequence</span>(<span class='kw'>sequences</span>, enforce_sorted = <span class='fl'>TRUE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>sequences</th>
+      <td><p><code>(list[Tensor])</code>: A list of sequences of decreasing length.</p></td>
+    </tr>
+    <tr>
+      <th>enforce_sorted</th>
+      <td><p>(bool, optional): if <code>TRUE</code>, checks that the input
+contains sequences sorted by length in a decreasing order. If
+<code>FALSE</code>, this condition is not checked. Default: <code>TRUE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+
+    <p>a <code>PackedSequence</code> object</p>
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>For unsorted sequences, use <code>enforce_sorted = FALSE</code>. If <code>enforce_sorted</code>
+is <code>TRUE</code>, the sequences should be sorted in the order of decreasing length.
+<code>enforce_sorted = TRUE</code> is only necessary for ONNX export.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>3</span>), dtype = <span class='fu'><a href='torch_dtype.html'>torch_long</a></span>())
+<span class='kw'>y</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>), dtype = <span class='fu'><a href='torch_dtype.html'>torch_long</a></span>())
+<span class='kw'>z</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>6</span>), dtype = <span class='fu'><a href='torch_dtype.html'>torch_long</a></span>())
+
+<span class='kw'>p</span> <span class='op'>&lt;-</span> <span class='fu'>nn_utils_rnn_pack_sequence</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>x</span>, <span class='kw'>y</span>, <span class='kw'>z</span>))
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nn_utils_rnn_pad_packed_sequence.html b/static/docs/dev/reference/nn_utils_rnn_pad_packed_sequence.html
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+++ b/static/docs/dev/reference/nn_utils_rnn_pad_packed_sequence.html
@@ -0,0 +1,299 @@
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+</div><!--/.navbar -->
+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Pads a packed batch of variable length sequences.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-utils-rnn.R'><code>R/nn-utils-rnn.R</code></a></small>
+    <div class="hidden name"><code>nn_utils_rnn_pad_packed_sequence.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>It is an inverse operation to <code><a href='nn_utils_rnn_pack_padded_sequence.html'>nn_utils_rnn_pack_padded_sequence()</a></code>.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_utils_rnn_pad_packed_sequence</span>(
+  <span class='kw'>sequence</span>,
+  batch_first = <span class='fl'>FALSE</span>,
+  padding_value = <span class='fl'>0</span>,
+  total_length = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>sequence</th>
+      <td><p>(PackedSequence): batch to pad</p></td>
+    </tr>
+    <tr>
+      <th>batch_first</th>
+      <td><p>(bool, optional): if <code>True</code>, the output will be in ``B x T x *`
+format.</p></td>
+    </tr>
+    <tr>
+      <th>padding_value</th>
+      <td><p>(float, optional): values for padded elements.</p></td>
+    </tr>
+    <tr>
+      <th>total_length</th>
+      <td><p>(int, optional): if not <code>NULL</code>, the output will be padded to
+have length <code>total_length</code>. This method will throw <code>ValueError</code>
+if <code>total_length</code> is less than the max sequence length in
+<code>sequence</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+
+    <p>Tuple of Tensor containing the padded sequence, and a Tensor
+containing the list of lengths of each sequence in the batch.
+Batch elements will be re-ordered as they were ordered originally when
+the batch was passed to <code><a href='nn_utils_rnn_pack_padded_sequence.html'>nn_utils_rnn_pack_padded_sequence()</a></code> or
+<code><a href='nn_utils_rnn_pack_sequence.html'>nn_utils_rnn_pack_sequence()</a></code>.</p>
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>The returned Tensor's data will be of size <code>T x B x *</code>, where <code>T</code> is the length
+of the longest sequence and <code>B</code> is the batch size. If <code>batch_first</code> is <code>TRUE</code>,
+the data will be transposed into <code>B x T x *</code> format.</p>
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p><code>total_length</code> is useful to implement the
+<code>pack sequence -&gt; recurrent network -&gt; unpack sequence</code> pattern in a
+<code>nn_module</code> wrapped in <code>~torch.nn.DataParallel</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>seq</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>0</span>), <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>,<span class='fl'>0</span>,<span class='fl'>0</span>), <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>,<span class='fl'>5</span>,<span class='fl'>6</span>)))        
+<span class='kw'>lens</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>,<span class='fl'>1</span>,<span class='fl'>3</span>)
+<span class='kw'>packed</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_utils_rnn_pack_padded_sequence.html'>nn_utils_rnn_pack_padded_sequence</a></span>(<span class='kw'>seq</span>, <span class='kw'>lens</span>, batch_first = <span class='fl'>TRUE</span>,
+                                            enforce_sorted = <span class='fl'>FALSE</span>)
+<span class='kw'>packed</span>
+<span class='fu'>nn_utils_rnn_pad_packed_sequence</span>(<span class='kw'>packed</span>, batch_first=<span class='fl'>TRUE</span>)
+
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt;  1  2  0
+#&gt;  3  0  0
+#&gt;  4  5  6
+#&gt; [ CPUFloatType{3,3} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt;  2
+#&gt;  1
+#&gt;  3
+#&gt; [ CPULongType{3} ]
+#&gt; </div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/nn_utils_rnn_pad_sequence.html b/static/docs/dev/reference/nn_utils_rnn_pad_sequence.html
new file mode 100644
index 000000000..114fcfaf3
--- /dev/null
+++ b/static/docs/dev/reference/nn_utils_rnn_pad_sequence.html
@@ -0,0 +1,276 @@
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+otherwise." />
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+
+      
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Pad a list of variable length Tensors with <code>padding_value</code></h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-utils-rnn.R'><code>R/nn-utils-rnn.R</code></a></small>
+    <div class="hidden name"><code>nn_utils_rnn_pad_sequence.Rd</code></div>
+    </div>
+
+    <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='kw'>sequences</span>, batch_first = <span class='fl'>FALSE</span>, padding_value = <span class='fl'>0</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>sequences</th>
+      <td><p><code>(list[Tensor])</code>: list of variable length sequences.</p></td>
+    </tr>
+    <tr>
+      <th>batch_first</th>
+      <td><p>(bool, optional): output will be in <code>B x T x *</code> if <code>TRUE</code>,
+or in <code>T x B x *</code> otherwise</p></td>
+    </tr>
+    <tr>
+      <th>padding_value</th>
+      <td><p>(float, optional): value for padded elements. Default: 0.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+
+    <p>Tensor of size <code>T x B x *</code> if <code>batch_first</code> is <code>FALSE</code>.
+Tensor of size <code>B x T x *</code> otherwise</p>
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p><code>B</code> is batch size. It is equal to the number of elements in <code>sequences</code>.
+<code>T</code> is length of the longest sequence.
+<code>L</code> is length of the sequence.
+<code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is any number of trailing dimensions, including none.</p>
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>This function returns a Tensor of size <code>T x B x *</code> or <code>B x T x *</code>
+where <code>T</code> is the length of the longest sequence. This function assumes
+trailing dimensions and type of all the Tensors in sequences are same.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_ones.html'>torch_ones</a></span>(<span class='fl'>25</span>, <span class='fl'>300</span>)
+<span class='kw'>b</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_ones.html'>torch_ones</a></span>(<span class='fl'>22</span>, <span class='fl'>300</span>)
+<span class='kw'>c</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_ones.html'>torch_ones</a></span>(<span class='fl'>15</span>, <span class='fl'>300</span>)
+<span class='fu'>nn_utils_rnn_pad_sequence</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>a</span>, <span class='kw'>b</span>, <span class='kw'>c</span>))<span class='op'>$</span><span class='fu'>size</span>()
+
+}
+</div><div class='output co'>#&gt; [1]  25   3 300</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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--- /dev/null
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+    <div class="page-header">
+    <h1>Adaptive_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_adaptive_avg_pool1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 1D adaptive average pooling over an input signal composed of
+several input planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_adaptive_avg_pool1d</span>(<span class='kw'>input</span>, <span class='kw'>output_size</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape (minibatch , in_channels , iW)</p></td>
+    </tr>
+    <tr>
+      <th>output_size</th>
+      <td><p>the target output size (single integer)</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nnf_adaptive_avg_pool2d.html b/static/docs/dev/reference/nnf_adaptive_avg_pool2d.html
new file mode 100644
index 000000000..6b80f5494
--- /dev/null
+++ b/static/docs/dev/reference/nnf_adaptive_avg_pool2d.html
@@ -0,0 +1,243 @@
+<!-- Generated by pkgdown: do not edit by hand -->
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+    <h1>Adaptive_avg_pool2d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
+    <div class="hidden name"><code>nnf_adaptive_avg_pool2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 2D adaptive average pooling over an input signal composed of
+several input planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_adaptive_avg_pool2d</span>(<span class='kw'>input</span>, <span class='kw'>output_size</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor (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>
+    </table>
+
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+    <div class="page-header">
+    <h1>Adaptive_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_adaptive_avg_pool3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 3D adaptive average pooling over an input signal composed of
+several input planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_adaptive_avg_pool3d</span>(<span class='kw'>input</span>, <span class='kw'>output_size</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor (minibatch, in_channels , iT * iH , iW)</p></td>
+    </tr>
+    <tr>
+      <th>output_size</th>
+      <td><p>the target output size (single integer or triple-integer tuple)</p></td>
+    </tr>
+    </table>
+
+
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new file mode 100644
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+    <div class="page-header">
+    <h1>Adaptive_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_adaptive_max_pool1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 1D adaptive max pooling over an input signal composed of
+several input planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_adaptive_max_pool1d</span>(<span class='kw'>input</span>, <span class='kw'>output_size</span>, return_indices = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape (minibatch , in_channels , iW)</p></td>
+    </tr>
+    <tr>
+      <th>output_size</th>
+      <td><p>the target output size (single integer)</p></td>
+    </tr>
+    <tr>
+      <th>return_indices</th>
+      <td><p>whether to return pooling indices. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+
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+    <h1>Adaptive_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_adaptive_max_pool2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 2D adaptive max pooling over an input signal composed of
+several input planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_adaptive_max_pool2d</span>(<span class='kw'>input</span>, <span class='kw'>output_size</span>, return_indices = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor (minibatch, in_channels , iH , iW)</p></td>
+    </tr>
+    <tr>
+      <th>output_size</th>
+      <td><p>the target output size (single integer or double-integer tuple)</p></td>
+    </tr>
+    <tr>
+      <th>return_indices</th>
+      <td><p>whether to return pooling indices. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+
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new file mode 100644
index 000000000..4254c42eb
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+    <h1>Adaptive_max_pool3d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
+    <div class="hidden name"><code>nnf_adaptive_max_pool3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 3D adaptive max pooling over an input signal composed of
+several input planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_adaptive_max_pool3d</span>(<span class='kw'>input</span>, <span class='kw'>output_size</span>, return_indices = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor (minibatch, in_channels , iT * iH , iW)</p></td>
+    </tr>
+    <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>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/dev/reference/nnf_affine_grid.html b/static/docs/dev/reference/nnf_affine_grid.html
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+    <div class="page-header">
+    <h1>Affine_grid</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-vision.R'><code>R/nnf-vision.R</code></a></small>
+    <div class="hidden name"><code>nnf_affine_grid.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Generates a 2D or 3D flow field (sampling grid), given a batch of
+affine matrices <code>theta</code>.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_affine_grid</span>(<span class='kw'>theta</span>, <span class='kw'>size</span>, align_corners = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>theta</th>
+      <td><p>(Tensor) input batch of affine matrices with shape
+(\(N \times 2 \times 3\)) for 2D or  (\(N \times 3 \times 4\)) for 3D</p></td>
+    </tr>
+    <tr>
+      <th>size</th>
+      <td><p>(torch.Size) the target output image size. (\(N \times C \times H \times W\)
+for 2D or \(N \times C \times D \times H \times W\) for 3D)
+Example: torch.Size((32, 3, 24, 24))</p></td>
+    </tr>
+    <tr>
+      <th>align_corners</th>
+      <td><p>(bool, optional) if <code>True</code>, consider <code>-1</code> and <code>1</code>
+to refer to the centers of the corner pixels rather than the image corners.
+Refer to <code><a href='nnf_grid_sample.html'>nnf_grid_sample()</a></code> for a more complete description. A grid generated by
+<code>nnf_affine_grid()</code> should be passed to <code><a href='nnf_grid_sample.html'>nnf_grid_sample()</a></code>  with the same setting for
+this option. Default: <code>False</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+
+
+<p>This function is often used in conjunction with <code><a href='nnf_grid_sample.html'>nnf_grid_sample()</a></code>
+to build <code>Spatial Transformer Networks</code>_ .</p>
+
+  </div>
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diff --git a/static/docs/dev/reference/nnf_alpha_dropout.html b/static/docs/dev/reference/nnf_alpha_dropout.html
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+
+      
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Alpha_dropout</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-dropout.R'><code>R/nnf-dropout.R</code></a></small>
+    <div class="hidden name"><code>nnf_alpha_dropout.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies alpha dropout to the input.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_alpha_dropout</span>(<span class='kw'>input</span>, p = <span class='fl'>0.5</span>, training = <span class='fl'>FALSE</span>, inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>p</th>
+      <td><p>probability of an element to be zeroed. Default: 0.5</p></td>
+    </tr>
+    <tr>
+      <th>training</th>
+      <td><p>apply dropout if is <code>TRUE</code>. Default: <code>TRUE</code></p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>If set to <code>TRUE</code>, will do this operation in-place.
+Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Avg_pool1d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
+    <div class="hidden name"><code>nnf_avg_pool1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 1D average pooling over an input signal composed of several
+input planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_avg_pool1d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='kw'>NULL</span>,
+  padding = <span class='fl'>0</span>,
+  ceil_mode = <span class='fl'>FALSE</span>,
+  count_include_pad = <span class='fl'>TRUE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape (minibatch , in_channels , iW)</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>the size of the window. Can be a single number or a
+tuple <code>(kW,)</code>.</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the window. Can be a single number or a tuple
+<code>(sW,)</code>. Default: <code>kernel_size</code></p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit zero paddings on both sides of the input. Can be a
+single number or a tuple <code>(padW,)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>ceil_mode</th>
+      <td><p>when True, will use <code>ceil</code> instead of <code>floor</code> to compute the
+output shape. Default: <code>FALSE</code></p></td>
+    </tr>
+    <tr>
+      <th>count_include_pad</th>
+      <td><p>when True, will include the zero-padding in the
+averaging calculation. Default: <code>TRUE</code></p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nnf_avg_pool2d.html b/static/docs/dev/reference/nnf_avg_pool2d.html
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+    <div class="page-header">
+    <h1>Avg_pool2d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
+    <div class="hidden name"><code>nnf_avg_pool2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies 2D average-pooling operation in \(kH * kW\) regions by step size
+\(sH * sW\) steps. The number of output features is equal to the number of
+input planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_avg_pool2d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='kw'>NULL</span>,
+  padding = <span class='fl'>0</span>,
+  ceil_mode = <span class='fl'>FALSE</span>,
+  count_include_pad = <span class='fl'>TRUE</span>,
+  divisor_override = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor (minibatch, in_channels , iH , iW)</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>size of the pooling region. Can be a single number or a
+tuple <code>(kH, kW)</code></p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>stride of the pooling operation. Can be a single number or a
+tuple <code>(sH, sW)</code>. Default: <code>kernel_size</code></p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit zero paddings on both sides of the input. Can be a
+single number or a tuple <code>(padH, padW)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>ceil_mode</th>
+      <td><p>when True, will use <code>ceil</code> instead of <code>floor</code> in the formula
+to compute the output shape. Default: <code>FALSE</code></p></td>
+    </tr>
+    <tr>
+      <th>count_include_pad</th>
+      <td><p>when True, will include the zero-padding in the
+averaging calculation. Default: <code>TRUE</code></p></td>
+    </tr>
+    <tr>
+      <th>divisor_override</th>
+      <td><p>if specified, it will be used as divisor, otherwise
+size of the pooling region will be used. Default: <code>NULL</code></p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Avg_pool3d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
+    <div class="hidden name"><code>nnf_avg_pool3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies 3D average-pooling operation in \(kT * kH * kW\) regions by step
+size \(sT * sH * sW\) steps. The number of output features is equal to
+\(\lfloor \frac{ \mbox{input planes} }{sT} \rfloor\).</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_avg_pool3d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='kw'>NULL</span>,
+  padding = <span class='fl'>0</span>,
+  ceil_mode = <span class='fl'>FALSE</span>,
+  count_include_pad = <span class='fl'>TRUE</span>,
+  divisor_override = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor (minibatch, in_channels , iT * iH , iW)</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>size of the pooling region. Can be a single number or a
+tuple <code>(kT, kH, kW)</code></p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>stride of the pooling operation. Can be a single number or a
+tuple <code>(sT, sH, sW)</code>. Default: <code>kernel_size</code></p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit zero paddings on both sides of the input. Can be a
+single number or a tuple <code>(padT, padH, padW)</code>, Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>ceil_mode</th>
+      <td><p>when True, will use <code>ceil</code> instead of <code>floor</code> in the formula
+to compute the output shape</p></td>
+    </tr>
+    <tr>
+      <th>count_include_pad</th>
+      <td><p>when True, will include the zero-padding in the
+averaging calculation</p></td>
+    </tr>
+    <tr>
+      <th>divisor_override</th>
+      <td><p>NA if specified, it will be used as divisor, otherwise
+size of the pooling region will be used. Default: <code>NULL</code></p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/nnf_batch_norm.html b/static/docs/dev/reference/nnf_batch_norm.html
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+    <h1>Batch_norm</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-batchnorm.R'><code>R/nnf-batchnorm.R</code></a></small>
+    <div class="hidden name"><code>nnf_batch_norm.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies Batch Normalization for each channel across a batch of data.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_batch_norm</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>running_mean</span>,
+  <span class='kw'>running_var</span>,
+  weight = <span class='kw'>NULL</span>,
+  bias = <span class='kw'>NULL</span>,
+  training = <span class='fl'>FALSE</span>,
+  momentum = <span class='fl'>0.1</span>,
+  eps = <span class='fl'>1e-05</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor</p></td>
+    </tr>
+    <tr>
+      <th>running_mean</th>
+      <td><p>the running_mean tensor</p></td>
+    </tr>
+    <tr>
+      <th>running_var</th>
+      <td><p>the running_var tensor</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>the weight tensor</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>the bias tensor</p></td>
+    </tr>
+    <tr>
+      <th>training</th>
+      <td><p>bool wether it's training. Default: FALSE</p></td>
+    </tr>
+    <tr>
+      <th>momentum</th>
+      <td><p>the value used for the <code>running_mean</code> and <code>running_var</code> computation.
+Can be set to None for cumulative moving average (i.e. simple average). Default: 0.1</p></td>
+    </tr>
+    <tr>
+      <th>eps</th>
+      <td><p>a value added to the denominator for numerical stability. Default: 1e-5</p></td>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/dev/reference/nnf_bilinear.html b/static/docs/dev/reference/nnf_bilinear.html
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+
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+    <div class="page-header">
+    <h1>Bilinear</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-linear.R'><code>R/nnf-linear.R</code></a></small>
+    <div class="hidden name"><code>nnf_bilinear.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a bilinear transformation to the incoming data:
+\(y = x_1 A x_2 + b\)</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_bilinear</span>(<span class='kw'>input1</span>, <span class='kw'>input2</span>, <span class='kw'>weight</span>, bias = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input1</th>
+      <td><p>\((N, *, H_{in1})\) where \(H_{in1}=\mbox{in1\_features}\)
+and \(*\) means any number of additional dimensions.
+All but the last dimension of the inputs should be the same.</p></td>
+    </tr>
+    <tr>
+      <th>input2</th>
+      <td><p>\((N, *, H_{in2})\) where \(H_{in2}=\mbox{in2\_features}\)</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>\((\mbox{out\_features}, \mbox{in1\_features},
+\mbox{in2\_features})\)</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>\((\mbox{out\_features})\)</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+
+    <p>output \((N, *, H_{out})\) where \(H_{out}=\mbox{out\_features}\)
+and all but the last dimension are the same shape as the input.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nnf_binary_cross_entropy.html b/static/docs/dev/reference/nnf_binary_cross_entropy.html
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+    <h1>Binary_cross_entropy</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
+    <div class="hidden name"><code>nnf_binary_cross_entropy.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Function that measures the Binary Cross Entropy
+between the target and the output.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_binary_cross_entropy</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>target</span>,
+  weight = <span class='kw'>NULL</span>,
+  reduction = <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"mean"</span>, <span class='st'>"sum"</span>, <span class='st'>"none"</span>)
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
+    </tr>
+    <tr>
+      <th>target</th>
+      <td><p>tensor (N,*) , same shape as the input</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>(tensor) weight for each value.</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'</p></td>
+    </tr>
+    </table>
+
+
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+    <div class="page-header">
+    <h1>Binary_cross_entropy_with_logits</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
+    <div class="hidden name"><code>nnf_binary_cross_entropy_with_logits.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Function that measures Binary Cross Entropy between target and output
+logits.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_binary_cross_entropy_with_logits</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>target</span>,
+  weight = <span class='kw'>NULL</span>,
+  reduction = <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"mean"</span>, <span class='st'>"sum"</span>, <span class='st'>"none"</span>),
+  pos_weight = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>Tensor of arbitrary shape</p></td>
+    </tr>
+    <tr>
+      <th>target</th>
+      <td><p>Tensor of the same shape as input</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>(Tensor, optional) a manual rescaling weight if provided it's
+repeated to match input tensor shape.</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'</p></td>
+    </tr>
+    <tr>
+      <th>pos_weight</th>
+      <td><p>(Tensor, optional) a weight of positive examples.
+Must be a vector with length equal to the number of classes.</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nnf_celu.html b/static/docs/dev/reference/nnf_celu.html
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Celu</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
+    <div class="hidden name"><code>nnf_celu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies element-wise, \(CELU(x) = max(0,x) + min(0, \alpha * (exp(x \alpha) - 1))\).</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_celu</span>(<span class='kw'>input</span>, alpha = <span class='fl'>1</span>, inplace = <span class='fl'>FALSE</span>)
+
+<span class='fu'>nnf_celu_</span>(<span class='kw'>input</span>, alpha = <span class='fl'>1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
+    </tr>
+    <tr>
+      <th>alpha</th>
+      <td><p>the alpha value for the CELU formulation. Default: 1.0</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+<div class="row">
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+    <div class="page-header">
+    <h1>Conv1d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-conv.R'><code>R/nnf-conv.R</code></a></small>
+    <div class="hidden name"><code>nnf_conv1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 1D convolution over an input signal composed of several input
+planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_conv1d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>weight</span>,
+  bias = <span class='kw'>NULL</span>,
+  stride = <span class='fl'>1</span>,
+  padding = <span class='fl'>0</span>,
+  dilation = <span class='fl'>1</span>,
+  groups = <span class='fl'>1</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape (minibatch, in_channels , iW)</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>filters of shape (out_channels, in_channels/groups , kW)</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>optional bias of shape (out_channels). Default: <code>NULL</code></p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the convolving kernel. Can be a single number or
+a one-element tuple <code>(sW,)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit paddings on both sides of the input. Can be a
+single number or a one-element tuple <code>(padW,)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>the spacing between kernel elements. Can be a single number or
+a one-element tuple <code>(dW,)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>split input into groups, <code>in_channels</code> should be divisible by
+the number of groups. Default: 1</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/nnf_conv2d.html b/static/docs/dev/reference/nnf_conv2d.html
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+    <div class="page-header">
+    <h1>Conv2d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-conv.R'><code>R/nnf-conv.R</code></a></small>
+    <div class="hidden name"><code>nnf_conv2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 2D convolution over an input image composed of several input
+planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_conv2d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>weight</span>,
+  bias = <span class='kw'>NULL</span>,
+  stride = <span class='fl'>1</span>,
+  padding = <span class='fl'>0</span>,
+  dilation = <span class='fl'>1</span>,
+  groups = <span class='fl'>1</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape (minibatch, in_channels, iH , iW)</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>filters of shape (out_channels , in_channels/groups, kH , kW)</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>optional bias tensor of shape (out_channels). Default: <code>NULL</code></p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the convolving kernel. Can be a single number or a
+tuple <code>(sH, sW)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit paddings on both sides of the input. Can be a
+single number or a tuple <code>(padH, padW)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>the spacing between kernel elements. Can be a single number or
+a tuple <code>(dH, dW)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>split input into groups, <code>in_channels</code> should be divisible by the
+number of groups. Default: 1</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/nnf_conv3d.html b/static/docs/dev/reference/nnf_conv3d.html
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+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Conv3d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-conv.R'><code>R/nnf-conv.R</code></a></small>
+    <div class="hidden name"><code>nnf_conv3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 3D convolution over an input image composed of several input
+planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_conv3d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>weight</span>,
+  bias = <span class='kw'>NULL</span>,
+  stride = <span class='fl'>1</span>,
+  padding = <span class='fl'>0</span>,
+  dilation = <span class='fl'>1</span>,
+  groups = <span class='fl'>1</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape (minibatch, in_channels , iT , iH , iW)</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>filters of shape (out_channels , in_channels/groups, kT , kH , kW)</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>optional bias tensor of shape (out_channels). Default: <code>NULL</code></p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the convolving kernel. Can be a single number or a
+tuple <code>(sT, sH, sW)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit paddings on both sides of the input. Can be a
+single number or a tuple <code>(padT, padH, padW)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>the spacing between kernel elements. Can be a single number or
+a tuple <code>(dT, dH, dW)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>split input into groups, <code>in_channels</code> should be divisible by
+the number of groups. Default: 1</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/nnf_conv_tbc.html b/static/docs/dev/reference/nnf_conv_tbc.html
new file mode 100644
index 000000000..6f567569e
--- /dev/null
+++ b/static/docs/dev/reference/nnf_conv_tbc.html
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+    <div class="page-header">
+    <h1>Conv_tbc</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-conv.R'><code>R/nnf-conv.R</code></a></small>
+    <div class="hidden name"><code>nnf_conv_tbc.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 1-dimensional sequence convolution over an input sequence.
+Input and output dimensions are (Time, Batch, Channels) - hence TBC.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_conv_tbc</span>(<span class='kw'>input</span>, <span class='kw'>weight</span>, <span class='kw'>bias</span>, pad = <span class='fl'>0</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape \((\mbox{sequence length} \times 
+batch \times \mbox{in\_channels})\)</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>filter of shape (\(\mbox{kernel width} \times \mbox{in\_channels} 
+\times \mbox{out\_channels}\))</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>bias of shape (\(\mbox{out\_channels}\))</p></td>
+    </tr>
+    <tr>
+      <th>pad</th>
+      <td><p>number of timesteps to pad. Default: 0</p></td>
+    </tr>
+    </table>
+
+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Conv_transpose1d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-conv.R'><code>R/nnf-conv.R</code></a></small>
+    <div class="hidden name"><code>nnf_conv_transpose1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 1D transposed convolution operator over an input signal
+composed of several input planes, sometimes also called "deconvolution".</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_conv_transpose1d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>weight</span>,
+  bias = <span class='kw'>NULL</span>,
+  stride = <span class='fl'>1</span>,
+  padding = <span class='fl'>0</span>,
+  output_padding = <span class='fl'>0</span>,
+  groups = <span class='fl'>1</span>,
+  dilation = <span class='fl'>1</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape (minibatch, in_channels , iW)</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>filters of shape (out_channels, in_channels/groups , kW)</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>optional bias of shape (out_channels). Default: <code>NULL</code></p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the convolving kernel. Can be a single number or
+a one-element tuple <code>(sW,)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit paddings on both sides of the input. Can be a
+single number or a one-element tuple <code>(padW,)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>output_padding</th>
+      <td><p>padding applied to the output</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>split input into groups, <code>in_channels</code> should be divisible by
+the number of groups. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>the spacing between kernel elements. Can be a single number or
+a one-element tuple <code>(dW,)</code>. Default: 1</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/nnf_conv_transpose2d.html b/static/docs/dev/reference/nnf_conv_transpose2d.html
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+    <div class="page-header">
+    <h1>Conv_transpose2d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-conv.R'><code>R/nnf-conv.R</code></a></small>
+    <div class="hidden name"><code>nnf_conv_transpose2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 2D transposed convolution operator over an input image
+composed of several input planes, sometimes also called "deconvolution".</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_conv_transpose2d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>weight</span>,
+  bias = <span class='kw'>NULL</span>,
+  stride = <span class='fl'>1</span>,
+  padding = <span class='fl'>0</span>,
+  output_padding = <span class='fl'>0</span>,
+  groups = <span class='fl'>1</span>,
+  dilation = <span class='fl'>1</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape (minibatch, in_channels, iH , iW)</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>filters of shape (out_channels , in_channels/groups, kH , kW)</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>optional bias tensor of shape (out_channels). Default: <code>NULL</code></p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the convolving kernel. Can be a single number or a
+tuple <code>(sH, sW)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit paddings on both sides of the input. Can be a
+single number or a tuple <code>(padH, padW)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>output_padding</th>
+      <td><p>padding applied to the output</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>split input into groups, <code>in_channels</code> should be divisible by the
+number of groups. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>the spacing between kernel elements. Can be a single number or
+a tuple <code>(dH, dW)</code>. Default: 1</p></td>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/dev/reference/nnf_conv_transpose3d.html b/static/docs/dev/reference/nnf_conv_transpose3d.html
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+composed of several input planes, sometimes also called &quot;deconvolution&quot;" />
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Conv_transpose3d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-conv.R'><code>R/nnf-conv.R</code></a></small>
+    <div class="hidden name"><code>nnf_conv_transpose3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 3D transposed convolution operator over an input image
+composed of several input planes, sometimes also called "deconvolution"</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_conv_transpose3d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>weight</span>,
+  bias = <span class='kw'>NULL</span>,
+  stride = <span class='fl'>1</span>,
+  padding = <span class='fl'>0</span>,
+  output_padding = <span class='fl'>0</span>,
+  groups = <span class='fl'>1</span>,
+  dilation = <span class='fl'>1</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape (minibatch, in_channels , iT , iH , iW)</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>filters of shape (out_channels , in_channels/groups, kT , kH , kW)</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>optional bias tensor of shape (out_channels). Default: <code>NULL</code></p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the convolving kernel. Can be a single number or a
+tuple <code>(sT, sH, sW)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit paddings on both sides of the input. Can be a
+single number or a tuple <code>(padT, padH, padW)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>output_padding</th>
+      <td><p>padding applied to the output</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>split input into groups, <code>in_channels</code> should be divisible by
+the number of groups. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>the spacing between kernel elements. Can be a single number or
+a tuple <code>(dT, dH, dW)</code>. Default: 1</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nnf_cosine_embedding_loss.html b/static/docs/dev/reference/nnf_cosine_embedding_loss.html
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index 000000000..e6c7b12f9
--- /dev/null
+++ b/static/docs/dev/reference/nnf_cosine_embedding_loss.html
@@ -0,0 +1,269 @@
+<!-- Generated by pkgdown: do not edit by hand -->
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+    <h1>Cosine_embedding_loss</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
+    <div class="hidden name"><code>nnf_cosine_embedding_loss.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Creates a criterion that measures the loss given input tensors x_1, x_2 and a
+Tensor label y with values 1 or -1. This is used for measuring whether two inputs
+are similar or dissimilar, using the cosine distance, and is typically used
+for learning nonlinear embeddings or semi-supervised learning.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_cosine_embedding_loss</span>(
+  <span class='kw'>input1</span>,
+  <span class='kw'>input2</span>,
+  <span class='kw'>target</span>,
+  margin = <span class='fl'>0</span>,
+  reduction = <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"mean"</span>, <span class='st'>"sum"</span>, <span class='st'>"none"</span>)
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input1</th>
+      <td><p>the input x_1 tensor</p></td>
+    </tr>
+    <tr>
+      <th>input2</th>
+      <td><p>the input x_2 tensor</p></td>
+    </tr>
+    <tr>
+      <th>target</th>
+      <td><p>the target tensor</p></td>
+    </tr>
+    <tr>
+      <th>margin</th>
+      <td><p>Should be a number from -1 to 1 , 0 to 0.5 is suggested. If margin
+is missing, the default value is 0.</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'</p></td>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/dev/reference/nnf_cosine_similarity.html b/static/docs/dev/reference/nnf_cosine_similarity.html
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+<div class="row">
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+    <div class="page-header">
+    <h1>Cosine_similarity</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-distance.R'><code>R/nnf-distance.R</code></a></small>
+    <div class="hidden name"><code>nnf_cosine_similarity.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Returns cosine similarity between x1 and x2, computed along dim.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_cosine_similarity</span>(<span class='kw'>x1</span>, <span class='kw'>x2</span>, dim = <span class='fl'>1</span>, eps = <span class='fl'>1e-08</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>x1</th>
+      <td><p>(Tensor) First input.</p></td>
+    </tr>
+    <tr>
+      <th>x2</th>
+      <td><p>(Tensor) Second input (of size matching x1).</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int, optional) Dimension of vectors. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>eps</th>
+      <td><p>(float, optional) Small value to avoid division by zero.
+Default: 1e-8</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+    \mbox{similarity} = \frac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert _2 \cdot \Vert x_2 \Vert _2, \epsilon)}
+$$</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Cross_entropy</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
+    <div class="hidden name"><code>nnf_cross_entropy.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>This criterion combines <code>log_softmax</code> and <code>nll_loss</code> in a single
+function.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_cross_entropy</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>target</span>,
+  weight = <span class='kw'>NULL</span>,
+  ignore_index = <span class='op'>-</span><span class='fl'>100</span>,
+  reduction = <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"mean"</span>, <span class='st'>"sum"</span>, <span class='st'>"none"</span>)
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(Tensor) \((N, C)\) where <code>C = number of classes</code> or \((N, C, H, W)\)
+in case of 2D Loss, or \((N, C, d_1, d_2, ..., d_K)\) where \(K \geq 1\)
+in the case of K-dimensional loss.</p></td>
+    </tr>
+    <tr>
+      <th>target</th>
+      <td><p>(Tensor) \((N)\) where each value is \(0 \leq \mbox{targets}[i] \leq C-1\),
+or \((N, d_1, d_2, ..., d_K)\) where \(K \geq 1\) for K-dimensional loss.</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>(Tensor, optional) a manual rescaling weight given to each class. If
+given, has to be a Tensor of size <code>C</code></p></td>
+    </tr>
+    <tr>
+      <th>ignore_index</th>
+      <td><p>(int, optional) Specifies a target value that is ignored
+and does not contribute to the input gradient.</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'</p></td>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/dev/reference/nnf_ctc_loss.html b/static/docs/dev/reference/nnf_ctc_loss.html
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Ctc_loss</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
+    <div class="hidden name"><code>nnf_ctc_loss.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>The Connectionist Temporal Classification loss.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_ctc_loss</span>(
+  <span class='kw'>log_probs</span>,
+  <span class='kw'>targets</span>,
+  <span class='kw'>input_lengths</span>,
+  <span class='kw'>target_lengths</span>,
+  blank = <span class='fl'>0</span>,
+  reduction = <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"mean"</span>, <span class='st'>"sum"</span>, <span class='st'>"none"</span>),
+  zero_infinity = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>log_probs</th>
+      <td><p>\((T, N, C)\) where C = number of characters in alphabet including blank,
+T = input length, and N = batch size. The logarithmized probabilities of
+the outputs (e.g. obtained with <a href='nnf_log_softmax.html'>nnf_log_softmax</a>).</p></td>
+    </tr>
+    <tr>
+      <th>targets</th>
+      <td><p>\((N, S)\) or <code>(sum(target_lengths))</code>. Targets cannot be blank.
+In the second form, the targets are assumed to be concatenated.</p></td>
+    </tr>
+    <tr>
+      <th>input_lengths</th>
+      <td><p>\((N)\). Lengths of the inputs (must each be \(\leq T\))</p></td>
+    </tr>
+    <tr>
+      <th>target_lengths</th>
+      <td><p>\((N)\). Lengths of the targets</p></td>
+    </tr>
+    <tr>
+      <th>blank</th>
+      <td><p>(int, optional) Blank label. Default \(0\).</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'</p></td>
+    </tr>
+    <tr>
+      <th>zero_infinity</th>
+      <td><p>(bool, optional) Whether to zero infinite losses and the
+associated gradients. Default: <code>FALSE</code> Infinite losses mainly occur when the
+inputs are too short to be aligned to the targets.</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/nnf_dropout.html b/static/docs/dev/reference/nnf_dropout.html
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+<div class="row">
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+    <div class="page-header">
+    <h1>Dropout</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-dropout.R'><code>R/nnf-dropout.R</code></a></small>
+    <div class="hidden name"><code>nnf_dropout.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>During training, randomly zeroes some of the elements of the input
+tensor with probability <code>p</code> using samples from a Bernoulli
+distribution.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_dropout</span>(<span class='kw'>input</span>, p = <span class='fl'>0.5</span>, training = <span class='fl'>TRUE</span>, inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>p</th>
+      <td><p>probability of an element to be zeroed. Default: 0.5</p></td>
+    </tr>
+    <tr>
+      <th>training</th>
+      <td><p>apply dropout if is <code>TRUE</code>. Default: <code>TRUE</code></p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>If set to <code>TRUE</code>, will do this operation in-place.
+Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+
+  </div>
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diff --git a/static/docs/dev/reference/nnf_dropout2d.html b/static/docs/dev/reference/nnf_dropout2d.html
new file mode 100644
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+<meta property="og:description" content="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).
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+probability p using samples from a Bernoulli distribution." />
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+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Dropout2d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-dropout.R'><code>R/nnf-dropout.R</code></a></small>
+    <div class="hidden name"><code>nnf_dropout2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Randomly zero out entire channels (a channel is a 2D feature map,
+e.g., the \(j\)-th channel of the \(i\)-th sample in the
+batched input is a 2D tensor \(input[i, j]\)) of the input tensor).
+Each channel will be zeroed out independently on every forward call with
+probability <code>p</code> using samples from a Bernoulli distribution.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_dropout2d</span>(<span class='kw'>input</span>, p = <span class='fl'>0.5</span>, training = <span class='fl'>TRUE</span>, inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>p</th>
+      <td><p>probability of a channel to be zeroed. Default: 0.5</p></td>
+    </tr>
+    <tr>
+      <th>training</th>
+      <td><p>apply dropout if is <code>TRUE</code>. Default: <code>TRUE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>If set to <code>TRUE</code>, will do this operation in-place.
+Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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diff --git a/static/docs/dev/reference/nnf_dropout3d.html b/static/docs/dev/reference/nnf_dropout3d.html
new file mode 100644
index 000000000..498cd6d0c
--- /dev/null
+++ b/static/docs/dev/reference/nnf_dropout3d.html
@@ -0,0 +1,258 @@
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+
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+<meta property="og:description" content="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).
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Dropout3d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-dropout.R'><code>R/nnf-dropout.R</code></a></small>
+    <div class="hidden name"><code>nnf_dropout3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Randomly zero out entire channels (a channel is a 3D feature map,
+e.g., the \(j\)-th channel of the \(i\)-th sample in the
+batched input is a 3D tensor \(input[i, j]\)) of the input tensor).
+Each channel will be zeroed out independently on every forward call with
+probability <code>p</code> using samples from a Bernoulli distribution.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_dropout3d</span>(<span class='kw'>input</span>, p = <span class='fl'>0.5</span>, training = <span class='fl'>TRUE</span>, inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>p</th>
+      <td><p>probability of a channel to be zeroed. Default: 0.5</p></td>
+    </tr>
+    <tr>
+      <th>training</th>
+      <td><p>apply dropout if is <code>TRUE</code>. Default: <code>TRUE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>If set to <code>TRUE</code>, will do this operation in-place.
+Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nnf_elu.html b/static/docs/dev/reference/nnf_elu.html
new file mode 100644
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+<meta property="og:title" content="Elu — nnf_elu" />
+<meta property="og:description" content="Applies element-wise,
+$$ELU(x) = max(0,x) + min(0, \alpha * (exp(x) - 1))$$." />
+
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+
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Elu</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
+    <div class="hidden name"><code>nnf_elu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies element-wise,
+$$ELU(x) = max(0,x) + min(0, \alpha * (exp(x) - 1))$$.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_elu</span>(<span class='kw'>input</span>, alpha = <span class='fl'>1</span>, inplace = <span class='fl'>FALSE</span>)
+
+<span class='fu'>nnf_elu_</span>(<span class='kw'>input</span>, alpha = <span class='fl'>1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
+    </tr>
+    <tr>
+      <th>alpha</th>
+      <td><p>the alpha value for the ELU formulation. Default: 1.0</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>)
+<span class='kw'>y</span> <span class='op'>&lt;-</span> <span class='fu'>nnf_elu</span>(<span class='kw'>x</span>, alpha = <span class='fl'>1</span>)
+<span class='fu'>nnf_elu_</span>(<span class='kw'>x</span>, alpha = <span class='fl'>1</span>)
+<span class='fu'><a href='torch_equal.html'>torch_equal</a></span>(<span class='kw'>x</span>, <span class='kw'>y</span>)
+
+}
+</div><div class='output co'>#&gt; [1] TRUE</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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diff --git a/static/docs/dev/reference/nnf_embedding.html b/static/docs/dev/reference/nnf_embedding.html
new file mode 100644
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--- /dev/null
+++ b/static/docs/dev/reference/nnf_embedding.html
@@ -0,0 +1,282 @@
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+    <div class="page-header">
+    <h1>Embedding</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-embedding.R'><code>R/nnf-embedding.R</code></a></small>
+    <div class="hidden name"><code>nnf_embedding.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>A simple lookup table that looks up embeddings in a fixed dictionary and size.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_embedding</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>weight</span>,
+  padding_idx = <span class='kw'>NULL</span>,
+  max_norm = <span class='kw'>NULL</span>,
+  norm_type = <span class='fl'>2</span>,
+  scale_grad_by_freq = <span class='fl'>FALSE</span>,
+  sparse = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(LongTensor) Tensor containing indices into the embedding matrix</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>(Tensor) The embedding matrix with number of rows equal to the
+maximum possible index + 1, and number of columns equal to the embedding size</p></td>
+    </tr>
+    <tr>
+      <th>padding_idx</th>
+      <td><p>(int, optional) If given, pads the output with the embedding
+vector at <code>padding_idx</code> (initialized to zeros) whenever it encounters the index.</p></td>
+    </tr>
+    <tr>
+      <th>max_norm</th>
+      <td><p>(float, optional) If given, each embedding vector with norm larger
+than <code>max_norm</code> is renormalized to have norm <code>max_norm</code>. Note: this will modify
+<code>weight</code> in-place.</p></td>
+    </tr>
+    <tr>
+      <th>norm_type</th>
+      <td><p>(float, optional) The p of the p-norm to compute for the <code>max_norm</code>
+option. Default <code>2</code>.</p></td>
+    </tr>
+    <tr>
+      <th>scale_grad_by_freq</th>
+      <td><p>(boolean, optional) If given, this will scale gradients
+by the inverse of frequency of the words in the mini-batch. Default <code>FALSE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>sparse</th>
+      <td><p>(bool, optional) If <code>TRUE</code>, gradient w.r.t. <code>weight</code> will be a
+sparse tensor. See Notes under <code>nn_embedding</code> for more details regarding
+sparse gradients.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>This module is often used to retrieve word embeddings using indices.
+The input to the module is a list of indices, and the embedding matrix,
+and the output is the corresponding word embeddings.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+<div class="row">
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+    <div class="page-header">
+    <h1>Embedding_bag</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-embedding.R'><code>R/nnf-embedding.R</code></a></small>
+    <div class="hidden name"><code>nnf_embedding_bag.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Computes sums, means or maxes of <code>bags</code> of embeddings, without instantiating the
+intermediate embeddings.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_embedding_bag</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>weight</span>,
+  offsets = <span class='kw'>NULL</span>,
+  max_norm = <span class='kw'>NULL</span>,
+  norm_type = <span class='fl'>2</span>,
+  scale_grad_by_freq = <span class='fl'>FALSE</span>,
+  mode = <span class='st'>"mean"</span>,
+  sparse = <span class='fl'>FALSE</span>,
+  per_sample_weights = <span class='kw'>NULL</span>,
+  include_last_offset = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(LongTensor) Tensor containing bags of indices into the embedding matrix</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>(Tensor) The embedding matrix with number of rows equal to the
+maximum possible index + 1, and number of columns equal to the embedding size</p></td>
+    </tr>
+    <tr>
+      <th>offsets</th>
+      <td><p>(LongTensor, optional) Only used when <code>input</code> is 1D. <code>offsets</code>
+determines the starting index position of each bag (sequence) in <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>max_norm</th>
+      <td><p>(float, optional) If given, each embedding vector with norm
+larger than <code>max_norm</code> is renormalized to have norm <code>max_norm</code>.
+Note: this will modify <code>weight</code> in-place.</p></td>
+    </tr>
+    <tr>
+      <th>norm_type</th>
+      <td><p>(float, optional) The <code>p</code> in the <code>p</code>-norm to compute for the
+<code>max_norm</code> option. Default <code>2</code>.</p></td>
+    </tr>
+    <tr>
+      <th>scale_grad_by_freq</th>
+      <td><p>(boolean, optional) if given, this will scale gradients
+by the inverse of frequency of the words in the mini-batch. Default <code>FALSE</code>.                                            Note: this option is not supported when <code>mode="max"</code>.</p></td>
+    </tr>
+    <tr>
+      <th>mode</th>
+      <td><p>(string, optional) <code>"sum"</code>, <code>"mean"</code> or <code>"max"</code>. Specifies
+the way to reduce the bag. Default: 'mean'</p></td>
+    </tr>
+    <tr>
+      <th>sparse</th>
+      <td><p>(bool, optional) if <code>TRUE</code>, gradient w.r.t. <code>weight</code> will be a
+sparse tensor. See Notes under <code>nn_embedding</code> for more details regarding
+sparse gradients. Note: this option is not supported when <code>mode="max"</code>.</p></td>
+    </tr>
+    <tr>
+      <th>per_sample_weights</th>
+      <td><p>(Tensor, optional) a tensor of float / double weights,
+or NULL to indicate all weights should be taken to be 1. If specified,
+<code>per_sample_weights</code> must have exactly the same shape as input and is treated
+as having the same <code>offsets</code>, if those are not <code>NULL</code>.</p></td>
+    </tr>
+    <tr>
+      <th>include_last_offset</th>
+      <td><p>(bool, optional) if <code>TRUE</code>, the size of offsets is
+equal to the number of bags + 1.</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <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/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='kw'>input</span>,
+  <span class='kw'>output_size</span>,
+  <span class='kw'>kernel_size</span>,
+  dilation = <span class='fl'>1</span>,
+  padding = <span class='fl'>0</span>,
+  stride = <span class='fl'>1</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>output_size</th>
+      <td><p>the shape of the spatial dimensions of the output (i.e.,
+<code>output$sizes()[-c(1,2)]</code>)</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>the size of the sliding blocks</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>a parameter that controls the stride of elements within the
+neighborhood. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit zero padding to be added on both sides of input.
+Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the sliding blocks in the input spatial dimensions.
+Default: 1</p></td>
+    </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">
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diff --git a/static/docs/dev/reference/nnf_fractional_max_pool2d.html b/static/docs/dev/reference/nnf_fractional_max_pool2d.html
new file mode 100644
index 000000000..838b7aa83
--- /dev/null
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+    <div class="page-header">
+    <h1>Fractional_max_pool2d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
+    <div class="hidden name"><code>nnf_fractional_max_pool2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies 2D fractional max pooling over an input signal composed of several input planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_fractional_max_pool2d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>kernel_size</span>,
+  output_size = <span class='kw'>NULL</span>,
+  output_ratio = <span class='kw'>NULL</span>,
+  return_indices = <span class='fl'>FALSE</span>,
+  random_samples = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>the size of the window to take a max over. Can be a
+single number \(k\) (for a square kernel of \(k * k\)) or
+a tuple <code>(kH, kW)</code></p></td>
+    </tr>
+    <tr>
+      <th>output_size</th>
+      <td><p>the target output size of the image of the form \(oH * oW\).
+Can be a tuple <code>(oH, oW)</code> or a single number \(oH\) for a square image \(oH * oH\)</p></td>
+    </tr>
+    <tr>
+      <th>output_ratio</th>
+      <td><p>If one wants to have an output size as a ratio of the input size,
+this option can be given. This has to be a number or tuple in the range (0, 1)</p></td>
+    </tr>
+    <tr>
+      <th>return_indices</th>
+      <td><p>if <code>True</code>, will return the indices along with the outputs.</p></td>
+    </tr>
+    <tr>
+      <th>random_samples</th>
+      <td><p>optional random samples.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>Fractional MaxPooling is described in detail in the paper <code>Fractional MaxPooling</code>_ by Ben Graham</p>
+<p>The max-pooling operation is applied in \(kH * kW\) regions by a stochastic
+step size determined by the target output size.
+The number of output features is equal to the number of input planes.</p>
+
+  </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>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='kw'>input</span>,
+  <span class='kw'>kernel_size</span>,
+  output_size = <span class='kw'>NULL</span>,
+  output_ratio = <span class='kw'>NULL</span>,
+  return_indices = <span class='fl'>FALSE</span>,
+  random_samples = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>the size of the window to take a max over. Can be a single number \(k\)
+(for a square kernel of \(k * k * k\)) or a tuple <code>(kT, kH, kW)</code></p></td>
+    </tr>
+    <tr>
+      <th>output_size</th>
+      <td><p>the target output size of the form \(oT * oH * oW\).
+Can be a tuple <code>(oT, oH, oW)</code> or a single number \(oH\) for a cubic output
+\(oH * oH * oH\)</p></td>
+    </tr>
+    <tr>
+      <th>output_ratio</th>
+      <td><p>If one wants to have an output size as a ratio of the
+input size, this option can be given. This has to be a number or tuple in the
+range (0, 1)</p></td>
+    </tr>
+    <tr>
+      <th>return_indices</th>
+      <td><p>if <code>True</code>, will return the indices along with the outputs.</p></td>
+    </tr>
+    <tr>
+      <th>random_samples</th>
+      <td><p>undocumented argument.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>Fractional MaxPooling is described in detail in the paper <code>Fractional MaxPooling</code>_ by Ben Graham</p>
+<p>The max-pooling operation is applied in \(kT * kH * kW\) regions by a stochastic
+step size determined by the target output size.
+The number of output features is equal to the number of input planes.</p>
+
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+    <h1>Gelu</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_gelu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Gelu</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_gelu</span>(<span class='kw'>input</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="gelu-input-gt-tensor-"><a class="anchor" href="#gelu-input-gt-tensor-"></a>gelu(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>Applies element-wise the function
+\(GELU(x) = x * \Phi(x)\)</p>
+<p>where \(\Phi(x)\) is the Cumulative Distribution Function for
+Gaussian Distribution.</p>
+<p>See <a href='https://arxiv.org/abs/1606.08415'>Gaussian Error Linear Units (GELUs)</a>.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <h1>Glu</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_glu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>The gated linear unit. Computes:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_glu</span>(<span class='kw'>input</span>, dim = <span class='op'>-</span><span class='fl'>1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(Tensor) input tensor</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) dimension on which to split the input. Default: -1</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$GLU(a, b) = a \otimes \sigma(b)$$</p>
+<p>where <code>input</code> is split in half along <code>dim</code> to form <code>a</code> and <code>b</code>, \(\sigma\)
+is the sigmoid function and \(\otimes\) is the element-wise product
+between matrices.</p>
+<p>See <a href='https://arxiv.org/abs/1612.08083'>Language Modeling with Gated Convolutional Networks</a>.</p>
+
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+    <div class="page-header">
+    <h1>Grid_sample</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-vision.R'><code>R/nnf-vision.R</code></a></small>
+    <div class="hidden name"><code>nnf_grid_sample.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Given an <code>input</code> and a flow-field <code>grid</code>, computes the
+<code>output</code> using <code>input</code> values and pixel locations from <code>grid</code>.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_grid_sample</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>grid</span>,
+  mode = <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"bilinear"</span>, <span class='st'>"nearest"</span>),
+  padding_mode = <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"zeros"</span>, <span class='st'>"border"</span>, <span class='st'>"reflection"</span>),
+  align_corners = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(Tensor) input of shape \((N, C, H_{\mbox{in}}, W_{\mbox{in}})\) (4-D case)                    or \((N, C, D_{\mbox{in}}, H_{\mbox{in}}, W_{\mbox{in}})\) (5-D case)</p></td>
+    </tr>
+    <tr>
+      <th>grid</th>
+      <td><p>(Tensor) flow-field of shape \((N, H_{\mbox{out}}, W_{\mbox{out}}, 2)\) (4-D case)                   or \((N, D_{\mbox{out}}, H_{\mbox{out}}, W_{\mbox{out}}, 3)\) (5-D case)</p></td>
+    </tr>
+    <tr>
+      <th>mode</th>
+      <td><p>(str) interpolation mode to calculate output values <code>'bilinear'</code> | <code>'nearest'</code>.
+Default: <code>'bilinear'</code></p></td>
+    </tr>
+    <tr>
+      <th>padding_mode</th>
+      <td><p>(str) padding mode for outside grid values <code>'zeros'</code> | <code>'border'</code>
+| <code>'reflection'</code>. Default: <code>'zeros'</code></p></td>
+    </tr>
+    <tr>
+      <th>align_corners</th>
+      <td><p>(bool, optional) Geometrically, we consider the pixels of the
+input  as squares rather than points. If set to <code>True</code>, the extrema (<code>-1</code> and
+<code>1</code>) are considered as referring to the center points of the input's corner pixels.
+If set to <code>False</code>, they are instead considered as referring to the corner
+points of the input's corner pixels, making the sampling more resolution
+agnostic. This option parallels the <code>align_corners</code> option in  <code><a href='nnf_interpolate.html'>nnf_interpolate()</a></code>, and
+so whichever option is used here should also be used there to resize the input
+image before grid sampling. Default: <code>False</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>Currently, only spatial (4-D) and volumetric (5-D) <code>input</code> are
+supported.</p>
+<p>In the spatial (4-D) case, for <code>input</code> with shape
+\((N, C, H_{\mbox{in}}, W_{\mbox{in}})\) and <code>grid</code> with shape
+\((N, H_{\mbox{out}}, W_{\mbox{out}}, 2)\), the output will have shape
+\((N, C, H_{\mbox{out}}, W_{\mbox{out}})\).</p>
+<p>For each output location <code>output[n, :, h, w]</code>, the size-2 vector
+<code>grid[n, h, w]</code> specifies <code>input</code> pixel locations <code>x</code> and <code>y</code>,
+which are used to interpolate the output value <code>output[n, :, h, w]</code>.
+In the case of 5D inputs, <code>grid[n, d, h, w]</code> specifies the
+<code>x</code>, <code>y</code>, <code>z</code> pixel locations for interpolating
+<code>output[n, :, d, h, w]</code>. <code>mode</code> argument specifies <code>nearest</code> or
+<code>bilinear</code> interpolation method to sample the input pixels.</p>
+<p><code>grid</code> specifies the sampling pixel locations normalized by the
+<code>input</code> spatial dimensions. Therefore, it should have most values in
+the range of <code>[-1, 1]</code>. For example, values <code>x = -1, y = -1</code> is the
+left-top pixel of <code>input</code>, and values  <code>x = 1, y = 1</code> is the
+right-bottom pixel of <code>input</code>.</p>
+<p>If <code>grid</code> has values outside the range of <code>[-1, 1]</code>, the corresponding
+outputs are handled as defined by <code>padding_mode</code>. Options are</p><ul>
+<li><p><code>padding_mode="zeros"</code>: use <code>0</code> for out-of-bound grid locations,</p></li>
+<li><p><code>padding_mode="border"</code>: use border values for out-of-bound grid locations,</p></li>
+<li><p><code>padding_mode="reflection"</code>: use values at locations reflected by
+the border for out-of-bound grid locations. For location far away
+from the border, it will keep being reflected until becoming in bound,
+e.g., (normalized) pixel location <code>x = -3.5</code> reflects by border <code>-1</code>
+and becomes <code>x' = 1.5</code>, then reflects by border <code>1</code> and becomes
+<code>x'' = -0.5</code>.</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+
+
+<p>This function is often used in conjunction with <code><a href='nnf_affine_grid.html'>nnf_affine_grid()</a></code>
+to build <code>Spatial Transformer Networks</code>_ .</p>
+
+  </div>
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+    </nav>
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diff --git a/static/docs/dev/reference/nnf_group_norm.html b/static/docs/dev/reference/nnf_group_norm.html
new file mode 100644
index 000000000..2eefc5dc4
--- /dev/null
+++ b/static/docs/dev/reference/nnf_group_norm.html
@@ -0,0 +1,253 @@
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+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Group_norm</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-normalization.R'><code>R/nnf-normalization.R</code></a></small>
+    <div class="hidden name"><code>nnf_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='kw'>input</span>, <span class='kw'>num_groups</span>, weight = <span class='kw'>NULL</span>, bias = <span class='kw'>NULL</span>, eps = <span class='fl'>1e-05</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>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>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/nnf_gumbel_softmax.html b/static/docs/dev/reference/nnf_gumbel_softmax.html
new file mode 100644
index 000000000..84200f758
--- /dev/null
+++ b/static/docs/dev/reference/nnf_gumbel_softmax.html
@@ -0,0 +1,251 @@
+<!-- Generated by pkgdown: do not edit by hand -->
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+
+
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+optionally discretizes." />
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+    <div class="page-header">
+    <h1>Gumbel_softmax</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
+    <div class="hidden name"><code>nnf_gumbel_softmax.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Samples from the Gumbel-Softmax distribution and
+optionally discretizes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_gumbel_softmax</span>(<span class='kw'>logits</span>, tau = <span class='fl'>1</span>, hard = <span class='fl'>FALSE</span>, dim = <span class='op'>-</span><span class='fl'>1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>logits</th>
+      <td><p><code>[..., num_features]</code> unnormalized log probabilities</p></td>
+    </tr>
+    <tr>
+      <th>tau</th>
+      <td><p>non-negative scalar temperature</p></td>
+    </tr>
+    <tr>
+      <th>hard</th>
+      <td><p>if <code>True</code>, the returned samples will be discretized as one-hot vectors,        but will be differentiated as if it is the soft sample in autograd</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) A dimension along which softmax will be computed. Default: -1.</p></td>
+    </tr>
+    </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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+    <h1>Hardshrink</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_hardshrink.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the hard shrinkage function element-wise</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_hardshrink</span>(<span class='kw'>input</span>, lambd = <span class='fl'>0.5</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
+    </tr>
+    <tr>
+      <th>lambd</th>
+      <td><p>the lambda value for the Hardshrink formulation. Default: 0.5</p></td>
+    </tr>
+    </table>
+
+
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+    <div class="page-header">
+    <h1>Hardsigmoid</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_hardsigmoid.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the element-wise function \(\mbox{Hardsigmoid}(x) = \frac{ReLU6(x + 3)}{6}\)</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_hardsigmoid</span>(<span class='kw'>input</span>, inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>NA 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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+
+
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+    <h1>Hardswish</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
+    <div class="hidden name"><code>nnf_hardswish.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the hardswish function, element-wise, as described in the paper:
+Searching for MobileNetV3.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_hardswish</span>(<span class='kw'>input</span>, inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$ \mbox{Hardswish}(x) = \left\{
+  \begin{array}{ll}
+  0 &amp; \mbox{if } x \le -3, \\
+  x &amp; \mbox{if } x \ge +3, \\
+  x \cdot (x + 3)/6 &amp; \mbox{otherwise}
+  \end{array}
+  \right. $$</p>
+
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+    <div class="page-header">
+    <h1>Hardtanh</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
+    <div class="hidden name"><code>nnf_hardtanh.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the HardTanh function element-wise.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_hardtanh</span>(<span class='kw'>input</span>, min_val = <span class='op'>-</span><span class='fl'>1</span>, max_val = <span class='fl'>1</span>, inplace = <span class='fl'>FALSE</span>)
+
+<span class='fu'>nnf_hardtanh_</span>(<span class='kw'>input</span>, min_val = <span class='op'>-</span><span class='fl'>1</span>, max_val = <span class='fl'>1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
+    </tr>
+    <tr>
+      <th>min_val</th>
+      <td><p>minimum value of the linear region range. Default: -1</p></td>
+    </tr>
+    <tr>
+      <th>max_val</th>
+      <td><p>maximum value of the linear region range. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nnf_hinge_embedding_loss.html b/static/docs/dev/reference/nnf_hinge_embedding_loss.html
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+    <div class="page-header">
+    <h1>Hinge_embedding_loss</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
+    <div class="hidden name"><code>nnf_hinge_embedding_loss.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Measures the loss given an input tensor xx and a labels tensor yy (containing 1 or -1).
+This is usually used for measuring whether two inputs are similar or dissimilar, e.g.
+using the L1 pairwise distance as xx , and is typically used for learning nonlinear
+embeddings or semi-supervised learning.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_hinge_embedding_loss</span>(<span class='kw'>input</span>, <span class='kw'>target</span>, margin = <span class='fl'>1</span>, reduction = <span class='st'>"mean"</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" 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>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>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/dev/reference/nnf_instance_norm.html b/static/docs/dev/reference/nnf_instance_norm.html
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+<meta property="og:description" content="Applies Instance Normalization for each channel in each data sample in a
+batch." />
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+    <div class="page-header">
+    <h1>Instance_norm</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-instancenorm.R'><code>R/nnf-instancenorm.R</code></a></small>
+    <div class="hidden name"><code>nnf_instance_norm.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies Instance Normalization for each channel in each data sample in a
+batch.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_instance_norm</span>(
+  <span class='kw'>input</span>,
+  running_mean = <span class='kw'>NULL</span>,
+  running_var = <span class='kw'>NULL</span>,
+  weight = <span class='kw'>NULL</span>,
+  bias = <span class='kw'>NULL</span>,
+  use_input_stats = <span class='fl'>TRUE</span>,
+  momentum = <span class='fl'>0.1</span>,
+  eps = <span class='fl'>1e-05</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>running_mean</th>
+      <td><p>the running_mean tensor</p></td>
+    </tr>
+    <tr>
+      <th>running_var</th>
+      <td><p>the running var tensor</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>the weight tensor</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>the bias tensor</p></td>
+    </tr>
+    <tr>
+      <th>use_input_stats</th>
+      <td><p>whether to use input stats</p></td>
+    </tr>
+    <tr>
+      <th>momentum</th>
+      <td><p>a double for the momentum</p></td>
+    </tr>
+    <tr>
+      <th>eps</th>
+      <td><p>an eps double for numerical stability</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/nnf_interpolate.html b/static/docs/dev/reference/nnf_interpolate.html
new file mode 100644
index 000000000..b0a3ff2a8
--- /dev/null
+++ b/static/docs/dev/reference/nnf_interpolate.html
@@ -0,0 +1,295 @@
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+    <h1>Interpolate</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-upsampling.R'><code>R/nnf-upsampling.R</code></a></small>
+    <div class="hidden name"><code>nnf_interpolate.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Down/up samples the input to either the given <code>size</code> or the given
+<code>scale_factor</code></p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_interpolate</span>(
+  <span class='kw'>input</span>,
+  size = <span class='kw'>NULL</span>,
+  scale_factor = <span class='kw'>NULL</span>,
+  mode = <span class='st'>"nearest"</span>,
+  align_corners = <span class='fl'>FALSE</span>,
+  recompute_scale_factor = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(Tensor) the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>size</th>
+      <td><p>(int or <code>Tuple[int]</code> or <code>Tuple[int, int]</code> or <code>Tuple[int, int, int]</code>)
+output spatial size.</p></td>
+    </tr>
+    <tr>
+      <th>scale_factor</th>
+      <td><p>(float or <code>Tuple[float]</code>) multiplier for spatial size.
+Has to match input size if it is a tuple.</p></td>
+    </tr>
+    <tr>
+      <th>mode</th>
+      <td><p>(str) algorithm used for upsampling: 'nearest' | 'linear' | 'bilinear'
+| 'bicubic' | 'trilinear' | 'area' Default: 'nearest'</p></td>
+    </tr>
+    <tr>
+      <th>align_corners</th>
+      <td><p>(bool, optional) Geometrically, we consider the pixels
+of the input and output as squares rather than points. If set to TRUE,
+the input and output tensors are aligned by the center points of their corner
+pixels, preserving the values at the corner pixels. If set to False, the
+input and output tensors are aligned by the corner points of their corner pixels,
+and the interpolation uses edge value padding for out-of-boundary values,
+making this operation <em>independent</em> of input size when <code>scale_factor</code> is kept
+the same. This only has an effect when <code>mode</code>  is <code>'linear'</code>, <code>'bilinear'</code>,
+<code>'bicubic'</code> or <code>'trilinear'</code>.  Default: <code>False</code></p></td>
+    </tr>
+    <tr>
+      <th>recompute_scale_factor</th>
+      <td><p>(bool, optional) recompute the scale_factor
+for use in the interpolation calculation.  When <code>scale_factor</code> is passed
+as a parameter, it is used to compute the <code>output_size</code>.  If <code>recompute_scale_factor</code>
+is ```True`` or not specified, a new <code>scale_factor</code> will be computed based on
+the output and input sizes for use in the interpolation computation (i.e. the
+computation will be identical to if the computed `output_size` were passed-in
+explicitly).  Otherwise, the passed-in `scale_factor` will be used in the
+interpolation computation.  Note that when `scale_factor` is floating-point,
+the recomputed scale_factor may differ from the one passed in due to rounding
+and precision issues.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>The algorithm used for interpolation is determined by <code>mode</code>.</p>
+<p>Currently temporal, spatial and volumetric sampling are supported, i.e.
+expected inputs are 3-D, 4-D or 5-D in shape.</p>
+<p>The input dimensions are interpreted in the form:
+<code>mini-batch x channels x [optional depth] x [optional height] x width</code>.</p>
+<p>The modes available for resizing are: <code>nearest</code>, <code>linear</code> (3D-only),
+<code>bilinear</code>, <code>bicubic</code> (4D-only), <code>trilinear</code> (5D-only), <code>area</code></p>
+
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+    <h1>Kl_div</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
+    <div class="hidden name"><code>nnf_kl_div.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>The Kullback-Leibler divergence Loss.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_kl_div</span>(<span class='kw'>input</span>, <span class='kw'>target</span>, reduction = <span class='st'>"mean"</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
+    </tr>
+    <tr>
+      <th>target</th>
+      <td><p>tensor (N,*) , same shape as the input</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'</p></td>
+    </tr>
+    </table>
+
+
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+    <div class="page-header">
+    <h1>L1_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_l1_loss.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Function that takes the mean element-wise absolute value difference.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_l1_loss</span>(<span class='kw'>input</span>, <span class='kw'>target</span>, reduction = <span class='st'>"mean"</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
+    </tr>
+    <tr>
+      <th>target</th>
+      <td><p>tensor (N,*) , same shape as the input</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'</p></td>
+    </tr>
+    </table>
+
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diff --git a/static/docs/dev/reference/nnf_layer_norm.html b/static/docs/dev/reference/nnf_layer_norm.html
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+    <div class="page-header">
+    <h1>Layer_norm</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-normalization.R'><code>R/nnf-normalization.R</code></a></small>
+    <div class="hidden name"><code>nnf_layer_norm.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies Layer Normalization for last certain number of dimensions.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_layer_norm</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>normalized_shape</span>,
+  weight = <span class='kw'>NULL</span>,
+  bias = <span class='kw'>NULL</span>,
+  eps = <span class='fl'>1e-05</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="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>normalized_shape</th>
+      <td><p>input shape from an expected input of size. If a single
+integer is used, it is treated as a singleton list, and this module will normalize
+over the last dimension which is expected to be of that specific size.</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>the weight tensor</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>the bias tensor</p></td>
+    </tr>
+    <tr>
+      <th>eps</th>
+      <td><p>a value added to the denominator for numerical stability. Default: 1e-5</p></td>
+    </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">
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diff --git a/static/docs/dev/reference/nnf_leaky_relu.html b/static/docs/dev/reference/nnf_leaky_relu.html
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+    <h1>Leaky_relu</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_leaky_relu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies element-wise,
+\(LeakyReLU(x) = max(0, x) + negative_slope * min(0, x)\)</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_leaky_relu</span>(<span class='kw'>input</span>, negative_slope = <span class='fl'>0.01</span>, inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
+    </tr>
+    <tr>
+      <th>negative_slope</th>
+      <td><p>Controls the angle of the negative slope. Default: 1e-2</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
+    </tr>
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+
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diff --git a/static/docs/dev/reference/nnf_linear.html b/static/docs/dev/reference/nnf_linear.html
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+    <h1>Linear</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-linear.R'><code>R/nnf-linear.R</code></a></small>
+    <div class="hidden name"><code>nnf_linear.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a linear transformation to the incoming data: \(y = xA^T + b\).</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_linear</span>(<span class='kw'>input</span>, <span class='kw'>weight</span>, bias = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>\((N, *, in\_features)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means any number of
+additional dimensions</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>\((out\_features, in\_features)\) the weights tensor.</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>optional tensor \((out\_features)\)</p></td>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/dev/reference/nnf_local_response_norm.html b/static/docs/dev/reference/nnf_local_response_norm.html
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+    <div class="page-header">
+    <h1>Local_response_norm</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-normalization.R'><code>R/nnf-normalization.R</code></a></small>
+    <div class="hidden name"><code>nnf_local_response_norm.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies local response normalization over an input signal composed of
+several input planes, where channels occupy the second dimension.
+Applies normalization across channels.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_local_response_norm</span>(<span class='kw'>input</span>, <span class='kw'>size</span>, alpha = <span class='fl'>1e-04</span>, beta = <span class='fl'>0.75</span>, k = <span class='fl'>1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="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>size</th>
+      <td><p>amount of neighbouring channels used for normalization</p></td>
+    </tr>
+    <tr>
+      <th>alpha</th>
+      <td><p>multiplicative factor. Default: 0.0001</p></td>
+    </tr>
+    <tr>
+      <th>beta</th>
+      <td><p>exponent. Default: 0.75</p></td>
+    </tr>
+    <tr>
+      <th>k</th>
+      <td><p>additive factor. Default: 1</p></td>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/dev/reference/nnf_log_softmax.html b/static/docs/dev/reference/nnf_log_softmax.html
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+    <div class="page-header">
+    <h1>Log_softmax</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
+    <div class="hidden name"><code>nnf_log_softmax.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a softmax followed by a logarithm.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_log_softmax</span>(<span class='kw'>input</span>, dim = <span class='kw'>NULL</span>, dtype = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <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>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+
diff --git a/static/docs/dev/reference/nnf_logsigmoid.html b/static/docs/dev/reference/nnf_logsigmoid.html
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+
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+
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+
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+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
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+    <div class="page-header">
+    <h1>Lp_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_lp_pool1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 1D power-average pooling over an input signal composed of
+several input planes. If the sum of all inputs to the power of <code>p</code> is
+zero, the gradient is set to zero as well.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_lp_pool1d</span>(<span class='kw'>input</span>, <span class='kw'>norm_type</span>, <span class='kw'>kernel_size</span>, stride = <span class='kw'>NULL</span>, ceil_mode = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="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>norm_type</th>
+      <td><p>if inf than one gets max pooling if 0 you get sum pooling (
+proportional to the avg pooling)</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>a single int, the size of the window</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>a single int, the stride of the window. Default value is kernel_size</p></td>
+    </tr>
+    <tr>
+      <th>ceil_mode</th>
+      <td><p>when True, will use ceil instead of floor to compute the output shape</p></td>
+    </tr>
+    </table>
+
+
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Lp_pool2d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
+    <div class="hidden name"><code>nnf_lp_pool2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 2D power-average pooling over an input signal composed of
+several input planes. If the sum of all inputs to the power of <code>p</code> is
+zero, the gradient is set to zero as well.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_lp_pool2d</span>(<span class='kw'>input</span>, <span class='kw'>norm_type</span>, <span class='kw'>kernel_size</span>, stride = <span class='kw'>NULL</span>, ceil_mode = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="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>norm_type</th>
+      <td><p>if inf than one gets max pooling if 0 you get sum pooling (
+proportional to the avg pooling)</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>a single int, the size of the window</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>a single int, the stride of the window. Default value is kernel_size</p></td>
+    </tr>
+    <tr>
+      <th>ceil_mode</th>
+      <td><p>when True, will use ceil instead of floor to compute the output shape</p></td>
+    </tr>
+    </table>
+
+
+  </div>
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diff --git a/static/docs/dev/reference/nnf_margin_ranking_loss.html b/static/docs/dev/reference/nnf_margin_ranking_loss.html
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+    <div class="page-header">
+    <h1>Margin_ranking_loss</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
+    <div class="hidden name"><code>nnf_margin_ranking_loss.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Creates a criterion that measures the loss given inputs x1 , x2 , two 1D
+mini-batch Tensors, and a label 1D mini-batch tensor y (containing 1 or -1).</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_margin_ranking_loss</span>(<span class='kw'>input1</span>, <span class='kw'>input2</span>, <span class='kw'>target</span>, margin = <span class='fl'>0</span>, reduction = <span class='st'>"mean"</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input1</th>
+      <td><p>the first tensor</p></td>
+    </tr>
+    <tr>
+      <th>input2</th>
+      <td><p>the second input tensor</p></td>
+    </tr>
+    <tr>
+      <th>target</th>
+      <td><p>the target tensor</p></td>
+    </tr>
+    <tr>
+      <th>margin</th>
+      <td><p>Has a default value of 00 .</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'</p></td>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/dev/reference/nnf_max_pool1d.html b/static/docs/dev/reference/nnf_max_pool1d.html
new file mode 100644
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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='kw'>input</span>,
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='kw'>NULL</span>,
+  padding = <span class='fl'>0</span>,
+  dilation = <span class='fl'>1</span>,
+  ceil_mode = <span class='fl'>FALSE</span>,
+  return_indices = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape (minibatch , in_channels , iW)</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>the size of the window. Can be a single number or a
+tuple <code>(kW,)</code>.</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the window. Can be a single number or a tuple
+<code>(sW,)</code>. Default: <code>kernel_size</code></p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit zero paddings on both sides of the input. Can be a
+single number or a tuple <code>(padW,)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>controls the spacing between the kernel points; also known as
+the à trous algorithm.</p></td>
+    </tr>
+    <tr>
+      <th>ceil_mode</th>
+      <td><p>when True, will use <code>ceil</code> instead of <code>floor</code> to compute the
+output shape. Default: <code>FALSE</code></p></td>
+    </tr>
+    <tr>
+      <th>return_indices</th>
+      <td><p>whether to return the indices where the max occurs.</p></td>
+    </tr>
+    </table>
+
+
+  </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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+
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+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Max_pool2d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
+    <div class="hidden name"><code>nnf_max_pool2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 2D max pooling over an input signal composed of several input
+planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_max_pool2d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='kw'>kernel_size</span>,
+  padding = <span class='fl'>0</span>,
+  dilation = <span class='fl'>1</span>,
+  ceil_mode = <span class='fl'>FALSE</span>,
+  return_indices = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor (minibatch, in_channels , iH , iW)</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>size of the pooling region. Can be a single number or a
+tuple <code>(kH, kW)</code></p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>stride of the pooling operation. Can be a single number or a
+tuple <code>(sH, sW)</code>. Default: <code>kernel_size</code></p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit zero paddings on both sides of the input. Can be a
+single number or a tuple <code>(padH, padW)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>controls the spacing between the kernel points; also known as
+the à trous algorithm.</p></td>
+    </tr>
+    <tr>
+      <th>ceil_mode</th>
+      <td><p>when True, will use <code>ceil</code> instead of <code>floor</code> in the formula
+to compute the output shape. Default: <code>FALSE</code></p></td>
+    </tr>
+    <tr>
+      <th>return_indices</th>
+      <td><p>whether to return the indices where the max occurs.</p></td>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/dev/reference/nnf_max_pool3d.html b/static/docs/dev/reference/nnf_max_pool3d.html
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+    <div class="page-header">
+    <h1>Max_pool3d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
+    <div class="hidden name"><code>nnf_max_pool3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 3D max pooling over an input signal composed of several input
+planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_max_pool3d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='kw'>NULL</span>,
+  padding = <span class='fl'>0</span>,
+  dilation = <span class='fl'>1</span>,
+  ceil_mode = <span class='fl'>FALSE</span>,
+  return_indices = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor (minibatch, in_channels , iT * iH , iW)</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>size of the pooling region. Can be a single number or a
+tuple <code>(kT, kH, kW)</code></p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>stride of the pooling operation. Can be a single number or a
+tuple <code>(sT, sH, sW)</code>. Default: <code>kernel_size</code></p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit zero paddings on both sides of the input. Can be a
+single number or a tuple <code>(padT, padH, padW)</code>, Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>controls the spacing between the kernel points; also known as
+the à trous algorithm.</p></td>
+    </tr>
+    <tr>
+      <th>ceil_mode</th>
+      <td><p>when True, will use <code>ceil</code> instead of <code>floor</code> in the formula
+to compute the output shape</p></td>
+    </tr>
+    <tr>
+      <th>return_indices</th>
+      <td><p>whether to return the indices where the max occurs.</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/nnf_max_unpool1d.html b/static/docs/dev/reference/nnf_max_unpool1d.html
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+<div class="row">
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+    <div class="page-header">
+    <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='kw'>input</span>,
+  <span class='kw'>indices</span>,
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='kw'>NULL</span>,
+  padding = <span class='fl'>0</span>,
+  output_size = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>the input Tensor to invert</p></td>
+    </tr>
+    <tr>
+      <th>indices</th>
+      <td><p>the indices given out by max pool</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>Size of the max pooling window.</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>Stride of the max pooling window. It is set to kernel_size by default.</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>Padding that was added to the input</p></td>
+    </tr>
+    <tr>
+      <th>output_size</th>
+      <td><p>the targeted output size</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nnf_max_unpool2d.html b/static/docs/dev/reference/nnf_max_unpool2d.html
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+    <div class="page-header">
+    <h1>Max_unpool2d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
+    <div class="hidden name"><code>nnf_max_unpool2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Computes a partial inverse of <code>MaxPool2d</code>.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_max_unpool2d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>indices</span>,
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='kw'>NULL</span>,
+  padding = <span class='fl'>0</span>,
+  output_size = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>the input Tensor to invert</p></td>
+    </tr>
+    <tr>
+      <th>indices</th>
+      <td><p>the indices given out by max pool</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>Size of the max pooling window.</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>Stride of the max pooling window. It is set to kernel_size by default.</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>Padding that was added to the input</p></td>
+    </tr>
+    <tr>
+      <th>output_size</th>
+      <td><p>the targeted output size</p></td>
+    </tr>
+    </table>
+
+
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Max_unpool3d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
+    <div class="hidden name"><code>nnf_max_unpool3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Computes a partial inverse of <code>MaxPool3d</code>.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_max_unpool3d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>indices</span>,
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='kw'>NULL</span>,
+  padding = <span class='fl'>0</span>,
+  output_size = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>the input Tensor to invert</p></td>
+    </tr>
+    <tr>
+      <th>indices</th>
+      <td><p>the indices given out by max pool</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>Size of the max pooling window.</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>Stride of the max pooling window. It is set to kernel_size by default.</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>Padding that was added to the input</p></td>
+    </tr>
+    <tr>
+      <th>output_size</th>
+      <td><p>the targeted output size</p></td>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/dev/reference/nnf_mse_loss.html b/static/docs/dev/reference/nnf_mse_loss.html
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+      <a href="../articles/extending-autograd.html">Extending autograd</a>
+    </li>
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+</li>
+<li class="dropdown-header">Examples</li>
+<li>
+  <a href="../reference/index.html">Reference</a>
+</li>
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+  <a href="../news/index.html">Changelog</a>
+</li>
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+  <a href="https://github.com/mlverse/torch/">
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Mse_loss</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
+    <div class="hidden name"><code>nnf_mse_loss.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Measures the element-wise mean squared error.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_mse_loss</span>(<span class='kw'>input</span>, <span class='kw'>target</span>, reduction = <span class='st'>"mean"</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
+    </tr>
+    <tr>
+      <th>target</th>
+      <td><p>tensor (N,*) , same shape as the input</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Multi head attention forward</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
+    <div class="hidden name"><code>nnf_multi_head_attention_forward.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Allows the model to jointly attend to information from different representation
+subspaces. See reference: Attention Is All You Need</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_multi_head_attention_forward</span>(
+  <span class='kw'>query</span>,
+  <span class='kw'>key</span>,
+  <span class='kw'>value</span>,
+  <span class='kw'>embed_dim_to_check</span>,
+  <span class='kw'>num_heads</span>,
+  <span class='kw'>in_proj_weight</span>,
+  <span class='kw'>in_proj_bias</span>,
+  <span class='kw'>bias_k</span>,
+  <span class='kw'>bias_v</span>,
+  <span class='kw'>add_zero_attn</span>,
+  <span class='kw'>dropout_p</span>,
+  <span class='kw'>out_proj_weight</span>,
+  <span class='kw'>out_proj_bias</span>,
+  training = <span class='fl'>TRUE</span>,
+  key_padding_mask = <span class='kw'>NULL</span>,
+  need_weights = <span class='fl'>TRUE</span>,
+  attn_mask = <span class='kw'>NULL</span>,
+  use_separate_proj_weight = <span class='fl'>FALSE</span>,
+  q_proj_weight = <span class='kw'>NULL</span>,
+  k_proj_weight = <span class='kw'>NULL</span>,
+  v_proj_weight = <span class='kw'>NULL</span>,
+  static_k = <span class='kw'>NULL</span>,
+  static_v = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>query</th>
+      <td><p>\((L, N, E)\) where L is the target sequence length, N is the batch size, E is
+the embedding dimension.</p></td>
+    </tr>
+    <tr>
+      <th>key</th>
+      <td><p>\((S, N, E)\), where S is the source sequence length, N is the batch size, E is
+the embedding dimension.</p></td>
+    </tr>
+    <tr>
+      <th>value</th>
+      <td><p>\((S, N, E)\) where S is the source sequence length, N is the batch size, E is
+the embedding dimension.</p></td>
+    </tr>
+    <tr>
+      <th>embed_dim_to_check</th>
+      <td><p>total dimension of the model.</p></td>
+    </tr>
+    <tr>
+      <th>num_heads</th>
+      <td><p>parallel attention heads.</p></td>
+    </tr>
+    <tr>
+      <th>in_proj_weight</th>
+      <td><p>input projection weight and bias.</p></td>
+    </tr>
+    <tr>
+      <th>in_proj_bias</th>
+      <td><p>currently undocumented.</p></td>
+    </tr>
+    <tr>
+      <th>bias_k</th>
+      <td><p>bias of the key and value sequences to be added at dim=0.</p></td>
+    </tr>
+    <tr>
+      <th>bias_v</th>
+      <td><p>currently undocumented.</p></td>
+    </tr>
+    <tr>
+      <th>add_zero_attn</th>
+      <td><p>add a new batch of zeros to the key and
+value sequences at dim=1.</p></td>
+    </tr>
+    <tr>
+      <th>dropout_p</th>
+      <td><p>probability of an element to be zeroed.</p></td>
+    </tr>
+    <tr>
+      <th>out_proj_weight</th>
+      <td><p>the output projection weight and bias.</p></td>
+    </tr>
+    <tr>
+      <th>out_proj_bias</th>
+      <td><p>currently undocumented.</p></td>
+    </tr>
+    <tr>
+      <th>training</th>
+      <td><p>apply dropout if is <code>TRUE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>key_padding_mask</th>
+      <td><p>\((N, S)\) where N is the batch size, S is the source sequence length.
+If a ByteTensor is provided, the non-zero positions will be ignored while the position
+with the zero positions will be unchanged. If a BoolTensor is provided, the positions with the
+value of <code>True</code> will be ignored while the position with the value of <code>False</code> will be unchanged.</p></td>
+    </tr>
+    <tr>
+      <th>need_weights</th>
+      <td><p>output attn_output_weights.</p></td>
+    </tr>
+    <tr>
+      <th>attn_mask</th>
+      <td><p>2D mask \((L, S)\) where L is the target sequence length, S is the source sequence length.
+3D mask \((N*num_heads, L, S)\) where N is the batch size, L is the target sequence length,
+S is the source sequence length. attn_mask ensure that position i is allowed to attend the unmasked
+positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend
+while the zero positions will be unchanged. If a BoolTensor is provided, positions with <code>True</code>
+is not allowed to attend while <code>False</code> values will be unchanged. If a FloatTensor
+is provided, it will be added to the attention weight.</p></td>
+    </tr>
+    <tr>
+      <th>use_separate_proj_weight</th>
+      <td><p>the function accept the proj. weights for
+query, key, and value in different forms. If false, in_proj_weight will be used,
+which is a combination of q_proj_weight, k_proj_weight, v_proj_weight.</p></td>
+    </tr>
+    <tr>
+      <th>q_proj_weight</th>
+      <td><p>input projection weight and bias.</p></td>
+    </tr>
+    <tr>
+      <th>k_proj_weight</th>
+      <td><p>currently undocumented.</p></td>
+    </tr>
+    <tr>
+      <th>v_proj_weight</th>
+      <td><p>currently undocumented.</p></td>
+    </tr>
+    <tr>
+      <th>static_k</th>
+      <td><p>static key and value used for attention operators.</p></td>
+    </tr>
+    <tr>
+      <th>static_v</th>
+      <td><p>currently undocumented.</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/nnf_multi_margin_loss.html b/static/docs/dev/reference/nnf_multi_margin_loss.html
new file mode 100644
index 000000000..3b288405a
--- /dev/null
+++ b/static/docs/dev/reference/nnf_multi_margin_loss.html
@@ -0,0 +1,272 @@
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+<meta property="og:title" content="Multi_margin_loss — nnf_multi_margin_loss" />
+<meta property="og:description" content="Creates a criterion that optimizes a multi-class classification hinge loss
+(margin-based loss) between input x (a 2D mini-batch Tensor) and output y
+(which is a 1D tensor of target class indices, 0 &amp;lt;= y &amp;lt;= x$size(2) - 1 )." />
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+    <div class="page-header">
+    <h1>Multi_margin_loss</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
+    <div class="hidden name"><code>nnf_multi_margin_loss.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Creates a criterion that optimizes a multi-class classification hinge loss
+(margin-based loss) between input x (a 2D mini-batch Tensor) and output y
+(which is a 1D tensor of target class indices, <code>0 &lt;= y &lt;= x$size(2) - 1</code> ).</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_multi_margin_loss</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>target</span>,
+  p = <span class='fl'>1</span>,
+  margin = <span class='fl'>1</span>,
+  weight = <span class='kw'>NULL</span>,
+  reduction = <span class='st'>"mean"</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
+    </tr>
+    <tr>
+      <th>target</th>
+      <td><p>tensor (N,*) , same shape as the input</p></td>
+    </tr>
+    <tr>
+      <th>p</th>
+      <td><p>Has a default value of 1. 1 and 2 are the only supported values.</p></td>
+    </tr>
+    <tr>
+      <th>margin</th>
+      <td><p>Has a default value of 1.</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>a manual rescaling weight given to each class. If given, it has to
+be a Tensor of size C. Otherwise, it is treated as if having all ones.</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'</p></td>
+    </tr>
+    </table>
+
+
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Multilabel_margin_loss</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
+    <div class="hidden name"><code>nnf_multilabel_margin_loss.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Creates a criterion that optimizes a multi-class multi-classification hinge loss
+(margin-based loss) between input x (a 2D mini-batch Tensor) and output y (which
+is a 2D Tensor of target class indices).</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_multilabel_margin_loss</span>(<span class='kw'>input</span>, <span class='kw'>target</span>, reduction = <span class='st'>"mean"</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
+    </tr>
+    <tr>
+      <th>target</th>
+      <td><p>tensor (N,*) , same shape as the input</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'</p></td>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/dev/reference/nnf_multilabel_soft_margin_loss.html b/static/docs/dev/reference/nnf_multilabel_soft_margin_loss.html
new file mode 100644
index 000000000..36fe68438
--- /dev/null
+++ b/static/docs/dev/reference/nnf_multilabel_soft_margin_loss.html
@@ -0,0 +1,254 @@
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Multilabel_soft_margin_loss</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
+    <div class="hidden name"><code>nnf_multilabel_soft_margin_loss.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Creates a criterion that optimizes a multi-label one-versus-all loss based on
+max-entropy, between input x and target y of size (N, C).</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_multilabel_soft_margin_loss</span>(<span class='kw'>input</span>, <span class='kw'>target</span>, <span class='kw'>weight</span>, reduction = <span class='st'>"mean"</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
+    </tr>
+    <tr>
+      <th>target</th>
+      <td><p>tensor (N,*) , same shape as the input</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>weight tensor to apply on the loss.</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'</p></td>
+    </tr>
+    </table>
+
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+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Nll_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_nll_loss.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>The negative log likelihood loss.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_nll_loss</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>target</span>,
+  weight = <span class='kw'>NULL</span>,
+  ignore_index = <span class='op'>-</span><span class='fl'>100</span>,
+  reduction = <span class='st'>"mean"</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>\((N, C)\) where <code>C = number of classes</code> or \((N, C, H, W)\) in
+case of 2D Loss, or \((N, C, d_1, d_2, ..., d_K)\) where \(K \geq 1\) in
+the case of K-dimensional loss.</p></td>
+    </tr>
+    <tr>
+      <th>target</th>
+      <td><p>\((N)\) where each value is \(0 \leq \mbox{targets}[i] \leq C-1\),
+or \((N, d_1, d_2, ..., d_K)\) where \(K \geq 1\) for K-dimensional loss.</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>(Tensor, optional) a manual rescaling weight given to each class.
+If given, has to be a Tensor of size <code>C</code></p></td>
+    </tr>
+    <tr>
+      <th>ignore_index</th>
+      <td><p>(int, optional) Specifies a target value that is ignored and
+does not contribute to the input gradient.</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'</p></td>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/dev/reference/nnf_normalize.html b/static/docs/dev/reference/nnf_normalize.html
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+    <div class="page-header">
+    <h1>Normalize</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-normalization.R'><code>R/nnf-normalization.R</code></a></small>
+    <div class="hidden name"><code>nnf_normalize.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Performs \(L_p\) normalization of inputs over specified dimension.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_normalize</span>(<span class='kw'>input</span>, p = <span class='fl'>2</span>, dim = <span class='fl'>1</span>, eps = <span class='fl'>1e-12</span>, out = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of any shape</p></td>
+    </tr>
+    <tr>
+      <th>p</th>
+      <td><p>(float) the exponent value in the norm formulation. Default: 2</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the dimension to reduce. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>eps</th>
+      <td><p>(float) small value to avoid division by zero. Default: 1e-12</p></td>
+    </tr>
+    <tr>
+      <th>out</th>
+      <td><p>(Tensor, optional) the output tensor. If <code>out</code> is used, this                            operation won't be differentiable.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <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>
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diff --git a/static/docs/dev/reference/nnf_one_hot.html b/static/docs/dev/reference/nnf_one_hot.html
new 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>One_hot</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-embedding.R'><code>R/nnf-embedding.R</code></a></small>
+    <div class="hidden name"><code>nnf_one_hot.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Takes LongTensor with index values of shape <code>(*)</code> and returns a tensor
+of shape <code>(*, num_classes)</code> that have zeros everywhere except where the
+index of last dimension matches the corresponding value of the input tensor,
+in which case it will be 1.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_one_hot</span>(<span class='kw'>tensor</span>, num_classes = <span class='op'>-</span><span class='fl'>1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensor</th>
+      <td><p>(LongTensor) class values of any shape.</p></td>
+    </tr>
+    <tr>
+      <th>num_classes</th>
+      <td><p>(int) Total number of classes. If set to -1, the number
+of classes will be inferred as one greater than the largest class value in
+the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>One-hot on Wikipedia: https://en.wikipedia.org/wiki/One-hot</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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+
diff --git a/static/docs/dev/reference/nnf_pad.html b/static/docs/dev/reference/nnf_pad.html
new file mode 100644
index 000000000..de1e931a3
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+    <div class="page-header">
+    <h1>Pad</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-padding.R'><code>R/nnf-padding.R</code></a></small>
+    <div class="hidden name"><code>nnf_pad.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Pads tensor.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_pad</span>(<span class='kw'>input</span>, <span class='kw'>pad</span>, mode = <span class='st'>"constant"</span>, value = <span class='fl'>0</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(Tensor) N-dimensional tensor</p></td>
+    </tr>
+    <tr>
+      <th>pad</th>
+      <td><p>(tuple) m-elements tuple, where \(\frac{m}{2} \leq\) input dimensions
+and \(m\) is even.</p></td>
+    </tr>
+    <tr>
+      <th>mode</th>
+      <td><p>'constant', 'reflect', 'replicate' or 'circular'. Default: 'constant'</p></td>
+    </tr>
+    <tr>
+      <th>value</th>
+      <td><p>fill value for 'constant' padding. Default: 0.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="padding-size"><a class="anchor" href="#padding-size"></a>Padding size</h2>
+
+    
+
+
+<p>The padding size by which to pad some dimensions of <code>input</code>
+are described starting from the last dimension and moving forward.
+\(\left\lfloor\frac{\mbox{len(pad)}}{2}\right\rfloor\) dimensions
+of <code>input</code> will be padded.
+For example, to pad only the last dimension of the input tensor, then
+<code>pad</code> has the form
+\((\mbox{padding\_left}, \mbox{padding\_right})\);
+to pad the last 2 dimensions of the input tensor, then use
+\((\mbox{padding\_left}, \mbox{padding\_right},\)
+\(\mbox{padding\_top}, \mbox{padding\_bottom})\);
+to pad the last 3 dimensions, use
+\((\mbox{padding\_left}, \mbox{padding\_right},\)
+\(\mbox{padding\_top}, \mbox{padding\_bottom}\)
+\(\mbox{padding\_front}, \mbox{padding\_back})\).</p>
+    <h2 class="hasAnchor" id="padding-mode"><a class="anchor" href="#padding-mode"></a>Padding mode</h2>
+
+    
+
+
+<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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+<div class="row">
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+    <div class="page-header">
+    <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='kw'>x1</span>, <span class='kw'>x2</span>, p = <span class='fl'>2</span>, eps = <span class='fl'>1e-06</span>, keepdim = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>x1</th>
+      <td><p>(Tensor) First input.</p></td>
+    </tr>
+    <tr>
+      <th>x2</th>
+      <td><p>(Tensor) Second input (of size matching x1).</p></td>
+    </tr>
+    <tr>
+      <th>p</th>
+      <td><p>the norm degree. Default: 2</p></td>
+    </tr>
+    <tr>
+      <th>eps</th>
+      <td><p>(float, optional) Small value to avoid division by zero.
+Default: 1e-8</p></td>
+    </tr>
+    <tr>
+      <th>keepdim</th>
+      <td><p>Determines whether or not to keep the vector dimension. Default: False</p></td>
+    </tr>
+    </table>
+
+
+  </div>
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diff --git a/static/docs/dev/reference/nnf_pdist.html b/static/docs/dev/reference/nnf_pdist.html
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+
+      
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Pdist</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_pdist.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Computes the p-norm distance between every pair of row vectors in the input.
+This is identical to the upper triangular portion, excluding the diagonal, of
+<code>torch_norm(input[:, None] - input, dim=2, p=p)</code>. This function will be faster
+if the rows are contiguous.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_pdist</span>(<span class='kw'>input</span>, p = <span class='fl'>2</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape \(N \times M\).</p></td>
+    </tr>
+    <tr>
+      <th>p</th>
+      <td><p>p value for the p-norm distance to calculate between each vector pair
+\(\in [0, \infty]\).</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>If input has shape \(N \times M\) then the output will have shape
+\(\frac{1}{2} N (N - 1)\).</p>
+
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diff --git a/static/docs/dev/reference/nnf_pixel_shuffle.html b/static/docs/dev/reference/nnf_pixel_shuffle.html
new file mode 100644
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+<div class="row">
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+    <div class="page-header">
+    <h1>Pixel_shuffle</h1>
+    <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>
+    <div class="hidden name"><code>nnf_pixel_shuffle.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <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>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_pixel_shuffle</span>(<span class='kw'>input</span>, <span class='kw'>upscale_factor</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>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>
+
+
+  </div>
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diff --git a/static/docs/dev/reference/nnf_poisson_nll_loss.html b/static/docs/dev/reference/nnf_poisson_nll_loss.html
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+    <h1>Poisson_nll_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_poisson_nll_loss.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Poisson negative log likelihood loss.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_poisson_nll_loss</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>target</span>,
+  log_input = <span class='fl'>TRUE</span>,
+  full = <span class='fl'>FALSE</span>,
+  eps = <span class='fl'>1e-08</span>,
+  reduction = <span class='st'>"mean"</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
+    </tr>
+    <tr>
+      <th>target</th>
+      <td><p>tensor (N,*) , same shape as the input</p></td>
+    </tr>
+    <tr>
+      <th>log_input</th>
+      <td><p>if <code>TRUE</code> the loss is computed as \(\exp(\mbox{input}) - \mbox{target} * \mbox{input}\),
+if <code>FALSE</code> then loss is \(\mbox{input} - \mbox{target} * \log(\mbox{input}+\mbox{eps})\).
+Default: <code>TRUE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>full</th>
+      <td><p>whether to compute full loss, i. e. to add the Stirling approximation
+term. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>eps</th>
+      <td><p>(float, optional) Small value to avoid evaluation of \(\log(0)\) when
+<code>log_input</code>=<code>FALSE</code>. Default: 1e-8</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'</p></td>
+    </tr>
+    </table>
+
+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Prelu</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
+    <div class="hidden name"><code>nnf_prelu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies element-wise the function
+\(PReLU(x) = max(0,x) + weight * min(0,x)\)
+where weight is a learnable parameter.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_prelu</span>(<span class='kw'>input</span>, <span class='kw'>weight</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>(Tensor) the learnable weights</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+    <h1>Relu</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_relu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the rectified linear unit function element-wise.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_relu</span>(<span class='kw'>input</span>, inplace = <span class='fl'>FALSE</span>)
+
+<span class='fu'>nnf_relu_</span>(<span class='kw'>input</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
+    </tr>
+    </table>
+
+
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+
+      
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+<div class="row">
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+    <div class="page-header">
+    <h1>Relu6</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_relu6.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the element-wise function \(ReLU6(x) = min(max(0,x), 6)\).</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_relu6</span>(<span class='kw'>input</span>, inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/dev/reference/nnf_rrelu.html b/static/docs/dev/reference/nnf_rrelu.html
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+      <a href="../articles/using-autograd.html">Using autograd</a>
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Rrelu</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_rrelu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Randomized leaky ReLU.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_rrelu</span>(<span class='kw'>input</span>, lower = <span class='fl'>1</span><span class='op'>/</span><span class='fl'>8</span>, upper = <span class='fl'>1</span><span class='op'>/</span><span class='fl'>3</span>, training = <span class='fl'>FALSE</span>, inplace = <span class='fl'>FALSE</span>)
+
+<span class='fu'>nnf_rrelu_</span>(<span class='kw'>input</span>, lower = <span class='fl'>1</span><span class='op'>/</span><span class='fl'>8</span>, upper = <span class='fl'>1</span><span class='op'>/</span><span class='fl'>3</span>, training = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="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>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>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/nnf_selu.html b/static/docs/dev/reference/nnf_selu.html
new file mode 100644
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@@ -0,0 +1,259 @@
+<!-- Generated by pkgdown: do not edit by hand -->
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+<html lang="en">
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+<meta property="og:title" content="Selu — nnf_selu" />
+<meta property="og:description" content="Applies element-wise,
+$$SELU(x) = scale * (max(0,x) + min(0, \alpha * (exp(x) - 1)))$$,
+with \(\alpha=1.6732632423543772848170429916717\) and
+\(scale=1.0507009873554804934193349852946\)." />
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+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
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+    </li>
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+      <a href="../articles/using-autograd.html">Using autograd</a>
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+<li>
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+</li>
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+</li>
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+  <a href="https://github.com/mlverse/torch/">
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+     
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+</li>
+      </ul>
+      
+    </div><!--/.nav-collapse -->
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+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Selu</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_selu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies element-wise,
+$$SELU(x) = scale * (max(0,x) + min(0, \alpha * (exp(x) - 1)))$$,
+with \(\alpha=1.6732632423543772848170429916717\) and
+\(scale=1.0507009873554804934193349852946\).</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_selu</span>(<span class='kw'>input</span>, inplace = <span class='fl'>FALSE</span>)
+
+<span class='fu'>nnf_selu_</span>(<span class='kw'>input</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>)
+<span class='kw'>y</span> <span class='op'>&lt;-</span> <span class='fu'>nnf_selu</span>(<span class='kw'>x</span>)
+<span class='fu'>nnf_selu_</span>(<span class='kw'>x</span>)
+<span class='fu'><a href='torch_equal.html'>torch_equal</a></span>(<span class='kw'>x</span>, <span class='kw'>y</span>)
+
+}
+</div><div class='output co'>#&gt; [1] TRUE</div></pre>
+  </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>
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+</div>
+
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+
diff --git a/static/docs/dev/reference/nnf_sigmoid.html b/static/docs/dev/reference/nnf_sigmoid.html
new file mode 100644
index 000000000..97dff7ee3
--- /dev/null
+++ b/static/docs/dev/reference/nnf_sigmoid.html
@@ -0,0 +1,238 @@
+<!-- Generated by pkgdown: do not edit by hand -->
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+<meta property="og:title" content="Sigmoid — nnf_sigmoid" />
+<meta property="og:description" content="Applies element-wise \(Sigmoid(x_i) = \frac{1}{1 + exp(-x_i)}\)" />
+
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+    </li>
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+    </li>
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+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
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+      <a href="../articles/getting-started/neural-networks.html">Neural networks</a>
+    </li>
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+</li>
+      </ul>
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+        <li>
+  <a href="https://github.com/mlverse/torch/">
+    <span class="fab fa fab fa-github fa-lg"></span>
+     
+  </a>
+</li>
+      </ul>
+      
+    </div><!--/.nav-collapse -->
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+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Sigmoid</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_sigmoid.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies element-wise \(Sigmoid(x_i) = \frac{1}{1 + exp(-x_i)}\)</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_sigmoid</span>(<span class='kw'>input</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
+    </tr>
+    </table>
+
+
+  </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> 1.6.1.</p>
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+    <div class="page-header">
+    <h1>Smooth_l1_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_smooth_l1_loss.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Function that uses a squared term if the absolute
+element-wise error falls below 1 and an L1 term otherwise.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_smooth_l1_loss</span>(<span class='kw'>input</span>, <span class='kw'>target</span>, reduction = <span class='st'>"mean"</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
+    </tr>
+    <tr>
+      <th>target</th>
+      <td><p>tensor (N,*) , same shape as the input</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'</p></td>
+    </tr>
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diff --git a/static/docs/dev/reference/nnf_soft_margin_loss.html b/static/docs/dev/reference/nnf_soft_margin_loss.html
new 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>Soft_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_soft_margin_loss.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Creates a criterion that optimizes a two-class classification logistic loss
+between input tensor x and target tensor y (containing 1 or -1).</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_soft_margin_loss</span>(<span class='kw'>input</span>, <span class='kw'>target</span>, reduction = <span class='st'>"mean"</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
+    </tr>
+    <tr>
+      <th>target</th>
+      <td><p>tensor (N,*) , same shape as the input</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'</p></td>
+    </tr>
+    </table>
+
+
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+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/nnf_softmax.html b/static/docs/dev/reference/nnf_softmax.html
new file mode 100644
index 000000000..3118c0ddf
--- /dev/null
+++ b/static/docs/dev/reference/nnf_softmax.html
@@ -0,0 +1,252 @@
+<!-- Generated by pkgdown: do not edit by hand -->
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+    <h1>Softmax</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
+    <div class="hidden name"><code>nnf_softmax.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a softmax function.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_softmax</span>(<span class='kw'>input</span>, <span class='kw'>dim</span>, dtype = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(Tensor) input</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) A dimension along which 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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+    <div class="page-header">
+    <h1>Softmin</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_softmin.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a softmin function.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_softmin</span>(<span class='kw'>input</span>, <span class='kw'>dim</span>, dtype = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(Tensor) input</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) A dimension along which softmin will be computed
+(so every slice        along dim will sum to 1).</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.      If specified, the input tensor is casted to <code>dtype</code> before the operation      is performed.
+This is useful for preventing data type overflows. Default: NULL.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>Note that</p>
+<p>$$Softmin(x) = Softmax(-x)$$.</p>
+<p>See <a href='nnf_softmax.html'>nnf_softmax</a> definition for mathematical formula.</p>
+
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+    <h1>Softplus</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_softplus.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies element-wise, the function \(Softplus(x) = 1/\beta * log(1 + exp(\beta * x))\).</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_softplus</span>(<span class='kw'>input</span>, beta = <span class='fl'>1</span>, threshold = <span class='fl'>20</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
+    </tr>
+    <tr>
+      <th>beta</th>
+      <td><p>the beta value for the Softplus formulation. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>threshold</th>
+      <td><p>values above this revert to a linear function. Default: 20</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>For numerical stability the implementation reverts to the linear function
+when \(input * \beta &gt; threshold\).</p>
+
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diff --git a/static/docs/dev/reference/nnf_softshrink.html b/static/docs/dev/reference/nnf_softshrink.html
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+    <div class="page-header">
+    <h1>Softshrink</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_softshrink.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the soft shrinkage function elementwise</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_softshrink</span>(<span class='kw'>input</span>, lambd = <span class='fl'>0.5</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
+    </tr>
+    <tr>
+      <th>lambd</th>
+      <td><p>the lambda (must be no less than zero) value for the Softshrink
+formulation. Default: 0.5</p></td>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/dev/reference/nnf_softsign.html b/static/docs/dev/reference/nnf_softsign.html
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+    <h1>Tanhshrink</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_tanhshrink.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies element-wise, \(Tanhshrink(x) = x - Tanh(x)\)</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_tanhshrink</span>(<span class='kw'>input</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
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+    <div class="page-header">
+    <h1>Threshold</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_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'>nnf_threshold</span>(<span class='kw'>input</span>, <span class='kw'>threshold</span>, <span class='kw'>value</span>, inplace = <span class='fl'>FALSE</span>)
+
+<span class='fu'>nnf_threshold_</span>(<span class='kw'>input</span>, <span class='kw'>threshold</span>, <span class='kw'>value</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
+    </tr>
+    <tr>
+      <th>threshold</th>
+      <td><p>The value to threshold at</p></td>
+    </tr>
+    <tr>
+      <th>value</th>
+      <td><p>The value to replace with</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/dev/reference/nnf_triplet_margin_loss.html b/static/docs/dev/reference/nnf_triplet_margin_loss.html
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+<meta property="og:description" content="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
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+    <div class="page-header">
+    <h1>Triplet_margin_loss</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
+    <div class="hidden name"><code>nnf_triplet_margin_loss.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Creates a criterion that measures the triplet loss given an input tensors x1 ,
+x2 , x3 and a margin with a value greater than 0 . This is used for measuring
+a relative similarity between samples. A triplet is composed by a, p and n (i.e.,
+anchor, positive examples and negative examples respectively). The shapes of all
+input tensors should be (N, D).</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_triplet_margin_loss</span>(
+  <span class='kw'>anchor</span>,
+  <span class='kw'>positive</span>,
+  <span class='kw'>negative</span>,
+  margin = <span class='fl'>1</span>,
+  p = <span class='fl'>2</span>,
+  eps = <span class='fl'>1e-06</span>,
+  swap = <span class='fl'>FALSE</span>,
+  reduction = <span class='st'>"mean"</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>anchor</th>
+      <td><p>the anchor input tensor</p></td>
+    </tr>
+    <tr>
+      <th>positive</th>
+      <td><p>the positive input tensor</p></td>
+    </tr>
+    <tr>
+      <th>negative</th>
+      <td><p>the negative input tensor</p></td>
+    </tr>
+    <tr>
+      <th>margin</th>
+      <td><p>Default: 1.</p></td>
+    </tr>
+    <tr>
+      <th>p</th>
+      <td><p>The norm degree for pairwise distance. Default: 2.</p></td>
+    </tr>
+    <tr>
+      <th>eps</th>
+      <td><p>(float, optional) Small value to avoid division by zero.</p></td>
+    </tr>
+    <tr>
+      <th>swap</th>
+      <td><p>The distance swap is described in detail in the paper Learning shallow
+convolutional feature descriptors with triplet losses by V. Balntas, E. Riba et al.
+Default: <code>FALSE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'</p></td>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/dev/reference/nnf_unfold.html b/static/docs/dev/reference/nnf_unfold.html
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Unfold</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-fold.R'><code>R/nnf-fold.R</code></a></small>
+    <div class="hidden name"><code>nnf_unfold.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Extracts sliding local blocks from an batched input tensor.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_unfold</span>(<span class='kw'>input</span>, <span class='kw'>kernel_size</span>, dilation = <span class='fl'>1</span>, padding = <span class='fl'>0</span>, stride = <span class='fl'>1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>the size of the sliding blocks</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>a parameter that controls the stride of elements within the
+neighborhood. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit zero padding to be added on both sides of input.
+Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the sliding blocks in the input spatial dimensions.
+Default: 1</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+
+<p>Currently, only 4-D input tensors (batched image-like tensors) are
+supported.</p>
+
+
+<p>More than one element of the unfolded tensor may refer to a single
+memory location. As a result, in-place operations (especially ones that
+are vectorized) may result in incorrect behavior. If you need to write
+to the tensor, please clone it first.</p>
+
+  </div>
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+
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+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Implements Adam algorithm.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/optim-adam.R'><code>R/optim-adam.R</code></a></small>
+    <div class="hidden name"><code>optim_adam.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>It has been proposed in <a href='https://arxiv.org/abs/1412.6980'>Adam: A Method for Stochastic Optimization</a>.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>optim_adam</span>(
+  <span class='kw'>params</span>,
+  lr = <span class='fl'>0.001</span>,
+  betas = <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0.9</span>, <span class='fl'>0.999</span>),
+  eps = <span class='fl'>1e-08</span>,
+  weight_decay = <span class='fl'>0</span>,
+  amsgrad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>params</th>
+      <td><p>(iterable): iterable of parameters to optimize or dicts defining
+parameter groups</p></td>
+    </tr>
+    <tr>
+      <th>lr</th>
+      <td><p>(float, optional): learning rate (default: 1e-3)</p></td>
+    </tr>
+    <tr>
+      <th>betas</th>
+      <td><p>(<code>Tuple[float, float]</code>, optional): coefficients used for computing
+running averages of gradient and its square (default: (0.9, 0.999))</p></td>
+    </tr>
+    <tr>
+      <th>eps</th>
+      <td><p>(float, optional): term added to the denominator to improve
+numerical stability (default: 1e-8)</p></td>
+    </tr>
+    <tr>
+      <th>weight_decay</th>
+      <td><p>(float, optional): weight decay (L2 penalty) (default: 0)</p></td>
+    </tr>
+    <tr>
+      <th>amsgrad</th>
+      <td><p>(boolean, optional): whether to use the AMSGrad variant of this
+algorithm from the paper <a href='https://openreview.net/forum?id=ryQu7f-RZ'>On the Convergence of Adam and Beyond</a>
+(default: FALSE)</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='kw'>optimizer</span> <span class='op'>&lt;-</span> <span class='fu'>optim_adam</span>(<span class='kw'>model</span><span class='op'>$</span><span class='fu'>parameters</span>(), lr=<span class='fl'>0.1</span>)
+<span class='kw'>optimizer</span><span class='op'>$</span><span class='fu'>zero_grad</span>()
+<span class='fu'>loss_fn</span>(<span class='fu'>model</span>(<span class='kw'>input</span>), <span class='kw'>target</span>)<span class='op'>$</span><span class='fu'>backward</span>()
+<span class='kw'>optimizer</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/stats/step.html'>step</a></span>()
+}
+
+}
+</div></pre>
+  </div>
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diff --git a/static/docs/dev/reference/optim_required.html b/static/docs/dev/reference/optim_required.html
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+    <div class="page-header">
+    <h1>Dummy value indicating a required value.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/optim.R'><code>R/optim.R</code></a></small>
+    <div class="hidden name"><code>optim_required.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>export</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>optim_required</span>()</pre>
+
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+Nesterov momentum is based on the formula from
+On the importance of initialization and momentum in deep learning." />
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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='kw'>params</span>,
+  lr = <span class='fu'><a href='optim_required.html'>optim_required</a></span>(),
+  momentum = <span class='fl'>0</span>,
+  dampening = <span class='fl'>0</span>,
+  weight_decay = <span class='fl'>0</span>,
+  nesterov = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>params</th>
+      <td><p>(iterable): iterable of parameters to optimize or dicts defining
+parameter groups</p></td>
+    </tr>
+    <tr>
+      <th>lr</th>
+      <td><p>(float): learning rate</p></td>
+    </tr>
+    <tr>
+      <th>momentum</th>
+      <td><p>(float, optional): momentum factor (default: 0)</p></td>
+    </tr>
+    <tr>
+      <th>dampening</th>
+      <td><p>(float, optional): dampening for momentum (default: 0)</p></td>
+    </tr>
+    <tr>
+      <th>weight_decay</th>
+      <td><p>(float, optional): weight decay (L2 penalty) (default: 0)</p></td>
+    </tr>
+    <tr>
+      <th>nesterov</th>
+      <td><p>(bool, optional): enables Nesterov momentum (default: FALSE)</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+
+
+<p>The implementation of SGD with Momentum-Nesterov subtly differs from
+Sutskever et. al. and implementations in some other frameworks.</p>
+<p>Considering the specific case of Momentum, the update can be written as
+$$
+  \begin{array}{ll}
+v_{t+1} &amp; = \mu * v_{t} + g_{t+1}, \\
+p_{t+1} &amp; = p_{t} - \mbox{lr} * v_{t+1},
+\end{array}
+$$</p>
+<p>where \(p\), \(g\), \(v\) and \(\mu\) denote the
+parameters, gradient, velocity, and momentum respectively.</p>
+<p>This is in contrast to Sutskever et. al. and
+other frameworks which employ an update of the form</p>
+<p>$$
+  \begin{array}{ll}
+v_{t+1} &amp; = \mu * v_{t} + \mbox{lr} * g_{t+1}, \\
+p_{t+1} &amp; = p_{t} - v_{t+1}.
+\end{array}
+$$
+The Nesterov version is analogously modified.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='kw'>optimizer</span> <span class='op'>&lt;-</span> <span class='fu'>optim_sgd</span>(<span class='kw'>model</span><span class='op'>$</span><span class='fu'>parameters</span>(), lr=<span class='fl'>0.1</span>, momentum=<span class='fl'>0.9</span>)
+<span class='kw'>optimizer</span><span class='op'>$</span><span class='fu'>zero_grad</span>()
+<span class='fu'>loss_fn</span>(<span class='fu'>model</span>(<span class='kw'>input</span>), <span class='kw'>target</span>)<span class='op'>$</span><span class='fu'>backward</span>()
+<span class='kw'>optimizer</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/stats/step.html'>step</a></span>()
+}
+
+}
+</div></pre>
+  </div>
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diff --git a/static/docs/dev/reference/pipe.html b/static/docs/dev/reference/pipe.html
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+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/utils-pipe.R'><code>R/utils-pipe.R</code></a></small>
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+    </div>
+
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+    <div class="page-header">
+    <h1>Dataset wrapping tensors.</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>tensor_dataset.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Each sample will be retrieved by indexing tensors along the first dimension.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>tensor_dataset</span>(<span class='kw'>...</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>...</th>
+      <td><p>tensors that have the same size of the first dimension.</p></td>
+    </tr>
+    </table>
+
+
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+    <h1>Abs</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_abs.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Abs</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_abs</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="abs-input-gt-tensor-"><a class="anchor" href="#abs-input-gt-tensor-"></a>abs(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the element-wise absolute value of the given <code>input</code> tensor.</p>
+<p>$$
+    \mbox{out}_{i} = |\mbox{input}_{i}|
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_abs</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>1</span>, <span class='op'>-</span><span class='fl'>2</span>, <span class='fl'>3</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1
+#&gt;  2
+#&gt;  3
+#&gt; [ CPUFloatType{3} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Acos</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_acos.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Acos</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_acos</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="acos-input-gt-tensor-"><a class="anchor" href="#acos-input-gt-tensor-"></a>acos(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the arccosine  of the elements of <code>input</code>.</p>
+<p>$$
+    \mbox{out}_{i} = \cos^{-1}(\mbox{input}_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_acos</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.8976
+#&gt;  1.4614
+#&gt;  1.3778
+#&gt;  2.7205
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Adaptive_avg_pool1d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_adaptive_avg_pool1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Adaptive_avg_pool1d</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_adaptive_avg_pool1d</span>(<span class='kw'>self</span>, <span class='kw'>output_size</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>output_size</th>
+      <td><p>the target output size (single integer)</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="adaptive-avg-pool-d-input-output-size-gt-tensor-"><a class="anchor" href="#adaptive-avg-pool-d-input-output-size-gt-tensor-"></a>adaptive_avg_pool1d(input, output_size) -&gt; Tensor </h2>
+
+    
+
+
+<p>Applies a 1D adaptive average pooling over an input signal composed of
+several input planes.</p>
+<p>See <code><a href='nn_adaptive_avg_pool1d.html'>nn_adaptive_avg_pool1d()</a></code> for details and output shape.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Add</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_add.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Add</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_add</span>(<span class='kw'>self</span>, <span class='kw'>other</span>, alpha = <span class='fl'>1L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor/Number) the second input tensor/number.</p></td>
+    </tr>
+    <tr>
+      <th>alpha</th>
+      <td><p>(Number) the scalar multiplier for <code>other</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="add-input-other-out-null-"><a class="anchor" href="#add-input-other-out-null-"></a>add(input, other, out=NULL) </h2>
+
+    
+
+
+<p>Adds the scalar <code>other</code> to each element of the input <code>input</code>
+and returns a new resulting tensor.</p>
+<p>$$
+    \mbox{out} = \mbox{input} + \mbox{other}
+$$
+If <code>input</code> is of type FloatTensor or DoubleTensor, <code>other</code> must be
+a real number, otherwise it should be an integer.</p>
+    <h2 class="hasAnchor" id="add-input-other-alpha-out-null-"><a class="anchor" href="#add-input-other-alpha-out-null-"></a>add(input, other, *, alpha=1, out=NULL) </h2>
+
+    
+
+
+<p>Each element of the tensor <code>other</code> is multiplied by the scalar
+<code>alpha</code> and added to each element of the tensor <code>input</code>.
+The resulting tensor is returned.</p>
+<p>The shapes of <code>input</code> and <code>other</code> must be
+broadcastable .</p>
+<p>$$
+    \mbox{out} = \mbox{input} + \mbox{alpha} \times \mbox{other}
+$$
+If <code>other</code> is of type FloatTensor or DoubleTensor, <code>alpha</code> must be
+a real number, otherwise it should be an integer.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_add</span>(<span class='kw'>a</span>, <span class='fl'>20</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>1</span>))
+<span class='kw'>b</span>
+<span class='fu'>torch_add</span>(<span class='kw'>a</span>, <span class='kw'>b</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0.3169  0.3608 -1.4928  0.4897
+#&gt;  0.5190  1.1967 -0.6570  1.3255
+#&gt; -1.0508 -0.3731 -2.2268 -0.2443
+#&gt; -0.9534 -0.2757 -2.1294 -0.1469
+#&gt; [ CPUFloatType{4,4} ]</div></pre>
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+
+      
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Addbmm</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_addbmm.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Addbmm</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_addbmm</span>(<span class='kw'>self</span>, <span class='kw'>batch1</span>, <span class='kw'>batch2</span>, beta = <span class='fl'>1L</span>, alpha = <span class='fl'>1L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) matrix to be added</p></td>
+    </tr>
+    <tr>
+      <th>batch1</th>
+      <td><p>(Tensor) the first batch of matrices to be multiplied</p></td>
+    </tr>
+    <tr>
+      <th>batch2</th>
+      <td><p>(Tensor) the second batch of matrices to be multiplied</p></td>
+    </tr>
+    <tr>
+      <th>beta</th>
+      <td><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></td>
+    </tr>
+    <tr>
+      <th>alpha</th>
+      <td><p>(Number, optional) multiplier for <code>batch1 @ batch2</code> (\(\alpha\))</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="addbmm-input-batch-batch-beta-alpha-out-null-gt-tensor-"><a class="anchor" href="#addbmm-input-batch-batch-beta-alpha-out-null-gt-tensor-"></a>addbmm(input, batch1, batch2, *, beta=1, alpha=1, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Performs a batch matrix-matrix product of matrices stored
+in <code>batch1</code> and <code>batch2</code>,
+with a reduced add step (all matrix multiplications get accumulated
+along the first dimension).
+<code>input</code> is added to the final result.</p>
+<p><code>batch1</code> and <code>batch2</code> must be 3-D tensors each containing the
+same number of matrices.</p>
+<p>If <code>batch1</code> is a \((b \times n \times m)\) tensor, <code>batch2</code> is a
+\((b \times m \times p)\) tensor, <code>input</code> must be
+broadcastable  with a \((n \times p)\) tensor
+and <code>out</code> will be a \((n \times p)\) tensor.</p>
+<p>$$
+    out = \beta\ \mbox{input} + \alpha\ (\sum_{i=0}^{b-1} \mbox{batch1}_i \mathbin{@} \mbox{batch2}_i)
+$$
+For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, arguments <code>beta</code> and <code>alpha</code>
+must be real numbers, otherwise they should be integers.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>M</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>))
+<span class='kw'>batch1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>4</span>))
+<span class='kw'>batch2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>4</span>, <span class='fl'>5</span>))
+<span class='fu'>torch_addbmm</span>(<span class='kw'>M</span>, <span class='kw'>batch1</span>, <span class='kw'>batch2</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  -0.0019  -2.7238  14.9011   0.2573   1.0161
+#&gt;  -4.0793  -7.3787  -3.1121  -3.8778   7.2987
+#&gt;   8.5516  -8.2610  -2.8574   1.6860   6.3424
+#&gt; [ CPUFloatType{3,5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_addcdiv.html b/static/docs/dev/reference/torch_addcdiv.html
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Addcdiv</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_addcdiv.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Addcdiv</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_addcdiv</span>(<span class='kw'>self</span>, <span class='kw'>tensor1</span>, <span class='kw'>tensor2</span>, value = <span class='fl'>1L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the tensor to be added</p></td>
+    </tr>
+    <tr>
+      <th>tensor1</th>
+      <td><p>(Tensor) the numerator tensor</p></td>
+    </tr>
+    <tr>
+      <th>tensor2</th>
+      <td><p>(Tensor) the denominator tensor</p></td>
+    </tr>
+    <tr>
+      <th>value</th>
+      <td><p>(Number, optional) multiplier for \(\mbox{tensor1} / \mbox{tensor2}\)</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="addcdiv-input-tensor-tensor-value-out-null-gt-tensor-"><a class="anchor" href="#addcdiv-input-tensor-tensor-value-out-null-gt-tensor-"></a>addcdiv(input, tensor1, tensor2, *, value=1, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Performs the element-wise division of <code>tensor1</code> by <code>tensor2</code>,
+multiply the result by the scalar <code>value</code> and add it to <code>input</code>.</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p>Integer division with addcdiv is deprecated, and in a future release
+addcdiv will perform a true division of <code>tensor1</code> and <code>tensor2</code>.
+The current addcdiv behavior can be replicated using <code><a href='torch_floor_divide.html'>torch_floor_divide()</a></code>
+for integral inputs
+(<code>input</code> + <code>value</code> * <code>tensor1</code> // <code>tensor2</code>)
+and <code><a href='torch_div.html'>torch_div()</a></code> for float inputs
+(<code>input</code> + <code>value</code> * <code>tensor1</code> / <code>tensor2</code>).
+The new addcdiv behavior can be implemented with <code><a href='torch_true_divide.html'>torch_true_divide()</a></code>
+(<code>input</code> + <code>value</code> * torch.true_divide(<code>tensor1</code>,
+<code>tensor2</code>).</p>
+<p>$$
+    \mbox{out}_i = \mbox{input}_i + \mbox{value} \times \frac{\mbox{tensor1}_i}{\mbox{tensor2}_i}
+$$</p>
+<p>The shapes of <code>input</code>, <code>tensor1</code>, and <code>tensor2</code> must be
+broadcastable .</p>
+<p>For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, <code>value</code> must be
+a real number, otherwise an integer.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>t</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
+<span class='kw'>t1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>1</span>))
+<span class='kw'>t2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
+<span class='fu'>torch_addcdiv</span>(<span class='kw'>t</span>, <span class='kw'>t1</span>, <span class='kw'>t2</span>, <span class='fl'>0.1</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0.5168 -1.0842 -0.2506
+#&gt; -0.4703 -1.5705 -0.2148
+#&gt; -0.5364 -0.8793 -0.2657
+#&gt; [ CPUFloatType{3,3} ]</div></pre>
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diff --git a/static/docs/dev/reference/torch_addcmul.html b/static/docs/dev/reference/torch_addcmul.html
new file mode 100644
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--- /dev/null
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+    <div class="page-header">
+    <h1>Addcmul</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_addcmul.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Addcmul</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_addcmul</span>(<span class='kw'>self</span>, <span class='kw'>tensor1</span>, <span class='kw'>tensor2</span>, value = <span class='fl'>1L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the tensor to be added</p></td>
+    </tr>
+    <tr>
+      <th>tensor1</th>
+      <td><p>(Tensor) the tensor to be multiplied</p></td>
+    </tr>
+    <tr>
+      <th>tensor2</th>
+      <td><p>(Tensor) the tensor to be multiplied</p></td>
+    </tr>
+    <tr>
+      <th>value</th>
+      <td><p>(Number, optional) multiplier for \(tensor1 .* tensor2\)</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="addcmul-input-tensor-tensor-value-out-null-gt-tensor-"><a class="anchor" href="#addcmul-input-tensor-tensor-value-out-null-gt-tensor-"></a>addcmul(input, tensor1, tensor2, *, value=1, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Performs the element-wise multiplication of <code>tensor1</code>
+by <code>tensor2</code>, multiply the result by the scalar <code>value</code>
+and add it to <code>input</code>.</p>
+<p>$$
+    \mbox{out}_i = \mbox{input}_i + \mbox{value} \times \mbox{tensor1}_i \times \mbox{tensor2}_i
+$$
+The shapes of <code>tensor</code>, <code>tensor1</code>, and <code>tensor2</code> must be
+broadcastable .</p>
+<p>For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, <code>value</code> must be
+a real number, otherwise an integer.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>t</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
+<span class='kw'>t1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>1</span>))
+<span class='kw'>t2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
+<span class='fu'>torch_addcmul</span>(<span class='kw'>t</span>, <span class='kw'>t1</span>, <span class='kw'>t2</span>, <span class='fl'>0.1</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.0254  0.7760  0.0074
+#&gt; -0.0154  0.9929  0.0002
+#&gt;  0.0596  0.5947  0.0134
+#&gt; [ CPUFloatType{3,3} ]</div></pre>
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+    <div class="page-header">
+    <h1>Addmm</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_addmm.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Addmm</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_addmm</span>(<span class='kw'>self</span>, <span class='kw'>mat1</span>, <span class='kw'>mat2</span>, beta = <span class='fl'>1L</span>, alpha = <span class='fl'>1L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) matrix to be added</p></td>
+    </tr>
+    <tr>
+      <th>mat1</th>
+      <td><p>(Tensor) the first matrix to be multiplied</p></td>
+    </tr>
+    <tr>
+      <th>mat2</th>
+      <td><p>(Tensor) the second matrix to be multiplied</p></td>
+    </tr>
+    <tr>
+      <th>beta</th>
+      <td><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></td>
+    </tr>
+    <tr>
+      <th>alpha</th>
+      <td><p>(Number, optional) multiplier for \(mat1 @ mat2\) (\(\alpha\))</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="addmm-input-mat-mat-beta-alpha-out-null-gt-tensor-"><a class="anchor" href="#addmm-input-mat-mat-beta-alpha-out-null-gt-tensor-"></a>addmm(input, mat1, mat2, *, beta=1, alpha=1, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Performs a matrix multiplication of the matrices <code>mat1</code> and <code>mat2</code>.
+The matrix <code>input</code> is added to the final result.</p>
+<p>If <code>mat1</code> is a \((n \times m)\) tensor, <code>mat2</code> is a
+\((m \times p)\) tensor, then <code>input</code> must be
+broadcastable  with a \((n \times p)\) tensor
+and <code>out</code> will be a \((n \times p)\) tensor.</p>
+<p><code>alpha</code> and <code>beta</code> are scaling factors on matrix-vector product between
+<code>mat1</code> and <code>mat2</code> and the added matrix <code>input</code> respectively.</p>
+<p>$$
+    \mbox{out} = \beta\ \mbox{input} + \alpha\ (\mbox{mat1}_i \mathbin{@} \mbox{mat2}_i)
+$$
+For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, arguments <code>beta</code> and
+<code>alpha</code> must be real numbers, otherwise they should be integers.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>M</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+<span class='kw'>mat1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+<span class='kw'>mat2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='fu'>torch_addmm</span>(<span class='kw'>M</span>, <span class='kw'>mat1</span>, <span class='kw'>mat2</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -1.3813 -0.7177 -0.6912
+#&gt;  1.3986 -0.4737 -0.7401
+#&gt; [ CPUFloatType{2,3} ]</div></pre>
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Addmv</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_addmv.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Addmv</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_addmv</span>(<span class='kw'>self</span>, <span class='kw'>mat</span>, <span class='kw'>vec</span>, beta = <span class='fl'>1L</span>, alpha = <span class='fl'>1L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) vector to be added</p></td>
+    </tr>
+    <tr>
+      <th>mat</th>
+      <td><p>(Tensor) matrix to be multiplied</p></td>
+    </tr>
+    <tr>
+      <th>vec</th>
+      <td><p>(Tensor) vector to be multiplied</p></td>
+    </tr>
+    <tr>
+      <th>beta</th>
+      <td><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></td>
+    </tr>
+    <tr>
+      <th>alpha</th>
+      <td><p>(Number, optional) multiplier for \(mat @ vec\) (\(\alpha\))</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="addmv-input-mat-vec-beta-alpha-out-null-gt-tensor-"><a class="anchor" href="#addmv-input-mat-vec-beta-alpha-out-null-gt-tensor-"></a>addmv(input, mat, vec, *, beta=1, alpha=1, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Performs a matrix-vector product of the matrix <code>mat</code> and
+the vector <code>vec</code>.
+The vector <code>input</code> is added to the final result.</p>
+<p>If <code>mat</code> is a \((n \times m)\) tensor, <code>vec</code> is a 1-D tensor of
+size <code>m</code>, then <code>input</code> must be
+broadcastable  with a 1-D tensor of size <code>n</code> and
+<code>out</code> will be 1-D tensor of size <code>n</code>.</p>
+<p><code>alpha</code> and <code>beta</code> are scaling factors on matrix-vector product between
+<code>mat</code> and <code>vec</code> and the added tensor <code>input</code> respectively.</p>
+<p>$$
+    \mbox{out} = \beta\ \mbox{input} + \alpha\ (\mbox{mat} \mathbin{@} \mbox{vec})
+$$
+For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, arguments <code>beta</code> and
+<code>alpha</code> must be real numbers, otherwise they should be integers</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>M</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>))
+<span class='kw'>mat</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+<span class='kw'>vec</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>))
+<span class='fu'>torch_addmv</span>(<span class='kw'>M</span>, <span class='kw'>mat</span>, <span class='kw'>vec</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0.6406
+#&gt; -0.0850
+#&gt; [ CPUFloatType{2} ]</div></pre>
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+    </li>
+    <li>
+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/neural-networks.html">Neural networks</a>
+    </li>
+  </ul>
+</li>
+<li class="dropdown">
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
+    Articles
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+      <a href="../articles/using-autograd.html">Using autograd</a>
+    </li>
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+      <a href="../articles/extending-autograd.html">Extending autograd</a>
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+  </ul>
+</li>
+<li class="dropdown-header">Examples</li>
+<li>
+  <a href="../reference/index.html">Reference</a>
+</li>
+<li>
+  <a href="../news/index.html">Changelog</a>
+</li>
+      </ul>
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+        <li>
+  <a href="https://github.com/mlverse/torch/">
+    <span class="fab fa fab fa-github fa-lg"></span>
+     
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+</li>
+      </ul>
+      
+    </div><!--/.nav-collapse -->
+  </div><!--/.container -->
+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_addr.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Addr</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_addr</span>(<span class='kw'>self</span>, <span class='kw'>vec1</span>, <span class='kw'>vec2</span>, beta = <span class='fl'>1L</span>, alpha = <span class='fl'>1L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) matrix to be added</p></td>
+    </tr>
+    <tr>
+      <th>vec1</th>
+      <td><p>(Tensor) the first vector of the outer product</p></td>
+    </tr>
+    <tr>
+      <th>vec2</th>
+      <td><p>(Tensor) the second vector of the outer product</p></td>
+    </tr>
+    <tr>
+      <th>beta</th>
+      <td><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></td>
+    </tr>
+    <tr>
+      <th>alpha</th>
+      <td><p>(Number, optional) multiplier for \(\mbox{vec1} \otimes \mbox{vec2}\) (\(\alpha\))</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="addr-input-vec-vec-beta-alpha-out-null-gt-tensor-"><a class="anchor" href="#addr-input-vec-vec-beta-alpha-out-null-gt-tensor-"></a>addr(input, vec1, vec2, *, beta=1, alpha=1, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Performs the outer-product of vectors <code>vec1</code> and <code>vec2</code>
+and adds it to the matrix <code>input</code>.</p>
+<p>Optional values <code>beta</code> and <code>alpha</code> are scaling factors on the
+outer product between <code>vec1</code> and <code>vec2</code> and the added matrix
+<code>input</code> respectively.</p>
+<p>$$
+    \mbox{out} = \beta\ \mbox{input} + \alpha\ (\mbox{vec1} \otimes \mbox{vec2})
+$$
+If <code>vec1</code> is a vector of size <code>n</code> and <code>vec2</code> is a vector
+of size <code>m</code>, then <code>input</code> must be
+broadcastable  with a matrix of size
+\((n \times m)\) and <code>out</code> will be a matrix of size
+\((n \times m)\).</p>
+<p>For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, arguments <code>beta</code> and
+<code>alpha</code> must be real numbers, otherwise they should be integers</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>vec1</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>4.</span>)
+<span class='kw'>vec2</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>3.</span>)
+<span class='kw'>M</span> <span class='op'>=</span> <span class='fu'><a href='torch_zeros.html'>torch_zeros</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>))
+<span class='fu'>torch_addr</span>(<span class='kw'>M</span>, <span class='kw'>vec1</span>, <span class='kw'>vec2</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1  2
+#&gt;  2  4
+#&gt;  3  6
+#&gt; [ CPUFloatType{3,2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/torch_allclose.html b/static/docs/dev/reference/torch_allclose.html
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+    </li>
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+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
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+
+      
+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Allclose</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_allclose.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Allclose</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_allclose</span>(<span class='kw'>self</span>, <span class='kw'>other</span>, rtol = <span class='fl'>1e-05</span>, atol = <span class='fl'>0</span>, equal_nan = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) first tensor to compare</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor) second tensor to compare</p></td>
+    </tr>
+    <tr>
+      <th>rtol</th>
+      <td><p>(float, optional) relative tolerance. Default: 1e-05</p></td>
+    </tr>
+    <tr>
+      <th>atol</th>
+      <td><p>(float, optional) absolute tolerance. Default: 1e-08</p></td>
+    </tr>
+    <tr>
+      <th>equal_nan</th>
+      <td><p>(bool, optional) if <code>TRUE</code>, then two <code>NaN</code> s will be compared as equal. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="allclose-input-other-rtol-e-atol-e-equal-nan-false-gt-bool-"><a class="anchor" href="#allclose-input-other-rtol-e-atol-e-equal-nan-false-gt-bool-"></a>allclose(input, other, rtol=1e-05, atol=1e-08, equal_nan=False) -&gt; bool </h2>
+
+    
+
+
+<p>This function checks if all <code>input</code> and <code>other</code> satisfy the condition:</p>
+<p>$$
+    \vert \mbox{input} - \mbox{other} \vert \leq \mbox{atol} + \mbox{rtol} \times \vert \mbox{other} \vert
+$$
+elementwise, for all elements of <code>input</code> and <code>other</code>. The behaviour of this function is analogous to
+<code>numpy.allclose &lt;https://docs.scipy.org/doc/numpy/reference/generated/numpy.allclose.html&gt;</code>_</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_allclose</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10000.</span>, <span class='fl'>1e-07</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10000.1</span>, <span class='fl'>1e-08</span>)))
+<span class='fu'>torch_allclose</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10000.</span>, <span class='fl'>1e-08</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10000.1</span>, <span class='fl'>1e-09</span>)))
+<span class='fu'>torch_allclose</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1.0</span>, <span class='fl'>NaN</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1.0</span>, <span class='fl'>NaN</span>)))
+<span class='fu'>torch_allclose</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1.0</span>, <span class='fl'>NaN</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1.0</span>, <span class='fl'>NaN</span>)), equal_nan=<span class='fl'>TRUE</span>)
+}
+</div><div class='output co'>#&gt; [1] TRUE</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/torch_angle.html b/static/docs/dev/reference/torch_angle.html
new file mode 100644
index 000000000..7105df85e
--- /dev/null
+++ b/static/docs/dev/reference/torch_angle.html
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+
+      
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Angle</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_angle.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Angle</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_angle</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="angle-input-gt-tensor-"><a class="anchor" href="#angle-input-gt-tensor-"></a>angle(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the element-wise angle (in radians) of the given <code>input</code> tensor.</p>
+<p>$$
+    \mbox{out}_{i} = angle(\mbox{input}_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='fu'>torch_angle</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>1</span> <span class='op'>+</span> <span class='fl'>1i</span>, <span class='op'>-</span><span class='fl'>2</span> <span class='op'>+</span> <span class='fl'>2i</span>, <span class='fl'>3</span> <span class='op'>-</span> <span class='fl'>3i</span>)))<span class='op'>*</span><span class='fl'>180</span><span class='op'>/</span><span class='fl'>3.14159</span>
+}
+
+}
+</div></pre>
+  </div>
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+    <div class="page-header">
+    <h1>Arange</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_arange.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Arange</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_arange</span>(
+  <span class='kw'>start</span>,
+  <span class='kw'>end</span>,
+  step = <span class='fl'>1</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>start</th>
+      <td><p>(Number) the starting value for the set of points. Default: <code>0</code>.</p></td>
+    </tr>
+    <tr>
+      <th>end</th>
+      <td><p>(Number) the ending value for the set of points</p></td>
+    </tr>
+    <tr>
+      <th>step</th>
+      <td><p>(Number) the gap between each pair of adjacent points. Default: <code>1</code>.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). If <code>dtype</code> is not given, infer the data type from the other input        arguments. If any of <code>start</code>, <code>end</code>, or <code>stop</code> are floating-point, the        <code>dtype</code> is inferred to be the default dtype, see        <code>~torch.get_default_dtype</code>. Otherwise, the <code>dtype</code> is inferred to        be <code>torch.int64</code>.</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="arange-start-end-step-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#arange-start-end-step-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>arange(start=0, end, step=1, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a 1-D tensor of size \(\left\lceil \frac{\mbox{end} - \mbox{start}}{\mbox{step}} \right\rceil\)
+with values from the interval <code>[start, end)</code> taken with common difference
+<code>step</code> beginning from <code>start</code>.</p>
+<p>Note that non-integer <code>step</code> is subject to floating point rounding errors when
+comparing against <code>end</code>; to avoid inconsistency, we advise adding a small epsilon to <code>end</code>
+in such cases.</p>
+<p>$$
+    \mbox{out}_{{i+1}} = \mbox{out}_{i} + \mbox{step}
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_arange</span>(start = <span class='fl'>0</span>, end = <span class='fl'>5</span>)
+<span class='fu'>torch_arange</span>(<span class='fl'>1</span>, <span class='fl'>4</span>)
+<span class='fu'>torch_arange</span>(<span class='fl'>1</span>, <span class='fl'>2.5</span>, <span class='fl'>0.5</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.0000
+#&gt;  1.5000
+#&gt;  2.0000
+#&gt; [ CPUFloatType{3} ]</div></pre>
+  </div>
+  <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="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Argmax</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_argmax.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Argmax</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_argmax</span>(<span class='kw'>self</span>, dim = <span class='kw'>NULL</span>, keepdim = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the dimension to reduce. If <code>NULL</code>, the argmax of the flattened input is returned.</p></td>
+    </tr>
+    <tr>
+      <th>keepdim</th>
+      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not. Ignored if <code>dim=NULL</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="argmax-input-gt-longtensor-"><a class="anchor" href="#argmax-input-gt-longtensor-"></a>argmax(input) -&gt; LongTensor </h2>
+
+    
+
+
+<p>Returns the indices of the maximum value of all elements in the <code>input</code> tensor.</p>
+<p>This is the second value returned by <code>torch_max</code>. See its
+documentation for the exact semantics of this method.</p>
+    <h2 class="hasAnchor" id="argmax-input-dim-keepdim-false-gt-longtensor-"><a class="anchor" href="#argmax-input-dim-keepdim-false-gt-longtensor-"></a>argmax(input, dim, keepdim=False) -&gt; LongTensor </h2>
+
+    
+
+
+<p>Returns the indices of the maximum values of a tensor across a dimension.</p>
+<p>This is the second value returned by <code>torch_max</code>. See its
+documentation for the exact semantics of this method.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_argmax</span>(<span class='kw'>a</span>)
+}
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_argmax</span>(<span class='kw'>a</span>, dim=<span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  2
+#&gt;  3
+#&gt;  3
+#&gt;  3
+#&gt; [ CPULongType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Argmin</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_argmin.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Argmin</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_argmin</span>(<span class='kw'>self</span>, dim = <span class='kw'>NULL</span>, keepdim = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the dimension to reduce. If <code>NULL</code>, the argmin of the flattened input is returned.</p></td>
+    </tr>
+    <tr>
+      <th>keepdim</th>
+      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not. Ignored if <code>dim=NULL</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="argmin-input-gt-longtensor-"><a class="anchor" href="#argmin-input-gt-longtensor-"></a>argmin(input) -&gt; LongTensor </h2>
+
+    
+
+
+<p>Returns the indices of the minimum value of all elements in the <code>input</code> tensor.</p>
+<p>This is the second value returned by <code>torch_min</code>. See its
+documentation for the exact semantics of this method.</p>
+    <h2 class="hasAnchor" id="argmin-input-dim-keepdim-false-out-null-gt-longtensor-"><a class="anchor" href="#argmin-input-dim-keepdim-false-out-null-gt-longtensor-"></a>argmin(input, dim, keepdim=False, out=NULL) -&gt; LongTensor </h2>
+
+    
+
+
+<p>Returns the indices of the minimum values of a tensor across a dimension.</p>
+<p>This is the second value returned by <code>torch_min</code>. See its
+documentation for the exact semantics of this method.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_argmin</span>(<span class='kw'>a</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_argmin</span>(<span class='kw'>a</span>, dim=<span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  2
+#&gt;  3
+#&gt;  3
+#&gt;  3
+#&gt; [ CPULongType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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+
+      
+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Argsort</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_argsort.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Argsort</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_argsort</span>(<span class='kw'>self</span>, dim = <span class='op'>-</span><span class='fl'>1L</span>, descending = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int, optional) the dimension to sort along</p></td>
+    </tr>
+    <tr>
+      <th>descending</th>
+      <td><p>(bool, optional) controls the sorting order (ascending or descending)</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="argsort-input-dim-descending-false-gt-longtensor-"><a class="anchor" href="#argsort-input-dim-descending-false-gt-longtensor-"></a>argsort(input, dim=-1, descending=False) -&gt; LongTensor </h2>
+
+    
+
+
+<p>Returns the indices that sort a tensor along a given dimension in ascending
+order by value.</p>
+<p>This is the second value returned by <code>torch_sort</code>.  See its documentation
+for the exact semantics of this method.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_argsort</span>(<span class='kw'>a</span>, dim=<span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  3  1  1  2
+#&gt;  2  2  0  3
+#&gt;  1  3  2  1
+#&gt;  0  0  3  0
+#&gt; [ CPULongType{4,4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
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+</div>
+
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+
+      
+
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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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_as_strided.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>As_strided</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_as_strided</span>(<span class='kw'>self</span>, <span class='kw'>size</span>, <span class='kw'>stride</span>, storage_offset = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>size</th>
+      <td><p>(tuple or ints) the shape of the output tensor</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>(tuple or ints) the stride of the output tensor</p></td>
+    </tr>
+    <tr>
+      <th>storage_offset</th>
+      <td><p>(int, optional) the offset in the underlying storage of the output tensor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="as-strided-input-size-stride-storage-offset-gt-tensor-"><a class="anchor" href="#as-strided-input-size-stride-storage-offset-gt-tensor-"></a>as_strided(input, size, stride, storage_offset=0) -&gt; Tensor </h2>
+
+    
+
+
+<p>Create a view of an existing <code>torch_Tensor</code> <code>input</code> with specified
+<code>size</code>, <code>stride</code> and <code>storage_offset</code>.</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p>More than one element of a created tensor may refer to a single memory
+location. As a result, in-place operations (especially ones that are
+vectorized) may result in incorrect behavior. If you need to write to
+the tensors, please clone them first.</p><pre>Many PyTorch functions, which return a view of a tensor, are internally
+implemented with this function. Those functions, like
+`torch_Tensor.expand`, are easier to read and are therefore more
+advisable to use.
+</pre>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='kw'>x</span>
+<span class='kw'>t</span> <span class='op'>=</span> <span class='fu'>torch_as_strided</span>(<span class='kw'>x</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>), <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>))
+<span class='kw'>t</span>
+<span class='kw'>t</span> <span class='op'>=</span> <span class='fu'>torch_as_strided</span>(<span class='kw'>x</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>), <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>), <span class='fl'>1</span>)
+<span class='kw'>t</span>
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.2727  0.7072
+#&gt; -0.7263  0.1785
+#&gt; [ CPUFloatType{2,2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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+      <a href="../articles/using-autograd.html">Using autograd</a>
+    </li>
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+      <a href="../articles/extending-autograd.html">Extending autograd</a>
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Asin</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_asin.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Asin</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_asin</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="asin-input-out-null-gt-tensor-"><a class="anchor" href="#asin-input-out-null-gt-tensor-"></a>asin(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the arcsine  of the elements of <code>input</code>.</p>
+<p>$$
+    \mbox{out}_{i} = \sin^{-1}(\mbox{input}_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_asin</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -1.3162
+#&gt;  0.6367
+#&gt; -1.0489
+#&gt;     nan
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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diff --git a/static/docs/dev/reference/torch_atan.html b/static/docs/dev/reference/torch_atan.html
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+    <div class="page-header">
+    <h1>Atan</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_atan.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Atan</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_atan</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="atan-input-out-null-gt-tensor-"><a class="anchor" href="#atan-input-out-null-gt-tensor-"></a>atan(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the arctangent  of the elements of <code>input</code>.</p>
+<p>$$
+    \mbox{out}_{i} = \tan^{-1}(\mbox{input}_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_atan</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -1.2005
+#&gt; -0.9267
+#&gt; -0.8862
+#&gt; -0.8779
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Atan2</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_atan2.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Atan2</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_atan2</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the first input tensor</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor) the second input tensor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="atan-input-other-out-null-gt-tensor-"><a class="anchor" href="#atan-input-other-out-null-gt-tensor-"></a>atan2(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Element-wise arctangent of \(\mbox{input}_{i} / \mbox{other}_{i}\)
+with consideration of the quadrant. Returns a new tensor with the signed angles
+in radians between vector \((\mbox{other}_{i}, \mbox{input}_{i})\)
+and vector \((1, 0)\). (Note that \(\mbox{other}_{i}\), the second
+parameter, is the x-coordinate, while \(\mbox{input}_{i}\), the first
+parameter, is the y-coordinate.)</p>
+<p>The shapes of <code>input</code> and <code>other</code> must be
+broadcastable .</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_atan2</span>(<span class='kw'>a</span>, <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -1.8237
+#&gt;  0.1158
+#&gt; -0.1993
+#&gt; -1.5104
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/torch_avg_pool1d.html b/static/docs/dev/reference/torch_avg_pool1d.html
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+    <div class="page-header">
+    <h1>Avg_pool1d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_avg_pool1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Avg_pool1d</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_avg_pool1d</span>(
+  <span class='kw'>self</span>,
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(),
+  padding = <span class='fl'>0L</span>,
+  ceil_mode = <span class='fl'>FALSE</span>,
+  count_include_pad = <span class='fl'>TRUE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iW)\)</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>the size of the window. Can be a single number or a tuple <code>(kW,)</code></p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the window. Can be a single number or a tuple <code>(sW,)</code>. Default: <code>kernel_size</code></p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit zero paddings on both sides of the input. Can be a single number or a tuple <code>(padW,)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>ceil_mode</th>
+      <td><p>when <code>TRUE</code>, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape. Default: <code>FALSE</code></p></td>
+    </tr>
+    <tr>
+      <th>count_include_pad</th>
+      <td><p>when <code>TRUE</code>, will include the zero-padding in the averaging calculation. Default: <code>TRUE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="avg-pool-d-input-kernel-size-stride-null-padding-ceil-mode-false-count-include-pad-true-gt-tensor-"><a class="anchor" href="#avg-pool-d-input-kernel-size-stride-null-padding-ceil-mode-false-count-include-pad-true-gt-tensor-"></a>avg_pool1d(input, kernel_size, stride=NULL, padding=0, ceil_mode=FALSE, count_include_pad=TRUE) -&gt; Tensor </h2>
+
+    
+
+
+<p>Applies a 1D average pooling over an input signal composed of several
+input planes.</p>
+<p>See <code><a href='nn_avg_pool1d.html'>nn_avg_pool1d()</a></code> for details and output shape.</p>
+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Baddbmm</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_baddbmm.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Baddbmm</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_baddbmm</span>(<span class='kw'>self</span>, <span class='kw'>batch1</span>, <span class='kw'>batch2</span>, beta = <span class='fl'>1L</span>, alpha = <span class='fl'>1L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the tensor to be added</p></td>
+    </tr>
+    <tr>
+      <th>batch1</th>
+      <td><p>(Tensor) the first batch of matrices to be multiplied</p></td>
+    </tr>
+    <tr>
+      <th>batch2</th>
+      <td><p>(Tensor) the second batch of matrices to be multiplied</p></td>
+    </tr>
+    <tr>
+      <th>beta</th>
+      <td><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></td>
+    </tr>
+    <tr>
+      <th>alpha</th>
+      <td><p>(Number, optional) multiplier for \(\mbox{batch1} \mathbin{@} \mbox{batch2}\) (\(\alpha\))</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="baddbmm-input-batch-batch-beta-alpha-out-null-gt-tensor-"><a class="anchor" href="#baddbmm-input-batch-batch-beta-alpha-out-null-gt-tensor-"></a>baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Performs a batch matrix-matrix product of matrices in <code>batch1</code>
+and <code>batch2</code>.
+<code>input</code> is added to the final result.</p>
+<p><code>batch1</code> and <code>batch2</code> must be 3-D tensors each containing the same
+number of matrices.</p>
+<p>If <code>batch1</code> is a \((b \times n \times m)\) tensor, <code>batch2</code> is a
+\((b \times m \times p)\) tensor, then <code>input</code> must be
+broadcastable  with a
+\((b \times n \times p)\) tensor and <code>out</code> will be a
+\((b \times n \times p)\) tensor. Both <code>alpha</code> and <code>beta</code> mean the
+same as the scaling factors used in <code>torch_addbmm</code>.</p>
+<p>$$
+    \mbox{out}_i = \beta\ \mbox{input}_i + \alpha\ (\mbox{batch1}_i \mathbin{@} \mbox{batch2}_i)
+$$
+For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, arguments <code>beta</code> and
+<code>alpha</code> must be real numbers, otherwise they should be integers.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>M</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>5</span>))
+<span class='kw'>batch1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>4</span>))
+<span class='kw'>batch2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>4</span>, <span class='fl'>5</span>))
+<span class='fu'>torch_baddbmm</span>(<span class='kw'>M</span>, <span class='kw'>batch1</span>, <span class='kw'>batch2</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;   2.7673  3.8142 -2.1566  0.8392 -0.1574
+#&gt;  -3.5008 -3.0245  1.5608  0.8823 -3.8403
+#&gt;  -0.6895  2.3852 -1.9370 -0.4578  3.7591
+#&gt; 
+#&gt; (2,.,.) = 
+#&gt;   2.8193  4.0901 -1.0565 -1.4749  4.1633
+#&gt;  -1.5332  2.0761  0.5535  0.1496  0.8969
+#&gt;  -3.9711  2.0648  2.1003  2.6881 -5.2599
+#&gt; 
+#&gt; (3,.,.) = 
+#&gt;   7.2532  0.1823 -3.8754 -1.7195  0.9933
+#&gt;   0.3700  0.0226 -2.8153  1.1894 -0.8335
+#&gt;   7.0359 -0.9349  3.8406 -1.1739  7.6222
+#&gt; 
+#&gt; (4,.,.) = 
+#&gt;  -0.0215 -1.5237  1.3790 -1.2662 -1.5822
+#&gt;  -0.8591  4.3549 -1.2652  0.5812 -1.1035
+#&gt;   1.0322  2.8543 -1.4274 -1.8826  2.4363
+#&gt; 
+#&gt; (5,.,.) = 
+#&gt;  -2.5454  3.1478  1.1194 -0.0983 -1.0287
+#&gt;   0.0967 -0.5739 -4.6500  2.1894 -1.3741
+#&gt;   1.2304  0.7907  2.5194 -4.3185 -2.9295
+#&gt; 
+#&gt; (6,.,.) = 
+#&gt;   1.1406  2.5135 -1.7900  3.7070 -0.5922
+#&gt;  -0.3170 -1.6689  1.1494 -1.8042  2.0719
+#&gt;   0.1365 -3.1741 -0.1377  0.1946  0.7151
+#&gt; 
+#&gt; (7,.,.) = 
+#&gt;  -4.1217 -1.1835  1.0868 -0.7996  1.3881
+#&gt;  -0.9917  1.4596  0.1372 -0.8100  0.4499
+#&gt;  -2.5818 -3.8951 -3.5722 -0.5064 -0.2227
+#&gt; 
+#&gt; (8,.,.) = 
+#&gt;   2.7398 -1.6400 -2.1485 -2.2978 -3.7031
+#&gt;  -2.1908 -1.4017  1.6094  1.8951 -0.0367
+#&gt;  -1.2369  3.2816  1.2695  3.6934 -0.3198
+#&gt; 
+#&gt; (9,.,.) = 
+#&gt;   1.3105  1.0761  0.6626  0.9699 -0.2097
+#&gt;  -0.5385  0.6569 -1.8404 -0.2079  0.6920
+#&gt;  -0.0850 -1.7620 -0.3243 -1.3616  1.3040
+#&gt; 
+#&gt; (10,.,.) = 
+#&gt;  -2.5976 -3.9216 -1.1513  1.6483  1.9870
+#&gt;   0.3967 -1.2132  1.1914  1.6283  0.8361
+#&gt;   2.9039  1.0315  1.8009 -3.4176 -0.4645
+#&gt; [ CPUFloatType{10,3,5} ]</div></pre>
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+    <div class="page-header">
+    <h1>Bartlett_window</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
+    <div class="hidden name"><code>torch_bartlett_window.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Bartlett_window</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_bartlett_window</span>(
+  <span class='kw'>window_length</span>,
+  periodic = <span class='fl'>TRUE</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>window_length</th>
+      <td><p>(int) the size of returned window</p></td>
+    </tr>
+    <tr>
+      <th>periodic</th>
+      <td><p>(bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). Only floating point types are supported.</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned window tensor. Only          <code>torch_strided</code> (dense layout) is supported.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>If `window_length` \eqn{=1}, the returned window contains a single value 1.
+</pre>
+
+    <h2 class="hasAnchor" id="bartlett-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#bartlett-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>bartlett_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Bartlett window function.</p>
+<p>$$
+    w[n] = 1 - \left| \frac{2n}{N-1} - 1 \right| = \left\{ \begin{array}{ll}
+        \frac{2n}{N - 1} &amp; \mbox{if } 0 \leq n \leq \frac{N - 1}{2} \\
+        2 - \frac{2n}{N - 1} &amp; \mbox{if } \frac{N - 1}{2} &lt; n &lt; N \\
+    \end{array}
+    \right. ,
+$$
+where \(N\) is the full window size.</p>
+<p>The input <code>window_length</code> is a positive integer controlling the
+returned window size. <code>periodic</code> flag determines whether the returned
+window trims off the last duplicate value from the symmetric window and is
+ready to be used as a periodic window with functions like
+<code>torch_stft</code>. Therefore, if <code>periodic</code> is true, the \(N\) in
+above formula is in fact \(\mbox{window\_length} + 1\). Also, we always have
+<code>torch_bartlett_window(L, periodic=TRUE)</code> equal to
+<code>torch_bartlett_window(L + 1, periodic=False)[:-1])</code>.</p>
+
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Bernoulli</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_bernoulli.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Bernoulli</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_bernoulli</span>(<span class='kw'>self</span>, <span class='kw'>p</span>, generator = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor of probability values for the Bernoulli
+distribution</p></td>
+    </tr>
+    <tr>
+      <th>p</th>
+      <td><p>(Number) a probability value. If <code>p</code> is passed than it's used instead of
+the values in <code>self</code> tensor.</p></td>
+    </tr>
+    <tr>
+      <th>generator</th>
+      <td><p>(<code>torch.Generator</code>, optional) a pseudorandom number generator for sampling</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="bernoulli-input-generator-null-out-null-gt-tensor-"><a class="anchor" href="#bernoulli-input-generator-null-out-null-gt-tensor-"></a>bernoulli(input, *, generator=NULL, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Draws binary random numbers (0 or 1) from a Bernoulli distribution.</p>
+<p>The <code>input</code> tensor should be a tensor containing probabilities
+to be used for drawing the binary random number.
+Hence, all values in <code>input</code> have to be in the range:
+\(0 \leq \mbox{input}_i \leq 1\).</p>
+<p>The \(\mbox{i}^{th}\) element of the output tensor will draw a
+value \(1\) according to the \(\mbox{i}^{th}\) probability value given
+in <code>input</code>.</p>
+<p>$$
+    \mbox{out}_{i} \sim \mathrm{Bernoulli}(p = \mbox{input}_{i})
+$$
+The returned <code>out</code> tensor only has values 0 or 1 and is of the same
+shape as <code>input</code>.</p>
+<p><code>out</code> can have integral <code>dtype</code>, but <code>input</code> must have floating
+point <code>dtype</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))<span class='op'>$</span><span class='fu'>uniform_</span>(<span class='fl'>0</span>, <span class='fl'>1</span>)  <span class='co'># generate a uniform random matrix with range c(0, 1)</span>
+<span class='kw'>a</span>
+<span class='fu'>torch_bernoulli</span>(<span class='kw'>a</span>)
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_ones.html'>torch_ones</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>)) <span class='co'># probability of drawing "1" is 1</span>
+<span class='fu'>torch_bernoulli</span>(<span class='kw'>a</span>)
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_zeros.html'>torch_zeros</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>)) <span class='co'># probability of drawing "1" is 0</span>
+<span class='fu'>torch_bernoulli</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0  0  0
+#&gt;  0  0  0
+#&gt;  0  0  0
+#&gt; [ CPUFloatType{3,3} ]</div></pre>
+  </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="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Bincount</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_bincount.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Bincount</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_bincount</span>(<span class='kw'>self</span>, weights = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(), minlength = <span class='fl'>0L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) 1-d int tensor</p></td>
+    </tr>
+    <tr>
+      <th>weights</th>
+      <td><p>(Tensor) optional, weight for each value in the input tensor.        Should be of same size as input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>minlength</th>
+      <td><p>(int) optional, minimum number of bins. Should be non-negative.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="bincount-input-weights-null-minlength-gt-tensor-"><a class="anchor" href="#bincount-input-weights-null-minlength-gt-tensor-"></a>bincount(input, weights=NULL, minlength=0) -&gt; Tensor </h2>
+
+    
+
+
+<p>Count the frequency of each value in an array of non-negative ints.</p>
+<p>The number of bins (size 1) is one larger than the largest value in
+<code>input</code> unless <code>input</code> is empty, in which case the result is a
+tensor of size 0. If <code>minlength</code> is specified, the number of bins is at least
+<code>minlength</code> and if <code>input</code> is empty, then the result is tensor of size
+<code>minlength</code> filled with zeros. If <code>n</code> is the value at position <code>i</code>,
+<code>out[n] += weights[i]</code> if <code>weights</code> is specified else
+<code>out[n] += 1</code>.</p>
+<p>.. include:: cuda_deterministic.rst</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_randint.html'>torch_randint</a></span>(<span class='fl'>0</span>, <span class='fl'>8</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>5</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>())
+<span class='kw'>weights</span> <span class='op'>=</span> <span class='fu'><a href='torch_linspace.html'>torch_linspace</a></span>(<span class='fl'>0</span>, <span class='fl'>1</span>, steps=<span class='fl'>5</span>)
+<span class='kw'>input</span>
+<span class='kw'>weights</span>
+<span class='fu'>torch_bincount</span>(<span class='kw'>input</span>, <span class='kw'>weights</span>)
+<span class='kw'>input</span><span class='op'>$</span><span class='fu'>bincount</span>(<span class='kw'>weights</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.0000
+#&gt;  0.7500
+#&gt;  0.0000
+#&gt;  0.2500
+#&gt;  0.0000
+#&gt;  0.0000
+#&gt;  0.5000
+#&gt;  1.0000
+#&gt; [ CPUFloatType{8} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_bitwise_and.html b/static/docs/dev/reference/torch_bitwise_and.html
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+    <div class="page-header">
+    <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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_bitwise_and.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Bitwise_and</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_bitwise_and</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>NA the first input tensor</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>NA the second input tensor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="bitwise-and-input-other-out-null-gt-tensor-"><a class="anchor" href="#bitwise-and-input-other-out-null-gt-tensor-"></a>bitwise_and(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the bitwise AND of <code>input</code> and <code>other</code>. The input tensor must be of
+integral or Boolean types. For bool tensors, it computes the logical AND.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <h1>Bitwise_not</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_bitwise_not.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Bitwise_not</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_bitwise_not</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="bitwise-not-input-out-null-gt-tensor-"><a class="anchor" href="#bitwise-not-input-out-null-gt-tensor-"></a>bitwise_not(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the bitwise NOT of the given input tensor. The input tensor must be of
+integral or Boolean types. For bool tensors, it computes the logical NOT.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Bitwise_or</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_bitwise_or.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Bitwise_or</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_bitwise_or</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>NA the first input tensor</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>NA the second input tensor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="bitwise-or-input-other-out-null-gt-tensor-"><a class="anchor" href="#bitwise-or-input-other-out-null-gt-tensor-"></a>bitwise_or(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<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>
+
+  </div>
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+    <h1>Bitwise_xor</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_bitwise_xor.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Bitwise_xor</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_bitwise_xor</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>NA the first input tensor</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>NA the second input tensor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="bitwise-xor-input-other-out-null-gt-tensor-"><a class="anchor" href="#bitwise-xor-input-other-out-null-gt-tensor-"></a>bitwise_xor(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the bitwise XOR of <code>input</code> and <code>other</code>. The input tensor must be of
+integral or Boolean types. For bool tensors, it computes the logical XOR.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Blackman_window</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
+    <div class="hidden name"><code>torch_blackman_window.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Blackman_window</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_blackman_window</span>(
+  <span class='kw'>window_length</span>,
+  periodic = <span class='fl'>TRUE</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>window_length</th>
+      <td><p>(int) the size of returned window</p></td>
+    </tr>
+    <tr>
+      <th>periodic</th>
+      <td><p>(bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). Only floating point types are supported.</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned window tensor. Only          <code>torch_strided</code> (dense layout) is supported.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>If `window_length` \eqn{=1}, the returned window contains a single value 1.
+</pre>
+
+    <h2 class="hasAnchor" id="blackman-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#blackman-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>blackman_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Blackman window function.</p>
+<p>$$
+    w[n] = 0.42 - 0.5 \cos \left( \frac{2 \pi n}{N - 1} \right) + 0.08 \cos \left( \frac{4 \pi n}{N - 1} \right)
+$$
+where \(N\) is the full window size.</p>
+<p>The input <code>window_length</code> is a positive integer controlling the
+returned window size. <code>periodic</code> flag determines whether the returned
+window trims off the last duplicate value from the symmetric window and is
+ready to be used as a periodic window with functions like
+<code>torch_stft</code>. Therefore, if <code>periodic</code> is true, the \(N\) in
+above formula is in fact \(\mbox{window\_length} + 1\). Also, we always have
+<code>torch_blackman_window(L, periodic=TRUE)</code> equal to
+<code>torch_blackman_window(L + 1, periodic=False)[:-1])</code>.</p>
+
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+    <div class="page-header">
+    <h1>Bmm</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_bmm.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Bmm</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_bmm</span>(<span class='kw'>self</span>, <span class='kw'>mat2</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the first batch of matrices to be multiplied</p></td>
+    </tr>
+    <tr>
+      <th>mat2</th>
+      <td><p>(Tensor) the second batch of matrices to be multiplied</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>This function does not broadcast .
+For broadcasting matrix products, see <code><a href='torch_matmul.html'>torch_matmul</a></code>.</p>
+    <h2 class="hasAnchor" id="bmm-input-mat-out-null-gt-tensor-"><a class="anchor" href="#bmm-input-mat-out-null-gt-tensor-"></a>bmm(input, mat2, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Performs a batch matrix-matrix product of matrices stored in <code>input</code>
+and <code>mat2</code>.</p>
+<p><code>input</code> and <code>mat2</code> must be 3-D tensors each containing
+the same number of matrices.</p>
+<p>If <code>input</code> is a \((b \times n \times m)\) tensor, <code>mat2</code> is a
+\((b \times m \times p)\) tensor, <code>out</code> will be a
+\((b \times n \times p)\) tensor.</p>
+<p>$$
+    \mbox{out}_i = \mbox{input}_i \mathbin{@} \mbox{mat2}_i
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>4</span>))
+<span class='kw'>mat2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>4</span>, <span class='fl'>5</span>))
+<span class='kw'>res</span> <span class='op'>=</span> <span class='fu'>torch_bmm</span>(<span class='kw'>input</span>, <span class='kw'>mat2</span>)
+<span class='kw'>res</span>
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;   0.1611  3.2873 -2.7979  0.5577 -0.6578
+#&gt;   0.4613 -0.3262  0.8856  1.8179  0.5950
+#&gt;   0.3713 -1.0938 -0.0862 -0.2558 -0.2166
+#&gt; 
+#&gt; (2,.,.) = 
+#&gt;   1.7443 -0.3017 -4.1491 -3.1967 -0.9234
+#&gt;   0.3326 -0.1091 -0.1212 -1.4089 -0.1950
+#&gt;  -0.5194  1.0499 -2.9961 -0.3035  0.2586
+#&gt; 
+#&gt; (3,.,.) = 
+#&gt;  -4.2654  3.1454  5.5731  0.0939 -0.2378
+#&gt;   2.7372 -1.7179 -4.6256  0.3196 -1.2640
+#&gt;  -2.6296  1.7937  3.1864  0.0494 -1.7055
+#&gt; 
+#&gt; (4,.,.) = 
+#&gt;   0.2021  1.0450 -1.9129 -0.0005 -0.9617
+#&gt;   1.6322 -2.6664  0.4473 -2.3600  1.0421
+#&gt;  -0.8634  2.2971 -1.7369  1.3270 -1.4233
+#&gt; 
+#&gt; (5,.,.) = 
+#&gt;   0.9213 -1.8910 -2.8053  0.2184 -0.4071
+#&gt;   0.1079  0.2815 -0.0232 -0.0372  0.7164
+#&gt;   0.4005 -0.3513 -0.0439 -0.3791 -0.6494
+#&gt; 
+#&gt; (6,.,.) = 
+#&gt;  -0.4160 -2.6689 -1.4779 -0.2370  3.2740
+#&gt;   1.1628  2.6820 -0.3655  2.7943 -3.7204
+#&gt;  -0.0693  0.9010 -0.0905 -1.5431 -1.8316
+#&gt; 
+#&gt; (7,.,.) = 
+#&gt;  -0.6875 -6.1682 -0.6918 -0.4328  1.8613
+#&gt;   1.3210  0.0232  0.2676  0.9049 -1.5467
+#&gt;  -0.5596  5.2267 -0.2623 -1.0450 -0.3493
+#&gt; 
+#&gt; (8,.,.) = 
+#&gt;  -0.2389  0.4121 -0.2401  0.1842  0.9670
+#&gt;   0.4465 -1.6942  2.7656  1.1619 -3.8290
+#&gt;   0.6562 -2.4667 -1.6473 -1.5007 -2.2954
+#&gt; 
+#&gt; (9,.,.) = 
+#&gt;  -0.1681 -1.9747  2.9924  1.4263 -1.4143
+#&gt;  -0.9687  1.3590 -2.0486 -0.9397 -0.0612
+#&gt;   0.7662 -0.1328  1.9483  0.1000  1.3884
+#&gt; 
+#&gt; (10,.,.) = 
+#&gt;  -0.9815 -0.6052 -0.7606  0.2180 -3.1664
+#&gt;   0.0858  1.0146  0.1378 -1.6044  0.3625
+#&gt;  -2.1684 -2.1501 -2.8241 -1.3085  0.4587
+#&gt; [ CPUFloatType{10,3,5} ]</div></pre>
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+    <div class="page-header">
+    <h1>Broadcast_tensors</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_broadcast_tensors.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Broadcast_tensors</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_broadcast_tensors</span>(<span class='kw'>tensors</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensors</th>
+      <td><p>a list containing any number of tensors of the same type</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="broadcast-tensors-tensors-gt-list-of-tensors-"><a class="anchor" href="#broadcast-tensors-tensors-gt-list-of-tensors-"></a>broadcast_tensors(tensors) -&gt; List of Tensors </h2>
+
+    
+
+
+<p>Broadcasts the given tensors according to broadcasting-semantics.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0</span>, <span class='fl'>3</span>)<span class='op'>$</span><span class='fu'>view</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
+<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0</span>, <span class='fl'>2</span>)<span class='op'>$</span><span class='fu'>view</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>))
+<span class='kw'>out</span> <span class='op'>=</span> <span class='fu'>torch_broadcast_tensors</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>x</span>, <span class='kw'>y</span>))
+<span class='kw'>out</span>[[<span class='fl'>1</span>]]
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0  1  2
+#&gt;  0  1  2
+#&gt; [ CPUFloatType{2,3} ]</div></pre>
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diff --git a/static/docs/dev/reference/torch_can_cast.html b/static/docs/dev/reference/torch_can_cast.html
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+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Can_cast</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_can_cast.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Can_cast</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_can_cast</span>(<span class='kw'>from</span>, <span class='kw'>to</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>from</th>
+      <td><p>(dtype) The original <code>torch_dtype</code>.</p></td>
+    </tr>
+    <tr>
+      <th>to</th>
+      <td><p>(dtype) The target <code>torch_dtype</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="can-cast-from-to-gt-bool-"><a class="anchor" href="#can-cast-from-to-gt-bool-"></a>can_cast(from, to) -&gt; bool </h2>
+
+    
+
+
+<p>Determines if a type conversion is allowed under PyTorch casting rules
+described in the type promotion documentation .</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_can_cast</span>(<span class='fu'><a href='torch_dtype.html'>torch_double</a></span>(), <span class='fu'><a href='torch_dtype.html'>torch_float</a></span>())
+<span class='fu'>torch_can_cast</span>(<span class='fu'><a href='torch_dtype.html'>torch_float</a></span>(), <span class='fu'><a href='torch_dtype.html'>torch_int</a></span>())
+}
+</div><div class='output co'>#&gt; [1] FALSE</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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+  </div>
+</div>
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+</div>
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diff --git a/static/docs/dev/reference/torch_cartesian_prod.html b/static/docs/dev/reference/torch_cartesian_prod.html
new file mode 100644
index 000000000..4409b7195
--- /dev/null
+++ b/static/docs/dev/reference/torch_cartesian_prod.html
@@ -0,0 +1,254 @@
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+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Cartesian_prod</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_cartesian_prod.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Do cartesian product of the given sequence of tensors.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_cartesian_prod</span>(<span class='kw'>tensors</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensors</th>
+      <td><p>a list containing any number of 1 dimensional tensors.</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>)
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>)
+<span class='kw'>tensor_a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='kw'>a</span>)
+<span class='kw'>tensor_b</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='kw'>b</span>)
+<span class='fu'>torch_cartesian_prod</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>tensor_a</span>, <span class='kw'>tensor_b</span>))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1  4
+#&gt;  1  5
+#&gt;  2  4
+#&gt;  2  5
+#&gt;  3  4
+#&gt;  3  5
+#&gt; [ CPUFloatType{6,2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
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+</div>
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diff --git a/static/docs/dev/reference/torch_cat.html b/static/docs/dev/reference/torch_cat.html
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+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Cat</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_cat.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Cat</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_cat</span>(<span class='kw'>tensors</span>, dim = <span class='fl'>1L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensors</th>
+      <td><p>(sequence of Tensors) any python sequence of tensors of the same type.        Non-empty tensors provided must have the same shape, except in the        cat dimension.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int, optional) the dimension over which the tensors are concatenated</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="cat-tensors-dim-out-null-gt-tensor-"><a class="anchor" href="#cat-tensors-dim-out-null-gt-tensor-"></a>cat(tensors, dim=0, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Concatenates the given sequence of <code>seq</code> tensors in the given dimension.
+All tensors must either have the same shape (except in the concatenating
+dimension) or be empty.</p>
+<p><code>torch_cat</code> can be seen as an inverse operation for <code><a href='torch_split.html'>torch_split()</a></code>
+and <code><a href='torch_chunk.html'>torch_chunk</a></code>.</p>
+<p><code>torch_cat</code> can be best understood via examples.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+<span class='kw'>x</span>
+<span class='fu'>torch_cat</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>x</span>, <span class='kw'>x</span>, <span class='kw'>x</span>), <span class='fl'>1</span>)
+<span class='fu'>torch_cat</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>x</span>, <span class='kw'>x</span>, <span class='kw'>x</span>), <span class='fl'>2</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.8422  0.3027  0.4810  0.8422  0.3027  0.4810  0.8422  0.3027  0.4810
+#&gt; -1.2264  0.6089  1.3649 -1.2264  0.6089  1.3649 -1.2264  0.6089  1.3649
+#&gt; [ CPUFloatType{2,9} ]</div></pre>
+  </div>
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+<div class="row">
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+    <div class="page-header">
+    <h1>Cdist</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_cdist.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Cdist</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_cdist</span>(<span class='kw'>x1</span>, <span class='kw'>x2</span>, p = <span class='fl'>2L</span>, compute_mode = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>x1</th>
+      <td><p>(Tensor) input tensor of shape \(B \times P \times M\).</p></td>
+    </tr>
+    <tr>
+      <th>x2</th>
+      <td><p>(Tensor) input tensor of shape \(B \times R \times M\).</p></td>
+    </tr>
+    <tr>
+      <th>p</th>
+      <td><p>NA p value for the p-norm distance to calculate between each vector pair        \(\in [0, \infty]\).</p></td>
+    </tr>
+    <tr>
+      <th>compute_mode</th>
+      <td><p>NA 'use_mm_for_euclid_dist_if_necessary' - will use matrix multiplication approach to calculate        euclidean distance (p = 2) if P &gt; 25 or R &gt; 25        'use_mm_for_euclid_dist' - will always use matrix multiplication approach to calculate        euclidean distance (p = 2)        'donot_use_mm_for_euclid_dist' - will never use matrix multiplication approach to calculate        euclidean distance (p = 2)        Default: use_mm_for_euclid_dist_if_necessary.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
+
+    
+
+
+<p>Computes batched the p-norm distance between each pair of the two collections of row vectors.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+
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+    <div class="page-header">
+    <h1>Ceil</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_ceil.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Ceil</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_ceil</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="ceil-input-out-null-gt-tensor-"><a class="anchor" href="#ceil-input-out-null-gt-tensor-"></a>ceil(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the ceil of the elements of <code>input</code>,
+the smallest integer greater than or equal to each element.</p>
+<p>$$
+    \mbox{out}_{i} = \left\lceil \mbox{input}_{i} \right\rceil = \left\lfloor \mbox{input}_{i} \right\rfloor + 1
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_ceil</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0
+#&gt;  2
+#&gt;  1
+#&gt;  1
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
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+<div class="row">
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+    <div class="page-header">
+    <h1>Celu</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_celu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Celu</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_celu</span>(<span class='kw'>self</span>, alpha = <span class='fl'>1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>alpha</th>
+      <td><p>the alpha value for the CELU formulation. Default: 1.0</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="celu-input-alpha-gt-tensor-"><a class="anchor" href="#celu-input-alpha-gt-tensor-"></a>celu(input, alpha=1.) -&gt; Tensor </h2>
+
+    
+
+
+<p>See <code><a href='nnf_celu.html'>nnf_celu()</a></code> for more info.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+
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+    <div class="page-header">
+    <h1>Celu_</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
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+    </div>
+
+    <div class="ref-description">
+    <p>Celu_</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_celu_</span>(<span class='kw'>self</span>, alpha = <span class='fl'>1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>alpha</th>
+      <td><p>the alpha value for the CELU formulation. Default: 1.0</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="celu-input-alpha-gt-tensor-"><a class="anchor" href="#celu-input-alpha-gt-tensor-"></a>celu_(input, alpha=1.) -&gt; Tensor </h2>
+
+    
+
+
+<p>In-place version of <code><a href='torch_celu.html'>torch_celu()</a></code>.</p>
+
+  </div>
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+    <div class="page-header">
+    <h1>Chain_matmul</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_chain_matmul.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Chain_matmul</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_chain_matmul</span>(<span class='kw'>matrices</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>matrices</th>
+      <td><p>(Tensors...) a sequence of 2 or more 2-D tensors whose product is to be determined.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
+
+    
+
+
+<p>Returns the matrix product of the \(N\) 2-D tensors. This product is efficiently computed
+using the matrix chain order algorithm which selects the order in which incurs the lowest cost in terms
+of arithmetic operations (<code>[CLRS]</code>_). Note that since this is a function to compute the product, \(N\)
+needs to be greater than or equal to 2; if equal to 2 then a trivial matrix-matrix product is returned.
+If \(N\) is 1, then this is a no-op - the original matrix is returned as is.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>4</span>))
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>))
+<span class='kw'>c</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>, <span class='fl'>6</span>))
+<span class='kw'>d</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>6</span>, <span class='fl'>7</span>))
+<span class='fu'>torch_chain_matmul</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>a</span>, <span class='kw'>b</span>, <span class='kw'>c</span>, <span class='kw'>d</span>))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  -3.4661   0.3385   1.5381  -5.5143  -5.5374  -1.9805  -6.2549
+#&gt;   8.6568  -0.4593  -0.5739   5.2981   4.8523   1.4588   4.3676
+#&gt;  27.5014  -3.0642   0.2539  -0.8520   1.0841  -1.8489   4.2574
+#&gt; [ CPUFloatType{3,7} ]</div></pre>
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+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Cholesky</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_cholesky.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Cholesky</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_cholesky</span>(<span class='kw'>self</span>, upper = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor \(A\) of size \((*, n, n)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is zero or more
+batch dimensions consisting of symmetric positive-definite matrices.</p></td>
+    </tr>
+    <tr>
+      <th>upper</th>
+      <td><p>(bool, optional) flag that indicates whether to return a
+upper or lower triangular matrix. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="cholesky-input-upper-false-out-null-gt-tensor-"><a class="anchor" href="#cholesky-input-upper-false-out-null-gt-tensor-"></a>cholesky(input, upper=False, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the Cholesky decomposition of a symmetric positive-definite
+matrix \(A\) or for batches of symmetric positive-definite matrices.</p>
+<p>If <code>upper</code> is <code>TRUE</code>, the returned matrix <code>U</code> is upper-triangular, and
+the decomposition has the form:</p>
+<p>$$
+  A = U^TU
+$$
+If <code>upper</code> is <code>FALSE</code>, the returned matrix <code>L</code> is lower-triangular, and
+the decomposition has the form:</p>
+<p>$$
+    A = LL^T
+$$
+If <code>upper</code> is <code>TRUE</code>, and \(A\) is a batch of symmetric positive-definite
+matrices, then the returned tensor will be composed of upper-triangular Cholesky factors
+of each of the individual matrices. Similarly, when <code>upper</code> is <code>FALSE</code>, the returned
+tensor will be composed of lower-triangular Cholesky factors of each of the individual
+matrices.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='kw'>a</span>, <span class='kw'>a</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>()) <span class='co'># make symmetric positive-definite</span>
+<span class='kw'>l</span> <span class='op'>=</span> <span class='fu'>torch_cholesky</span>(<span class='kw'>a</span>)
+<span class='kw'>a</span>
+<span class='kw'>l</span>
+<span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='kw'>l</span>, <span class='kw'>l</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>())
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>, <span class='fl'>2</span>))
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span>(<span class='kw'>a</span>, <span class='kw'>a</span><span class='op'>$</span><span class='fu'>transpose</span>(<span class='op'>-</span><span class='fl'>1</span>, <span class='op'>-</span><span class='fl'>2</span>)) <span class='op'>+</span> <span class='fl'>1e-03</span> <span class='co'># make symmetric positive-definite</span>
+<span class='kw'>l</span> <span class='op'>=</span> <span class='fu'>torch_cholesky</span>(<span class='kw'>a</span>)
+<span class='kw'>z</span> <span class='op'>=</span> <span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span>(<span class='kw'>l</span>, <span class='kw'>l</span><span class='op'>$</span><span class='fu'>transpose</span>(<span class='op'>-</span><span class='fl'>1</span>, <span class='op'>-</span><span class='fl'>2</span>))
+<span class='fu'><a href='torch_max.html'>torch_max</a></span>(<span class='fu'><a href='torch_abs.html'>torch_abs</a></span>(<span class='kw'>z</span> <span class='op'>-</span> <span class='kw'>a</span>)) <span class='co'># Max non-zero</span>
+}
+}
+</div></pre>
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+    <div class="page-header">
+    <h1>Cholesky_inverse</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_cholesky_inverse.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Cholesky_inverse</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_cholesky_inverse</span>(<span class='kw'>self</span>, upper = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input 2-D tensor \(u\), a upper or lower triangular           Cholesky factor</p></td>
+    </tr>
+    <tr>
+      <th>upper</th>
+      <td><p>(bool, optional) whether to return a lower (default) or upper triangular matrix</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="cholesky-inverse-input-upper-false-out-null-gt-tensor-"><a class="anchor" href="#cholesky-inverse-input-upper-false-out-null-gt-tensor-"></a>cholesky_inverse(input, upper=False, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the inverse of a symmetric positive-definite matrix \(A\) using its
+Cholesky factor \(u\): returns matrix <code>inv</code>. The inverse is computed using
+LAPACK routines <code>dpotri</code> and <code>spotri</code> (and the corresponding MAGMA routines).</p>
+<p>If <code>upper</code> is <code>FALSE</code>, \(u\) is lower triangular
+such that the returned tensor is</p>
+<p>$$
+    inv = (uu^{{T}})^{{-1}}
+$$
+If <code>upper</code> is <code>TRUE</code> or not provided, \(u\) is upper
+triangular such that the returned tensor is</p>
+<p>$$
+    inv = (u^T u)^{{-1}}
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='kw'>a</span>, <span class='kw'>a</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>()) <span class='op'>+</span> <span class='fl'>1e-05</span> <span class='op'>*</span> <span class='fu'><a href='torch_eye.html'>torch_eye</a></span>(<span class='fl'>3</span>) <span class='co'># make symmetric positive definite</span>
+<span class='kw'>u</span> <span class='op'>=</span> <span class='fu'><a href='torch_cholesky.html'>torch_cholesky</a></span>(<span class='kw'>a</span>)
+<span class='kw'>a</span>
+<span class='fu'>torch_cholesky_inverse</span>(<span class='kw'>u</span>)
+<span class='kw'>a</span><span class='op'>$</span><span class='fu'>inverse</span>()
+}
+}
+</div></pre>
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index 000000000..5a3bf06fd
--- /dev/null
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+    <div class="page-header">
+    <h1>Cholesky_solve</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_cholesky_solve.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Cholesky_solve</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_cholesky_solve</span>(<span class='kw'>self</span>, <span class='kw'>input2</span>, upper = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) input matrix \(b\) of size \((*, m, k)\),                where \(*\) is zero or more batch dimensions</p></td>
+    </tr>
+    <tr>
+      <th>input2</th>
+      <td><p>(Tensor) input matrix \(u\) of size \((*, m, m)\),                where \(*\) is zero of more batch dimensions composed of                upper or lower triangular Cholesky factor</p></td>
+    </tr>
+    <tr>
+      <th>upper</th>
+      <td><p>(bool, optional) whether to consider the Cholesky factor as a                            lower or upper triangular matrix. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="cholesky-solve-input-input-upper-false-out-null-gt-tensor-"><a class="anchor" href="#cholesky-solve-input-input-upper-false-out-null-gt-tensor-"></a>cholesky_solve(input, input2, upper=False, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Solves a linear system of equations with a positive semidefinite
+matrix to be inverted given its Cholesky factor matrix \(u\).</p>
+<p>If <code>upper</code> is <code>FALSE</code>, \(u\) is and lower triangular and <code>c</code> is
+returned such that:</p>
+<p>$$
+    c = (u u^T)^{{-1}} b
+$$
+If <code>upper</code> is <code>TRUE</code> or not provided, \(u\) is upper triangular
+and <code>c</code> is returned such that:</p>
+<p>$$
+    c = (u^T u)^{{-1}} b
+$$
+<code>torch_cholesky_solve(b, u)</code> can take in 2D inputs <code>b, u</code> or inputs that are
+batches of 2D matrices. If the inputs are batches, then returns
+batched outputs <code>c</code></p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='kw'>a</span>, <span class='kw'>a</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>()) <span class='co'># make symmetric positive definite</span>
+<span class='kw'>u</span> <span class='op'>=</span> <span class='fu'><a href='torch_cholesky.html'>torch_cholesky</a></span>(<span class='kw'>a</span>)
+<span class='kw'>a</span>
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>))
+<span class='kw'>b</span>
+<span class='fu'>torch_cholesky_solve</span>(<span class='kw'>b</span>, <span class='kw'>u</span>)
+<span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='kw'>a</span><span class='op'>$</span><span class='fu'>inverse</span>(), <span class='kw'>b</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.4124  0.7031
+#&gt;  1.7785  1.9837
+#&gt; -1.8951 -2.2718
+#&gt; [ CPUFloatType{3,2} ]</div></pre>
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+    <h1>Chunk</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_chunk.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Chunk</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_chunk</span>(<span class='kw'>self</span>, <span class='kw'>chunks</span>, dim = <span class='fl'>1L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the tensor to split</p></td>
+    </tr>
+    <tr>
+      <th>chunks</th>
+      <td><p>(int) number of chunks to return</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) dimension along which to split the tensor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="chunk-input-chunks-dim-gt-list-of-tensors-"><a class="anchor" href="#chunk-input-chunks-dim-gt-list-of-tensors-"></a>chunk(input, chunks, dim=0) -&gt; List of Tensors </h2>
+
+    
+
+
+<p>Splits a tensor into a specific number of chunks. Each chunk is a view of
+the input tensor.</p>
+<p>Last chunk will be smaller if the tensor size along the given dimension
+<code>dim</code> is not divisible by <code>chunks</code>.</p>
+
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+    <div class="page-header">
+    <h1>Clamp</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_clamp.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Clamp</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_clamp</span>(<span class='kw'>self</span>, min = <span class='kw'>NULL</span>, max = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>min</th>
+      <td><p>(Number) lower-bound of the range to be clamped to</p></td>
+    </tr>
+    <tr>
+      <th>max</th>
+      <td><p>(Number) upper-bound of the range to be clamped to</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="clamp-input-min-max-out-null-gt-tensor-"><a class="anchor" href="#clamp-input-min-max-out-null-gt-tensor-"></a>clamp(input, min, max, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Clamp all elements in <code>input</code> into the range <code><a href='https://rdrr.io/r/base/Extract.html'>[</a></code> <code>min</code>, <code>max</code> <code>]</code> and return
+a resulting tensor:</p>
+<p>$$
+    y_i = \left\{ \begin{array}{ll}
+        \mbox{min} &amp; \mbox{if } x_i &lt; \mbox{min} \\
+        x_i &amp; \mbox{if } \mbox{min} \leq x_i \leq \mbox{max} \\
+        \mbox{max} &amp; \mbox{if } x_i &gt; \mbox{max}
+    \end{array}
+    \right.
+$$
+If <code>input</code> is of type <code>FloatTensor</code> or <code>DoubleTensor</code>, args <code>min</code>
+and <code>max</code> must be real numbers, otherwise they should be integers.</p>
+    <h2 class="hasAnchor" id="clamp-input-min-out-null-gt-tensor-"><a class="anchor" href="#clamp-input-min-out-null-gt-tensor-"></a>clamp(input, *, min, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Clamps all elements in <code>input</code> to be larger or equal <code>min</code>.</p>
+<p>If <code>input</code> is of type <code>FloatTensor</code> or <code>DoubleTensor</code>, <code>value</code>
+should be a real number, otherwise it should be an integer.</p>
+    <h2 class="hasAnchor" id="clamp-input-max-out-null-gt-tensor-"><a class="anchor" href="#clamp-input-max-out-null-gt-tensor-"></a>clamp(input, *, max, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Clamps all elements in <code>input</code> to be smaller or equal <code>max</code>.</p>
+<p>If <code>input</code> is of type <code>FloatTensor</code> or <code>DoubleTensor</code>, <code>value</code>
+should be a real number, otherwise it should be an integer.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_clamp</span>(<span class='kw'>a</span>, min=<span class='op'>-</span><span class='fl'>0.5</span>, max=<span class='fl'>0.5</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_clamp</span>(<span class='kw'>a</span>, min=<span class='fl'>0.5</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_clamp</span>(<span class='kw'>a</span>, max=<span class='fl'>0.5</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.0344
+#&gt; -0.8505
+#&gt;  0.5000
+#&gt;  0.0837
+#&gt; [ CPUFloatType{4} ]</div></pre>
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+    <div class="page-header">
+    <h1>Combinations</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_combinations.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Combinations</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_combinations</span>(<span class='kw'>self</span>, r = <span class='fl'>2L</span>, with_replacement = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) 1D vector.</p></td>
+    </tr>
+    <tr>
+      <th>r</th>
+      <td><p>(int, optional) number of elements to combine</p></td>
+    </tr>
+    <tr>
+      <th>with_replacement</th>
+      <td><p>(boolean, optional) whether to allow duplication in combination</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="combinations-input-r-with-replacement-false-gt-seq-"><a class="anchor" href="#combinations-input-r-with-replacement-false-gt-seq-"></a>combinations(input, r=2, with_replacement=False) -&gt; seq </h2>
+
+    
+
+
+<p>Compute combinations of length \(r\) of the given tensor. The behavior is similar to
+python's <code>itertools.combinations</code> when <code>with_replacement</code> is set to <code>False</code>, and
+<code>itertools.combinations_with_replacement</code> when <code>with_replacement</code> is set to <code>TRUE</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>)
+<span class='kw'>tensor_a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='kw'>a</span>)
+<span class='fu'>torch_combinations</span>(<span class='kw'>tensor_a</span>)
+<span class='fu'>torch_combinations</span>(<span class='kw'>tensor_a</span>, r=<span class='fl'>3</span>)
+<span class='fu'>torch_combinations</span>(<span class='kw'>tensor_a</span>, with_replacement=<span class='fl'>TRUE</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1  1
+#&gt;  1  2
+#&gt;  1  3
+#&gt;  2  2
+#&gt;  2  3
+#&gt;  3  3
+#&gt; [ CPUFloatType{6,2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Conj</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_conj.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Conj</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_conj</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="conj-input-gt-tensor-"><a class="anchor" href="#conj-input-gt-tensor-"></a>conj(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the element-wise conjugate of the given <code>input</code> tensor.</p>
+<p>$$
+    \mbox{out}_{i} = conj(\mbox{input}_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='fu'>torch_conj</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>1</span> <span class='op'>+</span> <span class='fl'>1i</span>, <span class='op'>-</span><span class='fl'>2</span> <span class='op'>+</span> <span class='fl'>2i</span>, <span class='fl'>3</span> <span class='op'>-</span> <span class='fl'>3i</span>)))
+}
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/torch_conv1d.html b/static/docs/dev/reference/torch_conv1d.html
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+      
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Conv1d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_conv1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Conv1d</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_conv1d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>weight</span>,
+  bias = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(),
+  stride = <span class='fl'>1L</span>,
+  padding = <span class='fl'>0L</span>,
+  dilation = <span class='fl'>1L</span>,
+  groups = <span class='fl'>1L</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iW)\)</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>filters of shape \((\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kW)\)</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>optional bias of shape \((\mbox{out\_channels})\). Default: <code>NULL</code></p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the convolving kernel. Can be a single number or      a one-element tuple <code>(sW,)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit paddings on both sides of the input. Can be a      single number or a one-element tuple <code>(padW,)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>the spacing between kernel elements. Can be a single number or      a one-element tuple <code>(dW,)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by      the number of groups. Default: 1</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-"><a class="anchor" href="#conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-"></a>conv1d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -&gt; Tensor </h2>
+
+    
+
+
+<p>Applies a 1D convolution over an input signal composed of several input
+planes.</p>
+<p>See <code><a href='nn_conv1d.html'>nn_conv1d()</a></code> for details and output shape.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>filters</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>33</span>, <span class='fl'>16</span>, <span class='fl'>3</span>))
+<span class='kw'>inputs</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>))
+<span class='fu'><a href='nnf_conv1d.html'>nnf_conv1d</a></span>(<span class='kw'>inputs</span>, <span class='kw'>filters</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;  Columns 1 to 8  -1.9392   5.0621   3.0437  -3.5636  -7.5239   0.8669  -1.8524   5.9130
+#&gt;    2.7449   1.6015  -7.6731 -11.0644   5.8939  -1.2001   3.9313 -10.1132
+#&gt;  -11.4874   2.1056  -1.0310  -0.7005  12.9240  13.1916  -4.5442  -7.9363
+#&gt;    6.0818  -9.9529   0.0051   0.6025   3.2260   4.7597  -4.4539 -10.4886
+#&gt;   -1.4388  -8.6063   1.5505   2.9923   2.8992  14.6708   6.9700  -3.7005
+#&gt;   -9.8520  12.1492   1.1354   4.2429   9.8903  -2.6861  -3.1849 -10.1582
+#&gt;    0.9200  -8.3914   9.7954   0.3859   3.4860   1.9175   5.8735  -2.8894
+#&gt;    4.4610   9.0711  17.1165   6.0199   2.8014  -5.3435   6.4462   4.5218
+#&gt;   11.4783  -7.2977   1.7851  -4.6470  -1.2914  -9.0278  -1.2200  -6.6805
+#&gt;   -3.9618   1.4190  -2.4918 -10.8161  -8.2211  -5.6850   2.8294  13.3770
+#&gt;  -10.4270   0.3910   0.6235  -5.0395  21.1592  -8.8681  17.0434  -5.3047
+#&gt;    1.7043   3.1305   2.8700  -7.3307   9.2678 -18.8262  -3.5185 -12.6776
+#&gt;    1.6884   0.3176  -3.8358  -6.1441  -3.3914   1.6720  -7.0779   3.5488
+#&gt;    3.2735  16.9618  17.5538   0.2881 -10.9703 -12.2581   3.3125   4.4180
+#&gt;    8.6331   3.8098   5.7529  -1.6187  -7.9152  -8.1661   5.5171  -5.6580
+#&gt;    0.6991   1.0305  -2.0063  -0.6770   0.6176   4.7773   0.3323  -3.2240
+#&gt;   -9.2985  -1.7995 -12.3496  -3.1609   5.0106   7.7908  -1.5697   2.7021
+#&gt;    3.3292   5.3498  -8.4542   8.5496   3.7287   8.7445  -0.4681  -2.2675
+#&gt;   -2.9159   3.5349   6.1476   5.6574   4.7744   8.0564  10.0144   1.8210
+#&gt;   -0.5171   2.9428   0.2183  -5.6238  -0.0016   1.3590 -11.7849   5.4779
+#&gt;    1.1912  -4.2315   6.3105  11.3349  -3.7946  -2.0639  -4.9689  -3.7005
+#&gt;   -0.1363   9.0812   4.5614 -11.8651   3.0685  -3.2603   3.8185  13.1912
+#&gt;    3.5125   5.6862  12.7178  -2.9094  -0.3736  -8.8316  -1.4607  14.7057
+#&gt;   -1.3676   8.3705   4.6200  -3.2310  -0.7064  -8.3868   3.6637  -4.2383
+#&gt;    5.9052   0.8962  -5.4688  -3.9047   3.9755  -7.4258  -3.7159  -4.1862
+#&gt;    5.6656  -2.3616   4.2609   4.4746  -5.8625   0.7051 -10.9567  -3.4700
+#&gt;    4.3938  -6.4445   2.0676  -6.3462  -6.4908   4.7513   4.0558   8.4510
+#&gt;    0.3763   5.5158   1.8604  -8.6866   5.9295   4.9654   4.8272   2.5466
+#&gt;   17.9655   8.7774  -1.5719 -14.2141 -20.2145   2.7196   9.0862  15.8173
+#&gt;    5.4497   0.2770   2.3034  -5.7729   6.9052 -10.3341   6.1157  12.3847
+#&gt;    0.3482  -0.2238  -2.3005   2.0549  -6.7614   5.0227   1.1398  -2.9690
+#&gt;    0.6327   2.3686 -12.6709   7.0034  -8.0163  -5.9293 -11.2388 -14.8064
+#&gt;    0.2650  -7.3227   1.5752  -4.9176   3.5857  -0.4669   3.1913   9.6913
+#&gt; 
+#&gt; Columns 9 to 16  -0.6154   1.3591  -1.9612   0.4248  -1.6151   1.2296   4.3672  -4.2460
+#&gt;   -7.1212 -13.5480 -14.6091  -3.4657   5.4540  -4.5291  -9.4951   0.2237
+#&gt;    1.6766  -3.5983  11.4963   6.9899  -8.0171   9.3252  -8.0778   8.0385
+#&gt;   -0.5801   0.8058   0.0117   6.5013 -14.7542  -4.9332  -5.9705  -4.4002
+#&gt;   -1.5819   5.1493  -1.1527  -3.3323  -0.4756   4.6146   7.0533   0.6563
+#&gt;    5.3804   3.8913  -0.5100  -9.8165   0.2300  -3.5009  -6.6559   3.3777
+#&gt;   -0.0898   5.3951  -7.4505  -5.7412  -9.1515  -1.4072   3.7023  -0.0851
+#&gt;   14.1511  -1.9829   3.4283 -18.5947  14.6786  11.9702  -3.5608   6.4718
+#&gt;   -4.5561  -4.5728   3.2706  11.1624  -8.9752  11.4919   2.2541   1.3316
+#&gt;    4.0303  -4.7324 -12.9543   6.6408  -5.0110   4.1167   7.7090   2.6313
+#&gt;    4.4952  -5.2520   1.7760  -6.0764  -6.2064  -1.1744   8.9036  -2.6773
+#&gt;   -2.8759  -5.6710   2.2459  15.7782  -6.0662   7.1497  -6.0907  -2.9721
+#&gt;   -9.1744   2.8917   1.9773   0.9547  -0.6993  -3.8925  -0.6842   2.3955
+#&gt;    3.8871  -0.8197 -13.3794 -14.7574   0.7448  15.9117  -9.6009  -7.5170
+#&gt;    2.6610   6.0994  -0.0780  -4.1576  -5.7099  -6.5641   2.3007  -2.9237
+#&gt;    7.6397   6.8257  12.3299   1.2675   6.8290  -5.7057  -2.1383  10.3878
+#&gt;   -2.1706  -1.7073   5.0162  -6.2541  13.7454  12.6861   1.8961   0.4629
+#&gt;   -1.2440  -7.6432  -0.4992  -1.2575  10.5352   2.9736 -10.3638   5.8272
+#&gt;   14.9095   4.4100  -2.4995   3.1485   3.0959  -0.2141  -9.3531  -1.4908
+#&gt;   -6.5168  -7.7695  -1.8647  -1.1292  -0.2509  -5.0355   1.0347   4.6722
+#&gt;   11.7160   0.7139   9.5218   6.5213   0.6176   2.5592  -8.4594  11.1248
+#&gt;    5.5684   3.4908  -1.9734  -7.9368  -3.8589  -3.0954   1.1574   2.2952
+#&gt;    2.7959   3.8238   1.0927   3.6093  -8.5999  11.6195 -13.2602 -10.0014
+#&gt;  -14.1328   1.1295   2.3065  -4.9148  -2.6217  -4.5627   3.0950  -8.5041
+#&gt;  -10.2239   0.4371   4.8232  -8.7538  11.7149  10.3083  -8.6122   3.4551
+#&gt;   -5.8450  -2.0972   8.2066   9.2659  -6.3882   7.8466   5.1257 -12.5129
+#&gt;    7.3825   7.2185  13.2157  14.9490   1.9005  -4.8808   6.6319 -18.2276
+#&gt;   -3.9029   1.7394  -3.1885   0.6199   0.5334   2.0353  -3.9030  -3.5253
+#&gt;   -8.5593  14.4150   7.3401  11.3958  11.8662   5.8342   6.8932  -3.1852
+#&gt;   -2.5997  -0.6474  -2.3001 -10.4394  -3.2690  -3.9822  -1.4075  -1.2428
+#&gt;    8.3899   6.7432  -2.6195  -1.0633   5.4585  -1.1791  -8.8594   8.3103
+#&gt;   -4.9787  -5.6692   0.3629  -9.5947   0.4275  -1.2912   6.3715  11.1544
+#&gt;   -7.0017   3.6194   4.6785   0.8878 -13.7756 -18.8658  -3.2674  -0.3311
+#&gt; 
+#&gt; Columns 17 to 24  -6.0395  -4.7292  -1.4530  -4.8371  -6.1932  -9.3966   0.8125   2.0888
+#&gt;   -0.5817  10.3544  -4.8773  -3.6496  -6.6131   6.7948   4.6345  -2.0666
+#&gt;   14.9900   9.2408   5.8285  -1.6623   2.7469   2.4549  -1.3833   1.8332
+#&gt;   14.1976  10.3182  -5.4717   1.2565  -0.1235  12.0829   1.2812  -7.6003
+#&gt;    6.9293 -16.4129   2.7948   2.7357   8.2863  -4.6122   3.1267   6.8616
+#&gt;   -5.1138   1.7644  -6.3663   3.8744  -5.9589  -7.8464  -0.0432   0.4551
+#&gt;    5.1275  -7.7724  -4.9620  -2.2237   1.0306  -7.3156 -10.0563   9.8865
+#&gt;  -14.1796  -9.8568   6.6058   9.1759 -11.6857 -16.6484   2.2584   1.1150
+#&gt;   11.9193  -0.6228   3.4260   7.1265  -0.4470   3.1540  -2.8272  -7.4899
+#&gt;    2.8415 -12.4887  -3.0011   0.7632   3.5436  -9.2418  -1.0072   1.4292
+#&gt;   -9.4365   5.8049  -1.2157  -3.8951  -5.8822   5.8152   4.6455  -0.5333
+#&gt;   18.2522  -1.5752  -4.8467   1.0388   2.2340   5.2267  -1.0198  11.7728
+#&gt;   17.5104   2.4909  -3.4580   0.9312   5.0226  -2.0512  -2.9678  -5.2989
+#&gt;  -13.4170 -21.2946  13.3405 -11.3831  -8.1909 -15.7700  -4.5563   8.0102
+#&gt;   -6.4040  16.9159   7.6974 -13.6738   0.2983   2.0692   4.1760  -3.5950
+#&gt;    0.1877   3.2537   6.0387  -3.7541  -1.9845   6.3741   3.9154   0.3985
+#&gt;   11.2750  13.8730   2.6284  -8.0898   4.9685   3.8585  -1.6134   0.7810
+#&gt;   -1.6800  -0.2496   2.2689   9.4880   2.9581  12.4249  14.5996  -4.2152
+#&gt;  -15.7468   4.1156  -3.2533   4.7198  -4.8878  -1.3850   3.2005   1.4606
+#&gt;    7.0839   2.5130  -8.0207  -3.6627  -1.8095  -0.1537   4.1842   1.8173
+#&gt;    2.4382   0.9138   8.8720   4.3209  10.5049  -9.9046  10.8212  -7.2577
+#&gt;   -0.4045   2.7304  -1.1687  -2.7728  -3.1842   1.7765  -0.3995  -4.3213
+#&gt;   -6.0051 -18.5712   9.9075  -0.0024  -6.7645  -8.8143  -4.6291   6.9310
+#&gt;   -9.9842   5.2304   2.5916  -4.3784   6.4098  -0.5394 -11.6019  -8.8339
+#&gt;   11.7127  -5.1780  -0.0861  -4.5638   3.1813  -0.6096   0.8383  13.1129
+#&gt;    4.9336  -8.6516  -0.7450  -5.7607  -1.4587  -4.2274  -1.1462  -9.8302
+#&gt;   -2.3611  14.8394   5.1314  -8.4575   0.8626   4.2962   1.5338  -4.2622
+#&gt;    2.2767  -2.0903  -2.6380  -5.4283  -2.6349  -1.3169   2.9036  -0.2084
+#&gt;  -10.6204   3.2733   7.2570  -4.0742 -12.2701  -6.4754   7.6803 -14.7380
+#&gt;   -5.0862  -8.7578  -2.1929   3.4163  -4.6247   7.9152   3.4955  -1.4093
+#&gt;   -6.3402   6.7090   4.4255  -3.5398  -6.3926  -3.1649  -1.6904  -1.0705
+#&gt;    0.6983   3.3284  -1.1082   5.6871   4.6380   9.5359   0.1505  -9.2344
+#&gt;   15.3411   1.7856  -0.4800   2.8846   0.5410  11.9808   8.6936  -2.9945
+#&gt; 
+#&gt; Columns 25 to 32   8.4758   3.2485   1.3263  -2.1658   7.2784 -10.1472   1.9357  -0.4875
+#&gt;    4.0942  -6.9749 -13.5927   9.9495 -10.8870  -1.3890   3.6737  -3.3366
+#&gt;   -2.4957 -11.0305  -6.0567  -0.1481   9.4782  -5.2385  -5.3943   4.6520
+#&gt;    2.2831  -3.7755   5.0576   1.4039  -6.0726   6.9990   7.9663 -10.6788
+#&gt;    5.5881 -10.9483   8.5682 -11.4979  21.9150  -9.3776 -12.8329  -1.7029
+#&gt;  -12.9199   2.2646   1.3169   4.1906   6.0932   5.2066  -2.3822  -7.1427
+#&gt;    0.8463   2.1142   3.4666  -6.1619  -7.3718   9.1168  -3.5694   1.0355
+#&gt;   10.7768   2.4936  11.9295   1.5696 -10.3159  -2.7693   2.2250  -8.7409
+#&gt;   -1.3807  -5.8368 -15.0976   3.2400 -10.8282   3.8730   3.9030  -0.4394
+#&gt;   -9.3837   8.0380  10.6763  -8.6889   7.4962  -2.3875  -0.1039   3.1041
+#&gt;   15.0613  -7.2106  -5.8828   4.8567  -0.0310   1.0219  -7.0270  -0.9296
+#&gt;   -1.4862 -14.9527  -4.5142   3.0598  -6.2907  -4.4331  14.7986  -8.1580
+#&gt;    2.6705   6.5091  -1.2496  -6.2086  -2.7199  -2.8468   7.2839   3.7043
+#&gt;    8.7040  -9.3363  11.9846  -8.2819  -6.8337   6.1654 -11.6724  -3.5310
+#&gt;   14.9324  13.4981  -8.9747  -4.8106 -10.7791  12.1841   2.5933   0.9230
+#&gt;   13.6497  -5.9087   7.7415  -0.5427   2.5481   4.3269   5.2614  -0.4958
+#&gt;    5.9884  -9.1013  13.2570  -7.2317  -8.4937  -5.0591  -2.1794   2.3220
+#&gt;    5.3694 -14.7493  -2.2215   3.0352   3.3232 -11.6518   3.3380  -2.8690
+#&gt;    3.3107  -2.0289  -4.8124  -1.7406  -6.7335   1.2736  10.8123  -5.5763
+#&gt;   -8.7857   7.7820   1.7681  -5.7144   2.1087  -1.2935   6.2793  -2.0212
+#&gt;  -15.4372  -3.5755   0.4401   4.6833   2.8549  -6.9048   0.5300   6.9146
+#&gt;   -5.8586  17.0472   3.2858   1.9170   3.8974   0.8677 -10.0318  16.0038
+#&gt;   -6.5340   1.3313  -3.3355   2.3734  -4.4620   1.1076   5.0707  -4.9228
+#&gt;   -1.1953   3.2453   6.1584  -9.9091  -8.1963   8.7176  -5.4719  -0.6555
+#&gt;    9.7245  -5.9719  -5.5202  -8.8272  -2.5195   0.6149   3.3568   5.8306
+#&gt;   10.0505 -13.1304   5.4540 -11.7057  -3.8276  -6.2989   5.6240 -16.6291
+#&gt;    3.9620 -14.6415  -4.4895   5.0164   3.0707  -6.9295   6.5739   3.2594
+#&gt;    6.4580  -9.0752  -8.0518  -3.2322   4.8367  -0.1585   1.1076   2.3886
+#&gt;   16.6152   8.2122 -15.0584   1.5698   2.2651  -2.3922  10.5806  -2.1872
+#&gt;    3.6539   6.7948  -3.7955   1.5235  -0.1542   2.7298   7.3141   0.8973
+#&gt;   -5.4835   3.2712   1.4302  -7.2277  -3.1467   7.3340  -1.2581   6.5278
+#&gt;    8.8211   6.4511   0.4508  -2.5657   0.8169  -5.3580   0.0615  -0.6817
+#&gt;   -3.7658  -6.2467  -0.7776  11.3720   5.1722   7.1388  -7.9943   4.8153
+#&gt; 
+#&gt; Columns 33 to 40   8.6171   7.4242   1.8350   2.3095   1.4498  -8.4101   1.4130   0.0554
+#&gt;   -1.6881  -3.1905 -10.1875 -14.0532   2.1352  -0.6725  -7.3952  -1.2065
+#&gt;  -11.9393 -10.0697  -5.0396   5.4406   7.1564   5.4031   6.5729   7.1854
+#&gt;   -1.1113  -3.1415  -6.1276   0.8331  -2.2253   0.4918  -2.5745  -8.2295
+#&gt;   -1.8134 -13.2332 -12.2761  -7.6760  -8.7211  -0.2142   1.8931  10.3959
+#&gt;   -6.3212   9.9672   6.5620   1.7724   5.8659  -2.9735  -6.2767 -10.8691
+#&gt;    6.6070 -14.5145   4.7604   2.6366  -1.2538  -2.4690  -2.2550 -11.2636
+#&gt;    5.7745   1.3711  -1.4180 -12.2406   5.5435  -2.6493   3.3535  10.0547
+#&gt;   -2.2914  -4.7679  -2.6354  -5.2423   0.4516   2.4769   0.4755   5.5777
+#&gt;   13.9415   0.2480   0.8020  13.4989  -7.3414   0.6097  -3.6648  -4.7836
+#&gt;   -1.9710   2.4796 -14.5737   3.7869  -4.3465   1.6049   8.0136   5.6078
+#&gt;    8.9937 -10.2986   0.4741   0.9593  -0.0722   6.5673 -15.7288  -0.0970
+#&gt;   -3.0580  -4.8259   2.6330   4.5046   1.2412   1.2833  -6.8520   6.0733
+#&gt;   19.7095  10.2863  -1.2508   7.7044   3.7236  -6.2476  -5.4432   6.9685
+#&gt;   -6.2856   5.0749  -4.6435  -5.6642  -6.4963   1.3583   5.8303  -5.2723
+#&gt;   -6.8327   7.1009   1.0545  13.0128   0.6232  -4.4436   5.3381   2.9114
+#&gt;    4.9522  -7.9362 -16.4873   2.2234   1.4765   2.3503   9.0933   8.3663
+#&gt;    6.3653  -3.8334  -7.6281 -11.2745   4.7442  -1.6257   7.3943  15.8016
+#&gt;   -7.4445  -1.4786  -2.2314 -12.0941  -0.6881  10.1857   2.8515  10.0926
+#&gt;   -5.7967  -6.4754   4.8557   3.4277   3.8330  -1.9905 -18.7623 -12.2126
+#&gt;  -12.7489  -8.2549   4.3713  -7.0047  -0.9216   1.1699   2.6289   3.5111
+#&gt;   -6.1219   4.8058  -3.4355   7.8423  -1.7072  -9.7325  -6.4437   4.2030
+#&gt;    7.7782   4.0045  19.9890   3.0283   4.5056  16.5469  -5.7926   3.4264
+#&gt;   13.4204  10.1940  -1.3420   7.6205   1.3880  -2.0436  10.2775   6.1513
+#&gt;   -5.7765  -6.8705   8.4631  -1.6095   8.7580  -0.0233 -11.4192  -4.0085
+#&gt;   10.2234  -5.4722  -5.4740   7.0522  12.6428  -0.8548   8.3546  14.4354
+#&gt;    5.5446   9.5760  -7.5310   7.8422  -5.6884   8.1071  10.2373   9.7893
+#&gt;    2.6095   1.1579  -0.6507   4.8192   3.0473  -5.1181  -4.4558   6.0310
+#&gt;    9.2626   6.7385  14.8097  -3.9879   4.5355 -14.5104   0.1930   2.5541
+#&gt;    4.9256   4.8284  10.0454  -0.5807  -4.0650  -3.6238 -15.5092  -1.8986
+#&gt;   -0.5726   0.3441   5.7173   8.2974   3.6005  -8.6032  -1.3940 -12.4486
+#&gt;    4.1821   9.0040  -3.3337   1.6628   1.3810  -5.4552   0.3616  -4.8888
+#&gt;   -0.4909   6.2546  -0.9669   6.9366  -0.3558  -6.7471  -3.0725 -12.0050
+#&gt; 
+#&gt; Columns 41 to 48  -8.2843   4.1627  -9.2066   1.1866  -8.6254  -2.0750   1.5929  -0.6789
+#&gt;    0.9104 -15.8556  -8.8004  -2.5957   1.4070 -12.5140  -3.4297  -1.8037
+#&gt;    1.6306  -2.2232  -1.4897  24.0795  13.0168   7.4404  -5.5602  -1.6311
+#&gt;   -5.1574  17.7188   6.3567  -8.4696  -0.9507   6.3167 -26.5885  14.2686
+#&gt;    7.1526   6.4220  -7.5260   8.3354 -17.7470   6.9888   5.8661   5.3227
+#&gt;    7.3520   9.0862   5.2303   4.3617   2.3903 -13.2851  -8.7022   9.4803
+#&gt;   -1.0648   7.2604  -9.7395  -8.9616   2.4903  -9.9758   4.4388   5.3299
+#&gt;    6.3518  12.8072  -9.5355  -0.6330   8.0765  -3.3519  25.0816  11.4433
+#&gt;  -11.2174  -5.8939   5.3469   3.2387   8.7287  15.2322  -0.9303  10.4744
+#&gt;   14.3205  -6.1806  -4.3585  12.0610 -18.1874   0.7745  -2.0500  -6.2361
+#&gt;   -1.9409   6.0303 -14.0147   3.1559   5.8128  -9.2374   2.7300  -8.4599
+#&gt;    3.3133  -0.6815 -20.0191   3.4106   1.8044  -1.5587  -4.6563   1.2868
+#&gt;    3.8531  -4.9154   8.3035 -13.7146   3.5576   5.2042 -20.7035  11.6480
+#&gt;  -17.5621   6.9807   8.8214  -6.4896  -8.2771  11.1657   1.2894   7.0215
+#&gt;    0.3366   7.2566  -1.7966 -23.2965   6.2045   6.7148 -10.0865  12.0572
+#&gt;   -1.8600  10.6898  -0.7430   7.6799  11.3518   2.5778  -0.5178  -5.7240
+#&gt;   -2.2901   5.0679  12.7800  -1.3705   3.6508  -0.8481  -4.6365  -6.8374
+#&gt;    7.2507   1.3439  -8.4469  13.5135   0.2989   0.3833  -2.4522  -4.9250
+#&gt;    9.3477   3.1048  -4.4668  -5.2266   0.8285  19.2942  11.2295   5.0283
+#&gt;    4.9897  -4.2612   4.8910 -11.7271   6.3791   2.6028  -3.6656   2.0408
+#&gt;    7.5806   5.3110   2.1232  11.5513  -4.5345  -0.3127   9.7307  -4.6856
+#&gt;   -1.4535 -14.6608  17.5748   3.4663  -4.9230   4.4870  12.7810  -8.4799
+#&gt;   11.3412 -15.1403  -0.3003   6.3041 -12.7827  11.2627  -4.1196   1.3282
+#&gt;   -6.0969  -2.7584  -1.1272  -6.0139  -9.9496   6.3426   2.9772  -5.2248
+#&gt;   -6.6534   1.2610   5.2604  -0.9078   2.6687   5.5641  11.2080   4.3217
+#&gt;  -16.8381  -0.8241  -3.9536  -7.8504  -3.7651  18.2988  -3.7778  -3.2625
+#&gt;    1.4984  -2.0963   4.1146  10.2833   4.0339  19.6106  -3.8761 -12.6146
+#&gt;   -5.9259  -3.5328   2.1454   2.7817   4.6381   6.2012   3.8716  -3.4074
+#&gt;   -0.3086  14.8235   1.3368 -13.3648   9.4660  11.4596   2.5582   8.5313
+#&gt;    0.9569  -2.1296  -2.7922   2.1363  -6.2216  -0.3625  -2.1549  -1.6005
+#&gt;   -5.1335   7.9228   7.3517  -8.3503   3.3215   5.6427   7.6625  -5.2101
+#&gt;    3.8252  -4.2529  -8.9876  -4.0487   2.7030  -1.8167  -2.8292   9.4868
+#&gt;   -1.2154   0.4104  -2.6581  16.1400   8.5149 -17.6945   3.7941 -17.0860
+#&gt; 
+#&gt; (2,.,.) = 
+#&gt;  Columns 1 to 8  10.0387   5.4708  -6.3734   2.1558  -4.8035  -0.9792   0.3938  -2.9347
+#&gt;    4.3439   0.1890   9.6327   9.5763  13.4223  -1.5037   5.3226   0.6079
+#&gt;   -3.6064   4.4535   3.5067  -6.0042  15.1842  -4.4269  -3.6917   3.6661
+#&gt;   -2.6294  -1.3005  12.9892   6.3745   6.4265   0.1348  -7.1079   3.3662
+#&gt;  -15.6274   6.3366  -2.9989  -1.6680  -0.9202  -6.2965  -9.3487   6.1968
+#&gt;   12.9490   7.2405  -0.9496  19.4406  19.6362  -4.4806  -0.2145  -7.6033
+#&gt;   10.5872   7.2060  -6.0659   2.0209  -4.7614   0.5210  -3.7657   6.9106
+#&gt;    1.5741  -6.6284  -5.2211  -2.1328  -1.6123  -9.4595   1.1185  -2.6316
+#&gt;   -6.7133  -3.4881  11.8812 -14.7721  -6.0150   6.5723  -0.5993   4.4268
+#&gt;    2.1109  -0.8600  -4.3527   5.8283 -19.6201   2.2504   4.4910  -2.6555
+#&gt;    2.2285   0.6531  -2.5456   3.2600  -0.1149  -6.1569  -0.8513 -10.9193
+#&gt;    0.6550   7.5810 -10.6247  14.7980  12.3875   1.7739  -1.4805   9.6636
+#&gt;    3.5144   5.8005  -7.9258  -7.9768   2.0918  -2.3824   1.1336   4.4373
+#&gt;    3.9089  16.6491   6.3135   1.1714  -0.1699  -5.7123   0.3445  -6.9033
+#&gt;    2.1465  -4.6260  -4.2637  -6.6357   0.4658 -14.7272  -2.2871  -1.3554
+#&gt;   -6.6704  -5.3987  -7.2616  -6.2063  16.4898  -9.9293  -3.7227   0.4519
+#&gt;   -3.7417  -1.3909  -8.7551  -3.2635  -0.4101  -4.8708   1.0206   3.4510
+#&gt;   -8.0268 -13.9665  -2.0084  -0.5276   6.5104   0.2762   4.9685   0.6996
+#&gt;   -5.2428 -11.7157   3.5161  -7.2069  -9.3662 -11.2496   0.7623   3.3160
+#&gt;   11.4105   5.4018   2.9417   6.3190  -0.5390   8.0009  -0.7611  -0.0764
+#&gt;   -3.5791  -7.1524  11.4355   2.6900  -2.2364  -8.0876  -0.1940   1.3643
+#&gt;    6.2868  -8.3695   3.4881  -8.1108 -13.3025  -6.5792   9.2919  -8.9605
+#&gt;    3.0644   2.1191   4.5545  -3.8662   0.2318   2.0430   5.2194  -1.3587
+#&gt;    2.8715   8.0543  -0.4770   3.3060   3.6380   3.8188   1.4443  -7.1562
+#&gt;    5.6883  -1.8343  -4.5909 -11.2525   9.4542   4.8489   0.6133  -4.9701
+#&gt;   -5.7903   9.9498   8.5145   3.1517  -2.9569   4.8534  -6.0148   9.1335
+#&gt;  -26.7042  -1.0212  -1.9860  -8.6255 -13.8608   6.9840  -6.9400  -5.2177
+#&gt;   -5.3356  -0.9771  -1.8447  -4.2235   7.4861   1.6444  -3.4178  -1.8249
+#&gt;   -2.7627  -9.5803 -14.8031 -18.4314  -6.0312   8.8467  -6.3099  -5.9350
+#&gt;   12.5973 -14.7640  -3.8700  -2.5082  -1.0931   0.9760   8.9502  -7.6603
+#&gt;    5.4796 -10.3674  -0.6473  -6.9611  -0.4527   1.9616  -0.9417   1.1845
+#&gt;   15.3209   1.8884  -8.4978  10.5386  11.8920  -0.6137   5.7161  -3.5002
+#&gt;    4.3078   8.2875   4.1719  -2.0938   6.3321  10.0098  -5.0424 -10.5911
+#&gt; 
+#&gt; Columns 9 to 16   4.1597   2.1692   2.2054   4.1077   3.4318   0.1999   5.8228  11.8961
+#&gt;    4.9303   1.2193   7.3910   0.9713  -4.2890  10.2032   3.0738   0.6304
+#&gt;  -12.8485  -2.4395   2.8374  -1.3661  -5.2101   4.7085  -7.1550   0.3047
+#&gt;    2.3786   1.9684  -0.5510   2.3542  12.7663   7.7894  -2.7148  -7.6698
+#&gt;   -1.2276  -4.7408   4.8722  -1.0672  -5.0058   6.7234  11.2136   5.9072
+#&gt;   -6.3154  -4.3397  -3.6689  -7.0067  -4.6603   3.8145  -4.4339  -2.6450
+#&gt;    8.4286   1.4060  -0.0919   3.8065   7.8747   2.1162  10.6630  -8.4432
+#&gt;  -13.4430  -5.6906  -0.6097  -1.6807  -8.9876   1.3663  -4.5168   1.5895
+#&gt;   -4.9139  -2.6789   3.2314  -1.8767  -3.4357   3.1801  -1.3308 -11.0145
+#&gt;    0.4473   9.2804   9.8407  -0.9183  -7.9442   3.8241   2.9361   0.6527
+#&gt;   13.1485  -8.5842  -1.0406  -0.7228  13.1383   8.0534  -9.9279   3.9771
+#&gt;    1.5954  -3.8329   1.7396   9.4148 -17.0650  -2.6608  -1.2154   0.5246
+#&gt;    2.3919  11.6748   2.4551   5.2270  -4.3684   2.5156  -1.6903  -1.5021
+#&gt;  -12.5887   6.1945   6.9322 -11.3265   5.0488   4.8533   5.1383  12.7364
+#&gt;    9.3312  -4.7861  -2.6901   3.4044  11.0314  -0.8612 -10.4005  -5.7105
+#&gt;   -1.3443   4.0103   3.4640  -5.2949 -10.3093   4.1236  -5.3233   0.1790
+#&gt;   -1.3425   6.7567 -10.8357   3.3593  10.2135   0.9623 -15.2821  11.1393
+#&gt;   -0.9694   3.0059   4.2774  -0.5631  -6.2557   4.7646  -1.6835  -0.3867
+#&gt;   -4.9846 -18.5112   6.2997  -0.1264  -0.4779  10.0308   0.4328  -0.9063
+#&gt;    0.4264   5.3685  -3.0136   3.1583  -5.4775   0.2059   1.8156  -4.0099
+#&gt;   -4.9440  -5.6953   3.6705  -1.9392  -3.9051  -4.7664  -1.5824   2.0965
+#&gt;   -3.1212   1.3969   5.6954  -3.2546  -0.9011  -0.0827  -6.7062  -0.3926
+#&gt;   -7.4393   3.3147   9.8627  -4.7677  -2.2820   0.1915   4.7477  -4.1498
+#&gt;   -0.0417  -1.9506 -13.5259  -4.2987  -2.8120  -2.9729  -0.7631  -0.9877
+#&gt;   -6.0731  -7.7160  -0.7670  -3.9131  -4.4119 -13.2602  -0.7533   1.8420
+#&gt;   -8.2742   2.9680   0.1263   2.1729   7.1899  10.2709  -8.6530   4.4435
+#&gt;    3.8645   3.1229   2.9930   2.0002  14.6195  -1.7418  -1.6529   4.5627
+#&gt;   -3.6491   3.1947   2.4108   2.2028  -8.9881   4.2088   2.1915   0.4089
+#&gt;   10.8190  11.3701  -0.3371  -1.5075  -8.2668  -5.0072   9.9827  -5.3444
+#&gt;    8.5812 -12.1142   8.8235  -8.1469   0.6937  -3.8408   3.2769  -1.0715
+#&gt;    1.4975   6.2713   4.2284   1.1946   3.6433   3.4696   0.2590   2.0327
+#&gt;   -0.5495  -5.7626 -14.2627 -10.9216 -13.5098  -0.0631 -17.7195  -4.3136
+#&gt;    5.6840   5.3397   0.6496   4.0252  -1.9396  -6.4027  17.9018   4.6041
+#&gt; 
+#&gt; Columns 17 to 24   1.0671  -5.6735  -1.7356   0.2826  -1.7342   2.7489   0.3404  -8.4617
+#&gt;   -2.8290  -2.3481  -3.2571  -6.7371  -8.7289   3.6104  -8.7822   0.8914
+#&gt;   -8.2273  -3.3133   7.0569   3.7742  -0.0975   2.9954   1.5037  -6.7629
+#&gt;   -1.6117   2.9180  -5.7385  -1.8675  -0.2565   2.1521   8.4325   9.3827
+#&gt;   -3.8608   3.2983 -10.8959  15.2713   8.4308   1.3514  -4.7878   3.2051
+#&gt;    5.3297  -7.1427  -1.5392  -9.7090 -14.8433  -3.4915  -5.8743  -5.7523
+#&gt;    0.5973   0.3342  -5.6272   1.9873  -6.8259  -5.4839  -3.9521  -2.4994
+#&gt;   -2.7891  -2.7704  -1.3523  10.0187   5.2736  -7.4167   1.3432  -8.6362
+#&gt;   -4.7752   2.2692   4.1968   4.5852   3.0411  -3.7009  11.2278   8.1053
+#&gt;    0.2368  19.0208   6.9000  -6.1644  -5.8023   1.9438 -18.6880   6.7844
+#&gt;    5.5147   1.0650   5.9031   5.7364  -5.3463   9.3030  -0.9224 -13.0262
+#&gt;  -17.2922  -2.0803  -6.3797  -1.3416   0.6258  -9.0174 -13.1975  -0.2615
+#&gt;    0.7377   4.0114 -12.3645  -3.9963  -6.7659  -3.4750  -1.0186   6.0733
+#&gt;    2.8350  14.5161   0.5353   3.6602  -6.8281   3.6620  -0.2507  -5.5430
+#&gt;   -2.5208 -11.8489 -12.3997   3.7713 -12.2817   0.5379  12.8663  -8.6197
+#&gt;   -8.6146  -6.3982  -0.1499   7.1789   4.8402   2.8423   5.8598  -3.7231
+#&gt;    4.8239   6.2888   9.8213   0.6283   2.7577   6.7405  -6.1247  -6.6673
+#&gt;  -17.6537  -0.8464   2.1170   6.4050  15.8401   8.8400  -2.0488  -1.3690
+#&gt;    4.5079  -8.5434  -1.2726   3.7948  -7.1957   2.6481   6.4863   0.5812
+#&gt;    7.6805  10.0123   2.5964  -1.2343   0.0422  -7.6710  -8.5354   7.6572
+#&gt;   -1.4183  -3.0296  -8.3676  -0.9816   2.2705  -8.2214   4.9600   0.5649
+#&gt;    6.0839  -8.0292   7.8519   1.4806 -11.9069  -3.2227  -0.5125   4.3146
+#&gt;   -2.2727  18.6645  -4.2197  -3.6080  -0.2691  -5.0248  -0.0947   2.2153
+#&gt;   -0.0806  -8.0914  -7.5449  -2.3888 -13.5686  -1.0365   4.5406   0.6311
+#&gt;    7.5030   6.6010  -2.6141   2.4009   0.8367  -9.9619  -5.4632  -4.2336
+#&gt;  -14.9680   2.9012   9.4707   0.2266  -1.4075   0.9262  10.3921   5.3077
+#&gt;   -6.0033   5.8817   6.0979  -3.2846  -6.3061  15.2949   4.8469   3.9848
+#&gt;   -2.4820  -1.5915  -1.1837  -2.3656  -5.6047   3.5448  -3.5234   1.9242
+#&gt;    0.6128   3.7932  -9.4951  -4.1153  -3.4303  -4.9710   3.5952  -6.4762
+#&gt;    0.0694   2.2889  -3.6859   5.7302   1.9990  -8.6007   3.5309   0.0725
+#&gt;    7.6183 -10.2740  -3.2182  -6.3023  -9.8338   1.2554   4.5090   4.7307
+#&gt;  -10.2709  -5.4335   6.6342   7.5687   1.4987  -0.7195  -3.6944  -2.4728
+#&gt;    3.5139  -2.6378  -1.1257  -1.1352  14.2617   4.2697  -0.8229  -5.1419
+#&gt; 
+#&gt; Columns 25 to 32  -2.8605  -7.6106  -7.7580   0.0828   0.7966  -3.0951   7.2010  -1.2648
+#&gt;    2.0580 -15.4701  -5.6807   0.7402 -10.2822  -6.9050   1.6919   5.5862
+#&gt;    4.2127   5.8471   5.8205   2.6206  -9.8988  -0.7593  -3.4190  -4.3496
+#&gt;   -5.3343  -1.6717  11.1063   4.4985   4.5081  -4.4372  -8.2722   9.1665
+#&gt;   -7.1482  -1.3140  12.1533   3.3619  -4.2655  -2.3480   3.9023  -4.4744
+#&gt;    9.3125  -1.2498  -5.1631   5.4271  13.0314  -7.1259  -8.3223   0.6921
+#&gt;   -2.9091   5.0933   4.8840   2.0200  -1.0488   6.1469   4.2876 -10.2045
+#&gt;   -0.3533  -3.6134  -6.0261  -7.1966   5.2609   3.5250  -1.5032 -16.6616
+#&gt;    1.2913   3.2078  -1.6241  -3.3776 -11.8939   5.4887   4.0506  -0.1919
+#&gt;   -8.2025   0.1463  -1.5267  -1.6365   2.9047   7.5340  -0.1444  -3.6001
+#&gt;   -3.0462 -11.6913   3.6328   5.5470   0.8737  -5.2903  -2.0251 -11.3414
+#&gt;   -6.2072 -17.4478  -1.3415 -10.6948  -9.0720  -8.5059  13.6543   8.4352
+#&gt;   -1.0737   2.0677   0.7558  -6.9782  -1.3208  -0.6046  -1.0724   5.6944
+#&gt;   -3.2665  -9.1576  -2.8227  -2.6193   0.7109   6.3560  -2.6093  -8.2808
+#&gt;   -5.2311 -14.1676   2.0376   5.2761  -5.3529  -4.6507  -7.6715   9.9249
+#&gt;   -0.3210  -2.3175   3.3561   2.0451   1.7077   2.1029  -1.6021  -3.2309
+#&gt;   -6.9755   0.3410   4.5739  -7.0452  -3.8745  -1.8202  -8.3847   0.8312
+#&gt;    8.3349  -2.4430   2.4211  -5.4273  -3.1578  -8.6491  -2.8242  -2.8450
+#&gt;   -1.3986  -6.5020   1.0437  -3.8064   1.6528   7.6997   9.7848 -15.6016
+#&gt;   -4.6690   8.1407  -3.5937  -1.1580   5.3055  -1.5233   0.8949   1.6268
+#&gt;   -0.2378   3.9313   0.3446  -3.1099  -9.2425   7.5811   3.0254   5.1405
+#&gt;    5.6826  19.7336   6.1109   7.8935  -3.8027   2.6965   9.0919   0.8480
+#&gt;   -3.9442  -0.9370  -3.3676  -3.1504   8.1480   4.8276  -6.7637  -0.8035
+#&gt;   10.4251  -7.3698   0.1856   2.1287   0.8263  -9.4881  -1.4680   7.9508
+#&gt;    2.9594  -1.7114 -10.8204 -12.7198  -6.2216   0.6603   7.5504  -8.6227
+#&gt;  -10.6754  -9.4401   5.8064  -2.4149  -8.7198  -0.0471  -0.9241  -0.9043
+#&gt;   -6.0706  -2.4452  -0.1827   1.3683   5.4613   5.0386 -19.4988   6.0188
+#&gt;    5.6529  -4.6562  -2.3702   5.7741  -4.5701  -4.9322   4.5069   2.3271
+#&gt;    2.7945 -11.9716  -5.3644  10.6809   3.2669  -0.1990   3.8550   1.4302
+#&gt;    2.9058   6.3966   1.0677  -0.8874   7.7929   4.9075  16.4945 -15.9696
+#&gt;    5.8018   8.0413  -0.4099   3.6087  -2.0954   7.9919   4.6651  -4.0117
+#&gt;    3.3499  -1.8451  -2.4768 -12.3844  -9.8948  -9.7921   3.4200   2.0898
+#&gt;    2.5268   8.7827  -1.3587   8.8208   4.3436  -7.2032   0.6781  20.7918
+#&gt; 
+#&gt; Columns 33 to 40  -0.2995   1.1191   6.7945   0.7649  -7.2045   4.2842   1.4715   4.9328
+#&gt;    6.7228  -2.6851  -1.3990   2.2491  -2.1846   3.4793  -9.1367   8.4017
+#&gt;    0.8091  -5.9336  -2.8827  -4.2341  -7.1728  -4.6606   2.7360   0.1913
+#&gt;    4.4369  -2.8495   0.7949  -5.1449   2.9258  -4.1219   4.4489 -10.4940
+#&gt;    7.8574  -5.7745   5.0699  -2.4012  -4.0257  -8.5597   6.4500 -10.9222
+#&gt;    9.8428  -6.9184  -0.2836  -2.8993  -1.3804  10.3463 -10.2509   5.5472
+#&gt;  -10.6990   3.1359 -12.1451 -14.6491  -2.3592   8.7361  -0.8053   3.4501
+#&gt;   -1.3851   1.3700   7.2683  -8.2200 -15.7723  14.6232   2.2407   9.4232
+#&gt;   -5.4510   1.5006  -1.0942   0.4086   1.5158  -3.9327   1.4447   2.6731
+#&gt;   -5.7838   4.5294  -3.6341  -9.8300   0.0597   8.1621  -8.7432  18.0181
+#&gt;   12.2544  -6.2685   1.2426  -9.7115  -4.1616   0.8954  -2.7068  13.7022
+#&gt;   -4.0988  -1.2120   5.0910   6.4699  -0.2618   5.1909 -10.1513  -1.7987
+#&gt;   -6.0932  -3.5896  -3.2720  -0.4075   8.3522  -3.3826  13.1417 -11.4535
+#&gt;   -3.0120  13.0322   6.9227 -14.9900  -1.2819  -2.6046  18.4206  10.6289
+#&gt;   -0.5705  -0.9780  -2.3203   1.0331  14.8899   6.4160  -1.8385   1.1234
+#&gt;   -3.5166  -6.2960   8.3617   2.9712  -7.6379   4.2812   1.3522  -5.7266
+#&gt;   -3.9419   2.6470  -1.9473  -8.9569  -0.7584   3.3980  14.5743 -10.2095
+#&gt;   10.6213  -3.2907   8.2270   2.4184  -0.6461  -5.0426   2.6884  -1.6167
+#&gt;   10.2942   6.1348   7.0960  -2.5108   0.6207  -6.1762  -3.7501  -4.4878
+#&gt;   -0.6256  -1.0605 -17.0469   2.6994  -4.2948   3.7081  -1.2754  -5.0648
+#&gt;    8.3084   6.0258   2.5210   0.6074  -4.6763   4.5620 -11.2779  -1.9649
+#&gt;   -0.7987   1.9118  -6.1674  -0.0607   1.3645   2.5053  -7.2885  16.7281
+#&gt;   -3.0225   9.0359   0.1897  -2.3280   7.3198   3.2974  -6.1207  10.8499
+#&gt;   -8.0671  -2.6701  -0.0388  -3.3756  10.8742  -1.6988  -0.5941  10.7106
+#&gt;    5.3680   1.4411   1.7096   9.8732   1.6266   4.1371  -2.7059 -14.2736
+#&gt;   -5.2262  -9.5127   1.3886  -3.0064   3.0941  -2.8367   9.5156  -0.6969
+#&gt;   18.5352   5.2560   7.0702  -4.4319   2.4627  -6.6754   6.4994   0.0374
+#&gt;    1.6114  -1.9833   0.0352   2.6434  -2.1654  -4.8480  -0.1521   1.9257
+#&gt;    3.0409  -1.2848  -3.1187   8.3898  12.7157   1.7213   5.3244   4.2758
+#&gt;    4.5793   2.3647   5.9688   5.6638  -0.5780   0.8704 -11.4496  -0.9863
+#&gt;    0.9182   4.2894  -5.4648  -0.2197   2.0977   1.9876  -3.1105  -4.9824
+#&gt;  -14.5905 -15.3422   7.1894   8.6722   3.7829  -3.4129  -8.0301   1.0123
+#&gt;    8.1811   5.3511   1.0564   0.9844  -6.6549  -9.0158 -14.3013  -3.1582
+#&gt; 
+#&gt; Columns 41 to 48   5.6332   2.2932  -4.5964   6.4198  11.1123   0.0795  -2.1457   5.0331
+#&gt;   -4.1047  -4.9831   2.4336   3.1466   0.0397  -5.9841  -5.2063  -8.4516
+#&gt;    6.4033   4.3415   9.4705  -7.1406   2.5291  -5.1500   3.6671   0.6115
+#&gt;   -2.4928  -6.3541  -8.5856   2.8727   4.4635   6.7641  -9.0326   0.6842
+#&gt;   13.9401 -10.6037  21.6569   3.9669   1.1742  -6.8804   4.2434  -7.7035
+#&gt;    3.0021   2.0676  -8.8853  -1.3246  11.0303   7.7684  -4.2882 -12.0431
+#&gt;    6.5844  -6.9448  -9.4087   2.1672  -5.5186 -11.8524  -3.4786   8.0810
+#&gt;   17.4261   3.3265   7.0836   7.8669   8.9668   3.7108  -2.3977   3.5640
+#&gt;   -9.8889   6.9500   5.6497  -5.3105  -2.7911  -0.2394  -6.0394  11.5696
+#&gt;    0.1476 -10.2242   7.2625   2.4323  -7.7611   3.3847   5.2573   7.1294
+#&gt;   -5.4409  -2.7633  12.5376  10.5969  -1.3762  -8.4004   6.0051   3.8142
+#&gt;   10.6442   2.4963   1.8327   7.9667  -5.6774  -7.6771 -15.2554   0.1189
+#&gt;    9.5391 -11.4385  -8.3000  -5.2080  -0.5006  -8.3721  -2.1355   1.9657
+#&gt;    7.3890   3.6394  -2.9041  12.4842   9.8456  16.7926   8.8750  10.0341
+#&gt;   -0.5811  -3.7149 -16.8386  12.0227   6.3468 -12.0353 -10.9658  -9.1868
+#&gt;    8.5468  -6.7005  -6.6056  -3.4504   7.5381   1.1473   3.6462 -11.4767
+#&gt;   -6.7523   4.0718   7.9301   5.5260  -1.3192   1.4566   2.8061   9.2092
+#&gt;   -2.0472  -4.2016  14.1996   3.8699  -2.8507  -3.6281   8.0653  -7.3780
+#&gt;    2.4319   6.0648   0.1928   3.5088  -1.0562  -6.6013  -5.3961   1.4732
+#&gt;    7.8926 -16.3563  -0.5517   0.7026  -1.6527   5.8850   1.0421   5.9831
+#&gt;   -4.6519   7.9746   1.9849  -8.3457  -2.4586  -0.6843 -12.2213 -11.0160
+#&gt;   -5.6572  -1.6221   0.3711 -10.3329  -6.3663  -5.1670   0.8120   7.7180
+#&gt;    2.9991  -0.7780  -1.8604   3.9307  -7.3936   6.4536   3.1914  11.0404
+#&gt;   -5.8615  12.9612  -4.8467  -1.3229   0.6688  -0.5670  -3.7521  -0.3018
+#&gt;   -5.4614   6.8128   6.2624   3.8757   4.4406   3.3667   2.2684   1.9731
+#&gt;   13.3009  13.8089   1.5255   8.6716  -0.1487  -3.5703  -3.4931  22.3602
+#&gt;   -8.1353   2.2880  11.7727  -2.9184  -7.3796   5.0952   4.3697   1.0045
+#&gt;    2.8701  -1.0420   0.2520  -2.1208   1.2186   1.5434   0.4500   2.5658
+#&gt;    2.6756 -13.7405 -13.2246  12.2021   8.1566  -5.2898   7.2462   6.0131
+#&gt;    3.0103 -10.4379  -7.5588   5.1218  -4.4178  -5.3754   4.2676   8.6003
+#&gt;   -6.4673  -2.9102 -10.8066 -10.0045  -3.7663   2.5766   6.2474  -2.0447
+#&gt;   -2.6220   0.7522   2.0396   8.4006   5.3325  -6.8119   0.7586 -12.5327
+#&gt;   -8.5465   0.3816  -4.5711  -0.3143   2.2783   2.4071   1.1744   0.8159
+#&gt; 
+#&gt; (3,.,.) = 
+#&gt;  Columns 1 to 8  -2.9465   6.7491   3.4035  -3.6492   5.0866  -1.3004   0.2449  -5.4939
+#&gt;   13.0754   3.2661   4.7796  -1.4391 -15.0498   0.9982  -7.4219   1.7322
+#&gt;   11.5146   6.5782  11.1510   8.3414  -9.8245  -1.5042   1.6307 -16.1146
+#&gt;    4.3577  -2.5324 -10.9075 -12.6360   2.2983   2.4125  -4.1170   0.6302
+#&gt;    3.8053  -1.5692   4.8981  13.4494  -0.2783   1.3898   0.4411   1.7879
+#&gt;   -1.1363  15.0526  -0.3598   2.6539  -1.6215  -6.7269   2.1990 -10.1371
+#&gt;    6.0341  -1.9518  -9.8891   2.2049   2.9623 -10.9494  -2.2168   2.7169
+#&gt;   -8.4840  13.4977   6.8483  10.2970   6.8995  -8.4330  -6.5020  -1.1835
+#&gt;    1.5872 -10.8291   1.1714   2.6117  -3.2293   5.4288   0.8903  -3.5801
+#&gt;    2.9126  -2.0079  -6.5620  -9.1304  -0.2895  -9.4825  17.6155   4.1331
+#&gt;    3.8673  13.2476   2.4900   3.8707   6.4898 -12.6353   3.6338   3.7556
+#&gt;    2.7610  -1.5891   5.9757  -6.3396   5.4864 -13.9254  -3.6302   9.7405
+#&gt;   -2.6819 -10.4204  -7.2094  -5.8542  -3.6023   3.0869 -13.3427   5.9596
+#&gt;  -12.0655   4.0919   1.9086 -14.4974  -6.2530  -2.7969  -5.5693   4.2034
+#&gt;    0.6609  -7.6478  -8.0833   8.2815  -1.7428  -1.3188 -25.3015  -1.3408
+#&gt;   -0.8759   7.2017   3.8076   2.0407   0.2781   0.7483  -5.6527  -2.9568
+#&gt;    1.1847   9.2117   1.6848   0.1483   8.1943   0.1440   1.0069   6.9198
+#&gt;    3.9311  11.6083   7.9933  -2.4647 -10.8112   2.0084   1.4939  -2.1771
+#&gt;    0.8121 -13.0258   9.5050   5.3774  -4.4842  -7.9937 -14.3164   1.3361
+#&gt;   -4.9986  -1.7274 -14.8807  -8.0427   7.7506  -6.5877  15.2168   7.6586
+#&gt;    1.5007  -5.3454  -0.8189   7.9164  -6.3935  -0.6613  -0.0635 -10.9799
+#&gt;   -4.9972 -11.5440  -4.6368   1.8109  -7.5331  -2.3971   3.0837 -11.0933
+#&gt;    3.2319  -5.5267   4.5814  -6.7636  -3.9420  -5.8516  -1.9070   7.6854
+#&gt;   -6.6653  -8.1467  -3.8014   4.9708  12.5034   1.7842  -3.7049  -7.7959
+#&gt;   -6.6782  -3.3112  -5.6619   6.2035  -0.0263  -2.6124  -1.3062  -4.2076
+#&gt;   -2.1601   4.4574   9.6345   5.7250   5.8007  12.2038   0.2432  -0.1653
+#&gt;   -1.3970  -4.2147   7.8978  -3.8411   7.8179   4.9035  -6.9380   5.4670
+#&gt;    2.5700   0.8235   5.6162   2.9144  -4.4624  -0.7601  -3.3247  -6.3907
+#&gt;    0.6469  -3.6597 -11.5515   3.2741   9.6091  15.3186  -0.6391   1.1884
+#&gt;   -6.5224  -2.7463  -5.8976  -8.7366  -0.5906 -12.1694   1.5458   8.0285
+#&gt;   -3.5449 -10.1203  -9.3352   3.2810  -2.5780   2.0167   2.7202  -8.8663
+#&gt;   -5.0322   2.0523   3.1426  11.1675   4.0442  12.3353  -6.2100  -0.0758
+#&gt;   -5.0508   1.4000 -11.1320 -10.0043  -5.4281  -5.8816   4.5383  -0.3971
+#&gt; 
+#&gt; Columns 9 to 16   6.5449   2.0045  -2.8130  -2.7645  -0.0235   1.1601   1.0732  -2.3921
+#&gt;    3.6279   0.0634 -12.7863   1.7218  -2.3510  -5.9596  -0.6172   2.1836
+#&gt;    4.9811   1.3213   1.2502   0.1739  -6.4937  -1.0192  -1.0153   1.6010
+#&gt;   -0.5681 -10.2578 -12.6408  -5.5517  -6.3547  -5.5246  -4.2951   8.5464
+#&gt;   -1.0979  -0.3486  -4.0974   8.9978  -7.3452   5.2716   2.8365   1.7610
+#&gt;   -0.1936   9.5106  -5.1467 -10.5008   2.0831   2.8340  -3.6761   5.0899
+#&gt;   -8.7012   2.7162  -2.0035   1.0220  -8.0065  -1.0102   3.4017  -0.2082
+#&gt;   -1.0910   6.3155   5.8994   2.0598  12.9580   3.7722  -7.1164   1.6312
+#&gt;    9.4666  -0.2722   2.1142   8.7033  -5.7696   5.8161   0.7354  -1.4988
+#&gt;    0.6759  -1.8277  -9.4586  10.9843  -0.8031  -5.1404   7.8896 -13.2030
+#&gt;  -10.3155  -1.6863  -4.6110  -1.5937  -0.4920  -1.6325  -1.9721   8.3756
+#&gt;   -7.2470   3.6684  -2.2973  10.8370   0.2955   3.1221   3.6971  -7.4725
+#&gt;   -3.0602   3.7926   2.8544  -3.2670  -5.1942   1.4339  -4.6983  -5.8587
+#&gt;   -5.4354  10.1609  -4.9332   4.1405   4.1991   2.8666  -5.2437  11.7911
+#&gt;   -1.0875   7.6569  -1.9310  -9.0522   4.2194   1.2091  -1.2352  11.8580
+#&gt;    5.4735   0.9263   0.3010  -8.7748   7.6049  -3.1868  -2.7634   6.5455
+#&gt;   -5.7583  -4.8841   1.8643   1.1487   3.9973  -7.8404  -1.2693   2.1135
+#&gt;    2.5554  -9.4165   1.5076   1.1334  -2.5302   0.3660  -3.9877   2.9519
+#&gt;    1.4318  -0.4066   8.2863  -2.3862   3.9937   3.7839  -1.3307  -4.2478
+#&gt;   -7.1912   1.5832  -3.5860  -1.8168  10.1623   1.8318   4.0577  -2.3840
+#&gt;    6.4235   1.5555   2.0653   3.3499  -0.8222   3.1654   2.7664   3.6706
+#&gt;    8.9828   4.9193   3.5951   8.4067   0.3966  -2.6981  -0.4956 -13.2621
+#&gt;   -1.0758  -0.1383  -3.1166   0.7782  -6.2175   3.8216  -4.6978  -5.9193
+#&gt;   -3.2667   3.1698  15.1409   7.3503  -0.2144 -11.8199   3.2289  -3.4907
+#&gt;    2.1314   1.8190   5.8104   6.4171   3.3890  15.9284   0.2489   5.5254
+#&gt;   -3.1983  -6.0544  -2.0201   4.5444  -6.5154  -0.5975  -4.5367   1.9974
+#&gt;   -3.9888   2.1322  -0.4907   4.7185  -3.9945  -8.7659  -2.5032  -6.1154
+#&gt;    7.7187   5.8723  -3.5478   0.8724   0.6887  -3.1265   1.5609  -2.7086
+#&gt;   14.9549   3.9259   0.2833   0.5005  11.6841   3.9938  -9.0293  -4.2390
+#&gt;   -4.8966   1.3657   5.3542  -3.5113  -1.2462  11.2799  -6.2368  -3.7606
+#&gt;    3.7314  -1.1981  -0.5762  -8.8711   1.3083  -6.9604   0.4904   3.6096
+#&gt;   -2.1686  -5.0282  13.2207   5.9258   2.0903  13.1469   6.6172   6.0303
+#&gt;    1.0073  -8.7611   1.3372   0.2772 -10.6286  -1.2899   8.4865   1.0604
+#&gt; 
+#&gt; Columns 17 to 24 -11.7194  -3.4742   6.7228  -3.2348   6.6872   2.7962  -3.8216 -16.4645
+#&gt;    6.3566  -2.0647  -2.4954   5.1539   2.6215  -1.7163  -7.0786  -3.4445
+#&gt;    2.5244  -1.3425   3.3126  -2.9585  -8.8994 -12.6755  11.0108  16.4860
+#&gt;    9.7978  -3.0523  -0.5564   6.7757   8.1707   5.7241  -1.3237 -18.5762
+#&gt;   -5.9852  10.7143  -2.2588  -2.7854 -18.7064 -13.0260   4.9254   1.3460
+#&gt;    5.0855 -11.0771  -6.2980   8.4517  -4.4639   2.9253   0.8851 -11.2825
+#&gt;   -4.9577   0.0768  -1.7747   0.3088   1.3094  -9.3061  -7.2066  -1.4033
+#&gt;   -4.6579  -5.2138  -0.9962  -9.4146  -0.8550   0.0451  -4.0479 -11.2631
+#&gt;    4.9886   1.4922   1.5610  -8.2084   3.1271  -1.9722  13.3146   9.7915
+#&gt;   -0.4112   3.1523   6.8147  -6.1784  12.6918   1.5125  -5.6114   4.0260
+#&gt;    0.3532  -5.5388   1.2163   4.0721 -10.7757 -11.1506 -19.9871   9.2129
+#&gt;    2.8650 -12.4232   4.4705   2.9344  14.7577 -16.6749   1.7247   0.4227
+#&gt;    8.7762   0.5141  -9.4361  16.8197  -1.0409  10.8817   8.5502   1.0062
+#&gt;  -13.2626 -14.6037   7.8303  -5.6896  -9.6640  13.5902  -0.7065 -27.8346
+#&gt;   -1.7589  -8.7919   6.2547  11.9314  -2.6878   6.3434 -10.0364  -4.8781
+#&gt;    2.3292  -3.5457  -1.8573   9.5039   2.0050   4.4728  -2.5737  -1.5500
+#&gt;    3.6692  -2.5691   2.4715  -4.3304  -7.8685  -1.5144  -0.6543  10.2911
+#&gt;   -2.6615   3.5720   0.6263  -3.3037  -9.7682  -9.8934  -0.3650  -3.0510
+#&gt;    6.1805   4.0238 -15.4475   3.1536  -4.5537   0.5307   1.4736   3.2388
+#&gt;   11.1083  -1.7856  -9.8816   0.2387  21.6622   8.1461  -6.9643  -0.1668
+#&gt;   -1.1595   2.2325   1.3895 -17.5522   3.2825  12.1828  12.0439   7.0735
+#&gt;   -0.7802  10.8209   7.4489  -1.9758  -3.6576   8.3138   5.8285  11.1171
+#&gt;    2.5402 -10.0043  -2.2162  -3.5739   1.8395  -2.3006   7.2071  -3.8794
+#&gt;   -4.4282   6.6475   6.0691   6.0688  -4.5014 -20.3268  -2.6398  -0.2482
+#&gt;    3.2296   5.2469  -9.3186   7.3619   9.4032   2.2472   9.3938   1.0058
+#&gt;    1.9222 -10.2521   2.8342  -0.2041  -5.4550 -21.9493   8.6508  -3.0456
+#&gt;   -2.2264  10.3704   1.2869  -7.2307   5.6671   5.0697  -2.9211  17.3764
+#&gt;    3.6620   3.2212  -3.4542  -1.4203  -7.2373  -4.5592   5.3546   0.5723
+#&gt;   -8.2548  -1.8444  -2.9206   6.5498   7.7863   8.8395  -1.5226  -6.9835
+#&gt;   -0.8807   0.8944  -1.4556  11.3731  -2.2037  -5.9420  -5.6442  -5.9234
+#&gt;    1.9328   3.0542   0.8298  -3.4456   5.9718  17.0679   4.0231  -2.3350
+#&gt;   -0.2588   1.7942   6.3263   9.9000  -2.6712  -8.2161  -8.1444  -6.1425
+#&gt;   -6.1458   7.8033   3.2257   8.8162  15.9783   0.5267 -12.7499  -3.1776
+#&gt; 
+#&gt; Columns 25 to 32  -2.0157   4.1089   7.2682   3.6886  -2.9480  -8.2878   6.8678  -5.5896
+#&gt;   -4.0806 -12.2172  12.4462  -0.5027   6.8148   3.1091  16.0016  -0.6848
+#&gt;   -0.2405  -4.5660  -0.8068   6.6606  12.8058   6.2472  -0.3635   1.0788
+#&gt;    7.7058   1.8378  -7.6407  -0.3896  -7.3546   0.3916   0.5564   1.8342
+#&gt;    1.9186  13.8919  -8.3621  -3.6377   1.1202   3.6434   6.2728  -1.4171
+#&gt;  -14.0436 -19.9002   8.4650  18.4132   7.9181 -12.8233  -0.5752   0.3696
+#&gt;   12.6063  -3.5674  -4.0703  -7.0508   5.3739   0.8898  -2.3077  11.6459
+#&gt;    8.3819  -2.0539  14.4301   8.0666  -2.7405  -2.2027   9.5361  11.3209
+#&gt;   12.0999   5.2191   7.2094  -4.4292  -1.6779   5.8554  10.1625  -8.1495
+#&gt;   -0.5939   4.0770  -0.6233 -16.4889  -9.4226   1.3257   0.7432 -12.6853
+#&gt;    3.7446   6.1835  -6.3783 -18.7896   3.6412   8.0568   7.2069  -1.3634
+#&gt;    7.3596  -5.7071  13.2045  18.7954  17.2428  15.4416   1.3781   1.8619
+#&gt;   -0.9375   1.7349  -7.3584  11.9405  -8.6807  -2.0350  -3.8199  10.3507
+#&gt;  -16.6160  17.3572   7.1191  -8.0920  -5.6566   5.2656   2.5318  -5.1723
+#&gt;    2.5319   4.4528   1.4150   6.7175  -9.4336   0.0099   7.0687  11.2796
+#&gt;   -6.9945   5.7012   0.7478  10.7271  -5.0086   2.3393  -0.5579   7.2747
+#&gt;   15.7752  17.5875 -18.5369  -9.9868  -5.4545  13.4179  -2.3004   2.9022
+#&gt;    5.9505  -2.6579  -0.1754  -1.5275   6.0338  11.6684   9.5316   4.0963
+#&gt;    5.0278 -10.0900  -1.7168   4.4559   3.7852   2.4527  12.2397   8.3822
+#&gt;    4.6039  -0.4500   0.2139   4.2664   2.9937  -0.7525  -8.7684  14.3166
+#&gt;    0.4572  -5.7957   0.0972   6.9616  16.6140   1.5037   3.2261  -8.7615
+#&gt;   -8.5531   4.3549   7.4059  -8.5630  -7.7292 -17.0612   0.8232  -9.3367
+#&gt;  -11.3403 -12.7695  10.4222  -2.2739   4.2038   0.2435  -1.7741 -10.3972
+#&gt;   -7.3868   7.8150  -3.7302   2.9652  -3.8516  -3.7140  -8.3359   0.2041
+#&gt;   -0.5373  -2.7766  -3.5409   1.3619  17.1908  -2.1378   9.3833   8.4610
+#&gt;   12.0572  13.5887   0.1190   0.4920  -6.8214  13.0381   3.7484 -14.7119
+#&gt;    8.6603  13.5787 -14.9618 -13.6073  -0.9042  14.7748   8.6161 -16.1236
+#&gt;   -8.3565   2.8309   2.6476   3.6061   3.8109   3.7507  10.7297   1.4240
+#&gt;   13.0005  17.4906  16.0882   4.8015  -6.7219  -3.7315  -0.7771   3.3905
+#&gt;   -6.2958 -10.8272  10.8498  -4.4420  -2.2415 -21.2301   2.6585   0.1607
+#&gt;    0.6630  -3.9846  -4.0316  -4.9802  -0.7418  -2.7902  -3.6381   7.4480
+#&gt;    6.6112  -1.8158   5.6075   9.8480  -4.0444  -1.3972  -0.8709   8.1595
+#&gt;  -13.8851  -8.0466  -0.3055   0.3725  11.3732  -2.1039  -5.3575 -10.2266
+#&gt; 
+#&gt; Columns 33 to 40   4.7198  -0.0901   0.2457   3.8183   4.5227  -5.3886  -2.8884  -7.6746
+#&gt;  -10.9014  -0.4934  -2.5116  -1.1350   4.2911  -1.6436  -0.0854  -1.2101
+#&gt;   -3.7498  -3.9846   1.1016  -2.7053   0.9169   4.6343   8.4728   7.9549
+#&gt;    3.8178 -14.2022  -3.3856  13.3016  -1.6856  -4.3524   3.5354  -0.1561
+#&gt;    1.1652   5.5548   3.8279  -0.7134  -0.3133  -0.4691 -10.8889   1.7277
+#&gt;   -8.2708 -21.7539   1.9627   7.8289   5.8654   3.9668   4.7696  -2.9379
+#&gt;   13.8735  -7.8829   0.2100  14.1288  -5.4307  -3.8724  -3.1266   1.1431
+#&gt;   -1.7572   1.0280   2.6630  -1.8167  -2.3524   5.4642   2.0585  -2.4154
+#&gt;    0.7114  -1.9926  -3.9740  -1.2824  -5.1682   7.0500   7.5277  -0.9279
+#&gt;   11.6471  -1.4907   8.2696   1.9277   6.3606   4.6438  -2.1029  -3.6280
+#&gt;    2.9605  -3.9471  -4.6455  16.6811  -0.1063 -16.2037   1.9691   5.2829
+#&gt;   -7.8695  -6.0213 -10.7647  -1.2503  -7.0724  -0.9308   6.0930   6.8161
+#&gt;   -0.7300  -1.2248  -1.1506   0.7065   7.9529   3.5382  -2.1695   3.3102
+#&gt;   14.5596  -2.1343  -8.5324  14.5377   3.7628   5.9530   1.7875  -9.9836
+#&gt;   -3.1529  -6.2340   3.3469  13.8249  -0.8234  -4.9439  -0.5999   4.5493
+#&gt;   -1.7360   3.7705  -2.2131  -4.3659   7.7011  -1.1756  -4.5246   2.1410
+#&gt;   11.6924  -4.7556   3.6914   1.0142  -8.0472  -3.8438   1.6591   7.1282
+#&gt;   -3.6113   5.4425   1.3223 -13.9324  -2.0020  -8.5013  -0.6448  -1.2193
+#&gt;  -10.7613   8.6417  -5.3610   1.0033   4.5142  -6.6560   2.0836  -6.6214
+#&gt;    4.9961 -18.0090  -0.2932  -1.6754  -7.1008   6.1781   3.5243   3.0648
+#&gt;   -7.7127  -7.6799   4.8339  -4.9892  -1.6488   4.7189   8.1236  -5.9356
+#&gt;    5.9332  12.1854   7.8402  -7.2979   5.1513  11.0374   1.6301  -5.2815
+#&gt;   -0.2638   5.0955  -2.2106   2.1170   1.0265  -1.5688   1.9216  -6.3343
+#&gt;   -0.9763   7.2011  -6.1354  -0.6408   7.9626   7.7870  -4.8278   0.9937
+#&gt;    4.3236 -11.2081  -6.7775  -7.9818  -6.1075  12.4051  12.9151   4.5897
+#&gt;    6.0191  -5.1637   1.5112  -1.0737  -1.0153   1.2376  -1.9025   2.0195
+#&gt;    6.6469  -1.1426  -6.0725   5.3781   2.0921  -0.6679  -6.2060  -2.2261
+#&gt;    1.7494  -0.3657  -4.7677  -4.1958   5.4964   1.0836  -3.4167  -6.8927
+#&gt;    6.6011  -4.7835   0.5737  -1.7932   1.1056  -8.7393  -6.7464 -15.0448
+#&gt;    2.1360  12.7848  -8.4477   2.5211   1.2313  -8.4252   4.2519  -9.1689
+#&gt;    6.4285  -4.4071   4.5360  -1.8803  -0.9859   5.9919   0.6359  -7.9721
+#&gt;    4.2081  -4.5014   2.3329 -20.6892   4.4741   3.5308   2.4164  15.9934
+#&gt;    4.5220   5.8845  -0.0032  -6.1738   7.4844  -0.3890  -2.8013  -0.7244
+#&gt; 
+#&gt; Columns 41 to 48   1.3805  -4.9380   0.8535   7.9801  -8.7107  -9.8639   8.0869  -0.9497
+#&gt;    0.3161   3.5444   7.2082   8.0190   2.0419  -5.0187  -5.7169  -5.0288
+#&gt;   -1.6709  -3.9399  -2.3093   5.7695   9.9227  -5.0367  -9.8780   9.4281
+#&gt;   12.9532   3.8538   6.7989  -0.0335  -9.1111   9.1214 -13.1752 -10.1677
+#&gt;   -3.4043  11.1772  -3.0363 -10.4170 -12.1433  13.0533  -6.6098  16.1098
+#&gt;   -2.2308  10.2838  11.9694  22.5632   9.2597  -9.2814  -4.7671  -8.3936
+#&gt;   -1.8761  -3.3415   9.8528  -6.0485  -3.2158   5.9935   4.5926   2.2405
+#&gt;    7.0757  -0.5773  -8.1496  -5.5039   6.8523   4.2756  -0.2683  -0.3216
+#&gt;    7.0463 -11.1999  -5.8586 -13.1925   2.2496  13.2353   3.6401   4.8425
+#&gt;   -2.4496  -1.1532   5.7173  -8.0488  -9.3571   1.8937 -12.0181  -9.3051
+#&gt;    5.3051   2.1933  -1.0898   4.3864  -4.0046  -6.7609  -1.0443  -2.4709
+#&gt;   -9.9847   5.9119   4.9733   4.5662  -3.3084   6.2302  -4.7580  -4.9996
+#&gt;   -6.9757  -0.6847  -7.5730  -0.9035  -9.4966  -0.2145 -11.6262  -3.3807
+#&gt;    0.7097  -1.2748 -10.2944   7.9670  -9.4208   6.7085  -9.3770   4.6652
+#&gt;    6.1547  -0.3353  -4.2997   1.4726 -16.2000   0.7614   5.2241 -17.7287
+#&gt;   -2.9057   1.5622  -0.2821   8.3046   8.2795  -4.9097  -5.4188  -2.7664
+#&gt;    4.4802 -12.3069  -9.8593  -0.4712  -6.6066  -2.9359  -6.0635   2.7054
+#&gt;    1.1890  10.8593   0.3688   0.8153   1.8687   0.4189  -9.7313   6.2856
+#&gt;   -5.0629   1.6361 -14.5558  -3.1869   2.2867  10.2302   5.5203  -3.1036
+#&gt;    3.6322  -1.2016   0.4924  -4.0717   8.7493  10.2594  -4.7146   0.1071
+#&gt;    0.4285  -5.8888  -4.1580  -6.1036   6.5381  -0.7054   0.9250   0.8008
+#&gt;   -0.5785 -12.7201  -1.6306  -0.0454   5.2061  -1.0831  13.5820   4.5648
+#&gt;   -5.3590   6.5304  -2.3736  -1.0916   1.6488   2.4671  -9.6795  -8.8535
+#&gt;   -1.8945   0.9741   2.7552   9.1384  -8.9336  -8.3000  13.0976  -0.9421
+#&gt;   -3.7371  -6.2150  -4.6558   4.8962  12.4271   5.5632   0.1805   3.3066
+#&gt;    8.0820  -6.3138  -1.6506   1.9567 -13.2407  12.0384   0.7110   7.1839
+#&gt;   -0.8565 -20.9714 -12.0888   1.1083  -1.9453   6.3443  -6.5816  -1.4029
+#&gt;   -4.7264  -3.8826   5.0662   8.9830   5.1964   4.6282   5.2139   3.2613
+#&gt;   -0.6169  -9.8690  -3.7684 -14.4861  -6.3009   7.8225  10.5893 -13.2439
+#&gt;   -0.7888   8.9360   4.1799   6.5549  -0.8809   1.1476  10.6641  -5.9324
+#&gt;   -0.2343 -11.8711  -3.0718  -6.1033   6.0796   6.5202   4.4145  -2.7017
+#&gt;    7.7586  16.7302   5.6891   9.7125   3.2340   2.8381  -2.1649   3.7011
+#&gt;   -6.3852  -7.4833  17.1817   6.7457   9.0055 -18.5461   0.9357   5.4377
+#&gt; 
+#&gt; (4,.,.) = 
+#&gt;  Columns 1 to 8   9.3815   0.5067  -0.4746   0.8557  -0.2518   4.1846   5.2638  -3.6556
+#&gt;   -6.6823  -1.5660   6.0075   9.0911   2.7352  -1.7840  -3.8560  -6.8980
+#&gt;   -7.3805  -7.3294  -1.7421   4.9221   4.4610  -5.2977  13.2838  -5.6296
+#&gt;   -8.0408   5.5889  20.9149   2.8095  -0.5835  -3.4809  -0.6350  -7.5940
+#&gt;    6.2594  -0.2308 -10.0346  -5.3197  12.1477 -16.1269  12.2065  -2.3300
+#&gt;   -2.1598  -2.0914   5.8031   9.2689  -7.6693   1.4311  11.7978  -1.2850
+#&gt;  -10.8211   1.6311   2.8033   4.9707   2.0051   4.8654  -0.6368   7.6161
+#&gt;    3.8561  11.1155  -7.9433 -15.2565   1.6465  12.6285   6.8346   5.1274
+#&gt;   -8.0238   1.3107   4.4103  -1.0850   6.5162   3.9244  -4.6141   5.3600
+#&gt;   13.5724  -2.2656  -2.8300   8.3306  -9.0484   4.6498   3.2783  -9.2353
+#&gt;   -2.2913   3.2422   1.3463  -6.4135  -7.6607  -2.3304  -8.4497   5.1440
+#&gt;  -11.9072  -5.6344  -2.7368  10.5469  -0.4579   1.7915  -0.8638   8.5353
+#&gt;    0.3101  -9.1636   4.1188   7.0597   0.7060  -5.3735   4.1513 -11.9009
+#&gt;   16.7822  -1.0691  -7.0354   0.3090   5.2873   9.0019  17.5214  -6.9909
+#&gt;   -8.0635   4.3959   4.6130  -7.4464  -5.2318  -0.3592 -12.1825  -0.4734
+#&gt;  -11.4288  -2.7838   2.9892  -3.8081  -2.3873  -4.6520   6.1755  -9.5882
+#&gt;    8.8121  -1.9857 -13.4389  -4.5692  -1.5536  -2.9367   1.3208  -1.1337
+#&gt;   -1.7257 -12.6874  -0.4554  -4.5100   3.7578  -6.8492   5.5284 -10.4876
+#&gt;   -4.8348  -3.4583  -2.9028  -9.0365   4.7328 -13.9674  -1.3429  -3.5877
+#&gt;    3.4851  -2.4339  11.5437   9.1441 -11.9711   4.1880   1.8360  -3.6242
+#&gt;   -7.7222  -2.2043  -1.8311  -3.0957  -2.2801   2.7201  -2.8722  -3.5286
+#&gt;    9.3062   0.6945  -8.5838   8.0356   7.3675  11.4115  -2.4561  -6.8050
+#&gt;    2.2952   0.2877  -4.3856   5.6378  -1.8479  10.9303  -4.5143  -2.9657
+#&gt;    4.5877  -1.1598 -17.2627   0.1477  12.8224  -0.1667 -11.2564  13.6121
+#&gt;   -0.7933  -8.7304 -12.4579 -10.0943  -0.3981  -0.0413  -5.6221  -1.8680
+#&gt;    6.7075   0.8151   5.1015   8.2805   8.1205  -2.6840  10.6711  -3.1135
+#&gt;    1.9171  -1.7400 -13.7950   6.0659  15.5111  -4.1585 -11.8906 -12.8805
+#&gt;   -2.8296 -11.4839  -1.9139   6.7354   6.7694  -3.3406   3.3734  -6.6906
+#&gt;    2.0097 -10.8567  12.4634 -11.8622  -1.2709  16.1235  -2.8331  -7.6479
+#&gt;   -5.2550  -1.2211   2.3181  -5.3669  -7.9116   7.4502  -3.0042   2.0301
+#&gt;   -4.1020  -3.3707   5.1646   2.0484   4.8935  -0.5884   2.7162  -2.3101
+#&gt;    0.8329  -4.2334  -3.8673  -7.0354  -7.4673 -10.5435   5.8531  -6.1334
+#&gt;   -7.3772  -4.5909  -1.9161   6.6259   3.6650  -3.4721 -15.8138  -0.5154
+#&gt; 
+#&gt; Columns 9 to 16  -2.2739   0.8283  -3.6091  -3.3817  -6.4904  -1.6953 -11.6233   9.6465
+#&gt;    3.1725  -2.0295   3.1037  -1.5620  -2.9030   4.3717   6.3062 -11.6154
+#&gt;    2.6086   5.4694   2.0637   1.0243  -6.3620   7.1537  -8.1323  -1.4023
+#&gt;   -0.9116   1.0728  -8.3562  -0.1994 -11.0499  10.4495  -2.3881  -3.9351
+#&gt;    0.2697   6.3687   5.7472  11.9531   4.1077  -0.6103  -0.8076  -4.0339
+#&gt;   -1.4583   1.7878   5.7407  10.0656  -1.1768  -4.7450  -2.7035   0.6984
+#&gt;    5.8765   3.3211   0.2865  -6.4075  -2.6308  -0.7213  -9.3295  -0.1074
+#&gt;    1.0711   2.5402  -9.0822  -2.6449   1.8962 -19.1070   0.1262  12.6525
+#&gt;   -1.8196   3.4460  -6.8566  -2.2804  -2.1786  16.4890  -3.9616  -0.4180
+#&gt;    5.6616  -5.4913  -2.2831  -6.6371  -1.7186  -8.4043  -2.4147   1.9391
+#&gt;    5.0043   4.6891   2.4815  -3.2522   2.3669  -9.7451   3.7433  -7.3471
+#&gt;  -18.1050  -5.6349   4.8301  -6.6695  -0.6437   9.9406   2.0819  -5.4838
+#&gt;    4.1282   0.2126 -14.6296 -10.7571  -3.8312   3.3462   0.6422  -0.6044
+#&gt;   -0.7692   4.0226 -13.8080  -6.6801  -1.7263  -5.4621 -12.8972   7.8290
+#&gt;   -6.2263 -11.7697  -9.2510 -11.1648  -6.6947   9.1236   2.8376  -4.2448
+#&gt;   -3.7582 -10.3329  -4.1958   7.8152  -3.6387  -2.1770   8.8520  -4.5941
+#&gt;   12.4033   5.5779  -6.7851  -3.5180   2.8896  -7.5427  -5.9868   4.3383
+#&gt;    1.5012   2.6535  -4.5133   5.1882  -3.7772   4.8348  12.0824   1.6978
+#&gt;   -9.2360   1.2548   3.9144  -6.1924   5.6979  -5.8403   2.9249   5.4458
+#&gt;    6.0617  -2.4988   4.3071  -4.7455   6.6305  -7.2826   4.2941   8.8152
+#&gt;   -1.3790  -4.0888   2.1732   6.6667   5.7116   1.4884  -3.3303  12.0984
+#&gt;    4.6833   0.4357   2.2986  -6.3403   5.6785  -1.1201  -0.8818  15.9166
+#&gt;   -0.0499  -7.0754   0.1177  -4.0228  -1.0969   3.6127  -5.4017   2.4523
+#&gt;   -8.1815   5.6716   5.0038  -6.6016  -1.2290   5.5799   4.2395  -8.0558
+#&gt;   -2.3371  -8.0296  -9.6448  -2.5299   6.7300   0.8519  -0.9271  10.0746
+#&gt;   -5.0255  11.4261  -3.3775  -4.8680  -1.5682   8.3731 -10.4490  -0.6064
+#&gt;   -3.4161  -4.5474 -10.1656  -3.8014   3.1146  -0.8437  13.1989   4.0744
+#&gt;   -3.1343  -1.5437   1.7597   4.2420   1.9727   2.9057   1.0924   2.3585
+#&gt;   -7.2000 -11.6088  -6.2610   5.7236  -3.6506   2.5864   9.5943   4.3496
+#&gt;   -0.0143   3.5620   3.9286  -1.7451   9.7302  -5.7654  -0.1410   7.4993
+#&gt;    0.2777 -11.5208  -4.9884  -1.0496   4.1476  -4.7511  -5.0362   6.0351
+#&gt;    1.9071   7.9963   6.2941  -0.4414  -4.3196  14.0197  -1.9299  -2.8859
+#&gt;    0.1823  -7.3858   0.8481  12.6923  -1.2820   7.4711   7.2569  -0.1897
+#&gt; 
+#&gt; Columns 17 to 24  -6.3902  -8.0915   2.8120  -5.4677 -12.1083   0.7293  -0.1854 -11.1693
+#&gt;   -5.4198  -0.0203  -7.4073  -7.8397   0.7944   5.9828   6.9035   4.1706
+#&gt;    7.0808  -2.9617  -0.4648  11.5878  16.2616   6.1828  10.2831   2.6774
+#&gt;   -4.3103  -3.4252  14.0725   8.9026   6.5727   4.0217   7.1244   7.2774
+#&gt;   12.8870   3.5684   1.6039  -6.3148   7.0076  -4.8579   3.2048   8.0879
+#&gt;   -0.9314 -10.3732   7.2991   8.0166  -0.9176  -6.0767   5.2560   5.7824
+#&gt;    1.9082  -1.3171   5.0438   2.9462   0.3494  -3.3253  -6.1741  -3.2421
+#&gt;   -0.0169  -7.9599   2.0991  -6.8749  -6.8766  -2.0351  -1.9117  -6.2487
+#&gt;    1.5205  -2.9213  -8.4928   0.6182   9.0535   5.8862   2.8992   1.1099
+#&gt;  -17.0103   6.3513  -6.8099  -8.3324   7.9425  -3.0891  -8.2486  -5.5774
+#&gt;    0.2098  -3.9244   9.8871   6.5982  -1.9152  -2.5609  -3.6247   3.7114
+#&gt;   11.5530  -4.6364  -5.5291  -7.5888   6.5992  11.5859   2.2213  -1.9854
+#&gt;    3.0392  -1.2233   3.5053   5.5386   2.4279   1.6361  -1.3988   2.2867
+#&gt;   -0.8353 -16.6261   1.6398  -5.1569  -2.8185  -6.3679  -7.1365   4.2858
+#&gt;    9.6753   3.8341  19.2793   4.3791 -14.7301  -0.5535  -3.9796   7.3269
+#&gt;    9.4752   4.8538  -1.5916   6.8589  10.5771  -0.0706  -0.2979  -5.7065
+#&gt;  -14.1045  -2.6599  13.2903   9.9040   0.0470   3.2708   4.7037   8.0030
+#&gt;    2.8563   9.4973  -0.5336  -6.3764  11.0968  12.6636   6.9392   4.5617
+#&gt;   17.0599   7.5924   1.1280   3.5068  -8.0345  -0.6109   2.3953   6.2543
+#&gt;  -10.5462  -0.7995  -9.0660  -4.4262   5.4083  -0.0945  -0.8333  -2.9669
+#&gt;    4.1783   5.8788   6.2085   8.8895  -2.6238   1.2547  11.4961   1.8723
+#&gt;   -9.0594   3.5422   0.8785  -2.5702  -7.0482  -5.3648   1.9069  -5.1079
+#&gt;    1.7619   2.7703  -5.5693  -4.5051   2.3811  -5.2538 -12.9502  -2.4305
+#&gt;   -3.6373  -3.1968   4.4038   2.7994  -5.1367  -3.5099  -7.0419   2.2849
+#&gt;    4.3217  -3.8034  -3.7976  -1.6171   4.7725   2.1851   8.3179   2.8682
+#&gt;  -12.5976  -7.7804   6.1259   0.5927  -0.6537   8.4436 -10.1696  -7.8077
+#&gt;  -13.3516   4.7491  12.5992   4.6612   1.9889  -3.1061 -10.5402   5.2656
+#&gt;    0.2885   5.6938  -6.7537  -1.5300   2.2709  -0.7323   1.7429   2.3142
+#&gt;   -6.5488  10.0074 -12.3938  -6.8540  -7.6912   3.3745 -21.5179  -4.2562
+#&gt;    4.7510  -0.6089  -2.7716  -6.2754  -3.1802   6.1061  -3.5172  -3.9791
+#&gt;   -6.5220   6.6923   0.4181   3.3658  -0.3326  -3.0382  -0.1421  -1.9329
+#&gt;    2.9057  -4.5653  -2.1983  -6.5553   6.7875   6.1814   1.0676  -1.8543
+#&gt;   -5.1031  14.3404  -0.8404  -4.6729   5.7388  10.2501   8.3465   0.7604
+#&gt; 
+#&gt; Columns 25 to 32   1.2654  -3.3516  -5.7053  -7.8255   3.6590   6.0428   8.9688  -5.3477
+#&gt;    1.6726 -10.3291 -15.2812   8.2254  -8.4873   6.0614   2.9004  -1.9538
+#&gt;   -0.2418  -1.1665  -6.5616  -7.1512  -6.2113 -12.8616   3.0833   1.7057
+#&gt;   -5.6332  -2.7662   4.8149   6.1077  -2.6404 -12.7719  -3.4250   4.0562
+#&gt;    1.9879   2.2246   4.2971 -20.7711   5.8182  -1.4030   2.9192  -0.2680
+#&gt;    1.3256  -3.0180 -12.9403  -5.2071 -15.3067 -17.2713   7.8258  10.3330
+#&gt;   -1.4015   0.0434  -2.0839   1.2152   9.6107   4.4360   3.9643   5.6152
+#&gt;    9.0424   9.9949 -15.5909 -10.5344  -2.1971  -8.0964   1.6513   5.0014
+#&gt;   -2.9262  -6.5216   4.1790   8.5427   0.5833  15.8765  -3.4010   5.5310
+#&gt;    2.3271  -8.1133   4.3857  -4.7348  18.2139  -0.5364   9.7689 -13.9685
+#&gt;    4.1562  -5.3540  -6.7082  -6.2944  10.5866 -13.2188   3.0162   2.6646
+#&gt;   -9.2604  -5.5886   6.0124 -11.2143   6.3229   3.1214   3.9772  -4.5632
+#&gt;   -9.3874   1.6861   7.1812   4.9253   6.6248  -2.1831  -3.7881   9.2211
+#&gt;    1.0030   0.9090 -19.2417 -21.9289   6.8239  15.9540   5.5406  11.2358
+#&gt;   -5.7796   0.9200   4.3031  14.7015 -10.6792   1.3597  -1.3136   8.6494
+#&gt;    4.8535   7.4838   3.6532  -0.5258   1.4696  -9.6067  -3.3348   1.7364
+#&gt;    3.0247  -3.0456   0.0834  -0.6601   7.2650 -11.9222  -0.2793  -1.0224
+#&gt;    2.6200   2.4871  -5.9044   2.6964   1.6822  -1.0856  -3.1480  -5.5987
+#&gt;    2.7681   7.1753  -6.9790   2.2352  -0.2698  -2.5281 -10.9540   4.2393
+#&gt;    4.0676   0.8708  -1.2757  -2.0553  14.0620  -7.5132   5.7005  -0.9112
+#&gt;   -0.6234   5.1424  -4.4367   0.2276  -7.5168  -7.6055  -1.8155  -2.5680
+#&gt;   13.4083  -0.1383   1.6389  -5.3740  -1.2201   8.6550   8.2300  -3.3025
+#&gt;   -3.1029  -1.8290   1.5303  -0.7020  -3.4181   2.4569   3.0376  -3.1733
+#&gt;   -2.2551 -11.1942   2.0088  -8.2837  -4.5408   4.6051   1.7681   4.5066
+#&gt;    7.2819  -0.1614  -0.3589  -5.9853   0.2030  -1.1363  -3.0869   7.0417
+#&gt;  -13.9386 -15.7282   1.0017  -7.6061   8.7221  11.7999  10.0351   3.9576
+#&gt;    3.8211  -9.8737   6.2607   8.0226   6.9275  -4.7078  -2.8938  -0.5316
+#&gt;    4.9167  -6.1248  -5.2153  -9.3726   2.8991   8.4113   8.1048   1.2021
+#&gt;    4.3773   4.6809  -0.0869  14.9806   5.5849   9.2625  -1.1807  -8.1128
+#&gt;   12.0883   7.3920  -0.4220   0.9516   8.3091  -1.5727  -2.5052   4.5375
+#&gt;    3.4472   5.8978  -2.9269   4.7144   1.5507  -0.4913  -1.3357  -3.3784
+#&gt;   -3.9602   4.6302   5.0555  -3.3751  -5.7936   8.4193  -6.2159   7.7606
+#&gt;    9.1413   2.5148   4.1361  -7.9684 -14.1566  -4.1577  -2.6422  -4.2231
+#&gt; 
+#&gt; Columns 33 to 40  -1.8361   2.5977   1.2113   3.1584   2.4803   1.8712   9.6124   1.7342
+#&gt;    4.4894   6.4122  17.2042   6.2978  -1.3853  -2.7681   4.3347   5.1712
+#&gt;    5.1208  10.0671   2.5129   3.5565   6.1656 -14.1704  -4.6014 -10.7715
+#&gt;   -7.6823  -0.3134  11.0295  -2.8157  -2.1397   5.3854  -0.0939   1.8866
+#&gt;   -0.1719   1.3564   3.6817  -6.5371   3.6307  -5.8634   3.8469  -3.5201
+#&gt;   -8.8105   7.3172   6.9655  10.9895 -13.5253  -8.4398   2.9433  -6.6314
+#&gt;    1.8326  -1.7513   3.6676 -17.4745   2.9683   0.9017  -7.1492   7.2882
+#&gt;    0.3307   3.4832  -0.8127   0.1858  -4.9683   3.1905  -1.0582  -9.1252
+#&gt;    9.0351  -4.9397   1.1190  -9.4008  10.7649  -3.1090  -4.6578   5.9950
+#&gt;    3.8868 -13.8184   1.3700  -0.7495  -1.6484   8.8782   2.2646  11.2848
+#&gt;   -9.5724   1.3846   6.1086   6.2509  -7.7991  12.9296  -0.6927  -9.7555
+#&gt;   23.0605  11.2223  17.4334  -7.4375  -0.2172  -6.4786   5.3294  -1.2919
+#&gt;   -6.1336   3.0130  -4.3595   3.0022  -9.9482  -4.3258   2.4032  -1.9318
+#&gt;   -3.3434  -2.2167  -2.1505   7.3530   7.3721   4.3594   3.2649  -8.3715
+#&gt;   -2.9273   4.3399 -10.7637  -4.4302  -2.4921   1.7669 -13.7607  -5.5637
+#&gt;   -7.0023   4.0979  -6.1513   5.6763  -0.0058   0.6027   0.1248  -3.0934
+#&gt;   -9.5418  15.9676  -1.0498  13.0614  -7.0726   6.8900  -4.4018 -14.4214
+#&gt;    0.6100   4.3300   7.3375   9.5600  -4.7694  -6.5430   6.5318 -13.5396
+#&gt;    8.2764  -7.9832 -15.2114  -8.6635  -1.8658 -18.0196   7.6218 -16.1250
+#&gt;   -2.3019  -3.6860   3.7449   5.6943  -3.4252  11.4617   3.9088  15.7300
+#&gt;    5.4615  11.3182  -0.9004 -10.1088  -5.5132 -12.9972 -13.6019  -6.4688
+#&gt;   -0.8671  -2.3171 -21.9437  -7.3357  -0.8372  -5.7185  -7.5967  -4.5414
+#&gt;    9.1727 -10.9384  -5.1395  -4.3122   8.1625  -3.0933   4.3229   5.8023
+#&gt;   11.9205   3.1939  -7.0176  -4.2303   3.2226  -2.9097  -0.5847 -11.5925
+#&gt;   -0.5190   5.2495   5.5915   6.6845   6.1935  -6.8317   6.3080   3.2115
+#&gt;    8.1689  12.3485  -7.0101   0.4498  14.1312  16.4469  13.2634  -7.4017
+#&gt;  -11.1469 -10.3666   0.5546   4.3177  -0.9639   5.5811  -5.3490   1.2830
+#&gt;   -0.8181  -0.8712  -3.5820   2.8442   2.0308  -8.6577   2.0294  -3.3143
+#&gt;   -2.6309 -10.4239 -11.9457   3.5868  -1.8183   3.0974  -2.9380   4.6135
+#&gt;    4.8803 -10.9800 -10.3520  -6.0651  -6.1950  -1.9074   0.8738  -4.3113
+#&gt;    0.1633  -2.6691  -9.3632  -3.7809   3.1943  -2.9829  -4.8624  -1.2437
+#&gt;    3.5621  10.9607   0.5768  16.1664   1.4864   3.3760  15.1032  -4.1169
+#&gt;   -7.9020   4.5644   7.8298  -0.8661  -2.3574  -2.6209  -6.4854  13.1534
+#&gt; 
+#&gt; Columns 41 to 48   4.2696 -10.3398   9.8238   4.2835   7.1176   3.2086  -4.8103  -4.7768
+#&gt;    4.6720 -12.1254   8.2943  -9.1417  -7.3231 -14.2636   5.1581   6.0995
+#&gt;   -9.4891 -17.9925  15.2434 -13.9374 -13.2522  -2.1237 -10.3948  14.2589
+#&gt;    9.9647 -19.6312  14.1153 -18.9570 -12.0324  -1.3652   7.8780   8.0777
+#&gt;    3.3484  -8.8792   7.4224  -4.1295  -7.4020  -2.4369  -1.8368 -11.3500
+#&gt;   -4.8635 -10.7434  -3.5431 -10.1043 -16.2888   1.6730   8.8689  21.3476
+#&gt;   -9.5860   5.9653  14.7955  -4.6409   0.7384 -10.4116  -0.7186  -5.9172
+#&gt;   -8.3793   4.7615  -4.9623   7.8984   0.9772  -0.2394  -4.7146   1.8378
+#&gt;    3.5802  -2.7157  13.5225  -8.0359   0.0310  -6.2094  -5.0738   2.6893
+#&gt;    7.5559   6.5254   9.2457  -1.8114   8.8511   8.7851  -2.5641   6.4545
+#&gt;   -8.1791  -4.4865  -1.6442  17.4838 -14.0743  10.0539 -14.0073   1.7466
+#&gt;  -10.7323  -4.3227   0.6978 -10.4341   2.5087 -12.0362   0.2711  -6.4079
+#&gt;    4.7360   0.7439   1.9109  -3.7773  -2.6147  -6.3711  12.8484  -5.3801
+#&gt;   20.3780  -2.7399  14.9806  -1.5724   2.9260  -0.0032 -14.2525   1.7104
+#&gt;   -2.2088  -1.3158  -2.1839   6.6328  -9.5388   4.3666  -1.6111   0.6622
+#&gt;    7.3581  -6.4935   1.7580   5.1221  -7.6628  10.5954  -4.7309  14.6242
+#&gt;   -9.5354   4.9613  -1.1469  -4.6229  -1.4870  -1.8687  -9.3710  -6.9784
+#&gt;   12.1129 -17.8627   5.2556  -9.1915  -4.8105  -9.4641   4.0789   2.8252
+#&gt;    6.8028  -9.4066  -8.1274  -0.9585  -2.7877 -12.1167   4.1587   2.0131
+#&gt;    1.5452  20.2106  -9.0438   1.1733   7.0840  -5.9307  -0.9919   1.4335
+#&gt;   -5.7004  -3.4985  -9.4653 -19.3110  -7.3291   6.2590  -7.9861  10.7984
+#&gt;   -4.4106  20.2895  -2.6798  -4.3932   7.2682   3.7751  -7.3893   7.0946
+#&gt;   -2.6276   5.8938   7.2839 -17.2202  16.3209   4.5467   8.6853   0.1249
+#&gt;   -9.7645   2.4152 -12.1889  18.6565   8.5801   1.8104   0.8734   5.1252
+#&gt;   -0.1649  10.0940   5.6633  -9.5501  11.7118 -16.7442  -4.4452   1.0700
+#&gt;    5.2197  -8.8343  14.1881   0.3360   9.9435   0.9216   3.0978 -11.8234
+#&gt;    0.1632  -3.7635   0.4943   7.8677 -13.9478   6.3833  -2.3129   4.2759
+#&gt;    7.4062  -6.4231   7.2962  -7.2470   0.5831  -5.7529  -2.3694   7.5074
+#&gt;   13.1673 -10.4342  -0.7006   0.6684  11.4740  -6.3343  10.9019  -3.7141
+#&gt;    1.2306   5.4821  -7.6930   7.1085   4.4791  -4.2813   4.3127  -9.0234
+#&gt;   10.4695   6.8462  -3.1281 -12.0219   3.2788  -2.4583   0.0424   9.2522
+#&gt;    4.2526  -5.3190   5.6858  13.3091  16.0954   1.7858   2.0846  -3.7272
+#&gt;   -5.0684  -4.0662  -5.4795  -4.0989  -8.7127   6.9814  -7.9572  12.0195
+#&gt; 
+#&gt; (5,.,.) = 
+#&gt;  Columns 1 to 6  5.8336e+00  1.4469e+00  1.0283e+01  2.8077e+00 -3.6094e+00 -1.5380e-01
+#&gt;   4.4940e+00  7.0896e+00 -2.2995e-04 -9.4848e+00  3.5691e+00 -6.2526e-01
+#&gt;  -7.6153e+00 -8.3259e+00 -5.7668e+00  6.1621e+00  1.3068e+01  1.2865e+00
+#&gt;  -2.9380e+00 -6.5334e-02 -2.3914e-01 -2.6353e+00  5.0308e+00 -2.9705e+00
+#&gt;   2.1598e+00 -2.5440e+00 -2.9192e+00  9.8470e+00  2.7998e+00 -6.0515e-01
+#&gt;  -2.2917e+00 -3.6183e+00  1.3518e+01  1.4176e+01  8.4649e+00  1.1038e+01
+#&gt;  -9.1844e+00  1.2930e+00  2.3062e+00 -7.7915e+00 -5.1413e+00 -2.6392e+00
+#&gt;  -2.3330e+00 -6.8423e+00  5.9670e+00  8.3960e+00 -6.5738e-01  1.0778e+01
+#&gt;   2.1833e+00 -3.8961e+00 -1.4628e+01 -7.7291e+00  9.0423e+00 -1.1237e+01
+#&gt;  -5.5193e+00  4.8869e+00  1.0202e+01 -1.2893e+01 -8.4842e+00  7.4763e+00
+#&gt;  -4.1294e+00 -7.2557e+00  1.0769e+01 -7.6877e+00  4.7998e+00  8.2344e+00
+#&gt;   4.5731e+00  2.8154e+00 -3.8774e+00 -2.7598e+00 -4.8445e+00  3.2821e+00
+#&gt;   8.7788e+00 -2.6621e+00 -1.2134e+01  3.6804e-01 -1.8133e+01 -1.3970e+01
+#&gt;   1.1779e+01 -4.0730e+00  1.5088e+01  2.6929e+01 -2.6832e+00  9.8234e-01
+#&gt;  -3.6524e-01 -3.1626e+00 -5.6657e-01 -1.2514e+01 -1.3301e+01 -1.1408e+01
+#&gt;  -1.0719e+01 -8.0286e+00  1.0051e+00 -3.0784e-01 -7.7974e+00  8.4203e+00
+#&gt;  -5.8883e+00 -3.9020e+00 -1.9682e+00  7.4097e+00 -6.7653e+00 -1.0275e+00
+#&gt;   8.7535e-01 -2.5602e+00 -1.4949e+00  3.3937e+00  6.4309e+00  6.5225e+00
+#&gt;   7.1358e+00  2.9573e+00 -1.0967e+01 -9.2460e+00  7.7102e+00 -7.1687e+00
+#&gt;   2.9202e+00  3.9027e+00  2.9854e+00 -1.0808e+01 -1.0620e+01  5.8729e+00
+#&gt;  -8.5519e+00 -3.5855e+00  1.1963e+01 -5.1478e+00 -1.5484e+00  3.6894e+00
+#&gt;  -4.3179e+00  3.8282e+00  1.0106e+01 -3.9487e+00  5.7075e-01 -3.4411e+00
+#&gt;   1.0022e+01  1.6128e+00  2.7114e+00  1.1418e+01  4.2626e+00  6.4562e+00
+#&gt;   7.8232e-01  1.2545e+01 -6.5005e+00 -1.1441e+00  5.2024e+00 -8.6110e+00
+#&gt;   9.8817e+00 -1.0433e+01 -1.1409e+01  3.2136e+00 -3.5242e+00 -1.0030e+01
+#&gt;   9.1674e-01  9.9108e+00 -7.4078e+00  1.3646e+01  7.4239e+00 -7.9529e+00
+#&gt;  -1.8578e+01  3.7623e-01  6.8215e+00  9.9675e-02 -4.9704e+00 -5.8879e+00
+#&gt;   1.9956e+00  4.8642e+00  2.0417e+00  1.7247e+00  2.2569e+00  3.4207e+00
+#&gt;   7.1978e+00  5.8948e+00  4.4842e+00 -1.3967e+01 -4.8855e+00 -8.6459e-01
+#&gt;   1.0278e+01 -7.0889e+00  1.7776e+00  1.8428e+00  3.3566e+00  1.4120e+00
+#&gt;  -9.7108e+00 -3.2075e+00  5.6131e+00 -6.6512e+00 -2.8241e+00 -7.9182e+00
+#&gt;   4.5832e+00 -2.6150e+00 -1.1328e+01 -2.5173e+00 -6.5660e+00 -1.0109e+01
+#&gt;  -1.2307e+01  1.3603e+00  1.0558e+01  7.8136e-01 -1.3191e+00  5.4988e+00
+#&gt; 
+#&gt; Columns 7 to 12 -2.0645e+00 -6.6983e+00 -6.1994e+00 -1.6564e+00 -4.5502e+00 -2.8016e+00
+#&gt;  -1.3859e+01 -3.6551e+00  2.5218e+00  4.3770e+00 -1.4367e+01 -6.3245e+00
+#&gt;   1.2623e+01  1.2601e+01  6.3387e+00  2.2144e+01  3.9178e+00 -3.8783e+00
+#&gt;  -6.7408e+00  1.9949e+01  1.5427e+01 -4.1423e+00 -6.7698e+00 -6.9732e-01
+#&gt;   1.1015e+01  2.6099e-01 -8.3667e+00 -9.5767e+00  9.1215e+00 -3.1890e+00
+#&gt;   3.6462e+00  8.5545e+00  6.3157e+00  1.6946e+01 -1.4022e+01 -4.6502e+00
+#&gt;  -5.3064e+00  8.5455e+00  1.0837e+00 -2.1713e+00  1.0200e+01  1.3736e+01
+#&gt;   1.0995e+01 -6.3629e+00 -1.4443e+01 -2.3231e+00  5.3971e+00  1.7415e+01
+#&gt;  -1.0402e+01  1.0123e+01 -2.2828e+00  5.5813e+00  7.9876e+00  5.5451e+00
+#&gt;  -4.6158e+00 -9.5204e+00 -1.0898e+00 -5.7203e-01 -1.0733e+01  2.5479e+00
+#&gt;  -1.5395e-01 -2.5729e+00 -6.5111e+00 -6.7184e+00 -4.9331e+00  1.2344e+01
+#&gt;  -1.2416e+01 -5.9973e+00 -4.7462e+00  7.3203e+00  1.8486e+00 -2.4777e+00
+#&gt;  -1.2116e+00  5.0463e+00  1.1736e+01 -4.4435e-01 -1.2165e+00  1.0625e+00
+#&gt;  -2.2685e+00 -2.2781e+01 -1.9375e+01 -3.1510e-01  6.4274e+00  2.7375e+00
+#&gt;  -1.2431e+01  8.4353e+00  4.3142e+00 -9.1101e+00  4.7028e+00  1.2753e+01
+#&gt;   1.3462e+01  2.9800e+00  1.1975e+01  3.4457e+00 -5.2758e+00 -4.2949e+00
+#&gt;   8.3694e+00 -3.9662e+00 -3.5001e+00 -4.9132e+00  2.8551e+00  9.5853e+00
+#&gt;   1.1037e+01  2.3242e+00 -4.4274e-01 -1.8270e+00 -8.3952e+00 -8.1380e+00
+#&gt;  -4.9490e+00 -2.2897e+00 -3.0064e+00  9.1670e-01  1.5170e+01  6.6728e+00
+#&gt;  -8.2737e+00 -1.6769e+01  4.1306e+00  5.2120e+00  3.9413e+00 -4.8320e-01
+#&gt;   2.8436e+00 -5.8850e+00 -3.9048e+00  9.0619e+00  6.8739e+00  1.0564e+01
+#&gt;   7.0394e-01  5.0636e+00  1.0411e+01  1.8480e+01  2.1017e+00  4.0144e+00
+#&gt;  -6.5799e-01 -7.0307e+00 -9.2050e+00 -2.4637e+00  1.6049e+00  1.5621e-01
+#&gt;  -1.7425e+01  2.3908e+00  1.0688e+01 -1.8332e+00 -4.7906e+00 -3.3172e+00
+#&gt;  -3.5468e+00 -6.2092e+00 -9.6285e+00  2.9284e+00  8.3679e+00 -2.9525e+00
+#&gt;  -7.5318e+00 -3.8661e+00 -2.3946e+00 -2.8203e+00  1.3830e+00 -1.0135e+01
+#&gt;   5.2110e+00 -9.7605e+00  8.4143e-01  9.1831e+00  1.2640e+01 -1.0897e+01
+#&gt;   3.0835e-01 -2.8095e+00  5.8838e+00  1.2429e+01 -3.4084e+00 -7.6109e+00
+#&gt;  -1.6576e+01 -5.6398e+00  9.6835e+00 -5.9646e-01  4.0844e+00  8.8613e+00
+#&gt;   1.3851e+00  6.4901e+00  7.8120e+00 -1.1958e+01 -2.6831e-01  6.2586e+00
+#&gt;   2.4582e+00  6.5239e+00  4.0140e+00  7.7614e+00  6.1779e+00  5.0389e+00
+#&gt;   4.8228e-01  9.7339e+00 -3.6486e+00 -9.3653e+00 -1.9010e+01 -4.9112e+00
+#&gt;   1.7829e+00  6.7601e-01  1.4006e+01  1.0871e+01 -6.7866e+00 -9.9635e+00
+#&gt; 
+#&gt; Columns 13 to 18  4.0804e+00 -4.6274e+00 -4.0114e+00 -5.9758e+00  6.3923e+00  7.5205e+00
+#&gt;  -3.7319e+00  2.9354e+00 -4.8929e+00  1.6966e+01  8.5034e+00  5.2201e+00
+#&gt;   4.6159e+00  1.1864e+01 -4.0793e+00 -3.4063e-01  9.1695e+00  2.1266e+00
+#&gt;  -3.8896e+00 -1.0185e+01  1.2285e+01 -6.5640e+00  3.7502e+00  2.7837e+00
+#&gt;   4.8891e+00 -5.5146e+00  1.8514e+01  3.9871e+00  9.7924e-01  1.3543e+00
+#&gt;   1.0535e-01  3.7238e+00 -1.6232e+01 -1.2843e-01  7.9798e+00  1.4718e+01
+#&gt;   1.0952e+00  1.9138e+00 -2.8797e-01 -8.5654e+00  9.5860e+00  1.1858e+00
+#&gt;   4.6907e+00  3.2192e+00 -1.1024e+01  2.7595e+00  5.3012e+00  3.4819e+00
+#&gt;  -5.4489e+00  6.3026e-01  1.0845e+01 -1.5170e+01 -4.0282e+00 -1.3003e+01
+#&gt;   9.3268e-01 -1.1519e+01 -1.8905e+00 -1.4264e+01 -7.1144e-01  4.4427e+00
+#&gt;  -1.0634e+01  2.2329e+01 -1.6086e+01  1.0390e+01  4.2664e+00  8.4624e+00
+#&gt;   5.9640e+00 -7.8229e+00  2.2603e+00  2.0475e+00  1.4151e+01  6.4597e-01
+#&gt;   5.0788e+00 -9.5068e+00 -3.3177e+00 -7.0264e+00  3.7865e+00  6.8228e+00
+#&gt;   1.2794e+01 -1.0997e+01  7.9029e+00 -1.6953e+01 -6.4281e+00 -2.7841e+00
+#&gt;  -9.8346e+00 -6.9945e+00  2.0859e+00  5.6787e+00  2.7236e+00  9.3853e+00
+#&gt;   6.5656e+00 -1.4124e+00 -6.5000e+00  9.4045e+00 -4.7943e+00  4.2398e+00
+#&gt;   9.9943e+00 -1.2300e-01 -8.9361e+00  1.6648e+00  4.5557e+00  3.6385e+00
+#&gt;   5.6028e+00  3.6349e+00 -7.6901e-01  1.3754e+01 -1.9914e+00  8.9277e-02
+#&gt;  -1.6124e+00  2.1531e+00  2.8876e+00  7.7484e+00 -1.1959e+01 -9.7570e-01
+#&gt;  -3.1444e+00  5.9413e+00 -6.1925e+00 -1.2860e+01  2.8159e+00 -6.0415e+00
+#&gt;  -5.8180e+00  4.9203e+00 -1.8490e+00  6.6601e-01  6.4857e-01 -7.6449e-01
+#&gt;  -1.6410e+01  2.0862e+00 -4.4882e+00  2.3420e+00 -6.1667e-01 -1.0148e+01
+#&gt;   5.1785e+00 -7.9062e+00  5.6711e+00 -5.0990e+00  2.1522e+00 -1.4937e+00
+#&gt;   7.2697e+00 -6.1065e+00  5.7418e+00  8.9145e+00 -1.0260e+01 -1.1386e+00
+#&gt;  -3.6550e+00 -1.0537e+00 -1.2647e+01 -6.2766e+00 -1.7208e+00 -9.7136e+00
+#&gt;   9.5335e+00 -3.6806e+00  1.8300e+01  4.3355e+00  9.6918e+00 -3.4956e+00
+#&gt;  -4.4250e+00  4.3727e+00 -1.2206e+00 -1.1575e+01 -6.9490e-01  4.0831e+00
+#&gt;   3.5346e+00  2.9208e-01 -3.0783e+00  3.0060e+00 -1.8771e+00 -2.6873e+00
+#&gt;  -1.2386e+00 -1.7936e+00 -1.4961e+01 -1.1672e+00 -3.5595e+00  1.6893e+00
+#&gt;  -9.2694e+00  9.4057e-01 -6.9681e+00  6.0922e-01 -8.3566e+00 -7.3884e+00
+#&gt;  -7.1675e+00  1.3295e+00 -4.0344e+00 -2.7340e+00 -8.4022e+00 -4.8550e+00
+#&gt;   4.4742e+00  4.4855e+00  1.8283e+00 -4.6183e+00 -5.4392e+00  1.9086e+00
+#&gt;  -6.6812e+00  1.1725e+01 -7.5816e+00 -1.5030e+00 -5.6127e+00 -6.3232e+00
+#&gt; 
+#&gt; Columns 19 to 24  1.9075e+00 -5.1671e+00 -2.8861e+00  3.8863e+00 -1.0865e+00 -4.4868e+00
+#&gt;  -2.7110e+00 -2.7595e+00  7.3627e+00  6.8774e+00  5.3323e+00 -2.3386e+00
+#&gt;   6.4896e+00 -5.9896e+00  6.2405e+00  4.5535e+00 -5.5806e+00  2.2119e+00
+#&gt;   3.6726e+00  3.6630e+00  1.3051e+01  5.6107e+00 -1.4811e+00  6.2675e+00
+#&gt;   9.7341e+00  1.8405e+00 -3.9884e-01  4.5383e+00  7.2716e+00 -8.9318e+00
+#&gt;   1.6564e+00 -8.6805e+00  1.4102e+00  4.6522e-01 -6.2129e+00  2.2132e+01
+#&gt;  -3.2298e+00 -8.6574e+00  6.4470e+00 -6.9426e-01 -3.5898e+00 -1.3447e+00
+#&gt;   2.7328e+00 -4.1683e+00 -6.0934e+00 -1.8344e+00  7.0255e+00  2.4824e+00
+#&gt;   4.2133e+00  2.8368e+00  2.2248e+00 -2.2850e+00  6.1526e+00 -5.8541e+00
+#&gt;   5.0729e+00  4.4385e+00 -7.1746e+00  1.5144e+00 -9.0863e-01  5.8397e-01
+#&gt;  -2.6362e+00 -5.5934e+00  5.1918e+00  1.8751e+01 -2.0175e+01 -3.6741e+00
+#&gt;  -3.5194e+00 -1.6747e+00  4.5705e+00 -2.7844e+00  8.1209e-01 -8.7155e+00
+#&gt;  -2.0420e+00  2.1456e+00 -2.7647e+00 -1.0230e-01  5.2683e+00 -5.0584e-01
+#&gt;   5.6779e+00  4.0394e-01 -7.8927e+00  1.2400e+01 -8.8077e+00 -5.2084e-01
+#&gt;  -1.3497e+01  5.7529e+00  3.1664e+00  3.0371e+00 -9.5848e-01  2.2515e+00
+#&gt;   4.5942e+00 -6.2759e+00 -2.5370e+00  3.0009e-01  5.1784e-01  9.5865e-01
+#&gt;  -4.3198e+00  3.6395e+00  7.1711e+00  5.6559e+00 -7.3939e+00  1.6095e+00
+#&gt;   7.7820e+00  1.4765e+00  4.6742e+00  4.6878e+00  3.5061e+00  1.2920e+00
+#&gt;  -6.8726e+00  3.3282e+00 -4.0796e+00  3.6112e+00 -3.8519e+00 -4.4891e+00
+#&gt;  -6.0432e+00  3.8831e+00 -2.7348e+00 -5.3159e+00 -1.5399e+00 -2.8460e+00
+#&gt;  -2.5613e-01  7.7684e+00 -3.2013e+00 -8.2297e+00  2.1071e+00  2.4986e+00
+#&gt;  -3.3099e+00  4.5167e+00 -7.7357e+00 -1.7795e+00  5.5184e+00  5.7502e+00
+#&gt;  -2.8577e+00  3.6075e+00 -4.3231e+00 -3.0083e+00  2.1724e-01 -3.0792e+00
+#&gt;  -5.4847e-01 -8.1615e+00 -8.6641e+00 -7.0225e-01 -2.9733e+00 -1.7660e-01
+#&gt;   6.4026e-01 -3.5293e+00  4.6284e+00 -6.9687e+00  9.9917e-01 -1.6749e+00
+#&gt;   9.1805e-01  5.7766e+00  6.8738e-01  7.5628e+00 -4.7703e+00 -1.4434e+01
+#&gt;   3.9465e+00  7.3027e+00 -8.6644e+00  1.0841e+01 -1.1006e+01 -1.1051e+01
+#&gt;   4.9543e+00 -5.5705e+00 -2.7287e+00 -1.6229e-01  3.6888e-01 -5.1247e+00
+#&gt;   4.2543e+00 -4.6977e+00 -1.2617e+01 -6.9965e+00 -1.3153e+00  7.0018e-01
+#&gt;  -9.3969e+00  4.5462e+00 -3.1446e+00  1.9827e+00 -7.9423e-01  3.1600e+00
+#&gt;   1.6407e+00  3.5578e+00  4.7857e-01 -7.9521e+00 -2.6705e+00  3.1548e+00
+#&gt;  -4.9212e+00 -5.4186e+00 -7.7663e+00 -1.7866e+00  7.3785e+00  3.7391e+00
+#&gt;   1.0988e+01 -3.5739e+00  2.3192e+00  7.7857e-01 -6.1537e+00 -1.6571e+00
+#&gt; 
+#&gt; Columns 25 to 30  3.5060e+00 -6.6555e+00  6.6771e+00 -1.2302e+00 -1.3100e+00 -1.2005e+00
+#&gt;  -4.3183e+00  1.8904e+00  9.3727e+00 -1.4739e+01  4.6993e+00 -3.0541e+00
+#&gt;   1.2514e+01 -1.2208e-01  3.9735e+00 -6.9719e+00 -1.6826e+00  8.0822e-01
+#&gt;  -6.0562e+00 -8.4440e+00  2.2050e+00 -4.1130e+00  3.6959e+00  9.7097e-01
+#&gt;   9.8301e+00 -1.2278e+01  7.3278e+00  6.7034e-01 -5.4279e+00  1.7780e+00
+#&gt;  -7.4711e-01  1.1437e+00  1.3250e+01 -2.1135e+00 -6.4001e+00 -1.5373e+00
+#&gt;   2.6650e+00 -7.7118e+00  7.3130e+00  2.5775e+00  8.6126e+00 -7.0690e+00
+#&gt;   1.0283e+00  3.5418e+00  3.3780e+00  7.3297e+00 -9.9980e+00  1.2897e+00
+#&gt;   3.5313e+00  4.8007e+00 -2.0213e+00 -7.9567e+00  1.1169e+01 -6.5328e+00
+#&gt;  -1.3208e+00 -1.5740e+01  1.4278e+01 -1.6370e+01  1.0310e+01  8.5325e+00
+#&gt;  -4.1625e-01 -5.1591e+00  8.1490e+00  4.5922e+00 -4.2217e+00 -8.2004e+00
+#&gt;   1.0554e+01  5.0466e+00 -4.3511e+00  1.6601e+00  9.6846e-02 -4.3014e+00
+#&gt;   4.2997e+00 -1.0469e+01 -3.0786e+00  3.6426e+00  3.3674e+00  6.3131e+00
+#&gt;   7.9593e+00 -1.7446e+01  1.0004e+01  3.2395e+00 -1.7300e+01  3.9032e+00
+#&gt;   1.8155e+00 -7.1766e+00 -1.7172e-01  1.5534e+01 -7.6793e+00 -7.3116e+00
+#&gt;   4.7103e+00 -2.9560e+00 -6.8889e+00  6.8551e+00 -6.6955e+00  2.3126e+00
+#&gt;   4.2955e+00 -4.4392e+00  1.3627e+00  1.7298e-01 -9.5954e+00  1.4855e+01
+#&gt;  -6.1302e+00  2.0692e-01 -2.7076e+00 -1.4112e+01 -7.9239e+00  7.6179e+00
+#&gt;  -8.4681e+00 -5.5159e-03 -6.0258e+00  5.8443e+00 -1.1785e+01 -3.1252e+00
+#&gt;  -4.6787e+00  2.4320e+00  2.2983e+00  5.3531e+00  1.4982e+00  3.5188e+00
+#&gt;   3.6223e+00  1.5777e+00  5.3822e-01 -6.7858e+00 -3.5065e-01 -1.8204e+00
+#&gt;   8.0087e+00 -2.8586e+00  4.0240e+00 -1.1221e+01  4.6164e+00 -6.6989e+00
+#&gt;  -4.8832e+00 -3.2109e+00 -3.7940e+00  3.4574e+00  2.9035e+00 -3.2206e+00
+#&gt;   1.0745e+01 -3.6220e+00 -4.1194e+00  1.6118e+01 -5.1622e+00  3.7233e+00
+#&gt;  -1.7687e+00  4.3935e+00 -3.8096e+00 -2.1342e+00 -8.3650e-01 -3.9519e+00
+#&gt;   1.4233e+01 -5.6465e+00  6.9745e-01  7.9877e+00  2.0713e-01  9.6992e+00
+#&gt;   4.9125e+00 -1.0809e+01  6.4334e-03 -1.4920e+00 -9.3754e-01  8.2461e-01
+#&gt;   6.0181e+00 -5.9128e+00  7.4054e+00 -7.4188e+00 -4.3923e-01 -7.3626e+00
+#&gt;   5.6962e-01 -1.0416e+00  7.1098e+00 -1.2487e+00  1.3191e+01 -1.4202e+01
+#&gt;   3.8604e-02  1.5730e+00 -1.1650e+01  4.1796e+00 -8.5456e+00 -3.7950e+00
+#&gt;  -4.3958e+00 -2.9770e-01  4.9461e+00 -1.1407e+00  2.4189e+00 -3.2541e+00
+#&gt;   1.1090e+00  2.1914e+00 -1.9405e-02  4.0812e+00 -1.3151e+00  5.7624e+00
+#&gt;  -5.2244e+00 -6.3511e-01 -6.2743e+00 -8.5156e+00  3.3536e+00 -1.6210e+01
+#&gt; 
+#&gt; Columns 31 to 36  6.1982e+00  5.5916e+00  2.7885e+00  1.0189e+01  1.1431e+01  4.9566e+00
+#&gt;   1.0178e+01  4.4730e+00  1.5423e+00  3.3104e+00  5.3882e+00  3.8084e+00
+#&gt;  -2.0036e+00 -1.2035e+01  8.6341e-01 -4.6895e+00 -4.4213e+00 -2.1763e+00
+#&gt;  -8.4662e+00 -7.7066e+00  1.1197e+01 -5.2408e+00  2.4517e+00  5.3138e+00
+#&gt;   5.7486e+00 -1.4716e+01  3.6035e-01  2.1045e+01 -1.1319e+01  1.5318e+00
+#&gt;  -9.4682e+00  2.8308e+00  1.3540e+01 -1.7959e+00  1.5806e+01  1.0672e+01
+#&gt;   9.4510e+00 -3.5718e+00 -8.4978e-01  4.5584e+00  1.2796e+01 -1.5654e+00
+#&gt;  -1.1028e+01  7.9593e-01  1.3840e+01 -2.7557e+00  6.1373e+00 -1.2787e+01
+#&gt;   7.2767e+00 -1.7013e+01 -1.1136e+01 -8.7254e+00 -6.3872e+00 -1.1509e+01
+#&gt;   5.8077e+00  1.5022e+00  1.7553e+00  2.4263e+00  3.5528e+00 -5.0187e+00
+#&gt;   1.7892e+01  1.8655e+00  3.1090e+00 -2.4692e+00  1.1657e+01  1.1899e+00
+#&gt;   2.2901e+01  1.9642e+00 -6.3054e+00 -4.4449e-01  6.8761e-01  1.7599e+01
+#&gt;  -1.6541e+00 -4.3045e+00 -6.2966e-01 -5.8996e+00 -9.5683e+00  1.0512e+01
+#&gt;  -3.4407e+00  3.6485e+00  1.9150e+01  1.2891e+01 -1.2427e-01  4.2755e+00
+#&gt;  -4.0717e-01  5.4538e+00 -2.6612e+00 -5.5491e+00  1.7631e+00  1.7065e+00
+#&gt;  -1.6581e+01  3.3456e+00  7.9727e+00 -8.9622e-01 -9.1770e+00  6.0081e+00
+#&gt;  -3.6655e+00  4.2207e+00  8.4427e+00 -9.9253e+00 -2.2105e+00 -4.1150e+00
+#&gt;   1.6683e+00 -6.3756e-01  3.6346e+00 -4.0476e-01 -1.3779e+01 -7.5887e+00
+#&gt;  -1.9393e+00 -9.3332e+00  5.2066e-01  6.6675e+00 -1.9191e+01  5.3200e+00
+#&gt;  -7.8613e+00  1.3930e+01  2.7022e+00 -1.4145e+01  7.8802e+00  2.6400e+00
+#&gt;  -6.8321e+00 -9.7115e+00 -5.3862e-01 -1.0395e+01 -6.3867e-01 -5.0146e+00
+#&gt;  -6.9120e+00 -5.6570e+00 -4.9373e+00  1.2915e+00 -2.3262e+00 -2.1438e+01
+#&gt;  -1.6716e+00  6.6241e+00  9.7926e+00  2.5665e+00 -7.9879e+00  5.3613e+00
+#&gt;   8.8860e+00  3.0840e+00 -1.8972e+01  5.5391e+00 -1.3550e-01 -3.1654e+00
+#&gt;   3.7155e+00  8.4405e-01 -4.5309e+00 -2.8464e+00 -6.6865e+00  4.7325e+00
+#&gt;   4.9660e+00 -1.1579e+01  5.5520e+00  8.9030e+00 -8.2167e-02 -6.5898e-01
+#&gt;  -4.4488e+00 -1.1496e+01  3.3262e-01 -9.9417e-01 -1.5805e+01 -1.3600e+01
+#&gt;   2.1313e+00 -1.5164e+00  1.5813e+00  5.9362e+00 -5.3972e+00 -2.6751e+00
+#&gt;   3.9508e+00  5.6909e+00  1.2322e+00 -6.3192e-01 -4.4705e-01 -1.5460e+01
+#&gt;   9.0135e+00  4.3258e+00 -4.4283e+00  6.1289e+00  1.0678e+00  6.2983e+00
+#&gt;  -1.5593e+01 -5.7544e+00  4.4779e+00 -1.4258e+00  2.6896e+00 -4.6289e+00
+#&gt;   1.0181e+01  5.1590e+00 -8.8165e+00 -5.6228e+00 -2.1333e+00  5.3188e+00
+#&gt;   5.9610e+00  7.9238e+00 -1.0611e+01 -3.9474e+00  1.2250e+01 -4.6364e+00
+#&gt; 
+#&gt; Columns 37 to 42 -7.3558e+00  1.8478e+00 -1.3565e+01 -3.9673e+00  4.8040e+00 -4.2955e+00
+#&gt;   1.5991e+01  2.5160e+00 -6.4857e+00  2.9707e+00  3.3668e+00  1.6766e+01
+#&gt;   5.3307e+00  1.6203e+01  1.1577e+01  1.1139e+01  6.6203e+00  1.8992e+00
+#&gt;  -3.6759e+00  1.3892e+01  1.6713e+01  1.7167e+01  1.4447e+01  3.9491e+00
+#&gt;  -9.7422e+00  8.7446e-01 -9.0631e+00  6.8964e+00 -1.6838e+01 -1.0605e+01
+#&gt;  -2.2836e+00  2.2875e+01  7.8057e+00  1.1797e-01 -5.8903e+00 -1.8103e+00
+#&gt;  -5.7325e-01 -2.3644e+00  4.9360e+00  5.3952e+00 -4.1858e+00 -1.0905e+01
+#&gt;  -2.0070e+01 -2.1911e+00 -1.6904e+01 -1.2237e+01 -1.2856e+01 -8.6475e+00
+#&gt;   1.6517e+00 -8.2912e+00 -3.2250e+00 -4.3587e+00  7.6742e+00  7.9976e-01
+#&gt;   4.4946e+00  5.4656e+00 -9.9353e-01  7.4549e+00 -9.2468e+00  6.6536e+00
+#&gt;  -1.0185e+01  3.3628e+00  1.3991e+01 -3.1431e-01  7.8805e-01 -2.8357e+00
+#&gt;  -1.1942e+01 -5.9051e+00 -5.0902e+00 -8.9735e-01  5.6305e+00 -8.4694e+00
+#&gt;   9.8223e+00  7.3412e+00  3.5796e+00  5.7735e+00  1.9905e+00  9.3903e+00
+#&gt;  -2.2032e+01  4.9234e+00 -1.4373e+01 -4.7423e+00 -4.5105e+00 -5.1470e+00
+#&gt;   7.5400e-01 -2.7651e+00  1.7615e+00 -4.3036e+00  2.1885e+00 -9.9168e+00
+#&gt;  -4.3423e+00 -9.7685e-01  1.5548e+00  1.2219e+01  1.2048e+00  5.0471e+00
+#&gt;   1.3638e+00  1.4873e+01  8.5603e+00  6.7804e+00  6.8853e+00  5.3534e+00
+#&gt;  -5.7750e+00  1.0367e+01 -9.4022e+00  8.1251e+00 -4.1837e+00  6.0742e+00
+#&gt;  -1.4540e+01 -1.1080e+01 -7.2195e+00 -7.9642e+00 -1.1162e+01 -3.3161e+00
+#&gt;  -5.3722e+00 -6.4250e+00  5.9252e+00  1.1901e+00 -6.4091e+00  9.8618e+00
+#&gt;   8.5537e+00  8.0950e+00  2.3728e+00 -1.0542e+00 -4.2487e+00 -1.0509e+00
+#&gt;   1.3275e+01 -3.9895e+00 -1.9680e+00 -1.2985e+01 -1.3368e+01 -3.7960e+00
+#&gt;   3.4736e+00 -2.1850e+00 -3.7348e+00 -6.5829e-01 -2.7725e+00 -3.2504e+00
+#&gt;   3.1980e+00  6.1895e+00  5.6776e-01 -7.4301e+00  4.7530e-01 -4.2809e+00
+#&gt;  -1.7356e+00 -4.0475e+00 -1.5024e+01 -3.2310e-01 -3.7233e+00  2.0674e+00
+#&gt;  -1.5031e+01  4.4899e+00 -7.4734e+00  3.8080e+00  1.2273e+01 -5.1166e+00
+#&gt;  -2.8133e+00  3.3891e+00  1.1317e+01  4.7004e+00  2.1072e+00  4.1002e+00
+#&gt;   2.6798e+00 -8.6531e-01 -9.9506e+00 -3.2235e+00 -9.8978e+00 -6.3470e-02
+#&gt;  -3.7034e+00 -1.5443e+01 -1.6219e+01 -2.6023e+00 -6.0245e+00 -9.2024e+00
+#&gt;  -7.9181e+00 -1.0742e+01 -5.6723e+00 -7.1277e+00 -2.9193e-01 -1.3050e+01
+#&gt;   1.0241e+01  3.1233e+00  3.9747e+00  9.2563e+00 -1.0152e+00  6.2321e+00
+#&gt;  -2.0754e+00  2.9852e+00 -1.1903e+01  1.1825e+00 -7.8679e+00  1.7905e+00
+#&gt;   2.1825e+01  7.8318e-02  1.3404e+01  8.4809e+00  1.3420e+01  4.5805e+00
+#&gt; 
+#&gt; Columns 43 to 48 -1.8168e-01 -1.1012e+00 -7.1910e+00 -1.4037e+00 -1.7236e+00  1.2356e+00
+#&gt;   2.8378e-02  1.8888e+00  7.9651e+00 -1.2617e+00 -2.4814e+00 -4.5150e+00
+#&gt;  -6.1803e-01  7.5025e-01 -8.1981e+00 -1.4032e+00  3.2832e+00 -5.0151e+00
+#&gt;   1.1292e+01  7.0137e+00 -4.8292e+00  7.0170e+00  4.8443e+00 -3.9258e+00
+#&gt;   1.0790e+00  1.2487e+01 -2.6888e+00  3.3796e+00  1.9949e+00 -3.6263e+00
+#&gt;  -8.9870e-01 -1.6069e+01 -1.2749e+01 -8.4440e+00 -5.3007e+00 -2.0938e+01
+#&gt;  -2.7425e+00  6.9554e-01  9.1293e-01 -1.6858e+00  4.3919e-01  9.5408e+00
+#&gt;  -4.1454e+00 -1.6091e+01 -6.6829e+00 -3.1834e+00 -1.4984e+00 -8.7432e+00
+#&gt;   1.1823e+01  1.4894e+01  6.4397e-01  1.3332e+00  8.0127e+00  7.9523e+00
+#&gt;   1.2801e+00 -2.6508e+00  6.0092e+00  7.6999e+00 -5.3024e+00  1.2944e+01
+#&gt;  -4.6366e+00 -4.0845e-01 -2.9419e+00 -5.5225e+00  6.1049e+00 -3.9872e+00
+#&gt;  -4.0252e+00 -2.7645e+00  5.7635e+00 -8.9263e+00  3.6107e+00 -4.5948e+00
+#&gt;  -1.4826e+00  7.4472e+00  1.7991e+00  1.1321e-01  4.5220e+00 -3.4062e+00
+#&gt;   2.9610e+00  6.0132e-01 -1.1946e+01  9.7302e+00 -3.0436e+00  1.9629e+00
+#&gt;  -6.7898e+00 -2.9701e+00 -8.9971e+00  2.4158e+00 -2.8677e+00  8.1492e-01
+#&gt;  -9.7827e+00 -5.8758e+00 -9.7358e+00  7.3615e+00  2.2624e+00 -1.7039e+00
+#&gt;  -4.4273e+00 -3.6459e+00 -5.4875e-01  6.2453e+00 -7.5960e-01  1.4502e+00
+#&gt;   5.1361e-01 -3.6357e+00  7.6778e-01  1.0157e+01  1.0523e+01 -9.4993e+00
+#&gt;   6.1523e-01  2.7263e+00  4.0079e+00 -1.0994e+01  2.4581e+00  2.9155e+00
+#&gt;   8.1481e+00  4.0605e+00  1.8207e+01 -1.7273e+00  5.1386e+00  8.0329e+00
+#&gt;   3.2023e+00  2.3604e+00 -2.7896e+00 -3.9848e+00 -9.7489e+00  3.1861e+00
+#&gt;  -6.7553e+00  7.6011e+00  5.3619e+00  2.2031e+00 -6.0388e+00  1.1303e+01
+#&gt;  -7.0094e-01 -8.7572e+00 -3.5856e+00  1.3358e-01 -2.0777e+00 -4.5189e+00
+#&gt;  -9.5313e+00  5.6471e-01 -6.8210e+00 -9.6729e+00 -1.4461e+01 -7.9584e+00
+#&gt;  -1.6693e+00  7.4138e+00  4.3911e+00  4.3128e+00  8.3905e+00 -2.9648e-01
+#&gt;   3.5896e+00  1.1364e+01 -1.3229e+01 -8.8098e+00  6.6256e+00 -4.3239e+00
+#&gt;   1.0316e+01  3.7142e-01  1.2612e+00  6.3484e-01 -2.2128e-01  5.8600e+00
+#&gt;  -6.0903e+00  6.3052e+00 -2.4741e+00 -1.1545e+00 -2.2059e+00 -1.1759e+00
+#&gt;  -3.2627e+00 -2.4473e+00 -7.9768e+00 -2.2971e+00  1.7707e+00 -3.2508e+00
+#&gt;  -9.4117e+00 -7.5099e-01  6.0180e+00 -2.0775e+00  1.0671e+01 -2.2179e+00
+#&gt;   8.1073e+00 -1.9226e+00 -1.5990e+00  3.2765e+00 -8.1194e+00  5.6850e+00
+#&gt;  -1.9664e+00 -1.1811e+01 -8.8313e-01  1.0266e+01  7.1744e-01 -8.2211e+00
+#&gt;  -3.7228e-01  7.0762e+00  3.0507e+00  1.0752e+01  3.8020e+00  2.8253e+00
+#&gt; 
+#&gt; (6,.,.) = 
+#&gt;  Columns 1 to 8  -3.8431   7.3834  -5.6651   5.0708  -1.4517  -4.6431  -0.8883   7.4393
+#&gt;   -1.2381 -11.5041  -0.6932   4.2547  -4.0637  -2.9001   2.5411   4.2100
+#&gt;   -6.3624  -5.3346  -4.7683  -0.3192  -3.9026  -7.1352   1.4164   6.1695
+#&gt;    1.6059   1.4589  -9.0341   9.8772  -0.2623   9.6224   7.9349  24.2502
+#&gt;   -4.0875   2.9824 -14.0975 -13.2114   5.4580  -2.1398  -2.3853  -2.6447
+#&gt;   -1.3363 -18.6336  -5.9642   7.3051  -0.4355  -4.6710   2.6051   0.8527
+#&gt;   -7.5498  -4.6894  -8.6756   4.6694   0.6881   8.1117   3.9346  -2.5690
+#&gt;   -2.2236  -4.7131  -3.3760  -3.0355  -2.1877  -8.5541  -0.2228  -3.6215
+#&gt;    2.3723   8.7867  -2.0226   3.5694  -7.1310  -3.2541  -6.1476   9.9991
+#&gt;    6.3060   4.2074  -1.3340   8.1741 -10.7549   4.7283   8.2079  -9.2118
+#&gt;   -8.5755  -7.6608   6.8131  -5.8313   5.3371   4.4907  -4.8163  -8.6892
+#&gt;   -7.9691   1.5445  -1.8785   2.3053  -7.6918   6.7353 -13.6094   5.5143
+#&gt;   15.5013   5.4054   8.1804   4.2990  -0.1030  -4.2687   2.5195   7.4986
+#&gt;  -17.2673   0.8237 -16.5174   4.2845   3.3804  -5.8239   4.2698   5.4976
+#&gt;    6.2063   7.0952  10.5727   8.8347   4.9185  -3.6430  -1.8074  -5.1455
+#&gt;    3.3922   1.3143   0.6030  -1.0616  -0.5150  -5.7062  17.7802   0.9328
+#&gt;    5.6760   3.8643   2.0599  -3.4919  -2.9444  -5.4062   0.8802  -8.1386
+#&gt;    0.0862   0.0255   1.9659  -1.3832  -1.9223   1.4673   0.4598   5.9191
+#&gt;    1.4391  -2.7191  -3.6907   5.8302   6.3299  -1.9486  -9.4407   0.9470
+#&gt;   11.6963  -8.8488   2.8334  -1.9257   0.8096   6.1601   1.1925  -1.6437
+#&gt;   -5.1210   6.9766  -2.8494   0.2123  -0.9609   1.1285  -0.4353  -6.7621
+#&gt;    4.7367  -3.7483   2.3989  -2.3040   3.1682   1.1741   5.7830   0.1155
+#&gt;   -5.1974  -3.2208  -1.1463   5.1683  -5.4692  11.4663  -6.5940  -2.0914
+#&gt;   -1.8723   3.3740  -3.7008   2.6528  10.6767  -3.1249 -11.2269  -8.2101
+#&gt;    8.8385   6.7610   1.4517  -5.8783  -2.4141  -4.0425  -7.8986  -0.9080
+#&gt;  -10.1123  -2.7159 -17.9195   4.9543   1.3166   0.7267  -7.6073  11.2488
+#&gt;   -0.5219  13.5147  -5.8599  -2.4669   7.9078  -0.0490   5.8223   6.0661
+#&gt;   -3.6575  -3.2303  -7.4904   4.6595   1.8652  -4.7055   0.7859  -0.4844
+#&gt;    8.4408  11.4584   7.4887   5.0873  -7.1360  -0.4913  -9.3323  -3.5790
+#&gt;    0.4671  -1.1088  12.0600  -1.5976   1.3205   5.9996 -10.3857   7.2800
+#&gt;    3.3133  -2.4864  -1.4580   5.9497   3.3518   2.1192  11.6811   0.5496
+#&gt;    5.3611   0.5139   7.0425   1.4836  -3.1040  -6.4254  -9.0574  -7.4924
+#&gt;   -7.3011  -2.0470  -1.6937  -4.3892   2.0341  11.0654  14.2605   6.6830
+#&gt; 
+#&gt; Columns 9 to 16  -0.8120   7.4782  19.8912   7.9250   5.3954  10.3623  -8.3094  -4.5597
+#&gt;    4.0740  13.3418   1.6228   0.0288   4.8979   4.6911  -5.6091  -7.1796
+#&gt;   -3.8880  -6.3417 -17.9954 -10.8148  -4.0093   6.5191  -9.4727   5.2284
+#&gt;   -8.0646  -5.8118  -0.1473  12.2662  -2.3938  -6.0465  -5.6511   7.1533
+#&gt;   -1.3761  -0.7350  -4.5809  -8.0448  -5.2520   6.9067   5.5636   9.8490
+#&gt;  -16.5892  -2.6397   8.6574   8.2825  -1.1968 -10.3730  -2.1449  -3.6058
+#&gt;   -8.2305  -1.0772   8.4838   4.4443  -2.0186  -2.4281  -7.0722  -3.3671
+#&gt;  -10.1104   3.0282  14.4971  -4.5735  -5.7620  -5.8830  13.1251  -3.0741
+#&gt;   -2.6544   3.5008 -19.3625  -8.2454  -2.7320   9.9883  -0.9108  11.3159
+#&gt;   -6.7775  13.9699   2.8887  -0.1321   2.9471   4.9556  -2.7823  -2.2237
+#&gt;   -4.7958   1.1634   2.4740  -0.2882   5.4787  -6.7352  -8.1452  -4.8458
+#&gt;    8.6103  14.6142  -0.4122   8.9540  -5.1263  -4.9815  -4.4847  -0.6936
+#&gt;   -2.3469  -7.4132  -2.3151   4.9861  -3.6393  -5.7564  -3.9316   0.4889
+#&gt;  -10.0148  13.3231   6.2298  10.7633   7.8557  18.2105 -12.0861  -5.3622
+#&gt;   -1.1570   4.9031   9.5066   3.8215 -18.6123 -15.8685  -4.3792  -0.2539
+#&gt;    1.6511  -9.7728   3.4944   0.3973  -6.2884  -8.5203   3.0773   0.9752
+#&gt;   -5.0465 -11.3065  -7.4616  -0.1316   5.7957  -4.1953  -1.9611  -1.8844
+#&gt;   -0.3280   0.5115  -1.4591  -8.2698  -7.8082  -2.3492   1.2596   9.7844
+#&gt;    0.8887  -2.5462   3.2239 -12.8953  -4.2650  -3.7035  -8.1158   2.2499
+#&gt;    4.8492  -7.3734  -3.7737   6.6471   8.6360  -6.4765   9.5265  -4.8609
+#&gt;    0.5326  -6.0841  -5.3797  -3.3401  -9.5274  -6.0842  -0.4826   3.6246
+#&gt;    6.6203   3.3530  -8.8277 -10.0762   4.6060  19.2481   0.1302  -6.2784
+#&gt;   -7.6193  23.1043  -6.3003   5.5355   0.1201   5.3113  -2.4701  -2.0311
+#&gt;    1.4992  -5.5342  -0.1859  -6.0348  -6.4621   4.8979 -10.7997 -10.0918
+#&gt;    1.5731  -0.6728  -8.2198   3.5697   6.4091   0.7899  -2.1718  -0.9641
+#&gt;    0.2540  -0.2781  -0.2170   0.6706  11.4201  15.2664  -4.3636  -3.6404
+#&gt;    2.6073  -0.5216  -8.0851  -2.3002  10.7249   5.4236  -4.6967 -10.5057
+#&gt;    2.5230   3.8005  -1.1218  -4.7448   4.6458   8.1591 -12.9378  -7.1927
+#&gt;    4.7360   8.1318  12.3759  -5.6282  -2.6114  -3.5258  -4.7704   3.0365
+#&gt;   -2.2549   8.4677   6.0758   0.9920  -1.2759  -7.2451   0.8246   1.0953
+#&gt;    0.8551  -5.4134   5.1745   4.8013   5.0497  -3.0157  -7.4301  -3.2177
+#&gt;   -4.8331  -9.1480  -1.8889  -1.5710 -14.2567  -2.8050  10.4588   2.0963
+#&gt;    1.7096  -2.0408  -0.0545   4.2426   4.2510   0.7132  -2.9194  -5.0759
+#&gt; 
+#&gt; Columns 17 to 24   2.6041  -2.7814  -5.6413   5.0081  -2.8758  -8.5565  -5.6881   3.1331
+#&gt;    6.7941  -1.9458  -5.2941   7.7836  -0.4092  -8.0201   0.3970  -7.6578
+#&gt;    0.6905   2.0132  -0.4893   7.8418   4.8785  -8.9924   4.4282   4.7115
+#&gt;  -10.0949  -0.1184  18.6563 -11.3439   1.0212  12.8898  -2.0014  -4.3306
+#&gt;   12.8555  -8.2099   3.4299   8.5190   5.0619   3.3760  -0.6128  28.6188
+#&gt;  -13.3237   4.6439  -2.0882  -0.0384  13.4190  -3.7056   8.4316 -10.2330
+#&gt;    5.1761  -1.4201   0.2314  -5.0628   0.6908  -5.1780  -4.8495   4.2790
+#&gt;   -4.5325  -1.7420   4.1852  -3.8144  -2.4941   5.0209  -1.3389  -7.1552
+#&gt;    0.7916 -14.4147   5.5268   2.0693 -10.2866   2.5438   7.6568  -4.2905
+#&gt;   -3.2468  -1.1817  -4.1815  -4.7880 -12.0480  -2.4661   2.6152  -1.2081
+#&gt;    6.7382   1.8363   8.8386   3.6054   0.8293  -7.1265   2.7732   2.1463
+#&gt;    8.5587   1.4978  -9.8710   7.9441  -0.0867 -19.0129 -14.4886   5.7760
+#&gt;    3.7488   7.6406   0.6706  -5.7490  -1.9489  -5.2499  -9.1955  -3.6295
+#&gt;   -2.9359 -12.2543   3.0039   4.7083  -6.1880  -2.8042   6.8415   6.8957
+#&gt;    9.4746  10.8160   4.5054  -6.9951  -4.0581  -5.2669  -8.4338  -7.2506
+#&gt;   -1.9632   2.1132  -0.3668  -3.2008  -1.6090   6.6668  -6.0098   3.8223
+#&gt;   -3.8161  11.6790  11.3140  -3.6064   1.4355   7.5667  -5.0646  -6.0837
+#&gt;    4.2442   4.7703   4.7685   9.5189   0.7365   1.4151   6.9032  -0.9291
+#&gt;    3.7390  -2.5007  11.1561   6.3443  -1.7992   2.0021   5.2217   0.7952
+#&gt;   -5.4934   1.6850 -12.2096  -6.3842   6.5940   5.7759   1.8569  -6.4054
+#&gt;    2.5908   3.9067  12.3438   1.7453  -5.8490   7.9098  11.8704  -7.2959
+#&gt;    5.3583  -6.4650  -8.6559   2.7904  -2.9893  -4.1956   6.7185   7.5843
+#&gt;   -6.3543   4.7106  -3.1060  -3.7198   1.3608  -7.2809   2.6153  -2.4212
+#&gt;    6.5172   1.1924  -7.4937   8.5603  -2.7305  -7.6439 -14.9833   7.0846
+#&gt;   -4.3924  -9.8153  -6.3095   5.7879   4.7444   0.1552  -6.2586  -4.4975
+#&gt;   -0.6651 -12.2799  11.2119   2.7600  -5.4118  -1.2694 -15.0990   8.1280
+#&gt;   -7.8125   2.1491  23.4992  -7.1674  -8.4802   7.1878   0.1168   1.8585
+#&gt;    6.8342  -6.3625  -8.1900  10.2074  -1.0959  -5.1161   0.8583  13.1324
+#&gt;    4.5211  -3.6171 -11.9831  -2.4082   0.7759  -2.6898   0.0079  -0.5101
+#&gt;    6.6346  -7.8264  -1.1006   1.0998   1.8459  -6.5513   1.9777  11.5169
+#&gt;   -8.8377  -3.6688   1.6344  -2.5381  -4.4863   3.7497   3.9530  -5.3874
+#&gt;   -6.3750  -3.2493  -8.7438   3.8994   3.8035  -5.0823  -6.0544  -8.7810
+#&gt;    5.6659   5.4076  -2.7472  -4.8992  10.0706   4.0290  -1.8043   6.2159
+#&gt; 
+#&gt; Columns 25 to 32   0.7427  -0.3925   5.6627   0.5508  -7.6576  -5.1700   0.7781  -2.8209
+#&gt;    1.2037  -5.2113   7.5220   6.8838  -9.0041  -4.5219 -10.6045 -20.6301
+#&gt;   -9.1645  -3.7385  -7.8983  -1.0718   2.6469  -2.5640   1.1154   0.4253
+#&gt;   -5.8675  12.2688   4.7816 -12.9854   3.6264  13.4326  -7.6249  -6.3909
+#&gt;   -7.8522  -3.3144   5.0485  -5.7656  -2.5531   4.2993   4.7858 -10.8891
+#&gt;   -0.7370   7.1488  -2.1010   2.3537   9.4115   4.7932   2.8704 -14.1572
+#&gt;   -7.0131   1.6708  -3.7218 -13.1652   2.8087   4.4407  -6.9831  -5.7197
+#&gt;   -2.7847 -10.1558  -2.9248   6.2940  -4.3385   7.8863   2.4613  -8.9948
+#&gt;  -12.4222   2.5276   1.8685  -2.3223  -8.8269  -4.4598  -1.8154   4.0663
+#&gt;  -17.2300   6.4685   1.8689   5.3603   6.7398   3.3667  -4.8997  -9.4086
+#&gt;    1.2356   1.6288   3.5676 -10.3513  -2.2020   1.3730  -6.1823   5.2587
+#&gt;   -4.2661   0.7121  -6.2024   4.2070   0.5956 -10.9004   4.6460 -17.3319
+#&gt;  -10.7968   2.4177   1.1019   6.1138  -2.1474   5.7430   3.5664 -11.3855
+#&gt;  -12.5341   1.4357  12.5342   7.7619  -1.1197  -8.1808  -1.7186  -6.8173
+#&gt;    9.1906   6.2513  -8.2495  -4.5827   2.5592   8.0839   2.9613   1.8699
+#&gt;    2.1904  -7.0417   2.0164  -2.4970   7.5029  17.5735   1.9224  -9.7896
+#&gt;   -3.2724   3.9292  -8.3599  -5.7366  -5.1671   1.6543  -0.7723  10.2821
+#&gt;   -1.9706 -12.8953   4.1068   4.9962  -2.3567   6.1539  -1.5427 -10.7669
+#&gt;    7.3496  -8.1844   5.1235   1.2408   3.1449   2.0168   8.2432   7.9997
+#&gt;   -4.0664   3.4344   0.3256  11.2229   0.7095   1.4735   1.5725  -8.4207
+#&gt;   -1.6166   1.4562  -4.4718   3.3023   0.3778   7.5133   5.1532  10.1043
+#&gt;    3.3225  -8.8089  -3.0923   3.3325   1.5031   4.3136  -1.8835   6.7184
+#&gt;   -4.6379   7.2774   2.1881   8.0453  11.9964 -11.0276  -2.0683   0.7021
+#&gt;    9.7671   1.3784  -4.5355  -3.9622  -1.7377 -11.7821   2.3796   6.4589
+#&gt;    0.2182  -0.3091  11.9139   5.2245   3.4034   1.2695  17.0450  -4.1572
+#&gt;    3.5552   5.8776  -2.7593  -8.6977 -14.6022  -6.1475 -14.9508  -3.9013
+#&gt;   -6.6385  12.0537   4.1298 -11.5479  -1.1747   9.0885  -5.2279  10.9613
+#&gt;   -2.1465  -8.8670   1.5405   4.0049  -2.5045  -1.5200   2.9076  -5.5131
+#&gt;    3.7108  -4.0137   5.6951   0.1028  -8.8379   9.2006   5.8924  -3.7027
+#&gt;   10.8200 -14.8880  10.4045   0.1085  -3.0449   2.5855   7.8488   3.8576
+#&gt;    5.4769   2.0296  -3.8539  -6.6743   0.4720   7.5457   0.1194   3.6049
+#&gt;    7.3787  -4.8472  -7.0770   9.2727   0.9584   8.7025  -1.2500 -13.6226
+#&gt;   14.5414   2.2860   6.7500  -0.9226   3.1862   8.2491  -1.1172   7.9106
+#&gt; 
+#&gt; Columns 33 to 40  -5.6168  -6.4860   2.6363   2.5115  -8.7517  -6.7171  -6.6574  -3.3769
+#&gt;    5.3670  -0.1472   9.6242  -3.7355 -12.6752  -0.7998   4.8054  -9.7223
+#&gt;    2.3392   1.3709  -9.9185 -12.6104   5.3177   0.2155   7.2612  -7.5051
+#&gt;   -3.7129  -7.8671   6.5456 -17.9262   7.7747   7.7368   1.1430   5.7418
+#&gt;   10.9394   2.0021  -0.8691   0.4828  -1.2898  -7.6861  -6.4627  -5.5539
+#&gt;    1.8966  -0.3136   3.3956   2.6334  15.1075   7.4043  -4.7763 -12.4683
+#&gt;   -4.7089   6.0495   8.3688  -3.4752   5.0511   1.1077  -1.4599   5.3383
+#&gt;   -0.5215   8.8620   2.4174   1.6004  -6.2814   6.4169   0.1949   0.8299
+#&gt;   -5.9857  -0.3503   7.0461 -20.2335  -8.0556   2.9546   2.0319  -0.7711
+#&gt;    0.7776  -8.4149   3.4837   5.9291  -0.3201   8.3164  -3.6599  -1.4523
+#&gt;   11.1929   6.5765  -6.4420   0.0670   0.9790   7.1676 -11.9064   8.0665
+#&gt;   -0.3148  -6.7382   0.0216  -0.1783  -8.5459  -5.9415   4.3921   3.1296
+#&gt;   -1.3924   2.6817   7.5451   0.3968   4.4998  -1.5098   5.7484   1.6824
+#&gt;   -8.7046 -10.6875   4.4194  -1.1980  -1.0027  -0.4302  -2.3993 -19.0529
+#&gt;    4.8254   9.1486  12.5051   8.7380  -0.9360   0.1790   5.9854   7.6658
+#&gt;    6.9828   1.3313  -7.0587   2.1813  -0.8202  11.3124   4.2426   0.6668
+#&gt;    6.6578   1.5624 -13.2574  -7.5266   4.4840  -2.2918  -9.5036   7.7070
+#&gt;    9.1161  -4.9644  -1.1914  -5.7381  -7.8603  -6.3567   4.3512  -6.6192
+#&gt;   12.4280   8.9660  15.9672  -3.5249  -7.5437  10.3486  10.2216   3.0577
+#&gt;  -11.6479   2.1733  -6.1750   3.9584   7.0792   6.9122   2.5134   2.9520
+#&gt;    0.4872  -2.3682  -5.0912  10.5690   5.2461   5.8557   0.4970  -0.5975
+#&gt;   -4.6388  -7.6717   1.0318   9.5039   6.5533   3.0746  -1.8915   4.7517
+#&gt;    1.0830   0.5759  -2.3991  -0.7123   0.3355  -5.1589   9.4931 -12.9261
+#&gt;    0.8625  -2.6225  14.8504  21.5154  -4.1961  -3.3638  -9.7227   2.2374
+#&gt;   -5.3223   5.9341  -1.4369  -4.6652   6.2489   3.5743   2.6827  -0.7111
+#&gt;    1.3230  -6.6167  -3.3289 -15.8255  -6.4208 -15.8053 -13.3011   7.2120
+#&gt;    4.2213  -4.2158 -13.7303  -1.7531   1.1995   3.4246  -4.6039   9.5911
+#&gt;    1.8121  -2.1075   0.8085   1.5771  -5.6438   3.4963   0.4645  -5.3024
+#&gt;   -5.6359   9.3658   4.9352   4.7401  -8.7382  -2.3576   0.6898  -3.6523
+#&gt;   -4.4666   7.5691   2.2186   0.5155  -4.7183   3.1927   5.8108   5.7489
+#&gt;   -2.5874   1.1465  -1.3267  -5.5489   5.9748   7.1293   3.6966   4.8829
+#&gt;    8.8428  -6.9308  18.6298   6.8286   1.8857 -11.7044   0.6557   2.5373
+#&gt;  -14.6013  -8.3760 -10.6774  10.0636   8.9325   2.8039  -0.0727  -3.0897
+#&gt; 
+#&gt; Columns 41 to 48   6.9642  -0.6262   2.2064  -4.1543   0.0911   5.4757   8.7951   1.6013
+#&gt;    9.6895   7.9675   6.8978  -0.5035   2.4456   0.2763   5.9792   4.9250
+#&gt;   -2.0042   1.3626  -2.3506  -2.3861  -1.2558   9.7202 -16.8533  -5.8326
+#&gt;   -5.9632 -11.5851   3.4268   4.8003   1.3087   1.2331   6.3146  -5.7044
+#&gt;   -0.4662  -1.3376   1.9251  -0.6392  -1.0576   8.9563  -6.2924  -4.8836
+#&gt;  -10.5783  -2.8322   2.6248 -12.4610  -6.1862  -8.0927 -12.2853  13.1715
+#&gt;    2.2852   0.6102   4.1804   5.3986   8.5801   6.9560   7.8326  -5.5230
+#&gt;   -8.6194   6.2151   5.7252   3.9505  -8.3117  -3.1280   2.8561   7.1808
+#&gt;    9.7590   4.1079   2.2343  -1.7692  -0.3579   0.7964   2.9640 -11.9145
+#&gt;   -7.0074   1.1420  -4.2457   2.8591  -1.1378   1.1163   3.8781  12.4765
+#&gt;   -6.1290  12.2708 -11.1302   3.8243  -2.9552   8.6422  -6.0264  -7.4022
+#&gt;   10.0635   9.6584   2.4000   4.1588   7.9187   1.2381   0.4380   0.8508
+#&gt;   -6.0808   7.0076   9.3598   0.1588   8.6685   1.5170   8.1652   3.7259
+#&gt;   -2.2060  -8.3047  -3.0411  -8.1245  -3.1376  -4.5335   6.5306   8.1359
+#&gt;    4.8976  12.9044   0.6897   4.5309   2.5029   9.2748   4.8736  -7.1804
+#&gt;   -8.4457  -2.7248  -3.2400  -0.5759  -0.9208   5.8858   1.6766  -5.2751
+#&gt;   -5.7156   3.9459  -4.4457   4.1108   6.7611   3.5183  -6.4844  -3.0592
+#&gt;    3.3449  -6.2755   7.4682   5.7035   5.5690  -1.8141  -1.9710   0.6901
+#&gt;    6.5519  -6.2914  10.2937   0.9887  -4.0702  -0.5473  -3.3893  -5.1029
+#&gt;  -16.6018   3.3911   1.6646   7.8269  -8.0762  -7.4555   0.7765   9.0354
+#&gt;    0.2345   1.2441  -4.0380  10.4353  -2.5967  -7.1556  -6.2753   2.0644
+#&gt;   -0.1557   4.8470  -2.2030   3.2334  -0.7537   1.3210   2.5823   4.4626
+#&gt;   -3.8668  -1.3391  -4.5296  -6.3264   2.4471  -4.8476  -0.7466   8.3751
+#&gt;    5.7443   3.7541  -1.4076  -8.6073  -8.8374  -1.3317   0.0160  -9.8453
+#&gt;    5.1070  -1.5126   5.4086 -11.8511  -3.9446  -9.0676  -4.9246  -4.2725
+#&gt;    8.4218  -2.2294  -5.2719  -7.5469   1.8683   7.0001   1.4385  -7.4544
+#&gt;   -6.4732  -2.3976  -6.5612   3.7650   7.0920  11.5379  -9.4214  -3.5016
+#&gt;    1.3462   2.4093   2.7490  -5.4944   1.8231  -1.4234   3.8278  -6.4919
+#&gt;    0.9387   1.8054   3.5880  -1.4546   2.0829   4.4832  22.5412 -14.8157
+#&gt;   -2.4800   1.0776  -1.3618  -0.0731  -5.1824   0.0743   7.2691  -0.8040
+#&gt;   -0.0316  -8.2954   0.1998   5.2957  -0.0135   5.4739   6.7748   0.5406
+#&gt;    7.6616  -3.9551   4.7891  -9.6003  -4.8387 -10.1878 -10.6751   2.1577
+#&gt;   -9.2890  -3.0727  -7.7070   3.2493   0.9178  -6.5737   5.3924  -3.1437
+#&gt; 
+#&gt; (7,.,.) = 
+#&gt;  Columns 1 to 8  -2.3296   2.6220   2.7053   9.0223   4.6784  -8.9184  -0.2804  -2.5976
+#&gt;   -4.7880   2.0406  -0.7386   7.3500   4.1096  -9.1256  -8.0225  -2.0870
+#&gt;    8.0657  -9.0503  -9.4772   3.4253   0.7609 -10.2437   1.5640   2.8089
+#&gt;   -0.7459   8.8218   2.3357  -6.3770   4.8054   0.3979  10.4343  -5.6464
+#&gt;   13.4301  -5.0842 -10.3859  -1.6107  -3.8801   1.9869   6.5917  -1.8032
+#&gt;    3.5070   0.8849   2.6876   2.3717   8.3952  15.8351 -10.0632   1.4077
+#&gt;    8.9518   6.0343   1.6781  -7.7070   3.9087   2.8646   7.4742 -11.8379
+#&gt;   15.5281   2.9651   5.2348   4.3290   4.4333   0.6046 -12.8930 -12.1883
+#&gt;   -0.1533  -8.4006  -8.8320   7.7445   0.3838 -23.2785  11.6583  -4.4223
+#&gt;   -5.7791   3.1234  -2.5414  -1.6776 -10.7481   2.6806   3.2963  -2.1340
+#&gt;  -12.2836   4.3460  -0.4746  -6.8613  -7.8982   3.5393  -9.3517   4.6484
+#&gt;   -7.9799  12.9991   5.6166   7.0813   5.2047   5.1364  -8.4394 -10.7971
+#&gt;   -3.3649  -1.8186   1.0821  -1.8201   0.5154   9.5670  -3.4895  -4.5462
+#&gt;    2.7008   2.7005  -6.8547  15.7891  -5.4901 -12.3276  -7.1192  -5.3654
+#&gt;   -8.3179  16.8236   3.6043  -6.3824   8.4656   1.5503 -19.6949  -5.3716
+#&gt;    8.5076  -5.4067   3.3730  -4.3485  -2.0729   9.7513  -8.3290   8.3339
+#&gt;   -8.5696  -6.8044  -3.2408  -9.7262 -14.2968   3.2913   2.8368   5.8087
+#&gt;   -2.4612  -1.7636  -1.2623   3.2902  -0.2236   0.0514   0.9184   8.6516
+#&gt;   11.4526   4.2531 -10.1231   5.7154  -0.8421  -0.4120 -10.9707  -1.2357
+#&gt;    0.6259  -7.3116  -1.8922 -13.2551  -1.0134  13.2029  -2.3882  -6.5237
+#&gt;   -2.6817  -5.8175   2.5477   2.7028   4.5282   2.4201   8.0314   2.1558
+#&gt;   12.3809   1.4826  -1.4175   5.9531   1.5363 -11.6131   2.6520  -0.7645
+#&gt;   -4.5831   7.5182   2.1211   8.1539   1.2662   2.5533  -1.0333  -6.3098
+#&gt;   -5.4063  17.3063   1.2650  -4.5729   0.5667   0.0456 -10.8855  -3.3304
+#&gt;    2.7603 -22.3222  -9.7392   3.0270  -7.3130  -5.0089   0.9636  -7.4817
+#&gt;    2.7866   2.3214   5.1668   4.9353  -1.0383 -10.4922   4.7751  -6.8500
+#&gt;  -15.2789  -3.5497  11.7025   9.0850 -19.2380  -0.5861  14.1731   5.6085
+#&gt;    9.0317  -2.9111  -7.7741   2.9326  -0.2974  -7.5001   0.2570  -0.6743
+#&gt;   -1.7161   8.5792   0.8657  -4.5549   6.9916  -0.7809   0.9465 -10.1163
+#&gt;    6.1382   8.3956  -2.8173  -1.6776   2.0230  -0.0545  -5.4137  -2.1501
+#&gt;    9.6015  -1.1818  -2.2238  -0.3524   2.0411   1.0046   7.4119   0.4235
+#&gt;   -8.8153  -3.8452  -5.2957  -7.5852  -0.7384   6.8361 -14.5995   2.1828
+#&gt;  -13.5845   2.8668  13.2323 -10.7866  -6.0058  -1.1561  14.1621  -1.2926
+#&gt; 
+#&gt; Columns 9 to 16  -7.1634  -6.0972 -11.7513 -10.4073  -0.8159   2.5930   4.2177 -10.0934
+#&gt;   -2.2544 -11.3961 -11.6843  -2.3137  -8.5706 -10.0163   0.3512   4.9911
+#&gt;   11.9232   2.4842  10.0709  -8.5920  -8.1947  -4.8634   5.0969   4.6391
+#&gt;    1.5472  -4.8407  13.2325  -0.1264  -5.5050   3.4931   0.4756   5.8156
+#&gt;    5.0784  -2.8242  -1.0371   8.3050  -7.4883   5.5563  -2.4524 -11.9313
+#&gt;   -4.0314   2.1286   3.4392  -7.1105  -5.7933  -4.3309  11.4669  14.5174
+#&gt;   -0.2058  -6.9356   1.3772  -1.9061  -0.6770   7.9332 -14.9127 -13.5586
+#&gt;  -12.2776  -4.7740  -1.5404  -3.5163   7.0517  -7.9475  -0.8112   7.3606
+#&gt;    9.7158 -11.3029  12.3129   1.6495  -0.3677  -8.4003   6.5051   0.1048
+#&gt;   -1.4861  -4.0380  -5.0327   5.3579   6.3946  -1.1793 -11.0562 -12.8570
+#&gt;    0.2693 -12.0579   0.8015  -0.8628  -7.8605  -0.5489  -1.3176 -15.1082
+#&gt;   -7.4996 -12.2279  -5.1412   1.7302   3.5835  -5.4725   4.9118   3.7829
+#&gt;    3.3948   8.1425  -2.5546   9.6217   1.8721   1.7571  -3.5932  -8.2782
+#&gt;  -11.4339 -15.8573 -16.3337  -6.0683 -16.1905   7.7043  15.7809 -10.0472
+#&gt;   -2.1120   7.9248   2.0360   6.3012  -1.7394  14.4151   0.3460  -2.1862
+#&gt;    7.2541  12.2710   4.0430   3.7129  -6.8094   6.2648   7.8792   2.5758
+#&gt;   -6.9766   3.3028   0.9064  -5.1602   5.7350   3.2716  -1.7567 -14.7903
+#&gt;    0.7560   4.0701  -3.0778   5.6728  -2.0256  -1.9973  -0.0311  -6.1567
+#&gt;   -6.8235   3.2236   1.2478  12.4593   3.9118  -2.7748 -21.0220  -3.1948
+#&gt;   -4.4552  -5.8244  -7.0327   4.2571   5.7014 -16.1163  -9.0073  15.7807
+#&gt;   -3.1768   3.3695   7.5084   1.9599  10.0857   6.9237  -2.1567  10.6698
+#&gt;   12.1337   4.5736   1.5905  -1.4781   9.7632   0.4483   9.8829   3.7366
+#&gt;    2.1638   5.9205  -5.8842   3.1058   1.5394 -10.3690  -5.2912   1.1492
+#&gt;   -3.4409   8.9310   2.8766   3.5897  -6.0677  10.1503  17.7390  -2.9372
+#&gt;   -4.6623  -3.7795  -6.0469   1.1800  -4.1813  -7.3442   2.9797  -1.6414
+#&gt;   -4.3184   1.7215   4.3932  -6.3918  -8.4958  -6.0978  18.0118  -7.7651
+#&gt;    4.5519   6.9310   7.5572   1.7235   6.0236   3.7269  -2.1889 -17.1142
+#&gt;    8.9957  -2.0600  -6.0540   1.6865  -7.9547  -8.3940   5.2172  -6.8774
+#&gt;    2.5671   4.0671  -2.3180  21.7114  -0.1279  -0.2939   6.8952  -3.6456
+#&gt;    1.7494   3.4129  -9.8035   5.2906   6.6857  -3.6860  -2.1410  -1.5441
+#&gt;    1.2420   3.9898   8.4892  -0.7814  -2.5251   4.7544  -7.1279   2.6486
+#&gt;   -2.8809   9.5647  -2.0376  -9.1030  -4.2586  10.2570  15.3708  -9.0445
+#&gt;   10.9928  -3.3645  -2.9894 -12.0953   0.2006  -1.0859  10.1212  -6.4035
+#&gt; 
+#&gt; Columns 17 to 24   2.7812   5.4220  -4.8518   1.6159   3.3163  -0.9405  -1.4396  -0.8000
+#&gt;   -0.7204   1.9521 -11.0833   7.5696   6.9420  -0.1180  -0.0605  -4.4413
+#&gt;   -0.9011   6.4584  -2.6853   6.1056   0.2922  -2.6252   2.7197  -3.1311
+#&gt;    2.9205 -14.7327   6.8285   5.4563  -6.8013   3.2424   5.1511   0.8192
+#&gt;   15.2468   3.6672  -8.8271   4.7590  -5.2288   3.2019   5.0552 -13.5845
+#&gt;   11.3536   4.5270   8.1449 -10.5235  -0.8249   7.0970   5.2966   8.2157
+#&gt;  -12.3210   4.9029  -5.9232  -7.0295  -5.2866   2.5831   3.4113  -7.1652
+#&gt;    7.4477   5.9604  -1.1821  -4.0429   2.6185  -2.0651   0.9707   1.4009
+#&gt;   -7.7386  -2.0487 -11.2013  10.8187   2.0498   1.4085  -7.2806  -6.4198
+#&gt;    4.2641  -6.3158   1.5590  -5.2147  -4.5397  -1.8869   2.6813  -1.0346
+#&gt;    0.5320  11.1622  -2.4356   0.2217   1.1017   1.4617   1.7884   4.4048
+#&gt;    1.2410   4.4032   2.1811  -4.8886  -6.3978  -8.0271  -6.3209  -4.5243
+#&gt;   -4.8644 -16.1335   8.0001  -0.4512  -0.0640   4.5421  -6.0189  10.2372
+#&gt;   10.8666  -2.3392 -14.6404  -6.6768   6.3468   3.3562  -4.9963   3.7271
+#&gt;  -16.8252  -2.4095   4.3875   4.9275   8.5122   2.0512   0.3071  11.8187
+#&gt;   -3.2934 -13.0638  12.3757   0.8423  -7.7589 -14.8246   0.6222   6.8520
+#&gt;    4.0858 -12.8416  10.5734   0.2094   4.0656  -0.8490   0.2859   3.8753
+#&gt;    7.9200  -3.2439  -2.2367   1.8224  -4.3161  -3.6697  -2.3160 -10.1752
+#&gt;   -2.4015  -6.4772  -2.8352  13.3998   6.2246   3.7878   1.7386   3.4412
+#&gt;   -2.6908  -5.1969  12.1578 -18.5621   2.7941  -5.0791  -2.7870  -4.6360
+#&gt;    0.4089   3.1580  -5.9018   8.9719   4.3680   3.1811   5.9165  -0.9216
+#&gt;    4.0521   6.9054 -11.9093  -2.7798  11.2565   2.5289   1.6041   2.8995
+#&gt;   -7.3418  -5.9512   2.9487  -7.1501  -6.3839   3.1446  -1.2858   8.3549
+#&gt;    5.4442   7.4219 -11.5866  -5.0148   7.2163   2.7395   2.7243  11.2979
+#&gt;    3.5898   5.2307   2.9838   0.8806   1.8287  -5.2266  -7.1442 -13.2070
+#&gt;    6.0116 -13.3017 -11.0431  -1.5814  -0.7696  -4.5543  -5.2704  -7.5289
+#&gt;    4.3283 -11.9296  -8.7027  11.9328  -4.8087   6.9332  -0.5710  12.0423
+#&gt;    4.5342  -0.4509 -11.4358   2.1943   5.0237  -1.9125  -2.5020   0.6515
+#&gt;   -9.9458  -1.3030  -4.9437   3.8813  -0.5505  -0.7792  -3.2231  19.6330
+#&gt;   -4.7559   5.9901   6.6762  -1.1477  -2.8810  -3.1458  -1.0710   0.7236
+#&gt;   -8.8685  -7.1802  -4.9199   4.3553   9.2855  -1.5581   6.4446   2.6088
+#&gt;    5.5704  -0.1899  11.7272 -13.7112  -7.5853  -4.6329  -8.1911 -11.9715
+#&gt;    0.8659   9.6754   6.2192   4.4845  -6.6850  -0.1068   4.6408   1.6410
+#&gt; 
+#&gt; Columns 25 to 32   0.1021   3.4989   7.5064   8.2781  -1.5580 -10.7283   6.7941   6.3417
+#&gt;   -8.8004  -2.8351  -0.6603  -4.9096  -4.8975   3.5512   7.6414  10.3805
+#&gt;    4.3131  -5.1323 -10.3353  -6.8733   4.1013   8.1932  -9.6053   7.0819
+#&gt;   -2.9337 -10.0615  -2.3167  -1.6669  -5.6632  10.8148   6.1126   7.1849
+#&gt;   -2.6452   4.3381  -4.6695  -6.1262   8.3285  11.5907  -7.4366  -3.3797
+#&gt;   -0.2017  -2.2431  -6.9068  16.7291   8.9836   1.6426  -2.0245  -4.8889
+#&gt;   -2.5906  -7.4285  -3.3424  -4.9273  -4.7368   3.6852   9.7596 -10.0672
+#&gt;    8.3528  -4.5681  -0.9854   9.0356   5.1900 -13.4860   5.7939  -5.0061
+#&gt;    2.0299  -2.1688  -1.0443 -17.0904  -0.0215   3.8864 -14.0175   2.9196
+#&gt;    3.3222   3.1324   5.8982  16.2515  -9.2976   8.5235  -4.8997 -17.1181
+#&gt;  -14.4956   8.2583   1.0591   7.5273 -19.3976  10.6573  19.1982 -11.3771
+#&gt;   -3.1710  13.0068  -7.3756  -0.5681   0.7774   9.9759  11.0113  -7.3256
+#&gt;   -3.8406  -0.8126  -5.5768   2.0762   0.5199  -0.5397  -1.2605  12.0127
+#&gt;   -3.2277   2.1645   1.0629  14.9632   1.8371  -5.9239 -13.8835  15.9642
+#&gt;  -12.8184  -2.6658  -2.0042  -0.0844  -7.2206   6.6421   9.7363   2.4991
+#&gt;   -1.6525  -4.6428  -9.1115   8.3921   3.7704  -7.8665  10.2522   0.1801
+#&gt;   -3.7896   2.4306  -1.3701   0.3734  -7.4602   4.2084  11.4515  12.0422
+#&gt;   -2.4753   2.0030  -0.5177  -6.5314   1.2207   4.5226  -2.2401   4.3960
+#&gt;    2.8886   3.2794 -10.2895  -0.2028   6.2209   1.5111   5.6472  -0.8896
+#&gt;    0.0851 -14.5293  -1.1060   9.2817  -0.9770  -3.4438  13.8217   0.0651
+#&gt;   17.0007  -6.9814  -8.2417  -1.7077   6.6519   4.8516   1.1276   2.4622
+#&gt;    0.2145  -7.9254   1.7342   6.1690  -2.1715 -10.6964  -9.7442  -2.3695
+#&gt;   11.2452   1.3741   6.4360   4.1201   5.1333   1.9648 -19.2341   0.7180
+#&gt;   -7.4940  12.5695   9.4383  -6.4369 -11.3819  -0.5540 -11.7748 -18.9718
+#&gt;    0.2186  -1.3956   0.0665  -2.0291  14.3456   0.3513   6.7104  12.9168
+#&gt;    5.1630   1.5422   3.2075  -2.9081  -5.7640  -4.0527 -11.5494   3.4883
+#&gt;    7.2746   9.1093   6.0529  -4.7185 -20.0544   2.4024   0.6728  -1.1473
+#&gt;   -5.1805  -0.7158  -1.3168  -0.3273   0.5368  -6.3159  -3.8350   4.0969
+#&gt;    4.8603   3.2546  19.2123   5.7714  -5.8778 -12.2052   2.0928  -2.4711
+#&gt;   -1.3025  -2.7034   2.7477   7.6757   5.7388  -4.8970   5.2955  -6.8052
+#&gt;   10.1599  -8.0182  -7.3798  -1.0700   0.6162  -6.7383   3.3595  -1.1978
+#&gt;  -13.4505   5.0993  -4.7529   2.2823   3.8764  -4.0923  -7.7800   0.6165
+#&gt;   -6.1131  -6.7054   6.5676 -10.4996   1.9459  11.2073   9.8336   1.6834
+#&gt; 
+#&gt; Columns 33 to 40  -0.3298  -4.2969  11.2754   9.8055   0.8983  -9.3014  -5.8688   3.6415
+#&gt;    6.0148  -5.7631  -1.8169  -0.5250  -3.9140  -0.2316  -8.7621   6.5795
+#&gt;    4.9109 -19.3549  -2.2391   3.8585  -3.1540   4.0717  -6.5839   0.5183
+#&gt;   -2.9798  -9.4008   4.7706   2.1077  -3.3910  -3.2499  -0.6783   1.5453
+#&gt;   12.7098   3.2865  -4.9499  10.0107  -3.5558  -0.0282  -3.1283   0.5524
+#&gt;    2.4122  -0.9889   5.2093  11.4160   4.1561  -3.1497 -16.0259   4.1585
+#&gt;   -7.8926  -0.9984  -1.2411   3.9572   0.3797   3.0215  -3.0578   0.1023
+#&gt;  -10.8219  16.1887   5.4966  -2.0509  -3.2074  -3.4945   1.6944  -2.2159
+#&gt;    0.3110  -8.4711  -3.5092 -15.3411   8.8879   5.6060   1.4967   1.9971
+#&gt;   11.3866  -7.1140   7.2142  -1.5308   1.5688   9.0305   4.3703  -1.1801
+#&gt;    6.6862  -2.6590  -2.9354  17.2175  -6.7822  -1.0260  -1.1077  -9.6485
+#&gt;   -0.9948  -5.5579   1.7111  -4.4484 -12.6108  -4.6243   2.0521  -5.5649
+#&gt;   -7.1225 -10.3208  -7.9880  -3.7788   5.9515  -3.9717   4.4162   4.4355
+#&gt;    1.6533   1.3678  10.8417  12.2366   5.4949 -10.2154  -2.8037  -9.9922
+#&gt;   -3.2760   9.1617 -10.6927  -7.5026  -5.4360  -9.0376  -1.5377   9.4721
+#&gt;  -12.4996   4.8649  -2.9894   4.7946 -11.5541  -0.3904   6.8418   0.8465
+#&gt;   -3.8046  -3.6546   2.3285   1.1938   5.6924  -7.7238   7.4887 -10.2338
+#&gt;    2.0710   0.0309   4.4036  -1.1317 -15.0928   2.7721   9.5530   2.0894
+#&gt;    7.4004   5.6765  -8.4092  -3.1803  -2.4403   4.3154  -3.0732   8.2051
+#&gt;  -13.4954  -3.2096   2.3950  -5.8532   1.8454   2.8569  -0.5035  -6.5992
+#&gt;   -1.3958   1.8985   4.7739  -7.2806  -6.6726  -0.9841  -2.1776   8.3176
+#&gt;    6.4731  -0.7357  -5.5632  -7.4430   6.2662  -3.5883  -1.6045  17.4411
+#&gt;    7.1276   5.1441  10.0146  -8.1522   7.7656   9.9407   1.4379  -5.7897
+#&gt;   14.1812   9.2245 -18.1448  -7.7124   6.5122   0.2088  -2.4817   0.2880
+#&gt;  -15.7896  -1.2188  -6.7288  -8.9549   4.9331   1.4936  -8.7471  -6.4209
+#&gt;    4.3099  -0.7303   1.7592   0.9840  14.6613 -17.9901   4.6189 -12.9752
+#&gt;   14.9605  -1.3112   0.1678   0.7928   5.7157  -3.6127   5.1273  -4.5057
+#&gt;    6.9167  -3.5551  -2.1515   5.1422   5.2428  -4.4654  -4.0587   4.0413
+#&gt;    1.3875  16.1840   6.9282  -2.2695  -1.2116  -3.0950   9.6788   1.2790
+#&gt;   -8.8776   9.9031   0.9821  -4.0823   0.2807   6.5130   4.3705   1.5543
+#&gt;   -4.8403   1.3496   2.6027   4.1189   1.5527   1.4183  -3.0095   8.0521
+#&gt;   -4.3142  -2.8906 -15.3153 -14.9150  -3.0554   1.5348   4.7710   0.2526
+#&gt;   -5.3966   0.1269   0.7146  -1.8717  -4.1178  -0.9931   5.4409   0.6686
+#&gt; 
+#&gt; Columns 41 to 48  -1.8616   5.7532   6.5965  -5.0088   0.0345  -0.7763  -1.6356  -2.0600
+#&gt;   -7.7402   1.2094   8.4545   2.6980  10.4990   5.3276  -1.5464  -8.0669
+#&gt;   -5.0384  -3.4051  13.2699  -1.1863  -9.1671   8.4433   1.3991  -1.6152
+#&gt;   11.0023  -2.5902  -2.1461  10.9165   3.5941  -6.6523  -9.4431   0.1495
+#&gt;    0.2131 -10.8118 -11.3988   0.1859   1.9479 -11.8650  -9.3801  -1.0290
+#&gt;    7.3107   7.5041  18.2200   9.3550   4.7096  17.1780   0.5387 -19.5252
+#&gt;    4.5644  -0.2605   3.7791  -2.1329   4.6946   1.2555   4.3208  -3.4587
+#&gt;   -2.5980  -5.5629  -2.0509   1.5486  -9.3856   2.5339  -6.3174   6.2199
+#&gt;  -10.2091 -13.6487   5.8027   8.9926   0.3840   0.6081   7.1941  17.2857
+#&gt;   -6.9849   6.1649  -2.8074  -7.9156   7.2325  -8.4858   1.0448   0.6305
+#&gt;   -0.9542   0.2664   2.1968  -6.6221  -3.5052   2.9299 -15.5179  -4.4278
+#&gt;    3.5436   1.7725   2.2777  -7.4842   7.5133   7.5347  -0.7854   5.3106
+#&gt;    8.3427  -0.7514  -4.5845  -7.2193  -3.9886   1.0598   9.0724  -4.7909
+#&gt;   -0.8176   1.7874  16.4429   5.9142   6.8127  -2.3502   3.9062  14.6298
+#&gt;   17.9763  -5.2250  -8.0704  -5.5407  -0.2623   4.9040  -1.4951  -2.7139
+#&gt;    4.4399   0.6390  -6.6045  -3.6975 -11.5393   6.4525   0.6170  -7.2279
+#&gt;   -8.9183  -0.5764  -4.1493  -9.9795  -5.9897  -8.3440  -6.1989   4.5492
+#&gt;   -4.5282  -1.2998 -12.6075  -5.2066  -4.1720  -7.8010  -6.3624  -2.1599
+#&gt;   -0.0395   0.5525  -1.2190  11.7965  -2.8265  -2.9007  10.7658  -6.0432
+#&gt;   -3.8849  -0.7795  -6.0214  14.7302   3.7549  -1.9099   4.5305   1.1395
+#&gt;    2.5980   5.7914   0.5088  -0.2587   1.6536   1.5198   0.6970   7.6877
+#&gt;   -3.4199   5.5684   5.8389   0.3654   0.7566  -4.6547   3.9163   7.2315
+#&gt;    0.8831   5.6800  -1.6161  -4.8695   8.0604   5.2900   6.0896   0.1284
+#&gt;   12.9451   0.7244   2.5631  -7.6859   4.2499   7.7468   8.8400  17.9649
+#&gt;  -12.1270  -1.1188   8.6782   8.2348  -0.1812   3.9842  12.4738   1.1312
+#&gt;  -15.5627   0.9205   4.8937   1.3959   0.9364  -5.9993   5.0125  15.9446
+#&gt;   -0.5960   8.9775   2.1488  -7.5156  -3.5539  -0.5377   5.5796  13.2049
+#&gt;   -2.0846   3.1420  10.8564   1.0379   7.3284   7.2150   8.0023  -0.7732
+#&gt;   10.4065   2.3664 -13.2031 -10.0701   3.7662   2.1972  13.5044   0.0219
+#&gt;    6.2028  10.0813  -7.7484   4.6236   1.5654   1.1147   0.6467  -9.8578
+#&gt;    0.7657   5.9682   5.6290  10.6157   0.1378  -0.1181  13.5057   3.3180
+#&gt;  -13.0670  -7.8584  -9.9599   2.3546   3.1039   0.7870   7.8630  -5.2758
+#&gt;    7.1305  12.3261   3.8330  -9.2226   8.3476   2.9376  -0.8149   0.6137
+#&gt; 
+#&gt; (8,.,.) = 
+#&gt;  Columns 1 to 8  -1.2317  -3.3196  -5.1569   1.9082   6.2612   4.3392 -11.2136   3.9773
+#&gt;  -10.3118   5.0747   3.5518  -0.0408  -3.1321   6.1357  -0.5448   1.5189
+#&gt;    2.7030   7.0978   1.1454   1.0692 -12.5975   9.5899  -8.7267  -2.5848
+#&gt;   -1.5421   2.0308  -2.2283   1.3549 -13.0727  -1.4142   5.1770   6.6988
+#&gt;    6.8411  -7.6045  -1.2982  10.8429   2.5187   0.5119  -2.7345  -0.1262
+#&gt;    7.9873  13.6170  13.8393  -9.4866   3.6657  11.1016   6.9682  14.5323
+#&gt;    2.0465   7.5210   6.8347  10.9886   1.0939 -13.6630  -9.4285  -5.0368
+#&gt;    4.0440  -1.7207  -2.3052  -5.0791  -1.0243   2.6739   4.8054  -8.1103
+#&gt;   -4.8036   6.8107 -12.3930   5.6116 -12.0874  -1.4229  -2.4287  -0.7909
+#&gt;  -14.0824 -10.3870   0.2212   9.2112  -3.7067  -1.3332  -0.7219   4.9144
+#&gt;   -5.8525  -2.8898   0.8723   0.2240  11.5024   1.5236 -12.9817 -10.0872
+#&gt;   12.6071   7.9935  -3.8990  -0.6208   0.1425  -0.8730   5.3491  -5.5887
+#&gt;    7.4526  -3.1527   4.4941  -8.1563  -8.9867  -4.2534   1.7696  -8.5009
+#&gt;  -12.8709  -9.0025   5.0548  13.1891   6.1871  14.4623  -7.5337  10.4154
+#&gt;    4.1507   4.8493 -14.9231  -4.2805  -1.6507   1.6043 -10.9938   4.4166
+#&gt;    5.6636  -3.7006  -0.5060  -9.6451  -8.8515   8.5477   4.9868  -7.4543
+#&gt;   -3.6501  -3.3526   4.7915  -0.3429   4.2049  -9.7057 -12.3466  -7.9951
+#&gt;    0.8060  -9.4069  -1.7457  -0.6788 -12.0969   1.4056   4.5861   2.7684
+#&gt;    3.0246  -7.8256   6.0450  -5.3646  -2.7373   1.2020   6.0695   1.5647
+#&gt;   -4.2576   5.7032  16.2630  -3.7573  -0.1373 -10.6970  13.5898  -1.4398
+#&gt;   -0.1086  15.7111 -11.1226   7.0051  -7.8891   5.1548   0.6955  13.2780
+#&gt;   -5.2106   0.1764  -1.0260   2.6907   4.2760  -3.5172  -6.5097  15.4163
+#&gt;  -10.7636  -2.0764  -1.6827   3.6295  -1.7870  17.8416  -0.4092  -1.7633
+#&gt;   15.1723  -1.2443  -0.7555   4.6745  12.0369   7.4046  -2.9553  -1.4032
+#&gt;   -7.1292   8.0863   2.0892  -8.8382   2.1167  -0.1061   2.3117   3.3048
+#&gt;    1.1223  -5.3102  -9.7623  14.4588   3.6329  -4.3042   1.6126  -8.9402
+#&gt;   -7.8986 -12.2560  -9.3964   8.4937   1.2416  -4.3892  -1.9670  -0.6979
+#&gt;   -2.9293   3.8861   7.1722   1.9240  -2.3646   2.8111   3.3984   2.8330
+#&gt;   -1.9388   7.8489   1.8083   0.6661  -6.2467  -0.2845   7.0653  -1.8716
+#&gt;    2.4168  -0.3931   3.8223  -7.7273   5.4307   2.2596  -1.1483   5.8083
+#&gt;   -5.0786   8.5213   8.2180   2.5309  -9.2810 -13.2719  -1.2012  11.6763
+#&gt;   15.2233  -4.5946  -6.8110   0.1354   1.0765   6.4974   2.4170   1.4068
+#&gt;   -1.7034  -1.2184  -3.5573   9.5502   6.9113   7.7414   1.7149   1.8343
+#&gt; 
+#&gt; Columns 9 to 16  -4.4885   0.5837  -5.2821   2.6704  -1.0122  -0.9631   4.5014  -0.3658
+#&gt;    0.1016   5.2009   7.1366 -12.2656  -0.6036   4.2722  -6.8218   5.7402
+#&gt;    1.8316  -0.9588   1.4152   1.6889   2.3983  -1.6298  -6.1908  -5.2894
+#&gt;    6.0208   5.0804   2.9412  -2.0281  -4.6607  -8.1584 -13.4804  -6.7688
+#&gt;    7.3720 -10.3945  -5.6677  -3.5158  -1.6095   8.3750   7.9127   5.1704
+#&gt;   15.4399   9.3323   1.6973   1.5998  -3.9071  -3.9725  -1.0085  -2.0901
+#&gt;    7.8788   1.0264  -0.4291   2.0364  -1.5741   3.5325  10.4612   3.0324
+#&gt;    8.7283  -9.3981   0.4778  -6.7209   6.4405  10.4767   9.5827   5.6104
+#&gt;   -7.3756  -0.7232   6.2456   3.4731   6.6796  -1.3627  -3.0187  -1.6944
+#&gt;  -13.1355   2.9039   0.0459   8.7924  -5.9811   0.8449   3.8136   5.9508
+#&gt;    4.6568   1.1027  -6.3764  -9.4723  -1.3233   6.0747   2.4053   6.8966
+#&gt;   -0.5289   2.6472  -9.4330   8.1228  -5.9426   3.4072   0.4823  -9.4226
+#&gt;   -4.7529  -1.0135   2.1678   4.4102 -10.6251  -5.6192  -3.6697   1.6366
+#&gt;   15.0061   6.5173  -4.8301   8.6828   6.0451   4.5656   1.2344  10.9157
+#&gt;    0.4303  -6.5763  -4.8707  -2.3870  -9.4889   4.1435   3.3063  -1.1898
+#&gt;   -0.2692  -2.4433   6.2689  -1.5584  -3.4070  -1.3909   0.1893  -0.4532
+#&gt;    2.5107  -1.5668 -10.2115  -5.5648  -1.3647  -8.0708   2.0161   1.6512
+#&gt;   -3.5940  -6.3579  -3.8869  -7.9065  -0.9742   6.7063  -8.4719   1.3576
+#&gt;   -3.7089  -1.8038  -0.6117  -9.8607   3.3698  22.9605  -3.6562   4.8463
+#&gt;    1.6361   2.5420  11.4186  14.3684   2.3310  -8.3540   0.9381  -0.0351
+#&gt;    0.1027  -9.0479   0.9032  -1.0097  -2.8629  -1.9554  -2.0904 -15.8881
+#&gt;   -0.7654  -4.2773  15.1426   9.0580   6.0057  -6.6287  -1.0927   2.0078
+#&gt;   -6.9771   6.4067  -5.5631   6.5593   2.6215   6.9967   1.5216  -3.5922
+#&gt;    1.4184   1.0813  -7.7687   3.8479 -12.0237  -2.4082   0.5225   7.0061
+#&gt;    6.2708   3.9158   6.9341   6.5268   4.7231  -0.6777   9.4545   7.8420
+#&gt;   -6.4314  -2.6474  -6.6454   3.6796   2.9888  -4.9358   3.7865  -6.6632
+#&gt;  -17.2334   3.7974  -2.9756  -0.2797   9.6011 -11.6500 -10.3269   2.0359
+#&gt;    0.7378   0.3811   5.8920   7.2694   1.1036   2.4982   2.2517   8.2471
+#&gt;  -18.3964  -4.0590   5.4808  11.2866  -9.6668   3.9698   0.3985   4.5973
+#&gt;    7.9941   3.3524   3.4157  -1.5425   4.7768   6.5502   3.1802  -2.4336
+#&gt;    4.0940  -2.9506   9.5420   2.8346  -0.2584   0.2626  -4.8959  -3.7916
+#&gt;   -9.1633   1.4314   6.1877  -1.1202  -9.1444   1.4858   7.0451   6.2695
+#&gt;   10.0667   4.4522   7.0475   4.2658   3.3789 -17.7391  -3.6927  -4.9239
+#&gt; 
+#&gt; Columns 17 to 24   1.1590   2.1596  11.5904   5.3307  -5.9865  -4.5522  -0.3769   0.3412
+#&gt;   -9.1646 -13.4521   4.3623  -8.3640   5.4370   1.4135  -8.5062   5.5152
+#&gt;  -14.5086  -7.4210  -5.6404   7.7579   1.1688  10.6242  -0.2768  11.8314
+#&gt;   -1.4226   0.2549  -1.3752  -5.4226   5.6763  13.8060  -9.4249   3.5398
+#&gt;   -9.5247   6.5736  -5.2558   2.7476  -6.4570   0.6130  -3.5709  10.0829
+#&gt;   -3.8160 -14.4933   0.9134   6.0329  13.7381  12.3846   4.8808   9.9607
+#&gt;    0.2729   3.9416   4.0583  -6.8990  -5.9573   2.6702   2.2853   1.4887
+#&gt;   12.3245  -0.8259  -2.8609  -1.4567   6.0206  -1.0838  -5.6750   0.6743
+#&gt;   -3.5316  -4.2598   2.0282  -6.4856  -1.9813  12.7992  -6.5678  -2.8261
+#&gt;    6.2282   2.9076  -1.2547  -3.2117   1.5294  -7.2852  16.2665 -14.1529
+#&gt;    0.7745   0.8543  -0.5128   4.6769  -8.7667  -2.6857  -4.1179   1.5439
+#&gt;  -11.6602   4.0413   0.9736 -12.6496  -3.9132  -4.5023  -5.2052  10.3583
+#&gt;   -5.7737  -1.4171  -2.9368 -12.0729  -6.1664   6.3264  -4.3236 -11.8384
+#&gt;   -2.1509   2.6165   6.5411  19.5070  -6.2026  -4.6205  -6.2938  12.5549
+#&gt;    5.9643   4.1513   1.4794 -10.6649 -11.2228   1.7241 -16.8030 -10.8468
+#&gt;   -0.0405   8.2949 -10.8254   0.8035   6.2583  -1.9041 -10.9151   3.7618
+#&gt;   -5.1036  -8.0058  -4.0197  -2.9640   0.6133  -6.3221   1.5265   4.5398
+#&gt;   -5.0752  -6.4282  -8.9374  -3.0473   3.9440  -5.0527  -8.7547   8.2707
+#&gt;    5.9394   7.3741  -2.8414  -7.4727  -4.4240  -2.3974  -6.3999 -11.8137
+#&gt;   12.5434   3.6482  -2.6699 -11.8778   1.9999  -0.5057  13.6465 -12.6433
+#&gt;   -1.5947   2.9400   1.1899  -5.5050  -7.8701   4.1474   8.1508  -3.8619
+#&gt;    8.0020   5.4543   6.7161   4.7623   2.0368   3.6720  17.7880  -7.5285
+#&gt;    1.6826   0.6658   0.7317   2.1400   1.9443  -4.3019  -1.9108  -5.3094
+#&gt;   -1.0087  -5.2954   0.7836  12.4790  -6.3694  -0.6966   1.5099  -0.7596
+#&gt;   -3.8252  -3.9653  -0.9693   2.4905  -4.2343   5.8967  -8.7370   7.5073
+#&gt;   -7.3033   2.0156   8.0625   1.1625   3.2388   1.3559 -15.0233  10.9750
+#&gt;    3.0027  -0.0993   8.0798   0.3630 -10.8551 -11.8839   2.5450 -10.9695
+#&gt;   -5.2685  -1.5443   8.3662   2.4834  -2.0784   5.2307  -3.9741   5.1350
+#&gt;   11.5153   2.6065   4.4680 -19.1725  -8.3254   5.6935 -17.2886 -11.1543
+#&gt;    8.7581   5.1499  -0.3413   3.5133   0.3349   4.7417  10.8582  -3.1654
+#&gt;    3.0637   3.2390   1.0381  -1.9561   7.5077  -0.0968   3.0674  -2.0990
+#&gt;    6.2414  -4.6814  -7.8332  -3.0702  15.8663  -9.5176  -0.0717  13.9568
+#&gt;   -0.3057   5.3359   7.4240   3.4238  -4.8786   1.0715   6.7994  10.0825
+#&gt; 
+#&gt; Columns 25 to 32   2.0486   0.8708   0.5544   6.1321  -0.0544   0.4094  -8.8153  -5.4935
+#&gt;    6.3016  -5.4775  -8.4473  -3.2009   6.2352 -22.3775  -9.4374  -5.1860
+#&gt;    9.5832  -9.2713   4.0406   1.7950 -11.8648   5.4144   0.1795  12.1535
+#&gt;    4.7111  -1.0170   1.0492   9.0870   2.7757  -0.9608 -10.3316   1.5690
+#&gt;   -5.8797  -3.1423  -7.8039   3.9120 -13.6611  -4.9237  -3.0517  -3.6668
+#&gt;   15.4324   4.1853   0.8577   0.5581  18.6150   1.9960   4.7495  -8.0974
+#&gt;   -6.1923   2.8643  -1.6250   2.7741  -1.5373  -8.7042  -4.4776   8.0329
+#&gt;  -14.5298   7.1356  10.3003 -18.2865   6.0448  -6.1983   2.5545 -14.4642
+#&gt;   -2.0427 -10.6095   4.6922   5.5868  -8.7672  10.6886   7.3272  12.1353
+#&gt;   -8.0065   8.2435   2.7104  11.1526   2.6338   6.5038   9.6109   0.4804
+#&gt;    8.7848  -7.9189 -11.4750  -1.4746   1.1250 -21.5257  -6.2951  -8.1664
+#&gt;   -1.6037  -7.7364   4.2800  -4.0937  -2.2630 -22.1018   5.3075   8.7757
+#&gt;   -7.1919   1.8938   1.7081  -1.9652   1.0867   0.5521  -5.5878   3.1036
+#&gt;   -8.7931   4.0906   3.5745   3.2660   5.1747  19.7471   0.3309  -4.6035
+#&gt;   -0.8961   3.6014  -7.5782  -7.8223   1.9755 -24.2763 -28.2850   3.6287
+#&gt;    5.2228   7.5141   6.2048  -8.2092   3.0490   2.1969  -3.0558   0.5392
+#&gt;   -7.7035  -7.1654   1.9086  -1.3701   2.0369  -3.1623  -4.1980  -9.3112
+#&gt;   -9.7242 -13.4276  -1.7406  -2.1395  -5.7136  -8.6700  -1.6221  -0.1690
+#&gt;   -4.8184  -4.3520  -2.0174 -11.0387  -8.3672 -11.4500  -5.3209  -0.8526
+#&gt;    9.3625  11.6845  -1.4533  -7.0848   6.4473   2.9298  14.2704  -0.9498
+#&gt;  -14.4770   0.0319   7.0016  -2.4201  -8.2706   6.8810  -3.5194   1.5913
+#&gt;    7.1092   4.3264  -4.7503   3.5927  -1.4041  13.2379  14.9831   1.1556
+#&gt;   -1.9736  13.7259   6.9042  -3.2147  -0.8209   9.0448   6.1615   5.7822
+#&gt;    0.2902  -3.1471  -9.7509   2.9221  14.1677   3.5735  -3.9131  -2.1705
+#&gt;    1.0779   0.8206  -0.5985 -10.3218  -7.7895   4.4428   8.3950  -0.8942
+#&gt;    0.3501  -3.8845   7.8536   8.3398  -5.4798  -0.5387  -2.7007  -1.9552
+#&gt;   -0.0627   3.5151  -0.9520   9.7943   3.1222   9.1339  -2.0090 -10.5566
+#&gt;   10.0780  -3.1242  -3.4301   1.3516   0.4802   6.2696   1.3840   1.7056
+#&gt;   12.8798  12.5197   2.2178   0.8452   8.7783  14.1163  -7.8866  15.9475
+#&gt;    0.0998   2.8998  -7.4788  -5.0958   2.5650   0.1309  13.2316   6.2798
+#&gt;    1.4239   3.1518   6.5176   2.6320   3.3761  13.7262   1.3285  -0.7827
+#&gt;   -3.9298  -6.0559  -2.6825  -3.6432   4.8206 -11.5525   5.8214  -1.3849
+#&gt;   10.8922   4.9468 -10.5936   3.9613   1.7132   6.7847  -0.9719   6.4322
+#&gt; 
+#&gt; Columns 33 to 40   6.7518   0.0637  -0.5057  -6.0995   2.2678  -0.1418  -0.0894  -1.8291
+#&gt;   -7.2281   3.5500  11.5275  -1.1611  -4.6947  -8.4302  -1.5892   8.6065
+#&gt;    1.3153   8.3020  -0.0371  -8.4444   8.9109   3.2795   1.4463  -3.0666
+#&gt;   -2.7541   4.7649   1.0819  -4.4879   6.4546  -0.9405  -1.2156 -11.2372
+#&gt;   -2.3492  -2.5303  10.0099  -0.1765  -0.1695   7.3308 -14.3103  -1.7786
+#&gt;    1.4942   2.9634   2.2040 -13.2088   6.4048 -10.0300 -21.0272  -8.5312
+#&gt;    4.9926 -10.2556  -0.7436   0.4673   8.5513  13.2979  -4.5161   3.3698
+#&gt;    1.1766   1.7118  -5.1453  -7.9682   0.4434   2.5463  -1.0197   2.4115
+#&gt;   -9.9690   7.2995   8.9152  -1.4745  -2.5456   0.4566  19.7125  -3.3651
+#&gt;    3.5575  -8.2574   7.9604  -3.8160  10.6529  -0.6370  -3.8722   9.4154
+#&gt;    4.2048  -4.6499  -0.0380   4.7352   0.2503   9.0735 -11.5653   5.9082
+#&gt;   16.7160  -6.6212  13.1925  -3.3743  -6.5159  -4.9169  17.4261  -1.2669
+#&gt;    0.0743  -3.5589  -7.6503   9.8061  -4.1787  14.1485   4.9883   4.0758
+#&gt;   -3.2329   5.0001   7.1155  -1.9461   0.9129  10.2497  -6.3972   2.6947
+#&gt;   -3.2503  -4.7514 -16.5120   9.3119  -6.0590   8.9492  -3.6170   4.6540
+#&gt;    3.9317   7.7332  -8.8682  -3.6867   6.9156   8.7876  -3.0058  -2.1417
+#&gt;   18.4717   6.0510  -8.5210  14.9308   8.1312   7.9611   3.8285   6.3706
+#&gt;   -7.6681   7.9707  19.1257  -4.4328   2.4504  -1.9814   3.8325   1.0614
+#&gt;  -11.5544  -4.2736  -4.6343  -2.8643  -7.1619  -5.1235   3.1156   4.9920
+#&gt;    7.5518  -6.5793   0.3576  11.3374   1.5424   0.5173  -1.5897   6.1460
+#&gt;   -5.7413   5.3503  -5.2814  -8.5277  -2.9417  -9.4174  -8.9315   0.9418
+#&gt;    4.5978 -11.5649 -20.8196   7.9550   4.7317 -10.5978  -9.1658  -1.1714
+#&gt;   -5.5631  -3.6984   3.9669  -2.8908   4.0008  -4.4950   4.9831   1.4214
+#&gt;    2.4905 -10.9587 -11.9720   4.8999  -4.7274  -4.5001  11.1181  -1.0998
+#&gt;    5.9553   3.1635   5.5144   4.0108   0.9412  -2.8774   3.0265  -2.3413
+#&gt;   14.4224  -5.8882   4.3856   1.2994  -4.3984   7.7702  11.2618  -2.3455
+#&gt;    2.1057  -2.5446 -10.4532   2.0138  -2.5983  11.3659  -7.3662  -0.2779
+#&gt;   -0.6512  -2.6942   2.0764   3.4600   1.7283  -6.8488  -5.9745  -2.4836
+#&gt;   -5.8764  -7.6335   1.2505   1.7193  -3.9201   6.6460   1.1170   5.6142
+#&gt;    6.3060  -5.5585   1.4505   6.7922  -9.8482   4.4323  -1.1784  -3.5730
+#&gt;   -5.8433   2.3117  -6.2789  -1.9090   6.1445  -4.9370  -8.7354  -1.8873
+#&gt;    2.0338   6.2214   8.1495  -2.3130  -0.6697   2.0147   7.2954   0.6291
+#&gt;    0.8160   1.2701   2.8749  -3.5246   0.4742   1.0937  -9.5596 -12.5237
+#&gt; 
+#&gt; Columns 41 to 48  15.4829   2.2627  -3.8973  -9.7836   1.1493   0.5589   1.7641  -0.8840
+#&gt;    2.3187   3.1692   3.7884  -1.5596   0.8787   1.6910   7.6269 -11.8201
+#&gt;  -11.8418  -8.2104  -0.5398   3.7971  -1.2872  -2.1573   0.2527  -3.5010
+#&gt;   -6.8187  -5.1291   3.2852  -6.8728   7.9513   9.5984   0.9122 -15.2566
+#&gt;    3.4903  -2.7251   3.8807  -8.0941  -2.2015  -7.3066   5.0231  -3.6624
+#&gt;   -3.9741   6.1723   9.8998  -3.8340 -12.1560   1.7174  -3.7623   5.3394
+#&gt;   10.6500  -7.2663  -7.8058   5.2731  16.6877   2.3567   1.8999   4.1658
+#&gt;   15.6738   5.8046  -3.3873  -9.3194   2.3546   0.0086   4.8869  11.7072
+#&gt;   -1.9869  -5.9006   0.9127   4.3662   6.3242  -4.6508   4.0298 -11.5901
+#&gt;   -1.9954   2.3345  -2.9135   4.3971   1.6289   4.1065  -0.1429   2.5092
+#&gt;   -2.3726   7.7030 -12.0237  -4.2762  -3.5185  11.9282  -2.6169  -6.9259
+#&gt;    5.7853  -2.6772   0.3960   6.8302  12.4755  -5.4547  -8.6617  -4.1516
+#&gt;   -7.4025  -2.6990   7.7098   5.0920   5.8451   6.7012   2.1474  -5.9251
+#&gt;   27.1486  18.9463  -7.2993 -15.4892   3.6741  12.9396  17.7948  -0.8815
+#&gt;    0.9300   0.4986 -18.4950  13.5777   1.7858  12.1219   8.9036 -12.1938
+#&gt;   -5.6350   1.4006   2.6860  -0.2889   1.0460  -2.4814   3.2362  -1.8450
+#&gt;    0.8046  -4.1961 -17.4817 -12.7517   2.5014  12.6322 -11.9623  -2.0113
+#&gt;    0.5882  -9.0460   4.0003 -11.7903   0.3451  -4.5666   1.3767  -1.3709
+#&gt;    0.4105 -14.2709   0.5560   3.4148   0.2631  -7.3312  16.2476  -3.8253
+#&gt;   -1.3780  -1.0883  10.1791  18.6183  10.5953   8.9525  -7.7608   5.9384
+#&gt;   -4.4125   0.3892 -11.2676   3.9380   0.6758 -10.2265  -4.2889   3.6558
+#&gt;  -10.4216  -2.8228   2.2852   8.5535  -4.5853  -3.4975   7.6815   7.8351
+#&gt;   -2.5886   5.6191   1.3650  -0.8839  -2.8282  -5.7420   6.9080   2.4722
+#&gt;    9.7513  12.6118   4.1706   3.4328  -7.2018   0.3146   7.1430   8.7496
+#&gt;   -2.4896 -10.3852  13.3378   8.6283  15.3907  -5.1322  -3.1432  -2.0923
+#&gt;   14.0131  -2.0976   9.5618  -7.1335   1.5136  -1.0878   4.2914  -7.3082
+#&gt;  -12.1128  -0.6549  -1.8092  -4.4573 -12.9953  11.5015  -5.7832 -17.2620
+#&gt;   -3.0415  -1.2888   8.2886  -7.2860   0.6435  -5.1612   8.2681  -2.4388
+#&gt;    2.5986  -6.4298  -1.4084   0.7011  -1.4112 -10.7474   5.2707  -4.9578
+#&gt;   -2.4491 -10.8547  -1.1192   0.5329  -3.5352 -12.4681   9.8951   4.4189
+#&gt;   -1.2082  -7.2711  -4.0508   3.8085   1.4658  -7.3905   2.7460   2.0371
+#&gt;   11.4228   3.3356  16.4942   9.5570  -0.5468   3.4326   2.3980   2.5207
+#&gt;   -8.8880   2.7444   1.3878  -1.5233   7.9070  -4.8843 -10.5793   8.9785
+#&gt; 
+#&gt; (9,.,.) = 
+#&gt;  Columns 1 to 8  -0.0401  -5.3587  -1.5157   6.2732 -13.6976  -6.7935  -1.4780   5.7833
+#&gt;    8.0295   7.2195  -2.0625   3.9150   2.6615   2.6223  -4.3817 -13.8680
+#&gt;    0.0117   1.2248  -2.0779   7.9729   1.2167  -5.9579   7.6680  -3.5438
+#&gt;    7.9207   2.3120   1.5136   5.0809   1.6964   0.2778   3.1050  -2.5285
+#&gt;  -12.4954   0.1997   1.6190  -5.3132  -4.5581  -7.3059   4.6492  18.3984
+#&gt;    6.6989   8.0327   3.4943   8.5599  -5.4795 -11.8677 -10.8685  -5.4175
+#&gt;   -3.5288  -6.9770   6.5822   2.4493  -7.6729   9.9095   0.4195   6.9523
+#&gt;   -0.0140 -10.2004   5.0841  -0.5328  -7.4023   1.9564  -1.8222  -3.6415
+#&gt;    7.1545  -5.4684  -8.4736   8.6685   6.2781   0.8422   6.3377  -4.6109
+#&gt;  -12.4599   2.0575  -1.8555  -5.7501   0.2061   6.6149  -2.3379  10.9490
+#&gt;   -4.1286 -10.9646   6.1999   3.4294  -0.2630  -4.1317  -0.5053   2.8262
+#&gt;    3.9036   3.3610  -4.8625   3.4878   1.6393   4.8154  -9.4592 -10.1161
+#&gt;    0.6990   1.2127  -3.1301   0.2518   9.4377   0.9270   2.3993  -3.1561
+#&gt;    0.8206   2.4236   5.4289   8.1309 -11.2299 -15.7591  -2.9568  10.8937
+#&gt;    1.0354  -0.7454   0.9291   0.7489   4.2153 -12.2105   2.7888  -1.5642
+#&gt;   -2.7756  -4.6378   3.1716  -1.5000   2.3015  -4.6238   7.1705  -0.5005
+#&gt;  -13.5580  -7.1779  11.3490  -5.8058  -7.8655   5.0732   8.6333   1.6669
+#&gt;   -9.2148   3.8687  -6.5184  -9.2128  -0.0878   9.0653   9.8284  -3.4182
+#&gt;    6.1588   4.3002  -3.3343  -3.2978   8.4184   5.9972   2.9810  -4.6630
+#&gt;    5.8986   4.7918   3.7645   2.8260  14.0795  11.1743  -5.7443  -2.8881
+#&gt;   10.9387   3.0948   5.1992  -0.2369  -1.1449   2.5200   0.5243  -9.9567
+#&gt;   -1.2741   0.0109   4.8255  -7.3392  -9.4379   1.2815   8.6107  15.7691
+#&gt;   -2.9800   2.0068  -2.5025  -1.9067   5.0608   6.0216 -14.4670   2.7249
+#&gt;   -6.0917   3.4364   0.1186  -2.7239  -2.3238 -11.5371  -5.3078   4.5473
+#&gt;    8.3556   2.3350   8.2108   7.8274  -1.7551  12.6956 -10.0731  -6.1833
+#&gt;   -5.7983 -11.7023   1.5639   4.6513  -9.0626   1.8596  -4.6818   4.4735
+#&gt;   -1.6112  -4.2717   2.3752  -5.5697   2.6525   2.7888  11.7979  10.5297
+#&gt;   -0.1627  -1.7397  -0.4723   0.6410  -2.0990  -0.0016   4.6182   9.5013
+#&gt;   -8.1699  -8.9439   0.4498  -5.0688  -2.0963  -2.8840   1.0486  18.9727
+#&gt;    2.1265  -8.2606   1.2341  -6.4923  -5.1748   5.5922 -12.1637   8.1622
+#&gt;   -0.6031   4.6973   9.2818  -2.4909  -2.8083   0.5951   4.9702   4.3780
+#&gt;   -3.7960   7.0361  -6.2691   2.5651   1.7682   1.3532   4.4861 -15.8085
+#&gt;   16.6168  -2.5662   8.5518   3.7907  -4.6948   3.5654  -7.3757   2.9434
+#&gt; 
+#&gt; Columns 9 to 16   6.8515  -5.7924  -1.1463  -0.8660  -8.4046  -3.4402   9.0900   1.6555
+#&gt;   -6.3973 -12.5254  -2.8980   0.0926 -14.1004  -8.7530   3.4872  13.0476
+#&gt;    1.9196   1.9927  -1.6439  10.9017   8.9194 -15.2162   4.5042   6.2516
+#&gt;   -7.2779   1.0948  -5.1268  -8.5474  -2.6354   1.4645   0.1540  -0.7131
+#&gt;   -6.0449   3.3190  -3.4381   0.3023  14.8809   4.8864  -6.5688  -7.9893
+#&gt;    1.5443  -2.8698 -10.6467   4.7107 -10.2424 -16.8032  -0.7090  12.1916
+#&gt;    3.6293   1.0568  -0.8823  -8.6903   5.1829   9.4234  -5.7579  -8.1969
+#&gt;   14.9818   1.6012  -7.0659   9.8859  -3.9187   4.4756  -6.4286  -2.3969
+#&gt;   -0.1391   2.9228   8.1983   4.6002  12.6124   2.5987  -4.0217  -2.2259
+#&gt;   -4.5055   8.3775   2.7163  -8.3042  11.1703   7.6232  -5.9136  -9.1705
+#&gt;   -3.7365   2.1000   1.3560  -2.7441  -1.3666   7.7541   2.7336  -1.9950
+#&gt;  -15.9188   0.5446  -4.0162 -14.9346  -9.2775   1.3314   9.5464  -4.7217
+#&gt;   -9.2875   5.1200  -5.8243   3.4159   0.6104  -7.7458  -1.5204   0.6096
+#&gt;   11.4252   9.9446 -10.1528  -6.4593   6.5204   3.7000  -9.1748  -1.6567
+#&gt;   -4.0282  -9.9866   2.6477 -10.0355 -13.7235   1.0370  11.0200   3.7142
+#&gt;   10.3721  -8.4207  -1.2325   4.2280  -2.5999 -10.9444  11.3093   5.2491
+#&gt;   -3.1942   7.6872  -0.6778   7.6504   5.6901   4.6751   0.7692  -3.9619
+#&gt;   -2.4893  -4.0110  -4.5442   6.8061  -2.1776  -3.1564  -4.6753  -2.0282
+#&gt;   -1.2711   6.2013   4.9302   6.2452  -1.9400  -4.4155  -1.8291  -7.0316
+#&gt;   -5.7117   8.8139 -16.1747  -4.5036  14.2471  11.4689  -8.3989  -4.5683
+#&gt;    0.8491  -1.9457   3.1733   3.2209  -2.4952  -4.1152  -5.4848   1.1790
+#&gt;    6.7018  -4.1316   1.7074   4.9630  10.7363  -2.7869  -5.5036  -2.6856
+#&gt;   -3.8230   9.8087   6.6230  -9.9396 -11.6228   2.0427  -8.1820   2.7967
+#&gt;   -5.7637  -7.6294   6.3921  -5.0245   3.3726 -11.1227   2.6857   0.1126
+#&gt;   -3.9921  -0.1089  -4.7720   7.4220   8.9761   4.3805  -3.3061   3.9361
+#&gt;   -5.4848   3.4496  -7.0230   3.5695  -7.5833   3.4404  -2.9668  -3.6015
+#&gt;   -7.7163   1.8404  12.2752   0.5312   2.3088  -3.5838 -19.5577  -4.0711
+#&gt;    1.6340  -3.3698  -1.8235   3.9398   4.3444 -12.4113  -1.6669   7.3683
+#&gt;    7.9665  -8.2360  -3.6457   0.1929   0.0513   4.3488  -6.7815   1.8229
+#&gt;    8.2299   3.8260   1.7625   0.0936 -10.6032  20.4361   1.5287  -2.6170
+#&gt;    9.5839   0.4424  -1.7036   2.1978   7.0456  -6.7660  -6.6564  -1.6229
+#&gt;  -11.3345  -9.5121  -3.7039   4.2622   0.3526   5.8893  14.7855  -0.0100
+#&gt;    9.3577  -4.2840   3.5418  -5.2092  -7.5807   5.2774   2.8565   6.9503
+#&gt; 
+#&gt; Columns 17 to 24  -1.1178  -5.7008   5.9227   6.2578  -2.9593  -2.4386  -2.4347   0.0266
+#&gt;    8.8592   0.1163  -0.8759   7.1805   3.2065   3.1771   0.5320 -10.5708
+#&gt;    3.3209  -3.6363   4.7289  -2.2140   5.6121  -0.1514   0.9353   1.3090
+#&gt;   -4.0686  -4.7128  -7.4325   3.3564   7.8527  -9.6121  -3.3140  -0.8054
+#&gt;    1.9529  -2.7376  14.5873  -2.6028   9.5261   8.4248 -11.6912   3.6645
+#&gt;   11.4815   4.3680  -6.4507   6.2285  11.1233  -3.5196   9.1821  -2.0697
+#&gt;  -11.0958   7.5177  10.8553  -3.7892  -3.6787  -1.0752  -1.3976  -1.7058
+#&gt;    7.3960   2.9949   5.6724   9.3524  -3.9775   3.0908  14.5466  -0.2611
+#&gt;   -0.9222  -0.8765  -2.0030  -2.0776  -4.7682  -3.0885  -0.7070  -0.7704
+#&gt;    1.1207  -9.1757   7.6451  -8.5055 -10.1224  -0.3377   7.9083   4.9216
+#&gt;    1.3136  -0.8340  16.4055   0.2280   9.2684  -1.8509  -3.4839  -3.6320
+#&gt;    1.5548   6.2844   7.3618  -1.0777   7.4141  -9.8509  -0.1014  -3.9631
+#&gt;   -2.5788  -2.9492  -9.8552  -6.0596  -2.2006   0.4549   1.3011   0.4578
+#&gt;    3.1423  -4.5543   9.1342   6.1294   3.6404  -5.0029  -1.3460   0.3086
+#&gt;   -3.1659   9.7089  -7.4630  -1.8297  13.9918 -11.4251  -0.7111   7.1855
+#&gt;   -4.0978 -11.9546   0.4081  -3.7149   6.9790   4.6450  -1.3808   8.5917
+#&gt;   -7.5492  -4.6058   0.6708   3.8632  -1.5932   2.6781  -1.2377  -3.7517
+#&gt;    4.3857  -4.5138   0.1166   3.3124  -4.9680  -0.8735   2.4706  -5.2127
+#&gt;    8.3726  12.4269  -5.2034   2.3746   4.9432  -3.9395   5.0369   9.4501
+#&gt;   -1.8028   1.0127   1.6399   5.2724  -0.4377  10.1737   3.4244  -4.2763
+#&gt;    0.3288   0.7244   2.7199  -3.2596  -2.9759  -4.2100   4.0019   7.3318
+#&gt;    3.9588   0.3420   0.8685  -9.8066 -13.4092   6.7500  -1.0379   2.6942
+#&gt;    2.9159   3.9992  -0.0392 -11.7874   1.1412  -0.9075   8.6785  -2.6231
+#&gt;    7.3890  14.5941  -5.1672 -12.2903   8.6983  -2.5509  -3.1014   0.8061
+#&gt;    0.5636   4.2588  13.8632   0.7863   6.2155  14.8437   1.6649  -8.9861
+#&gt;    3.5527   5.6034   7.2890   5.6278   3.2482  -1.6864 -14.5621  -1.5483
+#&gt;    3.3780  -3.9101   2.6302 -10.9969  -9.1415   1.6487  -6.3810  11.2122
+#&gt;    7.1961  -0.5232   1.3476  -3.9508   0.5511   8.8228  -3.5775  -1.3342
+#&gt;   -8.6096   1.2976  -8.8711 -15.1234  -7.6538  13.2325   3.1092  -1.0854
+#&gt;    1.0315  -4.0530   2.4703  -6.2399  -8.1853  -0.9545  -0.2738  -1.9879
+#&gt;   -1.0833   0.3655 -10.8048  -5.1990  -6.1180  -0.3845  -3.4704   1.9010
+#&gt;    6.7201   4.3863  -1.9426  16.8580   9.8743   0.6026   6.0198   4.3914
+#&gt;   -7.5689  -7.8332   7.3660  -1.1380  -7.6509   7.4930  -3.3141   1.5017
+#&gt; 
+#&gt; Columns 25 to 32  -2.6328  -4.5319  -7.1864   4.8594   4.1551  -1.6908  16.8185  -2.0045
+#&gt;    4.2067  -9.0978  -5.9054  -5.2251   2.4363  17.6033   7.5333  -9.7814
+#&gt;   -2.7554  -2.3861   5.9767   1.9709  14.0948 -10.1872  -1.7323  -8.9705
+#&gt;    1.9248  -8.7555   5.2803  -3.3823  -6.6890   0.9320   2.7809  11.6424
+#&gt;   -1.1710  -8.0971  -1.5792   5.1196   6.0805 -15.9685  -9.8254   2.1945
+#&gt;    3.4229  13.3123   1.7412   7.7443 -12.8203   0.1005  18.0222   0.6522
+#&gt;   -2.4779  -4.0404  12.9208   5.1077  -3.9396 -13.3427   4.9993   0.5800
+#&gt;    0.7383   0.5066   0.5028   5.0332 -15.0639  13.3385   8.5822   8.0929
+#&gt;    4.1591 -13.0543   3.7959  -4.2798   3.8052  -4.1710  -9.2550   0.0112
+#&gt;  -19.3894  -1.0482  12.2905   2.6449  -1.2021   2.8008   2.7482 -11.1357
+#&gt;   -5.2213  -2.2390   0.3899   6.5409  -4.4120  12.9182   2.6424 -12.3500
+#&gt;    5.1960   0.1905  -1.8664  -0.4012  11.7711   3.6295  11.3020  -9.2769
+#&gt;    0.8557   4.5771  -1.8557   5.4125   4.7146 -16.4669   2.4940   8.4388
+#&gt;   -6.5435 -10.4828  -7.4447   9.1780   2.5317   2.5086  16.7430  10.3668
+#&gt;   18.0602  -0.5392  -4.3564   1.4777   0.6322   9.7552  17.1769   0.3597
+#&gt;    1.8624  -2.1766  -0.1327   7.9368  -3.7893   0.1420   1.6865   6.7944
+#&gt;  -11.7524   3.9455  -0.4495   7.7694   0.2370  -2.8936  -6.5377   4.2342
+#&gt;   -2.8456  -6.5280  -0.5771   1.7983  -0.3862  10.8229  -7.4962  -9.9794
+#&gt;   12.8165   3.8421   3.3570   5.9936  -5.2011  12.9821   5.5467 -15.9329
+#&gt;  -10.5982  10.2841  -4.3786  -6.7834  -7.9043 -11.2157  -5.7437  13.0609
+#&gt;    8.4495   9.0868  13.1325   1.7125  -6.6945  -0.2319  -0.6027 -11.0652
+#&gt;    6.3865   5.2043  -6.4957   3.5516   3.2219 -12.3862   0.1227  -5.2055
+#&gt;   -5.2216  12.6346   3.7573  -7.7498  10.6991   3.1593   8.4007  -4.3679
+#&gt;   12.5380   7.2983 -11.7928   2.5340  12.8789  -0.3407   2.7112  -1.0381
+#&gt;   -0.9214   6.4992 -13.0042   6.0661   3.9832  -7.1026   5.6302  13.4000
+#&gt;   -2.1768  -6.6846 -13.8017   1.8433   4.4714   1.3138   4.8917  14.7906
+#&gt;   -4.2788   3.8993  13.7874  -4.6234   8.0018  12.5315  -5.2471   0.7957
+#&gt;    7.5338   2.3114  -4.8775  11.4832   7.3880  -5.8596   6.9446  -5.0459
+#&gt;   17.4258   8.2201   3.7529  12.9822  -5.2978  -5.2244   9.5446  -4.8894
+#&gt;    0.1072   4.9824  -9.7295   7.1216   3.4568  -2.5361  -4.1698   0.6386
+#&gt;    9.3592   0.1783  11.7393   8.9554 -11.0262  -4.5985   4.8218   2.9281
+#&gt;   -0.3339  -9.8969 -12.6341  -3.9049  -5.7417   2.9067   1.3615   6.0554
+#&gt;   -5.0467   1.7893  -5.2092  -2.5334  16.4103   2.5706 -15.9351  -9.0296
+#&gt; 
+#&gt; Columns 33 to 40   6.2091   0.1991   3.5344  -0.9969  -8.6031   4.7295  -6.6684  -3.4680
+#&gt;   17.4568  -2.6171  -9.9754  -0.0237   4.8433   4.7948  10.5709  -7.4558
+#&gt;   -9.0177   0.9439   3.9046  10.7104   9.0576  -1.2505  12.4538  14.4930
+#&gt;   -4.1310  -9.1202  -6.1499  -3.6964   1.2821   9.2477   3.8688  -0.2891
+#&gt;  -23.1013   0.4968   1.2061  12.0732  -5.0711  -2.3337  -1.7073   1.1353
+#&gt;    6.6329   3.2435   5.2979   4.1493  -5.4980   0.2584   9.4383  15.8394
+#&gt;   -7.8822   4.2218  12.1040   1.0965  -1.9303   2.0269   0.1280   1.8397
+#&gt;    0.7127  12.8749  -5.7977  -0.1436  -7.1988  -1.4078   1.6558   5.8912
+#&gt;    9.7196   0.6410  -1.4691   5.3841   5.9562   2.7362  -2.6035  -0.7117
+#&gt;   15.7405  -5.8802  15.0010 -13.5364   4.4596  -5.1669   3.4519   2.5614
+#&gt;   -1.9596 -11.9555  -2.3222   7.3614  -0.3040   5.6089  -6.7231  13.2914
+#&gt;   12.0305   1.3015  15.0780  -3.8119   8.8774 -10.5482   2.1028   7.0329
+#&gt;  -12.0409   9.9197  -0.9035   1.5472   0.0250   6.7408   2.9665  -7.3522
+#&gt;   15.3074  -5.0977  -0.5890  -3.2326 -11.9586   5.3730   0.3302  -7.4397
+#&gt;   -5.2714   0.3554 -10.4245  -0.2297  -1.6945  11.1466  -3.9075 -14.8633
+#&gt;  -12.3549   3.3810  -8.4166   0.6233   6.1302  -6.4055   4.9078   0.5862
+#&gt;  -16.7691 -15.3161   2.1979   9.4139   7.0512   9.3076  -2.2918   7.5642
+#&gt;    3.4194  -0.4941  -3.7123   4.0891   2.5900  -2.9446   7.2037   5.0012
+#&gt;    2.6715  -9.1567  -1.0215  -2.7875   1.2827  -7.7540  -6.0772  -5.9831
+#&gt;  -10.1948  11.5526  -3.2811 -12.5704   5.6780  -1.6276  -1.2724   5.6591
+#&gt;  -11.3361   4.0491   5.9046   2.5699  -0.1597  -1.3786   1.2125  -0.9712
+#&gt;    2.1922   7.5539  -4.0624  -5.0876 -13.0993   6.8749  -0.6148 -12.8928
+#&gt;   15.7846  11.2008   0.2007  -6.8360  -1.3051  -4.3575   4.8508  -3.4005
+#&gt;   16.5002  -6.7961   2.8574   4.1257  -0.2058  -1.5497   6.5875  -8.5155
+#&gt;  -13.5434   2.0469  -6.2474  11.2225  -2.8352  -0.5849  -6.3699   2.1378
+#&gt;    5.5689   6.3972   6.5323   5.2079  -2.2170  12.5505  -3.4878   8.8321
+#&gt;    0.2990 -12.0754   3.3492  -2.5455  -1.8806   7.3532 -14.6075   7.3780
+#&gt;    6.0513  -0.3128   0.7940   1.6838   2.5102   0.3200   3.7384  -2.9474
+#&gt;   15.1318   4.2079 -10.3092  -9.1043   0.4524   7.4112  -9.8660  -9.8504
+#&gt;   -6.5203  10.6804 -12.3515   4.4717 -13.9374   2.9997  -8.6741  -8.5732
+#&gt;    2.0869  -7.0619   2.1865  -4.9065   6.5477   1.0061   2.3397  -5.1603
+#&gt;    5.8797   1.4344   1.5147   5.2506  -1.2364   1.2565   3.4842   1.2513
+#&gt;  -13.3068  -3.7998 -10.6292   1.2736  -4.6309  -2.5332  -5.4764   6.7562
+#&gt; 
+#&gt; Columns 41 to 48  -3.5574   7.5599   2.0042   3.4539  -0.8589  -3.3040   3.3101 -11.0791
+#&gt;    5.4038   7.0159  -6.2993  -3.0544   3.6550   1.1032  -5.8003   1.3008
+#&gt;    2.7843  -1.0163   7.7313   8.6528   8.1484  15.0378   4.8930   7.2796
+#&gt;    5.7043  -7.2706   2.1721  13.6769  -5.2003  -4.8240  -0.6589 -10.6869
+#&gt;    6.5594   1.1363  -2.4785  -1.1522  -7.2464   1.6686  -0.2455  -6.7628
+#&gt;  -28.2058  -8.7507   6.6609  -3.9997   5.7921   3.9306  -4.3053   7.3204
+#&gt;    7.9689  -9.8504   7.8741   6.0412  -4.4477   6.1368   5.7541  -2.4991
+#&gt;   -2.6163   7.9938  -1.1072  -6.1312   2.9033   8.3875   0.8004  10.6133
+#&gt;   19.3417  -5.7705  -5.2083   7.7241  -2.8094   4.8042   6.2480   5.1922
+#&gt;   -2.4543   2.1262  -6.9594  -5.6646   0.9048   6.0474   4.4061  -8.3221
+#&gt;    0.7589   0.3771   9.3862  -1.1914   0.0915  11.6177  -1.5250  -8.1866
+#&gt;    1.8998  -2.8191  11.5197  -7.9579 -10.5968   3.2860  -2.8600  -4.5691
+#&gt;    1.3142  -1.5478   1.9273   2.2043   0.2335  -3.0520   4.8588   1.8683
+#&gt;   -1.5740   0.3910  -7.8552  -1.5160   0.6581  -3.3445   3.0745  -6.4489
+#&gt;    3.7713  -3.5507   9.1197  11.5505 -17.4841  -9.6546   1.2523  -8.5845
+#&gt;   -0.8830   5.1521  -1.8431   8.3170  -3.1605  -1.8789   0.2834   1.7550
+#&gt;   -0.2878   2.9302   2.4239   5.1226  15.0652   8.3873   3.6716   1.3386
+#&gt;   -3.3260  20.2701  -8.4066 -11.0285   9.3202   4.9653  -7.8801   6.4595
+#&gt;    7.3999  -1.3009  -4.7659  -3.0299  -4.3380  -0.7846  -0.0295   0.2334
+#&gt;   -1.2469  -2.6782   5.1911  -5.6863  -3.0801  -5.2409  -4.3599  -1.7330
+#&gt;   -3.7087  -3.1212  -5.9343   1.8252  -3.9633  -1.2791   0.7218   3.9059
+#&gt;    3.5941   0.7342  -3.0248  -4.0062  -5.4251  -6.4783   3.4834  -3.6418
+#&gt;   -5.5708  -7.3934   0.5518  -4.8714   1.8200   3.0631   4.8407  -0.5243
+#&gt;   -4.6621  -2.6307  -2.7314  -1.5075   0.3348   0.6291   8.1803   3.7558
+#&gt;    3.7802   2.4882   1.2630  -3.1052  -4.1978  -9.7503 -11.2655   4.2330
+#&gt;    1.9003  -1.8347   2.2605  12.1232   2.9234  -1.2920   9.1332  -2.7259
+#&gt;    9.4641  -7.7054  -3.1332   2.6496   2.0928   0.5605   3.8503  -3.1029
+#&gt;    0.6528   3.8899  -4.9479   3.0866   0.6270  -0.2846   7.4450  -2.0769
+#&gt;   -9.5134   3.7089  -7.7251   4.0346  -8.5719  -3.4793  11.6490 -10.6066
+#&gt;    0.7984   0.9333  -1.8003 -10.2657  -3.6976  -5.1811   0.4680  -1.1693
+#&gt;   -0.9469  -3.2107  -4.2615   6.3643   1.2269  -0.2931   1.4162  -0.5164
+#&gt;  -10.6698  18.0495   5.9027 -11.9230  -3.0949   1.2751  -9.0796  12.7728
+#&gt;   -1.7290   4.4678   2.5966  -5.5755  -6.3106  -7.8328  -2.7642  -8.1503
+#&gt; 
+#&gt; (10,.,.) = 
+#&gt;  Columns 1 to 8  14.4710   4.6432  -6.8133   3.7200  -2.6654  -4.0008  -9.0046  -0.3256
+#&gt;    2.6093   0.3767  10.7315   3.0589  -4.4227  -9.5532   5.4612   9.7847
+#&gt;    2.9513   6.5225  -1.8419  -5.8784   4.0442   1.9532  -6.0345   4.1286
+#&gt;   -3.2467  -0.1254   7.7224  -2.7941  -8.4919  16.0064   0.4471  -3.8678
+#&gt;  -10.8613   3.1102   7.4981  -2.7834   0.5129  10.9916  -5.3026   5.3011
+#&gt;   11.3749  10.6835   5.6861   5.2611  14.7803   6.1482  -0.0210  -5.5726
+#&gt;    4.0023   0.2153  -3.2174  -8.3354   4.2527   2.1872  -2.9610   2.3508
+#&gt;    2.4431  -1.0216   0.5010  -7.4281  -7.5019   3.3549  -0.7031  -3.9651
+#&gt;   -6.0749 -11.0627   1.2948   5.7971 -17.0622  -2.2280  -7.2597  -1.4245
+#&gt;    3.7310  -1.6254   1.3617   3.5650  -6.1422  -2.6705  -0.4709   4.5567
+#&gt;   -9.8843   0.6354   4.8846  -4.8381   4.8865   2.0070  -4.4283  15.0839
+#&gt;   13.7834  -6.3143   2.9675   5.2545 -11.7613  -6.4473   7.2150   8.5128
+#&gt;   -9.4301  -3.4960  -1.4475   2.3521   5.7713  -3.7340  -2.5474   3.0973
+#&gt;    9.2346  13.2348  -4.6010 -14.8336  -1.9623   9.7488 -11.0311   6.8809
+#&gt;   -6.4975  -8.5517   0.3187   1.9106   8.6420   3.1085  -3.8641   4.4096
+#&gt;    4.7967  -3.6075   5.8131 -14.5341  -4.3010   7.2033   5.2022   2.8585
+#&gt;  -16.6634   7.5446  -5.7558  -6.7430  11.2488   5.6837   3.0768   4.0601
+#&gt;   -3.8207  -7.3227   5.7054  -2.1779 -10.0372  -1.7432   3.8939   4.9310
+#&gt;   -5.1015 -10.4106   9.9179  -2.8573   8.5237   4.0813  -1.1077   2.8891
+#&gt;   -1.4281  -2.5566  -9.9267   5.1031   1.8095  -3.2307   8.0328  -4.4473
+#&gt;    2.0832  -1.8587  -3.9835   8.5245   3.7592   5.9499   8.6746  -9.7774
+#&gt;    1.0120   2.9970 -10.7454  -0.7120   9.0160  -3.3007 -19.0601  -3.6214
+#&gt;    6.0270   4.3670   0.2912   6.4658  -4.9769   5.0067   2.1219   1.2629
+#&gt;    3.2517  -6.9647   5.6150  -1.4422  10.5213   0.8685 -14.3389  -1.6863
+#&gt;    4.7981   3.6669   2.1774   6.5129   6.3029  -4.2212  -7.1775   0.6849
+#&gt;    4.1268   9.8775   0.3653   1.7378 -18.7752   3.0751  -3.1661  -5.7616
+#&gt;   -9.5046   1.5973  -6.7126   1.0743   0.1221  -0.4248  -6.5908   7.3890
+#&gt;    7.7763  -0.7585   1.1607   1.1947   0.5593  -4.3177  -7.9144   4.9266
+#&gt;   -6.5896 -22.8091  -5.1244   8.9467  -5.3432   6.1592   7.9922   2.2006
+#&gt;   -3.3691  -3.1167  -1.4385   6.0750  -4.5615   3.9180  -4.1986  -6.5412
+#&gt;    5.1560   2.3632   2.0595  -9.7522   5.4654   3.2598   0.3563   2.0999
+#&gt;    2.0908  -5.3153  11.3245   2.5395  -1.7546  -3.3645 -12.8333  -8.2892
+#&gt;    8.8760   1.2254  -5.4517  11.1036  -5.5584   2.3060  -0.5097  -3.2205
+#&gt; 
+#&gt; Columns 9 to 16  14.5479   6.6808  -2.0072   4.2442  -0.1356   0.8253   6.9246  -2.2184
+#&gt;   -0.9735   7.6945   2.7680  11.0302   4.8427   6.4257  10.5309   4.8268
+#&gt;   -5.8721   3.2921  -5.6136  -5.5610   5.9241  -7.7979 -10.2809  -3.3400
+#&gt;   -7.4690   7.2934   1.1521  -4.7303  -9.2411  -0.9633  11.4470  -2.2516
+#&gt;   -5.9393   5.4131  -6.2276   4.5821   1.3449  12.5757   6.3698   3.5507
+#&gt;  -11.1041   2.2527   4.1273  14.2510  -4.9141  -2.6712  -6.8055  -2.1377
+#&gt;    3.3734   3.5293  -5.9588  -4.5543 -11.7529  -1.3317   5.3928  -7.5389
+#&gt;   15.7623  -2.3318  -3.0392  12.6354  -3.0868   8.5040  -6.3759   8.9538
+#&gt;    0.6534  -7.9175  -5.0673  -8.4351  -3.4650  -6.8837  -0.1515  -1.9018
+#&gt;    4.6077  -3.1372  -1.9121  -1.9368  -4.0526   1.7457  -6.9543   1.4152
+#&gt;    3.6549   5.7417  -3.6029   0.4043  -3.4691  16.9400  10.5767   7.9510
+#&gt;   -9.4562  15.0964  -5.0772   7.7207   3.4396   8.2146  -3.8646  -2.3953
+#&gt;    0.0823   7.2684  -9.4364   5.7086  -4.1273  -6.8211   2.5935   4.2336
+#&gt;    8.7147  -0.8841  -8.3409   7.1285  -8.4872  -7.4519   3.1871   4.1935
+#&gt;    5.3103   1.8878  17.6789  -5.2104   1.2377   5.0434  21.0108  13.5516
+#&gt;   -3.9218  -6.2199  13.1058  -8.2961  -0.5789 -10.8592 -10.2193   0.0549
+#&gt;    2.8405  -4.7218  -4.1269  -2.0875  -7.2561   7.3622   2.0732   5.6603
+#&gt;   -0.9309   1.1359  -8.0774   6.8599   6.1417   3.7662  -3.6261   4.2135
+#&gt;  -10.3160  11.8156  -6.0465  -4.8211   9.5632   6.6152  11.8579  -1.8774
+#&gt;   -1.8500  -7.1162  -6.5278  -0.8735 -13.7540   2.1832  -7.1184  -3.5752
+#&gt;   -0.0181  -9.3558   3.7128  -1.4525   7.1921   4.6078  -3.3366  -0.9247
+#&gt;    5.4197 -10.4160   2.7022  -4.6919  -2.1032  -7.5483  -4.9965  -2.0029
+#&gt;    4.1998  -3.4277  -7.8864  13.4715   0.7598  -4.7825  -7.5446   2.3903
+#&gt;    0.9815  -1.8990  17.6363  -1.1371   7.7331 -11.9590  -2.6291   4.4274
+#&gt;   -1.2596  -6.1834  -8.3681  10.2132  -5.4146   3.1174  -0.3621  -4.6778
+#&gt;    7.4613  12.8624  -4.8624   2.9646  -7.0720  10.1873   1.1685   3.4204
+#&gt;    5.3303  -4.5529 -10.5102  -5.4973  -1.3891   4.8987   0.6605   3.0140
+#&gt;   -3.1125  -4.1321  -1.0305   1.3574  -3.4780  -6.3012  -0.8300  -3.5251
+#&gt;   -0.1821 -14.8435   0.8051   6.1117 -10.4716  -5.6095   2.3493  -2.9083
+#&gt;    0.2080  -0.7521 -10.1554  12.1975  -8.0559  -5.7300  -4.0059  -5.5448
+#&gt;   -8.6250 -11.8101   9.1740  -9.0937  -5.1054  -7.0094  -4.4310 -11.1649
+#&gt;   -3.8040  11.3338  10.4777  -0.9818  -0.9765  -8.1743  -9.9641  -0.8615
+#&gt;    6.4034 -19.4679   1.4102   2.1337   1.8203  -8.4996  -9.2010  -6.2137
+#&gt; 
+#&gt; Columns 17 to 24   4.1192  10.9130  -6.0056  -2.1138   8.5664   5.1662   2.6595  -1.3731
+#&gt;    4.0597   1.1002 -10.7103  -0.0916  14.5790   6.5852  -4.4298   4.4207
+#&gt;   -1.4257  13.8238   0.7385   1.6014  -1.3178   1.8495  -7.2067   9.7789
+#&gt;    3.9501   8.0051   4.3776   0.5641  -5.2933  -0.1487  -9.0504   1.1126
+#&gt;   -6.9081   9.5299  -0.6400  -6.7744   4.4750   1.6003  -0.6061   6.1316
+#&gt;   -5.1433  19.4295   6.6673  -4.3950   5.2266  21.6277   6.4043  -0.2752
+#&gt;   -5.0103   1.6013   3.9599  -7.4589   2.6976  -0.9549   4.2999  -6.8065
+#&gt;    7.4476   2.7529  -8.5339   2.3087   6.9197   3.1030   7.2101  -0.2283
+#&gt;    3.3662  -9.1169   0.4361   3.1868  -6.0411 -11.1444 -13.0576   1.2490
+#&gt;    4.5949  -6.2269 -15.8484   8.1420  -0.9314  -8.6579   5.3700  -5.1534
+#&gt;   -5.6058   4.9588  -6.7369  -1.1946   6.5778   8.9600   4.0150  14.8578
+#&gt;    2.0496   6.4323   3.3880  -1.0165   6.4750   0.2090  -8.1803  10.0744
+#&gt;   -1.8714   3.9368   6.5303 -12.0373   1.5525  -4.4925  -7.8129  -3.0793
+#&gt;   -3.1583   2.3940  -1.7749   8.2701  16.7172  -4.3440  19.2828   4.4032
+#&gt;   -5.6533   8.0105   3.2189 -10.4303   6.5888   1.4778  -2.9537 -12.4291
+#&gt;   -0.1350   5.0839   2.8423  -0.2953  -8.4328  -1.9774  -6.2660  -0.4665
+#&gt;   -2.4046  -4.0025  -1.5798   8.4617   3.0757  -9.2885   8.9053   0.2256
+#&gt;   10.1778   8.4762 -11.0694   2.4767  -2.3942  -1.1300  -9.0895   7.2365
+#&gt;    6.0020  -3.4611  -0.4594   0.9693  -1.6207   5.6691  -9.7336   8.2720
+#&gt;   -4.1412 -11.0522   5.2162   0.8411  -7.0415  -2.3906  10.2475  -0.7341
+#&gt;    4.9153   9.5439  -3.9681   4.3850  -1.4356 -10.0032   7.7016  -7.3656
+#&gt;    6.4802  -4.0935 -10.7369  -1.4834  -4.3873  -8.8123  -4.6086 -15.6554
+#&gt;    6.2670  -9.3744   1.1737   7.7070   8.1785 -11.6734   7.9465   6.8682
+#&gt;   -8.1977  -2.7518   7.7775   0.7281   0.3783  16.3828   8.0922  -5.0288
+#&gt;  -10.7717  -7.5790  13.4127   1.0349  -0.0678  -3.4017   7.5944   5.2857
+#&gt;   -0.9464   2.7255  -1.0871   2.3097  11.4077  -9.7370   1.1826   3.8529
+#&gt;   11.7870   4.3044  -3.5921  -1.1888  -6.8060 -16.7895  -0.6630  -1.1693
+#&gt;   -1.0368   2.5376  -0.7813  -1.5229   1.7481  -1.2655  -3.1261  -0.6197
+#&gt;   -4.1932  -0.5911   7.1051   3.7213  -4.3230  -6.3889  -4.1842 -16.6919
+#&gt;    1.0108  -9.6746   6.1159  -4.2510   4.1172  -4.3838  -4.8486   2.5938
+#&gt;   -2.0697   0.1736   5.6889   8.3053 -11.4090  -6.4399  -3.8511 -16.8787
+#&gt;   -7.3775   8.6551  -6.2978  -8.3835  -0.3105  14.6197  -0.5400   4.4779
+#&gt;    3.8251   4.0045   2.8259  -1.9221  -8.0333   0.4056   8.8029   1.6251
+#&gt; 
+#&gt; Columns 25 to 32  -4.0010   5.2869  -3.7124  -0.3850  -3.4061  -6.2136  -2.9467   1.8112
+#&gt;    6.8547  -3.1490   8.5090  -5.2604 -11.8212 -10.6668  -5.7048  10.8873
+#&gt;  -14.7036  -6.5548   1.6447   0.0889   5.9533   2.4828   3.0379   6.4476
+#&gt;  -14.1212   9.7051   9.6831  -3.6053   6.0167   5.2231   3.8789  -2.0557
+#&gt;    4.5233  -3.6778  10.6239  -7.6003   0.1486   1.1952 -13.5926   3.7119
+#&gt;  -13.0996   0.2291   0.1330   1.1188   4.8989  -2.9820  -7.7579  -8.0266
+#&gt;   -2.3823  -1.5344  -4.1569 -10.4308   5.7587  -0.8466   0.3815   7.2276
+#&gt;   -2.2049  12.2634 -12.7209  14.9204  -0.0622   4.5250 -15.3472 -10.9432
+#&gt;  -12.5341  -0.4378  -6.1007  -6.7649   3.6043   3.8794   6.9019   2.4826
+#&gt;    7.9965  -8.9160  -0.5156  -7.4211   8.0894  -3.8668   0.0773  -6.1718
+#&gt;    5.9205  -1.6424   5.9659  -0.5204  -5.9036   2.7446   0.0560   1.6184
+#&gt;   -8.7911  -3.9587  -1.6014  -7.6753 -11.9352   1.3038   0.7882   6.5391
+#&gt;   -5.1887  -5.5627   3.9967  -0.1711   7.8940  -4.6065   0.4770   5.9022
+#&gt;   -7.2264  12.3871   1.4501 -10.5747   4.2974  -8.2536 -12.8194   1.3355
+#&gt;    3.5932   4.4770  16.5684  -0.1417  -2.6059  -9.0042   4.2586   8.0855
+#&gt;   -0.5715  -1.7804   8.4751  12.0436  -7.5025   9.8106  -0.4650   7.0134
+#&gt;   -6.5063   6.9795 -11.7314   0.3454   2.3116   0.8328   2.1417  -3.1126
+#&gt;    2.0151   2.1232  -0.6683   7.5267  -2.6835   5.6750  -9.6292   4.5255
+#&gt;   16.3539   3.2921   3.0363   0.3554  -5.7509   4.3470  -4.5798  -1.6581
+#&gt;    5.5927   1.7704  -9.3390  -2.6007   1.8508  -0.0748  11.5658  -5.3670
+#&gt;   -2.5411   2.5914   1.2881   2.5088   2.1225  -4.2355  -1.0349 -11.8804
+#&gt;   -4.2727   1.1581  -2.0757   5.9721   5.6380  -9.3281   2.2306  -4.2831
+#&gt;    0.2923   0.8767   5.1367  -3.8978   0.7931  -7.1608   0.3274  -3.0203
+#&gt;    0.5253  -0.7469  -2.1695   4.9469  -0.0681  -0.1998  -6.7128   9.1915
+#&gt;    1.4316   6.7870 -11.9403  -1.0241   2.7523   4.2159  -5.2916  -1.1015
+#&gt;  -11.7556   9.3717  -7.0557   2.2428  -4.9803  -3.5776   8.9442   8.4118
+#&gt;   -1.3536  -8.4380  11.1821   6.6431   7.1123  -8.1119  -0.0093   1.8174
+#&gt;   -1.5371  -3.4120  -0.2186  -0.4295  -9.3453  -2.9871  -3.1140  11.2028
+#&gt;    4.3040 -10.9187  -3.9711  -0.6291 -11.9327   0.6901  -2.0339   7.4203
+#&gt;    5.5496  -4.4382  -9.7760   0.0982  -6.9906   6.1753   3.3855  -9.9118
+#&gt;    0.1587   1.0764  -7.0968   0.8729   1.8725  -1.1229  -0.2826   2.1209
+#&gt;    4.8244   5.7039  -7.7527   0.6460   7.2443   4.3282   2.4718   3.3759
+#&gt;    6.2145   3.8019   0.1981   7.4547  -7.1679  -5.8358   4.5452  -5.1186
+#&gt; 
+#&gt; Columns 33 to 40  -2.7463  -2.0798  -2.5194   7.7455  -0.6367  -7.5936  -5.9359  -8.5481
+#&gt;   -3.3242  -0.7103   3.6360  -5.1043 -11.5722   0.8882  -4.3279 -10.3382
+#&gt;    5.4391   1.6034 -13.6770   5.5982  -6.3312   2.5836  13.0854   3.0990
+#&gt;   -6.0943  12.4081 -17.8393  -3.5582   6.9505   5.7650  -0.7693  -3.1509
+#&gt;  -10.7622  10.3388   6.1969   1.7298  -0.2892  -8.7279   7.2891   4.4228
+#&gt;    5.4087 -14.6842 -17.9690   5.9490  -2.2659  13.3579   4.7606  -2.1772
+#&gt;    2.5449  13.5166  -2.2249   4.7754  11.4285 -14.5705   8.6252   7.5728
+#&gt;   19.4670  -5.2286   7.7477   7.1915   6.3136  -4.3305  -4.1927  -8.4972
+#&gt;    4.4044  10.9534  -6.1862   1.3248  -2.4400   0.9649   2.6163  -2.4498
+#&gt;   -1.7708   8.6231   1.7543   8.6069  -4.8777  -3.1206  -3.1282   1.0544
+#&gt;    3.7684  -4.9195   9.6077 -13.7415   6.4011  -6.7006  -4.1248   2.6999
+#&gt;  -17.3474   6.9439  -2.9371   1.2146  -4.3929  -9.4780  -5.4103   1.3074
+#&gt;   -5.3908   7.3266  -1.6835   3.3961  -2.2084  10.3198  -4.2306   3.3221
+#&gt;    7.5038   5.8080  -9.9477  16.2983  -0.9450  -7.1822 -13.0129  -3.8743
+#&gt;   -9.1869  -3.6647   2.8171  -5.9277   8.1256   2.9991  -7.9105 -10.7075
+#&gt;   -4.0340  -5.9935   8.3321  -8.2468   7.8384   0.0060  -0.6545   4.3919
+#&gt;    9.0310  -3.1660  -2.9173  -1.1388  -3.1402   0.4344  -8.0797   8.5828
+#&gt;    0.4831   5.4021   4.9846  -0.3507 -10.9405   5.4938  -2.6585   0.7319
+#&gt;   -7.5524  15.0568  -1.5017  -5.3615  -5.8247   1.2927   9.2929  -2.6711
+#&gt;   10.3731  -9.5647   0.9368   3.4030   1.5107  -0.1655  -2.6225  10.0625
+#&gt;   -0.6429  -9.5170  -5.2768  -0.2312   0.9537  -2.8683  12.2291  -1.5264
+#&gt;    3.5289 -11.2751  11.3402   9.9709  -1.9313   3.9201   7.5383   1.0913
+#&gt;    5.3155   7.0822  -7.8948  10.0645 -10.6934   3.1523  -3.9992  -7.4225
+#&gt;   -8.3727   5.3745  10.6670  -3.6268  -0.9629   0.4118   7.7708  -0.6398
+#&gt;    3.7513 -17.4524  -4.8192   4.7726  -3.1548  -8.3748  -2.4123   5.3698
+#&gt;    4.3340  16.0960  -2.9203   4.5094  -6.5853  -6.5885  -5.4492  -4.2702
+#&gt;   -2.1476   3.2810   5.6629 -10.6544  -4.9819  -1.7934  -2.1906   2.5553
+#&gt;   -2.3297  -2.1629   2.2620   1.2632  -7.7004  -4.1165   4.5297   2.8979
+#&gt;   -7.5091 -11.5038  11.4662   5.7960  -7.0260  -8.6066  -4.9869   2.1815
+#&gt;   -0.8860  -7.0743   8.7135  -6.5663   3.8496   1.9958   0.8628  -1.2757
+#&gt;    4.4538  -4.4086  -3.7311   2.2965  -1.3821  -4.7465   6.0835   2.8220
+#&gt;   -8.2009   1.5255   5.5705   1.2606  -4.9644  17.2563 -10.5917   1.6127
+#&gt;   -4.1038 -18.1976   6.0696  -7.1599   3.0302  -1.8089   2.2677   6.4528
+#&gt; 
+#&gt; Columns 41 to 48  -5.0606   6.2417  -2.8217   5.1493  -3.5163  -5.6563  -3.6681  -2.1512
+#&gt;   -4.0298   5.3893  -1.1602   1.5378  -8.1873  -8.5062  -2.0137  16.1354
+#&gt;   -4.4442   0.7066  -1.4783  -1.9802  12.1887   9.8826   7.7054   1.4902
+#&gt;   -4.4092   2.3773  -7.5726   6.1283  -4.4703   0.7849  -3.1640  13.5514
+#&gt;    1.8827   1.6585   6.9649   1.0963  -7.3246   9.6848  11.5215 -10.1604
+#&gt;  -20.4653   0.4185  10.8977   8.1515   9.7242 -13.3273  -6.8315   2.1960
+#&gt;   -0.5615  -7.1728   0.5999  11.9708   1.6972   4.7447  -6.3877  -1.4654
+#&gt;   -4.5520  -3.8538   7.9964  -1.3561   1.0739   2.5283   9.4726  -9.1893
+#&gt;    3.7516  -3.8982 -13.4918  -5.1956 -13.5435  10.5221   1.4650   6.7804
+#&gt;    4.4838   0.6042   0.4535   9.5781  -4.8421   6.4397   7.0565  -4.1577
+#&gt;    1.5536  -0.4799  12.1631   3.3254  14.9297  -3.2445  -4.3268  14.8057
+#&gt;    4.6843   2.0066  -8.5335   0.4175  -9.3473 -13.4129  12.0193   3.7965
+#&gt;   -1.3756   6.6072   0.3018  -0.5654   1.6305   7.2815  -1.4047  -3.2000
+#&gt;   -6.8800  -2.6158  -2.4326   0.5977  -2.6295  -3.3078 -11.7381 -17.4652
+#&gt;    7.0267  12.5131   3.4610  -6.0618  12.0014  -5.4000  -8.3710   5.1105
+#&gt;    0.9499   0.8749  -2.2749  -8.7281  16.2070   3.9815   2.2909  -7.3912
+#&gt;    1.4547   4.1245   5.9095   8.6042  19.5148   6.1538  12.7950  -3.8956
+#&gt;    2.6229   8.2897   0.5670  -1.1295  -9.9689   1.0132  11.4646  11.5797
+#&gt;    2.0644   3.0247   9.6181  -0.0726  -7.5189  -1.8044 -13.0203  -1.8426
+#&gt;   -1.6236 -16.4245  -4.8513   1.6835   0.2332  -7.5492 -11.0121  -0.0169
+#&gt;    4.3706  -0.3532   0.9759  -1.4393  -6.0106   6.8486   0.1169  -4.7158
+#&gt;   -1.3000  -3.4431  -4.1769   0.9913  -2.8124  10.1722  -9.3012 -10.8872
+#&gt;    8.3708   6.1809  -5.0569   3.9997 -10.6746   8.9321  -9.1896  -8.8022
+#&gt;   -7.5716   0.4196   4.5086 -11.1168   1.9562 -11.1500   5.5407  -8.1387
+#&gt;   -2.4173  -4.9560   1.5303   8.2358  -9.4596  -2.5966  -7.0269  -5.9465
+#&gt;   -6.8669   3.2344 -13.4096  -3.5862  -7.4090   2.1559   9.1505  -7.6138
+#&gt;    5.5773  -5.7943  -0.3198  -9.7214   3.1791   4.4402  -8.7427  -5.3370
+#&gt;   -5.3191   2.7368  -2.3253   2.5054  -4.4633  -4.3220  -7.9961  -2.8243
+#&gt;    8.4724   4.7369  -6.1556  -0.8707  -7.7329   6.5241 -15.5860   2.7061
+#&gt;   10.8530   3.3671   1.5378   9.2073 -13.9800   6.7152 -10.1386   9.1299
+#&gt;    0.7500  -5.5101  -1.0907  -0.3966   1.0970   4.4621 -12.0540  -8.8876
+#&gt;   -4.4861  -0.3344  -8.7108  -2.5508   0.6081  -5.0729  12.3159   2.2751
+#&gt;    4.0570   0.1595 -12.4873  -0.3785  -4.9597  -3.9137  -8.8278   7.8069
+#&gt; 
+#&gt; (11,.,.) = 
+#&gt;  Columns 1 to 8   3.7937   3.3443  -2.4370  -0.7493   2.5299   0.4709   6.0562  -0.3626
+#&gt;   10.6307  11.9516   9.8038  -0.7408  -3.8674  -4.6190  10.3573  -1.0870
+#&gt;  -10.6999  -3.8307  -0.2548 -10.6186  16.3173  -1.7586  11.4501  -5.5764
+#&gt;   -7.8635  -4.3516   3.3598 -10.4165   3.8293   7.3386   5.5245   0.6330
+#&gt;   -7.1622  -8.3438   4.7686   0.1439  13.4165  -8.6863  -3.6452   3.5247
+#&gt;    1.9894   1.6171   0.9422   1.7833   0.1280  -4.0095   3.9278 -11.9568
+#&gt;  -11.5697  -2.7070  -0.9483   4.8115   1.9425   7.3465  14.8355   4.7956
+#&gt;    1.2308   0.3228  -0.8247   8.3839   1.2995   1.8578  -5.8216   1.6002
+#&gt;   -0.7065  -3.7597   1.1004  -4.9003   7.1400   2.3689   2.0262  -6.3006
+#&gt;   -0.6387  -5.4112   0.7874  10.8427 -11.5375  -2.7082  -0.1494  -4.1081
+#&gt;    3.1051  -4.3198   9.7297  -9.6335   9.2616   2.5857   1.3609   3.3331
+#&gt;    2.5867  12.2692  -7.2361   5.3503  -8.8797  -0.2300   4.9374   4.7238
+#&gt;   -3.8307  -0.3206  -1.1478  -4.8669  -1.1158   1.6285   2.0032   2.0974
+#&gt;    0.6222  10.6310  -0.8089  -1.0949  -3.1346  -3.2931   8.5881   4.4816
+#&gt;    5.0675  -4.1099  -5.7666  -5.5653   7.9136   3.2587   0.1896   1.7996
+#&gt;   -5.7315  -7.7680  -2.8600  -8.7944   8.2526  -2.3587  -1.6003   0.5170
+#&gt;   -5.9382   1.3654  -3.4797   3.0315  -0.8547   0.4773  -6.1367  -3.9820
+#&gt;    3.6287  -1.2956   6.1237  -0.9495   4.2141  -3.6351  -0.1131   1.4238
+#&gt;   -0.0918   6.8673   3.0499  -2.8238  -5.1383   1.6259  -2.6161  14.0690
+#&gt;   -4.7757   6.0958   0.4287   5.5608 -14.7232   8.1290   3.6246  -2.3185
+#&gt;    5.0560  -4.1627   0.3608   3.1132  -3.0299  -8.4261  -4.3013 -10.9617
+#&gt;   -8.4905  -9.3292   6.5858   6.3036   7.6876   4.1438   1.9595  -2.7165
+#&gt;   -4.8891  13.0413  -5.9035   6.4807 -10.3121  -7.3228   5.7568   5.6401
+#&gt;    7.2047  -1.7132  -5.7729  -2.8612  -6.7071   5.8677  -8.1895   5.1844
+#&gt;   -4.3016   9.8202  -0.6803  -0.6256  -0.9157 -13.1843   0.1588 -12.9502
+#&gt;   -7.4285   3.6688  -1.6415   0.9138  -5.5771   8.7736   6.6882   1.6432
+#&gt;   -4.4428  -9.3043  -0.6134   1.9890   0.4854   0.2168   0.8523   5.6972
+#&gt;   -4.3809   3.4880   6.5377   1.8884   3.3033   3.2709   5.9732  -1.5907
+#&gt;    0.6638  -7.0466   0.8941  -2.5001  -1.2203   5.6382  -5.7607   1.6291
+#&gt;    1.0479   5.6997  -2.9219  -2.9017   5.6748   0.8129  -0.8491   8.1921
+#&gt;   -4.0707  -1.5495  -0.4425  -0.2246  -0.4532   5.4448   1.6117   1.8572
+#&gt;    2.2864  -6.7336  -0.1332  -1.3750  -1.8187 -10.8077  -6.0010  -7.3450
+#&gt;    2.0508  -9.1659  -1.8159  -9.7242   4.7545   2.1221   6.2191   3.5645
+#&gt; 
+#&gt; Columns 9 to 16   8.3584   0.9567   0.6699  -0.5915  -2.5452 -13.7650  -4.9275   5.8179
+#&gt;   -2.4895  -2.6484 -14.1346   3.4432  -5.7197  -1.3415  -4.3216  -5.3908
+#&gt;  -18.3576  -4.5139   4.7621   4.8123   6.5106  13.7852  -3.4981  -4.8045
+#&gt;    9.2857  -1.9781  -7.5667  -5.5825  -1.8052  -0.4736   3.6928  -5.4759
+#&gt;   -2.0240 -10.3960   5.4997   0.7885  -5.6982   3.8130   5.7453  -3.5903
+#&gt;    1.8793  19.2363 -11.2523   2.2805   5.5907  -0.9210 -17.2159 -12.7291
+#&gt;    6.9888  -1.4846   0.4863   5.9328   3.3185   3.3167  -0.3102   1.2225
+#&gt;    2.3845  15.0868  -8.9965   4.0807   7.5237  -3.5945  11.1996  -3.3507
+#&gt;   -3.0860  -8.1743   2.3691   2.3801   1.7114   8.3665   6.5482  -2.0371
+#&gt;   15.6948  -7.8191  13.9383  -2.4419  -4.7495  -1.9464   5.8553  -0.1548
+#&gt;  -15.9031   5.0678 -13.2995   2.1257  -2.7215  -7.4292   4.4587   4.5165
+#&gt;  -16.9304  -5.6543   6.8485   5.5245   5.5420  -6.3474  15.3867  -4.1328
+#&gt;   -2.4458  -1.8298   6.2050  -0.8572  10.9190   5.0564  -5.9771   5.1932
+#&gt;   21.3183  24.2537  -2.5838  -3.2858  -1.3310  -2.3132  -5.4575  -1.7254
+#&gt;    2.4244  -8.1272 -20.0456   1.9128   4.9572  -3.7852  -8.8739  14.7509
+#&gt;   -7.7226   7.3655  -4.0851  -0.8517  -1.0395   8.1234   0.8589  -0.0638
+#&gt;    0.7190  -9.0523  -4.8855  -1.0134  11.8126   5.6483  10.1249   7.5263
+#&gt;   -6.6636   0.0449  -5.7391  -0.3796  -3.3397  11.2182  13.0471  -7.0371
+#&gt;  -13.1443   6.5986  -2.9793   5.8134  -1.6611  -4.4457  -2.6233  -0.1726
+#&gt;   -4.1520  -0.7067  11.2879 -16.1209   5.7117  -4.4844  12.8697  -1.6114
+#&gt;    0.5737  -4.6153   5.4920   6.8388   1.4059   0.1756  -3.6811   9.9860
+#&gt;    8.5067  -2.7990  12.9833   4.9783  -0.9485  -0.5419  -7.0666   4.7147
+#&gt;   11.7475   8.2290  16.3922  -7.0700   1.0669  -6.5360  -6.9774  -3.5325
+#&gt;   10.1249   2.4669  -9.1289   4.8102   1.4256   1.2654  -3.4537  -8.7730
+#&gt;  -11.1659  -6.1313  -7.3278  -3.7938   6.7892   5.7347  -6.2605  -9.0258
+#&gt;    5.8518  -8.8304   6.6573  -5.1409  -0.4738  -9.2835   8.0228  -5.5465
+#&gt;    3.2390  -5.8512   6.1396  -1.0107  -3.1562  -9.1976   4.1317   6.5381
+#&gt;   -1.3794  -0.1016   0.4497   1.0687  -4.4595  -3.7331  -5.9836  -5.8984
+#&gt;   11.1390   8.7555  -0.2233  -6.3424  -2.9114  -2.0943   7.2410   5.7420
+#&gt;   -7.3059  10.4932   5.4640  -5.7067   5.3278  -8.3470  -0.7442  -1.5355
+#&gt;   10.8857   2.7782  -2.0679  -0.5128  -4.5647   0.6927  -7.5803  -1.9672
+#&gt;   -7.0913 -17.9866 -14.8429  -7.0096   4.3522  17.9882  -3.2681  -6.9015
+#&gt;    5.2191  -6.0421   5.3403  -0.5887 -13.5154  -1.0066  -0.4395  -3.0554
+#&gt; 
+#&gt; Columns 17 to 24   0.9972  -1.9477   1.6763  -2.3057  -0.0854   3.7226  -7.4466  -5.6549
+#&gt;   -5.1076  -8.0219   5.7801   9.0155   3.0495  -4.0400  -2.1674  14.0211
+#&gt;    9.0838   1.6779   0.6311   0.6824  -4.0252  -2.5605  11.8455   7.5120
+#&gt;   -2.6526   4.4219  -7.9159   1.1084   9.5066 -14.6283  15.8692  -0.9054
+#&gt;   10.4089  -0.7032   1.3462   3.7963   1.9185   1.8751   4.7767   8.6929
+#&gt;   -0.5876 -17.7739 -18.2536  -5.8443 -14.0135  11.5308   3.2224   9.4312
+#&gt;    7.6872  -0.3288  -3.6229  -8.7525  -9.0824  -6.2285   6.2582   0.0213
+#&gt;    3.7409 -18.3179  -4.3719   1.7264  -5.7159   5.2569  -5.1051  -5.0240
+#&gt;    5.8859   8.8848   4.3772   4.3765  17.8811 -13.3823  15.7412   8.6556
+#&gt;   -5.4387   4.9601   9.8092  -0.7505   0.6326  -0.9829 -14.8984  -6.9666
+#&gt;   -1.4902   5.8527   6.8104  -4.0063  -4.3960   6.1615  -5.9452  -1.8706
+#&gt;    0.1634   6.8792   1.2098   4.5989 -13.9686  -9.3451   2.5431  -1.0003
+#&gt;    5.2184   2.6057  -7.0273   6.8009  -0.7976  -9.5926   3.1235 -11.6438
+#&gt;    3.1717  -6.2223  -0.2322   5.0085   8.1052  -4.8153   6.6578  -0.5550
+#&gt;   -7.6704  -8.0143 -12.8910  -4.4232   2.8942 -13.4675  -0.6811   8.4524
+#&gt;    3.1801  -6.9591   0.8362   2.7003  -4.3841  -3.0317  -2.6469   1.4808
+#&gt;    6.2480  13.2894  13.2017  10.5169   0.7862  -4.6049   0.0015 -19.5002
+#&gt;    2.4041   3.9094   7.5511  14.6806   7.4684   7.5682  -8.9797  -6.0516
+#&gt;   -5.6842  -8.3527  -8.3326  -2.1025  -5.2128   2.7183   9.3746  13.0983
+#&gt;   -2.8523   8.9349  -0.9335   2.3307  -5.6938  12.1805   2.8683  -0.5086
+#&gt;   -5.1087  -9.1301   2.2784 -11.1650  -1.2309   0.3493   7.1337   8.3113
+#&gt;   -8.6389 -14.4029  -4.8800 -16.3632   2.6205  10.3695   2.4457   2.6485
+#&gt;   -9.6999   0.2376  -4.7306   2.2126  -5.2387  -7.5828   0.0944  -4.6289
+#&gt;    0.5303   3.0929  -6.9547  -9.1865   2.5571  -4.3240  -9.0880   1.8058
+#&gt;    9.2528  -0.0228   2.6048   8.5337  -0.6221   4.3056   2.3981   3.9403
+#&gt;    6.5828  12.7984   6.0844  -1.9288   8.5991 -15.2191  11.7522  -3.9922
+#&gt;   -6.9965   7.6586   1.7081  -6.5360   9.9816 -12.8057  -2.2373  -0.3058
+#&gt;    0.9783  -7.0158  -2.5609  -3.4667   4.9569  -2.6522   8.1639   8.7794
+#&gt;   -7.5215   4.5414 -11.6417  -3.7401   6.8329 -10.8399 -11.5499  -7.0909
+#&gt;   -6.8994   1.9352  -4.4629  -2.6767  -2.5938  15.0271  -6.1265  -0.9355
+#&gt;    0.3384  -7.7840  -4.9926  -8.5295  -2.9699  -0.4821   0.3740   3.2112
+#&gt;    9.3060   4.3025   4.0113  12.1607   2.8971   7.9864  -7.2097  -1.0170
+#&gt;   -7.0919   2.8168   9.4173  -8.0272   4.4756   4.6679   7.5488  -6.8999
+#&gt; 
+#&gt; Columns 25 to 32  -2.5597  -0.9806   1.1756  -2.2707   2.0862   3.8241   2.4741  -0.2315
+#&gt;    8.8242   3.8039  -5.1815  -4.6034   4.5181  -1.6453  -0.0998   6.8647
+#&gt;    0.1486 -10.2521  -5.4038   4.4365  -8.6100  -5.6858   2.6825   3.4047
+#&gt;   -9.4426  -7.7393   0.9135  -1.3056 -12.3088  -9.0515  -0.2296  -9.9137
+#&gt;   -0.4161 -11.5724  -1.7100  14.7900   2.3039  -2.5595  15.5342  -1.0142
+#&gt;    1.4336   2.4076   7.7402  -0.7544 -14.6283   0.0158   5.6794  14.8158
+#&gt;    1.7489   1.9878   7.4678  -1.1058  -3.7967  -3.6468  -1.5439 -10.8562
+#&gt;    9.5973   3.6431  10.6990  -3.1217  -6.6937  10.5877  -0.3785   1.5894
+#&gt;   -1.2239  -3.8936   1.1776   2.3063   7.8297  -9.1275  -3.9944  -0.2406
+#&gt;  -18.9179  -1.6482   1.3639   3.3204  -9.9353   2.7277  -1.7784  -0.1369
+#&gt;    0.2878  -5.0676  -6.9829   4.1097  -0.9337   2.8044  12.0952  -4.5898
+#&gt;   19.3418   8.9425 -10.2133  12.6115  -0.8296 -12.2732  15.3609  -7.7891
+#&gt;   -1.9803  -1.4505  -3.4261  -1.6209  -7.6682 -16.4376  -0.5219 -12.4175
+#&gt;    6.5593 -14.7310   6.9862  12.6301 -15.9163   1.1773  15.5185 -23.9902
+#&gt;    3.9017   4.4758  -2.4765 -10.5747  -2.4816  -3.9151  -3.5416  -4.3298
+#&gt;   -0.5229  -0.2976  -3.1351   4.9900 -11.7597  -5.8190  -0.1210   6.0816
+#&gt;   -3.0197  -8.0523  -0.5537   1.9743  -8.3856   6.8797   5.7334 -14.1970
+#&gt;    2.3897  -5.7025  -4.9972  -3.0315  -2.6838   2.7946   3.3642   0.5335
+#&gt;    5.8338  -0.5129  -9.5341  -0.1588  -1.2615  -1.2980  -2.9985  -4.5124
+#&gt;    1.8187   7.9621  10.5903   7.5557  -3.6478   3.6597 -15.2382  -1.9051
+#&gt;   -7.7924  -1.5262  11.8106  -3.8921  -6.4083   7.1495  -6.2546   0.6946
+#&gt;   -4.4436   2.8346  -1.8207  -3.0062   0.8136   4.1881  -9.1924  -2.0089
+#&gt;    0.5156   3.4063  -5.6720   1.4006  -4.1586  -3.0419  11.9767  -2.0378
+#&gt;   13.1782  10.1899 -10.2059   1.9461   5.3552  -2.0539   7.2243  -0.4714
+#&gt;   14.8124   7.7521   3.0912   3.8999   0.6046  -4.0801  -4.4360  -3.8308
+#&gt;   10.8627  -3.8267   3.1769  11.1920  10.7253  -3.7321  19.8521  -9.5062
+#&gt;  -12.8728  -4.4582  -4.5226  -3.0155   8.2595 -10.6669  -3.0094   5.6649
+#&gt;    8.6318  -3.1883  -5.9631   8.2891   3.7500  -7.9245  10.3776   2.9566
+#&gt;    5.2366   0.5301  10.3018  -5.5081  10.5544  -8.2135   1.5258  -1.1528
+#&gt;    8.3177  -2.0067 -11.9109  -5.3523   1.9335   1.8869  -0.5693  -3.4301
+#&gt;   -3.9508  -1.8663   7.4325  -2.8548 -10.7083   3.5088 -16.6890  -2.0355
+#&gt;   13.7662  23.5428   5.6057  -1.4791   3.4818   5.9160   3.6912   5.0746
+#&gt;   -5.0284   1.7475  -9.1356 -13.0927   8.3176 -13.0039  -5.1364   5.9707
+#&gt; 
+#&gt; Columns 33 to 40  -4.1589   3.7303  -1.6060   4.4635   6.7841  -0.2459  -6.4094   4.1113
+#&gt;   -6.7551   1.7594   3.1004   8.3066   5.0678   7.1857  -9.3370   2.3213
+#&gt;   -0.1991   4.0297   1.6417  -2.3390  -4.1655  -6.4183  -2.2333   1.1998
+#&gt;    7.2590  -1.4032  -4.5108  -1.6939   2.2592   9.0839   2.4611  -5.0034
+#&gt;   -4.2084  -1.7376   2.7075  12.6171   1.8772  -0.4516  -9.4619  13.7944
+#&gt;   -1.3379 -11.1412  -8.2708   3.2338   1.0159  16.4468   5.2993  -9.7824
+#&gt;    3.1983   1.4617  -1.5322   2.2830  -2.2331 -10.2190   2.0204   1.8553
+#&gt;    6.2902 -15.2740   1.7517   5.8250   4.6263   2.4940  -0.8620  -0.6863
+#&gt;    5.6052  12.4055   4.6088  -1.7553  -7.0513 -11.5828   8.4270   6.6339
+#&gt;  -14.0078   5.9296  -9.6404   4.1751  -5.6653  -0.9729  -4.9558   0.0111
+#&gt;   -2.7454   4.8666   4.4243   4.6143  -3.3006  -4.7780  -5.3619  -1.4123
+#&gt;   -2.6294   4.2099  -1.5582   9.5657   4.8314  -2.8027  -1.7586  -1.1307
+#&gt;    2.5317   3.1138  -9.2983  -5.4344  -6.7084   5.2707   1.2960  -0.8324
+#&gt;    1.4242   5.9347   0.3738  24.5331   7.4974   2.2504  -2.4404   8.9424
+#&gt;    0.6349  -1.4011  -0.8091  -4.0849  -0.0983   8.4596  -0.0663  -9.4661
+#&gt;   -1.6276 -10.1689  -5.7899  -4.1300   1.1119   3.3097  -2.9323  -6.6952
+#&gt;    5.5432   8.0423  -9.6663  -4.0656  -8.8668  -0.3644   2.3113  -1.2062
+#&gt;    8.5209  -3.5148  10.9451  10.1472   2.2316  10.6279  -8.4558   9.2929
+#&gt;    6.5243  -3.0288   6.4580  10.3934   3.4049   5.9547   0.0918   8.7563
+#&gt;    0.2904  -0.4497  -3.3537  -2.1805  -7.5533  -6.7769   2.9008   0.8825
+#&gt;    1.2091  -2.4707  -0.3328  -2.1079  -5.1798   5.7991   2.5772   6.4596
+#&gt;  -12.9408  13.9249 -10.0330  -2.6644  -1.8878  -6.9288  -0.0266   1.6690
+#&gt;   -6.6501   3.6091   1.2646  12.2899   6.1853   5.2655   1.3089   0.0149
+#&gt;   -3.9240  11.4311   4.0317  -2.7872   6.6256  -5.9100   2.8111 -15.5841
+#&gt;    7.4118  -1.0198 -12.4237   7.8476  -4.0985  -1.3785   8.0114   7.0813
+#&gt;    3.7799  17.8669   3.8149  14.0909  -3.0418   1.0252  -5.0919   3.1712
+#&gt;   -4.4973  14.6470   6.9466  -7.5049  -4.7671   5.9034   6.4002  -6.0712
+#&gt;   -2.2247  10.2474  -1.3700  10.7991   3.9240  -1.4399  -4.0167   2.5126
+#&gt;   10.2650  -8.5700   1.3659  -0.8875  -6.8651   1.5008   0.9678   8.9425
+#&gt;    1.4923  -0.8188  -1.8771   7.8341   3.9058  -0.7921   0.2714  13.5229
+#&gt;    8.5662  -5.0737  -4.2252  -2.5420  -6.5291   0.9155   6.9037   5.0505
+#&gt;   -3.8251   0.2528  -3.4308  -1.7888  -9.9637   8.6423 -13.1106  -5.5976
+#&gt;  -10.9219   4.3952   2.6134  -2.2916  10.3237  -1.8059  -4.2353 -10.4665
+#&gt; 
+#&gt; Columns 41 to 48  -3.6266  -0.4672   2.8204  -7.6010   3.9181   9.2954   2.6130   6.3411
+#&gt;    7.9702  19.1315   6.0261 -15.5574  -5.9639   1.2486  12.3981  -5.7620
+#&gt;    3.2285   6.0073  -2.0663  -3.3976   0.9136   5.8913  -8.4124  -6.3986
+#&gt;   -3.9151  -1.9902  -4.1941   1.9037  -5.3702  -2.1098   1.3982  -2.1048
+#&gt;   -2.2419  16.0544  10.6936  -9.0379   9.9122  -0.5433  -4.9791  -3.1856
+#&gt;    3.6397   2.7877   5.7401   3.0013 -11.6554  -5.9296   6.7184   9.0771
+#&gt;    4.1236   9.6333   3.7581  -3.0192   2.7629  -2.8612  -7.0557  13.5971
+#&gt;    3.6429  -6.6866   9.4158   7.3372   1.7895  -2.1698   0.2234   6.4218
+#&gt;    4.0710   0.1480  -3.4028  -2.7800  -5.6400  -1.7989  -2.0286 -11.1465
+#&gt;   -7.4089   7.6994  -2.5482  -8.9713   2.6950 -12.3743  19.5381  24.1576
+#&gt;    4.4235  10.9627  -4.0982   4.2861  -4.1998   8.3667   5.4484   9.5414
+#&gt;   17.3988   1.8155   2.6434 -10.1270   8.6963  -2.9827  -7.1048   4.0567
+#&gt;   -1.7410   3.7351  -2.7580  -2.8044  -2.9091  -6.2967   1.5733   4.1887
+#&gt;   -5.8837   1.3889  -1.2355  12.3573  -2.8756  -0.6569  -0.8305  -5.1950
+#&gt;   -1.9896   7.3914   5.7877  14.3839  -8.1825   3.6732  -0.3187 -10.0375
+#&gt;   -8.6129  -4.4320   3.6439   4.8098   8.3265  -2.1005  -8.5312  -7.4874
+#&gt;    6.7591  -7.3323  -5.6843   5.3391   5.3316   7.7282   0.8120  12.7123
+#&gt;   -2.6858   5.1234  12.0991  -3.9599  -2.8497  -3.1925   1.4564  -7.4550
+#&gt;    0.2012  15.4164  -1.8286   7.1329   2.8999 -15.3037   0.6159  -6.8872
+#&gt;    7.9285  -4.5910  -5.1719  -0.6516   1.4483 -14.7208  -2.8044  11.5302
+#&gt;   -1.9663   3.7511  -1.9429   6.7636   0.4475 -10.1221 -14.3627  -3.2124
+#&gt;   -4.4533   0.4802  -1.9435   2.4874   6.0485  -8.6816  -2.8959 -10.7583
+#&gt;   -2.7271   5.3359  -9.3921   1.7631   3.2884  -7.7299  11.4036   5.9107
+#&gt;   -5.8764  -9.3208   6.4196   7.2703  -6.1126  -0.9637   0.4326  -4.2808
+#&gt;   -0.9386  -1.1206   4.6074   3.8725   7.6005  -2.5125 -13.6471  -4.9348
+#&gt;    4.3929  -4.1937  -8.6613  -6.9293   8.8661  12.8171   5.5993   4.4867
+#&gt;  -21.8241  10.1130  -1.3049   4.8887   1.5002  -4.6623  -2.2722   8.9416
+#&gt;    3.5709  11.4422   7.7696  -4.8174   3.1494  -0.0924  -2.4787  -6.4510
+#&gt;   -9.3814  -3.7620  -0.3368   2.9283  -0.9704  12.2476  -0.6800   4.0082
+#&gt;   -6.5955   1.0856  -3.0095   4.7550   4.1424  -7.7957   2.3028  -5.0474
+#&gt;   -8.4253  -8.6888  -3.1362   6.6683   5.5780  -2.4277  -4.7206  -3.3897
+#&gt;   -8.8405  -2.5572  19.1457  -3.5793  -1.0315  -8.6304   0.1489  -6.8972
+#&gt;   -7.1471  -1.5294  12.7806   0.0976  -7.4513  -6.1026 -16.2612  -3.3594
+#&gt; 
+#&gt; (12,.,.) = 
+#&gt;  Columns 1 to 6  1.9582e+00  1.5163e+00 -4.4381e+00  9.3688e+00  8.7846e+00 -2.0308e+00
+#&gt;   5.8028e+00  9.3980e+00 -5.7602e+00  1.1914e+01  1.2539e+01  1.8618e+00
+#&gt;   3.4829e+00  4.4942e+00 -2.4770e+00  1.5647e+00  1.1298e+01  4.5241e+00
+#&gt;  -4.4228e+00  1.4689e+00  5.5802e+00 -2.4450e+00 -7.5374e+00 -4.3167e+00
+#&gt;  -6.8749e-01 -8.7819e+00 -1.1497e+01  4.7102e+00 -7.3499e+00  1.1296e+01
+#&gt;   1.1915e+00  9.6680e+00 -2.5148e+00 -6.1214e+00  1.3626e+01  1.4301e+01
+#&gt;  -2.3837e+00  2.1458e+00  4.3229e-01  5.4384e+00 -4.4749e+00 -3.2883e+00
+#&gt;  -4.6722e+00 -6.2104e+00  3.4471e-01  3.8749e+00  3.6713e+00  1.3000e+01
+#&gt;   3.1968e+00 -5.2109e+00  5.6345e+00  6.7392e+00 -5.0622e+00  2.4158e+00
+#&gt;   6.5981e+00 -6.6407e+00 -1.9040e+00  4.7759e+00 -8.4528e-01 -1.0665e+01
+#&gt;   9.2419e+00  2.8670e+00 -8.0240e-01  4.2599e+00  2.8237e-04  1.9178e+00
+#&gt;   7.0195e-01  1.6882e+01 -5.4213e+00  1.7245e+01  4.3374e+00 -5.9541e+00
+#&gt;  -8.4089e+00  1.1619e+01  2.2067e+00 -6.1709e-01 -1.8723e+00 -6.2607e+00
+#&gt;  -1.7670e+00  2.8082e+00 -1.2422e+01  2.3112e+01  8.8860e+00  1.4510e+01
+#&gt;  -1.2881e+00  8.7961e+00  1.2179e+01 -1.1539e+01 -6.3778e+00 -6.9043e+00
+#&gt;   3.5359e+00 -5.0793e+00  2.0676e+00 -9.0520e+00  1.0036e+00 -4.4830e+00
+#&gt;   2.4774e+00  1.4790e+00  6.3722e+00  5.8493e-01  1.8942e+00 -1.4599e+00
+#&gt;   2.4485e+00  3.9772e+00 -6.7222e+00  8.5166e+00  3.2430e+00  1.3068e+00
+#&gt;  -8.5607e+00 -3.6612e-01  6.1566e+00  4.9916e+00 -9.4561e+00  1.2934e+01
+#&gt;  -4.8112e+00  2.9644e+00  1.5391e-01 -7.4910e+00  7.7132e+00 -5.3493e+00
+#&gt;   3.3310e+00 -1.2281e+01  8.0321e+00  2.1130e+00 -9.9446e-01 -2.2052e+00
+#&gt;   5.6940e-01 -1.1635e+01 -9.0541e+00 -4.4985e+00 -3.7620e+00 -6.5610e+00
+#&gt;  -1.0743e+01  1.3157e+01 -2.9189e+00  1.0627e+01 -2.7263e+00 -4.0187e+00
+#&gt;   8.8342e+00  5.1593e+00  5.8149e+00  4.1091e+00 -5.3924e+00  2.4114e+00
+#&gt;  -4.2684e+00  4.9763e+00 -6.1863e+00  1.1412e+01 -2.6835e+00  1.3388e+01
+#&gt;   7.9342e-01  8.7995e+00  2.8206e+00  1.3025e+01  3.8911e+00 -3.0966e-01
+#&gt;   3.1382e+00 -4.0861e+00  1.4922e+01 -2.2620e+00 -2.3958e+00 -3.7674e+00
+#&gt;   9.2701e+00  4.3355e+00 -1.0704e+01  1.1377e+01  6.5893e+00  8.0414e+00
+#&gt;   5.4321e+00  5.2070e+00 -3.4322e+00  6.5029e+00 -6.9887e+00 -8.0402e+00
+#&gt;  -4.3460e+00  5.4807e+00 -9.5027e+00  2.0356e+00 -1.0137e+01 -6.3756e+00
+#&gt;   2.7251e+00 -6.7244e+00  6.4854e+00 -3.7559e+00 -4.2434e+00  2.4309e+00
+#&gt;  -4.7270e+00  5.5261e+00  1.2363e+00 -1.2075e+01  7.2748e+00  2.3007e+00
+#&gt;   7.1656e-01 -9.0506e-01 -3.9412e+00 -4.8804e+00  2.8452e+00 -9.9935e+00
+#&gt; 
+#&gt; Columns 7 to 12 -6.9090e+00 -5.8569e+00  3.5948e+00 -1.1790e+01 -3.1694e+00  6.1202e+00
+#&gt;  -8.2967e+00  3.7801e+00  1.6258e+00 -7.7937e+00  1.0489e+00  3.0526e+00
+#&gt;   2.8955e+00 -2.5378e+00  6.5976e+00  4.3344e+00 -9.5210e+00  1.3229e+00
+#&gt;  -6.0320e+00 -5.3527e+00 -4.4327e+00  5.5768e+00  6.8403e+00  1.0133e+01
+#&gt;   1.1643e+01  5.2540e+00  7.5433e+00  1.6085e+01 -8.3222e+00  5.7045e+00
+#&gt;  -5.0190e+00 -4.1345e+00  7.0509e+00 -1.8400e+00 -3.7420e+00 -7.7724e+00
+#&gt;   6.9741e+00  2.9107e+00 -4.5999e+00 -1.2082e+01 -1.8717e+00  7.6204e+00
+#&gt;  -6.3690e-01  9.3017e+00  5.9703e-01  7.2948e+00 -3.3143e+00 -1.1276e+01
+#&gt;   4.3913e-01 -5.6523e+00 -1.5859e+01 -3.2002e+00  3.8674e+00 -4.1639e+00
+#&gt;  -2.5547e+00  6.5887e-01  6.9181e+00 -1.3928e+01  6.7173e+00 -7.3907e+00
+#&gt;   2.7851e+00  9.8552e+00  3.5245e+00  1.1526e+01 -8.4232e-01  7.2696e+00
+#&gt;  -8.8759e+00  4.2260e+00 -7.2686e+00 -1.1937e+01  4.0806e-01 -4.9664e+00
+#&gt;  -1.3335e+00 -3.5270e+00 -5.0098e+00  1.6989e+00 -1.1594e+00  9.4589e+00
+#&gt;  -3.4792e+00 -9.4576e+00  2.9774e+00 -9.2488e+00 -2.8140e+01  1.9555e+00
+#&gt;   1.0540e+00  3.5825e+00  2.5980e+00  1.1157e+01 -1.2448e+00  2.2387e+01
+#&gt;  -1.1493e+01  4.1388e+00 -5.2556e-01  1.5224e+01 -9.9748e+00  1.8853e+00
+#&gt;   4.9094e+00  3.0075e+00  5.9231e+00  6.9954e+00  4.0012e+00  2.8866e+00
+#&gt;  -7.0807e+00  5.2841e+00  7.3297e+00  1.1766e+01  7.6081e-01  5.3239e-02
+#&gt;   9.4336e+00  1.0487e+01  1.6665e+00  1.3400e+01  1.0475e+01  6.7069e+00
+#&gt;   6.7666e+00 -8.2492e-01 -1.1049e+01 -2.3066e+01  6.0383e+00 -6.2987e+00
+#&gt;   7.8720e-01  6.0962e+00  1.0464e+01  5.2101e+00  2.8262e+00  2.5275e+00
+#&gt;   8.5303e+00 -5.5873e+00 -4.7950e+00 -9.2813e+00  1.3190e+00 -5.7839e-01
+#&gt;   7.4882e+00 -7.3008e-01  1.0160e+01 -8.7175e+00 -1.9188e+00 -8.6999e+00
+#&gt;   6.9142e+00 -2.4507e+00 -8.4099e+00  2.7714e+00 -8.9878e+00 -3.9398e+00
+#&gt;   1.0972e+01 -5.8331e+00 -1.1238e+01 -5.1030e+00  9.4947e-01 -5.4123e+00
+#&gt;   1.1761e-01 -1.0268e+01 -1.0456e+01 -2.2644e+00 -4.6218e+00 -2.4325e+00
+#&gt;  -1.1273e+01 -1.0211e+01  6.8191e+00  1.1760e+01  9.0462e+00  3.8576e+00
+#&gt;  -2.0217e+00 -1.8282e+00 -4.4059e-01 -4.6378e+00 -3.9322e+00  1.8231e+00
+#&gt;  -2.6160e+00  1.4471e+00 -2.7594e+00 -1.2062e+01  9.7545e+00  1.4638e+01
+#&gt;   1.1557e+01  6.1734e-01 -6.6312e+00 -2.9240e+00  3.2766e+00  3.6247e+00
+#&gt;  -5.4485e-01 -8.6700e+00 -4.9518e+00 -1.7252e+00  4.8794e+00  6.9322e+00
+#&gt;   1.1924e+00  3.0659e+00 -1.0207e+01 -1.2909e+00 -1.2420e+01 -7.3237e+00
+#&gt;  -2.5777e+00  5.0013e+00  6.8978e+00 -2.6915e+00 -5.6920e+00 -4.2401e+00
+#&gt; 
+#&gt; Columns 13 to 18  1.2030e+01 -3.2740e+00  1.8418e+00  4.3851e+00 -9.0849e-01 -5.2315e+00
+#&gt;  -5.9734e+00  5.4140e+00 -9.8742e+00  4.2244e+00 -5.1225e+00 -2.9482e+00
+#&gt;   2.2832e-01  5.8950e+00  6.0840e+00  8.5996e+00  8.1863e+00  6.5913e+00
+#&gt;  -1.0210e+01  7.5261e-01  6.5368e+00 -3.3902e-01  3.7261e+00  8.6245e+00
+#&gt;  -3.0129e+00  5.8725e+00  4.1971e+00 -9.3225e+00 -9.9618e+00  6.0806e+00
+#&gt;   1.4161e+01  1.0679e+01  3.6866e+00 -1.2610e+00 -1.2392e+00  6.1928e-01
+#&gt;   1.1042e+00 -9.2068e-01 -2.4594e+00 -6.0893e+00 -5.1094e+00  1.3665e+00
+#&gt;   2.4856e+00  1.7492e+01 -3.1000e+00 -7.4753e+00  7.6473e+00  8.3733e-02
+#&gt;  -7.6926e+00 -1.9211e+00 -1.2787e+01 -7.4857e+00  8.0192e+00  6.5071e+00
+#&gt;   1.2993e+01 -8.3812e+00  1.3754e+01 -1.0470e+01  1.2710e+01 -2.4153e+00
+#&gt;   1.0431e+01 -1.1703e+01  1.8209e+01  2.9263e+00 -3.3364e+00 -6.9785e+00
+#&gt;  -1.2995e+00 -1.3377e+01  1.5937e+01 -9.1478e+00 -3.9198e+00 -7.8333e+00
+#&gt;  -3.3543e+00 -5.0684e+00 -7.3708e+00  3.6521e+00  2.6349e+00  1.4546e+01
+#&gt;   1.6444e+01  1.4994e+01 -4.2387e+00 -1.6015e+01 -4.4054e+00  6.7506e-01
+#&gt;  -4.1046e+00 -8.1937e+00 -7.3669e+00  1.5166e+00 -2.7349e+00  2.2095e+00
+#&gt;  -6.0870e+00  1.9520e+00  9.1598e-01  1.3942e+01  1.2582e+00  2.5258e+00
+#&gt;   5.9907e+00 -5.0078e+00  8.4053e+00  6.7368e+00  9.4565e+00  6.8918e+00
+#&gt;  -1.5269e+01  3.9213e+00 -1.4352e+00  4.4186e+00 -4.0238e+00  7.0786e+00
+#&gt;  -1.3054e+01  1.0303e+01 -1.2526e+01 -3.8376e-01 -5.0172e+00  8.6113e+00
+#&gt;  -1.4660e+00 -2.2566e+00  1.7378e-01 -1.0619e+01  1.2341e+00 -6.8871e+00
+#&gt;  -4.1382e+00  6.8596e+00 -5.8995e+00 -5.2521e+00  5.4384e+00 -1.1286e+00
+#&gt;   1.4573e+01 -1.0460e+00 -8.5052e+00  4.5067e-01  2.5444e+00  1.7283e+00
+#&gt;   2.3258e+00  6.2800e+00  4.8757e+00 -6.5851e+00  5.8745e+00 -1.1268e+00
+#&gt;   1.2562e+01 -9.3875e+00  1.4156e-01 -6.8616e+00 -1.2719e+01 -5.0434e+00
+#&gt;  -3.9487e+00  7.1724e+00 -7.3348e+00  5.8512e-01 -4.1393e+00  9.3828e-01
+#&gt;   4.4327e+00 -1.0829e+01 -1.7106e-01 -3.6880e-01 -1.9792e+00  6.8310e-01
+#&gt;   2.4538e+00 -1.1562e+01  2.9378e+00  8.5902e+00  1.2170e+01 -2.4749e+00
+#&gt;   6.8260e+00 -7.0341e-01 -4.6220e+00  2.3372e+00 -6.9777e+00  1.1196e+00
+#&gt;   2.8531e+00 -1.0270e+01 -7.5814e+00  4.9443e+00 -5.1932e+00  1.1613e+01
+#&gt;  -2.5437e+00  3.0306e+00  8.1353e-01 -2.6043e+00 -5.9952e+00  8.3459e-01
+#&gt;  -8.6172e-01  5.4540e+00 -1.1564e+01  8.7192e-01  5.3836e+00  7.2653e+00
+#&gt;  -1.1370e+01 -4.5839e+00 -4.8711e+00 -1.0320e+00 -1.1259e+01  2.1886e-01
+#&gt;   8.5988e+00 -2.3520e+00  1.1205e+01  1.3373e+01 -6.8720e+00 -1.4589e+01
+#&gt; 
+#&gt; Columns 19 to 24 -1.4757e+00 -2.9086e+00  3.7875e+00  3.8046e+00  9.9135e-01 -5.2814e+00
+#&gt;  -7.8455e+00 -5.6955e+00  6.1365e+00  7.1745e+00 -5.9925e+00  5.1950e+00
+#&gt;   2.8120e+00  3.5108e+00 -2.3798e+00 -4.0304e+00  2.5297e+00 -1.7302e+01
+#&gt;  -1.6053e-01 -8.2070e+00  1.9308e+00 -2.7378e+00  1.2387e+01 -6.4349e-01
+#&gt;   1.3876e+00  6.8483e+00 -4.4044e-01 -2.2385e+01  1.0914e+01 -2.2045e+01
+#&gt;   3.9385e+00 -9.4922e+00 -1.9838e+01  2.6538e+01 -6.6371e+00  4.4715e+00
+#&gt;   4.1184e-01 -3.9045e-02  1.0639e+00 -9.6737e+00  7.3801e+00 -7.8772e+00
+#&gt;   2.6098e-01 -1.8976e+00 -7.4933e+00  8.8896e+00  7.5782e-01 -1.9326e+00
+#&gt;   6.7810e+00  1.8683e+00  2.1269e+01 -9.7725e+00 -2.6076e-01 -3.2745e+00
+#&gt;   8.4337e+00  9.0929e-01  4.1189e+00  8.4004e+00 -1.6291e+01  1.0513e+01
+#&gt;  -7.7256e+00  7.3382e+00 -5.5329e+00 -1.0845e+01 -1.3380e+01  7.6190e+00
+#&gt;  -8.1466e-01  8.6373e+00  8.0982e+00  4.8996e+00  2.9299e+00  4.8110e+00
+#&gt;  -4.1361e+00  3.3048e+00  6.1967e+00  4.1324e+00  3.5241e+00 -9.1599e+00
+#&gt;  -1.3167e+01  9.6421e+00  7.9457e+00  9.8987e+00  1.5261e+01  1.4769e-01
+#&gt;  -8.4662e+00  1.8609e+00  2.5667e+00  6.4801e+00  3.7954e+00  1.5022e+01
+#&gt;  -3.5729e+00  7.9538e+00 -2.6093e+00  8.2503e+00  7.0605e-01 -5.7706e+00
+#&gt;   4.2524e+00  6.8400e+00 -1.2863e+01 -5.7441e+00 -3.1656e+00  1.2086e+00
+#&gt;  -1.4751e+00 -6.2958e-01  7.7179e-02 -7.1986e+00 -5.4876e+00 -1.3063e+01
+#&gt;  -2.6628e+00  2.4780e+00 -5.7869e+00 -4.9347e-01  2.4905e+00 -6.2947e+00
+#&gt;   5.6991e-01 -2.4829e+00  5.8007e+00  1.4266e+01  7.6066e+00  3.1942e+00
+#&gt;   8.9414e+00  1.9897e+00 -2.3509e+00 -3.2521e+00 -4.0727e-01  5.1780e+00
+#&gt;   3.3468e+00 -7.8904e+00  4.2090e+00  8.5583e+00 -3.3650e+00  1.3906e+00
+#&gt;  -4.6435e+00 -2.9871e-01 -1.7427e+00 -3.7709e+00 -1.6402e+00  7.5157e+00
+#&gt;   1.5779e+00  4.6603e+00 -4.4591e+00  7.7573e+00 -2.2442e+00  9.8996e+00
+#&gt;  -8.6225e+00  4.1677e+00  1.2836e+01  1.1512e+01  2.0040e+01 -1.2154e+01
+#&gt;  -4.8182e+00 -2.1046e+00  7.7618e+00 -1.7149e+01  1.0615e+01 -6.0287e+00
+#&gt;   5.8925e+00  1.4365e+01  4.0609e+00 -1.6966e+01 -6.8104e+00 -3.8955e+00
+#&gt;  -4.4071e+00 -7.9534e-01  5.1252e+00  3.6052e+00 -1.1850e+00 -1.0037e+01
+#&gt;   3.3453e+00  5.4545e+00  1.8156e+01 -5.3970e+00  3.1606e+00 -1.2713e+00
+#&gt;  -4.0493e+00 -4.8913e+00  5.6615e+00 -5.7611e+00  6.6297e+00 -1.2146e+00
+#&gt;   2.5967e+00 -4.3332e+00 -2.1148e+00  4.6226e+00  5.3078e+00  1.4678e+00
+#&gt;  -4.7305e+00 -3.8472e+00  7.4686e+00  1.7495e+01  2.7979e+00  7.5927e+00
+#&gt;   4.9660e+00  4.7289e+00  9.5351e+00 -2.1106e+00 -4.0900e-01 -9.1845e-01
+#&gt; 
+#&gt; Columns 25 to 30  6.1161e+00  6.9226e+00 -2.8981e+00 -2.7580e+00 -1.0426e+01  3.3847e+00
+#&gt;   3.9221e+00  2.5279e+00  7.3571e+00  1.6244e+00  7.7329e-01 -1.2452e+00
+#&gt;  -3.1158e-01  3.9978e+00 -1.0066e+01  4.3027e+00 -2.6690e+00 -5.2712e+00
+#&gt;  -7.1519e+00  1.1379e+00 -8.2844e-01  6.3416e+00  6.2314e+00  2.7738e+00
+#&gt;   4.4686e+00  9.1622e+00 -3.0936e+00  3.4314e+00  2.2551e+00  5.1574e+00
+#&gt;   1.3488e+01 -2.1322e+00  1.0398e+00  8.0340e+00  7.3283e+00 -1.5809e+01
+#&gt;   3.7692e+00  1.9621e+00 -9.0556e-01 -2.4837e+00  6.5141e+00  5.7643e+00
+#&gt;   8.2587e+00 -2.0335e+01  4.0820e+00 -1.4321e+00  1.0502e+01 -3.8148e-01
+#&gt;  -8.9942e+00  1.9670e+00 -3.3506e+00 -9.3317e-01 -9.3726e+00  7.3447e+00
+#&gt;  -5.0825e+00  5.2144e+00 -4.7799e-02  9.0999e+00 -1.2141e+00 -7.9670e-01
+#&gt;   2.1925e+00 -1.4159e+00  7.0442e+00  5.4507e-01  4.1791e+00  2.5825e+00
+#&gt;   9.1459e+00 -4.1197e+00 -4.3125e+00 -9.0687e+00 -4.7147e+00 -7.2035e+00
+#&gt;   1.3595e+00  4.6402e+00 -5.2194e+00  1.3152e+00 -2.5326e+00 -8.4170e+00
+#&gt;  -4.4392e-02 -2.8086e-01  1.2058e+01  1.6194e+01 -5.1273e+00  3.0479e+00
+#&gt;  -1.0030e+01 -5.9885e+00  5.6329e+00 -2.0701e+00 -1.1666e+01  1.5481e+00
+#&gt;  -3.3927e+00 -7.3879e+00 -7.6752e+00 -1.6255e-01  5.5445e+00 -1.4005e+00
+#&gt;  -5.6587e+00  9.7032e-03  1.0599e+00 -1.8436e+00  6.6282e-01 -4.5359e+00
+#&gt;   1.1799e+00 -4.7255e+00  3.7418e-01  4.2854e+00  1.0477e+01  2.9613e+00
+#&gt;   4.8317e+00 -4.2625e+00  2.7001e+00 -7.4672e-01  1.2663e+01  1.4830e+01
+#&gt;   3.5157e-01  1.0214e+00  2.4288e+00  6.6755e+00  8.6543e+00 -2.0844e-01
+#&gt;  -1.1498e+00 -2.9015e+00  1.3124e+00  1.8678e+00 -7.2379e+00  3.8631e+00
+#&gt;  -1.4355e+01  5.2258e+00 -4.0159e+00  2.0858e+00 -1.2366e+01  1.5935e-01
+#&gt;   8.1982e+00 -4.8342e+00  8.7440e-01  1.4225e+01 -1.1004e+00 -4.4817e+00
+#&gt;  -2.3645e+00  2.5067e+00  8.5244e+00 -1.1762e+01 -1.4766e+01 -1.3830e+01
+#&gt;   9.2692e+00  1.6213e+00 -2.1836e+00  9.3800e-01  3.5906e+00  1.3250e+00
+#&gt;   4.7266e+00  4.2532e+00 -6.7926e-01 -7.2347e+00 -1.2477e+01 -4.3235e+00
+#&gt;  -1.0179e+01  6.6467e+00 -5.6658e-02 -1.1237e+00 -4.4639e+00  2.1199e+00
+#&gt;   2.4276e+00  5.1821e+00 -2.1755e-01  2.2693e+00 -2.6698e+00  3.4815e+00
+#&gt;   2.4108e+00 -3.8796e+00  9.8660e-01 -9.2692e+00 -1.0325e+01 -7.4074e-02
+#&gt;   3.4966e+00 -6.8966e+00 -3.5573e+00  3.1927e+00  6.6068e+00  9.5086e+00
+#&gt;  -3.8704e+00 -1.4158e+00 -1.1447e+00 -1.1132e+00  1.3367e+00  7.4420e+00
+#&gt;  -3.4355e+00  1.6264e+00 -6.1039e+00 -4.5346e+00 -5.4162e+00 -3.8093e+00
+#&gt;  -5.5183e+00  9.1193e+00  4.2706e+00  3.1916e+00  2.9840e+00  6.3735e-01
+#&gt; 
+#&gt; Columns 31 to 36 -9.0484e-02  6.9927e+00  7.3969e+00 -5.7673e+00 -4.3828e+00 -1.3971e+00
+#&gt;  -5.2099e+00 -7.5013e+00 -1.7246e+00  5.3855e+00 -3.3955e-01 -8.9563e+00
+#&gt;  -1.3297e+01  4.8407e+00  1.3566e+01  2.4213e+00 -4.6956e+00 -7.1628e+00
+#&gt;  -1.2235e+01 -1.1365e+01 -4.9232e+00  1.2284e+00  5.2631e+00 -2.5183e+00
+#&gt;   4.4032e+00 -4.1133e+00  1.1311e+01 -1.3455e+01 -1.4408e+01  1.0434e+01
+#&gt;  -9.5784e+00  6.0335e+00  1.1400e+01  5.1747e+00 -4.6522e+00 -1.6362e+01
+#&gt;   4.7644e+00  5.2691e+00  1.1483e+01 -7.0581e+00 -7.6905e+00  1.9435e+00
+#&gt;  -6.6241e+00  3.9515e+00 -3.2274e+00  9.5960e+00  5.0506e+00 -2.9879e+00
+#&gt;  -6.7060e+00 -1.1738e+01 -7.0699e+00  2.1243e+00  7.5988e+00  2.4997e+00
+#&gt;  -1.1083e+01  3.4505e+00  2.7356e+00 -7.9679e+00  7.6487e+00 -8.0102e-01
+#&gt;   4.3550e+00  1.3824e+01  2.6706e+00  1.2870e+01 -1.0729e+01 -6.7232e+00
+#&gt;  -1.0844e+01 -1.0815e+01 -2.4823e-01  6.4956e+00 -8.4392e+00  9.5622e+00
+#&gt;   1.8728e+00 -7.1832e+00  4.6905e+00 -1.2203e+01 -3.6089e+00  1.5070e+01
+#&gt;  -2.1337e+00 -2.3224e+00  2.3086e+00  4.2808e+00  7.1980e-01 -6.0761e+00
+#&gt;   1.5361e+01  7.7081e+00 -1.1713e+01 -6.5901e+00 -2.2062e-01  2.4049e+00
+#&gt;  -9.4625e+00  4.8361e+00 -1.4522e+00  4.2046e+00  7.4177e+00  3.3904e+00
+#&gt;   3.0925e-01 -1.4634e+00  5.1916e-01  3.0538e+00 -5.2062e+00 -5.0542e+00
+#&gt;  -9.3709e+00 -7.7323e+00 -1.2448e+00  1.0660e+00  1.5900e-01  7.7578e+00
+#&gt;   5.5954e+00  8.9109e+00 -5.5711e+00  1.0904e+01  8.6947e+00  2.3469e+00
+#&gt;  -5.4309e+00 -1.5257e+01 -9.2526e+00  6.4138e+00  8.7340e+00 -8.7541e+00
+#&gt;  -1.1727e+01  6.8796e+00 -2.3330e+00 -5.5755e+00  5.3497e+00 -4.8680e+00
+#&gt;   9.8880e+00  1.2896e+01 -8.6888e+00 -1.6464e+01 -4.0600e-01  1.3119e+00
+#&gt;   9.4101e-01  1.5961e+01 -2.2882e+00  1.3269e+00  1.3616e+01  4.3966e+00
+#&gt;   1.0615e+01  1.3888e+01  6.5107e+00  3.0149e+00 -1.4685e+01  1.2442e+01
+#&gt;  -4.8838e+00 -1.4796e+01 -4.2271e+00  1.0969e+01 -5.1779e+00 -2.2008e+00
+#&gt;  -6.8329e+00  1.9917e+00  1.6098e+01 -1.6539e+00 -2.4991e+00  1.3878e+01
+#&gt;  -7.0967e+00  2.3575e+00  1.1299e+01  6.6215e+00  2.3297e+00  1.5319e+01
+#&gt;   4.8587e+00  4.4289e+00  4.7356e+00  6.4253e-01  1.3700e+00  5.1514e+00
+#&gt;   8.7891e+00 -2.5138e+00 -9.8037e+00 -2.1609e+00  8.2152e+00  1.6041e+01
+#&gt;   8.2567e+00  1.0464e+01 -1.4057e+01 -1.0571e+00 -5.0778e+00  2.1363e+00
+#&gt;   8.5765e-01  4.0197e-01 -8.2174e+00 -1.3369e-01  1.1378e+01 -4.4959e+00
+#&gt;  -5.4334e+00 -8.5184e+00  3.4430e+00 -5.2772e+00  2.1432e+00  4.0265e+00
+#&gt;  -8.9269e+00  3.7571e+00  1.0541e+01 -8.2892e+00 -5.7320e+00 -5.3395e+00
+#&gt; 
+#&gt; Columns 37 to 42  6.5336e+00  5.4396e+00 -8.6902e+00 -1.0467e+00  5.2486e+00  1.2798e+00
+#&gt;   3.4265e+00 -6.1059e+00 -1.1453e+01 -3.6784e+00 -4.1583e+00  5.8550e+00
+#&gt;   8.0959e+00 -2.4088e-01 -7.0431e+00  3.7555e+00  1.2422e+00  6.9115e-02
+#&gt;  -5.0315e+00  4.6298e+00 -2.1419e+00 -4.1107e+00 -9.8939e-01 -6.2979e-01
+#&gt;  -1.0387e+01 -5.6365e+00 -9.6757e+00  6.3510e+00  1.2661e+01 -9.2142e-01
+#&gt;   2.4167e+00  8.2972e+00 -1.2247e+01 -7.8396e+00  4.6219e+00  9.4729e+00
+#&gt;   1.8849e+00  4.3804e+00 -4.3225e+00 -9.5198e-01  1.9971e+00  4.5372e+00
+#&gt;  -1.9068e+00  1.0075e+01  4.9945e+00 -6.3370e+00 -1.2891e+00  7.4768e-01
+#&gt;   1.0198e+01 -4.5966e+00  8.2938e+00  7.5842e+00 -1.1039e+01 -2.0984e+00
+#&gt;   1.0913e+01 -4.3925e+00 -1.6509e+00  8.5603e-01  1.0903e+00 -3.8952e+00
+#&gt;  -6.9291e+00 -6.5752e+00  9.8335e-01 -8.7769e+00  5.7905e+00  1.0136e+01
+#&gt;   6.6339e+00  4.6321e+00  3.5160e-01 -3.0522e+00 -8.9206e+00 -1.5932e+00
+#&gt;  -3.3305e+00  3.0761e+00  4.1607e+00 -7.2895e+00 -6.8976e+00 -7.5342e-01
+#&gt;  -3.9297e+00  1.5378e+01  5.4435e+00 -1.6367e+01  3.2887e+00  9.1972e+00
+#&gt;   1.5821e+00  1.3631e+00 -7.6733e+00 -1.4882e+01  1.9956e+00 -1.6447e+00
+#&gt;  -3.4146e-01  7.3848e+00 -5.5356e+00 -5.2053e+00  4.7189e+00  8.1394e-01
+#&gt;  -8.6958e+00 -2.2186e+00  1.5008e+01 -3.1278e+00  7.9075e+00 -1.6715e-01
+#&gt;  -1.1315e+00  5.3178e+00 -3.1307e+00  8.8815e+00  2.9763e+00 -7.6449e+00
+#&gt;  -9.3992e+00  1.2160e+00  2.1107e+00  4.0891e+00 -7.9666e+00  1.0358e+00
+#&gt;  -3.9945e+00  4.1562e-01  8.1565e+00 -5.9213e-01 -7.2816e+00 -2.8893e+00
+#&gt;   5.2877e+00 -3.8570e+00  2.7873e+00  4.8510e+00  1.2445e+00 -1.2769e+01
+#&gt;   1.8740e+01 -1.4332e+01  3.0651e+00  7.0418e+00 -4.5293e+00 -3.6274e+00
+#&gt;   6.7745e-01 -3.0371e+00  3.1109e+00  1.2090e-02 -5.3179e+00 -2.5002e+00
+#&gt;   5.2796e+00  1.0283e+00 -3.4523e-01 -1.0040e+01 -6.6017e+00  9.1503e+00
+#&gt;  -1.0775e+00  3.2323e+00  1.0403e+01  2.7762e+00 -1.5277e+01 -5.6901e-01
+#&gt;   9.0629e-01 -4.0061e+00  6.1235e+00 -1.2577e+01 -3.8271e+00  3.0505e+00
+#&gt;   7.4715e+00 -6.3082e+00  1.1744e+01 -9.8021e+00 -3.7131e+00 -1.7504e+00
+#&gt;   8.2256e+00 -2.3969e+00 -5.2840e+00  8.5097e-01  1.5515e+00  8.4764e+00
+#&gt;   7.9864e+00  4.1985e+00 -9.0979e+00  3.9316e+00  6.6061e+00 -2.8472e+00
+#&gt;  -2.7000e+00  2.2510e+00  5.7231e+00  2.7795e+00  5.0384e+00  6.0337e+00
+#&gt;   7.1882e+00  3.7472e+00 -1.3904e-01  3.8474e+00 -4.8376e-01 -5.2268e-01
+#&gt;  -4.1407e-01 -2.9709e+00  2.4135e+00  3.1045e+00 -7.5430e+00 -6.4270e+00
+#&gt;   5.8998e+00 -2.8439e-01 -2.7081e+00 -5.9818e+00  5.4839e+00  2.3218e+00
+#&gt; 
+#&gt; Columns 43 to 48  2.2402e+00 -1.5542e+00  1.4843e+00  3.2727e+00 -6.9243e+00 -2.8410e+00
+#&gt;   1.0847e+00 -5.9554e+00  5.8303e+00  2.4813e+00  4.7389e+00 -7.3507e+00
+#&gt;   8.4970e-01 -3.5377e+00  4.2821e+00  4.2873e+00  1.1666e+01  4.5596e+00
+#&gt;  -9.7468e+00 -8.9358e+00  4.5727e+00  5.4329e+00  8.6689e-01  1.0314e+01
+#&gt;  -4.8837e+00  5.9225e+00 -9.9869e-02 -4.2408e+00  3.9993e+00 -4.3992e+00
+#&gt;  -1.9045e+00 -7.1476e+00  9.9999e-01  1.5986e+01  1.2513e+01  7.1614e+00
+#&gt;  -1.7830e+00  5.9948e-01  7.8580e+00 -9.4462e+00  1.0200e+00  4.8011e+00
+#&gt;  -9.8234e+00  3.4861e-01  3.8357e+00  6.1562e+00  2.9061e+00  4.0485e+00
+#&gt;   3.5596e+00  1.4595e+00 -1.3254e+00 -6.1885e+00 -1.2089e+00 -3.0521e+00
+#&gt;   2.9925e+00  2.6702e+00  6.7610e+00 -2.3091e-01  6.8220e+00 -3.1444e+00
+#&gt;  -5.4020e+00 -2.3964e+00 -3.1865e+00  5.7555e+00  1.3291e+01 -8.5698e-01
+#&gt;  -2.8169e+00 -1.8385e+00 -9.0526e-01  1.9450e+00 -9.2923e+00 -4.7803e+00
+#&gt;  -3.0303e+00  3.2695e+00  3.0534e-01 -9.2509e-01 -3.9528e+00  1.1499e+00
+#&gt;  -2.5301e-01 -3.4039e+00  1.1043e+01  8.3450e+00 -6.2288e+00 -1.1530e+01
+#&gt;  -8.8186e+00 -2.2850e+00  1.1378e+01 -3.6004e+00 -2.0555e+00 -9.0479e-01
+#&gt;  -2.0104e+00 -4.7570e+00  1.1233e+00  1.9993e+00 -5.9040e+00  5.5085e+00
+#&gt;  -1.0149e+01  3.0721e+00  1.9551e-01  9.4429e+00 -5.2201e+00  1.5768e+00
+#&gt;  -4.0797e+00 -9.9219e+00 -7.6212e+00  2.4989e+00 -1.3341e-01  1.9144e+00
+#&gt;   3.4272e+00 -4.3545e+00 -6.7412e+00 -1.0484e+01 -5.8306e+00  8.4177e+00
+#&gt;   2.8215e+00  4.5339e+00  2.1628e+00 -1.6060e+00  1.7593e+00  8.6476e-01
+#&gt;  -3.3726e+00 -2.9092e+00  4.7258e+00 -7.0895e+00  6.2824e+00 -2.6824e+00
+#&gt;   4.2323e+00  1.7419e+01  3.6262e+00 -7.0972e+00  3.3825e+00 -1.9260e+00
+#&gt;   3.5111e+00 -3.4567e+00  2.1831e+00 -6.6562e+00  6.3965e+00 -8.3988e+00
+#&gt;   4.6389e+00  3.6323e+00 -3.3976e+00  2.6526e+00 -8.4421e+00 -3.6572e+00
+#&gt;   7.8457e+00  1.1802e+01  2.7869e+00 -7.3697e+00  2.0312e+00 -6.5736e+00
+#&gt;   9.7525e-01 -3.6469e-02 -1.6891e+00  6.5193e+00 -1.5194e+01 -4.8096e+00
+#&gt;   4.0414e+00 -2.8706e+00 -2.1379e+01 -3.6530e+00  9.7867e-01 -9.1130e+00
+#&gt;   5.1010e+00 -1.8387e+00 -3.7601e+00  7.1863e-01 -1.9257e+00 -6.5057e-01
+#&gt;   1.6760e+00 -4.5478e+00  1.5769e+00 -9.2460e+00 -1.5995e+01 -1.1016e+01
+#&gt;   1.6188e+00  3.1737e+00  1.3289e+00 -1.2460e+01  3.4080e+00  6.0096e+00
+#&gt;   3.7538e+00 -7.2063e-01  3.3712e+00 -9.0546e+00 -5.1337e+00  6.7879e+00
+#&gt;   7.7956e+00  6.9683e+00 -1.4220e+00  6.4978e+00 -5.2899e+00  7.5029e+00
+#&gt;   4.4002e+00  1.5309e+00 -6.6199e+00  5.7752e-01  5.1969e+00  2.1022e+00
+#&gt; 
+#&gt; (13,.,.) = 
+#&gt;  Columns 1 to 8  -0.9778   2.0864  -1.9347   3.3888  -0.1636  -3.8267   0.5605   9.9101
+#&gt;  -13.5658  -3.4669  -8.5129  -9.9463  -5.5977   2.4082  -5.8181   0.5399
+#&gt;  -11.2338   2.0017  -1.7188  -2.8144   0.6448  -6.0843  -8.3034  -0.3904
+#&gt;   -8.7948 -11.1114   5.0740   0.4245  -0.1223   2.4761   1.9931  -9.5092
+#&gt;    8.7837   5.0841  -2.5632  -0.0376  -5.2262   2.1127  -1.4961  -6.8694
+#&gt;   -8.1744  -3.6743  -6.1567  -8.8577  -0.2625 -15.8838 -11.3298  16.3652
+#&gt;    6.2160  -0.7043   2.2028   0.4975  -8.6895   0.5222   1.6079   7.0410
+#&gt;   -3.9090   4.1901   0.9985   3.0054   4.1974   0.7658  -6.6744  -1.5689
+#&gt;  -11.0796  -8.8534   0.9024  -2.9542   4.6354  11.8482   5.6382 -17.6922
+#&gt;   -0.9981   2.7919  -6.5282  17.6245 -11.2529   4.3189  -1.1030  10.6881
+#&gt;    5.6450  10.5769  -8.8890  -1.4596 -12.3727  -2.0341  -8.5240  16.0834
+#&gt;   -3.4532   7.7638 -14.1343   5.3774  -7.3675   7.2708  -4.6601  10.7336
+#&gt;   -5.7117  -0.5653   9.7594   3.0666   4.1041   2.5848   8.7299  -9.0438
+#&gt;   -7.4324   4.4691 -13.9857   8.0078  -2.5114  -3.7753  -1.4817  -4.4988
+#&gt;   -0.4866   1.1374   7.0974  -1.2004  -3.8848   2.9834  12.7753  -8.9348
+#&gt;   -6.1833  11.8951   7.6308   9.2798   1.5523  -4.2564 -12.1051   1.2364
+#&gt;   -4.1699  11.0643  -1.2436   7.4335   3.0640  -7.0257   0.4135   0.7183
+#&gt;    1.6508   5.3294  -2.6630  -3.7436   3.8429  -0.6369  -2.3379  -0.1505
+#&gt;    6.2069  -2.2236   7.1296  -6.1202  -3.0155   8.5069  -7.5743  -0.1698
+#&gt;   -1.1382 -11.1565   3.1580  -1.1949  -0.6722  -3.1941   3.2886  -3.5690
+#&gt;    4.6859  -5.8429   2.6151   5.2238  -2.2376   3.3885   1.7907   0.9217
+#&gt;   -2.9735   2.4143   4.0791   9.0949   2.4303   3.6517   5.2465  -0.7700
+#&gt;   -2.9390   2.5388  -0.8809  -4.5503  -4.8797   2.1322   0.6046  -0.2651
+#&gt;   -1.9825   6.9068 -10.2627   3.9346  -7.7332  -1.3122   0.7909   2.8877
+#&gt;   -8.7848  -5.0153  -3.2054  -7.6517  -0.0733  -3.7653  -8.5102 -11.1232
+#&gt;  -17.9673   0.4590  -5.5341   0.1872  -2.4148  -6.0210   8.3866 -10.3305
+#&gt;   -1.3640   3.2962   6.6594  10.2493   2.8223   3.0586  -1.5421  -3.4137
+#&gt;  -11.3088   5.3447  -5.6580   1.1274  -8.6965  -2.1191  -6.1493  -0.5026
+#&gt;   -1.8827   1.6988  20.9489   6.4057   3.9443  10.6856   8.5919 -10.9101
+#&gt;   10.8214  -5.2606   5.7944  -6.0466  -0.6325   1.4181   4.2511   4.7328
+#&gt;   -7.7921  -7.8776   3.9628   7.7676   5.7189   0.6693  -4.5340  -1.5418
+#&gt;   -7.7887   2.7145 -11.8478  -9.0489  -0.5368 -12.5737  -1.5724  -1.7395
+#&gt;    1.7873   2.7427  -2.4864  -1.1201   1.0337  -5.9219  -8.1600   2.8393
+#&gt; 
+#&gt; Columns 9 to 16  -1.8169  -7.9612  -2.1608   0.5852  -6.5375   8.1288   2.5874  -3.5900
+#&gt;   -1.4759  -1.2970  -1.1523  -3.5235   0.9351   8.4060   8.1361  -2.8203
+#&gt;   15.8027   5.2125  10.0109   1.2406   1.4232  -2.5722   7.9275  -7.1921
+#&gt;    5.4071   1.3940   6.5425  -5.8151   7.5485   9.8290  -5.3536  -4.6675
+#&gt;   16.1111  -3.6397   2.8263   5.2734 -12.4194  -6.2390   7.4967 -14.4427
+#&gt;    4.9413   6.7940  10.4025  14.9983   6.9236  17.2183  12.4610  -4.3952
+#&gt;    7.0620  -8.7988   5.7420   3.7065  11.9922  -9.3160   1.3450   8.9910
+#&gt;   -1.6379  -5.3000   2.2178   6.1383  -5.8877   1.0227  11.9440  -3.0353
+#&gt;    0.1425   3.7023   4.0341 -13.7450   2.3731  -1.8288  -0.2984  -2.9665
+#&gt;   -8.6226   7.2702   2.7093  -6.5834   1.2454  11.6249  -9.4339  -4.0186
+#&gt;   -5.5424  -7.9561  -1.6931   5.3224   2.0049   0.7928  -9.3779   7.3330
+#&gt;  -11.3829  -7.6404 -11.1493   8.6658   9.6880 -13.3295  14.4488   8.8575
+#&gt;   10.0761   6.4298  -3.1485   1.9403  -1.6768 -11.3805  -3.4466  -3.4310
+#&gt;    6.7750  -1.8505  -7.1245   9.9058  -2.0716  36.2091   7.2410   3.8925
+#&gt;  -10.3795  -2.4469   6.6220  10.7503  -7.5308   2.0596 -10.9463  -4.2121
+#&gt;   12.1840  -1.5373  -4.3675   5.1165  -5.7954   1.5911   0.4830  -8.5760
+#&gt;    2.5448   5.6987  -4.4362   0.4550   4.4976  -8.0434 -12.8337   7.6190
+#&gt;    1.0408  -3.0536 -11.0099  -6.1630 -16.3065  -3.2734  -1.0025 -14.9008
+#&gt;  -14.4417   5.8758   4.1159  11.8370 -18.3083   4.2844   0.5498   0.3004
+#&gt;   -0.6270   4.8943  -4.9521  -5.0111  27.1115  -3.8876   5.0781  15.2980
+#&gt;   -2.0936  -2.2204  10.7438   5.6234  -5.8773   1.8612  -0.1747  -2.0794
+#&gt;    1.7866  13.3086  14.1191  -7.1445  -0.3335   4.3896  -2.1735  -9.4727
+#&gt;   -8.2449   4.5875  -2.2806  -4.2115   1.3583   4.8266   1.2051   4.1941
+#&gt;  -13.0131   3.9361  -2.0040  14.2697  -1.9906  -0.3230   2.7687  -1.5652
+#&gt;    8.2772   3.4863  -6.8052   4.1047  12.7934 -11.4059   8.9971  15.3194
+#&gt;   -1.3278  -2.0004 -12.5763  -0.9432  -1.7532   4.4697   6.3850   2.4699
+#&gt;   -5.6669   2.9796   1.5793 -13.1081  -6.6368   9.7166 -18.8036   6.8036
+#&gt;    8.5978   4.7147  -3.3906  -0.8923   2.8644   5.3926   7.2355  -6.1755
+#&gt;   -1.9046   1.1967  -3.7383   3.0169   9.8420  -1.1192 -10.2677  -8.4321
+#&gt;   -6.7273   0.1615   2.1647   6.3368  -4.5556  -3.0376   0.5976   4.3843
+#&gt;    5.8365   5.5424   3.4668  -2.8281   2.3065   5.5648  -0.8226   1.8988
+#&gt;   -3.2143  -3.7337  -8.0035  10.7733  -5.3736  -0.8198  -2.4268   0.0483
+#&gt;   13.6660  -4.5237   4.5970  -5.1990  12.3013   1.6388   4.2768   2.5850
+#&gt; 
+#&gt; Columns 17 to 24   1.1647   0.0883  -1.8579   6.9675  -1.1364  -5.0060  -0.7780  -4.4172
+#&gt;    4.7413  -2.1396 -19.5925   1.1176  -3.8387   0.3339   5.2362  -2.5868
+#&gt;    3.0235   7.9256  10.5735   0.6956   4.5093  -3.4612   9.1438  -0.8495
+#&gt;    4.4381  -9.2104 -14.2792 -21.5529   2.7342  18.2734  -0.5226 -17.2673
+#&gt;   -6.9523   9.1747  15.3743   4.9407   3.6362  -6.5721  11.8230   7.4990
+#&gt;   -0.8719  20.3313 -11.1857 -15.8077  15.2127  14.0973   2.1633 -23.9395
+#&gt;   -1.3459   2.0318   2.8195 -12.9894  -9.3207   8.1929  -3.7738 -11.1509
+#&gt;    1.6179  12.0645   7.3246   1.8184  10.1276  -0.6606  -4.7457  -4.9631
+#&gt;   17.2713  -8.8859  -0.1720  -0.1655 -10.7213  -5.3521   3.2695  14.7218
+#&gt;   -1.6149   6.2540 -14.3293   8.2155  -0.2284  -1.6483   0.5684   5.5994
+#&gt;   -5.4084  -6.5135   9.4296  -1.7666  12.9097   0.5136  -2.3926   4.3374
+#&gt;   -9.4100  -8.4902  10.9871 -11.5960   4.4534 -10.3119   5.0998   8.0114
+#&gt;   -0.4976  -8.7225  -8.1794 -13.4504  -0.4677  -5.3711   1.6503  -4.6690
+#&gt;    6.1623  13.1666   3.0731 -12.0985   1.0742 -12.4761   4.2051  -2.4907
+#&gt;   -9.1310 -16.9587  -9.2522 -10.3044   2.7399   3.1901 -10.8720   1.1453
+#&gt;   -6.0153   3.4808   7.1725   1.4039  -2.9327   5.0683   7.5520  -9.3416
+#&gt;    1.9538   0.7618   2.5276  -3.9420   8.3673   0.7737   6.4622   0.6261
+#&gt;   -0.8007  -2.1855  -7.4393   7.3172   2.7565  -8.6415   9.3671   2.1703
+#&gt;   -7.8823  -5.1172  -1.7955  -3.6778  -0.2620   6.4934  -1.8294  -7.3872
+#&gt;    1.6145  18.7136  -6.2837 -13.3100  -2.6283  11.3937  -6.4198  -5.1121
+#&gt;   -3.6383   2.5777  -6.7709   7.2382  -7.8776   4.3315  -2.7850   2.6136
+#&gt;   11.7729   4.2469  -7.3782   8.6584 -11.0195  -6.8946  -9.9003   1.3134
+#&gt;   -2.4433   1.7564   4.3630   1.8874  -1.7856  -0.3932  -9.2346   8.8489
+#&gt;    0.1627  -3.4027   7.8790   1.4177   8.3922 -18.5751  -1.0273   9.8765
+#&gt;    4.4287   0.8575   0.5978  -3.5768  -7.0705   5.7572   5.8927   6.1881
+#&gt;    9.4920  -1.8612   9.4122   3.3498   3.9180  -2.6696   5.1081   2.6881
+#&gt;   -5.5692 -11.3642   2.3446  -0.4082  -1.7243  -6.4737  -2.4863  14.0122
+#&gt;    2.6065   6.2615   0.1544   2.1110  -3.4092  -7.5758   7.7624  -4.2724
+#&gt;    0.1356   3.7510   4.8822   3.8887 -11.2026  -0.8222  -0.8038  -0.4906
+#&gt;   -9.3040  -9.0981   1.7609   1.1214  -9.9225   7.2715 -11.7252   5.6880
+#&gt;    2.3609   2.7230 -13.1896  -7.3445 -10.8696   8.9974  -1.7979 -15.9049
+#&gt;   17.6879   1.3128  -3.2289  11.0028   5.5607  -2.8696  10.8506   0.0405
+#&gt;   -4.1897 -10.8829   1.3293  13.7790 -10.7932  -8.6982   0.8268   8.3045
+#&gt; 
+#&gt; Columns 25 to 32   3.3672   3.2518   4.6540   1.1434  -7.4825   2.9413   0.1813   0.1991
+#&gt;    6.0787  14.7320  23.1539  -1.4293   8.0082  -6.9817  -1.0013  -0.0750
+#&gt;   -7.4752  -4.8351   6.6102  -1.4242  -8.6344   2.4255  -3.6908  -1.9024
+#&gt;    1.1897  14.2074   8.5096 -11.0300   1.0316  -0.7676  -2.4233  -0.2152
+#&gt;   -2.3057  -5.1122  -2.3910   6.0738  -3.7626   4.5053   7.7907  -2.2122
+#&gt;  -15.4137   9.3436  15.0671  -6.1341   0.1437   7.1682  -4.1309   2.9222
+#&gt;   -4.2184  -2.2880  -4.5529   0.6118   5.1968  -5.0098   8.9606  -8.2621
+#&gt;    7.7482   3.0246  -9.0170  -1.3437   3.2428   8.4212  -0.0081  -4.5559
+#&gt;    5.0130  -1.6627   6.8645  -2.1399   0.6312  -8.5304  -0.6510  -5.3466
+#&gt;    3.0054 -13.8221  -4.8138  -9.6695  -1.7051  -1.0551   1.3081   5.2647
+#&gt;   -0.0134   3.9877  -5.8991  -1.4447  -2.5632  -2.9159   6.0396   4.4077
+#&gt;    9.1458   6.0467  14.2540   7.6451   4.6234  -9.4695   5.5932  -7.9882
+#&gt;   -4.3342   7.4167   3.5752  -6.1146   4.5217  -0.7637  -0.6503  -1.1054
+#&gt;   -6.8725   3.5239  -3.6538  -1.4832   5.1537  -2.9111  -1.3363  10.7392
+#&gt;    4.3785  16.9610   1.1975   0.3920  14.0748   4.1755  -3.6533   2.4858
+#&gt;   -1.7937   2.5136  -3.1279  -5.8645  -4.2897   9.6523  -9.5733  -0.6507
+#&gt;    6.7988   2.4583 -19.6900  -7.0410   2.4129  -0.6755  -0.0605   4.6293
+#&gt;    9.0016   2.7707   5.4987  -4.2748  -5.7277  11.2927  -1.2466   0.5758
+#&gt;    0.7269  -3.2907   1.9014   1.9705   2.9013   5.3157  -5.9270   8.9254
+#&gt;   -1.6971   4.8348   3.5307   8.9739   2.4663 -12.2650   0.3829  -7.1116
+#&gt;    2.6750  -7.4207  -3.1601   9.3425  -0.9851   3.5724   1.0315  -2.5465
+#&gt;   -4.2564  -6.5003  -3.5519   3.4889   8.9461  -7.1588  -0.3210   6.7513
+#&gt;  -12.1691 -13.5520  -0.9275  -8.4403  11.4199  -0.2876  -1.6594   2.5912
+#&gt;   -8.0832  -4.7692   0.3542   4.4135  -2.7930   7.5920  -2.3239   8.9804
+#&gt;    2.5175  10.1770   4.6237  10.5699   9.4463  -9.6478  -0.4713  -6.7494
+#&gt;    6.5276  -1.8750   0.6352   1.9519  -8.2804  -1.9805   1.3021   0.6536
+#&gt;   -5.9238 -16.8892  -6.7147   3.6698  -7.4369  -1.8938   2.4947   1.9169
+#&gt;   -6.3955  -2.6127   9.3821   3.4279   1.4402  -2.4267  -1.4215   3.7506
+#&gt;   -3.0882  -8.8381  -3.2720  -2.6979  -1.1780   6.0459   1.1284  -2.4717
+#&gt;   -2.0886   4.5918  -3.5290  -5.5408   5.0077  -2.1669   3.2118   0.8196
+#&gt;   -1.4853  -3.1820  -7.3037  -0.5624   3.1029  -0.4055  -4.3800  -1.5676
+#&gt;    9.8060  14.9224  14.7496  -2.9497   3.4719   7.5710  -9.0688  -4.5238
+#&gt;   -8.7652  -7.3070  12.1732   5.4162 -10.8798   0.5792   0.4540  -2.3674
+#&gt; 
+#&gt; Columns 33 to 40   0.5775  -4.4677  11.2720  -3.3636  -8.5356  -2.1139  -1.0137  -8.1068
+#&gt;    6.4036  -4.8619   3.5149   6.8066   2.5327  -2.4894   5.0787   4.2105
+#&gt;    1.0636  -5.4773   4.8422   3.3961  -0.7011   3.5336  10.6122   7.7750
+#&gt;    0.7237   6.9199   9.8021 -11.3355  -1.1520   5.2284   0.2712  -1.3648
+#&gt;    4.0831  -8.0998   7.4962  10.3927 -10.4632   9.3660  -7.0408  -4.8940
+#&gt;    6.6636   9.5090  16.7417   4.6306   5.3977   1.9157   6.5911   8.0977
+#&gt;    1.6209   3.8768   5.1800   0.6810  -8.0559   5.1859  -5.6461   1.7969
+#&gt;    6.6280   9.1175   1.8795  -0.1595  -5.5815  -3.8655  -2.4064  -5.0959
+#&gt;    5.0322 -11.1407  -5.2614   0.8215   6.3341   0.8956  -4.0355  -4.2597
+#&gt;   -4.4824  -2.1265   3.7489  -8.0417  -1.5849  -7.4492 -19.7344  -0.9276
+#&gt;   -3.6402   0.5646   5.4167  -8.9768  -4.1497   5.0244   9.2658   4.6353
+#&gt;    5.8962  -7.5954   6.8295   4.3948  -1.3111   0.0832   3.4058   0.9073
+#&gt;   -0.8869  -4.1317  -0.0667  -2.5821  -2.3452  -3.3751  -2.9564   0.0604
+#&gt;   -1.2905   1.2066  13.5431  -6.4651  -2.0152  -8.9456  -5.1113 -13.7680
+#&gt;    8.5245   3.3595  -9.1866  -9.1915   4.2073   8.5071   2.6865  -5.1196
+#&gt;   -5.5655   2.4154  -4.9278  -0.3808 -11.5661  -0.7320   7.0886  -0.3078
+#&gt;   -8.5356   5.7566  -1.0723 -10.9469   1.5997   3.7010   4.1550  -0.4964
+#&gt;   -1.3956  -3.8126   5.9658  -6.4291   1.7802  -2.6399   5.9742  -1.7544
+#&gt;    5.2109  -1.2798   2.6850  -2.7984   9.2020  -8.7254  -4.1873  -5.6979
+#&gt;   -7.0239  12.5927 -12.9334   1.1010  -2.5309  -9.0615   1.7940  11.0528
+#&gt;    5.8765   3.1526   2.6281   5.4769  10.3717   7.5385   2.4004  -8.3088
+#&gt;    4.6767   1.8066   0.1697   3.5908   1.6994   4.1112  -5.9328  -4.9845
+#&gt;    3.4727  -3.0859   0.9391  -6.9982   6.6553 -13.1016  -9.6493   1.4379
+#&gt;    2.4858  -8.1152  -3.2048  -0.5419  -0.9222   9.8688   1.8992  -2.2098
+#&gt;   -2.5455  -4.3628 -11.5113  10.2497   2.7842   0.7743   3.8009  -1.7875
+#&gt;    3.6694 -14.8864   3.9832 -11.6735 -13.5658  -1.5386   1.4304  -6.9780
+#&gt;   -1.4678  -6.4834  -2.2244 -12.9281  -0.8812  -0.0655 -10.2224  -2.8203
+#&gt;    3.9205  -6.0842   4.4426   5.9737  -4.7561  -3.2656   2.9719  -0.1410
+#&gt;    4.9297  -0.9006  -0.1484  -8.9344  -7.9250 -13.6350  -2.1800 -12.5243
+#&gt;    1.4256   8.0593   6.3374   4.8553   5.4058  -3.2912   4.1656  -5.6653
+#&gt;   -4.2563  10.1613   1.5826  -0.1803   2.4414  -4.1139  -3.5719   0.2643
+#&gt;   -4.9414 -14.7304  -1.8926  -8.8752   6.2763   8.3244   6.4805   7.4341
+#&gt;   -4.4275  -4.0429   5.5091  12.0350   2.2849   4.7607   1.4306   3.0357
+#&gt; 
+#&gt; Columns 41 to 48   1.3425   2.4318   0.2297   3.9487   0.8340  -3.4892  -1.8934  -2.5187
+#&gt;    2.5465   6.1363   4.7546  -1.1347   0.2565   4.8179  -6.1110   3.9354
+#&gt;   -0.9445  -5.7972   5.6068   0.1764   0.0661   5.9946   4.5057   4.3280
+#&gt;   15.6166  -9.8865   1.3323   6.3277  -8.4087   3.5651  -8.7914 -11.7654
+#&gt;    1.3859   1.2135  -6.7168  -0.0515  -2.8396   7.7291   3.0882   5.1256
+#&gt;    1.1402  -9.2210  14.8552  -6.0190   3.4786   5.9440   1.1656  -9.2151
+#&gt;    7.1774   7.7675  -4.4483   0.9239  -6.6282   2.3573  -4.6418  -6.5257
+#&gt;   -2.3929 -11.7843  -2.3346  -7.4551   3.8152   1.3559   7.4466   5.8157
+#&gt;    1.2616  -3.3774 -10.5876  -4.8113  -2.2306   6.2377   3.9167   3.2517
+#&gt;   -5.8087   3.9114   1.9733  -2.9279   0.6541   3.8726   3.8843 -10.0951
+#&gt;    3.9405  -2.6526   5.0703   4.1638   8.4561   2.9692  -5.7006  -8.5118
+#&gt;    3.6775   0.7867  18.1272 -15.8702  -4.6949  -8.0838   6.5894  16.8597
+#&gt;    0.6880   4.3539  -0.4666  -5.6496  -4.5788   0.7218  -4.0261  -3.4822
+#&gt;   -8.6434  -1.5937   0.8873   0.5744  10.9685   1.3127   0.1695  -6.8751
+#&gt;    8.5375   4.0668  -6.1528  -1.6856  -6.2984  -9.0808  -2.0758   8.7333
+#&gt;    1.9280  -0.7524   1.1469   4.2321  -5.0411  -2.8487   1.8767  12.3459
+#&gt;   -8.1954  -9.7367   4.8013   9.2316  13.0555  -4.6173  -6.3980  -2.1876
+#&gt;    2.9570  -3.0677   7.4724  -0.1404  -1.2668   3.9883   1.5399   4.4235
+#&gt;   14.3549  -4.9833  -5.5107  -0.5534  -1.3172   3.7463   8.8271  11.3629
+#&gt;   -3.7792  -0.2707   2.4581  -3.1972  -3.6777   5.9267  -4.8994 -14.2793
+#&gt;   -3.6525   8.5514  -8.2225   2.7873  -8.2155  -7.0475  11.2389   9.3106
+#&gt;  -12.0075   1.9691 -13.6354  -4.5603   1.7476   3.5187  -4.3323  -1.3716
+#&gt;    1.2376   0.8189   7.9525  -6.1161   1.2412   9.3051   5.9537  -2.9717
+#&gt;    5.0425   7.8080   3.6463  -7.0141   6.8462  -3.0663   0.1731   3.5539
+#&gt;   -6.2023   2.6357   4.5537   4.1061   0.4813   4.1727  -4.3997   8.9735
+#&gt;    9.4631  -2.4638  -1.9929   8.5272   7.0650   0.9482   1.4085  -1.6877
+#&gt;    0.3908  -2.8212  -2.1498   8.3580   0.0389  -3.4834   6.8670  -2.2132
+#&gt;   -1.8257   4.6533   0.2535   2.8972   0.3081   7.1296  -3.2923   2.2787
+#&gt;    1.3109  -0.3302 -13.9930   1.8319 -14.3261   5.9126  -2.5189  -4.8976
+#&gt;    2.0578   0.7190  -4.5069  -0.4589  -2.7491   2.7208  -2.3110  -0.6910
+#&gt;    1.3674  -1.9422  -7.2157   6.5781  -0.6388  -4.9945 -12.9072  -0.9459
+#&gt;    6.6912   3.1621  10.0633  -9.3664   5.1656   1.3459   1.4042  11.3953
+#&gt;   -1.4085  14.7674  -0.2491   5.4398 -12.1071   4.9412  -1.9091  -6.8413
+#&gt; 
+#&gt; (14,.,.) = 
+#&gt;  Columns 1 to 8   2.2600  -2.8558 -10.8461  11.0187   6.9782  -4.3478  -0.5539  -1.5358
+#&gt;    2.4124   0.4629   5.8994   0.1709  -6.1074   5.0492  -5.0420  11.9547
+#&gt;    7.1065  10.2237  -8.5146  -6.7201  -8.6567  -5.7960   7.6112   0.0106
+#&gt;   -3.6799  -1.3248   3.8999   4.1496  -6.4840   0.1998   2.3449  12.9582
+#&gt;    4.9348  -7.8063   2.6865   0.9718 -13.2628   1.7727  -7.2850   5.8127
+#&gt;    2.2097   7.6322  -3.9095  -4.9124   4.5758   5.9961   5.0590 -14.9655
+#&gt;  -10.5864   4.7010   7.0016  -0.9775  -3.7136  -1.1254   3.3070   5.5694
+#&gt;  -11.8419 -14.6315   5.5866  -3.0063   2.1711   1.3174  11.3221 -18.4676
+#&gt;    2.5500   2.4397  -4.5283   4.2481  -3.9721   0.8438   4.8044  16.6811
+#&gt;   -3.6356 -13.4748  12.1426   3.4413 -12.4099  15.8512  -8.8948  -9.8018
+#&gt;   -6.2980   1.1834   3.4216   0.1284  -3.2141 -15.6710  -2.9212 -12.4916
+#&gt;   -1.7433   5.9026 -11.5393  11.1219 -12.1876  15.6494  -0.2747  29.1426
+#&gt;    5.5712  -4.7204  -2.8677   1.5505  -4.0286   6.5050  -0.8830  16.4093
+#&gt;  -13.9082  -5.1252   8.5360  23.4810   2.1408  12.4500  -8.9152   0.8395
+#&gt;    7.6985  -1.4053  -3.2569   2.8858   2.0554 -14.6744   2.0157   6.5206
+#&gt;   -0.9287 -10.3851   9.1769  -6.0633   2.6984  -3.3501   7.5678  -5.8300
+#&gt;   -4.7567  -2.6000   5.0655  -1.2978  -9.6344 -12.7557  -0.9538  -8.4263
+#&gt;   -6.4511  -6.8126   9.9144  -3.1847 -17.0333  -6.7505 -10.2389  -1.3184
+#&gt;    8.3838   3.7580  -5.1037  -0.2602  -1.0659   1.9762   5.2730   0.7907
+#&gt;   -3.5293  -3.5601   5.3804  -4.2517  11.9572  14.2685  13.5047   0.6003
+#&gt;   15.3931   2.2727   4.3013 -15.7087   1.1502   2.3006  20.0940  -3.1557
+#&gt;    8.4442   1.0775 -10.7549   0.8859  14.6411  -2.5016   1.9741  -3.8478
+#&gt;   -8.8823   5.2919  -0.3432   4.1783  -6.0039  21.0042 -14.6209  -4.4580
+#&gt;   -0.8538  11.9851 -10.1445  13.7776  15.2895  -9.2509 -16.1284   3.7772
+#&gt;   -3.9917  -0.6193  -5.6516  -0.5427   4.6323   4.8671   9.3253   8.2588
+#&gt;   -1.6397  -8.5018  -3.3495  21.7717   1.2697  -3.9017  -2.1084   4.3235
+#&gt;    4.4836  -2.3586  15.3751   5.0692 -10.0125   3.3953  -7.7981  -6.8305
+#&gt;    3.2021   2.6053  -4.7890   5.3281  -0.1095  -1.1120  -7.5739   4.9639
+#&gt;    0.7935  -4.9545   1.3251  11.3611   9.0432   1.2695  -2.4978  -0.2546
+#&gt;   -0.5736  -3.8872  -7.7943   5.9322   2.4866  -6.5556  -3.9849  -6.4153
+#&gt;    3.0998   0.6605   4.3801  -0.3700   6.1907  -1.8227  14.0377  -6.7645
+#&gt;   -3.9354 -15.6906   3.4533   3.0959  -0.7309  -6.0984  -4.9267   5.3492
+#&gt;    0.9763   8.2680   2.8493  -6.9326  10.0556  -0.5642   1.0170  -0.6914
+#&gt; 
+#&gt; Columns 9 to 16  -1.4014  -3.8321   5.1040   5.4307  -7.5006   1.7403   9.2552   1.6595
+#&gt;   19.7852  -7.0015  -0.5662   3.0511  -0.7668  -6.8566   5.2111 -13.4175
+#&gt;    2.1381   3.3892  -9.8033   4.2055  -0.3977 -10.0748  -4.3383  -6.9594
+#&gt;   -1.0467  13.0893  -2.8339 -16.7426  -0.6585  -2.6023 -14.9551  -7.2855
+#&gt;  -14.9770  -1.0909   6.8344 -13.0934  -1.1271  12.6072   2.4682   9.3305
+#&gt;    6.3926  14.0606 -11.6504   7.0526   6.6679 -21.2735  -7.1550  -5.2344
+#&gt;  -10.0229  -1.6623   9.4548  -7.2335   3.8185  -1.4016   1.8229   2.1612
+#&gt;   -2.7789  13.5777  -2.1077  -0.8922  12.0194   3.5338   4.5966  -0.3275
+#&gt;   -8.2648   2.1932  10.4050  -5.2969  -3.5747   1.7460   0.5028   3.1883
+#&gt;    4.7326  -0.7221  -4.2855 -12.4920  -0.5439  14.1273   5.7346 -12.9161
+#&gt;   -1.4505  -1.4576  -8.1009  -8.1000   8.6470   0.5084   7.7073   0.7724
+#&gt;   -6.2689  -2.1733  10.3319   2.3879   7.4911 -13.9156   3.7235  -8.3498
+#&gt;   -4.3175  -0.9807  -0.8975  -5.6346  -4.6694  -3.4608 -10.7194   9.9853
+#&gt;  -10.1202  17.7336   3.3008   1.6841  -3.6197   8.7401  -3.9455 -18.8883
+#&gt;    4.1658   0.3644  -1.2469  -0.6469  -5.9142 -13.7340  -0.4149  10.0050
+#&gt;   -0.9701   6.6391  -9.5478  -2.9086   3.2133  -3.3332  -6.0938   0.6281
+#&gt;    2.8207  -2.7162 -14.8283  -0.2728   1.1317   4.3732  -8.9964   3.7575
+#&gt;    8.6114   4.2575  -8.0006  -2.7123   0.3408   9.4673   9.5063  -6.7093
+#&gt;  -18.3700   7.5151   4.1535  -3.4655  -1.7342  -2.5393  13.5231  10.7161
+#&gt;    6.8329  -4.0682  -0.6335  -3.5361   8.2864   6.3652 -21.4809  -1.1721
+#&gt;    6.2769   2.0555   0.3967   1.2667  -2.5802  -5.9748  -5.8337  -0.8392
+#&gt;   10.7900  -2.8396   9.8649   6.9794  -9.6329  -2.0757  -2.0149  -2.7491
+#&gt;   -3.5650  -0.8591  -0.1728  -0.2713   9.2215   6.3208   1.4432  -9.7395
+#&gt;    1.4093   0.2759   4.3701  11.6951  -4.7707 -16.6113  -2.4031   0.9886
+#&gt;   -8.2225  -9.9281   9.1841   3.6379  -0.0029  -3.8688 -12.0253  10.3216
+#&gt;   -9.6009  -0.7327  11.5823  -4.7354  -4.7562  10.2660  -1.7002   4.9996
+#&gt;    7.2495   9.9516  -0.1641 -15.7963  -2.8375  19.9997  -2.5914  -2.9705
+#&gt;    3.5448  -2.2903   6.5692   1.3634  -0.3906   1.1528   1.4527  -1.9760
+#&gt;    5.6957 -10.7337  11.6013  -3.2464   4.9270   9.5724  -1.8049  -3.0091
+#&gt;   -9.4110  -6.9045  16.7280   0.7745  -5.6757   1.5603   4.1562  10.4568
+#&gt;    0.4569   8.0082   2.0614   1.0410  -1.5383  -4.3348  -2.9376  -5.4775
+#&gt;    0.6965  -1.9393  -6.3455   9.7045  -5.7857 -12.4620   1.8762   8.5201
+#&gt;   17.7996  -7.9529   0.6533  -0.5032  -3.7281  -5.9325 -11.4193  -1.8686
+#&gt; 
+#&gt; Columns 17 to 24  -5.4768   4.9582  -5.0723  -1.1487   1.2050   2.4675 -12.0072   1.8139
+#&gt;   -5.8026  11.0532  11.4259   1.2884   5.8616   2.1643   3.0137 -12.2740
+#&gt;   -0.9038  -5.5463   9.6585 -10.6045  -4.5570  11.9275  -2.8928   7.6934
+#&gt;   -3.4287   1.2766   8.6902  14.7956   2.1803   4.1642   7.9175  -1.5596
+#&gt;   13.5894   7.7346   0.5477  -0.7536  -4.7169  10.0166  -2.0151  -0.2430
+#&gt;  -24.3139   5.1565  15.2477  -0.1144   4.2824  25.6640  16.5368  -6.7587
+#&gt;    7.5259  -2.6374  -3.7976   8.1169  -1.1820  -3.7018   5.2150  -4.7325
+#&gt;  -19.2848  -1.5864   3.5810   3.9467  -0.5479  -6.7748   0.3467 -11.3724
+#&gt;   11.6433 -10.6654  -8.9308   1.0838  -2.7418  -7.8942  -6.5439  11.6651
+#&gt;    7.1318  -1.3480  -7.2803   8.8523  -6.0541  -3.1280 -12.0955   7.0331
+#&gt;    3.7680  10.2324  -0.4844  -3.9459  -1.5617  -0.8020   4.5583 -11.5668
+#&gt;   -7.1870   6.4922 -14.8820   2.5774   5.4022  -0.3494  -1.0015 -11.7287
+#&gt;   -0.2821  -1.6047   2.6962   2.9507  -1.8278 -12.9270  -1.3581   0.9846
+#&gt;    2.3997   7.0725  -1.0677   9.4333   0.5657   1.0665  -8.7655  -2.6380
+#&gt;    3.0206   1.9449   5.2204   4.9902   5.4449 -14.4223  -1.8163 -15.3560
+#&gt;  -14.0065  -3.6739  12.6390  -8.1281  -5.1985  -4.4967  10.6623   3.3192
+#&gt;    5.4631  11.2123  -2.5677  -5.2898  -2.5867  -3.1398   0.2112  -5.9672
+#&gt;   -3.5132   5.2737  -0.4602  -3.8034  -3.5406   3.3348  -2.2505   3.2473
+#&gt;    4.8308  -4.0960   2.4735   3.0338  -5.2545 -11.1473  -6.1799  -5.1850
+#&gt;    0.6530  -2.3355   4.2858   8.0186  -2.0377  -6.1213   9.4021   0.5604
+#&gt;    9.7189  -9.7026  -3.8299   1.9838   9.6862   4.2679  -9.6970   2.4873
+#&gt;    4.9712   0.2527   0.0769  -0.6832   9.8139   0.5805  -7.5233   7.9888
+#&gt;   -3.3289  -1.1279  -4.6609   4.8636  -1.5157  -4.0133  -0.9233   3.9986
+#&gt;    7.0947   3.0456  -8.1862  -9.2550  -0.3196  -1.0323  -0.3905  -5.1577
+#&gt;    2.6294   0.5371   0.8569  -5.5430  -4.8138  -9.3191   2.2431  -1.7935
+#&gt;    0.1546   7.6907 -15.3715   4.2409   2.1672  -0.2653  -5.3247   2.7335
+#&gt;    5.6578  -6.4600 -11.3122   0.3388 -10.8876  -5.5536   0.7111  14.0562
+#&gt;   -0.0120   4.1150   0.6879  -8.6990  -2.3047   3.2561   3.3856   1.6094
+#&gt;   -1.6072  -0.9016  -4.6860   1.5532 -12.3131 -16.8786   3.2448  15.1862
+#&gt;   -3.2762   5.0642  -5.6370   5.6169   5.8870  -9.2878   2.9192   0.6714
+#&gt;    5.3539  -8.4478   3.3212   1.2104  -4.6266  -1.4858   3.5183   4.5395
+#&gt;   -5.1764   3.9487  -7.7713  -3.8079   2.3851   1.6164  -1.6969  -1.3513
+#&gt;   -1.2971   2.1094   5.8598  -3.8874   1.6435   2.4079   4.9317   9.3203
+#&gt; 
+#&gt; Columns 25 to 32  -2.6964  -1.1113  -1.4024  -4.4222   0.4144   9.6956   4.1457  -0.7109
+#&gt;   -2.4912  -4.8149  -7.1492   8.6070  10.3873  10.9661  15.5382  10.3474
+#&gt;   -4.3286   8.9290   3.6952   4.2518   4.0972   4.2747   1.9436   5.8343
+#&gt;  -10.3608   9.0934  -0.0294  -3.8719   3.6704  20.6640  -1.7353 -11.6331
+#&gt;   -3.2692 -12.3798  -8.7836  -1.2800  -8.2774  -2.7337   6.7594   5.1531
+#&gt;  -10.8345  17.4734   3.7571 -16.9622  -8.7740   8.9832   0.4001   8.4319
+#&gt;    3.5035   0.2823  10.0342  -0.4373   2.9493   5.5865  -0.7728  -9.2221
+#&gt;    2.6898   4.1650  -2.8318  -4.0260  -4.7243   0.6167  -6.4298   6.5094
+#&gt;    6.9303   2.7851   3.6933   2.7355  -2.6194   2.4837   1.1551  -1.1337
+#&gt;    5.5545  -2.9953   6.3803  -6.6386  -5.6648  -5.3221  -3.4175  -2.6752
+#&gt;  -13.3938 -10.6384  -1.1191   3.0185   7.5009   4.1620   6.9191 -12.4121
+#&gt;    9.8980   1.5878  14.3361  19.2343   5.1820   2.4226  11.3789  -1.4119
+#&gt;    5.7504  15.8322  -0.7426   2.8932  -1.3498   6.3948  -1.5811  -1.3767
+#&gt;    1.6785  -7.7196  -0.2856   2.1753 -18.4288   5.6670   5.3621   0.3818
+#&gt;    8.5308   9.8964 -10.8518   0.2661  10.4546  11.3260 -10.8128  -7.6275
+#&gt;    2.2311  11.8294  -7.7885  -1.7342   6.7255   0.0074  -2.4929   2.4669
+#&gt;   -4.5218  -2.5291  -2.2452   9.9933   1.6961   3.4532 -13.5014  -8.1877
+#&gt;  -12.8427  -3.1186  -5.3725   8.3846   4.8210   6.8567   1.7881  10.9682
+#&gt;   -2.3149  -7.5824  -6.9274   0.6322  -2.4300  -7.7349   8.2114   6.6951
+#&gt;   -9.0935   6.9586  12.4879  -3.2093  -3.3315   1.5685  -3.2253   3.4307
+#&gt;    2.5824   5.1793   6.4223  -1.0388  -5.2914   2.5218  -8.0272   2.3941
+#&gt;   18.3362   4.2767  -6.0339  -6.8537  -5.2728 -17.0345  -5.7337   3.2746
+#&gt;    3.2076  -2.0787  -7.9874   7.2432  -3.0282  -1.8623  -4.2151   7.4416
+#&gt;   14.9657   3.8570   0.1775  -0.9479  -3.9806  -8.6715   9.1971  -3.2055
+#&gt;   -8.5756   4.6311  -0.7387   8.2013  -8.3355  -6.6855  -2.8408  15.0039
+#&gt;   -0.6835  -1.5262  -5.0150   5.5284   1.3317  25.7027   1.1385   0.1626
+#&gt;    1.1703 -13.3847   3.2461  -3.9855  -1.0056   5.1104   8.3900  -8.6340
+#&gt;    1.9177   1.8562  -6.4541  -1.2059  -3.5174  -3.2030  13.2447  10.5126
+#&gt;    8.6436  17.8570  -7.1956 -12.4798  -2.5568  -4.4195  -9.2614   0.9336
+#&gt;   -1.2621   4.2302  -3.5884   2.9379  -9.5656 -13.0744  -6.9742  -0.7941
+#&gt;    5.1301   7.6236   4.3145  -5.1867   1.4815  -5.4339  -5.8556   2.0453
+#&gt;   -4.8896   5.7395  -3.8022   2.3572   4.5721   4.0151  -8.8917  10.3065
+#&gt;  -12.4749  -2.3375   5.8765   0.8283   4.7226  -1.8815  -0.2609   1.4435
+#&gt; 
+#&gt; Columns 33 to 40  -4.7302  -0.7203  -2.1366  -5.1505  -5.5650  -1.5344   2.8628   1.8069
+#&gt;  -10.0343   3.3875   6.5205  -4.0177   9.9163  -9.8909   9.0677   1.2172
+#&gt;   -6.3158  -4.3120   3.1926   5.0718  -1.0227   0.2026  -5.6831   7.3821
+#&gt;    0.9460  10.9301  -2.3823  -2.4416  -2.5480  -0.6224   9.1862   5.6608
+#&gt;    4.6530   7.5677 -12.8621  15.5798   1.2732   3.5899 -13.6831  -0.2983
+#&gt;  -12.2439  -6.5280   7.7131  -8.9455  -0.6136 -16.8031  13.5525  15.3239
+#&gt;   13.0103   7.8079  -2.0780  -3.9817  -1.9197  -4.8449   3.7999   2.5186
+#&gt;    1.6175 -14.0658  -3.0648  -9.7692  -1.3258  -3.1297   2.8956 -11.8217
+#&gt;    6.4766   2.5869   0.1051   6.9717   5.7080   3.5031   6.1024 -12.0477
+#&gt;    2.5707   6.8687  -1.8644  -2.8663  -4.0887  -1.3995  -4.8373  -4.1561
+#&gt;   -3.3495   7.8976   5.1014   3.9156   2.0625  -7.1106  10.1714  -7.5096
+#&gt;   -3.6825  -0.8773  -5.8448  -5.9601   2.6245  -9.4794   7.4325  -8.0925
+#&gt;   -1.6669   9.0294  -3.7196  -2.1797  -3.8907   2.0714  -5.2986   6.8930
+#&gt;   -4.4502   5.6893  -6.2273   6.1676  -9.2446  -3.0287   1.0945   4.0618
+#&gt;    0.9663  10.6646  -3.0525  -8.6235   2.1806   6.7143   1.0023  -4.7460
+#&gt;   -6.8601  -6.9040  -2.1362  -3.8128  -3.6172   6.8327 -11.7972   4.5291
+#&gt;    2.3521   7.5579   8.6607   4.4343  -0.7224   6.2762  -1.0277  -2.2570
+#&gt;   -7.8887   6.1623   0.2058   4.3952   2.9239   0.0894  -1.9110  -1.5387
+#&gt;    9.1913  -7.2619   4.7334   7.7999   5.8007  -4.7579 -12.2513 -10.2077
+#&gt;    6.6632  -5.6959   3.4835  -3.4153  -5.5553  -1.5467   2.9655  14.1140
+#&gt;    7.1015  -1.6608   1.8035   2.9720  -0.2277   5.2976  -8.8339 -14.5872
+#&gt;   -0.0507  -5.1413  -6.4168  -3.6183   1.7529  -2.9126   3.9377  -2.5140
+#&gt;   -7.9588   2.2475  -6.8236   2.3756   3.6878  -8.0904 -10.5043   4.3607
+#&gt;   -6.0041  -3.4032  -2.3349   2.0346  -0.0680   5.5060   1.9226   5.7583
+#&gt;    2.7211   3.8999  -3.5304   8.1900   4.6290  -1.9789  -0.2079  -0.1383
+#&gt;   -5.9205  -0.9211   0.3081  -2.4201   1.7209   7.1508   4.0471   2.5096
+#&gt;    3.2303  -2.8736  -1.5706   8.0534  -0.0957   6.4468 -11.9059 -13.3073
+#&gt;   -2.9459  -1.4798   0.3102   4.0339   3.9692  -4.6202   0.1579   4.8978
+#&gt;   -4.3827   0.0199 -13.2549 -13.5809   5.0189  -0.3153  -2.8449  -0.0908
+#&gt;   -2.7795   8.7480   2.1159  -1.6767   1.7355  -2.2025   3.2562   1.3900
+#&gt;   11.7586  -3.5436   2.9524   0.3429  -4.0160   0.9875   2.4724  -0.0485
+#&gt;   -9.9436   5.4469   1.0031  -1.0694   4.8070   2.0749  14.3860   3.8473
+#&gt;   -2.5094   2.3865   6.0782   0.3042  -0.8091  -5.7312  -7.5272   2.5913
+#&gt; 
+#&gt; Columns 41 to 48   1.1286   1.9196  -8.0738  -3.3653  -4.8662   1.7636   1.9130  -6.4182
+#&gt;    2.7282   0.1354   6.3312   5.3053   7.7725   4.0125  -6.5891  -6.6015
+#&gt;    3.7504   9.1643  -2.9494  -6.7499   3.4279  -0.8597  -3.7706   0.4302
+#&gt;    0.7339   2.0958  19.1199  -7.1387   5.1549   6.4956   0.8500  -9.3698
+#&gt;   -1.3320  10.0561  -9.1548  13.5957   0.0464  14.8015  -7.8637   6.5315
+#&gt;    3.4790  10.4465   3.7532 -17.1198   5.0716   8.5595   2.9782 -14.3744
+#&gt;    7.9216   7.3401   5.8683  -9.1900  -3.5437   4.5550   6.6951  -2.6652
+#&gt;   -2.8156  -2.9303  -8.3067 -10.7477  -1.2444  -5.9962  -6.4136  -0.3524
+#&gt;    5.8353   0.0697   5.0888  -6.5700  11.8449 -14.0458 -10.0617   7.1165
+#&gt;    9.5783   4.1892  -6.8969  -5.7255  -5.8262  10.0081  -3.0229   5.2248
+#&gt;    4.8607 -10.2742   5.4418  -4.6935  -3.9924   7.6789  11.0688  -7.2299
+#&gt;   -1.8997  -2.5301  12.9405  -5.5245  -7.6899  -4.1013  -6.5683 -11.3616
+#&gt;   -8.9428   0.9376  -4.3687   3.1147  -2.8850   0.5377   1.6129   5.0085
+#&gt;    7.5761  11.6445   4.5632  -7.4423  -5.8603  -0.9190   7.3045   0.3010
+#&gt;  -16.6320  -7.8229  -0.8025  -0.0247 -10.7229   4.1699   4.9983 -14.3914
+#&gt;   -4.2124  -2.1237  -2.7954  -2.3528  -8.9774  -1.7163   3.4692   6.9663
+#&gt;  -12.2076  -5.4039   1.0898   4.7608   2.7735  -1.9635  10.1507  -1.2104
+#&gt;   -7.8948  -5.6078  -8.2388  10.2654   6.9660   3.3335  -9.6765   9.3083
+#&gt;   -4.1451  -5.9400   4.4090  -1.9872  -8.4899   3.7365  -2.9364  10.1282
+#&gt;   -0.2870  12.6552  11.8432  -4.8058  -2.5164  12.0275  -4.0982  -1.4433
+#&gt;   -1.3412   5.3159  -6.5643   2.7085   2.4023  -2.4453  -8.0315  -5.0729
+#&gt;    9.8281   3.3709 -10.4745  -5.5053  10.6372  -9.6326   1.6871   0.0840
+#&gt;    4.7918  -2.5304   0.4281  -0.6118   0.6592  -5.7371   5.9862   7.2743
+#&gt;    0.9949  -8.8003  -3.4850   1.1671  -7.0995 -10.5485   5.8313  -1.8438
+#&gt;   -9.2603   9.2860   2.3040   2.3731   9.3122 -18.6149  -8.1081   5.1539
+#&gt;    6.4143   7.6940   1.7792   0.3514  -3.0460 -10.5961   3.1049   4.9386
+#&gt;    9.1956 -17.2597  -5.3138   4.9583 -17.3296  -1.2646  12.5870  22.1977
+#&gt;    5.4353   4.3994  -4.5703  -6.5473   0.3110  -0.4991   3.8774   0.1557
+#&gt;   -5.3731  -9.0090 -22.4950  -2.0026  -6.2803   3.7204   2.6534   6.7629
+#&gt;    0.4759  -2.5250   7.3919  -1.9523   0.1725  -1.8869   1.5488  -3.0972
+#&gt;    1.5629   8.1136   1.0751  -1.6913  -2.8480  -4.7111  -1.3944   1.6796
+#&gt;   -7.1196  -2.5497  -3.5083  12.7240  -1.8137 -11.9769 -13.8184   3.3666
+#&gt;   18.6126  -0.3100  11.7363   4.1982   5.2959  -9.3371  12.0391  -1.6972
+#&gt; 
+#&gt; (15,.,.) = 
+#&gt;  Columns 1 to 8  -3.1253  -2.4891  -2.5895   7.0758  -5.4317  -9.5299   3.6977  -0.1674
+#&gt;   -9.8030  -7.6711  -8.4548  -4.8719  -1.7880  -0.4484   0.5284   3.2480
+#&gt;    3.6320  -1.0659  11.7798   5.2171   5.7348 -10.4198  -0.4218  13.1514
+#&gt;    4.8075  -5.5062   6.3425  -7.3376  -4.3863  -4.5289   9.4405  13.9770
+#&gt;    3.8806 -16.2398   3.5059  -6.1362  12.2265  -9.0854  -5.5039  13.4235
+#&gt;   -4.1663   6.0868  -3.4945  11.1570  -3.6488 -11.6105  11.0163   6.5336
+#&gt;   -3.8031   3.1703  -2.2212   3.1016  -1.5322   5.4706   4.5173  -3.3172
+#&gt;    0.8993   3.7078  -6.8125   4.8599   8.6339   5.4187  15.7392  -5.3999
+#&gt;    0.9756  -1.8817  -0.0176   1.0504   2.2567  -0.4623  -5.9230   5.6332
+#&gt;    1.1827   4.1514   4.8478   3.5314  -4.7031  15.3405  -6.8330  -8.1256
+#&gt;    2.4200  -3.3245   1.6314  11.2973  -6.1368  -2.5005   9.0485  -5.0253
+#&gt;  -10.0487  -4.6163  -2.9459  -8.1504   2.7669   5.7930   3.2534  -8.4073
+#&gt;    8.2579  -4.6469   3.6854  -4.0573   8.4072  -7.8887   7.4235   0.3356
+#&gt;    9.0672 -22.3137   8.2970  -1.2394  11.0511  -2.6435   6.9328   2.2990
+#&gt;    4.3114 -13.1906   3.5634  -1.9399  -4.4585  11.5080  12.0772  -6.5503
+#&gt;    5.4362  -5.8136   8.6792  -0.4724   4.3796  -4.4385   9.6176   0.6723
+#&gt;    0.8973  -3.5184   5.9208   2.2987  -7.1067  -4.2047  11.8861 -10.2618
+#&gt;   -5.7227  -5.0894   4.4474 -10.8677   5.9358   2.5396  -0.2277   6.3298
+#&gt;    4.3897  -6.0229  -0.0670  -2.5611  17.0762   3.3938  -1.7386   3.7448
+#&gt;   -9.4934   6.9583  -8.5542  -1.5938   3.3045   0.0415  -4.6655   3.9101
+#&gt;   -1.2996  -0.7492  -3.2458  -4.5743  -4.1373   8.8601  -1.2482   2.2418
+#&gt;    8.7954   4.2748  -7.6379   9.9663  -7.5720  -5.1425   0.4114   8.5001
+#&gt;    9.7972   2.1686   7.2911  -4.3874   9.2126  14.3686 -13.0085 -14.7067
+#&gt;    6.9073  -0.1528  -0.5361  10.2091   3.5838  -1.9562   5.5775  -2.3897
+#&gt;   -2.0058   0.9751  -9.4071  -4.1356  10.8273  -7.2553   0.2022   2.7087
+#&gt;    8.8742  -9.5533   6.2028   9.2313  -4.6031  -7.4926   5.2301  -4.4256
+#&gt;   10.5665  -3.9474   6.7996   4.2394  -4.6927  -7.2510  -6.1554  -4.1069
+#&gt;   -0.2140  -8.0879  -1.9416   5.6772   3.9732 -12.6492   0.7084   4.7242
+#&gt;   -4.1366   0.9518  -0.7086   4.3093  -0.0127   2.9521  -8.6758 -15.1255
+#&gt;    3.7458   2.0063  -5.2624  -2.4859   3.5777   4.8612  -3.6627  -4.5873
+#&gt;    2.0283  -0.2667  -1.7340  -0.3327  -6.9421   0.0196  -0.1779   3.4340
+#&gt;   -1.3583   0.7448   1.4340  -5.0278   1.0488   6.6714   3.4176   1.0589
+#&gt;   -1.8821   1.7644  -8.2928   3.4571 -11.9411   3.0883  -7.5486   5.7396
+#&gt; 
+#&gt; Columns 9 to 16  -1.3996  -0.2212  -2.9046   7.0362   7.4061   7.8913  -0.2837  -1.7778
+#&gt;    8.4745   1.2357  -0.4998   0.1824  -3.7174   2.6377  12.4441  -1.5362
+#&gt;   -7.9753  13.8676   1.4375   1.5568  -1.7344  -7.5788   0.3910  -4.4924
+#&gt;   -0.2156  -3.7623  -8.0925   3.6557  16.9974  -2.7182  -4.0778  -6.0998
+#&gt;   -9.2072  -6.8382   0.9352  -6.8689  11.1395  11.9871  -5.7927   1.1290
+#&gt;    0.5936   0.7895 -15.9258 -12.0001  -1.8077 -13.7929  12.6847  11.3575
+#&gt;    7.7198  -0.4563  -1.6533   0.5332   1.7745  -7.2186 -13.4829  -6.3908
+#&gt;   -4.1707  -1.0304   6.4781   1.7351  -2.9058 -11.8763  -2.2188   7.6641
+#&gt;   -6.9670  -0.9226   2.9826  -2.0580  -2.8312   0.3905   0.6323  -7.8374
+#&gt;    0.1781  -5.4987   5.2169   5.0412  -1.8663  -6.3837  10.3154   2.5862
+#&gt;    7.0369   1.2855   1.4010   4.5597  -0.1614  -1.8498   2.9861   6.5672
+#&gt;   25.6376   1.0768  11.5551 -14.0244  10.1413   1.9486   0.2683   7.8230
+#&gt;    1.0909   4.2149   0.5620   0.7162   3.3697  -5.4716  -4.2234   5.7646
+#&gt;  -11.0719  -5.8712  11.0278   0.0785  -5.3192  -1.2503  21.8856  -2.2491
+#&gt;    9.8843   2.4702 -11.4462   3.8217  10.2331  -0.7071   1.2817  -0.4589
+#&gt;  -11.1059  13.3002   4.8504  -0.0257   3.8627  -4.9640  -4.3204  -1.3751
+#&gt;   -4.6346   3.1975   9.8365   9.9026  -5.1183   1.7183   6.9603   8.1594
+#&gt;   -2.4647   2.7544   9.4306   4.5861  -0.2958  -0.9157   7.9395   4.6285
+#&gt;    1.9963   2.2322   0.9410  -1.1384   7.8911  -0.7860  -8.6600  -0.1771
+#&gt;    5.3851  -3.2531   3.0231   4.9294  -8.7050  -7.7352  -8.3001  -3.8381
+#&gt;    0.4298  10.3184 -11.4358  -5.7089  -1.3504   6.2225   5.1214  -5.6078
+#&gt;   -8.9549  -6.2012  -4.3474  17.8379 -12.1752  -9.4749  -8.6853 -22.4279
+#&gt;    5.2647  -1.9981   1.7805  -1.6629  -3.6457  -6.5940  18.4017  10.6445
+#&gt;    7.9835  -7.1073  -1.1655 -11.3274  -6.1546  -4.1639   1.9358   3.5239
+#&gt;   -4.5755  12.9567  16.2086  -1.1543 -12.1783   6.4463   3.4809   3.4591
+#&gt;   -5.3961 -14.6684   0.0882   4.8349  12.0317   7.2562  -7.2496   5.0603
+#&gt;    0.5083  -0.7885   2.6800   7.8811   7.4976   2.3588 -10.2068   9.5285
+#&gt;   -5.1508   0.6026   7.5339   0.4525 -12.0128  -1.3036   2.9689  -6.5210
+#&gt;   -1.1429  -7.2266   1.6786   4.4667 -13.0362  10.3502  -5.1623  -2.0758
+#&gt;    7.7377   0.3442  -2.4271   2.0120  -4.1017  -0.1575  -5.2286   4.5640
+#&gt;  -12.6262   1.9976   1.4561   5.7899  -7.9257  -1.2511  -3.9091 -17.3013
+#&gt;    0.8465   4.1801  -2.0622  -0.0416   5.1187 -11.5897   2.2525   7.4036
+#&gt;   13.7671  10.8874  -3.5693   8.3849   3.0566   2.8306  -1.7121  -8.8746
+#&gt; 
+#&gt; Columns 17 to 24  -2.7890  -2.6777  12.4100  -0.4173  -2.7351  -2.3237  -1.1893  -7.8487
+#&gt;    2.8180  -0.6704  -1.1067  -5.3284   2.8287 -12.1802   0.6371  -7.8358
+#&gt;    5.0840 -12.8286  -8.1259   0.3413   1.5893   4.7990  -1.0848   7.9284
+#&gt;    0.7554  -2.6893   1.4951  -8.0301  -9.2258   9.2503  -4.3063  -2.9557
+#&gt;   -3.3535  -1.8424   2.6565 -14.7257  -0.4094   3.4802  -5.4106   0.2227
+#&gt;   -8.3111   0.9962 -13.7546   5.9317   7.1012   2.1884   0.1121   0.3053
+#&gt;  -12.9573  -4.9866   4.1322 -17.2575   8.8725   1.0928   0.7994  -1.9979
+#&gt;   -4.0501   3.7805  -5.9780   1.6578   6.6764   0.6974  -2.1380   1.1962
+#&gt;    7.4870  -1.9593   7.8647   2.1362   7.6960   1.4970   6.4040  11.4150
+#&gt;   -8.6788  10.7922   3.7114  -6.4831   7.8223  -0.4050   2.9311   8.9054
+#&gt;   -7.1412   1.8338  -5.3095  -2.4958 -11.7982   0.1850  -1.6287  -5.4001
+#&gt;   -5.0454   2.4634   5.7743  -3.5566  -3.0905 -10.1670  -6.1730   3.2453
+#&gt;    1.4025  -4.3798   3.3640   6.0507  -5.7250   7.3543   0.9277  -1.4232
+#&gt;  -12.3126  -7.0066  25.1666  -1.1720  -9.6095  15.6625  -5.4981  -6.9320
+#&gt;   -6.1307   2.9528  11.1720   3.3601 -14.1288   7.2203  -0.0050 -16.1877
+#&gt;    8.2280   0.4377   1.7582   1.3171  -8.7026  -1.1113  -3.6698  -5.1849
+#&gt;    6.9931  -7.4229  -8.2460   1.0635  -5.6589  17.8784  -4.5206  -4.7060
+#&gt;   12.6966   8.8376  -4.6713  -5.9602  -3.9046  -6.6923  -6.4629   0.8691
+#&gt;    9.7201   2.4825  -3.6458   5.0900   5.3519 -11.6224  -0.2309  -3.5843
+#&gt;   -4.8432  10.5841 -10.4051   0.8594   0.6298  -0.7780  -0.8480  11.4692
+#&gt;    1.1214   5.4748  -2.0440   0.1484   7.7655 -12.8579   0.9495  11.1897
+#&gt;    1.4060 -10.5693  -1.8325  10.9258   8.6519   7.6816   9.7604   1.3936
+#&gt;   -6.5766   3.0152   3.8706   2.6890  -4.8256  -3.7317   2.0358   2.5278
+#&gt;    2.6886  -9.6577   4.5351   9.3324  -5.7782  14.5360   6.2228 -11.9422
+#&gt;   12.6803  -4.0114   1.4691   7.7560  15.4859  -2.5307  -5.0918  -0.0953
+#&gt;   -0.4229 -15.7543  12.8897  10.0431 -14.1118   5.8578   8.0864  -8.7946
+#&gt;    5.5382   6.8417  10.2201   6.2617  -9.7157   0.1867   2.5066  -4.6980
+#&gt;    5.2566  -7.6473  13.5125   2.9201   4.6974  -0.1316  -0.3926  -3.7544
+#&gt;    4.4954  14.5592  23.9246  -7.5780  -3.5055   4.0718  15.7131  -5.0255
+#&gt;   -4.2636  -0.7075  -3.2000   6.7224  -1.7066  -0.7913   5.1736  -1.7236
+#&gt;    2.8973  -0.1939   2.0450   0.3506  14.3486   3.5940  -0.2480  -1.9980
+#&gt;   10.1309   7.5637 -12.5340   2.9218   3.7240   3.3869   2.1361  -9.2092
+#&gt;   -4.2256   0.7727   1.1605 -13.3239 -10.2843  -8.2181   0.6975   5.7283
+#&gt; 
+#&gt; Columns 25 to 32  -7.3705  -4.5355  -4.6654   8.5918   7.8764   2.7982   6.0937   4.9096
+#&gt;   -3.2256   4.6105  13.4156  11.3793  13.9300  -2.6096  -0.2455   1.8582
+#&gt;   -4.1965  12.2526   1.0939  -8.2841  -0.7019   1.2590   8.1224   8.2583
+#&gt;   10.3194   7.5178   1.8102  -2.6836  -0.2819  -6.5837   5.6283 -10.7460
+#&gt;    2.7923   1.5692  17.0556  -7.5243  -0.6599  -6.3663   5.6177   6.9881
+#&gt;   -2.1981   7.7804 -13.8299   7.0072  18.8106  10.8715   3.5986  -3.3465
+#&gt;    3.8715   1.4149  -1.5835 -15.3301   4.3764   3.0398   7.3337 -10.8537
+#&gt;   -0.5547   0.5229 -14.5510   4.3035   2.2922   5.5868  -0.3519  -6.5139
+#&gt;    8.5781   8.8679   8.2551 -13.8532 -10.1748  -6.1047  13.1753  -5.9603
+#&gt;    7.8875 -22.1076 -10.7262  -4.0644  -8.6112  -2.5865   4.4834  -8.3024
+#&gt;   -2.2896   3.7276  -8.8088   5.1738   7.0051   6.3038   1.5773   5.1471
+#&gt;   -3.9813   4.1158  16.0092   7.7633  18.0729 -10.1168   4.8557  -4.8928
+#&gt;   -1.5299  -0.7899  -1.8753 -10.5417  -3.6429   1.4227  -1.7932 -13.7711
+#&gt;    9.3151  -4.0839  -9.7988   0.5396  -0.2788   5.7625  18.9792   4.5048
+#&gt;    0.7024   8.3928  -2.2674   8.7297   6.4764  -7.2255 -11.1423   0.3319
+#&gt;   -8.2088   2.8323  -2.8998 -13.4854  -4.7871  -0.7063 -14.0605   9.1888
+#&gt;    7.5993   0.3761 -12.7373   4.7726 -10.1780  14.2810   8.6891  -3.3541
+#&gt;    1.7549   7.4356   8.0417  11.8169   1.0411   2.3458  -5.9812   3.5713
+#&gt;    2.0372  10.7705   0.0037  -4.1848   9.4260  -1.5995  -9.3472   3.6611
+#&gt;    2.9331 -10.5961  -4.6535  -6.9784 -16.6003   3.2779   1.2942 -13.4965
+#&gt;    7.2361   8.7838  -3.5954   8.2293   3.4667   1.6373  -3.4771  -5.9106
+#&gt;   -1.5328  -3.6081   4.7678   4.0522 -15.1855  -5.3358   1.1090  -3.7671
+#&gt;   -0.9779  -8.5687  -9.3836   3.6648   6.7702  -5.3110   5.8611  -7.0016
+#&gt;  -10.7062   0.9163  10.8128  10.8927  -6.5610  -2.6657  -3.5245  13.2871
+#&gt;   -6.6687   1.5307  -0.0698 -11.1103  -0.6281  12.6698   8.1904 -10.2510
+#&gt;   -2.4164   3.8756  -1.7883   2.0656  -9.5013  -5.1183  11.6215   7.6850
+#&gt;    7.4401 -12.6784  -7.0437   1.4436  -3.1311   0.7063   4.0412  -9.8384
+#&gt;   -3.0490   1.9418  11.6326   1.2181  -1.4382   2.8223   5.7756   6.7115
+#&gt;   -9.8401  -7.9711   2.8821   4.0277 -14.2939   2.5826  -5.7853  -1.2387
+#&gt;   -0.8754   9.9109   6.4537   7.3832   6.1161  -2.5829  -0.1030   2.8234
+#&gt;    4.1773   1.5992  -3.4398  -6.2480  -6.4559   6.2756  -4.9218  -1.6041
+#&gt;   -6.8433   5.2723  -0.3745  -7.0563  -9.0295  -1.5636  -1.9559   5.8586
+#&gt;   -5.0232  -4.6374  11.6069  11.3809  -2.0076   1.4440 -12.3505   2.9675
+#&gt; 
+#&gt; Columns 33 to 40  -5.6125   6.7731   8.4541  -2.1854   0.3412   2.5516   0.4211   6.6909
+#&gt;   24.7267  11.4926  -0.7755  -3.1323  -3.8439 -15.0334  -5.0555   6.1352
+#&gt;   -6.5297  -3.1846   5.3956  -0.9324 -18.1899   2.5683   0.1014  -4.2333
+#&gt;    2.6885   4.7499  -8.2193  -1.1181   2.0216   0.3198  -2.2306  -6.7962
+#&gt;   -7.3324 -17.0377  14.1088  -4.4187  -1.0388   3.1218   8.2675   6.9591
+#&gt;    2.8057  14.4177  -0.1052  -5.3618  10.1704  12.5540  -4.7404  -1.0310
+#&gt;    3.2645   4.1142   8.3417 -10.1458   2.0197  -1.9604  12.6541  -4.8555
+#&gt;   10.4826   3.7072  12.4159  -0.9389   6.7590   6.6212  -4.2486  -1.4669
+#&gt;    5.5648 -13.3981  -3.6252   3.9873 -13.3871  -6.9011   3.3795  -1.0039
+#&gt;   -8.5738  13.3355 -11.9097  -0.3358   8.6572  -4.5881   4.3793   5.3560
+#&gt;    4.9882   4.6433  -5.6991  -6.7133  -2.3059   1.3128   3.2571  -4.7954
+#&gt;   21.8481   2.4341   7.4053  -1.9258  -5.9918 -13.7795   9.5304  -8.2610
+#&gt;   -2.8034   0.1379   7.4667  -0.7562  -4.7330  -7.0494  -7.7486  -5.3417
+#&gt;   -0.2651   7.3359  10.2199 -12.9101   7.4875  18.9526  -0.3215  16.4356
+#&gt;    6.4327   1.3308  -1.0451   3.6919   6.1151 -10.2877 -17.5006   3.3733
+#&gt;   -7.3841   1.6269   5.3620   0.1772  -4.7273   1.0363  -3.8303   7.5537
+#&gt;  -11.0693  -7.3817  -5.9603  -4.6691  -2.9732   4.7557  -2.1609  -6.5708
+#&gt;    5.7738   3.7471   3.8845  -1.3242  -2.7638   0.9706 -13.0282   1.6208
+#&gt;   -4.4859   6.2482  -1.5561  12.2759  -0.3583   0.6379   0.3503  -2.8123
+#&gt;    8.1079   7.7002 -13.2443  -0.5972   1.9159  -2.3509   5.2845  -4.6995
+#&gt;  -12.6464  -2.1451  -2.0149   4.4907   1.3824   5.4930  -9.6339  -3.9409
+#&gt;  -12.5056  -3.6930   0.9160  12.3106   3.3540   3.8414   3.1190  -0.4137
+#&gt;   11.1568  10.4567   1.9502  -0.4310   3.7549   8.4122  -1.2994  -0.0369
+#&gt;    0.3792   0.1034  -0.2982   5.5100   9.8395  -8.4046  -8.9870   0.6755
+#&gt;    3.2959  -6.3545  14.4600  -3.1475 -12.6413   0.4253   1.5095   1.5729
+#&gt;    9.7122  -9.0172  10.6954  -2.7309 -10.5575   7.6955   7.7792   0.9114
+#&gt;  -16.1275   3.9434   4.8510  -4.9340   6.4149   9.6819  -0.8637 -12.9560
+#&gt;    0.8454   3.9225   4.1566   1.8728   1.0603  -0.9924   1.6692   1.5093
+#&gt;   -4.1258  -5.1589  -5.7234   6.6922  13.5036   2.5792  -2.2433   8.0794
+#&gt;    8.1036  -8.9591  -1.8927   6.3455  -4.2923   5.9356   5.4426   5.4370
+#&gt;   -9.6589   1.9065  -8.2652   3.1675   4.9384   3.5444   0.1585   1.6574
+#&gt;    7.6873  -1.7784  10.2622   0.2564  -6.5389 -13.5866 -13.6336  13.6436
+#&gt;    4.8743   3.9971  -4.7304  -2.6670   1.5260  -1.3921  -7.4538   4.8182
+#&gt; 
+#&gt; Columns 41 to 48   6.1353   3.7578  -0.0441   7.3223   4.9602  -2.1568   4.3757   0.8931
+#&gt;    0.5662  18.6103   4.0292   9.4775  -6.7890 -10.5541  -0.4426 -10.3427
+#&gt;  -13.2749  -6.6240   4.1254 -13.0459   1.7052  -2.8550   2.5767   6.2758
+#&gt;   -2.3137   7.0136  10.9082 -11.1078 -15.2089   1.0354  -2.6229  -8.0987
+#&gt;    6.4055 -14.9889  -6.5978   3.7650  -0.8987   8.4562  -1.4764  13.1080
+#&gt;   -4.4993  20.2693  17.5327 -14.8533   4.4216  -8.7556  12.3613  -5.5825
+#&gt;   -2.7250  -6.6039  -5.7230   3.6576   2.9245   9.2087  -3.5757   0.7079
+#&gt;    1.5939   2.1982   6.9100  -3.3073  -1.1629  -0.2601   9.5024  -1.7469
+#&gt;   -0.3790 -10.1971   0.5872   4.1371  -3.9639   3.0745  -6.6441  -6.2489
+#&gt;    2.3687  -3.5648  -7.2643   5.6019  -3.9771  12.3935   5.8484  -4.8982
+#&gt;   -2.8047  -1.5620  -2.2006  -1.3755  10.8788 -17.9937  15.4760  -3.8772
+#&gt;   17.2307   3.3495   0.5677  14.9326  -2.6751 -16.8204   3.8257 -11.9529
+#&gt;   -1.4403  -1.1898   1.4185  -7.8768   2.4049   4.3673 -11.8423   1.5068
+#&gt;   12.1322   1.2196   7.1309   0.2518   3.2303   4.1855   4.6652  13.1675
+#&gt;   -0.9723  13.1599  -8.4276  -5.5885  -1.6950 -17.1130  -6.1245   2.9842
+#&gt;  -11.4643   2.6667  -1.0499 -12.7139  -7.9057  -2.0608   1.6031   4.9724
+#&gt;   -4.7444  -2.6997  -0.7225  -7.9546   3.1268   9.3679   8.8378   5.3170
+#&gt;   -6.2729  -9.7272  -2.2612  -1.7600  -5.5337   0.5706   2.2268  -8.3842
+#&gt;  -11.3989  -8.9167   3.7355   4.0470 -11.4348   1.0019   1.8444  -0.2433
+#&gt;    1.7320  10.5308   9.5154   5.5511  -0.9445   7.9703  -7.1860  -6.2728
+#&gt;   -7.1761   3.9230  -2.2746   3.3040  -6.5579   1.0544   1.4223   2.5891
+#&gt;   -2.3831  -0.2480 -11.4843  -1.3215   6.8661   3.4761  -2.7993  -4.1668
+#&gt;    3.2639   1.6892  -3.8993   1.9522   2.8619   5.3807   0.8974   8.2397
+#&gt;   13.2428  -6.2506  -8.7147   2.0855   5.3620  -7.9402  -3.0805  -0.0242
+#&gt;    2.8766   9.3800   1.6777  -1.4231   3.3601   0.4491 -11.8244  -4.6924
+#&gt;   13.5989  -8.7720   4.8395   4.7784  -1.7019   5.3305 -12.3706   6.6611
+#&gt;   -8.1035 -13.6463  -2.9965   9.7603  -4.3336   3.1389  -0.6104   2.8124
+#&gt;   -4.6791  -2.6387  -2.1356   5.3763   4.3937  -1.0013   0.7160  -0.2833
+#&gt;    0.7437  -0.1479  -2.7407   5.0302  11.4307   1.8110   5.3611   9.9176
+#&gt;    1.6393  -3.3354  -6.9242  -0.4400  -0.5671   0.8072   2.0127   3.4851
+#&gt;  -11.8593  -1.0136  -1.7088   0.0624  -3.6022   8.4024  -2.4087   1.2273
+#&gt;    5.7540   2.5522  -6.3327   0.4532  -0.8195  -6.3937  -8.3132 -12.9178
+#&gt;    7.9770  -3.8467  -8.2244  -5.2552   5.2144  -5.7930   1.9104  -3.7280
+#&gt; 
+#&gt; (16,.,.) = 
+#&gt;  Columns 1 to 6  1.1062e+00  2.3523e+00  4.2966e+00  8.7650e+00 -1.7496e+00  5.0325e+00
+#&gt;  -5.2779e+00  7.7802e+00 -9.6564e-01 -1.5290e+00 -3.3332e+00  7.4104e+00
+#&gt;   1.1348e+01 -1.1255e+01 -1.1021e+01  4.2997e+00  3.5445e-01 -3.8183e+00
+#&gt;  -5.7209e+00  1.2489e+01 -4.0475e-01 -3.7800e+00 -7.7922e+00  8.2359e+00
+#&gt;   6.7294e+00 -3.2308e+00 -1.7050e+01 -1.9124e+00 -6.1422e-01  1.5671e+00
+#&gt;  -4.9611e+00 -1.9958e+00  1.3287e+01  1.2624e+01  1.1339e+01  9.8049e+00
+#&gt;   2.0276e+00 -7.0259e+00 -5.8926e+00  1.8932e+00  2.4937e+00  1.2010e+00
+#&gt;   1.8477e-01 -9.5830e-01 -2.9014e+00  1.1737e+00  1.1770e+01  3.3203e+00
+#&gt;   9.8410e+00 -4.3573e+00 -5.1784e+00 -5.9002e+00 -1.0852e+01 -2.7578e+00
+#&gt;  -1.1024e+00  4.8481e+00  2.5828e+00  4.5410e-01 -6.1700e+00  1.1684e+01
+#&gt;  -8.5110e+00  8.0595e+00 -5.6307e+00 -7.8825e+00  2.5881e+00 -9.7911e-01
+#&gt;  -1.2821e+00  3.0768e+00 -5.5347e+00 -2.0616e+00  1.7245e+00  1.6892e+01
+#&gt;  -1.4444e+01  9.7178e+00  3.3073e+00 -6.1807e+00 -4.5290e+00 -4.8493e+00
+#&gt;  -2.0301e+01  2.5582e+00  6.6547e-01  2.0842e+01  1.1694e+00  2.4476e+00
+#&gt;  -1.3845e+01  1.2386e+01  3.0308e+00 -2.3253e+00  6.9146e+00 -8.5794e+00
+#&gt;   1.4086e+00  3.6773e+00 -9.4792e+00 -3.8196e-01  5.4873e+00 -3.0524e+00
+#&gt;  -8.7695e+00  1.6707e+00 -1.0864e+01 -7.7258e+00 -9.6745e+00 -6.1782e+00
+#&gt;  -5.4825e+00  5.7056e+00 -6.1971e+00 -1.0549e+01 -1.0189e+01  3.0593e+00
+#&gt;  -1.1173e+00 -3.6139e+00 -5.3825e+00  4.5499e+00 -7.2917e-01 -2.3880e+00
+#&gt;  -1.2923e+01  1.5657e-01  4.4478e+00 -6.3199e+00 -3.0469e+00 -2.8037e+00
+#&gt;   8.3813e+00 -5.3089e+00 -4.8085e+00 -1.6525e+00  4.2677e+00 -2.8523e+00
+#&gt;   3.8522e+00 -7.8284e+00  7.1563e+00  1.7591e+01 -5.4518e-01 -1.3013e+01
+#&gt;  -2.0716e+00 -4.8086e+00  4.9970e+00  1.0184e+01  9.7747e+00  2.0513e+00
+#&gt;  -4.7721e+00  1.9632e+00  7.6009e+00  1.0404e+01  1.5373e+00  1.0064e+00
+#&gt;  -4.6576e+00 -9.8586e+00 -2.1044e+00  3.0080e+00 -3.4148e+00 -2.7283e+00
+#&gt;  -5.1921e-01 -4.3524e+00 -7.8099e+00  5.3708e+00 -4.4657e+00  8.4733e+00
+#&gt;   1.3220e+00  5.4338e+00  2.4668e+00 -2.7286e+00 -1.3265e+01 -8.2510e+00
+#&gt;  -3.3402e+00  1.4025e-01 -1.1569e+00  9.3844e+00 -6.8034e+00  1.1691e+00
+#&gt;   5.7934e-01  1.3726e+01  9.1059e+00 -4.1645e-01  1.2338e-01 -4.5460e-01
+#&gt;  -2.9531e+00  4.2259e+00  5.7111e+00  2.1363e+00  9.4939e+00 -5.9615e+00
+#&gt;   3.9652e+00 -2.2525e+00  2.5842e+00  7.1956e+00 -2.4090e+00 -5.7351e+00
+#&gt;  -1.6458e+01 -1.6657e-01  2.1476e+00 -7.4493e+00 -2.6677e+00  1.5434e+00
+#&gt;   5.9747e+00  1.4048e+00  4.2234e+00 -6.1436e+00 -3.5178e+00  6.7212e-01
+#&gt; 
+#&gt; Columns 7 to 12  6.4937e+00  1.5255e+00 -2.6174e+00  4.1989e+00  1.7143e+00  4.4914e-01
+#&gt;   9.2254e+00 -8.1909e+00 -8.5043e+00  3.4534e+00  1.3033e+00 -2.0266e+01
+#&gt;  -1.3503e+01 -2.4304e+00 -1.0212e+01 -1.1287e+01  8.7373e+00 -1.2957e+01
+#&gt;  -5.5402e+00  3.0597e+00  2.4944e+00  8.2812e-01 -1.5562e-01 -1.2708e-01
+#&gt;  -1.0826e+01  4.0194e+00  8.4735e+00 -9.6511e+00 -1.2049e+01  5.8889e+00
+#&gt;   1.3975e+01  1.0204e+01 -8.5457e+00  6.1153e+00  1.7690e+01 -8.2086e+00
+#&gt;  -1.0727e+00 -5.1974e+00 -4.0803e+00 -1.2308e+01 -2.4092e+00  8.5248e+00
+#&gt;   6.7251e+00 -3.3911e+00 -1.2863e+01 -4.8699e+00 -4.7742e+00  6.6995e-01
+#&gt;  -1.6866e+01 -6.9636e+00 -7.7978e+00 -1.3163e+01  1.3390e+00  3.1046e+00
+#&gt;   4.8430e+00 -1.0008e+01  3.5039e+00 -5.8474e+00  1.8435e+00  9.5515e+00
+#&gt;   1.4132e+01 -1.4396e+01  2.8810e+00  2.4206e+00 -4.8963e+00 -8.7100e+00
+#&gt;  -3.1799e+00 -3.6143e+00 -1.2477e+01  5.0919e+00 -1.8975e+01 -7.9723e+00
+#&gt;  -2.2462e+00 -4.7195e-01 -1.1273e+01 -6.9180e-01  2.9568e+00  7.2841e+00
+#&gt;   1.9165e+00  2.0633e+00 -7.4336e+00 -6.6721e+00 -5.2687e-01  1.1182e+01
+#&gt;   3.8574e+00 -2.5511e+00  2.8475e-01 -2.8109e+00  3.2679e+00 -9.2002e+00
+#&gt;  -5.4003e+00 -1.4693e+00 -4.2705e-01 -4.6539e-01  8.6899e+00 -4.1028e+00
+#&gt;  -2.3059e+00 -6.4408e+00  2.4362e+00  4.1849e+00 -4.1545e+00 -1.0140e+01
+#&gt;  -7.0114e+00  9.0500e-01 -6.6372e+00  1.4289e-01 -1.3913e+01 -8.6175e+00
+#&gt;  -2.1385e+00  7.1310e+00 -6.4398e+00 -4.6363e+00 -4.8200e+00 -5.8590e+00
+#&gt;   1.1300e+01 -6.0039e+00 -5.8085e+00 -9.1043e+00  2.4718e+00  1.1574e+01
+#&gt;   2.1517e+00  9.9852e-01  8.8147e+00 -8.0279e+00  1.1400e+00 -2.8017e+00
+#&gt;   3.9096e+00 -1.8214e+00  9.4604e+00 -5.9733e+00  1.2349e+01  8.4879e+00
+#&gt;  -2.8631e+00  3.8223e+00  3.6857e-01  5.3020e+00 -2.0941e+00  3.6934e+00
+#&gt;  -2.4222e-01 -9.1640e-02  6.8393e+00  3.0688e+00 -3.4666e+00  6.4445e+00
+#&gt;   2.3689e+00  4.8254e+00 -8.1025e+00 -9.1224e+00 -1.4370e+00 -3.5194e+00
+#&gt;  -1.7873e+01 -7.5773e+00 -3.3779e+00  5.3065e-01 -2.5650e+00 -1.9081e+00
+#&gt;   3.0292e+00 -9.5216e+00  8.8519e+00  8.2866e+00  2.9579e+00  9.7640e-01
+#&gt;   1.0347e+00 -2.8423e+00 -4.4655e+00 -4.7059e+00  4.2787e+00 -9.3091e+00
+#&gt;  -2.1331e+00 -4.7208e+00  3.7622e-01  8.2563e+00  1.1822e+01  8.3298e+00
+#&gt;   1.0366e+01  1.7794e+00  6.7672e+00  4.0003e+00 -1.1374e+01  1.2010e+01
+#&gt;  -1.0395e+00  5.3475e+00  4.3677e+00 -7.1196e+00  1.5527e+01  4.5863e+00
+#&gt;  -2.5943e+00  1.3862e+01 -1.3181e+01 -6.8664e+00 -7.1383e+00 -2.8625e+00
+#&gt;   8.1429e+00  3.7006e-01  1.8648e+01 -1.7464e+00 -7.7375e+00  1.7909e+00
+#&gt; 
+#&gt; Columns 13 to 18 -5.6074e+00 -3.4085e+00  7.3234e+00  4.4006e+00  2.6561e+00  5.5296e+00
+#&gt;  -4.2334e+00  3.4272e+00  7.4620e+00  9.8037e+00  3.7460e+00 -3.7765e+00
+#&gt;   1.7933e+00  8.1047e+00  1.3330e+00 -1.5064e+00 -4.2035e+00 -1.0850e+01
+#&gt;   2.6064e+00  1.2460e+01  6.2961e+00 -5.1986e+00  3.2517e+00 -1.3406e+00
+#&gt;  -2.6584e+00 -1.1686e+01 -2.5594e+00 -1.1187e+01  2.2860e+00 -1.0599e+01
+#&gt;  -7.2094e+00  1.3429e+01 -1.8517e+00  9.0273e+00 -3.4923e+00 -1.4232e+00
+#&gt;  -1.4405e+00 -8.0286e-01  9.0611e+00 -1.0482e+01 -7.6412e-01 -1.2579e+00
+#&gt;  -5.6488e+00 -4.4713e+00 -3.3972e+00  1.1581e+00 -5.3720e+00 -2.1390e+00
+#&gt;   1.6543e+00 -6.6934e+00 -1.6806e+00 -4.3857e+00  4.3596e-01 -4.8176e+00
+#&gt;   7.1809e+00 -1.0534e+01  1.2480e+01  1.8823e+00 -2.5779e+00 -1.0183e+01
+#&gt;  -1.1569e+01  6.5945e+00  7.0316e+00 -3.7626e+00 -9.0361e+00 -3.6413e+00
+#&gt;   1.0520e+01  3.3703e+00 -7.2867e-01  1.1533e+01  2.7200e+00  2.7042e+00
+#&gt;   5.0795e+00  3.8477e+00  5.7534e+00  5.5852e-01 -2.2018e+00  4.1012e+00
+#&gt;  -7.9938e-01 -2.1073e+01  7.6444e+00  9.3051e+00 -9.6337e-01  1.1523e+01
+#&gt;  -2.5777e+00  1.0652e+01 -3.3177e-01 -9.3445e+00  8.8366e+00  1.2526e+01
+#&gt;  -1.8051e+00  8.9351e+00 -7.4940e-01  2.2109e+00  3.5112e+00  6.2608e-01
+#&gt;   5.3026e+00  2.2802e+00 -1.5618e+00 -1.1538e+01 -1.6324e+01 -2.1797e-01
+#&gt;  -7.9441e-01 -1.9782e+00  2.2414e+00  1.0456e+01 -6.3631e+00 -1.1424e+01
+#&gt;  -2.1576e+01  2.4376e+00 -1.0401e+01  7.9473e+00  7.6992e+00  3.7250e+00
+#&gt;  -1.4687e+00  2.4617e+00 -8.8550e-01 -8.3288e+00  3.7216e+00 -2.8876e+00
+#&gt;   5.1585e+00 -4.0592e+00  2.2326e+00 -6.3612e+00  6.0288e+00  7.7542e-01
+#&gt;   5.1339e+00 -8.1341e+00 -8.6073e+00 -6.0124e+00  5.4194e+00  1.5091e+00
+#&gt;   5.1967e+00 -3.0833e+00  2.6684e+00  7.7853e+00  2.5172e+00  8.2850e-01
+#&gt;   3.7526e+00 -5.1123e+00  1.5545e-01  1.1436e+01 -1.3545e+00  1.4901e+01
+#&gt;  -8.7696e+00 -9.5002e+00 -1.1776e+01 -7.0670e+00  6.7120e+00  9.2688e+00
+#&gt;   8.0022e+00 -6.2738e+00 -3.2173e-01 -9.5505e-01  4.7932e+00  5.6999e+00
+#&gt;  -7.8021e+00 -6.1326e+00  7.7465e+00  7.6195e+00  8.4870e+00  1.3874e+00
+#&gt;  -7.3460e+00 -9.6441e+00  1.5864e+00  6.2023e+00  3.5650e+00 -7.2110e-01
+#&gt;  -3.2927e+00 -2.7632e+00  5.4078e+00 -7.6875e+00  4.6021e+00  7.5661e+00
+#&gt;  -1.2230e+01  2.1578e+00 -2.4639e+00 -3.6101e+00  2.1423e+00 -5.4368e+00
+#&gt;  -3.0876e+00 -5.9892e-01 -4.1137e+00 -3.3779e+00  1.2727e+01  1.0199e+01
+#&gt;   2.6479e+00 -8.2225e+00 -9.6021e+00  7.1308e+00 -3.5645e+00 -4.3502e+00
+#&gt;   1.8473e+00 -6.9284e+00  1.3093e+01  5.6988e+00  7.6180e+00 -8.3846e-01
+#&gt; 
+#&gt; Columns 19 to 24  7.3627e-01 -5.9731e+00  6.6536e+00  5.0267e+00 -3.4687e+00 -5.4246e+00
+#&gt;   5.9174e-01  1.1488e+00 -1.0466e+00  1.5471e+00 -2.7703e-01  1.7222e+01
+#&gt;   2.9076e-01 -2.9828e+00 -1.1044e+01 -4.9871e+00 -2.6339e-01  6.8426e+00
+#&gt;   1.6837e+00  1.6836e+00 -8.8908e-01  1.1217e+00 -9.1887e-02  9.2449e+00
+#&gt;  -3.5896e+00 -1.5933e-01  4.0711e+00 -1.1323e+01  3.7404e+00  1.1657e+01
+#&gt;   4.3868e+00 -3.5228e+00 -1.0065e+01  2.6119e+00  1.0244e+01 -2.4183e-01
+#&gt;  -2.1082e+00 -5.0763e+00 -4.7873e+00  1.2629e+00 -1.7532e+00  8.8094e+00
+#&gt;   1.8175e-01 -4.1472e+00  5.8678e+00 -7.3693e+00 -5.5440e+00  3.1073e+00
+#&gt;   4.5549e+00 -7.7966e-02  4.1041e-01  3.3153e+00 -1.2862e+01 -3.4978e+00
+#&gt;   4.3182e+00  1.3787e+01  9.4837e+00 -1.0485e+01  2.7736e+00 -5.2870e+00
+#&gt;  -4.6468e+00 -3.5307e+00  1.3198e+00  4.6244e+00  1.9277e+00  1.4982e+01
+#&gt;  -2.2590e+00 -3.7974e+00 -2.0574e+00 -5.1744e+00 -5.8982e+00  6.0544e+00
+#&gt;   7.7261e+00  4.5548e+00  3.1839e-01  2.2807e+00 -1.8303e+00 -1.0658e+01
+#&gt;  -2.1419e+00 -1.7195e+00  5.8053e+00  3.6411e+00 -3.6666e+00 -1.3808e+00
+#&gt;  -6.9503e+00 -1.5673e+00  2.3036e+00  1.2977e+01 -3.6986e+00 -4.0424e+00
+#&gt;  -7.9816e+00  5.0594e+00 -3.3944e+00 -1.1771e+01 -5.3699e+00  3.5985e+00
+#&gt;   2.1716e+00 -1.6266e+00  3.2117e+00 -5.0772e+00  6.2194e+00 -2.6357e+00
+#&gt;  -6.5571e+00  3.1025e+00  6.6873e-01 -1.7099e+01 -7.0586e+00  4.9848e+00
+#&gt;  -6.8468e+00  5.1036e+00  3.8933e-02  8.0019e+00 -3.4802e+00 -1.7174e+00
+#&gt;   6.3885e+00  2.1318e+00  3.9541e+00 -3.3173e+00  1.0848e+01 -5.0692e+00
+#&gt;   5.7441e+00 -1.0195e+00  4.1744e+00  4.5401e+00  1.6965e+00 -2.0420e+00
+#&gt;   7.0096e-02 -1.0259e+01 -4.7403e+00  7.3246e+00 -4.2524e-02 -9.8128e+00
+#&gt;   7.8280e+00  8.2722e-01 -6.3400e-01 -7.8921e+00  4.0342e+00 -6.3275e+00
+#&gt;  -4.8883e+00 -1.4970e-01 -6.8572e+00  7.5394e+00  1.5777e+00 -8.3016e+00
+#&gt;   1.1322e+01 -3.9290e+00  1.1352e+00  2.0000e+00  5.4427e-01  2.3393e+00
+#&gt;   3.7449e+00 -1.4576e+01  3.7942e+00 -2.8292e+00 -7.8410e-01 -1.9518e+00
+#&gt;   5.1286e+00  3.5064e+00  6.3571e+00 -1.1949e+01 -1.7064e+00 -1.3999e+01
+#&gt;  -6.0800e+00 -5.6762e+00 -3.2926e+00  2.5514e+00 -7.8099e+00 -1.9884e-02
+#&gt;  -3.1596e+00  6.6942e+00  1.2611e+01 -4.3601e+00 -2.3626e+01 -6.5944e+00
+#&gt;  -7.2940e+00 -1.0204e+01  5.5124e+00  8.1994e-01 -1.8847e-01 -2.6315e+00
+#&gt;  -1.8329e+00  2.2539e+00  2.2605e+00  6.7189e+00 -6.1848e-01  2.0646e+00
+#&gt;  -6.1003e-01 -7.5034e-01 -4.6136e+00 -7.3260e+00  6.4762e+00 -1.6502e+00
+#&gt;  -2.7058e+00 -5.2549e+00 -4.6255e-02 -2.0182e+00 -2.1587e+00  6.2375e+00
+#&gt; 
+#&gt; Columns 25 to 30  5.1233e+00  5.0286e+00  2.6590e+00  2.2515e+00  1.1992e+00  8.4146e+00
+#&gt;   9.4814e+00  6.4499e+00 -2.7137e-01  4.2821e-02  4.6112e+00  7.4243e-01
+#&gt;  -1.2391e+01  7.0394e+00 -1.0630e+01  2.4426e+00 -4.2618e+00 -4.7847e+00
+#&gt;   1.1533e+01  3.8799e+00 -3.3985e+00  2.8032e+00 -8.5456e+00  2.5490e+00
+#&gt;  -1.8559e+01  8.8160e+00 -5.9181e+00  2.3876e+00 -5.9590e+00  9.3294e+00
+#&gt;   3.2840e+00  1.7363e+00  6.7995e+00 -1.5756e+00  8.8201e+00 -7.6525e+00
+#&gt;  -8.3449e+00 -6.7816e+00  3.0615e+00  3.6525e+00 -2.1395e-01 -1.2624e+00
+#&gt;  -3.8154e+00 -1.3157e+01  3.4326e+00 -7.5243e+00  4.2768e+00 -4.5662e+00
+#&gt;  -7.4420e+00 -3.8675e+00 -2.1392e+00 -3.2964e+00 -1.2923e+01  1.7868e+00
+#&gt;  -2.2106e+00 -1.0292e+00 -4.8813e+00 -2.5015e+00  6.1327e+00  1.3463e+00
+#&gt;  -2.9320e+00  3.3000e+00 -4.1312e+00 -5.5715e+00  7.5949e+00  7.2804e-01
+#&gt;   7.7675e+00 -9.0562e+00  4.0172e+00 -7.4262e+00  7.2146e+00 -1.0835e+00
+#&gt;   1.9125e+00  1.8156e+00 -2.6505e+00  3.2869e+00 -6.9167e+00 -2.6447e+00
+#&gt;  -1.6817e+01 -1.4289e+01 -9.0178e-01  1.1828e+00 -1.0829e+00  7.4066e+00
+#&gt;   4.6046e+00 -2.0611e+00  1.0022e+01 -4.0054e+00 -8.3094e+00  3.4566e+00
+#&gt;   1.1643e+00  1.4705e+00 -7.2142e+00  1.3124e+00 -4.4851e-01 -4.0656e+00
+#&gt;  -1.1685e+00 -8.3800e+00 -7.2293e+00 -3.3617e+00  4.6644e+00 -7.2425e+00
+#&gt;  -2.5843e+00  5.1759e+00 -1.0891e+01  9.9750e-01  9.9458e-01 -2.1447e+00
+#&gt;  -1.0396e+01 -4.2850e+00  3.2901e+00  1.2966e-01  2.0225e+00  3.0206e+00
+#&gt;   8.9554e+00 -5.1237e+00  9.9581e-01 -7.2699e+00  4.8061e+00 -5.4607e+00
+#&gt;  -1.0005e+01  3.6013e+00  3.7541e-01 -3.4383e-01  1.2102e+00  1.4265e+00
+#&gt;  -7.7733e+00  8.2007e+00  4.5593e+00  3.0362e-01  2.4441e+00 -4.0336e+00
+#&gt;   9.1666e-01 -1.3503e+01  6.0334e+00  1.1291e+00  1.9338e+00  1.0270e+01
+#&gt;  -1.8784e+00 -5.1123e+00  5.5162e+00  2.7242e+00 -4.4748e-01 -6.8360e+00
+#&gt;  -7.8590e+00 -5.5001e+00 -1.4332e+00 -4.0083e+00 -5.0840e+00 -3.9921e+00
+#&gt;   4.6096e+00 -4.9101e+00 -5.2793e+00 -1.8341e+00 -2.1632e+00  1.3962e+00
+#&gt;  -2.6309e+00  1.2267e+01 -4.3781e+00 -5.8274e+00  4.7211e+00  1.3502e+01
+#&gt;  -6.5993e+00  3.8038e+00 -1.9661e+00  3.6923e+00  2.0397e+00  3.8195e+00
+#&gt;  -3.3981e+00 -2.2913e+00  5.1411e-01  7.9761e+00  9.0115e-01  8.9859e+00
+#&gt;   9.2428e-01 -1.1373e+01  1.0695e+01  2.6802e+00 -1.2878e+00  2.7543e+00
+#&gt;  -1.0323e+01  1.6291e+00  3.1996e+00  3.2008e+00  3.4868e+00 -2.1713e+00
+#&gt;   7.5225e+00  1.7948e+00 -5.1063e+00 -9.8844e+00 -4.4957e+00 -1.6431e+01
+#&gt;   7.8656e+00  8.3052e+00 -4.3064e+00  6.0204e+00  9.6694e-01  1.1735e+01
+#&gt; 
+#&gt; Columns 31 to 36  7.8981e+00  4.6716e-01  9.1192e+00  6.1724e+00  1.9819e+00  2.7730e+00
+#&gt;   6.2735e+00 -5.0336e+00  4.1666e+00  8.9138e+00  6.4594e+00  9.7013e+00
+#&gt;  -1.3202e+01 -1.7686e+01  8.6735e-01  3.1854e+00  5.6680e+00  4.7630e+00
+#&gt;  -2.8526e+00 -2.1498e+00 -6.8529e+00 -5.1817e+00  8.2548e+00 -1.2442e+01
+#&gt;  -5.0210e+00 -2.5293e+00  7.3916e+00 -1.9711e+01 -4.9559e+00 -6.0873e-01
+#&gt;   6.2391e+00 -9.9065e-01 -5.2801e+00  1.2112e+01  8.5207e+00 -5.7427e+00
+#&gt;  -6.3976e+00  9.8501e-01 -4.4478e+00  4.7107e+00  3.0327e+00 -8.8026e-01
+#&gt;  -6.0163e-01 -2.0219e-01 -1.4381e+01  1.1614e-01  3.0164e+00  7.1744e+00
+#&gt;  -5.7401e+00 -4.9621e+00  6.5895e+00  1.5225e+00  9.7609e-03  6.6442e-01
+#&gt;   2.7118e+00  7.8119e-02  1.5722e+00 -1.2200e+01  7.8679e+00  4.2014e-04
+#&gt;   5.3179e+00 -6.8347e+00 -2.4433e+00  9.0759e+00 -6.5207e+00  1.1099e+00
+#&gt;  -1.2456e+00 -1.3854e+00  1.2949e+01 -1.8100e+00  4.0563e+00  6.1291e+00
+#&gt;  -5.2673e+00 -3.3808e-01  3.4098e+00  2.9034e+00  6.0943e+00 -9.2761e+00
+#&gt;   5.7289e+00 -4.3777e+00  1.4237e+01 -8.2053e+00  1.9530e+01  7.8049e+00
+#&gt;   3.9308e+00 -5.6630e+00  4.6183e+00  6.5412e+00 -3.3655e+00 -6.8543e+00
+#&gt;  -6.8679e+00 -1.4019e+00 -2.9951e+00 -8.8762e+00  5.1456e-01 -3.5969e+00
+#&gt;  -1.0001e+01 -2.2497e+00  3.6360e+00  1.4877e+00  5.6955e+00 -7.8108e-01
+#&gt;   1.3087e+00 -6.4806e+00 -6.2973e+00 -1.8158e+01 -5.6897e+00  5.1906e+00
+#&gt;   1.4685e+01  1.5701e+01 -1.2001e+00 -1.2090e+00  2.7959e+00  1.0252e+00
+#&gt;  -9.5096e+00  8.3507e+00 -1.1105e+01  1.5248e+01  8.6121e+00 -1.1817e+01
+#&gt;   1.3106e+00  8.3270e+00  5.4541e+00 -2.1049e+00 -1.7916e+00  4.1066e+00
+#&gt;  -1.0683e+01 -4.3503e+00 -1.8285e+00  4.9063e+00 -6.9743e+00 -1.3843e+00
+#&gt;   6.3039e+00  1.6537e+00  5.3608e+00 -6.5532e+00  5.4682e+00  8.8575e+00
+#&gt;   2.9352e+00 -6.5402e+00  5.9713e+00  3.2167e-01 -1.3253e+00  4.7857e+00
+#&gt;  -7.1478e+00  1.0595e+01  1.4829e+01  1.0829e+01  4.1257e-01 -4.9931e+00
+#&gt;  -9.3587e-01 -8.5972e+00  9.1508e+00 -2.7947e+00  4.0810e+00  1.0099e+01
+#&gt;   1.0737e+01  2.1649e+00  6.8882e+00 -4.7508e+00  3.5292e-01  4.0821e+00
+#&gt;   1.1075e+00 -2.7300e+00  1.2028e+01  4.6867e+00  3.9253e+00  4.2040e+00
+#&gt;   7.6877e+00  3.6231e+00  1.1568e+01  1.2691e+01 -4.0414e+00  3.5469e-01
+#&gt;   2.5404e+00  9.3366e+00 -6.9511e+00  2.8369e+00 -1.4238e+01 -2.4976e+00
+#&gt;  -2.2425e-02  1.2984e+01  2.3252e+00  5.9544e+00  4.0155e+00 -1.5570e+00
+#&gt;  -8.6683e+00 -6.9898e+00 -2.5169e+00 -1.2687e+01 -1.1422e+00 -3.6780e-01
+#&gt;   1.1547e+00 -2.3473e+00  3.8827e-01  1.2182e+00 -1.4819e+01  3.1968e+00
+#&gt; 
+#&gt; Columns 37 to 42 -8.2986e-01  1.5808e+00 -8.5267e+00  8.2548e-01  6.7616e+00  4.5466e-01
+#&gt;  -5.4160e+00  4.2210e-02 -6.7870e+00  1.3731e+01 -1.4242e+00  2.8703e+00
+#&gt;   3.3299e+00  1.2675e+01  4.9779e+00  1.0628e+00 -4.1909e+00 -3.6972e+00
+#&gt;  -7.8707e+00  9.9748e+00  3.0096e+00 -7.6082e-01 -7.3238e+00  1.5020e+00
+#&gt;  -4.7687e+00  4.1794e+00  3.4387e-01 -6.1600e+00  2.0465e+01 -1.1869e+01
+#&gt;   4.9347e+00  1.9419e+01 -1.4836e+01 -2.6536e+00 -1.8038e+00  6.3195e+00
+#&gt;   2.6440e+00  4.4561e+00 -5.5239e+00 -3.5099e+00 -8.2062e-01  6.2203e+00
+#&gt;   1.2335e+01  7.1544e+00 -4.6199e+00 -8.6408e+00 -2.0380e+00  6.3335e+00
+#&gt;  -2.3501e+00 -1.4147e+00  5.5156e+00  8.5819e+00 -1.2062e+01  1.1047e+01
+#&gt;   4.9658e+00  1.5123e+00 -1.0143e+01  1.5395e+00 -1.1483e+00 -5.0319e-01
+#&gt;   9.1873e+00 -4.0729e+00 -4.2981e+00  4.9867e+00  5.9117e-01  1.2014e+00
+#&gt;   5.0660e-01 -4.0075e+00  2.7222e+00  3.5089e+00 -8.8519e+00 -1.6687e+01
+#&gt;  -2.1690e+00 -2.7477e+00 -8.3437e-02 -6.3983e+00  2.1571e+00 -6.2812e+00
+#&gt;   2.3533e+00  2.0906e+00 -1.2856e+01 -2.6172e+01  1.3576e+01  1.7861e+01
+#&gt;  -4.0977e+00 -1.2518e+01 -2.3761e+00  4.4950e+00 -5.2409e+00  5.0584e+00
+#&gt;  -9.2152e-01  6.5864e+00  7.8020e+00 -7.0217e+00  1.8967e+00 -1.4170e+01
+#&gt;   6.2868e+00 -3.7704e+00  4.5620e+00 -8.2222e+00  4.8203e+00 -2.4004e+00
+#&gt;  -3.6014e+00  6.6567e+00  1.9025e+00  5.1070e+00  3.6100e+00 -1.3140e+01
+#&gt;  -1.6723e+01 -3.2510e+00  3.4677e-01  4.1354e+00  9.5771e-01 -2.9183e+00
+#&gt;   3.4590e+00  2.0839e+00 -4.4422e+00 -6.4545e+00 -2.5781e+00  1.4163e+00
+#&gt;  -2.2038e+00 -3.9421e+00 -2.5150e+00  8.6248e+00 -1.0409e+01  1.0375e+00
+#&gt;   3.5168e+00 -6.7899e+00 -1.0509e+01  1.7630e+00  6.9609e-01  1.0334e+01
+#&gt;   1.7577e+00  1.4941e+00 -1.7930e+00 -3.1237e+00 -1.0071e+01  7.2025e+00
+#&gt;   6.0528e+00 -1.1943e+01  4.0831e+00 -6.3333e+00  1.2425e+01  3.2790e+00
+#&gt;  -3.0971e+00  3.1807e+00 -6.1176e+00 -9.5196e+00  4.3732e+00  1.8972e+00
+#&gt;   3.3534e+00  3.1717e-01  1.2562e+01 -2.0393e+00  1.7878e+00  3.5104e+00
+#&gt;   1.3278e+00 -1.2130e+01  6.3743e+00  5.0183e+00  1.5475e+00  3.2694e+00
+#&gt;  -2.0132e+00  1.8420e+00 -2.7093e+00 -1.2816e+00  1.2382e+01  8.1606e-01
+#&gt;  -8.1138e+00 -1.1665e+00 -6.9289e+00  9.1660e-01  5.5099e+00  4.4927e+00
+#&gt;  -7.5698e+00 -4.2022e+00 -2.4955e+00  1.3951e+00 -5.1773e+00 -4.4802e+00
+#&gt;  -2.1522e+00  4.0329e+00 -6.2254e+00  2.6406e+00  5.4958e+00  7.9409e+00
+#&gt;   8.9750e+00  7.5071e+00 -2.3948e+00  5.6136e+00 -6.3503e+00 -9.2532e+00
+#&gt;  -8.0334e-01 -7.1829e+00  3.7443e+00 -6.3808e-01  1.6234e+00  7.6806e-01
+#&gt; 
+#&gt; Columns 43 to 48 -1.5677e+00 -2.9915e+00 -2.7661e+00  3.5917e+00 -3.7266e-01 -1.7021e+00
+#&gt;  -7.0343e+00 -1.7440e+00 -1.0801e+00  3.7083e+00 -9.5518e+00  1.1644e+01
+#&gt;   1.4276e+00  4.6530e+00  4.7658e+00  4.9927e+00  9.4682e+00  1.1981e+01
+#&gt;   5.1168e+00 -1.3723e+00 -4.0565e+00  2.6561e+00  8.4619e+00  1.3204e+01
+#&gt;   1.7021e+00 -1.0051e+00 -3.5616e+00 -1.6672e+01  1.2281e+00  1.3375e+01
+#&gt;   1.9577e+00 -1.0872e+01 -3.0708e+00  8.6402e-01  5.1648e+00 -8.1927e+00
+#&gt;  -1.4588e+00 -3.2117e+00 -4.2848e+00  8.6730e+00  6.3471e+00  7.4469e+00
+#&gt;  -1.1409e+00 -3.9830e+00 -4.2742e+00  1.5370e+01 -2.0290e+00  3.3303e+00
+#&gt;   8.0693e-01  1.0572e+00  4.9300e+00  8.5671e+00 -1.6947e+01  1.8162e+01
+#&gt;   1.2017e+01 -1.8909e+00 -4.6078e-01 -5.2104e+00  1.0487e+01 -1.0465e+01
+#&gt;   1.1179e+00 -5.1893e+00  7.1756e-01  5.2863e+00  6.4620e+00  9.7821e-01
+#&gt;  -3.5414e+00  8.1094e+00  4.1925e+00  3.1710e+00 -5.1721e+00  5.7962e+00
+#&gt;   3.6400e+00  3.3028e+00  8.8489e+00 -4.8388e+00  1.1024e+01 -4.8040e+00
+#&gt;   2.9843e+00 -1.2802e+01 -1.2235e-02 -5.1402e+00  1.4085e+01 -8.2303e+00
+#&gt;  -4.5388e+00 -3.9963e+00  1.5188e+01  4.7872e+00 -3.0182e+00 -5.7262e+00
+#&gt;   7.1949e+00  5.2302e+00 -2.1220e+00 -7.4285e-01  3.7943e+00  6.3117e+00
+#&gt;  -9.9401e+00 -3.0102e+00  4.7872e+00 -2.3967e+00  2.4630e+01 -2.2904e+00
+#&gt;  -7.0986e+00  5.8050e+00 -2.6652e+00 -3.0579e+00 -2.7367e+00  7.1841e+00
+#&gt;   5.8417e+00 -1.6451e+00  5.4505e+00  5.0665e+00 -4.9857e+00  1.9282e+00
+#&gt;   6.6733e+00  6.7517e-01 -7.7643e+00  6.6629e+00  1.0348e+01 -1.3121e+01
+#&gt;  -3.1792e-02  2.0679e+00 -1.3601e+00  3.7340e+00 -5.3487e+00  5.0697e-01
+#&gt;   2.7926e+00 -3.8699e-01  6.6610e-01 -2.2246e+00 -3.5554e+00 -6.2905e+00
+#&gt;   4.7517e+00 -2.4412e+00  6.2003e+00 -1.6680e+00 -4.6255e-01 -1.2179e+01
+#&gt;  -2.3411e+00 -1.4320e+00  9.3129e+00 -5.4157e+00 -7.7290e+00 -5.5071e+00
+#&gt;  -4.1921e+00  1.9433e+00  1.1364e+01  8.3016e+00 -9.6522e+00  2.5074e+00
+#&gt;  -2.7066e+00 -9.0488e-01  4.5803e+00 -6.0346e+00  1.0435e+01  1.7717e+01
+#&gt;   4.0107e+00  5.0770e+00  6.2467e+00 -1.4272e+01  8.8144e+00  1.3842e+00
+#&gt;  -1.3449e+00  9.4606e-01 -1.6930e-01 -4.9765e-01 -5.1612e+00 -9.5667e-01
+#&gt;  -1.1142e+01  1.7190e+00  1.8346e+00  3.3680e+00 -1.6060e+01 -1.0255e+01
+#&gt;   3.6461e+00 -1.8809e+00 -2.5463e-02  2.9670e+00 -1.6904e+01 -6.1228e+00
+#&gt;  -3.0225e+00  1.1404e+00  3.5382e-03  3.9360e+00  1.8768e+00 -1.0632e-01
+#&gt;  -2.9910e+00  5.5240e+00  4.8275e+00 -1.0063e+01 -7.7438e-02  6.2757e+00
+#&gt;   8.0563e-01  1.0350e+01 -4.3406e+00 -4.0113e+00 -1.0990e+01  5.9592e+00
+#&gt; 
+#&gt; (17,.,.) = 
+#&gt;  Columns 1 to 8   1.3668  -3.4432   2.1390  -0.8427  -0.6320   1.9581   3.2341   1.9210
+#&gt;   -1.6544  -4.2853   0.9612  16.5762  -6.9679   0.1930  -3.5903   5.7247
+#&gt;    4.8006  -3.6107  -5.6760   8.4668 -16.0328  -7.7381   3.2784   2.5965
+#&gt;   -9.5673   2.8128   8.2458   1.0514  -0.7305  -5.2313   1.3575   6.6193
+#&gt;    6.6319  -5.2377  -0.6103  17.2627   9.5523   1.8141  -4.0966  -4.1852
+#&gt;   -3.9050 -12.3535  -0.4245  -2.6451 -14.1949   2.2731  -0.8757   8.6501
+#&gt;    4.0817  -4.6800   0.9710  -4.5444  -7.0173  -6.4987  -4.5614  -3.2444
+#&gt;    2.3754  -0.3210 -15.1469  -2.6496  -7.3515  -0.4677   1.9387   6.8919
+#&gt;    9.4497   3.2428  -9.6117   7.6118   1.2019  -5.9788   4.4539  -4.1950
+#&gt;   -0.5779  -2.4925   6.2938 -10.5802   3.1000   1.9519  -0.6499   2.8393
+#&gt;    0.5083  -1.2085   7.2838   7.6720 -15.7038  11.5571 -11.4855   9.2948
+#&gt;    8.7573  -5.7349  -1.5443  -4.0924  -0.9508   9.0159 -11.9210   6.9827
+#&gt;  -10.6537   2.2223  -5.6679  -6.0914   4.2831   1.0994   6.9468  -5.2568
+#&gt;   15.4910  -3.0078  -8.6448  -1.9103   2.2530  -2.6212  -1.8769  11.0320
+#&gt;   -0.7046  -0.8399 -10.9215   0.8463  -0.5661  10.0800  -0.9215   8.4464
+#&gt;    0.2006   3.3116 -15.5019  14.4619 -10.9747   4.4037  -4.4251   1.1613
+#&gt;  -12.2859   1.1584   7.0685  -7.0402   1.7751   7.7504   5.1132   2.3032
+#&gt;   -5.8107   2.9755   7.9711   8.7593  -3.4794  -5.4817  -2.0242   1.0860
+#&gt;    0.0560  -7.8120   4.4276   9.2264   1.7535  -7.2687 -11.2472  -2.2641
+#&gt;   -2.9812  10.3210  -3.2653 -15.6866  11.8189  -5.6440   5.6897  -4.3984
+#&gt;   -0.4527  -5.7626  -2.7274   1.7819   0.4515   1.4686   3.9425   2.5368
+#&gt;   -2.1137  -4.9138  -9.7005  -1.9540  -7.2742   5.7914   0.2371  -5.8429
+#&gt;   10.5286   0.8848   3.7341  -9.4200   4.7866  -0.8210  -2.6872  -4.3080
+#&gt;   -3.1698  -2.3465  -4.7972  -5.3810   0.8969  10.1857 -10.0679   2.6018
+#&gt;    7.2200  -5.9711  -9.7702   6.4634   2.4296  12.0672   1.9609 -10.3035
+#&gt;   -0.2531   7.8131  13.8678   0.3494   9.8688  -4.1610  -0.9882  -3.0283
+#&gt;  -13.5478  -5.8178  20.0050  -2.9637   1.9540  -0.5726  -0.7745 -10.1655
+#&gt;    3.4642   0.3604 -10.5769   6.6056  -3.8506  -1.7982  -6.6384  -8.2130
+#&gt;   11.2080  -1.5003 -26.1303  -9.7587  18.6554   4.8752  11.6313 -15.9210
+#&gt;    8.0520   0.6630  -4.3392  -2.7406  -0.6999  -2.6470  -4.0481   0.6635
+#&gt;    2.3905  -3.1174  -4.4570  -0.2210   1.7340  -6.2630  -3.1480  -2.8472
+#&gt;   -5.3666  -2.1554   3.5044   4.3067  -5.7222   6.9594  -5.5928   4.7046
+#&gt;   -2.3082  11.0496   1.2639   4.8452  -7.6340  -2.5461   0.2318 -10.4146
+#&gt; 
+#&gt; Columns 9 to 16   2.2131  -3.1289  -3.2092  -4.9386  -7.1049   0.2535   2.3358  -0.5907
+#&gt;    3.7709  13.5684   4.0799  -3.6861   3.2552  -1.5032 -12.4103  -7.7534
+#&gt;    4.7420   9.0173   1.7441   8.1694   2.2653  -3.1814  -6.5683  -1.1406
+#&gt;    1.6285  18.5031   2.6744  -1.6099   7.8342  -3.9538 -12.6472  -4.5839
+#&gt;   -0.3643   4.3094   2.8272  12.1150  -0.1179  -1.3630  -5.0676  -3.3576
+#&gt;   11.7261  14.8062   6.6564  -3.1429  -6.8643  -6.9250  -6.3080   1.7487
+#&gt;   -3.5739   8.8945  -2.3407  -1.1460  -1.9256  -0.0228   0.1383  -1.1746
+#&gt;    1.9408  -6.4063  -7.9212  -4.8813  -6.5500   7.1861  22.5862  -8.1295
+#&gt;  -14.4637   5.0205 -10.4692   5.7980   9.8879  -5.4975  -1.8670   1.2577
+#&gt;  -13.7285  -5.8090   3.1237  -4.0750  11.1732  11.5277 -10.1997   5.1817
+#&gt;    7.6963  -4.0519   5.8397  -1.9703   2.7619  -0.0227   1.1363  -6.3590
+#&gt;    3.1155  -2.2087   2.2321  -4.7861 -10.0136 -11.1697   7.4551 -12.0435
+#&gt;   -2.3962  -0.5803  -7.3797   3.8779  -3.3768   4.3138  -6.5188   8.7066
+#&gt;   -5.1084  -2.8763  -3.0044  -9.3940   0.4919   9.7544   1.0481  -0.0822
+#&gt;   -3.5620  -6.6338  -2.9303 -13.2935  -3.6175  13.6802  -2.6698   2.0818
+#&gt;    5.0874  -6.6636   5.0258   0.0068  -4.4974  10.3106   4.4157  -9.1281
+#&gt;   -0.5039  -9.0866  -7.8792   8.3116   3.1921   4.4632   0.7874   4.4261
+#&gt;    0.0949  -4.5344   1.1556   4.1645   0.8011   4.6032  -4.3907  -7.9854
+#&gt;   -2.7583 -13.8017   3.0664 -15.8004   6.5540   0.2059   6.4565  -4.6958
+#&gt;    2.7841   9.0344   6.6813   2.9996   5.0517  -1.1102  -1.6973  -0.8198
+#&gt;   -4.6734  -0.0891  -1.6388  -1.2797  -7.5500  -1.7393  -4.1495  -6.5000
+#&gt;   -1.9580  -0.0299  -6.7465   1.5278   7.1508   5.4712   2.8490  15.1900
+#&gt;   -5.7016  -4.6485  -2.2517  -8.5704  -4.5748   2.7904  -6.5105  11.1943
+#&gt;    3.4303  -8.1898  -0.5538  -4.7813  -2.6399  -2.9381   1.3538   8.3747
+#&gt;   -4.9123   6.4044  -4.0448  16.7914   1.0910  -4.7631   7.2308   4.9348
+#&gt;    3.3073   7.5156  -6.2439   3.1141 -13.5495  -8.9150  -4.9400  -2.1943
+#&gt;  -11.8695  -5.6923  -5.2384  -5.4031   7.6025   0.0664 -11.8833  -1.6594
+#&gt;   -0.1889   0.8857  -4.0587   2.2487   2.3278  -1.4802  -3.5833   3.3591
+#&gt;  -11.8833 -15.0646 -14.1268 -11.4514  -6.5296  10.8089  -1.2067   9.8783
+#&gt;   -0.4446 -13.2851   2.8717  -7.3882  -1.3503  12.6771  14.5548  -1.2611
+#&gt;   -1.6082  -1.7365  -1.3400  -7.8448  -1.3372  -0.4710   3.3958  -2.6139
+#&gt;    6.7788   3.9052   6.4834   9.8326  -8.8153   7.2564   5.8019   6.6102
+#&gt;   11.6655   7.3165   3.8755  17.4124  -4.0078   0.3736  -7.9917  -3.5397
+#&gt; 
+#&gt; Columns 17 to 24   3.5885   0.5112   4.1919   2.2928  -3.4592  -3.3438  -2.7507  10.1935
+#&gt;  -14.8107  -8.5995  -3.2987  -3.8913  11.1210 -11.2294  -5.3465  -0.9420
+#&gt;   -0.6395   1.4731   0.4671   1.7614   9.3827  -6.4671  -1.4682   2.0018
+#&gt;   -4.4671  -3.5339  -8.8812   4.4848   2.2799  -5.7539  -8.1888 -18.6924
+#&gt;   -3.6165  12.6336  -1.9317  -6.8450  15.0244   0.7580  11.5272   0.8536
+#&gt;    3.0117 -10.5515  -6.4697  -5.6312  12.4682   3.6703  -1.9525  -1.4758
+#&gt;    8.9683  -0.7668   1.5552  -3.6024  -0.3375   4.8706  -1.6415  -1.8996
+#&gt;   10.5285  -6.9695   9.4006  -1.6278  -1.7209  10.2509   3.6054  -2.3565
+#&gt;   -4.3185  -3.4627   1.3199   6.1191   0.4631  -6.6461  -3.7601  -8.1898
+#&gt;    4.3494   7.4639  -1.3335  -7.7843   7.8536 -13.4332  -5.3972  -0.3448
+#&gt;    9.0859   4.2671  -2.2402   1.1676   2.1955  -2.0298  17.5266  -5.1779
+#&gt;   -8.4949  -0.0489  -2.5038 -19.0814  -3.1472  -2.3482   0.5061  -0.1879
+#&gt;  -17.4451   1.1065  -9.1180   4.0942  -2.8670 -17.7986  -1.4602  -6.7647
+#&gt;   -9.2190   8.2250  -4.6333   2.7758  -7.1850  -5.8412   3.8636 -15.6275
+#&gt;  -14.7662  -8.8694  -4.6928   2.5367  -3.3641  -1.6041   0.2557 -16.6916
+#&gt;    0.6874  11.9075   5.7605  -2.5003  -8.5480   0.0249  -2.7186  -0.3809
+#&gt;    0.1377   2.5164   1.2167   2.3743 -13.1255   0.0631   6.6241  -2.3395
+#&gt;   -5.2050   1.7445  -6.4421  -2.0980   9.4729  -4.7849  -0.4018   2.3524
+#&gt;   -2.1437  -1.2305  -5.5264   1.8921   8.4241  10.7543   6.0593   4.3951
+#&gt;    7.3050   4.1177   1.4402  -7.1866  -7.6545  -3.9801   1.0020  -6.5904
+#&gt;    1.6969  -6.4428  -1.2667  -6.1963   1.3801   8.1921   1.4278  -2.9235
+#&gt;   -1.5682  -1.5774  -0.2605   7.9931   7.5534  -5.9387   2.5593   1.1506
+#&gt;   -5.8831   1.1837   1.8428   1.3193   3.3813  -6.9804   5.6135   0.7021
+#&gt;   -5.1809 -13.6561  -9.6489  -0.9701   4.7968  11.5547  -2.0378  -1.0602
+#&gt;   -8.4414  -3.6153   2.1264  -5.8963 -14.0751  -4.3650   8.6629  -0.1972
+#&gt;    1.7479  -0.8118   2.8183   6.3006   3.6819  -3.7023  -1.8702  -9.9025
+#&gt;    9.1341   0.4909  -3.8819   5.3517   0.1662  -3.4185   4.7450  -4.2207
+#&gt;   -7.9886   3.8488   4.5116   1.5594   4.2561  -0.6114   0.5441   9.0807
+#&gt;    0.4632  10.6365   7.8597  -5.6214  -9.5630   0.8920  -1.9616  18.4082
+#&gt;    1.9194   0.7698  -1.1071   1.7970  -1.2869  -0.3217  15.0823  -0.5134
+#&gt;   -1.1442   1.0544   8.4564   0.9455  -6.0304   3.1597  -7.5331  -2.5951
+#&gt;   -4.9870  -1.7763 -13.3783  -2.8915   9.3471 -12.7830   1.9093  -7.5257
+#&gt;    0.5892   0.2067   2.5230  -1.9130  -4.9706   1.5910  -1.7690   6.1105
+#&gt; 
+#&gt; Columns 25 to 32  -2.0029   3.9320   3.4067   5.3786   0.5396   3.2219  -7.7468  -4.7151
+#&gt;    0.2340  -7.7960  -0.9949   2.4682  -5.4832   1.0483  19.7407  -0.3670
+#&gt;    8.2586 -26.3016  -1.1887  -0.8150   3.0739   5.2194   6.4329  10.5169
+#&gt;    6.7984   5.2923 -11.1026   3.6038  16.4585  16.3690 -10.6448  -2.7222
+#&gt;    1.0594   0.1640  -4.0368 -17.2720   0.6828   0.8792  -4.4849   8.2630
+#&gt;   13.6407 -14.7086 -10.7549   8.5309  25.9234   1.2776  15.5606   8.0957
+#&gt;    2.6937   7.4711 -10.7229  -1.7906   5.5418   3.0545 -11.5271   6.0893
+#&gt;    7.4223  -8.4966  -2.2960  -4.8587   1.0670  -9.9737 -12.3587  -1.9898
+#&gt;   -5.3290  -0.9011   3.1267  -0.6015 -18.7713  15.6686  -0.6180   6.3267
+#&gt;   -1.1610   9.2603   3.2946   7.5929  -1.7813  -3.0358  -6.0294  -4.0077
+#&gt;    3.9938  -1.9535  -7.3432   2.1561  -3.5667 -10.5269  -0.0138   9.9390
+#&gt;  -12.0993  -0.7934   3.2908  -0.6219  -9.9757   6.9532  -3.9538   9.9249
+#&gt;   -0.3967   8.4286  -2.4337  -3.7470  11.7568  -5.6643   2.4034 -15.0496
+#&gt;    0.8066  -0.6670  16.3712  -1.1119  -6.3982  -0.2643   2.2696  -0.5712
+#&gt;    8.1521  11.5330  -4.9830  -4.5086   6.1124 -15.1762  -4.2291  -3.7102
+#&gt;   10.3675  -1.9513  -1.2383  -2.5966  11.4348  -1.6052 -13.1066 -14.3413
+#&gt;    7.5306  -6.6635   1.5598  -8.7047   1.1178  -9.2202  -2.9110  -3.3539
+#&gt;   -7.3380 -14.6635   0.9139  -6.4532  -3.7371 -10.3109   4.2459  -3.9466
+#&gt;    8.4953  12.9968 -17.9691  -6.0439  -1.3250  -4.7113   4.7177  13.5382
+#&gt;    3.7967   3.7300  -7.8984   3.7023   6.1214  -2.7543   8.7444   0.5483
+#&gt;    4.0348   0.0959   8.1114  -6.1435  -0.4961   8.7812   9.5133   9.0300
+#&gt;   -2.0379  -4.1883   4.7867   9.5205  -4.9854  -2.4336   2.5777  -4.4673
+#&gt;   -5.3450   3.8609  11.8456   6.9889  -4.2670  -7.3262   2.8602   2.9715
+#&gt;  -15.0469   6.1461   7.7922  -5.8133  -4.5396   1.1051  -3.7984   6.4299
+#&gt;   -3.2903  -9.3937   5.7421  -8.5343  -4.2489   0.9252  10.1617   5.1430
+#&gt;   -2.5213  -5.9571  12.3115  -1.6832  -0.2978   4.6345  -9.3846  -3.4147
+#&gt;    3.4115  16.7532   8.9324   5.0320  -8.1629  -1.3587  -6.7701  -2.1715
+#&gt;   -0.4575  -3.3817   3.9605   1.6008  -8.0968   4.3161   9.5901  -0.0280
+#&gt;    0.9238  27.4941   7.9045   1.6047  -5.1932  -5.2457  -4.6604 -23.6828
+#&gt;   -8.9074   9.7269 -11.1725   1.8938   0.9953  -4.0623   1.6491   2.7527
+#&gt;   13.1886  10.0342   1.2653   5.6036  10.1693   7.6246   0.1324  -5.8280
+#&gt;   -8.2246 -10.7013  -2.3507 -10.6499   9.7994 -16.3100   0.8994  -1.2278
+#&gt;   -5.7152   3.3416   9.0670  -2.8194   0.2029  14.9150   5.5999   7.0084
+#&gt; 
+#&gt; Columns 33 to 40  -6.8262  -1.1841  -1.4982  -1.3496   2.7896   0.1497  -1.9016   0.0235
+#&gt;   13.9558   6.3541   5.1794  -5.9051 -10.0455  11.4800   4.4661  -4.3158
+#&gt;   -3.8938  -5.5247   2.4443   6.2150   6.8373   7.9397  11.8457   0.3093
+#&gt;    6.4177   4.4580   9.2110   1.5865   5.1901   0.0157  -5.6327  13.2105
+#&gt;    6.8356 -10.1816  -2.8213  17.2493  -4.7139  13.5832   4.4130  23.2731
+#&gt;   -6.6588  -1.2380   8.0038   9.1993   5.0634 -16.7633  17.0086  -8.2435
+#&gt;    4.8066  -8.2511  -6.9907  10.9079  12.0255   0.1532   1.3351   8.3415
+#&gt;    1.2216   0.9603   1.6721   9.9872  -6.9341 -13.8973   3.4003   7.8173
+#&gt;   -1.1035  -7.6217  -4.5250 -10.1933  -2.7159   9.6300 -15.2344   7.5073
+#&gt;  -12.8378 -17.2390   0.4289  -6.9769 -13.0214 -11.8335   9.0580 -12.5285
+#&gt;   11.3366  -4.5438  -9.8580   3.4395   9.6712   1.6224  16.4277  -1.1459
+#&gt;    9.4785   9.2577  -5.2641  -0.5703  -2.3712  -6.0868 -22.6391  -9.3431
+#&gt;   -2.2929   1.5627  -4.2094  -6.9983  -1.2377  -2.2779 -14.3282   5.1031
+#&gt;    4.3634 -13.2845   2.8837   1.7756  -8.9107 -11.8874  -0.1907  17.6609
+#&gt;   17.4933   2.8129  -3.1234  -4.1698  -8.5263  -1.2697  -4.4828  -7.6263
+#&gt;    5.4847   3.4026  -0.1521   4.0699  -1.6453  -9.1976   5.3217   6.8270
+#&gt;   -1.1283  -4.7820  -0.6132  -3.5993  15.5907   0.4372   6.5704   0.1422
+#&gt;    1.7675  12.4804  13.3193   6.0499  -6.6079   3.7158   2.7083   3.6558
+#&gt;   15.0732   1.3094  -2.7776   1.4411   0.2781   8.4353   0.7465  10.3257
+#&gt;   -4.4394   8.8678  -4.8606  -5.7965   8.1754 -10.7260  -6.3006  -6.9240
+#&gt;    5.0459  -1.7674  10.6367   3.9742  -4.9796   7.1816   6.7132 -10.6704
+#&gt;   -6.2376   0.8917  13.0906  -2.3837   3.1197  -3.0004   2.5718  -4.6050
+#&gt;   -5.1556  -3.3456   1.8443  -5.9739 -10.6337   0.5083   2.3102  -8.9235
+#&gt;   10.2780   0.9049  -8.4920 -10.7988 -12.1641 -11.7005  -7.6098   3.2160
+#&gt;    3.6577   6.1075  -8.5969  -3.4325   3.5764 -12.4381 -19.4353  -1.1953
+#&gt;    0.7634  -4.8556  -8.9701  -9.6647   6.7192  -2.7212 -15.3708  21.7324
+#&gt;   -1.4057  -2.3539   4.2881 -11.4797  -8.8430  12.6676   1.0928  12.2919
+#&gt;    8.5077   0.4143   1.1196   0.0862   0.1472   2.7632   3.6282  12.2199
+#&gt;   -5.0323   2.9259  -7.1431  -4.1458  -7.3915  -8.6137  -5.6548   9.9677
+#&gt;    0.0409   8.6625  -3.9516   9.0051   7.6905  -4.4558  -9.8734  -1.3521
+#&gt;   -4.4540  -3.8072   4.0657   8.3726   5.7265  -6.1096  11.3597   4.5525
+#&gt;    2.6306  10.6202  -1.5673 -18.5260 -11.5257 -16.5503 -16.8518 -12.0477
+#&gt;    8.2940  10.5399   8.6244  -3.6574  12.9791   6.0812   6.7372  -1.1974
+#&gt; 
+#&gt; Columns 41 to 48  -4.3020  10.8055   4.6957  -4.9985  -6.2372  -2.7128  -8.4595  -7.3967
+#&gt;    5.1753 -12.4423 -10.8705  -3.6379   3.7710  -2.2297  -4.7982  -3.3469
+#&gt;   -7.6991 -13.7726  -3.3563  -5.0511   5.6848  16.8829   1.6898  -5.4550
+#&gt;   -7.1857  -8.5628   1.1988  -6.4742  -9.5060   3.8144   4.4995  28.3708
+#&gt;  -11.5917  -2.3357   0.6637   1.2002   0.7196  11.2064  -6.4569  -0.7988
+#&gt;   -6.6003  -4.5986  -5.1728  -1.9692  10.9260   0.4085  -5.4126 -22.6286
+#&gt;   -9.0757  10.3513   2.2290 -13.7965   3.6469  10.4488   8.3148   3.1308
+#&gt;   -3.1757   8.3759  -3.7542   2.8308   0.0075   1.7349   5.1876 -16.4447
+#&gt;    7.5323 -11.7255   5.3699   5.7143  -3.5338  -4.6624   1.6409  19.3834
+#&gt;   11.9529  -7.1491   2.4550   3.3643  -1.3929  -3.6870  -6.2465   2.1597
+#&gt;   -1.0344  -1.1941  -8.5014   3.1560  -3.3007   3.9206  15.1769 -11.7456
+#&gt;   -0.6963  -7.1328 -13.3182  -4.9291   2.4142  -2.7669 -10.8955  -5.4913
+#&gt;   -8.6100  -2.2302  -3.3250  -6.1107  -7.4750   3.7574   4.4175  13.1750
+#&gt;   -7.6901  -1.3427  25.0766  10.5373 -16.1542   3.2744 -17.0334  -8.1871
+#&gt;    1.1219  -3.7225  -2.0549 -13.2678  -4.5581   9.1610   2.4108   3.5842
+#&gt;  -11.2810  -1.9513  -0.7774  -6.2395   1.2964  13.9970   6.0814  -6.6790
+#&gt;  -10.3570  -5.9657  -1.0659  -8.6927 -18.1257   7.0295   8.2417   0.1360
+#&gt;  -12.1424  -7.3138 -10.4336  -2.6660   1.9307   7.5141  -2.1322   4.9691
+#&gt;    3.9055   9.3442 -18.5435   8.3435   8.2109  -4.1803   8.2996   4.8317
+#&gt;    9.6217   4.7113   1.9322   5.2811   3.3229  -4.8833   5.3930  10.3162
+#&gt;    0.5451  -7.2596  -3.4627  -1.3435   2.4908   1.4579  -7.3328  -6.2187
+#&gt;   17.7945   5.2902   9.3749   3.9251   7.0861 -10.4601  -0.5091   0.1525
+#&gt;    7.6007  -7.6981   3.6208  10.9601   5.4518  -1.5241 -16.4945   2.8296
+#&gt;    6.1518   1.8870   3.0516   1.0733  -4.8044  -8.6038  -2.2350  -3.6789
+#&gt;   -3.5250   5.8319   3.1770  -7.1390   2.8663   2.1237   2.9298  -4.8953
+#&gt;   -5.6806  -8.7227   5.6269   9.6688 -17.3416   6.3317  -6.7240  16.0170
+#&gt;   11.7895  -1.7393  -6.7264   0.7712 -17.2032   2.6500   2.9119  10.6580
+#&gt;    0.0527  -4.1213  -3.3495   2.4200   3.5814   2.3895  -4.0955  -0.2074
+#&gt;    0.5995  10.2260   3.0368  -5.2734 -13.2920  -8.8198 -12.0334   5.3303
+#&gt;    3.9332  12.1409  -4.3906   8.9993   1.0733  -6.0257  -0.2475  -5.4129
+#&gt;    0.8669   7.1621   1.8395  -9.9322  -2.5338  -0.8740  10.4499   2.6948
+#&gt;   -8.6909   5.1841   6.7866 -10.0677   6.1586   0.6929   1.9280   0.6756
+#&gt;    2.9781   3.8300   7.3013  -4.3447   1.3425   3.6420  -0.7538  -0.1901
+#&gt; 
+#&gt; (18,.,.) = 
+#&gt;  Columns 1 to 8   1.7693  -3.7437   0.4488  -1.4994  -6.6695  -3.2654  -3.9207   2.9121
+#&gt;    0.5893   6.8927  -0.7710 -13.0229   8.1895 -10.8628  -6.4562  -4.8553
+#&gt;   -1.3457  -6.7509  -0.4216  -6.1695   0.2155  -2.9639   1.1536  -4.4958
+#&gt;   -1.5678  20.3629  -7.6743  -4.7557   0.9429  -1.0841  -9.0395   1.3211
+#&gt;    4.8079  -1.0804  -0.2974 -13.3860  11.4514   8.6793   0.2175  14.0844
+#&gt;    6.3678  17.7403   2.4537  -3.2665   1.9183  -0.6195   2.5128 -15.3417
+#&gt;   14.1902  -1.5669  -0.8139   5.5219   1.6574  -1.2610  -2.1059   8.0694
+#&gt;    0.8491   4.1303   1.2346   4.2384   0.8954   1.8104  -8.1027  -2.6615
+#&gt;   -6.7503  -4.8570  -3.6724   0.1626  -3.2078  -9.2938  -9.8602   9.7555
+#&gt;   -4.3962  -8.7855   1.8805  -5.5878  -0.4761  -0.3574  -7.1436  -0.9219
+#&gt;   -4.0086  -1.0663  -6.3442  -0.5711  -1.4093   6.6411  18.4049   0.7341
+#&gt;    6.9307  -7.8223  -2.9164   3.7998  -4.4687 -10.0486  -9.5851  -7.9319
+#&gt;  -10.7289   2.7492   0.2853   1.7634   2.3138  -4.1133  -3.7710  -9.5835
+#&gt;   -3.4323   7.3113   4.7476  -1.9059 -17.2983  -0.7695   0.6150 -11.2911
+#&gt;   -1.9111  16.1997   3.4382   4.2202   3.9772  -5.0819   3.3507  -9.0817
+#&gt;   -5.7460  13.8628   1.4023 -13.6941   0.3724  -7.4148  -4.1202  -5.4349
+#&gt;   -9.6823  -4.2764 -18.3603   3.1510  -6.9110  15.3406  14.7010  -0.0527
+#&gt;   -3.3143  -4.1826  -7.3979 -13.9564   9.1485   3.6606  -2.2703  -8.8997
+#&gt;    8.4892  -0.2705   5.2918   4.8499  15.7377  -8.8092  14.7740   0.8690
+#&gt;   -2.7041  -0.9629   7.5889   0.2518 -12.1988   3.1537  -7.3136   3.2532
+#&gt;    1.2082  -2.5927   5.9001   3.5160  13.2167   4.2710  -8.6033   4.0416
+#&gt;    0.0327   2.2226   9.3338  11.1842   0.7902  -3.5978   0.3853  -2.3400
+#&gt;   -1.6999  -2.9742   5.7235   3.3782  -4.6991  -8.1012  -1.1316  -6.7495
+#&gt;    5.0286   3.9535   1.8890   9.1390  -0.0810   0.5031  10.0741 -10.5023
+#&gt;   -0.0041   1.3454   4.8857   0.1407  -3.1608  -2.2099  -2.6475   2.0598
+#&gt;   -7.0508  -4.2499  -9.8425   2.6331 -23.7175  -7.0619  -1.0543  10.4615
+#&gt;   -8.6743   0.2593  -0.1853  -2.3228 -10.3654   1.1706   8.3139  -0.9085
+#&gt;    3.8526   6.5251   5.5954 -10.5018  -5.3175  -3.6696  -5.7914  -4.6401
+#&gt;   -3.8558  10.8271  13.5630 -15.7800 -11.1479  -4.1164  -8.8653   6.4761
+#&gt;   -1.4505  -1.8530   7.7855   6.5016   0.1126   2.2911   3.1066   6.9591
+#&gt;    9.3867   8.2427   9.7405  -0.5214  -1.4383  -6.1565  -6.5163  -3.8440
+#&gt;    4.4435   1.3288 -13.7300  -1.5707   7.2329  -8.3706   5.4045  -5.1003
+#&gt;    6.2303   7.8291   3.5604   1.0735  -2.4621   8.0137 -10.1479   1.3674
+#&gt; 
+#&gt; Columns 9 to 16   1.7708   4.0457  -6.1123 -12.4369   1.5701   0.7788   1.5079  -4.0221
+#&gt;   15.0378   4.7770  -5.8520  -2.1910   8.2019   1.4738   5.3760   7.8546
+#&gt;   -6.1831  -1.8318   3.5178  -9.4994   8.1199   5.7272   2.8079  -6.8835
+#&gt;   -3.7350  -8.6199   5.4259   6.5652   4.2800 -11.8189  -4.6143   7.2360
+#&gt;  -10.7553   7.5053   7.4874  -5.7983   0.4043   0.8089   9.4067 -13.1004
+#&gt;    6.1953   4.4338  17.3723  -5.9549   1.7684   5.3632 -10.3271  -4.7114
+#&gt;   -9.9337  -6.1133   3.9953   1.3035  -4.7276  10.3715  13.9013  -7.9286
+#&gt;   -0.9040  -5.6797   5.1721  -3.8479  -4.4299  -2.9723   3.6749   3.6444
+#&gt;   -1.1017  -2.9652  -5.8445   3.9218   6.0071  -8.6968   6.5589  -1.1590
+#&gt;   -5.7768   4.6566  -0.1643  -4.3213  -4.9812   2.4639  -0.9566   7.4473
+#&gt;    4.8310  -4.1685  -8.4279   7.0530 -10.2627  15.2408   8.5978   6.6599
+#&gt;   15.0967  -7.4296  -6.2416   3.8482   2.8075   1.9646   8.2397   0.2924
+#&gt;   -5.9098   7.6005  -9.1337   4.8852   3.4711   1.1408   1.2984   7.4806
+#&gt;   10.6830   5.2838   7.3942  -5.7493  -9.6578   8.3334   1.8883  -3.4544
+#&gt;    8.0284  -3.2153  -4.6798   7.0326   2.2160   5.2296   8.3419  13.1458
+#&gt;    6.0632  -8.7457  -0.8001   4.1799  -1.1196  -2.1648  -2.0590   3.7524
+#&gt;   -5.6592  -7.7796  -6.0401  -3.0907  -4.5600   6.7824  -0.2442   7.5469
+#&gt;    1.3968  -0.8609   9.0593   2.6139   6.8111  -7.5359   1.6450   2.8085
+#&gt;    3.4499  -6.6892  -1.5266   9.7360   5.1715  12.3020  -0.3969   4.7609
+#&gt;    2.1820   6.0696  -8.8311  -0.4778   5.3647  -1.5951  -6.1490   6.5399
+#&gt;   -3.1861   0.5121   6.1372  -4.3520   6.2971   3.1386  -6.5192   0.6372
+#&gt;  -13.7539  14.8394  -4.5614  -7.6384   3.1100  -4.2105 -11.9115  -0.5960
+#&gt;    0.0991  10.2576   3.5545   2.5239  -1.5584  -0.7342  -1.4513   2.3753
+#&gt;    3.1298   2.8470   9.1367   2.1962 -10.9025   3.4857   1.3424   1.7096
+#&gt;   -3.4284  -3.9947   2.6576  -0.0517   9.5964   4.1337  11.2346  -0.2747
+#&gt;   -0.7268   1.8680  -3.1494  -5.5078  -1.8781  -5.5577   5.0735  -0.8478
+#&gt;  -19.5334  11.6953  -6.0222   5.4528 -12.5032   1.3430  -1.5628  -4.4609
+#&gt;    4.9712  14.0985  -4.1237  -5.1416   3.5854   7.1459  -0.9475   0.1677
+#&gt;    5.4993   9.4316 -20.0849   1.1029  -0.5751 -10.3690  -4.5713   0.9547
+#&gt;   -3.0163   1.9530  -5.8652  15.8317   1.0462  -4.7546  -1.0412   5.7411
+#&gt;   -6.4841  -3.1284   5.2417  -1.8846   1.6745  -0.6803  -6.0243  -1.8856
+#&gt;    7.1562  -1.8055   8.6672  -0.8442  -0.0713 -12.3860  15.8024   6.7052
+#&gt;   -8.2570   9.0252   2.5138   8.1018  -6.4767  -4.7385  -3.5294   1.8052
+#&gt; 
+#&gt; Columns 17 to 24   6.2775  -1.5850   3.2457  -9.9804  -1.6445  -7.0003  -9.3166 -11.5565
+#&gt;    7.9664   9.6289   0.2315  -1.3559   0.0530  -5.2808  -4.1676   0.7045
+#&gt;    6.4092   6.8486  -3.1486  -3.3920  10.4421  20.3564  17.9846   4.4097
+#&gt;   -2.3627   5.6153   2.1447  21.3260  -7.2783  -0.4885  11.7380  -6.2825
+#&gt;   -1.4116   5.0696 -12.0459 -13.7264   8.2601   0.4631  20.2876  -5.3802
+#&gt;   10.1130   8.0819  12.0196   4.4795   6.2384  19.2221  -2.6166 -11.2525
+#&gt;   -5.5405   0.7912   5.6823   1.7938  -3.9193   0.0173   1.0807 -13.1727
+#&gt;    7.3571  -4.1093   9.3866  -7.4530 -14.9705   3.8423 -10.9668  -0.5777
+#&gt;   -6.1378  -2.0439  -0.1679   6.4808  -5.9127   6.4555  13.2630   2.6399
+#&gt;   -3.6521 -10.5245   2.6363  -7.0387  11.3067  -7.2164   4.6194  -5.4668
+#&gt;   -4.9939   2.1794  -0.7131 -13.3611   8.2837  -1.1934  -6.7704   9.8353
+#&gt;   11.5898  -4.1928  -1.3052   3.4957  -5.8720   1.2028  -3.1091  13.1973
+#&gt;   -3.1264   6.7654  -2.8430  13.9791  -8.4931  -0.2467  -3.8826   2.6253
+#&gt;   -2.1554  -4.6526   0.3287  -4.4211  -2.7765  -9.5945  -1.2182  -6.0988
+#&gt;    3.5301  16.1572   6.2256   6.7041 -14.0738 -24.8879  -6.8695  17.6601
+#&gt;    2.0198   4.4124  -2.4822  -7.3260   1.6237   0.2821  12.2131   6.1603
+#&gt;   -3.5125  -6.0513  -9.0158   3.2189  -0.6211   7.8695  -0.4305  10.0394
+#&gt;    3.0957  -1.7018  -7.6300  -8.3000   3.9737   2.7196   2.5131   0.5913
+#&gt;    4.8698   2.4559  -4.4226  -1.1888  -9.9158  -9.0054  -1.9232  -4.1826
+#&gt;  -10.5941  -8.7925   2.0395  12.3159  -8.1303   6.5499   9.0866  -5.5161
+#&gt;    4.8163  -3.7302   3.6447   4.9581  11.8535   1.0523  10.8975  -9.3148
+#&gt;   -2.7097   5.0189   1.3801  -2.0891   7.1357   2.1787  -3.7618  -6.3489
+#&gt;   -0.1834  -3.4768  -6.1250   3.5673   1.2078  -8.4678 -10.4166   3.5552
+#&gt;    4.4301   4.1431  11.0031  -1.9690   5.4267  -5.9929 -14.1570   7.8287
+#&gt;   -0.0241   5.3399   0.5543  -5.0418  -2.8688   6.8181  17.5128   3.0657
+#&gt;    1.4544  -1.0651  -1.4330   6.9148  -1.1912   4.7612  -6.6168  -4.1147
+#&gt;    1.5356   2.1107  -8.3980  -1.6127  10.8442  -4.5871  -4.3506   3.3017
+#&gt;    3.8860   5.2318  -4.8024  -8.2091   3.6962   2.8229   7.1286  -7.4035
+#&gt;  -11.9108   2.0322  -1.0265 -10.2117 -13.0385 -10.3164  -3.1064  -8.6134
+#&gt;  -12.2115   0.2786  -3.1106  -5.7939  -2.2793  -8.2510  -7.1224   8.6989
+#&gt;   -1.0105   5.1231   6.0568   3.0091  -1.1277   0.4511   7.1284 -12.9398
+#&gt;    7.0544   4.4285   5.9728   2.4493   1.8328   6.6938  -0.2673  11.6947
+#&gt;   -8.0670   0.9918   3.2459 -10.6189  11.4817   6.1879   4.2604   6.4099
+#&gt; 
+#&gt; Columns 25 to 32  -0.8525  -4.2466   8.1149   8.9604   2.6708  -4.4176  -1.6742  -1.0590
+#&gt;   12.3265  -5.2732  -6.4582   3.1313   7.5117  -3.6575  -7.0726  -0.6175
+#&gt;    4.3506  -7.8010  -2.3238   9.1139   6.7009  -1.6155  -5.2580  -4.8739
+#&gt;    1.3846   4.9054  -1.5195 -15.9436   0.6421   2.2918  -3.9876 -14.4454
+#&gt;   -9.4614  -2.8986  -1.1720   3.6532   2.1531 -12.4642   9.8654 -13.5485
+#&gt;   -3.7789   2.6711   2.3726   1.3406  18.7266   1.4941 -17.1303  -9.9435
+#&gt;   -7.7549   6.6290  -8.2352   3.8351  -7.4765  -1.8254   5.3463  -0.9688
+#&gt;   11.4318   2.1752   3.5475 -11.0165   3.4342  10.1854  11.2030   6.3557
+#&gt;    7.8854 -12.9497 -10.9564   2.5554  -7.5959   4.8934  13.7491   2.9515
+#&gt;  -16.6380   8.1645  -7.0017   4.9986   2.3491   9.3661  12.6656   0.1412
+#&gt;  -10.5543   1.7138   2.8433  13.7616   5.4915 -18.3438   6.3564 -11.7630
+#&gt;   -1.1189   3.1494  -1.9130  12.0892   7.8868 -22.8812  13.7789   2.8608
+#&gt;    5.7502   5.8820  -5.7827 -10.7467  12.4053   2.1625   9.2320   0.1326
+#&gt;  -26.4053 -10.0236   5.1681  -8.0470  -8.4688   5.7817  22.6678 -15.7947
+#&gt;   14.9533  10.6294  -3.2660   0.0595   0.2008  -5.3989   0.6418   2.7737
+#&gt;    7.3439  -4.1394   4.6748 -14.2296  13.9774   4.8629  -6.9513   0.6267
+#&gt;   -3.2179   6.5492  16.0983  -5.5892   4.1199   5.6976   5.9562  -4.3062
+#&gt;   12.2855 -10.3834   5.0860 -14.8155   0.5852   2.1939  -2.0211  -5.9842
+#&gt;   15.2099 -15.7808  -3.3093 -15.0326  -1.9072   4.7694   3.5471  -3.8703
+#&gt;  -13.5151  13.7489  -7.8604  -9.9652  -1.5774  10.2144   0.0495   3.6213
+#&gt;    5.0646  -0.9133  -4.4108   6.7082 -13.5431   6.9828  -1.0883   2.8532
+#&gt;    1.4100   3.7741  -0.6558   0.9271 -15.5753  -6.1758   3.0588   7.6803
+#&gt;  -12.6247  -7.7815  -9.9065  10.6178   1.0637   2.7132   4.7961   5.1542
+#&gt;    6.4375   3.6851   6.0630   2.5964   2.2891  -7.7238   2.8372  -2.4290
+#&gt;   12.6501  -0.4962  -6.1870 -10.1276  -0.2421   4.6832   5.6642   2.8007
+#&gt;   -4.5591 -16.3988  20.4572  10.2898   0.5325  -6.2261   8.2257 -11.0654
+#&gt;  -14.5784  -8.0392  -2.5530   4.7269   3.0883  11.3417   6.5360 -17.0215
+#&gt;   -2.6200 -12.4883  -1.2617  -2.0253   7.9900  -4.3683  -1.7141  -4.1041
+#&gt;    1.9636  -0.0181   7.0525  -1.0082   9.4880  16.3890   7.7812   5.2593
+#&gt;   -0.7927  -0.0714  -5.3875  -5.7970 -10.7919 -11.7366   2.1444   4.8084
+#&gt;   -0.2244  -2.5639  -1.7997 -14.9589  -8.3325  20.9482  -0.5846  -3.4125
+#&gt;   28.0977   4.8458  12.7155 -10.4177   2.1977  -6.6505  -0.0407   3.5486
+#&gt;   -7.3188 -13.0014 -10.6939   4.8607  -7.7463  -5.1368 -19.2582  -5.9339
+#&gt; 
+#&gt; Columns 33 to 40  -0.1377   3.2506  -3.7871  -1.7106   5.6040  -3.9724  -3.5178   5.4732
+#&gt;    4.7593  -1.1129  -2.8694  -5.2186   0.6332  -1.6388   8.8995  -1.3257
+#&gt;    5.7330 -13.2376  -2.9613  -3.8430  -2.3290   5.7001  -7.2494  -3.2479
+#&gt;    5.2694  -4.3552  -4.1546   5.8831 -12.7448   0.4376  -0.3571  -2.8330
+#&gt;   10.3967  -1.6598 -11.2438   6.2785   3.1869  -1.2920  -1.5651 -13.4057
+#&gt;    4.9391   7.1792  14.1085 -13.6301  -8.4327  -1.1758 -10.8934   3.0144
+#&gt;   -3.3718   3.2028   7.2947   8.6978   5.3906   0.6700   0.1483  -6.6201
+#&gt;    1.6603   7.8946   9.3005 -11.7120 -11.4806   9.4585  -3.5366   7.3680
+#&gt;   -2.3639 -11.7350  -6.0241  13.2135  -7.9159   8.2889   2.2433  -9.5829
+#&gt;    7.4441  -4.4825 -10.8704  11.8347   5.2982  -2.7713   8.4805  -4.3410
+#&gt;    0.4683   0.3675   5.5605  -3.4918  13.9562 -10.8579  -3.2898   4.5480
+#&gt;   -1.2874   6.0127 -11.8734  12.2815  -2.5169   5.7772  14.3163 -14.1267
+#&gt;    6.2015  -2.7839  -5.4442   3.8890   5.3374  10.8819  -1.7560   2.8851
+#&gt;   -3.3453  -6.0401  -0.8963   0.5534 -13.0673   8.6007  -6.2398  -6.9812
+#&gt;    3.4145  22.1141  -0.1146  -1.6732   5.3828  -7.8777  -1.9819   3.0397
+#&gt;    5.0049  -6.3872   2.6951 -11.7042   2.1571  -0.2808   0.5163   7.2347
+#&gt;   -5.4106  -0.7201  -2.5963  -4.6721   5.6510  11.6991   0.5185  12.9348
+#&gt;   11.7139 -13.3170  -8.1163  -3.0640  -7.0170   1.7769   2.6703   4.6418
+#&gt;   -0.6666  -1.5284   3.7767  -3.0283  -4.3940  -1.2406  -0.5145 -10.3931
+#&gt;  -13.4537   2.4053   6.4562  -0.3350   0.6786   8.1114   5.2701   6.4626
+#&gt;   -3.6901  -3.4125  -0.1483  -1.0464  -0.2067   3.2730  -6.4035   1.1932
+#&gt;    4.7079   7.1522   1.0095 -11.3026  13.4431  -9.8529 -12.1500   0.1062
+#&gt;   -8.7493  -7.3526  -2.5875   8.2180 -11.8997  -0.3301   1.0430  -4.4056
+#&gt;    1.3124  17.5556  -2.8616   0.9150  10.7662  -3.2282  -5.5729 -12.4599
+#&gt;   -9.8113  -4.1152  10.6109 -15.3814  -0.4287  12.5030   5.3582  -1.4230
+#&gt;   -4.3885   3.8400 -16.4500  17.1638   1.1282   2.7942 -13.5514  -6.5250
+#&gt;   -1.5967  -3.5750 -20.1434  12.8107   5.2973  -7.3706  -5.8396  -3.9896
+#&gt;    2.6274  -3.5200  -4.1072  -5.4231   2.5422  -5.8367  -3.2230  -6.7909
+#&gt;   -1.3164   5.3932  -1.0555  -2.4966   3.7360  -7.8412   8.6574   9.8278
+#&gt;   -4.4774   7.7216   5.6121 -11.1173   3.9849  -4.7291  -2.4899   3.5248
+#&gt;   -6.6304  -0.4859   7.2998  -9.1267  -1.0349  -0.8976   0.7707   0.2001
+#&gt;   15.3553   4.8740   0.8778   2.2946  -2.4136   8.1221   3.2021   8.5878
+#&gt;   -3.5446 -14.0025  -1.2499  -6.4958   6.5118  -6.9399  -7.1943   3.8768
+#&gt; 
+#&gt; Columns 41 to 48  -3.5953   2.1697  -0.3344  -6.6644  -3.9017  -0.7598  -9.5340   6.0270
+#&gt;   -8.4097 -12.3432  -5.6333  -3.1630  -4.9298  -7.6671  -6.8117  -4.4287
+#&gt;   -2.6316   0.1110   8.7020  -1.0336  -4.1076  -3.1124  12.1268  10.2697
+#&gt;   -7.6029 -12.7724  -3.0518   9.1972  -1.7139   3.4533   3.1317  -6.5516
+#&gt;   17.7244  -0.6909   1.6023   0.4245  -9.7306  -6.2988  -2.0096   4.8661
+#&gt;  -17.8505  -6.5809   2.6257  -8.1040   1.1353   4.0209  -0.6767 -15.1927
+#&gt;   -0.7163  -4.5878  -4.0440  -9.7823   7.1896  13.7092   5.9171   4.5902
+#&gt;   -9.6751 -12.4562  -0.0980   7.2054   5.0217   3.4857   3.0155  -8.7498
+#&gt;    4.4006  -4.9509  -1.1345   8.0237  -4.5314  -3.9380   6.7367   5.3459
+#&gt;    8.7066  13.6964  -6.8727  -2.7678   8.2594   1.2589 -10.8770   9.1227
+#&gt;   -5.7971  14.9028  -2.1528  -2.3899   5.8608  -6.0840  -1.4284  13.2431
+#&gt;    2.4691  -1.6721 -16.9231  -7.3021   1.9050  -7.1693 -11.4543   7.3997
+#&gt;    6.1480  -1.5046   4.2482   4.0662   3.6572  10.8297   7.2994  -8.9193
+#&gt;   -8.8439 -20.0561  -2.0420   4.6577 -12.5582   4.0400  -7.8463  -3.2685
+#&gt;   -7.7507   2.0564   5.9774   9.1360   7.6873  11.6704   0.8791 -16.1470
+#&gt;   -1.9377  -3.0228  11.4520   3.7974   0.5614   0.6581   7.8290  -4.3634
+#&gt;   -4.5778  11.0574   7.6594   4.9709  -3.9845   1.9616   7.1617   7.5041
+#&gt;    4.8766  -0.6172   2.3325   5.5392  -4.7441  -5.1529   3.6519  -2.3442
+#&gt;    5.2498  -9.0746   8.3399  17.3116   3.0493   6.6338   4.5687   5.0531
+#&gt;  -11.1321  -4.8567 -15.2620  -5.3239  14.1225  -2.4464  -8.0929   3.0455
+#&gt;    3.6702   6.0884  -3.7725  -0.6496   6.3575   3.4182   4.6017   1.1628
+#&gt;   -4.4519   8.9508  -2.1166   1.6412   5.0754  -2.2529  -2.1845  -1.7842
+#&gt;    1.9706   5.9680  -9.0638   5.2057   1.3752   2.9317  -2.7724  -7.6801
+#&gt;    1.5172   6.3629   0.9934   6.1167  -5.9671  -6.4193   3.7734   2.2889
+#&gt;   -8.4000  -2.7407   0.8589   2.4671  -4.2783  -8.0097   1.4589  -9.7707
+#&gt;   -3.7666   0.7908  -9.2058   2.7238 -15.6450 -12.3411  -3.3576  14.9731
+#&gt;    7.9356  17.2258  12.0983   3.0539  -4.0637   4.3706  -2.5933  19.9890
+#&gt;    0.2995   3.2532   0.1612   1.3240  -3.8385  -5.4814  -3.7269   6.8137
+#&gt;    1.4511   8.7210  -0.7404  14.8264   9.6801  -0.8539  -6.6956  -2.7426
+#&gt;   -8.3648   5.7144  -6.9272   7.4207   2.4334  -1.5141  -0.9600 -10.3752
+#&gt;   -4.5589  -6.6866   5.0427   5.3583   3.3784   9.3046   5.3795   0.8498
+#&gt;    0.5552  -5.3834  -0.0234  -7.8309  -7.7514  -3.5336  -2.8804 -11.4011
+#&gt;   -7.8766  15.0146  -5.0480 -10.1385   0.5785  -5.2375  -1.8472  -1.3784
+#&gt; 
+#&gt; (19,.,.) = 
+#&gt;  Columns 1 to 8  -3.4611  -9.1404   1.7931  -3.0810   0.4750  -7.0759  -4.8471   4.2381
+#&gt;   -0.0352 -12.1933  -5.7169 -11.1747  -2.5628  -6.8346  -2.2831   4.9942
+#&gt;    2.6442  -0.2799   7.6961  -2.9925  13.2339  -2.4387 -17.7280  -7.4640
+#&gt;    5.7746   6.6950  -5.3038  -2.8800  -0.9693   2.5390   4.3223  -3.1995
+#&gt;   -6.1854   8.1444   7.4993 -17.7296  10.2539  11.1687 -10.5051  -5.8786
+#&gt;    2.8332   5.7838  -6.2065  -4.2929   7.3672 -15.5023 -13.5422  -3.5879
+#&gt;    8.9983   2.2253   3.9089  -3.8948  -3.0167   9.7603   5.8497   1.3103
+#&gt;   -1.8740 -10.7571   0.9592   9.4249   6.4506  -7.1429  -7.8860  -4.7393
+#&gt;    8.8798  -7.6927   2.0819   3.1199   4.4651  10.9876   2.2112  -5.5021
+#&gt;   -3.3376   1.1291   2.8319   6.3287  10.1260  -7.8622  12.9377  -0.1655
+#&gt;    4.4010   1.1070  -7.4041   2.4861  -3.1894 -11.1775   1.6313  -6.4075
+#&gt;   -4.3031   0.4363 -15.8812 -10.8058  -6.9135 -13.3837  14.2700  -1.7496
+#&gt;   -9.9263   8.3336 -15.6886  -0.5846   1.6988   1.5499   4.1669   9.1616
+#&gt;  -11.1320  -5.9512   4.1215  22.5764  -5.4150 -21.3222  -2.8292  -4.1807
+#&gt;   -2.0779   0.4513 -16.3865 -12.8173 -19.3269  -0.4818  23.1835   5.7976
+#&gt;  -11.8749   4.9889   9.5479   4.0342   2.1430  -1.6942  -4.7308   4.7222
+#&gt;   -3.8208   9.4871   1.4341  15.6271  -5.5153 -14.3294  -1.7244  -1.7959
+#&gt;   -5.8851 -10.5064   0.0830  -4.4312   0.6757  -5.7881 -10.0082   0.9816
+#&gt;   12.8905  -9.3879 -12.7090 -20.1896  -2.0752  13.9645   0.4113  -4.8586
+#&gt;    6.6401   8.4873   3.5662   8.7259   7.4143   4.1407   2.2664  -4.7658
+#&gt;    3.5540  -5.3497   1.8646 -15.3814  -8.4128  -1.2179   2.8765   3.3504
+#&gt;    5.7773   1.7763   3.1419   7.6839   7.3752   3.5489   0.2443   7.5274
+#&gt;   -6.2074   1.6690  -8.1071   3.1395   4.1027 -13.6262  11.1680   0.3504
+#&gt;    5.4216  10.8193 -10.5752   0.7090 -14.1337  -7.2826  11.8666   0.0769
+#&gt;    0.2185  -1.2183   5.5371  -5.9713  -2.5031  -1.1161 -17.1534  -7.7117
+#&gt;    1.5651   1.4154   2.4670   1.2006   5.9063 -11.6006  -1.4069  -1.2071
+#&gt;   -6.3910   6.6812   0.9082  -8.4090   2.6307 -11.4943   0.8303   7.1434
+#&gt;   -1.1001  -0.0420   1.2677  -2.3396  -0.7564  -2.7638  -7.4476   4.5147
+#&gt;   -6.8185  -1.0605  16.0993   1.8552 -11.7301  10.6714   8.6809  15.1086
+#&gt;    3.4357  -8.2866  -0.8393   1.7461  -2.2635   5.7537   6.1828   3.5534
+#&gt;    6.6863  -8.7180  13.1817   2.8627  -4.3145   7.4306  -2.6715   3.6868
+#&gt;    6.8550   8.8745  -5.7014  -4.9334   8.2615  -5.9185  -1.6588  -7.0063
+#&gt;   -3.1026   3.0432   1.5501   2.6255  -6.8630   1.4004  -2.0602   2.0817
+#&gt; 
+#&gt; Columns 9 to 16   5.2902   2.7383   4.8146  -5.3328  -5.9478   1.1382   2.2200  -1.5181
+#&gt;   -2.7915   3.4025   3.4455  -3.6296  -5.0638   7.8659   3.7163   1.6103
+#&gt;    8.8335  10.3378  10.0154  -0.9722  -4.3381   3.9938  -2.6561 -13.8199
+#&gt;   -8.0572  -1.6901  -5.5261  -0.2729  12.8816   9.8370  -4.1764 -15.8455
+#&gt;   -5.5871  -0.7823   9.1436   7.3097  -6.8795   5.3679   6.6581  12.6578
+#&gt;   14.6644   1.8670   8.3263  -3.8820   0.5929   9.8740   1.6168 -15.2564
+#&gt;   -0.2044  10.7061  -9.6726  -7.2059  -4.5816   8.3205   3.3814  -1.3716
+#&gt;   10.8472  -9.4861   9.4482   2.8386  -5.3880  -6.6471   6.8851   3.2060
+#&gt;   -5.0416   4.7347  -9.3375   2.8626  -1.3858  -7.3036  -3.2511  -6.0695
+#&gt;   -4.1657   1.4105   4.4514  -4.3708  -7.7883  -4.9683  -5.0988  -2.3729
+#&gt;    5.0074  -2.6819   0.2308  -3.8807   5.7511  15.1418   4.6777  -2.6857
+#&gt;   13.4803   9.9774  -7.9755   4.8428  -4.2181   1.4315   4.6671   9.2052
+#&gt;   -3.1098   5.3918  -2.5908   0.7142   0.2389  -4.2196  -0.5577   5.7543
+#&gt;    5.3973  -5.1033  -1.8019   3.3361 -17.7087   3.1050   1.6701 -15.3451
+#&gt;   -8.5191  -0.6737  -7.4726   8.3262   5.8445  13.9084   6.1187  -6.4196
+#&gt;   -2.8843  -1.8931  11.2683  -2.0956  -7.7679  -5.3379   4.7186  -9.8928
+#&gt;    3.8557  -4.4119   3.4556  -2.6384  11.2398   8.3716   1.6247  -1.3049
+#&gt;    3.0877  -5.1738  15.3108   3.6818   0.3692  -6.3290  -2.0822  15.2589
+#&gt;    0.3658  -4.6154   4.7221   7.3196   5.5705  12.4712   5.8117   6.3938
+#&gt;   -2.7410  -7.1528  -9.8978 -12.0244   5.8807  -0.8024  -7.3468  -3.7716
+#&gt;    5.8393  -1.4356   7.4594  11.1117   5.8228  -0.2386  -2.7089   3.7404
+#&gt;    2.3091   3.3260   1.5249  -4.2911  -4.1793  -3.8416 -10.7012 -10.4168
+#&gt;    1.9383   7.3857  -9.7228   7.2830  -3.6504  -3.0505   2.8108   0.9772
+#&gt;    2.5689  -0.5136  -1.8759   2.3639  -8.4020  -6.3512   8.6290  -0.0518
+#&gt;    2.5915  -2.2131  -4.5490   5.2644   4.0784  -2.1929  -6.3137   3.1056
+#&gt;    3.4506   0.3835  -6.8334  -9.9447   1.8448   2.3892   0.6988 -11.2923
+#&gt;   -2.8059   1.7178   8.3596   0.7915   5.9538  -1.8466  -9.0439  -4.7090
+#&gt;    4.0136   7.1306   4.5880  -2.7101  -6.3237  -0.2910   1.6246  -9.5799
+#&gt;   -4.8588   2.7241  -2.0466  -3.5706   2.3091  -0.0946   4.9093   5.8773
+#&gt;    0.2450  -1.9763  -3.7768   2.7259   1.5898   0.2287   2.6923  10.9416
+#&gt;    2.5144  -3.9102   4.8004   1.2450   1.5627   2.9760  -6.2275 -12.0590
+#&gt;   -5.9881  -9.1830   2.9870  -3.6907  -8.4951 -10.5845  -4.5769   6.8579
+#&gt;  -10.3085   6.1097   9.1956   9.3138  -0.6870 -20.9332  -0.4139   3.7591
+#&gt; 
+#&gt; Columns 17 to 24   1.2376   7.5606  12.5421  -3.0553  -0.9328   1.7316   0.6615  -2.5097
+#&gt;    9.4143  14.5409   3.9029  -0.7644   0.0921  -0.1401   1.1963   2.8162
+#&gt;   -6.1888  -4.1944 -11.8042  -7.1860  -5.6268  17.0825  12.8673   4.7076
+#&gt;    0.9620   5.0366  -8.5746  12.7652  -2.2362  -5.4341  -3.6892  -3.3061
+#&gt;   -9.0501  11.4634  -6.0280  -1.7396   3.4346  -5.4313  10.9943   5.4963
+#&gt;  -10.6759   6.0834   2.6129   5.1395  -6.9779  12.2807   4.5402   4.3892
+#&gt;   -7.7933  -5.6006   6.5909  -4.0752  -5.2934  -4.2789  -1.3369  -6.7048
+#&gt;   -5.6729   5.4822   0.3232  -1.6646  -9.2207   2.5597   1.4470  11.8789
+#&gt;   -1.7569 -17.5717  -2.9011   2.3722  -5.3322  -0.9906  -0.2374  -0.7140
+#&gt;   10.2958  -8.0127  -0.2486   6.8947  -1.4969 -16.7986  -7.5833  -3.8531
+#&gt;   -5.4006  18.8477  -0.7930  -5.5661   2.8746   5.6923  -1.0670  11.8340
+#&gt;    8.7225  13.2541  -0.0645   6.1721  -5.0030   7.3289 -12.8461   7.3463
+#&gt;   -1.2710   7.4224   0.0202   6.3414 -10.4809  -2.1036 -12.0762 -11.1617
+#&gt;   -1.3905   0.0062   6.2062  11.0585  -8.7239   0.8016   1.4885   3.2104
+#&gt;   11.7447  19.2056   1.4388  -5.1179  -4.0166  -2.2951 -12.5351   6.4644
+#&gt;   -3.2974  11.3161  -8.8209 -11.7499   2.9362   2.3316   6.7193  -0.9621
+#&gt;   12.9329  -0.1011  -7.3242   4.3339   1.0294  13.6734  -3.9086  -6.9845
+#&gt;   10.8593  14.5181  -7.2419   0.2592   5.3456  -1.1686   4.3092   8.9532
+#&gt;   -8.1362  12.2634  -5.9849  -5.3753  -2.7588 -18.1391  -2.4050   0.9766
+#&gt;  -15.9462 -14.0508   4.6497   6.9329  -4.4538   2.4160  -1.1990  -9.0743
+#&gt;   10.7729  -2.5267   3.3410  -1.3814   3.6126  -5.7625  -7.7225  -3.2399
+#&gt;   -0.4555 -18.3892   5.7101  -5.5647   3.8795   6.2622  -0.2600 -10.7557
+#&gt;    8.0861  -6.9856  -7.6936   6.8099  -9.6428  -1.8709  -3.9135  11.6752
+#&gt;    2.3393  -3.4244   1.7594  -1.7691  -1.8816  -1.3449  -3.1213   5.8446
+#&gt;  -17.0414 -12.5825  13.7659   3.2276   2.8819   9.7088  -0.0395 -16.3680
+#&gt;   -9.0849   2.8703  -4.8908   0.0538  -4.0284  10.7334   1.8612  14.3455
+#&gt;    2.7764  10.3519  -8.5610   1.2517   1.8367 -13.9965  -7.0438  -2.9352
+#&gt;   -7.8392  -0.7714   3.5866  -8.1745  -2.8952   2.8005   7.5204  -2.6269
+#&gt;   -3.5496   8.5775   8.3233 -22.9023  -6.5217  -6.1104  -1.7158  -5.2329
+#&gt;   -9.3595   4.4470   1.9405  -2.8629   6.0939  -0.0330  -5.4820  -0.4906
+#&gt;   -7.1914  -7.4423   5.7052  -8.5940  -1.9660  -9.3857  -3.4722 -16.1382
+#&gt;   -5.2057  -6.5660  -3.2930   6.4077   9.6433  10.5191   2.4833   6.9536
+#&gt;    3.6243 -12.0534   8.2019  -4.6935   6.8123  -4.5821  -1.7005  -2.0976
+#&gt; 
+#&gt; Columns 25 to 32  -5.2512   1.8249   1.1596   1.4756  -0.1453  -5.6668   4.3688   3.8027
+#&gt;    0.3378  -0.9915   3.1091  -8.2443   1.4756  11.2804   5.2564   4.3766
+#&gt;   -1.8313  -2.2099  12.7952   2.9058  -1.2613 -13.3544   3.3536   9.3037
+#&gt;    4.9129   5.6348  -3.1549   0.1487   5.6897  -5.9294  12.3421  -4.6768
+#&gt;    5.9648   7.6459   0.8358   5.6926   9.6688 -12.8750  -6.5378  10.4043
+#&gt;   -0.5012   1.3708  -1.9134   7.2386 -11.6159  -4.4659   1.1672   5.8006
+#&gt;    7.5550  15.2160   3.9736   0.6838   4.0331  -0.1188   5.0034  -2.9129
+#&gt;    4.0897  -0.8494   2.9359  13.4275  -6.4579  -3.2058   0.3327  -9.4161
+#&gt;   -1.2164 -10.9410   4.9078   3.7710   9.4618  -7.4789  -0.9974  -3.7066
+#&gt;   -2.3371   0.4635  -0.9240  -0.4963  -0.6820   0.7073  -0.4325   2.0849
+#&gt;    2.0346   5.2058  -2.9409   0.6210  -4.5806  -0.9869   2.5354  -1.3443
+#&gt;   -3.2821   6.8711  -2.9636 -14.8750  18.0725   1.8790   1.5808   2.6172
+#&gt;   -0.0502   0.7007  -1.9748  -2.0885   0.7059  -5.6614   7.9234   2.7092
+#&gt;  -17.1384   3.1040  12.6798   8.4108   4.2531  -9.5270  12.8214 -14.1993
+#&gt;   -4.2209  -7.2070   9.4186  -6.9256  -1.7514   5.9064   4.9219 -13.0215
+#&gt;    3.7964  -4.0864  16.0046   1.6829  -3.5307  -1.5526  -0.3633   3.1747
+#&gt;   -7.1834   6.0091   4.9318  -5.0777 -10.0517  -5.5497   4.9865  -3.2409
+#&gt;    1.5353   5.2731  -4.6185  -2.0486   0.8635  -3.6302   3.3154   6.9697
+#&gt;    9.7254   2.1400 -13.2873  -0.3283   2.1209  -7.9610  -1.0666  -3.1248
+#&gt;    9.8743   9.8609  -7.9352   3.1834   3.7730  16.6648  -5.2800  -2.7646
+#&gt;    0.1464  -6.8665  -3.2266  -7.7950  -5.8029  -2.8987  -2.3511   3.3740
+#&gt;   -0.1179  -1.6332   5.8092   0.0731  -2.9186  -1.1299  -1.0177   4.3289
+#&gt;   -6.9973  -2.5415   2.9499  -4.7806  -6.6248   1.9698   6.0161  -1.9432
+#&gt;   -7.5513  -8.0110  -1.7323  -0.9099  14.4731  -7.5086  -6.2971  -5.3039
+#&gt;   -2.6150   7.7648  -1.4753   0.4759   3.1144   1.4673  -5.3906  -0.5745
+#&gt;    3.3908   2.6249   4.3091   7.0905  13.8049 -15.0081  13.2431   6.3141
+#&gt;   -2.2864  -0.6594  -0.4841  -1.7268 -13.0835 -10.8193  -3.2489   3.3462
+#&gt;    2.7739   0.1309   6.8358  -4.1396   3.0261  -6.0489   0.8392   5.5245
+#&gt;   -9.4017  -2.3352   1.0868  -2.3716  -4.7258  -3.0849  -7.9274 -18.7819
+#&gt;   -2.2601   1.6484 -13.3630   5.3704  -2.1310   6.0107  -0.3032  -4.6949
+#&gt;    1.1735   0.0361   0.5446  -1.3908  -6.9785  -0.6287   2.1474  -4.2094
+#&gt;   -4.6960   2.4893   3.2426   5.1052  14.4704   0.1834  -1.8464   6.2223
+#&gt;   -0.3800  -3.7501   1.3461  -3.6039  -1.4326   9.7886  -5.2614   5.1459
+#&gt; 
+#&gt; Columns 33 to 40 -11.5152  -0.9243   1.7177   5.0535  -8.1015  -2.6109  -3.8127   5.8398
+#&gt;    4.1287  -9.3522  10.9055 -16.0773  -5.0454  -3.5528  13.8708   2.7029
+#&gt;   11.6871  -1.9325  -0.4353  -7.9579 -11.7032   5.5666   3.7455  -9.6129
+#&gt;    3.2296  13.6496 -11.7866   0.2916  -5.4664   7.4755  16.1655   9.7189
+#&gt;    2.0674  10.3068   9.1034   8.7561 -10.0243  -3.6387   4.6554   2.6122
+#&gt;    5.0319 -14.1564  11.3310   4.3536  -4.7910 -10.0291   4.5722  -3.0061
+#&gt;   10.4745  -4.0082  -3.6633  -5.0737  -3.4279  -0.3299  -4.7581  -5.4095
+#&gt;   -6.5930  -4.4209  15.1655   6.3988   0.2990 -16.0692   0.7111   2.0711
+#&gt;    5.5374  10.7280  -9.7765  -5.7725  -4.9483   7.8515  -2.0251  -9.8779
+#&gt;   -3.7189   2.4464   6.4076 -10.6928   2.0520   1.9248   4.6834 -10.2054
+#&gt;    4.2027  -0.9405   0.8029  -2.3511   6.8259  -9.3003   9.1939  -2.4253
+#&gt;    0.8544 -13.7303   0.4549 -12.2916   4.1789   1.0487   7.6946  -1.5183
+#&gt;    2.1501   1.2057  -7.6369  -3.0940  -0.9013   5.7293  -4.5890   4.1786
+#&gt;   -8.5443   9.4214   8.3380  12.1051  -6.0888  -4.7884   2.3212  -8.0710
+#&gt;   -1.2801   9.6789   0.0649  -1.9204   9.9172  -2.8573   9.9711  12.2731
+#&gt;   -4.4635   2.3057   4.5296   0.9816   1.7360   1.8344   7.3084  12.0034
+#&gt;    4.0429   6.1456  -2.1394  -6.9665  -5.5990  -5.1341  -3.4766 -10.4355
+#&gt;    3.1496   5.1559   4.6521  -1.9684  -6.8253   4.0424  12.2552  10.9414
+#&gt;   -3.6489   9.7629   6.5666   7.4750   5.6747  -2.3111   2.2481   8.5219
+#&gt;    3.8301  -5.4730  -6.6944  -6.1863  11.0954   0.7285  -7.3015  -0.2664
+#&gt;   -2.1023   0.9463   6.2356  -4.1167   5.8918   4.5609  -1.8199  -1.1138
+#&gt;   -9.0089   3.7983   3.3152  -7.7969   3.4476  -1.7216  -3.0805  -4.2707
+#&gt;    1.3314   4.4158   1.9641  -0.6657  10.2384   8.2146  -2.2257  -8.8144
+#&gt;   -9.1151  -0.4731   3.9524   7.4266   0.7322  -8.9711  -3.1905  -2.7870
+#&gt;    6.1514  -6.4834  -4.7259   3.6780   6.6178   1.3456  -7.7370   1.2610
+#&gt;   -1.2889  14.6657  -7.0592  -7.8284  -1.6898   5.6680   6.8909  -6.2393
+#&gt;    5.6675  15.7121  -6.1919   7.7758  -4.3231   4.1056  13.2871  -2.7665
+#&gt;    2.7247  -1.6288   8.2588  -1.6087  -7.6883   1.1752  -0.2560   0.0173
+#&gt;    1.0590   8.4544   2.4623   8.8080   0.8151  -0.4072 -19.2495   5.6410
+#&gt;  -14.9196   6.2735  -6.5225   6.6026   9.5773  -9.1956  -7.6193  12.3483
+#&gt;    2.9590  -0.0770   0.5244  -0.1409   1.3758  -2.8964  -4.8030   5.5306
+#&gt;   -6.0014  -5.4352  -4.3040  -3.9256   9.0386  -5.5042   0.7756   2.6890
+#&gt;    3.5337  -7.0212  -1.3884   5.6556   2.7715   8.9969  -1.3071   9.9345
+#&gt; 
+#&gt; Columns 41 to 48  -0.5824   1.4692   9.9275   3.6555  -0.8344  -3.8524  -1.7748   5.2643
+#&gt;    0.3203  12.2354  -3.6763   6.3472  -2.8340  -1.8693   2.3417   2.2867
+#&gt;   -5.5468  -1.2549  -6.0051   5.1252  -6.7402   7.3577   3.4990  -1.8936
+#&gt;    9.8346   5.3042  -2.1794   5.1964  -9.2770  -1.5883   6.6708  -0.7402
+#&gt;   -3.5805  -0.7470   1.2830   7.2845  -7.3766   3.7228   6.2811   0.2138
+#&gt;    2.6054  -4.6318  -0.8484   8.3144   3.5253  -5.8594   2.0936  -7.2572
+#&gt;   -2.0082   3.8686   0.8358  -0.5959   4.6445  -1.6382   5.7570   9.4667
+#&gt;   -2.7015  -7.0737   0.3860   0.0786  14.4266  -5.7703   3.4809  -9.3841
+#&gt;   -2.2334   2.7636  -4.3436   4.6516  -3.6256   8.9462   0.9035   0.8084
+#&gt;   -4.3726   7.2027  -4.3678   3.9060  -1.8193  -4.5488   3.8191   5.7538
+#&gt;   -6.2466   6.2129  -0.7223  -7.6462   8.4903  -1.9344   8.9862   3.6523
+#&gt;    7.0767   4.6762  -3.0891 -12.8673  13.1296  -1.3765  -2.7232   9.2269
+#&gt;    0.4489   1.8570   3.5168  -2.8925  -7.6406  -0.0952  -1.3527   3.7495
+#&gt;    1.5826   4.9068   2.2813  19.8114   5.6210   6.5733  -3.8362   9.5918
+#&gt;   -2.9860  10.9359   1.6390  -1.1453  -2.9503   2.8189  -8.0274  -2.1033
+#&gt;   -4.3303   0.2937  -1.2596  -4.8879  -6.8936   0.5022  -5.8503  -4.2147
+#&gt;   -1.3657  -5.2851  -0.5626  -2.0250   5.0959   0.7407   5.7787   6.5425
+#&gt;    4.1264  -2.3706  -5.5607   0.1691 -11.5336  -0.5482  14.1629  -1.3343
+#&gt;   -4.2715   7.0989  -3.2161  -3.4652   5.9091  -8.7639  -2.0256  -2.5622
+#&gt;    3.5216   1.1792  -1.5078  -2.9335  14.8755  -3.4561   3.2270  -4.9966
+#&gt;   -1.3233   1.7755 -12.4446  -2.2966   2.4633  -0.1826  -5.5273  -7.7662
+#&gt;   -3.8034   0.5859   6.7605  12.2746  -4.7226  -4.1606 -12.1284  -9.8226
+#&gt;    1.3134   7.2764  -9.0757  -4.2172   0.0349   7.8910  -2.4282   2.2683
+#&gt;   -6.9185 -10.8574   4.7544   2.8901   0.2314  -4.0512  -4.5323  12.0024
+#&gt;   10.8790   3.4761   4.8979   6.8556   2.2733   1.9687  -7.3329  -0.3296
+#&gt;   -0.8850  -1.0989   3.1610   5.7198   1.3077  -2.6804  11.3774   9.7755
+#&gt;   -3.7612   6.2740   1.3685  -9.1816 -10.4648  -2.2668  -1.5501   2.4334
+#&gt;   -4.7406   3.3968   3.4882   2.6706  -4.0044  -1.0474  -2.7413   2.1758
+#&gt;   -9.4952  -0.0138  -0.0833  -4.8732  -4.4048  -9.8041   1.0938  -8.0169
+#&gt;   -3.8376   3.0544   1.6981  -2.6817   9.1293  -8.2263  -0.0040   2.8741
+#&gt;    1.8922   0.5671  -3.0768   1.2282  -4.8935  -4.6556  -2.9761  -9.9547
+#&gt;   -0.0221 -11.1739   3.2565   5.4391  -6.3191   8.9794   1.6672   2.8193
+#&gt;    8.3047  -2.6720   0.5503  -3.5542  -5.0851   0.9157  -5.0840   1.2687
+#&gt; 
+#&gt; (20,.,.) = 
+#&gt;  Columns 1 to 8   2.2842  -9.3007  -2.5022  -4.5254   0.8086  11.4189   4.4972   0.0225
+#&gt;   -5.4441  -8.8693   5.7317 -12.6132  -4.9310  -1.5905  10.4928  -5.4723
+#&gt;    8.0980   2.5519   1.5367 -18.4978  -2.2771   5.7824   2.2589  -6.2605
+#&gt;  -10.7820  -1.2471   4.6014   5.3154 -15.6898  -5.4572  -3.4885  13.0038
+#&gt;   -1.3370  -4.8736  -6.5480   6.2534   8.5135  13.9670  -9.0490   3.2787
+#&gt;    6.8220  -6.1084  -4.2379  -9.0561  -4.4813 -11.1193   9.8193  12.2705
+#&gt;   -1.9170   5.1773   6.8824   7.3334   5.0908   2.4932  -0.6944   2.1303
+#&gt;    8.1684   3.4160  -6.2923  -2.1127   7.2822  -8.4506   2.5393  -4.3990
+#&gt;   -3.5237  17.5413   7.2349  -1.0963   1.3250   4.2543  -3.0632  -6.4548
+#&gt;   -2.1005  -3.6719   7.0955  -6.5324  19.2545   0.4258  -5.5677  -5.0242
+#&gt;   -9.1786 -10.5193   0.8223   0.1238  -6.2313   1.7179   8.5738  -8.5565
+#&gt;   -7.8284   1.7040   3.8873  -9.1008  -0.6783   2.9533  -1.6648 -14.6538
+#&gt;    9.3043   1.0702 -11.2738  10.9230 -10.2916   0.5239  -7.3030   3.1418
+#&gt;    2.2950  -6.2420   3.6485   3.5322  -0.9277  20.1958   0.5635  12.4370
+#&gt;   -7.1262  -8.2530  10.2255  -0.6592 -28.4965  -7.2944  21.0907  -1.4685
+#&gt;   12.3869 -14.2600   3.5160  -5.1331  -1.2875  -2.0544   5.2436  -3.6207
+#&gt;    1.0990  -1.7611  -4.9954   3.8427   1.6899  -1.0132  -8.4517   0.7244
+#&gt;   -1.9679  -5.4131  -5.4782  -3.1717  -1.4692  -1.9899 -16.9192 -15.8189
+#&gt;   -1.6072   3.0888   4.8682  -7.2044  -7.5279  -7.6817  -0.1098  -4.4967
+#&gt;    1.0395   4.9443   5.7307   8.6428  15.3588  -1.5626   3.9243   9.2448
+#&gt;   -3.0733   1.7062  -0.4861 -11.9732   5.9804 -14.3937  -2.1416  -2.3518
+#&gt;   15.2716   5.2276   3.2135  -0.4847  14.3549  -1.8188   2.1609   5.9673
+#&gt;   -8.9208   3.1274  -1.9101  -3.1453  -2.2634   1.1079 -12.2047  -1.1498
+#&gt;    4.2715   0.1498  -1.0524   6.5481 -11.2053 -10.4551   5.7463   8.0795
+#&gt;    6.6160  14.1188  -9.8353  -2.5755  14.6442   7.2921  -5.6188  11.5282
+#&gt;   -5.7045   2.6968   1.0832  13.1342 -10.1617   4.7363   1.5884   7.4962
+#&gt;    1.6970  -1.6772  -4.2640   6.2300  -7.0329  -9.8650 -12.3766 -11.0625
+#&gt;    8.7481  -3.2058   1.9772  -4.6278  -1.2608   5.2762  -0.4509  -2.0620
+#&gt;   -9.9497  -0.1287   0.6570  15.6611  -8.7315   5.2262  -7.7726 -11.2332
+#&gt;    0.8742   7.2435  -4.7075   6.3476   0.0331   9.3009  -8.1382   0.9533
+#&gt;    6.4679   2.6813  11.9876  -3.0380   6.1175 -13.9871   1.2410   3.8011
+#&gt;   10.9178   2.4582   5.3939  -0.3543   2.1917  -0.9866  13.4287   7.3312
+#&gt;    2.7657  -3.6383  -9.5479   4.4432  -3.9823  14.3085  -3.2065   3.5106
+#&gt; 
+#&gt; Columns 9 to 16  -4.1815 -11.5099  -5.2858   2.1357  -0.6340  -8.8014  -1.6580   2.0258
+#&gt;   12.6018  -6.4907  -1.7134  10.0354   5.7478   3.3890  -2.9349  -0.5419
+#&gt;    8.3475  -0.4711   4.3299   5.7077  -9.1401  -2.3767  14.7719  10.5088
+#&gt;    6.1800  -1.6572   8.5729  15.6553  12.7924  -8.0829  -8.9365   0.6729
+#&gt;    0.0958   4.4101   1.4468  11.9980  -9.2241   7.8956   3.2662   3.9322
+#&gt;   16.9663   5.6927   4.8464  -0.8725  12.3926   7.1392  -0.0534  15.9518
+#&gt;   -8.2486 -10.1814  -7.3882  -2.1658  -3.0324  -4.6042   3.3077   1.5265
+#&gt;   -0.4083 -12.7831   1.3060 -15.8912   5.3509  -4.3770  -0.0479   0.1638
+#&gt;   -0.8841 -14.2071   0.4834  -2.1798   0.5447  -0.2378   4.3046  -2.1199
+#&gt;    4.4221   1.6682  -4.2027 -17.1329  -7.9319   1.7415 -12.3031  -8.0263
+#&gt;    3.9206  -6.8473   5.9718   3.3969  14.0158 -20.7377  -5.5385  15.3233
+#&gt;    8.6910  -4.5725  13.4395  -9.9043 -14.6248  -0.2776  -9.8324   7.8371
+#&gt;   -5.9905  -4.5636  -7.8709  -7.8936  -2.7318   8.6649 -11.8396  -3.5448
+#&gt;    1.1642 -14.6849  -1.3807  -8.3010   4.8826  -3.0971  -7.9406   2.7303
+#&gt;  -10.4803 -13.4136  -2.9378   7.4990   9.2564 -12.6693  -6.4091   6.8663
+#&gt;   -1.5119   0.3176  -0.5733   1.2998   2.5392   0.0195  -0.1565  -4.6094
+#&gt;   -5.7074  -8.9894  17.5815  -1.2018   1.4961 -17.1923  -2.4444   0.8150
+#&gt;    0.5595   8.2031   4.3901   3.8901   2.6667   1.6572   1.0731 -14.1939
+#&gt;  -12.3769  -1.4230  -3.4196   5.7495  -6.0557   8.6327  -1.0485   5.1459
+#&gt;    1.3921  12.8169  -4.7885  -4.5789   2.4130   7.6160  -7.9005  -1.2045
+#&gt;    7.1584   6.3071   9.4819  -4.2199  -0.9562  -0.2124  16.1204   3.1242
+#&gt;    3.0037  -4.6943  -3.0364 -13.0083  -6.6571   6.2830  16.0910  12.3829
+#&gt;    2.1058   4.3323   2.2193  -7.8173 -12.5410  13.6322 -11.2586  -6.8660
+#&gt;   10.5044  -9.5241  -5.9055 -16.7476   0.0221   1.6616   3.0149  11.0270
+#&gt;   -0.4683   0.7242   8.5773   1.6223  -9.4850   3.5766   1.4956  -6.4105
+#&gt;    0.7010 -16.4320  10.0723  -2.6672  -0.6204 -13.9131   2.1235   6.2535
+#&gt;   -4.9614  -8.3707   3.8164   3.4402  -7.0477  -8.3158  -3.2705   3.0646
+#&gt;    3.6172  -8.7604  -2.9719  -4.5413  -2.4701   5.3452   7.9408   8.8628
+#&gt;  -14.9451  -2.3443 -18.9294  -6.1599   4.0042 -10.3584   1.3638  -8.8801
+#&gt;   -5.1800  11.7115  -5.9830  -1.8752  -1.1079   7.7158  -4.9851  -2.0916
+#&gt;   -8.7498   1.0072  -4.9560  -0.7679  -0.5172  -2.3869   8.2433  -4.0864
+#&gt;   13.9879   4.3474  -2.1083  -9.7008   6.9482   5.2922  -3.8754  -6.8229
+#&gt;   11.1854  21.4086   9.0164  -1.9481   2.6196  -4.5010  11.7534  -8.6801
+#&gt; 
+#&gt; Columns 17 to 24  -6.8623   0.9711   0.3639   0.8478  13.4453 -10.3790   5.3281  -3.1556
+#&gt;    1.1253   6.6452  -8.1128   7.6139  -5.4746   2.7172   5.0802   5.8007
+#&gt;    6.9472   6.4035 -12.3596 -12.7988  16.8413   6.8426  -2.7024  -8.3391
+#&gt;   -8.0927  -0.1453  -0.8154  20.4970  -1.6281 -12.2054  12.8511   9.4697
+#&gt;    3.1497   3.7875  -2.5723 -16.9826   0.7948   2.5969 -12.3331   8.1099
+#&gt;    9.0053   8.4317 -12.1390   8.9320  19.1877  -8.9690  -5.3526  -9.7213
+#&gt;   -0.2293   2.2816  -8.8143   5.8754  -3.9842   3.6941  -3.2832  -0.4515
+#&gt;    2.7337  11.0518   4.7096  -5.8642  -3.2091   4.9801  -0.5527 -15.4752
+#&gt;   -6.0244   5.1127  -4.9657  -3.6686 -13.1704  13.1213   5.9027  -0.3311
+#&gt;   -4.7060   6.5571   1.1623   3.1787  12.1805  -5.9795   2.6450  -3.8890
+#&gt;   -1.6306  -2.1122  -0.8350  -3.4539   7.9609  -4.4769   8.5061  -9.8306
+#&gt;   -1.3080   5.6213   5.7974  -5.7210   0.0955   8.9099   3.7288   3.9984
+#&gt;   -2.5704  -6.9725   1.8329   7.5978  -0.8593   0.5273  -3.3973  23.9328
+#&gt;   -7.1605  -1.4152   7.6666 -12.9347  21.7138  -1.2969  -4.8485  -4.0536
+#&gt;    1.5020 -14.0901  20.2565  16.0215  -4.5797 -17.2057  11.4516  22.0808
+#&gt;    5.7694  -0.7467   2.2154  -6.6873   9.5679  -0.5329  -0.9066  -0.5328
+#&gt;   -5.9617  -4.3717   3.7772  -9.3009   3.4248   3.6538   2.0167  -1.1754
+#&gt;   -8.4511   4.7735  -9.5639  -3.8635   2.8697  -7.0396   4.0187  -8.9156
+#&gt;    5.1888   0.6822   1.2531  -9.0151  -2.5080  -3.4085   8.2925 -11.9264
+#&gt;   -1.1109   2.7448   3.4192   2.9126 -16.1397   4.4868   5.2626   4.0020
+#&gt;    3.4241  -1.0331   0.1459  -4.1374   9.5474  -7.8095  -1.9151  -3.1304
+#&gt;   -1.8686   0.4939   2.9359   2.4186  -6.1717  -4.0475 -10.4613   1.0937
+#&gt;    3.8963  -7.4234   3.1208   9.0334   1.1222   0.8339  -7.1781   0.6802
+#&gt;    8.1094  -6.6328   9.6605  -8.4629   7.6693  -5.5535  -2.5057   9.2899
+#&gt;    5.0109  15.5296  -0.6742  -9.4445  -8.8333  24.9679   2.9495  -0.7685
+#&gt;   -0.6991  -2.9458  -4.8978  -8.7362  11.0254   1.6207   2.0617   8.8985
+#&gt;    1.6044  -6.5704  -0.9885  -0.9802  13.6871  -0.9607  -9.8014  -4.7119
+#&gt;    4.3083   4.4132  -3.6083  -8.3834   3.2557  -1.2220  -7.0236  -6.1360
+#&gt;    0.4155   8.9627   5.8059  10.8253  -9.9329 -23.8729   8.9552  -1.7645
+#&gt;   -2.3212  -1.2192   2.5327   4.5004 -10.6159   0.5372  -0.6923  -3.4753
+#&gt;   -0.5686   7.1069   1.3838   2.7925  -1.0928  -0.2820   6.5320  -9.9073
+#&gt;    1.7437   8.6192  -1.2021   0.1432  -8.0516  -2.0394  18.8280  12.6478
+#&gt;    2.0619   1.9974  -1.2226  10.7438   0.8942  -1.1889  -6.6737   4.5507
+#&gt; 
+#&gt; Columns 25 to 32  -1.3911   3.0692  -6.5327   6.1549  -1.8614   6.7446  -4.5028   6.1221
+#&gt;   20.5083  -8.3077   8.4241  -0.9615   2.6350  -0.4336   4.3162  -4.3943
+#&gt;    3.6221  -3.4589  -0.4485  -3.6896   0.0100   6.2156  -4.5433  -0.9005
+#&gt;    1.7241   9.3956  10.2362   1.6859  -2.2215  -2.3525 -14.7515   8.7909
+#&gt;   -4.7686 -10.5521   3.6614  -9.1587 -22.2191   5.6011  -3.2201 -10.5813
+#&gt;   13.0537  -4.9376  -3.7024 -11.0930   5.8585  -4.2476  -0.8783  10.5901
+#&gt;   -7.2132   9.4312   0.1471  -7.5342  -3.0692  -2.1450  -5.2526   2.1488
+#&gt;   -0.0374  -2.0891 -10.8902   6.4740  -6.8957  -3.8138  14.3612  10.8246
+#&gt;   -2.0578   2.9070   0.7636  -2.4083   4.8585  10.4305  -6.1290   3.2920
+#&gt;    6.0745  -3.8061  11.4818  -5.4890  -7.6171   4.5417  -1.2581   3.9617
+#&gt;   -1.9831   5.8574  -9.7204  -4.0234   6.3325 -18.3013   0.0386  -0.2576
+#&gt;   14.1160   4.1519   1.4994   6.3302   4.4596   5.4791   6.9900   2.0674
+#&gt;   -0.8832   9.1233  15.2962 -10.6723  10.2131   0.2640  -0.8931   5.4990
+#&gt;   -9.4974   1.8862  10.7015   3.6607   3.6988   0.3356   8.3397   3.7397
+#&gt;   -4.9341   8.5224   5.5406  -2.2485  15.9529 -16.2531  11.6592   4.1163
+#&gt;    2.3302 -10.0099   9.2156   4.9441  -0.7977   1.2424   5.3864  -1.9414
+#&gt;   -7.3847   7.0329   2.7319   3.1262   0.3203 -10.2225   0.6724  -8.4490
+#&gt;   10.0434 -11.5953   5.7548  13.7025  -8.6265   5.6097   6.8491   3.0921
+#&gt;    7.2897  -3.8818   0.8764  -2.4209  -1.4158   6.2946  -9.5363   2.0281
+#&gt;    3.1655  21.0592   0.5048  -4.6175  -8.7786  -9.3837  -1.6558  -7.4198
+#&gt;    3.3693  10.7326  -1.0920   0.7143  -0.5083   1.2072   3.9200  -0.7866
+#&gt;   -8.5052  -1.6198   0.3570 -15.7462   0.2321   4.3145  -2.1609  -8.6069
+#&gt;   -3.2597  -6.2662   2.6336   3.8607  11.7085   5.2244   8.3493   9.3229
+#&gt;    4.6654 -11.0545  -4.0304   0.3089   6.3779  14.5641   9.4906   2.8101
+#&gt;    8.3755   7.4700   1.5303  -7.4667  -3.8557  -2.9146  -2.2516  -8.7543
+#&gt;   -6.7077  -3.4594 -13.8202   7.8683   6.8754  19.9426  -2.8319  -0.6905
+#&gt;   -2.6036   8.8536   1.6484   9.1093   5.6961   9.1970   3.0565   0.7573
+#&gt;    2.1711  -9.3552   1.2404  -2.5395  -1.8879  13.0455  -2.4307  -4.2132
+#&gt;   -1.1247   1.1490  -6.4411  12.0462  -0.5293  10.0444   7.4590   8.2738
+#&gt;  -13.2797  -2.3689  -8.9095   5.2106  -2.0309  -9.6698  -4.5866  -1.4056
+#&gt;   -0.0742  -1.0281   2.2397   2.6643  -1.4644   4.5541   0.5894   2.8064
+#&gt;    3.0200 -11.9664   8.1987  -5.7961   3.0824  -5.5006   3.5044  -1.8273
+#&gt;    5.7003   8.4931  -1.9940  10.1808  -9.4862 -10.4904  -5.6016  -1.3174
+#&gt; 
+#&gt; Columns 33 to 40   0.2859   0.9840   2.2459  -5.8183  -5.7843   1.8694  -2.8571  -3.5816
+#&gt;   -2.0551   7.1783  -1.1572   1.5118  -5.3523 -11.4000   0.2914   8.7751
+#&gt;   -7.8758   9.6504   8.1545  -2.8151  -6.2475   5.7657  -0.4973   4.8358
+#&gt;   10.9517  -0.8912   0.9366  -1.4467  -3.5532   6.1168   7.2917   2.2090
+#&gt;    4.6189 -12.8922   9.2277  -9.2239 -10.5154  -3.4526 -10.3148  -8.3928
+#&gt;   -4.9455  16.6823  -0.9426 -14.8247   2.2910   3.6689  -7.7723  -3.2236
+#&gt;    1.7229  -1.7162   7.5840 -10.9975  -6.2808  -1.5537  -0.2561 -15.7143
+#&gt;   -5.6600  -7.6340 -11.1233  -9.4201   8.8585  -3.8014 -12.8213  -6.5898
+#&gt;   -1.7536   1.6171   1.4321  13.1670   1.2993  -1.2554   4.0438  13.5872
+#&gt;   -1.3886   8.3679  -6.1426   2.6054  17.4474  -2.2992  -3.5374   2.9025
+#&gt;   -8.0930   2.7702   9.6704  -6.5223  -3.7036   4.3249  -4.4369  -9.1160
+#&gt;   -3.8686  -0.0064   6.5268 -22.2443   1.2206  -1.4583   2.5408  -7.5054
+#&gt;   10.2556  -6.3264   0.3958   7.4226  -4.6827   0.1756   1.1814   3.9796
+#&gt;    1.3106  -1.2637   8.7511  -1.4127   2.6299  -0.2128 -12.0432  -0.7313
+#&gt;    1.5478 -12.4681  -7.7595  11.3704  -8.4924  -3.9687   1.2540 -12.7846
+#&gt;    0.1630  -7.1268   2.0465  -1.0191   2.5131   5.7533   8.5044  -0.3682
+#&gt;   -9.3258   2.9014   2.1419   3.9074  -2.2512   4.1822  -1.8971   2.1417
+#&gt;   -0.0288  -4.3303 -11.6703  -4.0828   0.8670  -4.3038  -8.3397  11.6375
+#&gt;    2.7956  -3.4149   0.8683  -3.5767  -0.4766  -4.0104  -3.5698  -7.3545
+#&gt;   -0.0359  11.6808   5.1896   2.8549   8.1802   7.2720   4.9197   0.1529
+#&gt;   -0.4764  10.4557 -12.9198  -1.9820   4.1237  -1.1226  -1.9904  -3.2599
+#&gt;   -2.6389   3.2737  -9.9861   3.6453  -6.9060  -7.1317  -1.5258  -1.1851
+#&gt;   -6.5330  -7.2562  -3.5095   2.0451  13.9918  -6.1458  -3.0096   3.0420
+#&gt;    9.2042  -6.6191  -0.0189   7.3594   5.9885  -5.6567   3.1459  -2.4510
+#&gt;    0.1703   7.4191  21.8283   5.8142   7.5045   3.8907   3.2357   6.2399
+#&gt;    7.4051  -2.9737   1.3531   3.2412  -8.0534   7.6714   2.4359  11.1779
+#&gt;    3.5949  -3.2263   4.4387   8.6041  -4.2712   3.5999  -1.2865  16.8223
+#&gt;   -3.5910   2.3213   6.4177  -3.0644  -4.7243  -1.8941   1.9550   6.2904
+#&gt;   -8.8605 -15.7645 -15.2876  11.0053  -2.5527   0.4685   2.4509  -1.1892
+#&gt;    1.3369 -10.9335  -1.8109  -4.6219   1.3658  -3.2621  -2.9472  -8.4462
+#&gt;   -0.2522   9.8344  -2.9380   2.2742   0.5041   2.9737   9.0709  -1.7162
+#&gt;    0.8950   0.3126  -0.5070  22.7424   9.5379   9.2431  -1.0588   6.9257
+#&gt;    1.0140   3.1717   7.1245   4.6121  -3.1624   0.2608   1.6049  11.6702
+#&gt; 
+#&gt; Columns 41 to 48   3.0304   0.0680  -7.5101  -7.4402  -2.6598  -2.8985  -5.8781   4.7761
+#&gt;   -4.8228   4.0899  -3.5155 -12.1874   2.6026  -4.9185   6.2257   7.5257
+#&gt;    8.5117 -15.6475  -0.8342   3.4969  -3.1766  15.5418  -9.4967  -2.2512
+#&gt;   -8.1561   0.9059  -8.3890  -1.3538 -10.3790   2.8180  11.7988   4.0264
+#&gt;    1.0914  -1.2328  -4.9416 -12.2846  14.0517   0.3156  -5.3539   4.6993
+#&gt;   -8.4228  -5.0796  11.6175   0.8570  -9.9459  -0.4718  -0.9525  -4.2221
+#&gt;   -4.2918   8.9565  -9.6403   9.7461   4.6375   1.3586   3.2488   8.1169
+#&gt;   -4.1697   6.8415  11.9651   0.1357  -7.7237  -1.6472   1.5552  -9.2615
+#&gt;   18.6786  10.7467  -5.8624  15.0461  -4.0063   2.0067   3.4129  -0.1880
+#&gt;    1.8563 -10.2713   0.6743  -3.7190  -1.5415  -5.4851  -6.5890   9.7558
+#&gt;    4.9810  -8.3962 -11.9026   4.6082   1.0926  -3.8416   3.4724  -6.4561
+#&gt;    5.9249  -8.2414  -0.5522  -3.7924  -1.8490   4.4591  -8.1456  15.5649
+#&gt;   -6.6237  -1.1452   5.2781 -15.0934   5.3808  -3.9231   3.4970  -5.5533
+#&gt;    5.1679   8.7257  15.9771  -7.2731   1.2025 -11.6389 -10.8809  -0.5952
+#&gt;    2.2360   1.5761   0.3329 -15.2636   1.0576  -6.3211   1.5507  -0.4872
+#&gt;  -16.3665  -1.6977   0.2362  -3.2404  -0.2077  11.0188  -3.7190  -5.1220
+#&gt;   -4.1080  -9.6898   2.4934  -6.1365  -7.9426   0.9949  -7.5373  -9.7230
+#&gt;   -4.5896  -5.6256   4.4149  -9.9634   4.7889  -2.4785  -1.6755  -1.0807
+#&gt;   11.3292   4.1457  -3.1557   0.4664   6.4328 -15.9121  18.2348   5.1840
+#&gt;   -5.4514   6.7970  13.5304   7.8403  -1.2932   5.4044  15.9420   1.2582
+#&gt;    3.3170  -6.9941   1.8947   2.6167  -2.5129  -3.3292  -1.8862   3.4555
+#&gt;    1.1166   8.8201 -11.9609   3.5047   4.7559   8.0647  -6.9492  -0.6075
+#&gt;   -0.1691  -8.5591  14.4825  -2.3782  -1.3802  -0.6122 -10.3782   5.3662
+#&gt;   14.2644   8.7466   3.7724   1.1308   9.7657   5.6476 -19.4576  -2.2649
+#&gt;   -1.1631  17.2467   4.2864   0.9452  -6.5103   5.2915  -1.7537  -2.0942
+#&gt;   17.6076  -6.2901  -2.1301   3.3579   2.4108  12.9935   0.7542  -3.4535
+#&gt;   20.8561 -18.3694 -13.3298  -5.0167   4.9272  -8.1185   8.3193 -13.0272
+#&gt;   -1.6616   5.9349  -3.2905  -0.6601   6.3905   6.9249  -6.5231   0.1840
+#&gt;  -11.0278  10.2004   4.1260 -13.8157   3.5648  -3.9353  -1.6771  -5.5769
+#&gt;   -7.6532   8.4180   1.4966  -0.0342   1.0259  -8.0851   7.3364  -5.2486
+#&gt;   -1.8772   4.3241  -3.9665   4.0804  -2.0016   2.0577   6.0984   5.7852
+#&gt;    1.8621  10.3355  18.7347   0.5608  13.8933  -1.9063  -3.9664   0.1928
+#&gt;  -13.8940  -2.1754 -17.3681   5.7886  -6.5112  13.9914  -5.8627  -0.0077
+#&gt; [ CPUFloatType{20,33,48} ]</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> 1.6.1.</p>
+</div>
+
+      </footer>
+   </div>
+
+  
+
+
+  </body>
+</html>
+
+
diff --git a/static/docs/dev/reference/torch_conv2d.html b/static/docs/dev/reference/torch_conv2d.html
new file mode 100644
index 000000000..e73ebb768
--- /dev/null
+++ b/static/docs/dev/reference/torch_conv2d.html
@@ -0,0 +1,342 @@
+<!-- Generated by pkgdown: do not edit by hand -->
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+  <meta charset="utf-8">
+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+    <h1>Conv2d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_conv2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Conv2d</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_conv2d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>weight</span>,
+  bias = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(),
+  stride = <span class='fl'>1L</span>,
+  padding = <span class='fl'>0L</span>,
+  dilation = <span class='fl'>1L</span>,
+  groups = <span class='fl'>1L</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iH , iW)\)</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>filters of shape \((\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kH , kW)\)</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>optional bias tensor of shape \((\mbox{out\_channels})\). Default: <code>NULL</code></p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the convolving kernel. Can be a single number or a      tuple <code>(sH, sW)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit paddings on both sides of the input. Can be a      single number or a tuple <code>(padH, padW)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>the spacing between kernel elements. Can be a single number or      a tuple <code>(dH, dW)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-"><a class="anchor" href="#conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-"></a>conv2d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -&gt; Tensor </h2>
+
+    
+
+
+<p>Applies a 2D convolution over an input image composed of several input
+planes.</p>
+<p>See <code><a href='nn_conv2d.html'>nn_conv2d()</a></code> for details and output shape.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='co'># With square kernels and equal stride</span>
+<span class='kw'>filters</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>8</span>,<span class='fl'>4</span>,<span class='fl'>3</span>,<span class='fl'>3</span>))
+<span class='kw'>inputs</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>4</span>,<span class='fl'>5</span>,<span class='fl'>5</span>))
+<span class='fu'><a href='nnf_conv2d.html'>nnf_conv2d</a></span>(<span class='kw'>inputs</span>, <span class='kw'>filters</span>, padding=<span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,1,.,.) = 
+#&gt;   1.7751  1.8102 -5.3800 -3.0488 -5.4477
+#&gt;  -5.7712  2.6865 -0.0846  7.5752  6.9191
+#&gt;   5.2896  3.0661  9.7657 -15.9121 -0.4981
+#&gt;  -9.0795 -1.5815  3.5357  9.3267  4.3401
+#&gt;   2.0030  4.1984 -14.2355  0.2379  5.6598
+#&gt; 
+#&gt; (1,2,.,.) = 
+#&gt;    3.7265  -0.2746  -1.2940  -1.8794   3.8898
+#&gt;   -1.3463   4.1611  11.7422   0.3257  -2.8260
+#&gt;   -3.1741  -9.4664   0.3786  -9.1628   2.2345
+#&gt;   -3.9877   8.6068   3.5048   4.0214   3.5298
+#&gt;    1.4579   0.8858   0.2262   0.5585   1.9909
+#&gt; 
+#&gt; (1,3,.,.) = 
+#&gt;   2.8495  7.5995  2.6102  4.4934 -4.1878
+#&gt;  -4.5630  3.2005  4.1013 -2.8311  2.1538
+#&gt;   3.3536  5.8931 -4.5269  4.2960 -4.2912
+#&gt;  -10.8705 -0.3137  6.4640  0.5994  6.9229
+#&gt;   7.0797 -0.3412 -1.9438  4.4663 -0.2123
+#&gt; 
+#&gt; (1,4,.,.) = 
+#&gt;  -1.0945 -0.2992  5.3942 -4.6463 -3.2643
+#&gt;   4.5450  0.4313  2.9448  8.1186  6.1091
+#&gt;  -5.4938  8.1003  0.6197  1.0535 -8.3222
+#&gt;  -6.4377 -8.4517  6.1147 -1.4768  1.7856
+#&gt;   0.4933  1.0665  3.6550 -1.2144 -6.8495
+#&gt; 
+#&gt; (1,5,.,.) = 
+#&gt;    1.9912   6.3521  -6.3074   5.0286   1.5800
+#&gt;    4.6302  -6.5545   1.2782  -9.8425   1.6922
+#&gt;   -1.9579  11.3642  -0.7866   2.6508   2.1056
+#&gt;   -4.8874   0.3786   3.4781  -8.8948  -6.2078
+#&gt;   11.8482   3.8567  -0.3391  -0.4456   5.6384
+#&gt; 
+#&gt; (1,6,.,.) = 
+#&gt;    3.2679   1.4055  -6.6733   9.1439  -0.1774
+#&gt;   -3.6459   1.0924  -3.4128  -9.4440   6.2553
+#&gt;    1.4464  -5.3091  11.0492  -4.1211  -7.3963
+#&gt;    7.6827   7.8154  -6.3949   2.2058  -7.1125
+#&gt;    0.1458   9.6769   3.2690   1.7532   0.0174
+#&gt; 
+#&gt; (1,7,.,.) = 
+#&gt;   -5.2135  13.6151  -3.6709   2.1498  -3.5713
+#&gt;   -0.4493  -4.8700   3.7786   2.1324   8.0006
+#&gt;    4.3950  -5.6337   7.7445  -8.1921   0.6682
+#&gt;  -12.5536   2.6553  -3.4464   6.5250  -5.0772
+#&gt;    0.1851   6.6151   1.0536  -3.3529   1.2139
+#&gt; 
+#&gt; (1,8,.,.) = 
+#&gt;    0.3621   2.2075   3.1951   1.3277  -2.4578
+#&gt;    5.0692   2.1762  13.6334  -4.1285  10.3525
+#&gt;    7.2175  -5.1844  -1.5165   7.2140  -6.2546
+#&gt;   -8.7801 -13.0705  -3.9751  14.0640  -9.8164
+#&gt;   -2.6648   1.6580   4.6547   0.6180  -0.3127
+#&gt; [ CPUFloatType{1,8,5,5} ]</div></pre>
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+
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+    <div class="page-header">
+    <h1>Conv3d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_conv3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Conv3d</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_conv3d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>weight</span>,
+  bias = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(),
+  stride = <span class='fl'>1L</span>,
+  padding = <span class='fl'>0L</span>,
+  dilation = <span class='fl'>1L</span>,
+  groups = <span class='fl'>1L</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)\)</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>filters of shape \((\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kT , kH , kW)\)</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>optional bias tensor of shape \((\mbox{out\_channels})\). Default: NULL</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the convolving kernel. Can be a single number or a      tuple <code>(sT, sH, sW)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit paddings on both sides of the input. Can be a      single number or a tuple <code>(padT, padH, padW)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>the spacing between kernel elements. Can be a single number or      a tuple <code>(dT, dH, dW)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by      the number of groups. Default: 1</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-"><a class="anchor" href="#conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-"></a>conv3d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -&gt; Tensor </h2>
+
+    
+
+
+<p>Applies a 3D convolution over an input image composed of several input
+planes.</p>
+<p>See <code><a href='nn_conv3d.html'>nn_conv3d()</a></code> for details and output shape.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='co'># filters = torch_randn(c(33, 16, 3, 3, 3))</span>
+<span class='co'># inputs = torch_randn(c(20, 16, 50, 10, 20))</span>
+<span class='co'># nnf_conv3d(inputs, filters)</span>
+}
+</div><div class='output co'>#&gt; NULL</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+<div class="row">
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+    <div class="page-header">
+    <h1>Conv_tbc</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_conv_tbc.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Conv_tbc</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_conv_tbc</span>(<span class='kw'>self</span>, <span class='kw'>weight</span>, <span class='kw'>bias</span>, pad = <span class='fl'>0L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>NA input tensor of shape \((\mbox{sequence length} \times batch \times \mbox{in\_channels})\)</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>NA filter of shape (\(\mbox{kernel width} \times \mbox{in\_channels} \times \mbox{out\_channels}\))</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>NA bias of shape (\(\mbox{out\_channels}\))</p></td>
+    </tr>
+    <tr>
+      <th>pad</th>
+      <td><p>NA number of timesteps to pad. Default: 0</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
+
+    
+
+
+<p>Applies a 1-dimensional sequence convolution over an input sequence.
+Input and output dimensions are (Time, Batch, Channels) - hence TBC.</p>
+
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+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_conv_transpose1d.html b/static/docs/dev/reference/torch_conv_transpose1d.html
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+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Conv_transpose1d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_conv_transpose1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Conv_transpose1d</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_conv_transpose1d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>weight</span>,
+  bias = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(),
+  stride = <span class='fl'>1L</span>,
+  padding = <span class='fl'>0L</span>,
+  output_padding = <span class='fl'>0L</span>,
+  groups = <span class='fl'>1L</span>,
+  dilation = <span class='fl'>1L</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iW)\)</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>filters of shape \((\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kW)\)</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>optional bias of shape \((\mbox{out\_channels})\). Default: NULL</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the convolving kernel. Can be a single number or a      tuple <code>(sW,)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p><code>dilation * (kernel_size - 1) - padding</code> zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      <code>(padW,)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>output_padding</th>
+      <td><p>additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple <code>(out_padW)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>the spacing between kernel elements. Can be a single number or      a tuple <code>(dW,)</code>. Default: 1</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-"><a class="anchor" href="#conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-"></a>conv_transpose1d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -&gt; Tensor </h2>
+
+    
+
+
+<p>Applies a 1D transposed convolution operator over an input signal
+composed of several input planes, sometimes also called "deconvolution".</p>
+<p>See <code><a href='nn_conv_transpose1d.html'>nn_conv_transpose1d()</a></code>  for details and output shape.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>inputs</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>))
+<span class='kw'>weights</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>5</span>))
+<span class='fu'><a href='nnf_conv_transpose1d.html'>nnf_conv_transpose1d</a></span>(<span class='kw'>inputs</span>, <span class='kw'>weights</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;  Columns 1 to 8   7.5092   1.7658  -2.2897  11.3350  -0.0320   9.0184   1.6378  -8.5314
+#&gt;   -1.4322  -3.9954   1.7131  -3.8299  -8.1916   1.0842  17.4139  -1.0314
+#&gt;    1.7253  -0.6135   5.5765   7.7010  16.5626   0.0593  -2.5966   0.6596
+#&gt;    7.4652   8.0367   8.5493   4.8215   0.3527  -3.4042 -15.8269   9.5207
+#&gt;   -7.8330  -1.6530  -7.7436  16.6798   2.0674  -2.3725  -9.2932  -7.2266
+#&gt;   -0.2451  -9.4369  -9.4362  -3.3380  -5.3237  -0.2217  18.2118   4.1885
+#&gt;   -0.4496   3.4870  -3.8555  -6.2449   3.7345  -4.6552  -8.5749   3.3828
+#&gt;    6.2772  -5.7245  -8.2870   8.6299  -0.3788  -4.0862 -12.6486  -4.0464
+#&gt;   -0.5047   0.3391  -8.3623   1.6752  25.0274   2.7063 -15.5494 -18.5210
+#&gt;   -4.6138  -2.7427   7.0834 -12.1850   7.8766   5.5000  -4.9467   8.5112
+#&gt;    5.8172   0.6223  -0.8831  -6.5954   4.4467  -3.4453   5.2012   2.3533
+#&gt;    0.1983   4.3668   0.8347  -0.4679  -0.0355 -10.2468 -10.3628   7.9219
+#&gt;    3.2171   1.4948   5.1908   5.6194   8.6378  -7.5130  -2.7667   8.3995
+#&gt;   -8.5638  -5.1357   0.2054  -1.2208   3.7810   6.8747 -11.9120   1.7171
+#&gt;  -10.3604   0.8502  -3.4820  -5.6220  -4.1899  -7.6872   6.2128  -4.1734
+#&gt;   -1.6649  -8.1770  -0.9832  -0.7593 -13.7185  -5.0273   4.9400   7.6807
+#&gt;   -0.5635  -9.3564  -7.4733  -0.7659  -3.2477  -1.7132  -6.3380  14.2290
+#&gt;   -0.9348   4.5867  -1.5974  -6.5680  -5.5891  -5.2598  -8.8694   8.8517
+#&gt;    1.0837   4.8790  -5.5669  -1.7414  -0.2322  14.3767  -1.7892   7.5502
+#&gt;   -1.5047   2.4748   5.7327   4.5888  -0.0441  -1.4232   5.7458   4.4760
+#&gt;   -1.0290   8.9721   1.3632   7.1096  -3.1683   4.9745  12.1966   1.6666
+#&gt;    5.3529  10.8940  -4.7734  -3.0531  -0.8778  -4.1124   8.3844 -10.1344
+#&gt;    7.6159   9.8673   2.7259   6.4998  11.2159  -5.1155  -2.6251 -12.3824
+#&gt;   -0.9453  -3.2646   8.0797 -11.7054   2.4791   2.1673  -8.9336  16.4105
+#&gt;   -2.7929  -8.9685   9.1113 -10.8023   8.3289  16.1866   1.6608  -1.5382
+#&gt;    3.4635  -6.9134   7.1730  -4.7356   9.2693   3.9543  -3.4936  13.5175
+#&gt;    3.6057   2.8769  -5.7268  -4.9874  11.7083  21.3028   3.9938  -9.9986
+#&gt;   -0.8990   0.3293  -0.7769 -10.3224  -0.2324   6.5558   2.0668  -8.2520
+#&gt;   -3.7813  -7.7431   1.1502  -1.2359   8.3363  -1.8446   6.6262   7.7890
+#&gt;   -7.2151   1.2037   6.9325  -4.6571   8.0520  -9.7497  11.2976 -12.2372
+#&gt;    4.1542   5.0674  -7.7885  -4.8527  -5.1850  11.2249  -6.6870  -0.6467
+#&gt;   -4.0202  -7.4219   0.9196   0.9713  -5.9721  10.8120 -11.7623  18.6043
+#&gt;   -3.0401  -8.4811   2.6108   1.0898  -3.3030   4.8920 -22.7463 -12.8264
+#&gt; 
+#&gt; Columns 9 to 16 -11.3871   6.6489   2.1386   1.4176 -16.9844  13.3540  36.5843 -16.1047
+#&gt;  -12.2285  -5.0270   0.4176 -11.8998  -3.2067  -5.2539  10.9849  -1.1460
+#&gt;   -3.7166   8.9264 -23.5993  -8.0172  12.9451  -1.0583 -15.8391  14.2367
+#&gt;    2.4835  -5.1355 -19.5458   4.9303  10.9628   6.8489  12.0889 -20.8065
+#&gt;   -1.2228  -4.5216   8.4793  -2.0482  10.2045  -7.3868   4.6700  17.5928
+#&gt;   19.5259   0.9556  -2.7849  -9.8909 -10.0789  14.4241 -16.0743  -1.7923
+#&gt;  -14.7718   0.6720  17.6044   1.4031  -7.3729   2.3456   7.9769  -5.7674
+#&gt;  -13.0827  11.1574   0.1594  -4.2948  -6.1294  16.2569 -27.6479  10.4936
+#&gt;    2.6115  -6.1979  -0.4239   0.5162   5.4815   2.2142   7.2077  -4.4299
+#&gt;    0.8763 -25.9816  -6.9282  -7.0469   5.0726   4.3156  -6.7208  -8.3778
+#&gt;  -11.1706 -13.3165 -17.6183  12.8667  -0.1709   2.1462  14.3785   1.1981
+#&gt;    3.7179  -6.1258 -10.7103   1.5823  -3.3837 -18.7970   3.7765   9.6193
+#&gt;   -9.2632   9.0765   2.8382   8.3116 -10.0603  -9.7842  23.6758  -0.5904
+#&gt;    4.3770   3.8825  -1.2259  -0.2087   6.6029  -8.3191  15.9908  -9.4910
+#&gt;    5.2189   1.0246  24.3959   9.9876  16.1522   2.3171 -10.4795   4.4048
+#&gt;   -7.3139   0.3050  -8.9961  -7.3436  -3.6458 -13.0024  -1.3321   7.4308
+#&gt;   -9.5448  -5.8965   1.0568  10.0638  -7.1460   1.8492   0.3626  10.6839
+#&gt;    5.5975 -11.2718  17.3997  -3.7610   4.5311 -11.0250  -8.0981   1.1792
+#&gt;  -13.1293   6.8548 -13.7434 -19.7634  -7.0857   7.8447  16.7162  -5.8272
+#&gt;   -6.6048   6.5744   5.2019   3.0699  -3.1838  -3.7632  14.0776  -5.3063
+#&gt;   -9.2272   4.5861   4.2751  -4.7052   4.1038 -10.0239   8.6336 -12.7005
+#&gt;  -15.6721  -7.9124  15.8599   3.2827   1.5979  16.0084   9.0554   4.3529
+#&gt;    9.0426  -0.5163  -0.7177 -12.6277  11.0778 -15.5229   5.6362   8.5260
+#&gt;   -3.5502  -3.4809   6.0038  -9.4615  -3.4595   6.3043   8.3116  11.0908
+#&gt;  -10.1339  15.4605  -3.8612 -12.8668 -15.8611   7.7750  -4.3362  -5.4805
+#&gt;   -0.9741  -8.9953  -8.0686   9.5095  -3.8287 -12.4131   5.5750   5.0586
+#&gt;   10.1511  -9.4778  19.2425 -19.7662  -8.8612  14.5213   3.6339 -11.1999
+#&gt;    7.4137  -8.5740 -15.2353  21.7661   4.4776  -6.8500  -6.5476   1.0748
+#&gt;   16.9917   1.6460   6.9789   5.3569  -1.3209  -8.1805  -9.5055  15.4783
+#&gt;   19.7126   1.5753   0.5239  -0.8367   0.7918 -10.4859   3.5727  -6.1451
+#&gt;    9.6539   6.1113  -1.4215  -1.1832   6.7106  -0.4782  -5.3751   0.9431
+#&gt;   -5.2650   2.0490  14.0690   5.9502 -23.8761 -17.0422   5.3440   6.7202
+#&gt;    0.0364   4.2985  -2.7618  10.1270   7.2346  22.9594  -8.9530 -12.4891
+#&gt; 
+#&gt; Columns 17 to 24   5.0461   7.1393  -4.2121  -2.7086   3.1919   5.6694   8.4918  -4.6152
+#&gt;  -10.5025  -7.5375   6.6254  -1.4796  -1.2396 -16.7928  -1.7118  -4.0963
+#&gt;    4.4701  -3.2115   4.5510  -2.9465   0.0178  -2.7616   2.3772  -6.2773
+#&gt;    1.0418   5.8846   2.3202   1.2672  12.6804   0.5868   6.5656  -8.7521
+#&gt;    0.9933  -8.4528 -14.6442 -13.8869  -0.2739  -3.4585  -2.3166 -20.1706
+#&gt;   13.5689   2.1767   3.2182  15.6104   2.1236   1.2023 -15.1057   6.0920
+#&gt;    5.2509  12.1414  -3.3489  -0.3299  10.6327  12.0399   6.9582  19.4964
+#&gt;    5.9499   3.5669  -4.9967  -8.3136  16.4572  -3.1373   7.9363  -1.5493
+#&gt;   -4.1027  -0.5909 -15.5389  -5.6274  -2.3088   1.9215  -2.9185  -9.8890
+#&gt;    4.2068  -2.6873  -2.4395  -4.3479  -6.7238  -6.0880 -12.2196  -5.3386
+#&gt;    0.0675  -1.6615 -16.3052   5.6699 -10.6777   1.2691   0.8664   1.3187
+#&gt;   -4.1581  -2.0602  -1.3760  -4.7957  15.5006   7.0492   7.2075   1.1087
+#&gt;   11.4466  -2.8854  -6.4908  -3.5479  -1.0935   1.8129  11.5216  -7.1288
+#&gt;   -2.1702 -13.4427 -10.4851 -14.5719   6.6614   3.9431  -0.3751 -17.0631
+#&gt;    8.8954 -15.5983   7.3785  -6.3945 -17.4699  -5.8576   1.7152   5.1607
+#&gt;    5.7983   5.5403  -1.8862  25.7451 -13.6046  10.8975  -7.8493   9.0120
+#&gt;   -7.8288  -5.0260   4.4384  -5.5562   6.1745   2.9839  -8.0770  -1.7941
+#&gt;    3.8588   2.9477  -1.0418  -0.9633  -3.2842   3.1351  -0.6501   3.0184
+#&gt;   -4.9276  12.2279  13.2807   0.4534   0.8122 -10.9757   7.0022  12.9495
+#&gt;   -6.8571   7.1047   1.4436  -4.8131  11.1064  -8.0598  -6.7032  -4.6377
+#&gt;   -0.4945  -1.9992  -0.9204  -2.0775   4.5513  -9.1970  19.4727  -6.7788
+#&gt;   -8.4687 -10.7235   1.2399   0.8373 -11.5367   7.3060  -4.3115   3.8187
+#&gt;    0.0777   3.6163   5.9119  -5.5425  -2.0467  -6.0968  -1.8903 -10.3778
+#&gt;  -12.9548 -10.2684  10.7147  -3.0850   0.1460   1.8934  -2.8139  -8.0602
+#&gt;   -5.8127  11.3679  23.3444  -6.8042   9.7215   0.7005   8.5951  15.4495
+#&gt;  -13.7565   4.9358 -10.4920   4.4700   5.7130  -2.3677  -7.3746  -4.8896
+#&gt;    5.7679   7.9859   4.3357  -8.2949  -1.1684  -1.2039  -2.5838  12.6753
+#&gt;   -9.8511   0.2315   0.0868   1.5931   9.8363  -7.6647   6.2375  12.4857
+#&gt;   -9.6911  -3.6033   0.3459  -8.1519  20.5941  13.4930 -15.9664  -6.7867
+#&gt;    4.9953  -7.6476 -12.2583 -13.9312   9.3277 -15.1152   7.0205 -10.9346
+#&gt;    1.8251  12.8465   8.8661  -6.7894  -5.5400  -8.6703  -0.9770  -1.5437
+#&gt;    7.5049   0.6881  -0.4029  -8.4497  -7.3942   4.8276   5.9813 -22.4572
+#&gt;    9.3542   4.8128 -18.0321  -7.1903   7.4505   8.9835   5.8222 -10.9540
+#&gt; 
+#&gt; Columns 25 to 32   6.3862 -13.3302  11.1319   4.7180   3.2073   7.9466  -2.5492  -6.9001
+#&gt;   -4.1443   1.9468  14.3526   2.2147  -4.3649  -1.3800  10.3766   2.3538
+#&gt;   -5.5984  -0.9744  20.5592  10.5067   0.7734   4.8639   5.6575  -4.2674
+#&gt;    5.8836   2.3992  -0.4241   7.4990   8.4222  -9.8820  -3.9143   2.9094
+#&gt;   -6.3367  13.7460 -16.2125  -6.6184  12.3727  -8.0381  -7.0010  -4.2844
+#&gt;  -15.1284  -9.3837  17.3818  10.3391  -6.7978  -7.0412   5.0071   3.3687
+#&gt;   22.8576   4.2799   3.9247  -8.8880   1.4415  -5.9465  -1.5270  -6.3403
+#&gt;   11.0400  -5.5754  -2.3710   2.9331  10.7125  -5.0097 -11.5741 -13.6864
+#&gt;   -0.7302  -7.3042 -22.2769  -3.0296   4.8445  -3.2851  -9.6227  -3.7467
+#&gt;  -19.9910   0.8801  15.4423   7.2928  -1.8487   2.2269   1.0567   2.8086
+#&gt;  -11.5817   0.3192   4.6724  -1.6993  -6.4201  -5.2266 -10.4855  -1.9111
+#&gt;    1.5360  13.3514  -2.8108  -0.8963   6.2879   3.1514  -3.8111   3.7554
+#&gt;   -4.3568   2.4317 -13.1502  -1.5801   0.0749  -4.2072   4.1061  -7.9029
+#&gt;   -2.3847   4.9557  -5.4506  -6.4766  12.1524  -3.1862  -6.4464  14.4038
+#&gt;    2.8651  10.2111   5.3075   7.2955 -12.2476  -1.0174  -2.5743  -9.8317
+#&gt;  -10.2342   4.2476   2.4423   1.7453   1.8884   8.2515  12.6821  14.2898
+#&gt;   -7.5861   3.9747  13.3662 -11.6256   4.9735  -1.1453   9.0942  -8.3465
+#&gt;   -2.7225  -0.4563   3.6159  -2.0268   7.0906   3.4329   5.7170  -5.0272
+#&gt;   -1.1599  -4.1004   5.8942   2.0763   1.1574  -9.0853   3.6334  14.5240
+#&gt;    8.1220   0.6985  -7.9109  -2.3143  14.3209  -8.1596   0.3304   3.9447
+#&gt;    5.7197  -5.4664 -14.2248  -7.1517  -0.7762   0.1096   7.9700   0.3414
+#&gt;    0.7410 -12.2951  -6.8423   2.7165  -4.8665  -0.1585  -2.9648  -7.9042
+#&gt;   -4.5787 -12.3927 -10.4110  -8.9851 -10.0936   6.6182  -8.9669  -0.8006
+#&gt;   -3.5018  -6.3532  -0.8541 -11.4317   6.5664  -5.0120   1.4957   0.2144
+#&gt;   24.9434   3.5060  12.1157  -8.8518   6.9327  -1.4099  10.5455   7.7316
+#&gt;   10.0264   2.2959  -3.4697  -3.3767  -7.6848   2.7017 -10.1378   6.6349
+#&gt;   -4.8995 -14.5042 -12.6239   2.2156   1.0402  20.8802 -17.5670  -1.9627
+#&gt;    6.5268   1.4303  14.2157 -12.5419  -5.7303   6.1321   0.1528   1.3539
+#&gt;  -12.5683   9.2066   5.2977 -12.3084  -1.3095  -2.0016   1.9560   2.1589
+#&gt;    9.9170  -0.3247   4.2993  -9.8070  -3.8887   2.7713 -11.7955  -5.8791
+#&gt;  -11.3715 -18.2381  -2.3663  -1.8315   2.1624   4.0394  -0.8909   0.9234
+#&gt;    6.7059  -9.0806  11.7172  -8.0449   7.2731  -2.4154  -6.7864   4.5915
+#&gt;    9.4971   3.7427  14.1617  -4.2874   1.0944  -2.3990  -6.5816 -10.5266
+#&gt; 
+#&gt; Columns 33 to 40  -6.3455  -1.8703 -15.4032  -7.0134   0.0208   4.1648  15.8100  28.9453
+#&gt;   -0.2216   7.7284   2.9620   0.3639   9.1237  -1.6198 -12.2399  -5.9175
+#&gt;    3.7651   0.1576  -8.9333  -9.0791 -11.2053  -2.2744 -11.9607  -3.9005
+#&gt;   17.8670  -3.7561  -8.5349 -16.0191  -2.2376  -4.5203  -1.3330   3.7970
+#&gt;   -3.0877  10.5827  11.0985 -12.3419   2.2013  21.9847   0.2310 -17.1320
+#&gt;    7.2605  -3.4555  -7.0256  22.1650   3.7467  -4.8336   4.7423 -13.1405
+#&gt;  -20.2477  -7.8561   0.6372   6.1106  11.2098   1.2973   6.5043   7.3840
+#&gt;   11.2250   2.4119  -7.7690   8.4223 -17.3652  15.1460  12.6436  -8.3118
+#&gt;   13.2364   0.5188  -1.7755  -8.5054   5.9982  10.0803  -3.5235  -1.4883
+#&gt;   11.7300  -9.9142  -2.5637  23.1697  -7.8644 -17.2795   6.9793  -8.2001
+#&gt;  -16.2262   7.3128  15.7658  -4.2027   8.8820  -1.2745  31.9361   0.3185
+#&gt;    1.1794   0.1328  -2.6065  -0.2457  -4.7804  -0.1214   4.5615  -8.4000
+#&gt;    4.9526  -0.0621  -3.1849  -5.1478 -12.8276 -10.0473   4.9044  -2.8794
+#&gt;   -1.6401   1.8186  11.2167 -15.1170  -6.1526  10.8328   3.0058  -2.7609
+#&gt;   -3.8121   3.5605  11.7003   8.8057   7.3935   9.8199  17.5917   0.2641
+#&gt;  -12.7404  -6.6300  23.2736  -0.9949 -13.6210 -14.9099   8.3208  -1.2268
+#&gt;   -1.2182  -1.0243  -1.3643  13.5720   1.6856  -0.1947   4.8962 -13.1538
+#&gt;   -3.8200  -5.2339   2.9613   9.7482  -3.4041   1.9207   3.8221 -10.3226
+#&gt;  -20.5769  -5.2420  14.3962  -7.3192  -1.0670  -5.8244  -7.1523  13.7788
+#&gt;   -3.4210  11.8004  -1.8985 -24.2199   5.1046  11.8401 -12.9386   9.0000
+#&gt;   -8.8798   6.7953  -4.5977  -0.4655  -1.1572 -10.4521 -17.8616  11.4842
+#&gt;    1.4247  10.5945  -0.6283   5.4646  -0.4637   2.7673   0.6746  -2.5160
+#&gt;   -8.2162   2.1856   8.7255 -17.7315   3.5021  13.9792  -0.5242   3.9809
+#&gt;   14.6361  -9.4655  10.5961   4.9845  -5.0705   6.2025  -2.0503 -11.3176
+#&gt;    2.5859  -2.6797   2.8491  -1.9721 -20.3271  -7.8121 -19.6944  -5.5982
+#&gt;    6.4359  -1.3808  -3.4707   7.9480  15.1759  -8.0214 -21.5689  -7.4061
+#&gt;   13.3360   0.9918  -1.9450  13.9269 -13.6345  -1.5715   6.1679  -6.9350
+#&gt;   -3.8513   8.5463  -6.2359   3.3214   6.2455 -11.9007  -5.4180 -17.7371
+#&gt;   13.6853  -3.6301   1.5904  -0.9607   1.7988   8.9489 -12.3001  -9.4200
+#&gt;   14.3144   7.7870  -1.3917  10.7065   3.2846  -7.5796  -0.4937 -13.6019
+#&gt;  -10.1535   0.3431   4.9430  -1.6263  -2.4084   9.9046  -2.4994   8.4613
+#&gt;  -11.6224  11.0871   3.0902   5.3550   5.7273   0.5542 -13.9770   5.3782
+#&gt;    4.7798  11.4943 -15.1648  -1.2837   0.3482   9.6982  15.4955   1.1748
+#&gt; 
+#&gt; Columns 41 to 48  -4.9976  23.7240  -1.7815   3.8400  -3.4735  10.8928  -3.4043   5.3599
+#&gt;   -7.3915   3.1934  -6.8950 -12.3522 -12.3706   6.8505   6.8892   1.9762
+#&gt;   -0.0715  -6.4888  11.4185   7.9727  -9.5411 -11.6563  -7.4205  19.0864
+#&gt;    4.1723  -1.9456  20.3811   3.8453   2.1266  13.0805   0.7215 -14.0059
+#&gt;  -13.7520  -2.6440   0.1240   2.0033 -19.2793  -8.4554   3.3368   7.8435
+#&gt;    0.9474   0.0677  -9.0905   8.6048   7.1732  -8.9498  10.6629  -6.7055
+#&gt;   12.3113  -4.2834   5.2673  -5.5216  14.5509  25.9458  -4.5646 -19.4167
+#&gt;    1.0545  -3.0002  -9.6247  20.9034  -9.3953  -8.2658 -17.1738  14.5474
+#&gt;   14.9427   1.7972   1.3384  -4.2754  -0.1040  -1.6516  -7.3495   7.4288
+#&gt;    0.4807  -2.4756 -13.8721 -17.2068  -1.2552  10.4579  14.9359   0.3989
+#&gt;    5.6744  11.8811  16.9016   4.7813  -4.1635   0.3091  10.3594  18.1963
+#&gt;   -7.1362   3.7953   4.2058  -6.5452   3.4073   6.2320  -0.0451  -1.7456
+#&gt;   -2.2142   3.8023  11.0165 -10.9944  -1.6709  -1.4512 -13.7373  -1.5756
+#&gt;   -7.9698  -2.3468   7.9848 -10.4275  -1.2309  -4.3872   1.3864  17.3116
+#&gt;    5.6839  -1.3079 -12.1567   5.1419   5.0424 -10.0917  -3.4104 -19.8380
+#&gt;   -0.7423   5.0781   9.0403  -5.9639   1.3515   9.2461  -2.3630  -0.6001
+#&gt;   -0.0186  -3.4549  -4.5367  -7.8862   3.3821  -8.4757   3.8030   6.7765
+#&gt;    0.1186   2.9832   4.4354 -11.9722   6.9402  17.6355   7.4197  -9.8151
+#&gt;   -5.0030   2.8123  -2.4880   2.9521  10.4449   2.8441   6.4338  15.1839
+#&gt;   -1.2206  -7.7154  12.4448  -0.4596  -4.7501   4.3778  -3.3127   0.9771
+#&gt;   -2.6349  12.9001   6.9091   0.6244   1.0455   1.8382  -4.4529 -24.9631
+#&gt;   13.8839  -3.9984 -13.0360   8.7732  19.8289   7.8284  -9.9964  -7.6309
+#&gt;    5.5527  -3.4324   6.1997   3.6301  -9.3164   7.0911   5.2206  12.3208
+#&gt;    4.0315  -7.2320  -4.3941   1.4198  -8.1051   3.8089  -0.3144  -3.1232
+#&gt;   -0.9606  -3.1572  -9.2182   8.4840  -5.6326  -0.4005  -2.5767  -2.6649
+#&gt;  -13.1766 -12.8322 -10.1581 -13.3435 -12.8123   0.3436   3.6834  -8.1418
+#&gt;  -12.2141   6.3279  -6.3403  18.5176   2.7415  -6.8329   7.6055 -12.8831
+#&gt;   -3.3953 -12.0585  -2.7113 -11.4844  -2.2880  11.4670  13.6869  -3.0174
+#&gt;   -4.0705 -11.9181   3.4695  15.9463  12.0978 -12.2546 -14.4044 -20.0763
+#&gt;  -11.9563 -11.6130   4.1540   3.7874   2.8458   0.4595   0.0348  -9.0737
+#&gt;   -1.6164   6.9030   1.9319  11.0896 -10.8507   2.4500 -16.2906   5.5908
+#&gt;  -22.5653  -0.2314  -5.6995  -0.9661   8.5033  11.1435  -5.7294 -15.6139
+#&gt;   -6.3774  12.9915  -3.6836  15.4483  -1.1037 -12.3339  -7.1406  17.2051
+#&gt; 
+#&gt; Columns 49 to 54   3.5690  -0.0477 -15.0157  -6.9384  -0.4864  -1.2965
+#&gt;    5.0945  -3.4426   2.6572  -0.0536   6.9838   3.3913
+#&gt;   -3.3324  -0.8539  -1.1119  -9.6154  -4.1228  -0.7313
+#&gt;  -13.3576  -5.4817 -19.9596  -8.3586  -1.1764  -0.7123
+#&gt;    3.6075  -2.7385  16.3039   3.1493  11.0811   4.8244
+#&gt;   13.1777  -2.9419  -5.6008   3.2825  -5.1851  -7.3926
+#&gt;   -3.9983   8.9675   4.6193 -12.1679  -1.6746  -5.8087
+#&gt;    1.7991  -7.8411  -1.2248  -1.0864  -4.2102   6.9872
+#&gt;   -2.4178   7.7497  -3.2832   1.6366   6.1673   3.2913
+#&gt;   10.5111   4.1524  10.0971  15.3716   0.9500  -1.6488
+#&gt;    7.9344  -5.6579 -17.3647  -1.3932  -0.2217   4.5559
+#&gt;   -1.4953  -1.9520  -1.7041  -5.4177   4.6078   0.7177
+#&gt;   10.0156  10.1209 -14.6283 -14.6672   5.8308   0.7388
+#&gt;    4.7466   3.5613  -9.8190  -1.6358   4.7033   4.0941
+#&gt;   -3.9140  -3.0370  -0.3425  -2.2664 -12.3400  -0.0259
+#&gt;    0.2508  -3.1653   0.5941   0.1653  -5.9538  -4.8980
+#&gt;   10.7472  -7.2793  -4.1071  -4.6229   6.7291   4.9974
+#&gt;  -17.5345  -1.0950  20.1220  -4.6122  -0.2784  -1.4945
+#&gt;   -1.7530 -18.5442 -11.9271   3.4168   6.6144   5.6142
+#&gt;   -0.2283  10.1079   7.8465  -4.4248  -3.0379  -1.7377
+#&gt;  -20.2794   5.7853   5.6740   5.6364   6.4354  -4.9923
+#&gt;    5.2906   6.5501  11.1736  -3.2384  -7.3907  -1.6246
+#&gt;    1.9878   0.6445   3.7551 -14.4261   8.9907   8.1813
+#&gt;    7.0414 -21.7505  11.8331  -6.3518  -1.0108   3.7186
+#&gt;    4.7247  -6.9536  -5.1810  -1.9012  -3.5109  -0.4010
+#&gt;  -12.9440   4.0629   6.3436   9.9396   9.9506   3.6067
+#&gt;    2.5080  -0.0307  -0.9016   8.1904  -5.3479   0.7115
+#&gt;  -16.8321   5.3424  11.2973   0.5175   2.7200   3.1669
+#&gt;    8.0001  -9.7692  -5.3772  -8.5085   0.2239  -0.2333
+#&gt;    5.0423   4.4927   5.9419  -7.3695   4.7928  -6.7803
+#&gt;  -12.1958  15.1992   2.2614  -6.3638  -4.7134   1.5851
+#&gt;    5.7907  13.0571   3.1658  -3.7021  -2.7384  -0.7511
+#&gt;    7.6262   5.5069 -14.7954  -6.1695   1.0086  -1.4901
+#&gt; 
+#&gt; (2,.,.) = 
+#&gt;  Columns 1 to 8  -6.0142  -1.0846   6.6132   8.8048 -12.7652   1.2229  -3.9324  -2.6862
+#&gt;   -0.0446   6.6856  -6.5846  -5.6414 -10.7667  -7.7234   7.4473  -4.3770
+#&gt;    0.5037  -0.0460 -10.5845   2.3327   8.3228  -2.7322 -22.1459 -10.1168
+#&gt;   -5.7167   5.9581  -4.6676  14.6979 -10.7694  -5.0956   5.0268  -4.5396
+#&gt;    6.1477  -5.5994  -2.4948  -0.3934   1.3337  -5.0898  14.0561   6.1115
+#&gt;    2.3034   8.0586  -2.1196 -20.7930   4.3909   0.3674  19.3874 -12.0856
+#&gt;    5.2677  -2.1861  -6.3592  -6.9201   6.9173  18.7995   7.0750   1.2366
+#&gt;    0.2273   5.6071   5.7069   2.8791  -4.8039  11.6470  -1.8406   8.5442
+#&gt;    3.8859   0.1148   0.7576   8.3424  -4.4211  12.0085  -0.4955   2.0801
+#&gt;    0.3176   3.4337   1.5268   5.9414  -7.9478  -5.3932  -4.4354 -13.2712
+#&gt;    1.8593  -1.4915  -5.0682   5.8562   0.5165  -0.4172   8.4257  -6.5291
+#&gt;    3.4373  -6.0908   0.2243  -0.2928  -9.5887   2.5851   2.9402   3.5356
+#&gt;   -3.5106   2.9929  -8.5752   6.3283  -3.7899  -6.4614  10.1288  -2.5069
+#&gt;    2.4390   2.6027  -0.8885   9.6251   5.1367  -3.5879  -5.5656   2.5122
+#&gt;    4.8990   3.6292   1.4210   0.9515  10.0889  -6.8010  -1.4446  -1.5657
+#&gt;    6.9035  -3.1722   0.7518  -1.6720   1.7043   2.1128  11.1847  -1.3870
+#&gt;    5.2967  -1.8563  -0.1792 -12.7039   4.0796  12.2812  -0.9159  -3.7733
+#&gt;    0.2552  -0.8279   1.7061  -0.1536   4.1987  -7.3316   0.2077   7.6047
+#&gt;    5.9567  -5.8254  -8.3397  -9.2084   2.7443  14.3128   4.3582  -6.9438
+#&gt;    0.7814   2.8716   1.6646   5.8078 -20.3834  -2.3605   1.1268  -0.8053
+#&gt;   -2.3551   0.4909   6.6684   3.9486  10.9811 -11.7314  11.4604   2.8302
+#&gt;   -1.4565   2.0059   5.4728   3.8797   4.5597  -3.9356   4.6334   0.0123
+#&gt;   -0.0046   1.4556  -4.7974   1.3683  -3.0290 -11.4229   0.2953   1.9865
+#&gt;    7.2449   0.2676 -10.0256   4.2182  -7.5904   2.9119   2.9824  -9.4551
+#&gt;    2.0966   4.9624 -14.5976  -3.3322  -9.3261  10.3485   1.7679  -4.7087
+#&gt;   -7.7456  -1.2422  -7.4841  -4.0955  -3.6555   5.0936  -0.8654  -5.2973
+#&gt;   -3.9772  12.2638  -7.6108 -11.2966   3.2104 -16.6242  16.9061   7.4571
+#&gt;   -4.5477  -1.7940   2.9916  -3.3879  -0.0793   1.1810 -10.4906  13.0804
+#&gt;   -1.1056   0.5224  13.1361   7.4463 -21.3352   5.1708   2.2224   2.0314
+#&gt;   -5.1690  -3.3034  -1.3244   0.8516  -6.6657   0.6877  -5.4285   8.8806
+#&gt;   -0.2279   4.6165  -7.8977  10.6867   8.4942  -6.3051 -11.0459  -2.7063
+#&gt;   -4.1818  -1.3874   2.6532  -1.7010  -2.5896 -11.7944   7.7187   2.3623
+#&gt;   -5.0766   7.3549  10.2380   2.8121   6.0108   3.6358  -2.7319   6.7999
+#&gt; 
+#&gt; Columns 9 to 16   8.5346  14.6493   4.7343  10.5360 -13.0589  -1.0024  11.6057  -3.5862
+#&gt;    4.0119   4.1767   5.8653  -5.8562   4.2334  12.2277   1.6284   0.2745
+#&gt;   -4.0777  15.2538 -12.9052  -3.1355   6.7297   7.5706   2.5126  -6.3892
+#&gt;    3.1940 -10.7549  -7.6170  -0.4112  -4.0976  10.6098  -8.3684 -18.4668
+#&gt;   -3.2415  -8.9600   9.3266  -3.2092   4.5961  -8.3287  -0.9100  13.9068
+#&gt;  -12.5100   7.5045   3.7754 -15.6258  -6.2668   8.9938 -13.1121  -4.5580
+#&gt;    3.6137  11.2081   5.2502   1.5275 -14.4869   1.1398  12.1204 -12.9243
+#&gt;    8.4021  -6.4794   3.9283  -3.9831  -6.3969  -0.2040  -6.2184   9.9300
+#&gt;   -7.6047   8.8412   7.8077  -1.5548  -9.9581  -8.0922  -6.1359  -2.4118
+#&gt;    8.0359   2.0868  -1.9051   8.4603  -3.9361   3.8858   7.5554   8.1774
+#&gt;    7.6974  -2.9048  20.6802   5.3417  -2.3613   0.4480 -10.0400   1.7520
+#&gt;    6.1837  -3.5641  -6.2898  10.2009   4.8104  -3.8270   0.0386   0.0012
+#&gt;   -7.4964 -14.7028   0.6050  -5.8951  -7.9125  10.8285  -8.6409  -6.7336
+#&gt;   16.2129  -2.0599   4.5490  14.7370   0.5633  -0.3146  14.6852   4.0331
+#&gt;   10.1456  12.4847   3.7984 -30.2292  15.8297   2.4371  -1.7537  15.3802
+#&gt;  -14.4920   2.9402  -1.1959   6.6652  -6.5744   4.3329 -13.1386   6.6488
+#&gt;    5.8032   2.7329  -4.3297  -0.8535  -7.0992  -0.5278   4.6635 -16.4815
+#&gt;    2.3530  -0.4281  -1.5351   7.8826  -8.8837  -3.4022   6.3356 -10.1218
+#&gt;   -1.2376  -5.7826   3.9019   2.5767  -7.1819  -5.1861  -7.0960   1.8692
+#&gt;    6.0140  -8.3999   6.2292   5.2188 -10.3160  -0.8979  15.7884  -6.4467
+#&gt;  -20.7020  -2.8492   0.1653  -3.5244  -2.1998   4.6702   6.5823  15.6303
+#&gt;    3.7195   4.3370   7.4293 -11.7746  -1.0114   1.6440  -2.4324  -4.7989
+#&gt;   -4.9267   2.0240   5.2009  -9.0453  11.2709   5.0153  -1.0949  -5.1142
+#&gt;   17.9737  -3.8305  -5.3019   9.6272   9.1048 -10.2334   0.1254  -1.2130
+#&gt;   -4.9468 -10.2155  -3.5833   7.5000   3.7759  -0.9806  -7.0401 -15.9829
+#&gt;  -11.1570   8.3210  -1.7366  16.9358  -2.5996  -0.5112  20.6505  15.1489
+#&gt;    0.5344 -13.4556  -0.9938   1.5593 -10.1299   6.0384 -10.6928  -0.0428
+#&gt;   -0.1160  -0.3928  -4.5734   1.5162   2.1136  -1.5655   7.2743 -19.0332
+#&gt;    0.5523 -21.2386 -10.6015  -5.0827  -3.9395  -1.8489  16.5821  13.4423
+#&gt;   -9.9921  -3.6219  -9.2106  -3.6926   8.7945   1.7177   4.8302  -4.3215
+#&gt;    1.5996  -6.0443  -6.0334   4.2433  -9.8292   7.8096  14.8247 -10.2697
+#&gt;   -8.0190  -9.6405  -7.5884  -0.6951  -5.1690  -2.9340  -1.3671  -7.9987
+#&gt;    1.9397   3.0446   1.6674   3.8388  -7.0710   1.6946  -3.0739 -10.7864
+#&gt; 
+#&gt; Columns 17 to 24   3.5820  -1.0037  -2.1060  -0.5021   0.4669   5.5732  10.5865   6.4133
+#&gt;   -6.1313  14.0074   6.3147  -1.0882  -9.1710   4.2064  18.5576   8.6620
+#&gt;   -3.8849  -0.1272   4.0714   3.2136   8.1579 -13.3529  -3.6735  13.2189
+#&gt;   -4.7662  -0.0601  10.1762   6.0038   1.5362   9.3377 -11.6679   8.0604
+#&gt;    1.7997  -2.3455   2.2832   3.4272   3.4207  -1.8314   2.3037  -3.0638
+#&gt;    1.0240   3.4894 -16.9478  -4.6843  -9.7957  -9.3539   6.6651   3.9057
+#&gt;   20.5544  -9.2036 -15.7903   4.6800  24.0699   9.6675  -6.7924   6.5766
+#&gt;  -10.8993  -6.8507   3.1501  11.1207   1.6813  -6.4768 -12.4266  -7.7825
+#&gt;    4.2514  -8.3347  -9.1796 -24.2727  12.2082   9.7718 -13.9615  -6.7914
+#&gt;    0.6135 -16.2445  -9.4782  -2.2328 -16.4114  -3.9198   8.7505   6.3720
+#&gt;   -9.1444   2.4820  -1.2928   4.8765 -12.1881  11.2062   5.9014  15.5521
+#&gt;    2.3696  10.4414  10.2085   3.4477   3.2197  -2.5829 -13.2049   4.9238
+#&gt;   -1.8964  12.5158  10.1099   2.6588 -12.3698  -5.9838 -21.7972   9.1706
+#&gt;   13.2557  15.0246   9.3085 -12.6739  -9.1985   2.9681   5.7537  15.4926
+#&gt;    9.1599   4.1577  17.0316   9.7164  19.2484   6.2472  16.2896   0.8428
+#&gt;   -4.2250 -10.6430   6.5479  15.9252 -21.9275  11.6068  -5.1693  -5.2555
+#&gt;    6.0267  16.3332  -9.3404   2.8688   1.6730   7.3361  -6.7439  10.3059
+#&gt;   -1.9348   0.8716   8.5999   5.4898  -9.7037  -4.2541  -5.6444   1.1883
+#&gt;    2.3370   2.7619  -2.5206   0.8832  -2.1079   6.1265 -12.1927   5.6679
+#&gt;   -4.9426   1.5295  -0.3042  -2.2229  10.5935  -2.2341  -1.6341  -2.5226
+#&gt;   -6.4120   1.1743   6.9378  -4.2038  -3.4596  -1.8725   8.1421   1.1722
+#&gt;  -10.0810  -5.4424  -5.6946  -1.5854  -0.2833   5.7211   6.0585  -5.0141
+#&gt;   -9.7978  -1.5636   4.4473 -14.4918   3.7050  11.9840  -5.3736 -11.1259
+#&gt;   15.8352   2.8404  -7.3328   1.7837  -8.1617   6.6040   5.5681  -9.2222
+#&gt;    5.3187  14.0619   6.0540   1.1464   2.2653   1.6969 -14.8623  -8.8745
+#&gt;   16.1223  15.0312  13.5814   1.4108   2.0266   6.3702   1.5893  -6.6857
+#&gt;   -2.5696  -9.5020 -18.7982  14.3826  13.0060   0.8541  -1.6138  -3.7581
+#&gt;   -6.5214   4.8643  11.3533 -14.8433  -9.9778   6.5444  -0.1860   1.6480
+#&gt;   -2.5516  -1.2378   7.9439  -0.4755   9.3799  11.1985   7.4678  -6.6077
+#&gt;    1.8068 -11.0359  11.8054  -5.1727   3.9557 -11.9220  -6.6456  -5.2651
+#&gt;   -2.0492   7.6972   4.5677   3.9965   0.5246  -0.9572  -6.5559  11.8385
+#&gt;   -9.7477  -7.5894 -10.1902   2.6149  -9.2074  -1.6027  -7.5080  -2.3908
+#&gt;   -5.0027  -7.5121   6.1391  -6.0998  -3.7722   4.9990  -3.5067   1.5032
+#&gt; 
+#&gt; Columns 25 to 32  -8.3326  13.5462   1.9926  11.7988  15.5449  -6.1573   3.6031  19.6798
+#&gt;   -4.5706  -3.6426   3.9641  15.6773  -3.2051  -1.9343   5.4558   0.1278
+#&gt;    4.5995  -7.8457   4.9201  -8.9976   0.6543  22.1168  -6.6297  -5.3975
+#&gt;   -5.8292  -5.0141 -25.5740 -14.8855  17.8364  -3.9283  -3.5939  11.8638
+#&gt;   -2.9779   6.4178  -3.7487  -2.5831   2.8021  13.1714 -11.4142  -8.3406
+#&gt;   -7.7044  -4.8954   0.2741  17.5627  12.6050  -2.7270  12.1657 -10.2586
+#&gt;    4.2050  -8.5113   6.5566 -16.4712   0.9855  -5.5393   9.4006 -10.6406
+#&gt;   -0.0686  15.5756 -16.7051   0.7950 -11.9573   3.5651 -21.6793   0.0906
+#&gt;    0.2562  -3.8317   4.3460  -8.2718   2.0697  -1.5980  10.5568  17.0559
+#&gt;   -1.0399  -0.3710  17.2835  -1.7320   5.8411 -12.8674 -11.0526   9.9299
+#&gt;    3.8539 -15.1293   0.4751   6.1226  19.0899  -4.5517  -0.3517  11.6450
+#&gt;    6.4945  -5.1039  -6.6794 -10.5553 -13.6099  -0.8634 -17.5812   6.6181
+#&gt;   -3.0515   1.0205   4.1219  -4.3147   2.5246  -7.1121   1.5620  14.6095
+#&gt;   18.7603  11.6428  -2.3918  -6.4536   7.2049  16.7210  -0.2319  -3.9229
+#&gt;    9.9841   7.8996  15.8140  11.8107   7.5064  10.9066  -4.1827 -10.6760
+#&gt;   -2.0325   1.6884  -0.6822  13.7890   1.1328   1.9457   5.2086  -4.3310
+#&gt;   -2.2291  -7.0635   4.8275  -1.9795 -11.7523   9.6408  14.6592 -18.0885
+#&gt;   -0.7313 -14.2401   2.8376  -6.9697   7.1563   1.0538   0.9391  -8.2351
+#&gt;    9.7196  -0.1118   0.6643   9.5535 -28.1107  -8.3479  -0.4787   4.7379
+#&gt;   -8.2879   4.8443  -4.5237   4.2574  17.2704  -0.7534  -0.2627   3.1055
+#&gt;   -9.5763  -2.6406  -1.1393  -7.1322  -8.8842  -4.2991   1.9625  -1.4488
+#&gt;   -5.2910   7.7209   6.4626  -8.6814  -2.7850  -4.2663   9.5212   3.8776
+#&gt;   15.9189  -3.6846   8.1071  -5.2958   1.2387   1.7237   7.1463   2.7731
+#&gt;   -8.0275  -3.3292  10.7864  -4.6377   7.3067  10.3989  -1.4576  -3.2657
+#&gt;   -0.7987   4.4697  10.1321  -4.5464  -7.7000  -9.2910  15.0833   1.8220
+#&gt;    0.1179  -7.4323  -3.5473 -10.9446  -9.0101   8.4796  -6.5946  10.8900
+#&gt;   -8.9529   5.2127   5.1256  -8.2877   1.0793 -14.6291  -5.0903   3.9544
+#&gt;   -0.0143 -21.6235   0.5062 -11.7285  -5.6302  -6.3563  -2.7603   7.7661
+#&gt;    2.9248  14.2674 -10.3140  10.7840  -0.0614 -22.1212  12.7547  -0.6719
+#&gt;    2.9547   0.1362   6.0842 -21.9553  22.4724  -7.6297   0.8820   8.7482
+#&gt;   12.7782  -0.8343  -1.8348   8.3165   0.6553  20.9635   0.0442  11.5359
+#&gt;    0.3486   3.8193   8.0152  -1.3174 -12.5557   0.1765   9.3488   1.8027
+#&gt;   11.4389  -2.4853  -9.1779   4.0988   8.2682  12.0934   0.5927   2.7791
+#&gt; 
+#&gt; Columns 33 to 40 -10.9232   9.9730  11.3402  -6.9985  -5.9817  12.0211   7.8166   6.7483
+#&gt;    9.9061  -6.1972   8.9170 -12.2403  -3.2537   9.1765   1.9542   2.0039
+#&gt;   18.2129   0.5420  15.0230  -4.1787   2.6993  -4.4323   3.1550  -7.6110
+#&gt;    5.7301  -6.9517   9.9634 -18.3600   5.4696  -0.6466  -0.9669  -9.7345
+#&gt;   -4.5438   1.7791 -16.2689  -7.2792   9.5836  15.4019  -3.9399  -3.3617
+#&gt;  -10.4459  -4.0898   2.6926  -4.3441   6.9440  11.0991  -6.8736   6.7650
+#&gt;   -2.2186   9.3846  -3.3784   1.2866 -18.9018  15.1361   8.9594   1.4436
+#&gt;    0.6342 -11.3456  12.0230 -18.2563   1.8400 -11.3758   6.0929  -9.9955
+#&gt;   -7.0029   5.5635 -17.5657 -16.0448  14.7556  -9.7206   5.8468  -1.3272
+#&gt;   -5.2170   2.0634   2.8360 -11.4897   0.4731   5.9903  -2.9786  -3.9552
+#&gt;   14.0826  -0.8618   9.7885 -23.5981  -4.6269   2.7839  18.1689   8.5669
+#&gt;   -4.7810  -1.5260  -0.6470   8.5800  10.0634 -10.7164 -20.5765   3.0483
+#&gt;    2.9208  -8.7283  20.0642 -24.0890  10.4879  -6.6390 -18.9042  -4.5116
+#&gt;  -10.1837  16.6957 -15.0471  19.0028   0.6059 -18.3325  -6.6769   2.8096
+#&gt;    4.3609  13.3973 -13.6800  -3.5755  -5.4699   0.7154  -3.8066  -5.3042
+#&gt;    2.7760  -6.4505   5.1796   3.7856  -1.1897  -4.0430 -11.1327   0.8289
+#&gt;    2.0836   3.1477  -9.6225   0.7284 -16.2915   6.1414   1.0982   7.2752
+#&gt;   -5.3480  -4.6832   9.4552  11.5688  -6.9190  -2.1506  -3.3013  -2.7810
+#&gt;  -20.8252   9.1013   7.4089  15.0868  -4.8587  11.5892  -0.7041   4.1646
+#&gt;   -1.9325  -4.6326   0.7821   1.1786   7.3145   8.5536  -0.0245  -9.3328
+#&gt;    6.2325  11.8251  -0.1127   2.6230   4.0537  -0.0211 -14.9967  -4.6132
+#&gt;   -1.6787 -16.4564   2.5823   1.8390   4.6178  -0.5814   5.4933  -0.9679
+#&gt;    8.7747   0.2013  11.2500  -0.7504   6.5001  -0.1652   5.5703   8.0887
+#&gt;   -2.6262 -12.8454   2.7855  -3.5945  -8.0783   9.8257 -12.3558  -9.6176
+#&gt;   -2.2488 -10.7800  11.4000  15.3984  11.2245   3.3056   1.3418 -12.2344
+#&gt;   17.4834  -8.8691   2.4820  15.6661   8.7345  -7.0508  14.9689  -9.3668
+#&gt;   -0.6897  -6.5376   9.5029  -1.4304   7.1311   3.8007   8.2219  -3.1152
+#&gt;    3.2937  -6.8141   3.4211   3.8443  -6.6478   1.1954  20.9570  -4.7195
+#&gt;    8.8615   6.6159   1.3878  -1.7054  14.7872  -1.8724 -13.2738  -4.3344
+#&gt;    1.1489   4.1738   3.6340  -4.3114  11.8795  14.0606  -8.6819   2.8621
+#&gt;   -1.2038  16.4240   9.4475  -8.9706 -16.1243 -11.0447   9.5779  -4.6219
+#&gt;    6.1986   6.9468 -12.7478   6.4257  -2.7053   6.1554  -4.7764  -0.9324
+#&gt;  -11.4281  14.2899 -13.6856 -24.8442  -6.2173   4.1451   4.1338  -6.8419
+#&gt; 
+#&gt; Columns 41 to 48   5.6167   5.1628  -5.3210  -0.4502  -2.5538  -2.1373  -9.5876  -1.2143
+#&gt;    8.6899  -2.9253  -6.4096   3.3858  -0.5011   2.3444 -14.5082   7.1908
+#&gt;    0.4306   4.3266  20.9429   1.8534   8.9586  -0.3715  -6.6717 -13.2715
+#&gt;   12.1743   5.3164   7.8754   3.0096  -5.3800  12.6414   4.4029  13.5507
+#&gt;  -11.9514   0.4266  -5.6734  14.3629   1.5135  14.8825 -29.7157   3.3493
+#&gt;    5.9194  -0.2439 -19.1017  -1.9195   1.1815   2.0446   6.3048 -10.8831
+#&gt;   -0.6203  -1.4123   1.0244  10.9377 -16.4396 -12.8790 -17.0253 -20.1500
+#&gt;  -12.0508   0.0932  13.4479 -16.1927  -6.1773  18.3777   2.4006  14.4940
+#&gt;   -4.6760  -9.2898  -1.0704  -4.9801   1.5868   9.6104   5.8968   3.7639
+#&gt;  -11.6862   0.4314   5.3906   2.6873  -4.3179  -1.1402  13.9832  -7.7592
+#&gt;   21.9782  -1.6958   3.9049   1.5796  -4.9585  -2.4052  -2.6968  -1.2419
+#&gt;    3.7931  -0.8146   4.0895  -6.1485  -0.6181   0.7770  10.5150   4.9762
+#&gt;    7.7402  -4.6891  -3.0619 -13.1808   7.5418  16.2776  -3.7161   9.7330
+#&gt;    5.3329  -1.7892 -14.2884   2.9087   7.3382  -1.2930  -5.4422   7.2745
+#&gt;    5.0099  10.2449  -3.2923  -0.9518 -13.3891 -12.5068  -0.2344  -0.5110
+#&gt;    5.9696 -13.7433  -3.7850  -1.8290   7.4685 -11.1664   3.2635  -0.9625
+#&gt;  -13.5953  -9.1116  -3.2895  -0.9582   2.0397   1.1494 -10.4343  -6.6435
+#&gt;   -8.7079   0.0132   5.1753  -3.4559 -21.4840  -4.6608   9.5996   6.8894
+#&gt;    4.5736  -2.7679  -7.2934   8.6137   9.2659   2.0525   5.1626  -4.1929
+#&gt;   11.8081   1.8777  -4.1571   3.6456  -4.3223  -1.5937 -14.4750   4.8976
+#&gt;    4.4412  -1.9357  -6.1994   6.9305  -3.3421  -5.9407   7.1103  17.2743
+#&gt;   -2.3524   2.3204   6.9110  -1.3481  -3.3249  -5.5464   0.2175   5.9876
+#&gt;   17.7796   9.1184   3.7129  17.5938   7.4084   5.2870  -4.9536   3.2263
+#&gt;  -12.8465   8.8358 -15.6219   7.6324   2.9546  16.7422  -7.0209  18.3724
+#&gt;   -4.4955  -0.8018 -15.7798  -0.8550   6.9244   2.1549  -1.5718 -11.6629
+#&gt;  -10.9435  -3.9419  14.7621  -0.4420   6.6204 -13.7846  10.7538  -0.4441
+#&gt;   -7.8047   2.1492  -7.8054   0.2371  -2.2775   4.9381  -0.1767   0.7649
+#&gt;  -17.0416  -6.5340  10.0033  -4.7815  -9.8913  -7.2958  10.0637 -14.3519
+#&gt;    4.1466  -2.1522 -11.0059   2.9710  11.4202   5.4275  -3.6718  -3.6446
+#&gt;   -1.2420  12.3298  -2.0738   6.5719  -4.2960   6.7944   0.7106  -3.0943
+#&gt;   10.3188  12.8648   5.4735   3.2250  -9.5462   1.5936  -6.0500   1.1283
+#&gt;   13.0340   6.3285 -20.2287  -3.2465   6.8826   0.3587   9.6409 -11.5264
+#&gt;   -6.4261   0.0833  -2.6842  -0.0688  -8.8890   0.4671  -5.4090 -20.9419
+#&gt; 
+#&gt; Columns 49 to 54  -0.7595  17.7225   0.3433  -1.6600  -3.5832   9.1820
+#&gt;   -8.7117   4.1735   2.5032 -17.7739   0.8178   7.2484
+#&gt;   -4.1945  -2.0523   7.8682  -3.6549   0.1049   2.7100
+#&gt;  -14.5688   4.8320 -23.8985   1.7606   5.9118   1.8369
+#&gt;    1.6188   6.0889  10.2977  -0.9904   3.6440  -4.2862
+#&gt;    9.5141 -24.1392   6.4523  -1.2006   1.7379  -8.0874
+#&gt;    9.3583   3.8137  19.6174   1.6485   7.6951  -8.1034
+#&gt;   -5.4193  -0.9837   3.1700   4.9500  -9.2599   0.8894
+#&gt;    4.3056   9.3035 -14.7993   7.2205   5.2470  -5.3278
+#&gt;   15.3968 -12.8037   5.6605   0.1833  -1.2213  -3.1601
+#&gt;    0.0873   4.3805  -1.4111  -4.5149  -4.4941   3.8518
+#&gt;    9.0395   4.8149  -9.1265  -1.8190   6.3632  -6.1823
+#&gt;  -12.6536  19.3748 -14.2283  -7.0402  11.6094  -2.7043
+#&gt;  -11.3499  19.5086 -16.5464   8.1848   3.2863   0.7498
+#&gt;    5.5220  -9.2326   3.3839  15.0107 -13.0285   6.1753
+#&gt;   -6.3849  -1.9659  11.4017  -7.0603  -0.7306  -1.1019
+#&gt;    9.0291   5.8749  -7.3415  -2.4878   2.6143  -1.3518
+#&gt;    8.1800   2.8767   8.7180 -11.3694   8.2303  -4.1627
+#&gt;  -10.2962   2.4211  -9.2412   7.2377  -1.2510   1.9823
+#&gt;   -8.6539  13.0832   7.9066  -3.5086   0.2032   1.5193
+#&gt;   -6.3620   9.8919   4.0757  -8.8798   5.1976   0.1869
+#&gt;    3.3345  -9.2125  16.6036  -0.0995  -0.2828  -8.4475
+#&gt;   -2.1083  -1.8617   9.7966 -11.4863   7.3926   0.3694
+#&gt;   19.2445 -11.1232   2.8487  -3.6586  -2.7781  -1.6589
+#&gt;   -2.4736 -10.0103  -8.4449  -0.9406  10.5947   4.5627
+#&gt;   -1.3361  -1.6820   6.5745   3.9665   5.0010  -1.3289
+#&gt;   25.4012 -19.8178   1.6182  -3.6655  -3.4616   5.2035
+#&gt;   10.4479  -1.4385   1.6253 -15.2111   8.9087   1.9456
+#&gt;   -2.0720   1.9840  11.0861 -10.8541  -3.9871  -3.0554
+#&gt;   14.9716 -11.3589   7.2884 -19.3158   6.6518  -5.0357
+#&gt;  -16.5899  20.3291 -10.1480   0.7029   0.1713   1.4221
+#&gt;   -6.5276  -8.9623 -12.1283  -2.3452  -2.4032   0.3382
+#&gt;   -8.0900  21.0676  -7.2271   3.0907   0.4850  -0.7093
+#&gt; 
+#&gt; (3,.,.) = 
+#&gt;  Columns 1 to 8  -5.1676   4.3854   8.9417  13.7912  12.8967 -12.5368 -13.4475 -29.8693
+#&gt;   -2.9827  -1.8464   3.5695  -0.5098   4.7262  -4.9420  -5.0182  -0.1195
+#&gt;   -6.6998   3.1935   1.3652   7.9787   8.3085   5.1705  13.3275 -12.1181
+#&gt;   -5.1322  -9.0147   3.3953  -4.9266  11.1200  -8.4146   6.8602  -9.5063
+#&gt;    2.0075  -7.4455  10.4439  -4.1741  -4.2386   0.7301  -0.8043  14.3971
+#&gt;    3.1661  -8.7687  -0.3247  11.4966 -10.5124   6.8284  11.4774  16.3061
+#&gt;   -5.6473  12.9050  11.0416 -12.2355  -6.9002  22.7572  16.5203  -8.3692
+#&gt;    0.4523   6.5059  -6.1685  17.3045   9.7165  -2.5745  -5.2398  -1.0340
+#&gt;   -5.5061  -3.3630  17.3200  -0.6499 -20.7999 -18.8599  -9.2682   0.6153
+#&gt;   -0.7136   3.3564  -8.2173  -3.9614 -13.1121   7.8562  13.0622  -2.5986
+#&gt;    2.5756  -7.8965   4.7584  11.4308  11.0917  -2.3474 -14.7677 -21.7201
+#&gt;    1.8753   1.5512  -3.0405   5.6328  -3.0582   4.0797   0.6847  -8.1307
+#&gt;   -4.0625  -3.4900  -2.0719  -3.6909  14.6355 -11.0580 -10.3131 -21.0268
+#&gt;   -2.2194 -10.6236   5.6066  -0.1072   1.3125 -10.6183 -14.6643  -8.7573
+#&gt;   10.3914  -2.6885  -6.3333  -0.9993  10.1637  12.6405  -7.0527   4.6743
+#&gt;   -0.3993   8.3141   0.6243 -13.4408   6.9399  13.3666   8.9177   1.1276
+#&gt;   -6.5183   1.5285   3.5984   4.8716 -13.2214  11.5015   7.9882  -3.1666
+#&gt;    4.9163   4.5215  -6.0134 -10.1000  -5.9447   1.8915  -1.2253  -5.6679
+#&gt;    6.7536   2.8255   2.7324  -3.4355  -3.2745 -12.4479  -0.5024   1.5993
+#&gt;   -1.7533 -10.5775  -0.8278  -9.8134   8.6638   8.0763  -2.4028   0.0364
+#&gt;    3.9839   0.3409 -10.4570 -13.2835  -3.3255 -25.1475  -4.1796  -1.4266
+#&gt;    0.5803   6.6940 -12.4528   3.6662  11.8859   7.8554 -22.8835  -4.3774
+#&gt;   -3.1301   2.5668   7.0398  13.0751  13.3319 -18.9986 -13.1365  -6.1700
+#&gt;   -3.4769   8.3327  10.7065  -6.9407   6.6296  -5.1751   6.9217   6.4474
+#&gt;    3.4457   2.2778   4.4909  12.8246  -1.8149   9.8114   2.1885   0.2819
+#&gt;   -5.9717  14.9626   8.9741 -12.3336  -9.5979  -9.1602   7.4589  -6.9046
+#&gt;    0.1160 -10.8806  -3.6631  -0.7203   1.4317  -2.1587   0.5743   5.3527
+#&gt;   -0.6568   9.3763   1.3580   7.9313 -11.8559  -2.6212 -13.2784   3.7245
+#&gt;   -4.3391  -8.4758  -0.1667  11.3493 -13.0984   4.5487 -13.8899   0.4504
+#&gt;    5.5732   3.5709  -0.4515   8.8835   1.6770   2.2673  -7.8797  -5.2212
+#&gt;    4.7617 -10.0378  -6.3020   1.3460   0.1302  -3.0625   5.0635  -9.6043
+#&gt;   -6.6632   6.7277 -11.9992  -7.4585   1.9142  -3.1063  12.8365  -9.4380
+#&gt;    2.1475  -7.1687  -7.4764  17.2946   1.3755 -11.5783   4.2725  -5.1783
+#&gt; 
+#&gt; Columns 9 to 16   0.3036 -11.3024 -18.0805   4.6069   2.8373  -2.5326   5.8040  12.0689
+#&gt;   10.6212   5.5497 -10.4617   5.0755  -3.4345   2.4996  14.7773  15.9369
+#&gt;  -15.8449  -9.3174   4.6682  -2.9860 -11.9860   1.1289 -16.7580   1.5834
+#&gt;    5.6135   9.3310  -8.1332  -2.8832   1.1820  -3.1934  -5.7096  18.0656
+#&gt;  -11.3107   9.9932   6.4118  -0.2207   7.3681   8.6864 -19.0237 -14.6856
+#&gt;   17.4867 -16.3963 -15.2217   5.0862   5.9360 -12.4534   2.1316   3.2917
+#&gt;   -2.0394  -3.1791  -7.6890   3.3832   7.2108 -10.2875   6.5990  -2.2819
+#&gt;   -3.1144  -1.4885  11.4863   2.6386  -1.2845   6.7801 -11.7524   6.4304
+#&gt;   -3.6454   3.3980  12.6641  15.3436   1.4798  16.5141  -2.4534 -24.2972
+#&gt;   11.7442  -7.5043  -8.0800   3.9334  -0.1270  -0.8541  15.4260   7.7498
+#&gt;   -1.2218 -17.2297  -9.4993  -4.2767   9.1431 -10.7485  15.9689  -6.6429
+#&gt;   -4.1928  16.0113   6.0789  -3.2893   5.0841   6.7080   0.5600   0.0273
+#&gt;    2.7119   9.5193  -3.4432  12.0904  -9.0164  -1.5356  -0.0627 -11.3350
+#&gt;   -2.9610   5.5429  -5.7038   3.2398  -5.6736   4.8195  -5.7769  -3.0289
+#&gt;  -12.3682  10.2531   4.4688   6.8857 -10.9107  -0.0482  -9.6106   9.1177
+#&gt;    1.4048 -19.3090   3.9878   0.6812   2.1601  -9.6886  20.3901  -0.2893
+#&gt;   -0.7541   3.8006   1.8998   3.7767   2.7931  13.7318 -12.1923  -3.5357
+#&gt;   13.0578   0.1626  -1.0595  12.7407 -17.9925  -1.1900  10.8697   1.0413
+#&gt;  -11.6378 -21.6557   4.4854   1.7284   4.5183   2.8117  13.8441  -4.5974
+#&gt;    5.4963  -4.4388  -8.9355 -10.3428  -5.6252  -4.1750   4.9992  11.5611
+#&gt;    0.0838   5.8764  -0.8031  -3.6421   3.3854   6.8787  -0.8951   2.3469
+#&gt;    0.8371  -6.5001  -1.8387  17.2207  -3.2285   6.6468  11.7196  -0.3122
+#&gt;  -19.0688  -2.4172  -1.5870   3.2479   2.5332  12.7106   3.1121   1.0850
+#&gt;   11.2773   0.2099   0.8492   8.3810  -1.3572   8.8184  -2.1157  12.7494
+#&gt;    0.5370 -16.0230  -1.0461  -8.3726   0.9168   1.3402  -2.3441   5.3462
+#&gt;   -3.3933   7.5931   7.3473   0.5098  18.5612 -12.9361 -13.2702  -1.5486
+#&gt;   15.3652 -16.7713  -5.8028  13.2746   8.0601   8.7573  14.7321  -6.6338
+#&gt;    5.0931  15.6257   2.4031  -9.0759   9.6109   1.2197   3.4899  -1.6881
+#&gt;    3.7917  -1.0522   6.1791  -0.4308  -2.5520  12.2615  -2.9855  -6.7026
+#&gt;    1.2122  16.0225  -9.8612   0.3502   0.6998  -7.9716   0.4053  -7.3333
+#&gt;   -0.9244   5.7186   6.0906   0.1273  -6.2419  -0.4498   4.3157   0.5761
+#&gt;   15.6654   8.1636   1.7944   5.9871  -0.0451   2.0708 -14.1700  -1.3609
+#&gt;   12.3706   8.1161  -2.2232  -6.1145  -9.7034 -13.3460  -5.0612  -5.0010
+#&gt; 
+#&gt; Columns 17 to 24  -5.4216   1.1401   4.8240   9.6962   3.8607   0.2286   1.8772   8.0972
+#&gt;  -12.9882  -2.5961   7.1409   5.0553   5.5103 -10.7665  -3.2226   9.5484
+#&gt;    8.4563  13.2828  -9.9814   1.0268   0.7073   5.7946   5.9229  -9.5130
+#&gt;   -0.4320   1.1234  -1.4031  -5.9275 -14.8847 -16.2643  -1.8429   8.5939
+#&gt;   -4.8680  -7.1420  -0.0741   6.3314  -5.4704  12.8090  -1.6785  -6.5486
+#&gt;   -1.7604  -6.0015   4.2669   3.6654   3.6607 -10.7026  -2.7560  13.7193
+#&gt;   -8.0264   8.6330  -6.5216  -2.4336 -19.8746 -14.3504  23.0912  -8.1561
+#&gt;   -4.8400  -1.5763   9.6763  -2.3567 -10.1442  13.7499  -1.6231   1.4223
+#&gt;   -0.7328  -0.3130  -6.6008  -9.5737  -4.6598  17.0784   9.6055  -7.4617
+#&gt;    2.0727  -0.0316   8.7479  10.5035  -0.3153  -9.0982   6.7437   0.4590
+#&gt;   -1.6304  -8.2152  -4.9396   4.1893  11.3071 -12.0622   5.2254  13.9001
+#&gt;   13.8572   5.2728   3.8509  -0.7684  -3.8357  -5.6192  -1.7142 -19.1967
+#&gt;   -9.3842  -4.7837   4.4577   0.5659  -8.0542   5.2786   6.0536   2.1250
+#&gt;  -10.2943  10.5885  -1.9853  -0.4070  11.9270   3.5373   6.2308  -1.1091
+#&gt;   10.6107 -13.3856   1.9552   6.5011   8.9395  -3.9631  -1.4105  19.9545
+#&gt;    3.2959   9.3271   9.6623  10.6403   6.1253 -11.9934  -3.3633   4.3472
+#&gt;   -1.4674  10.2126 -21.2552  -0.7752   0.0276   9.0277   7.2645  -4.4475
+#&gt;    4.0243   6.0111  -5.0659   5.3259   2.8305  -2.6396   8.4763 -10.3649
+#&gt;    4.2005   6.9034   0.8247   0.4297  -7.4189 -13.6058  -6.0547 -13.2314
+#&gt;  -12.7094   3.2149  -4.2815  16.8455 -10.1513 -13.3428   7.7253   4.8071
+#&gt;  -15.2911 -13.0665   9.3437 -16.9386   2.8923  -4.6578   0.3354  -0.0428
+#&gt;    5.4649  -3.7999 -10.9661 -12.9193  -6.2558   3.4290   5.6079  -2.3062
+#&gt;    3.1578  -8.0265   7.2415 -13.8662   3.5183  -4.8634   3.4179  -0.4553
+#&gt;   -9.0685   7.4247 -13.0376  14.5329   5.5948   0.6212   9.0086   4.1470
+#&gt;   -2.6753   5.3530   9.4994   5.8938   8.6655 -11.1075  -1.9245   1.6359
+#&gt;    1.5458   5.2331   2.9425 -14.0977   6.1124   3.0792  -4.7941  -7.4190
+#&gt;   -7.6383 -23.8248  19.9109  -2.6570  -6.8308  -9.9353   0.3604   7.9929
+#&gt;    0.0368  -9.2980  -3.0392  -7.3616   0.0033  -2.2695  13.9712  -8.1888
+#&gt;  -12.3978   7.4440  -8.1300  -8.2130  -3.6385   9.3517   4.1502  10.8084
+#&gt;    1.0350  -7.0157   9.2695 -15.1145  12.5815  -7.6600   7.5487 -14.7018
+#&gt;    4.6132  -2.3264 -19.3550  -2.4313  -2.9898   3.1919  -5.7208  11.5779
+#&gt;  -15.4273   5.1705  -1.6033 -13.2370  10.5671 -15.5728   3.8489  -2.4687
+#&gt;   -7.4581   2.8750   2.6568   1.0497   6.3311   6.8648   3.8989  18.4072
+#&gt; 
+#&gt; Columns 25 to 32  10.3351  -1.5769   3.4252  13.7836   3.9965   2.6405  16.7248   4.0101
+#&gt;   -6.0968 -15.4264  -9.0269   9.8713 -22.3232   8.2689  19.0507 -13.0055
+#&gt;    2.3186 -11.1995  10.9495   4.4354  -4.7394   3.4724   0.1577  -2.1127
+#&gt;   -9.4724   8.8434   2.6938   6.3921  -1.5134 -12.8669 -22.9442  16.9483
+#&gt;   -1.1854   0.2564 -11.3047  15.9508  -6.3271   7.8759  -5.3361   5.8605
+#&gt;   -1.7057 -14.7213  13.2095   5.8321   3.7307  -3.8520  11.9686  -4.2490
+#&gt;    5.9013 -11.1572   8.4920   6.8084  13.2017   8.7801  -2.5933  -7.4978
+#&gt;   -8.8815   0.0722 -16.2817  -0.4731   1.7651  -2.6391  -3.3044   1.3444
+#&gt;  -12.4770   5.3769  -1.0898   1.3739  -2.3164   7.6707 -16.3326   6.7719
+#&gt;    2.3154  -6.3129   3.6013 -15.2266  17.4062   2.8900 -13.4940  -3.5560
+#&gt;    2.1785  12.1000 -14.9234  24.7450   1.1970 -13.9475  14.0433   2.7353
+#&gt;    0.8050  -6.3989  -2.8261 -10.9769  -2.1723   0.5281 -11.9126  10.0164
+#&gt;    3.3175  -7.1518   2.9584   8.2955 -10.3765   8.6735  -4.7033  -1.7784
+#&gt;    5.4076  11.4840 -11.7991 -19.7505  -0.3950  -6.6199 -10.0510   9.1154
+#&gt;    3.0866  -5.5300   1.8951   5.5660  -8.8052   7.6707  -5.4482  -3.2680
+#&gt;    5.4140  -7.9153 -13.7417  -1.4279   3.5988  -5.9935  15.3883  -0.0959
+#&gt;    0.4607 -15.7977   3.4221   0.9693  -1.7245  -4.2609  -0.2560  -4.4233
+#&gt;   19.1620  -1.7033  -4.7711  -4.8742  -1.8636  15.7761  -1.1533   1.9570
+#&gt;  -11.7664   3.9348  -8.1049   9.3780   2.9715  -5.1961  10.0908  -4.4165
+#&gt;   11.1899   6.3989   2.3887  11.8247  -5.6958   4.8766   7.9577 -10.6484
+#&gt;    7.0079 -18.0267   5.4670 -14.0870  -8.7145  17.8836 -15.9463  -3.9792
+#&gt;    7.3750  12.1773  17.4754   6.8414  -6.4836  -1.8049  -7.0344  -2.9170
+#&gt;  -10.5822   2.6888  -7.6235  14.0420  -0.3742   9.2761  14.3565  16.5268
+#&gt;   -5.1296  16.3504  -3.7858  -2.4817  -9.0940  -8.8171  -2.8296  -4.2678
+#&gt;   -9.6838   6.5828   0.2946   2.0167  -2.9256  -4.5033   7.1349  -1.3210
+#&gt;  -19.8294   3.4722  -1.1876  -8.8626   0.2013  -4.8618   0.4699   2.4278
+#&gt;   -3.8120  18.5868  -8.3621  -8.0789   5.7242   4.3538  -2.0483  -1.8576
+#&gt;   -6.2936  -3.3374   3.2529   9.8045   0.4049  -7.7762   2.2055   1.9092
+#&gt;    3.5109  -6.2683  -9.1098  -6.1544 -17.4376 -10.8298  -6.6113 -10.7142
+#&gt;    9.2603   7.5734   9.4958  11.1101  -3.3366   4.6612 -11.9323   3.0409
+#&gt;   -0.3238  -0.1114 -14.5615   3.2727   1.9813   1.1815   3.8492  11.6906
+#&gt;   -7.5225   0.2525  -0.1607  -0.4146  -0.2028   7.9316   0.2772   3.3927
+#&gt;    1.3049   3.7183   4.6914   3.4221  -5.3761   1.5496 -13.5748  -0.1931
+#&gt; 
+#&gt; Columns 33 to 40 -15.0313   1.8031  -4.8586  -4.6742 -11.6179   3.4501  22.4728  -0.9740
+#&gt;   -0.2749  13.0984  -7.2302   1.5292  -1.8052  -9.8942  -6.9763   3.1699
+#&gt;   24.6312  -2.3724  -0.8381  13.1975  -3.2445   0.5209  -4.4298  10.9253
+#&gt;   -0.0687  13.3333   2.0191   6.4038   9.7615   3.1002  19.1632  -9.0256
+#&gt;   -0.0968   0.5706  12.8796  -9.6310  -7.3837 -11.1546  10.7900 -16.2938
+#&gt;   -1.7818 -11.3226   2.4213 -10.2419   3.2077  12.7258 -10.1224  -4.8499
+#&gt;   -6.9263   4.4384  -8.2918 -11.3904  -3.8657   3.3859  15.3510   8.6328
+#&gt;   -1.2913   3.9457   2.4575  11.6668  14.8740 -16.8759  -3.3993 -13.0933
+#&gt;   -7.8915   1.3880   0.4816   2.1369  -4.9386   1.8787  14.2630 -15.2881
+#&gt;   -9.5671   2.3661   2.7132   6.8460   2.9096  -0.1649  -7.9070  -6.0393
+#&gt;   -1.1403  -1.8965  16.7791 -13.6186  -2.9396  -2.6223   7.6169   2.8150
+#&gt;   -0.1347  -5.5610   2.4079   3.5049  -2.6137  -0.2816   8.3561  -3.0247
+#&gt;    1.2535  -0.1156   0.0462   5.2174 -10.3696 -10.7547  10.8972   1.7234
+#&gt;  -15.6739  -1.2947  -4.9985   4.1553 -12.7274   1.7979   1.6871   3.3341
+#&gt;   -6.1162  -2.7802   4.7909   0.9724   4.2063  -4.9608 -19.9319 -11.9932
+#&gt;   -6.8179   7.0258  -2.4303  -8.4642   3.9944  -5.5612  -6.3351  -8.7273
+#&gt;  -13.0319   5.8118  -7.3953   5.0442  -0.5523  -3.7281  -1.4637   2.2636
+#&gt;   -4.3980  -3.0759 -11.9548   7.8060   5.1759  -0.6060   0.5467   2.6746
+#&gt;   -3.4059  -3.0204   6.8857  -4.4586   2.2255  -6.7481   9.3621   5.8424
+#&gt;    4.5733   5.1204   5.8599 -10.3077  -2.6493   5.5195  -3.6791   5.6804
+#&gt;   -1.0876   6.6536  -6.7350   4.1673   9.5383   3.2740   7.0744   4.3193
+#&gt;    3.8565  -1.2565  -1.0224   3.9740   6.5049  -0.5460  -3.1893  -4.9150
+#&gt;    6.5424   6.4211   1.6067  -3.1930 -14.9358 -20.1992  -2.1942   2.8212
+#&gt;    1.7486  -5.1895   5.1360   1.5839   6.3303  -2.8323   0.7757  -2.0265
+#&gt;    0.0772   9.1729   2.0778  -7.1541  -0.9870  -7.1627  -4.6010   2.3400
+#&gt;    5.9069  -4.0067  -6.4865   2.9032  13.8364   4.1096   0.3373   5.0010
+#&gt;   -9.4567   2.4729   7.3092  -7.2793  10.0568   7.1594  11.3578   7.3535
+#&gt;   -5.0468  -0.9335  -6.8382  -0.4090   8.6911  -6.4320   1.3180   7.6303
+#&gt;   -3.5101  10.4786   7.3228  11.3595  -0.7661  15.2906   7.2171   1.2259
+#&gt;   10.3892  11.0709  -5.5317   3.5472 -11.1066  -5.2047  11.7419  -8.7145
+#&gt;    1.8839  -8.3203  -3.0281  -4.2512  -2.3415  -7.5409  -0.8022  -2.5320
+#&gt;   -0.3910  -6.9799   2.3695  -4.2693  -1.4531  -1.0846  -2.1721  -4.3543
+#&gt;   -2.7668   5.8976   4.6049   1.6244  -6.1064  -1.8341   7.4666 -11.8902
+#&gt; 
+#&gt; Columns 41 to 48   7.7372   7.6612   4.9961   9.5088  -0.9377   8.1575  -5.2924   9.6978
+#&gt;   -3.3669 -13.6178 -13.7287  -4.2277  -1.7368 -15.7717  -5.6129  -2.9614
+#&gt;   -9.4431  10.1433   9.5629  -0.3275   3.0597   0.7192  12.6009  -5.1750
+#&gt;   -2.2547  18.4198   6.7477  -0.9649  12.8249  -2.1745  16.9646   5.1527
+#&gt;   -8.7985  -9.3572  -8.6903 -19.3328 -14.4594  -4.7990  -2.9063   9.2818
+#&gt;    3.0428   4.9044  11.9585  -4.3834  12.0654  -1.3176  -0.4619   5.4780
+#&gt;  -17.4608  -0.6354  -6.8096   1.1482  16.4705   1.7212   6.8303   5.3250
+#&gt;    2.4513  -3.8897   0.1238  -0.6568  -8.5003   5.6151  -5.1935  -3.0746
+#&gt;   -0.7103   5.7388   4.3880   6.0856  -8.4717   4.7485   7.2855  -5.0299
+#&gt;  -16.8739   9.3228  -8.2642  -6.7540  10.7359 -24.7143  -5.7663   1.2593
+#&gt;  -10.1084  -2.8553  -4.1009   0.0149   5.2116 -13.3501 -11.1080   1.6188
+#&gt;    4.0397   0.4117 -13.2689  -4.3993 -10.6805   8.1024   1.7343  -2.2570
+#&gt;    1.2193  11.8672 -16.9305   0.1263   5.1343 -17.0045   6.5744  -0.8092
+#&gt;    6.5237  -9.3432 -13.9690  -1.1858  -3.0704  -0.7990  13.5370  -5.2877
+#&gt;   -2.0121 -16.2035  -5.6066 -15.6618  -0.7642 -11.4210 -10.8173  18.3928
+#&gt;    5.7853  -4.0129 -20.9615  -4.4775   4.0315 -15.3383  -3.8424 -10.4490
+#&gt;  -15.3268 -24.1635  -3.0229   3.2985  -8.1616  -4.1786  -7.2921 -14.2433
+#&gt;   -4.7535  -4.1444   6.8501  -5.0432   4.2619  -6.6299  -4.3127  -4.2475
+#&gt;   23.2268  -0.9254   3.1896  11.0457 -16.4595   2.3668  -7.2917  -0.9706
+#&gt;    7.0187  19.6362  -5.1443   1.1327   7.4589   6.7519   7.2424  -1.8203
+#&gt;    8.0685  -4.9535   8.4029  12.5493   8.5755   5.8927   3.8632  12.0752
+#&gt;    0.5056   3.4838   1.6995   4.6258  -1.5417  -4.6166  12.7847  -1.3103
+#&gt;    8.5583   4.1503   7.7805  12.8082  -3.9444  -7.1749  11.4802  -6.1888
+#&gt;   -8.4713  10.3415  -0.3942 -12.9463  10.6149 -15.2584  18.6078  -6.8641
+#&gt;   11.6664  14.0203   2.8554   1.4630  -6.2200  10.6229   6.2584  -2.0938
+#&gt;   -0.4953  -3.6202  -2.5227  -4.0894   0.5414   9.3649   9.8964  -9.8591
+#&gt;   -0.2743  17.6746   7.8169  -7.1310   3.9413  -3.3677   6.5510  -0.3007
+#&gt;   -1.3900  -9.2363   8.4504  19.6248  -5.4001   6.0143  -2.1323   5.8315
+#&gt;    6.1095   1.2179 -12.3948   1.1394  -2.9768   7.9932   7.3940  -4.7661
+#&gt;   -5.6988   7.4068   0.7307  -0.3264  -5.5465   7.8606  -4.8046   7.7227
+#&gt;   -8.6420  -8.1665  -3.3280   2.2567  -6.4153   2.3591 -13.7708  -5.0655
+#&gt;    0.8891 -10.3632   7.1763 -19.1660  -4.8587  -7.7581  -1.0388   3.0972
+#&gt;   -6.1099 -18.8754   1.4319 -15.8268  -7.0458   4.1923 -10.2896   8.3506
+#&gt; 
+#&gt; Columns 49 to 54  14.9748  -3.4864  17.0685   4.1284   5.1310   3.1262
+#&gt;    5.6224 -12.1718  -3.8469  -0.5642  -4.5141  -0.7448
+#&gt;    9.3552  -8.2074   6.0488   2.0903   2.6791  -1.8372
+#&gt;   -9.0054 -23.0041   3.7161   5.2067   1.8688   6.3038
+#&gt;  -11.7563  -6.0015  -7.3474   0.3525  -2.0993  -2.5835
+#&gt;  -13.5861  -6.8689  10.9663  13.7047   7.8267   2.6829
+#&gt;   13.8121 -22.0552  -1.0479   5.6581  -1.5251   9.2322
+#&gt;    1.3197  12.9777 -10.2431  12.2484  -3.5843 -10.8793
+#&gt;   -5.3845  -0.6163   8.8257  -5.9029  -5.1137   0.5133
+#&gt;   17.6449  -2.9233  -4.6607  -2.4684   0.8963  -1.1989
+#&gt;  -14.1206  -6.1064 -15.7182   2.0309   0.2568   1.0332
+#&gt;    0.0812  -8.6971  -1.7887 -10.9466  -5.6519  -3.4051
+#&gt;    2.2405  -8.5929   9.6886   3.0710  -6.6256   0.9887
+#&gt;    8.6771   3.4362   8.6513  -4.0919   0.3634   2.0812
+#&gt;  -13.8877   6.0659  -6.1025   0.3090   0.0363 -12.5456
+#&gt;   -4.0079   5.4196  -5.7510   3.8144   5.6892   6.7359
+#&gt;    8.7777  -8.2431 -18.0547   1.4337  -5.8826  -0.7521
+#&gt;   14.7119  -4.1838  -8.4559   1.5906   1.3187   1.6980
+#&gt;    6.8145   5.7901   4.4138 -11.7686  -6.8325  -1.9794
+#&gt;    8.1843 -15.1178  12.3738  11.1231   8.9858   5.7360
+#&gt;    3.6753  -7.6156   6.1509  -3.7130  -0.6571   6.1083
+#&gt;  -17.1941   8.6179  -6.8330  10.0978   2.7350   0.4185
+#&gt;    0.4965   0.3054   4.7808   3.6973 -11.2568  -0.1029
+#&gt;   10.2335  -5.3348   2.6063   6.3540  -3.5606  -5.8432
+#&gt;   16.7542   6.7847   4.2078  15.8172  -0.1679   3.4273
+#&gt;    7.2342  -2.5870   0.9174  -6.4394  -6.4908  -2.7477
+#&gt;    4.9985   5.3420 -10.3066   2.6977  -1.9458  -1.4674
+#&gt;   14.4275  -7.8555  -3.6470   8.3568  -7.4040  -1.1206
+#&gt;    8.1549  -6.2982   6.0323   3.3352  -4.9192   2.2477
+#&gt;    3.1596 -26.3176  14.0512   1.3184  -0.6189   5.3501
+#&gt;    7.4058  -9.5246  -8.5619  -0.7603  -0.3428  -0.0791
+#&gt;  -13.0259  -3.9025  -2.3515  -4.5802   0.1049   1.7601
+#&gt;   -2.3563  -8.6606  -0.9520   3.5444   4.4992   1.4307
+#&gt; 
+#&gt; (4,.,.) = 
+#&gt;  Columns 1 to 8   5.2575  -0.7766  -4.7114  -2.1789   1.4369  -2.6180 -14.3979  -7.7102
+#&gt;   -0.6840   3.8540   4.0819  -7.0903   0.5486   3.2613   0.5156  -2.7583
+#&gt;    1.5078  -1.5715  -0.6514   1.7072  -4.7081  11.5225  12.4029 -13.0238
+#&gt;    2.1011   3.4168  -3.8830 -10.5282   3.7449  -4.7472  -5.5063  15.4334
+#&gt;   -2.2274  -4.8909  -3.5595   3.3106  -2.7585  18.6218  -5.5809   2.8852
+#&gt;    1.0694   1.6039  -0.5700  -0.2159  -5.5109  -6.7220  23.5573  11.3252
+#&gt;    3.7584  -7.4698  -0.4780   1.7294   0.4013 -19.3640   1.8215   7.7201
+#&gt;   -2.5843   4.0710  -2.1787   6.5675 -10.0709   0.0916   5.4518   3.4391
+#&gt;    5.3328  -3.5622  -6.7090   1.1574   0.9005   2.7114  -5.1990   8.0508
+#&gt;    4.2813  -0.1993   0.7410  -0.6316   2.6139 -18.8781  -0.0484  11.1997
+#&gt;   -0.0241   1.8775  -6.0636   5.3528  -6.4943  -1.7180   7.9061   4.0923
+#&gt;   -1.1490  -1.5504   1.7649   8.4804   0.9798  -8.3027  -7.5732  -2.0555
+#&gt;    0.0750   0.7099   7.6419   0.5240   0.7933  -8.1904  -3.3562   1.2358
+#&gt;    1.0598  -6.0068   9.1573 -12.4051  11.3743  -3.3522 -12.1646  -2.2511
+#&gt;   -6.2586  -1.8536   3.6691   2.2931  -6.2947  -3.0303  -2.5080   1.3602
+#&gt;    0.0500   0.5524   0.8717  16.9023 -14.3267   5.7065   0.2353  -7.9640
+#&gt;    1.4073  -3.8425   1.7744  -0.1246  -8.9244   1.8492   7.0214   2.7224
+#&gt;   -0.8062  -8.8097   6.1076  14.5658  -1.1873  -3.1778  -6.6876   1.8218
+#&gt;    3.0836   1.6713   4.6119  -8.0687  -0.9293   5.9532  10.6747  -6.8836
+#&gt;    0.0772  -0.6474   1.2649  -6.3609   2.8105  -2.3691   1.9532   2.3342
+#&gt;   -4.4295   3.4954  11.7487 -16.2479  21.5013 -21.5518  -6.8721   2.5280
+#&gt;   -0.2906  -1.9987  -5.5446  -1.7154 -21.7141   7.0420   9.9933   1.3947
+#&gt;    0.6380  -2.0186   0.0800 -14.2780   2.1709   7.8208  -2.9369  -7.1446
+#&gt;    0.7143  -7.0579   1.3845 -10.0082   2.6686   7.1179  12.3550   0.2197
+#&gt;    3.8913  -3.4731   7.8313  -5.9778   7.2661  11.2723   9.3754  -4.7707
+#&gt;   -1.2250  -0.4714  -0.2368  -6.6252  15.8559  10.3191 -16.4309   9.5409
+#&gt;    2.7018  -8.1056  -3.2256   5.8232  -3.3702   6.1084  -6.3966   2.1613
+#&gt;    2.6144  -0.9473   1.5922  -9.9597   0.8925  23.8863  -8.0366  -0.6395
+#&gt;    1.6135  -5.0597  -2.7115   6.5976   1.5883   5.4543  -7.8440  12.3300
+#&gt;    0.8075  -2.5191   0.7634   1.1340  18.0483  -0.3822   4.6083  -0.3061
+#&gt;   -2.8758   1.6708   3.0058   0.2336 -11.2261   1.7554  -8.1278  -1.7567
+#&gt;    3.0654  -1.4678   6.2884  -0.8781  12.7105  -6.6499  -5.2371  11.4991
+#&gt;    1.9314  -2.7722   2.2711  -5.6332  10.9087  -1.3416 -11.0572  24.7921
+#&gt; 
+#&gt; Columns 9 to 16  -1.2083 -15.3246   8.7146  -1.2374   5.4216   6.7218  10.7859  10.5819
+#&gt;    1.5094  -7.1882   0.4673  -3.3958  -2.1308   3.8891  -7.6609  15.6788
+#&gt;   -0.2640  -0.7133  -1.0597  -9.3713  11.0734   5.6356   8.4794  -0.5972
+#&gt;    0.3838   2.0368  -7.6437   5.4887  -5.0681  -0.0557 -18.9789 -17.2678
+#&gt;   -4.2130   0.5865 -11.2981  10.6303  17.1320  14.3611   2.8967   5.3941
+#&gt;   -9.3434  12.1248   5.2709 -20.6078  -7.1341   4.6923  -2.7827   0.0394
+#&gt;   -7.0147   3.1226  -0.5927  -3.7602  -9.7529 -12.3875  10.0586  -3.4637
+#&gt;   -1.0193   1.8488   7.5552  -7.5836  13.5377  -5.2304  12.5765  -8.4428
+#&gt;   -7.7684   9.5295   1.4658  13.8395   6.0057  -5.9827   6.0963  15.2381
+#&gt;    8.2388  -5.2734   3.3562 -16.8448  -0.3477  15.4448  14.2227 -13.4883
+#&gt;   -9.6210  -3.2715 -15.0036  -9.4995  -5.0580   2.9196  -0.3870   7.2528
+#&gt;   -0.3533  -8.1353   5.5265   6.4311   4.6806 -10.3631  -6.2019  -5.8668
+#&gt;    1.9106  -1.0105  16.0779  15.1296   1.2079  10.8193  -8.7599  13.7758
+#&gt;   -4.2827   7.8034  -1.5330  10.3991   5.2379  11.6170   1.1458   6.5294
+#&gt;  -11.0512   8.3186   2.9383   0.0428  -4.6336  13.5094  -6.4548   3.0002
+#&gt;    1.3194   2.2313 -14.1495  -3.7014  -5.5203   6.9198  -6.8851   5.7840
+#&gt;    8.2647  -2.6567  -2.6202  -7.8955  -7.6242   0.0974   8.8808  16.0055
+#&gt;   -2.0806   6.1532   0.6778   1.4626  -5.6735  -2.1429   0.1467  -1.4459
+#&gt;  -13.8766 -15.7014   2.0942  -7.7894  -0.0281   6.3920   6.9762   3.7968
+#&gt;   -5.8858   3.6765 -13.4358  15.3547  10.2177  -8.6941  -5.6539  -0.1847
+#&gt;   -1.8102   7.2655  11.5626   6.9123   5.0585   4.9868   1.3962   3.2381
+#&gt;   -9.8563  -2.3639   3.7108   0.6650   6.4558   7.4911  -3.1464  -3.8051
+#&gt;    1.6512  -4.5645 -10.0018   0.6565   8.4675   3.2615  -2.7642  12.2847
+#&gt;    6.9485  -2.4238  -1.2727  -4.8454  -5.9742  -5.2209 -10.5011   0.8381
+#&gt;  -17.4365 -16.8198   9.4141  -8.2449   2.7744  -8.3985   3.4191   6.1630
+#&gt;    5.1889 -18.6333   1.6147   8.9228   6.0449 -13.3716  -4.9473  -0.2034
+#&gt;   -4.4799 -13.8313   6.1731   6.8246  -6.9032   6.1586  -3.5178   7.4175
+#&gt;   18.1998  -9.0402  -8.3661  -9.5611  14.3823 -16.7345   7.7613  -8.1148
+#&gt;    1.3935   3.7030  -9.5104   3.7962   3.8622  -2.2526 -26.7995 -12.9311
+#&gt;   13.7098 -10.0344   9.1681  -5.0523  11.3861  -5.4115   5.5574 -14.7097
+#&gt;   -4.9616  13.2860  11.9651  -7.7347   2.0093  -8.0394   8.9447   6.1681
+#&gt;    5.5470  -2.6187   5.7762   3.6974  -0.5290  -0.3782  -2.2819  -3.0252
+#&gt;  -13.9729   5.5229   9.1752 -11.8947   3.1524  -5.4284  -1.3380 -13.3338
+#&gt; 
+#&gt; Columns 17 to 24  -4.0013  -5.1752  -6.0625   4.8196   5.2421 -28.3150  -9.7699   3.5622
+#&gt;    1.6674   4.6116  13.3719   7.9474   2.6414 -14.0860  -6.6946   0.5103
+#&gt;   -2.2187   5.7972  12.1092  -7.5571  11.7682  11.0406  -1.4348   5.1614
+#&gt;   -4.8356  15.1994  -7.3824  -3.7967   8.6452   6.7505  21.6267  13.5475
+#&gt;   -3.7089   8.5722  -1.8550  -2.7300  -2.5289  -2.5775 -15.8576   6.0162
+#&gt;   -3.8329  10.1273  -1.0425  -0.3029  -2.3056  10.0746  16.1152  -3.9638
+#&gt;    1.7464 -19.6774 -19.1648 -13.7747  -1.4367   8.1978  -6.9395   0.5813
+#&gt;   -6.8336  -4.5565   2.3554  25.8708 -14.1392   1.6892  -4.4730  15.2685
+#&gt;    2.1956   2.0937  17.7277   5.4085  -3.9359  -3.5907   2.6210  -5.9122
+#&gt;   -5.0316   1.8280  16.5148  13.7234  -8.1850  -4.9112   4.7328  -1.5492
+#&gt;  -10.0506  -5.0259  -1.6797  12.7264  18.3157  -0.3624 -15.3497   6.4820
+#&gt;   -3.4658  -0.8802  -4.8918 -17.2658  -5.7206   6.5984   2.0777  -0.0184
+#&gt;   -6.0299  -3.1784   3.3672  -2.6883   0.0094 -13.6375   4.0191  -0.6468
+#&gt;   -5.7017  -2.0493  -5.4481  -8.1665 -10.9426   1.5246  -1.6671   4.0896
+#&gt;   -6.8264   4.2544  -3.6874 -17.2830  10.7918  -7.2712  -2.3039 -11.1600
+#&gt;    1.1271  11.1836   8.5850  -2.4075  -5.7719   9.7470  -2.4562   3.3406
+#&gt;   -2.1897  -2.1179 -17.6689  -2.3949 -12.7269  20.6583 -11.9956  -3.8708
+#&gt;    1.2004  -3.1057   2.9151   4.2559   3.2008   2.5393  10.7603   4.7212
+#&gt;   -3.8557 -14.9922  -6.0539   2.7624  -0.2656   2.4445  -1.9374   0.6772
+#&gt;   -0.3970   7.9793   0.1745   1.1208  13.4290 -13.2243   7.3049   9.0883
+#&gt;  -11.8745 -15.0219   5.4823   5.0307   5.2270   5.6098   9.6503  -2.4330
+#&gt;   -6.1362 -15.4137  -3.9300  11.9052  21.8574  -3.4039  -6.1206  -2.4324
+#&gt;   11.0580   4.3413   0.8168   4.3517   7.3365   2.0796  -6.2185 -11.2443
+#&gt;   -0.2470  30.6120 -19.1413   6.5058   2.0727   5.7792 -10.0254  -1.8081
+#&gt;   11.8609   3.9276   8.2051 -12.0045  -7.0559  -0.9327  14.2228   7.9384
+#&gt;    5.9959 -13.4739   7.5799  -2.7083   1.6936  -1.8175  -3.8089  10.8707
+#&gt;    1.4073 -22.4073   6.2444  27.8608  -2.8708 -20.2574  -6.5330   3.3457
+#&gt;   15.3571  -8.8060  -2.2854  -6.4514   4.5233   7.8882  -6.8926  10.6354
+#&gt;  -11.2777   2.7589   7.0358   0.5842  -6.7995   4.6300  14.2326  -4.0184
+#&gt;    7.1088  -2.1649  17.9472 -10.6835   8.4725   2.4021  10.1116   1.9024
+#&gt;   11.2095  -2.8578  -0.3721   2.0504   1.8419   4.2851 -12.4610  -8.6200
+#&gt;    7.4972   9.2567  -3.7825  -1.4415   6.3870   3.0675  -0.8579  -2.5643
+#&gt;    5.6105   2.4290   6.9615   7.9456 -14.6035  -3.5008   7.8312   9.9388
+#&gt; 
+#&gt; Columns 25 to 32  -5.9620  -0.0204   0.7475   9.0756  -3.2886  18.0729   2.6390  -4.6496
+#&gt;    3.8225 -10.5170   0.3081  -3.6348  -8.8446  -6.3224  -1.2951  -7.6951
+#&gt;    8.6342  -5.8201  -4.7599 -22.9321  -7.0737   7.5013  10.3996   0.5483
+#&gt;   -1.3710   2.4480  -4.4538   5.9430  -8.7171 -12.2358   0.2681  -0.0347
+#&gt;    0.8756   3.6222  -0.9614 -14.0501 -26.2135   1.7694  -0.0165   6.5674
+#&gt;    6.4971   0.9460  13.1037  -1.6323  -2.8180  -2.0558  18.2262   9.0415
+#&gt;   -2.4175  11.1986 -11.0026   7.0562  -2.8092  18.9180   7.7928   3.8166
+#&gt;   -8.2856 -13.5131  -0.7048  14.4170  -8.4773  -4.0482  -3.4315  -1.1430
+#&gt;   -2.0748   5.0509  20.2421  -6.3062  -4.5865   9.8380   4.2838  -6.8102
+#&gt;   -0.0933 -19.3106  -3.8142  -3.2241   1.4647  -5.6961 -14.4402  -7.4341
+#&gt;   17.4403   1.0406   5.2266   1.3735  -6.1249  -9.4619  23.3903   4.3756
+#&gt;   -3.7529 -10.5598  -3.0329  -4.4190  -7.7150  -5.7010  -5.7494  -1.9703
+#&gt;   -0.6847  -3.7366  10.7811   2.3640  -3.6072 -12.6992   0.9389  -5.6063
+#&gt;   -7.4758   7.8815  -6.4612  -4.4221  -0.9759   3.5567   4.8440   9.3159
+#&gt;   25.3374 -17.8505  12.3732  10.7709  18.8009  -5.8407  -5.7090   3.8853
+#&gt;    7.5111 -14.1989 -16.3555  -2.2136  -5.6583 -13.5274  -0.0328   5.3420
+#&gt;   13.1912  26.4525  -7.4691  -1.1784 -11.1190  -2.3671   0.5289  -2.9780
+#&gt;    4.1862  -6.5302  -2.3661  12.6600   6.0300  -1.9318 -10.2906  -6.6801
+#&gt;  -17.7918   9.5451  -9.6835   5.6130 -16.7753  -3.0325   7.0554   5.8254
+#&gt;  -22.2120  -2.5846   7.6869   8.7114  -0.6171  14.1924   6.8168  -5.8089
+#&gt;   -0.4439  -9.7532  -7.1138  -3.2425   6.1150   0.8034  -5.0221  -0.0487
+#&gt;  -10.1492 -14.6502   9.9484   0.8859  11.4973  -8.2778   0.7768   0.6372
+#&gt;   -6.4879 -17.8953  -1.6492 -17.8681  -9.8552 -18.8246  15.3990  -7.3329
+#&gt;   -1.6665   3.9535   5.0340   3.9076 -11.8002  -9.1399  -5.2809  -5.0410
+#&gt;   -3.3469   5.9649 -11.9179  -3.7345  -8.5747  -9.3139   8.8574   7.0436
+#&gt;    7.0585  -4.3879 -27.3535 -19.1823   4.8467   4.5816  -0.3912 -11.9455
+#&gt;    7.1808  -2.1510  24.0965   8.7902  11.2961 -12.1195   2.7546 -10.4150
+#&gt;    2.7881   7.5827 -14.9542   9.2557  10.8290  -0.5128   1.9514   1.5722
+#&gt;   -1.2498   8.7504  14.4664 -19.5480 -17.6853   3.3890  -6.2159  -2.8830
+#&gt;    7.8213  -4.4381   0.3780  -4.7539  -7.4555   5.2615  -6.1714   3.4048
+#&gt;  -15.4673  13.3153 -16.1077  13.0992  -6.0089  -7.7125  -5.6382   1.4185
+#&gt;    4.7547  23.4044 -13.5534  11.6583 -10.4539  -5.5645  -8.4052  -1.6373
+#&gt;    0.3435  25.7502   0.2418  15.5421 -19.9352   8.5725  -1.8372   9.1598
+#&gt; 
+#&gt; Columns 33 to 40  -9.9918 -11.2558   3.9723 -14.5038  -8.4011   9.2543  -2.6456   4.7494
+#&gt;    4.5114   6.6943  11.3654  -0.0903  -9.6895  -2.1129   7.3742   1.0460
+#&gt;    2.7906 -11.0089   2.9186 -13.0802 -12.9032  -3.4157   8.0848  -2.7310
+#&gt;    7.2988   6.9473  -1.1243 -11.3052   5.7676   1.4347  -4.3950   5.2950
+#&gt;   -7.3413  -1.2005  -5.3936  -2.5187  -2.0919  -0.0479  -5.7671   2.3640
+#&gt;    1.9062  12.7308  10.3243   0.4744  14.8923  -5.8763  -8.1324   7.6046
+#&gt;    2.3840   5.4675  -6.9501   2.0311  10.8832   2.1187   4.0296 -14.1913
+#&gt;    4.9866   5.4312  -3.2555   2.2842   2.6813  21.9236 -21.0662  20.4726
+#&gt;   -3.6316   7.4061  -8.3588  -2.7434   3.6586   9.4686   0.7693  -7.7623
+#&gt;    0.7137   6.6464  -4.6390   1.0950  11.6623 -24.1333   3.0299  -1.1488
+#&gt;   -3.5207  -2.1141  11.9836   3.4444 -14.7773   1.0229   4.6654 -13.1691
+#&gt;   -3.0715  -1.0213 -10.2503   2.3550  -0.1898   7.5209  -7.0452   6.6049
+#&gt;   -2.6758 -14.3462  -9.0073 -13.5991  -1.4814   8.9751  -3.8749  -1.8851
+#&gt;    9.0460  -9.4622  -9.8574  10.2383   2.5086   2.6030  12.4254   1.5024
+#&gt;   -4.2682  12.7862  17.4230   6.5026  21.6198  -2.0568  17.6738   1.6034
+#&gt;   -2.7451  -7.4888  -7.7958  15.0149 -10.1658 -10.0607  -7.6173   4.2733
+#&gt;    2.2079   5.9707  -0.9333  16.3094 -21.9698   4.4637   2.9631  11.9600
+#&gt;  -10.3675  -2.1195  -8.4635  -4.4985  12.7914   6.8941   2.6211 -14.6620
+#&gt;   -4.0349 -12.7328  -1.4392  -4.6275 -24.8379  -3.4389   3.9137   4.1427
+#&gt;    6.0577  -1.6129   8.2038 -19.6548  -6.0476   8.7963  -0.4679 -12.2020
+#&gt;   -3.0002  -8.7029  -0.0351  -9.2292   6.9334   4.8472 -20.2736   9.7402
+#&gt;   -2.4221   4.5754  10.2630  -2.9332 -24.3398  -8.4547  10.8109   1.6836
+#&gt;  -10.5356   1.4607  -6.1856 -12.5852 -20.3493  -4.0766   5.5376 -16.8254
+#&gt;    9.7520   0.1653  11.3911   7.0408   4.5201  -9.5305   9.2442  -7.5668
+#&gt;   16.5580  -0.0725  -4.5160 -25.7244 -24.1641   7.3483  -3.4566 -11.0987
+#&gt;    3.7697 -13.1604   2.8608  -4.5298  -8.1642   1.9944   3.6661  -3.8547
+#&gt;   11.1156  10.4773  -1.1748  -8.3283 -10.9928   4.2175  -0.1683 -13.2033
+#&gt;    3.8437   2.4859   0.4669  -5.6129 -20.1019   7.1668  -0.7152  -2.3682
+#&gt;    4.5508   0.4159  -4.1009  12.9912  -1.8456  -9.3103  -7.9641  -7.9680
+#&gt;   12.3593   5.3969  -5.1216   0.5135  12.7073   1.5207 -20.1804  -9.0448
+#&gt;  -14.0890  -0.8132   8.5154  -3.2237  -2.9342   9.6484   6.4929   1.7095
+#&gt;    2.9154  -9.1732   4.2327   7.1021  18.2222  -4.0201  -9.6024  -5.8148
+#&gt;   -9.7206   6.9108   1.1476 -12.0830  17.6767  12.9765   0.2746  -1.8375
+#&gt; 
+#&gt; Columns 41 to 48   4.0353   0.1683   9.3107   8.2381  -1.3212   3.5892  -1.6621  -1.4353
+#&gt;   -1.4281  -6.8974   8.5788  11.5415 -12.2345   4.2449  -0.6578  -9.1329
+#&gt;    0.3255 -16.8218   8.9563   6.1586  -5.9325   1.5943 -12.7069   0.0494
+#&gt;   -8.6682  -0.0979  -5.0230 -18.0362   1.3477   0.8359   6.3049  -5.8941
+#&gt;   -2.5150   8.4617  -0.8237 -13.2406 -10.5428   2.4460   4.5751  -3.0680
+#&gt;   -3.7884 -13.1109  -4.6681   5.6655  15.2157  24.1355  -3.4650  -4.3438
+#&gt;   18.4587 -19.5020  -4.1910 -11.6513  16.8113   5.3275   0.0685  -0.3817
+#&gt;  -25.7388   8.8890 -10.4926  17.7701 -12.6404  -4.9679  10.5607 -16.9835
+#&gt;    5.6624  13.9448  -4.3876 -11.9631   9.2845   2.9322  -7.0282  18.7209
+#&gt;   18.6680   8.3034   4.0959   4.1039   6.6948  10.9190 -10.6597   9.6531
+#&gt;    3.8021 -14.0758  -9.2871  -1.7017   4.5407  10.1670  -0.0628   5.1776
+#&gt;    3.8555  13.4692 -11.4556  -5.9488  -4.0233   3.8919  -3.8699  18.5399
+#&gt;    9.1737  -6.5645   8.9549  -6.1373  -6.4067  -8.1647 -12.1142  10.2670
+#&gt;   -0.1919  13.6022  -3.9105 -12.5652   3.4625  -6.3605  21.5382  22.8667
+#&gt;  -12.0776 -12.7406  -3.2441  -8.8713 -16.6236  -3.4332 -16.5411 -11.0148
+#&gt;    2.1752  -0.5010   4.6397  20.0059   4.0325  20.9525  -5.0529  10.0026
+#&gt;   11.2979 -13.2448  -6.3452  -0.1919 -13.3157  -3.8160  18.6384   3.6693
+#&gt;   19.1818 -10.1661  -4.4319  11.7715  -4.1373   3.2186  -6.8304  10.1579
+#&gt;   -8.3812  12.0222  -4.4667  21.4049  11.3710  -0.2993 -10.0765   5.5163
+#&gt;   13.9959  -4.1086   6.1736 -10.6470  12.8444   2.3222  -5.8774  -5.3935
+#&gt;   -0.5331   5.4453   4.0455  -5.6229  12.5445  -8.5009   8.2076  -3.6695
+#&gt;    4.3265  -7.1274   8.2978  11.0249  -2.2476  -3.3942 -18.0633  -8.2711
+#&gt;   -4.1544   1.3124  14.0326   4.7946  11.9610  -0.9016  -5.4485  -3.2986
+#&gt;    9.0508  -8.0588   6.0134  -7.5460 -10.5553   3.1587   5.2354   1.9173
+#&gt;   -0.2094   0.2436  16.5652   3.4599   6.7972 -10.4418   6.2919   7.9371
+#&gt;   -4.1450  -1.3889 -20.9035  -2.6052   5.3293   6.1418   7.7175  -0.7744
+#&gt;    8.3306  16.1748   3.2943   0.8919  -3.6691 -10.6401 -19.3199  -3.8398
+#&gt;   -5.7417   0.9074  11.0930   3.4065  -3.1414 -17.1465   6.0950  11.5081
+#&gt;   12.9956  -0.8061   3.5513 -15.9179   5.1410  14.5245   1.1184  -2.1028
+#&gt;   -4.1863  -4.7161   5.7921 -14.5177  -6.3096   1.9435   0.6193 -11.3959
+#&gt;   -7.0905   4.7091  -4.8918  14.0901   1.5160 -10.1038  -3.1061  -3.2290
+#&gt;    3.0677   4.3721  -3.8297  12.4544   1.4152   0.0946   4.9133 -11.2872
+#&gt;  -17.1241  11.0867  -3.6483 -12.6453  -6.9553   2.7518  12.5078   4.2787
+#&gt; 
+#&gt; Columns 49 to 54   4.1236  -5.4074 -12.9845   4.0738  -7.3491  -4.1963
+#&gt;   12.4328   1.6314  17.9378  -4.5684  -4.7763  -2.2439
+#&gt;   -6.6719  -3.1554  10.8061   0.8958   3.5459   0.9062
+#&gt;    3.1129   2.7201 -12.6108   8.5306   8.0650   0.7508
+#&gt;   -1.9852  -5.5406  -3.4580  -6.5936   1.2295  -4.1384
+#&gt;  -10.3391 -19.3431  -2.8895   3.9120   2.8137   0.7597
+#&gt;    2.0765  -8.5507 -15.7660 -11.2097   1.5208  -0.8458
+#&gt;    1.5014   8.8149  -7.4493   6.1569   2.9695   4.1294
+#&gt;    5.0225   7.8214   9.1012   2.8977   8.4803  -0.4028
+#&gt;   -3.0103  21.8172  15.3435   4.7424  13.1495  -0.4556
+#&gt;    6.2431  -4.7825  -1.3751  -1.7184   1.4160  -1.8794
+#&gt;    4.9141  -0.5876 -10.1879  -7.3055   6.2525  -5.8698
+#&gt;   11.1453  11.6135  -6.4222  -1.2066   8.8767  -2.9253
+#&gt;    3.2769   2.5901  -0.5101  -1.4117   1.3547  -5.7876
+#&gt;   -0.1879   6.6963  11.9052   3.0424  -4.1758   7.4905
+#&gt;   15.1550   2.3688   3.0109   3.7021  10.5098   0.7399
+#&gt;   -0.3722  10.1152  14.7701  -1.0260  -7.4072  -0.2253
+#&gt;    3.7488  -5.1289   8.8644 -11.3547   3.6755  -1.5011
+#&gt;    1.6978 -13.0118 -17.8606  -1.8947  -9.1641  -2.8292
+#&gt;   -2.8340   0.4534  -8.8220  -1.7407   6.7418  -6.5311
+#&gt;    1.6589  12.7459  -3.8227   7.0958   5.6626   0.4574
+#&gt;   -7.6468  12.5628  19.8869   9.2874   5.8936  -0.5988
+#&gt;    0.9610  -8.8247   6.0486  -8.6888  -3.9088  -3.7171
+#&gt;    6.2439  13.9843   8.3316  -1.0690   7.0906   1.0348
+#&gt;   13.8028  10.2469 -18.0544   0.0224  -9.1942   3.0021
+#&gt;  -21.6612  -1.9768   2.6255 -12.7041   3.8225   1.7540
+#&gt;   -9.8668   1.3828   0.1024 -15.2984  10.6509   3.1775
+#&gt;   -3.1621   1.6915  15.4808  -4.5523 -14.7115  -4.2481
+#&gt;    9.9829   5.5103   4.3329   7.8143   8.2728  -0.9571
+#&gt;   14.0987  -4.9220  -1.2361   0.7605   8.8626   3.5967
+#&gt;    3.6289  -6.6178  12.4771   6.3315  -0.0571   2.5123
+#&gt;   -4.1765 -11.3557  -9.0154   2.1146   2.9739   7.5828
+#&gt;   15.4765  -3.9233   4.7180   3.1266  -8.5665   3.3690
+#&gt; 
+#&gt; (5,.,.) = 
+#&gt;  Columns 1 to 6  4.7121e+00 -4.1392e+00  4.2671e-01 -2.3432e-02 -6.1502e+00 -1.5154e+00
+#&gt;   1.3131e+00 -4.6019e+00  1.2158e+00 -4.8398e+00 -1.1462e+01  2.5465e+01
+#&gt;   1.4356e+00  1.3472e+00  1.1290e+01  3.7592e+00  6.4035e+00 -6.0758e-01
+#&gt;   4.5615e+00  1.4797e+00  1.9293e+00 -5.1798e+00 -8.7327e+00 -1.6384e+00
+#&gt;  -6.0103e+00  4.7125e+00 -5.0213e+00  1.4314e+01 -6.1885e+00 -4.8113e-01
+#&gt;  -2.8093e+00 -4.3261e+00 -2.2340e+00 -1.1215e+01  4.4678e+00  1.1791e+01
+#&gt;  -2.8427e+00  1.3671e+00 -9.2753e+00  4.2584e+00  1.7855e+00 -4.0986e+00
+#&gt;   1.5708e+00 -7.1654e+00  1.9545e+01  3.3585e+00 -8.1278e-02 -7.8859e+00
+#&gt;  -5.4065e+00  2.4482e+00 -2.0467e+00  1.5009e+00  6.9066e+00 -1.9656e+00
+#&gt;   2.1044e+00  3.7346e+00 -6.2802e+00 -8.6725e+00  1.2277e+01 -6.8880e-01
+#&gt;   8.1300e-01 -8.0786e+00 -4.3819e+00 -9.5436e+00  1.9126e+00 -5.9912e+00
+#&gt;  -1.4374e+00  5.4119e+00 -5.9999e+00  2.2893e+00  2.6581e-02 -8.7797e+00
+#&gt;   7.9736e+00 -1.5308e+00  9.2061e+00 -7.8350e-01 -8.8262e+00  1.8192e+01
+#&gt;  -4.8003e+00 -2.4843e+00  3.7562e+00  4.5686e+00  3.4293e+00  3.2791e+00
+#&gt;  -3.5586e+00  5.8432e+00 -5.2028e+00 -9.8800e-01 -8.6339e-01 -2.3204e+00
+#&gt;  -2.6500e+00  6.8019e-01  3.3802e-01 -1.3496e+00  8.8721e+00  6.9262e-01
+#&gt;  -3.6989e+00  1.4064e+00 -5.8904e+00  1.0351e+01 -4.6007e+00  2.7872e+00
+#&gt;   5.0128e+00  5.0922e+00 -8.0095e+00  3.0661e+00 -6.7186e+00  5.2068e+00
+#&gt;   1.7794e+00  2.0529e+00 -7.1347e+00  3.7214e+00 -1.3019e-01 -2.3643e+00
+#&gt;  -6.2076e-01  4.1952e+00 -1.5084e+00 -2.8139e+00 -4.6309e+00  3.7450e+00
+#&gt;   6.2531e+00 -6.6878e+00  7.8619e+00 -4.6050e+00 -2.9871e+00  1.6594e+01
+#&gt;  -5.8875e-02  2.0826e+00 -1.1967e+00 -7.6659e+00 -1.1437e+01 -6.7666e+00
+#&gt;   2.4713e+00 -1.6468e+00  3.1288e+00 -3.1551e+00 -1.4913e+01 -1.1225e+01
+#&gt;  -2.2079e+00  2.6099e+00 -1.3939e+01  1.3748e+00  4.7271e+00  1.7565e+00
+#&gt;  -4.3267e+00  5.8627e+00 -1.1069e+00  7.6390e+00  1.2591e+01  1.3539e+01
+#&gt;  -2.5213e+00 -6.9652e+00  6.6769e+00  7.5201e+00  9.1303e-01 -1.4627e+00
+#&gt;  -4.4846e+00 -3.4159e+00 -1.8865e+00 -4.1773e-01  3.1519e+00  1.4703e+00
+#&gt;   8.6222e-01  1.6073e+00 -3.6558e+00  9.0493e+00  4.5090e+00 -1.7756e+00
+#&gt;  -9.5790e+00  6.6201e+00 -1.2950e+01  1.3573e+00  4.4204e+00  7.0487e+00
+#&gt;  -3.1445e+00  6.7386e+00 -1.0002e+01  3.3010e+00  1.4198e+00  2.3509e+00
+#&gt;   4.6680e+00 -3.8550e+00  3.9559e+00 -7.9135e+00 -7.9662e+00 -1.1992e+01
+#&gt;  -1.1950e+00 -2.9311e-01 -9.8995e-01 -4.7760e+00  5.8903e+00 -2.2477e+00
+#&gt;   2.2851e+00 -1.4962e+00  4.5850e+00 -2.0969e+00  2.1677e+00  1.1184e+01
+#&gt; 
+#&gt; Columns 7 to 12  5.3742e+00 -1.3278e+01 -2.2128e+01  6.2522e+00  5.7646e+00  7.7593e-01
+#&gt;   1.5700e+01 -9.3470e+00 -6.5623e+00 -1.3812e+00  1.0429e+01  1.2237e+00
+#&gt;   7.0832e+00 -1.3387e+00 -7.0009e+00 -2.4392e+00  4.7489e+00  1.3399e+01
+#&gt;  -5.7861e+00  7.8405e+00 -1.7810e+00  3.8876e+00  1.2636e+01  1.7642e+00
+#&gt;   4.4580e-01 -3.3932e+00  2.9900e+00 -1.8315e+01 -1.5403e+01 -6.6818e+00
+#&gt;   1.5445e+01  2.5324e+00  3.6166e+00 -6.3296e+00 -9.0035e+00  4.4681e+00
+#&gt;   7.2337e+00 -9.5093e+00 -5.4988e+00  2.0171e+01  7.6171e+00  7.0269e+00
+#&gt;   6.1839e+00  7.8078e+00 -1.0692e+01 -1.0780e+01 -7.1039e-01  1.2407e+01
+#&gt;  -9.1226e+00 -1.9685e+00  2.4767e+00 -3.5430e-01 -6.7079e+00 -1.3514e+01
+#&gt;  -1.3627e+00 -6.3488e+00  4.1866e+00  2.6592e+00 -1.3842e+01 -1.4554e+01
+#&gt;   2.0691e+00 -2.9694e+00 -1.1506e+01  3.2658e+00 -8.1041e-03  2.8423e+00
+#&gt;  -4.2165e+00  1.0902e+01  3.2166e+00 -3.0789e+00  2.4340e+00  4.6152e+00
+#&gt;  -1.3090e+01 -8.9970e+00 -1.2499e+01 -7.4733e+00 -4.9388e+00 -2.6453e+00
+#&gt;   7.3393e+00  1.1991e+01 -4.4998e+00 -6.4108e+00 -6.1439e+00 -4.5448e+00
+#&gt;   1.2258e+00  7.8415e+00  8.2456e+00 -6.4829e+00  6.0065e-01  1.2036e+01
+#&gt;  -6.3584e+00  3.3278e+00  1.2227e+00 -2.8865e+00 -1.4019e+01  1.4059e+01
+#&gt;   6.8571e+00  1.0581e+01 -3.4583e-01  4.9050e+00  6.1908e-01 -3.8844e+00
+#&gt;   1.0609e+01  1.9299e+00 -5.2231e-01  3.0143e+00  3.9725e+00  1.3190e+00
+#&gt;   1.1832e+00 -8.2013e+00 -9.8862e+00  3.3924e+00  7.1782e+00 -6.9648e+00
+#&gt;   1.6707e+01  2.9170e+00 -1.6524e+00 -6.8065e+00 -8.7750e+00 -3.1798e+00
+#&gt;  -2.5117e+01 -2.5851e+00  9.3680e+00  2.9989e+00 -1.5857e+00  5.0736e+00
+#&gt;   2.0603e+00  5.2293e-01 -4.5410e+00 -6.7505e+00 -3.5698e+00 -1.3509e+00
+#&gt;  -7.8929e+00 -2.2287e+01 -2.7721e+01 -1.6145e+01  9.2091e+00  8.1378e+00
+#&gt;   6.9181e+00  1.9560e+00  3.0176e+00 -6.2042e+00  3.1212e+00 -1.3767e+01
+#&gt;   4.7774e+00  1.7216e+00  1.1810e+01  1.3689e+01  8.3514e+00 -6.4704e+00
+#&gt;   9.4548e+00  1.5837e+01  1.5592e+01  1.7712e+00  1.3769e+01 -9.1083e-01
+#&gt;   1.3323e+01 -1.2507e+01  7.1013e-04  6.5178e+00 -1.1276e+01 -1.0068e+01
+#&gt;   1.8866e+01 -4.5490e+00  7.2334e-01  2.2100e+01  3.2098e+01  2.0325e+00
+#&gt;  -8.2690e+00 -5.8296e+00  1.2803e+01 -5.4233e+00 -5.1961e+00  1.4061e+01
+#&gt;  -1.0628e+01 -9.3202e+00  1.2784e+01 -1.8175e-01  1.3940e+00 -3.9709e+00
+#&gt;   4.2279e+00 -8.3057e+00 -1.4058e+01  8.9101e-01  8.8170e+00 -2.5214e+00
+#&gt;  -1.3956e+01  1.4538e+00  7.3590e+00 -1.6660e+00 -4.3110e+00 -4.6546e+00
+#&gt;  -1.3127e+00 -5.5786e+00  3.8559e+00  4.2540e+00  2.9314e+00 -2.6850e+00
+#&gt; 
+#&gt; Columns 13 to 18  1.1930e+01 -6.4788e-01 -1.0442e+01 -2.0240e+01  1.1644e-02  4.5082e+00
+#&gt;  -7.2730e+00 -6.3459e+00  4.9806e+00 -1.0142e+01  8.3479e+00 -5.6752e-01
+#&gt;  -8.7923e+00 -4.1251e+00  9.0765e+00 -3.8417e+00 -1.4755e+00 -2.2709e+00
+#&gt;   9.0956e+00 -3.4605e+00 -5.6987e+00  1.7234e+01  1.3073e+01 -9.2928e+00
+#&gt;   1.7260e+00  2.3374e+00  1.5466e+00 -1.9666e+00 -7.8690e-01  3.9154e+00
+#&gt;  -1.6471e+00  8.3004e+00  4.3186e+00 -3.6931e+00  1.6072e+01 -1.5788e+01
+#&gt;   1.4084e+01 -2.9063e+00 -6.5679e+00  1.2663e+01  1.9751e+00  1.8627e+01
+#&gt;   2.4873e+00 -1.0175e+01  1.5735e+01 -2.9948e+00  2.2098e+00 -5.5016e+00
+#&gt;   2.2975e+00 -2.1597e+00 -5.9502e+00  5.7177e+00 -1.9129e+00 -6.3321e+00
+#&gt;  -3.0592e+00 -1.2532e+01 -1.6628e+01 -7.9265e-01  1.9683e+00 -1.0201e+01
+#&gt;   7.7512e+00  3.6023e+00  8.0514e+00  4.1680e+00  4.3380e+00 -6.9590e+00
+#&gt;  -1.0490e+00 -1.6473e+00  1.7163e+00  8.5430e+00 -1.7873e+01 -4.1432e-02
+#&gt;  -1.3627e+00 -1.0659e+01  7.9768e+00 -6.7237e+00 -1.6839e+01 -6.2214e+00
+#&gt;   1.1808e+01  1.3893e+01 -1.4191e+01 -7.5561e+00 -1.7373e+01  6.7302e+00
+#&gt;  -3.0020e+00  5.1884e+00  4.1400e+00  3.6263e-01  6.5121e+00  3.0467e+00
+#&gt;   1.3495e+01 -1.1261e+01 -3.4114e+00 -6.2916e+00  1.7888e+00 -2.2022e+01
+#&gt;  -1.8014e+01 -6.0846e+00  1.5245e+01  3.4250e+00 -2.2113e+00  1.6600e+01
+#&gt;   2.0599e+00 -5.7345e+00  8.3602e+00  1.1356e+00 -7.2583e-01  1.5345e+01
+#&gt;   4.0177e+00  3.9430e+00  1.2406e+00  2.9329e+00 -1.0253e+01 -8.1627e+00
+#&gt;   6.5136e+00 -5.7634e+00 -1.2022e+01  1.0369e+01 -1.4031e+00 -3.0687e+00
+#&gt;   7.3289e+00 -5.0217e+00 -9.1185e+00 -7.9444e+00  8.1302e+00 -4.3823e+00
+#&gt;  -1.0513e+01 -1.1314e+01  3.7360e+00  1.6278e+00 -3.0589e+00 -7.2650e+00
+#&gt;   6.4786e+00 -4.7705e+00 -5.1027e+00 -7.2549e+00 -3.8462e+00 -1.3477e+01
+#&gt;   3.1919e-01  5.7666e+00  1.3536e-01 -8.2108e+00  1.2744e+01  9.9364e+00
+#&gt;  -5.2084e+00  1.5089e+01  1.4856e+01 -8.6932e+00 -4.7304e+00 -9.5339e+00
+#&gt;  -1.5008e+00  6.9193e+00 -2.2116e+00  1.1155e+01 -4.8783e-01 -8.7540e+00
+#&gt;  -1.1480e-01 -2.0923e+00 -2.5827e+00  1.0092e+00  7.8682e+00 -5.1561e+00
+#&gt;  -2.4299e+01 -1.8868e+00 -2.9056e-01  1.5654e+01  6.3220e+00 -1.2154e+00
+#&gt;   6.7699e+00 -4.3887e-01 -1.7045e+01 -2.9752e+00  4.9787e+00 -4.4855e+00
+#&gt;   4.8247e+00  1.3522e+01 -1.7036e+00 -5.4038e-01  3.3729e+00 -7.0467e+00
+#&gt;  -2.4993e+00 -7.9922e+00 -9.0398e+00 -5.3949e+00 -2.6596e+00  4.6461e+00
+#&gt;   9.6043e+00 -6.8071e-01 -1.4409e+00 -7.2577e+00 -2.4176e+00  1.5199e+01
+#&gt;   3.3081e+00  6.6585e+00 -6.5976e-01  1.9123e+00  6.9633e+00  9.2466e+00
+#&gt; 
+#&gt; Columns 19 to 24 -7.5810e+00  1.9367e+00  1.1137e+01  4.6425e+00 -4.8519e+00  3.3498e+00
+#&gt;  -1.1113e+01  6.4837e+00 -1.4595e+01 -6.1985e+00  1.1358e+01 -5.8455e+00
+#&gt;  -7.8337e+00  5.4322e+00 -1.8810e+00 -1.3986e+01  7.4964e+00 -1.4575e+01
+#&gt;   9.0311e+00  5.3640e+00  1.3258e+01 -6.2679e+00 -1.4864e+01 -1.7001e+01
+#&gt;  -6.9761e+00 -6.3965e+00  6.1917e-01  1.3480e+00  4.2407e+00  5.5030e+00
+#&gt;  -1.9657e+00  1.9085e+01 -4.7221e+00 -1.2309e+01 -9.5057e+00  2.2392e+00
+#&gt;  -1.5988e+00 -2.7310e+01  1.7692e+01  1.3578e+01 -1.2273e+00  9.5286e+00
+#&gt;  -5.6398e+00  1.6114e+01 -1.2142e+01 -2.6239e+00 -5.9258e+00  1.1628e+01
+#&gt;   1.1855e+01 -9.7676e+00  2.9456e+00  1.3398e+01 -8.7582e-01  7.0983e+00
+#&gt;   1.7253e+00 -3.5074e+00  8.9861e+00  9.1305e+00 -1.7082e+01  5.8522e+00
+#&gt;  -5.9769e-02  4.4901e+00  3.0901e+00 -8.1861e+00 -6.7580e+00 -1.0428e+01
+#&gt;   2.1544e+01 -9.4859e+00 -6.4954e-01  3.8079e+00 -2.3861e+00  1.0862e+01
+#&gt;  -1.5596e+00  1.6921e+00  1.9812e+00 -1.6297e+00  1.2623e+01 -1.8583e+00
+#&gt;   1.7143e+01 -2.9039e+00  5.2118e+00  5.5340e+00 -3.1253e+00  1.8416e+01
+#&gt;  -8.9750e+00  8.7990e+00  8.2344e-01 -1.5382e+01  3.2630e+00 -4.2075e+00
+#&gt;  -9.1886e-01  2.8298e+00 -1.3495e+01 -1.2100e+01  4.2630e+00  2.1146e+01
+#&gt;  -1.1993e+00 -1.4638e+01  7.4549e-01  1.5010e+01 -8.0299e+00 -8.8297e-01
+#&gt;   1.4656e+01 -1.1243e+01  2.5431e+00 -2.6352e+00 -1.0266e+01  1.3959e+01
+#&gt;   1.1386e+00 -9.0387e+00  2.7431e+00  2.2918e+00 -3.1314e+00  1.5853e+01
+#&gt;   1.2103e+00  2.0319e+00  3.4055e+00 -5.1472e+00  1.4683e+01 -8.0329e+00
+#&gt;  -1.1963e+01  5.1479e+00 -1.6543e+00  1.4991e+01 -2.1046e+00  9.3839e+00
+#&gt;  -3.9061e+00  1.6098e+01  6.4221e+00 -6.9825e+00 -5.4664e+00 -1.4462e+01
+#&gt;   3.3171e+00  3.6631e+00 -1.4556e+01 -1.0407e+00  4.4183e+00  8.0098e+00
+#&gt;   3.3501e+00 -3.1580e+00 -1.2794e+00  1.4548e+00 -1.1369e+01 -5.2852e+00
+#&gt;  -9.3622e+00 -5.4411e+00 -7.9050e+00 -5.5712e+00  3.2700e+00  5.3043e+00
+#&gt;   3.7840e+00 -7.8123e+00  7.0003e+00  3.4789e+00  1.2995e+00 -1.1004e+01
+#&gt;   4.8015e+00 -7.0745e-02  1.8126e+00  3.1094e+00 -3.1520e+00  1.7710e+00
+#&gt;   1.3527e+01 -6.4771e+00 -4.1707e+00  1.1839e+01 -3.9910e+00 -2.8013e+00
+#&gt;   6.1319e+00 -7.7846e+00 -1.1972e+01 -7.8127e+00  3.3484e+00 -8.5366e+00
+#&gt;   1.2122e+00 -8.7030e+00  9.0175e-02  1.0272e+00 -1.3791e+01 -8.2346e+00
+#&gt;  -8.4347e+00 -7.8919e+00  3.1654e+00  6.9850e+00 -3.4827e+00  9.7836e+00
+#&gt;  -6.6116e+00 -5.1150e+00 -1.7089e+00 -6.9539e-01 -7.1561e+00 -3.5353e+00
+#&gt;   1.3174e+00  4.1013e-01  1.0555e-01  3.5495e+00 -7.9443e+00 -4.3379e+00
+#&gt; 
+#&gt; Columns 25 to 30 -7.1197e+00 -2.5533e+00 -6.7593e+00  3.8385e+00 -4.2044e+00 -4.6364e+00
+#&gt;  -1.3072e+00 -7.9670e+00  4.6678e+00 -6.7488e+00  1.9721e+01 -1.2685e+00
+#&gt;  -1.5270e+01 -3.4695e+00  4.3683e-01 -8.9342e+00  1.4607e+01  4.5429e+00
+#&gt;  -5.3979e+00 -6.2467e+00 -6.3999e+00  1.6897e+01 -1.2749e+01 -1.7730e+01
+#&gt;  -3.8974e+00  1.7861e+01  1.1714e+00  5.0724e+00  1.2244e+01  2.2317e+01
+#&gt;  -2.0501e+00 -9.5200e+00 -1.1371e+01  3.3698e+00  1.2072e+01  1.2209e+00
+#&gt;  -2.8670e+01 -1.5304e+00  6.5713e+00  2.3335e+01 -1.9684e+00  1.9220e+01
+#&gt;   7.8404e+00  3.9758e-01  1.5486e+01 -6.4413e+00  2.0383e-01  6.2809e+00
+#&gt;  -4.7607e+00  1.3538e+01 -5.4859e+00  8.5047e+00  1.5112e+00  7.9595e+00
+#&gt;  -5.1492e+00 -3.1643e+00 -9.2862e+00  4.5060e+00  1.3000e+01  4.4906e+00
+#&gt;   2.4070e+00 -4.8888e+00  1.9903e+01  2.5987e+00  1.2074e+01  3.2714e+00
+#&gt;  -3.2577e+00 -2.6569e+00 -7.4322e+00 -9.7783e+00 -5.0959e+00 -1.0909e+01
+#&gt;   1.6897e+00 -1.2268e+01 -3.4326e+00 -9.5988e+00  1.1998e+01 -1.9460e+01
+#&gt;  -2.9513e+00  1.3643e+01 -6.7885e+00  1.2826e+01 -1.0763e+00  1.0531e+00
+#&gt;   3.6025e+00  1.1069e+01  2.5384e+00 -2.4595e-01  1.1113e+01 -1.3048e+01
+#&gt;  -9.0986e-01 -1.9103e+01 -7.5893e+00 -2.6130e+00 -4.7878e+00  2.0838e+00
+#&gt;  -2.4974e+01  5.0931e+00  9.1401e+00  7.0589e+00 -7.8296e+00  1.5907e+01
+#&gt;  -3.5827e-01  3.9912e+00 -5.3342e+00 -6.9986e-01 -1.1233e+01 -1.4593e-01
+#&gt;  -2.9047e+00 -9.9776e+00 -1.0195e+00  6.0493e+00 -1.4187e+01  4.6327e+00
+#&gt;  -1.9968e+00 -6.6989e+00 -4.5910e+00  4.3948e+00  8.6554e+00  8.5784e+00
+#&gt;   1.4163e+01  6.2239e+00 -1.6425e+01 -1.5927e+01 -3.8968e+00 -2.0515e+01
+#&gt;   8.4828e-01  4.4815e+00  1.5392e+01  5.2222e+00  1.1490e+01  3.5422e+00
+#&gt;   3.0056e+00 -4.4067e+00  5.8039e+00  5.1411e+00  5.3563e+00 -1.0490e+00
+#&gt;   6.8250e-01  8.0229e+00 -3.5188e-01  2.0258e+01 -7.8061e+00  1.3818e+01
+#&gt;   6.6439e+00 -1.1878e+01 -3.7895e+00 -9.5331e+00 -6.0823e+00 -2.9926e+00
+#&gt;   1.9294e+01 -6.9251e+00 -5.1325e+00 -7.0214e+00  9.5923e+00  3.7839e+00
+#&gt;   1.1241e+01  2.5613e+00 -8.3436e+00 -1.3926e+01 -1.7239e+00 -1.9718e+00
+#&gt;  -9.3955e+00  1.1306e+01  1.4373e+01 -1.1424e-01 -4.8170e-01 -4.3959e+00
+#&gt;   1.3172e+01  6.2202e+00  3.3152e-01 -1.6445e+01 -1.2597e+01 -5.3284e+00
+#&gt;  -3.0086e+00  2.3601e+01  1.9524e+00 -1.3146e+00  7.1911e+00 -1.8541e+00
+#&gt;  -1.2704e+01 -1.8338e+00 -2.5337e+00 -5.1111e+00 -2.6656e+00 -3.5587e+00
+#&gt;   1.1506e+01 -1.1557e+01 -6.8043e+00 -5.9856e+00 -1.6333e+01  1.4423e+01
+#&gt;  -1.0813e+01  1.0223e+01  1.4212e+01  1.0552e+01  4.4558e+00  6.5555e+00
+#&gt; 
+#&gt; Columns 31 to 36 -1.8291e+01 -4.2999e+00  4.5601e-01 -8.3161e+00  1.9145e+01  1.0535e+01
+#&gt;   9.6855e+00  2.7774e+00  1.9523e+00 -7.7770e+00  2.2417e+01 -4.9211e+00
+#&gt;  -1.4236e+00 -6.1047e+00 -6.8579e-01  4.1378e+00  8.3291e+00  1.4699e+00
+#&gt;  -8.3872e+00 -1.2020e+00  1.2573e+01  1.0246e+00 -1.4205e+01  4.4063e-01
+#&gt;   7.8635e+00  5.5809e+00 -1.7828e+01  7.8313e-01  2.0001e+01 -2.0676e+00
+#&gt;   1.0424e+01  5.9135e+00 -9.5414e+00 -8.8739e+00 -7.5911e+00  1.0424e+00
+#&gt;  -1.5620e+01 -5.5053e+00 -1.8575e+01 -8.0920e+00 -2.3011e+00  1.2082e+01
+#&gt;   8.0482e+00  2.0614e+01 -5.9597e+00  4.4061e+00 -4.9501e+00  1.2811e+01
+#&gt;   8.5973e+00 -1.3672e+01 -7.8232e+00  7.0040e+00  1.2301e+01 -6.5928e+00
+#&gt;   9.5528e+00  2.9149e+00  7.0402e+00  6.4693e+00 -4.0254e+00 -1.4571e+00
+#&gt;  -6.7823e+00 -6.3719e-02  4.3697e+00 -2.0017e+01  1.4132e+01  1.7834e+01
+#&gt;  -3.6959e+00  3.3713e+00  7.4339e+00 -2.4896e+00 -1.0360e+01 -1.0914e+00
+#&gt;   9.5771e+00  3.0994e+00  1.9979e+00 -2.8915e+00  5.6933e+00  2.0304e+00
+#&gt;   3.1315e-01 -8.2881e+00  1.6856e+01 -6.0014e+00  5.6838e+00  3.6677e+00
+#&gt;  -9.1044e-01 -5.0781e-01  1.0143e+01 -2.3547e+00 -5.7666e+00 -8.1764e+00
+#&gt;  -9.5259e+00  4.0185e+00  1.7433e-01 -7.3735e+00 -7.8219e+00  6.3988e+00
+#&gt;  -1.1048e+01  1.4390e+00 -4.7159e+00 -9.0483e+00  2.0214e+00  7.5921e+00
+#&gt;  -7.7220e+00  2.6837e+00  1.1349e+01 -1.4938e+00 -1.8059e+01 -1.6158e+00
+#&gt;  -3.8124e+00  1.1999e+00 -1.7797e+01 -1.3856e+01  8.6441e+00  1.6933e+01
+#&gt;  -3.9433e+00 -2.0375e+00 -5.7926e+00 -6.4940e+00  1.8035e+01 -1.3628e+01
+#&gt;   5.5925e+00  7.2992e-01 -1.0357e+01  2.6171e+01 -1.2837e+01 -1.1919e+00
+#&gt;  -5.5127e-02 -5.4829e+00 -6.2261e+00  4.4794e+00  1.4508e+00  1.0786e+01
+#&gt;   5.0026e+00 -1.6174e+00  1.2585e+01 -6.2515e-01 -2.6363e+00  1.1334e+01
+#&gt;  -7.4970e+00 -5.9508e+00  9.5613e-01 -5.3492e+00 -5.7499e+00 -5.3408e+00
+#&gt;   3.1062e+00 -5.7467e-01 -1.1296e+01  3.6180e+00 -1.8956e+00 -2.8660e+00
+#&gt;   2.6379e+00 -6.9742e+00 -8.4832e+00  5.0639e+00 -9.9193e+00  1.5181e+00
+#&gt;  -7.4655e-01  2.7439e+00 -2.4501e-02 -1.1316e+01  1.4168e+01 -5.5834e+00
+#&gt;  -6.4428e+00  5.2486e+00 -2.2777e+00 -2.4341e-01 -1.0685e+01  4.9076e+00
+#&gt;  -1.7347e+01  2.5239e+00 -7.9817e+00  1.2293e+00  1.0096e+01 -1.8109e+01
+#&gt;  -2.2250e-01 -4.9260e+00  1.4205e+01  1.0993e+01 -4.4525e-01 -7.9359e+00
+#&gt;   1.5869e+00 -4.8892e+00  1.5826e+01 -1.0400e+01  5.6197e+00  6.0068e+00
+#&gt;   1.0016e+01  1.2187e+01  3.9678e+00  1.8454e+01 -2.1363e+01  1.2461e+01
+#&gt;   2.9391e+00  7.7279e-01 -1.7982e+01  1.3988e+01  2.3683e+00  4.7453e+00
+#&gt; 
+#&gt; Columns 37 to 42 -1.4111e+01  5.3830e+00 -9.3397e-01 -2.0437e+00  1.3894e+01 -9.6059e+00
+#&gt;   7.0483e-01  1.0629e+01  1.4933e-01 -3.3972e+00  3.7201e+00 -7.8989e+00
+#&gt;  -6.2214e+00 -1.4707e+00 -6.0769e+00  1.4141e+01 -8.4486e+00  7.6925e+00
+#&gt;  -1.0395e+01 -5.1267e+00  1.5288e+01 -2.1768e+00 -2.9589e+00 -1.7916e+01
+#&gt;   6.7907e+00 -1.8053e+00  1.2054e+00 -1.1046e+01  1.4566e+01  1.1057e+00
+#&gt;   7.0647e-01  8.0419e+00 -1.6069e+01 -1.5754e+00 -1.8007e+01 -2.6630e+00
+#&gt;  -5.3209e+00 -2.2984e+00 -1.9713e+01  8.3384e-01  6.2710e+00  1.9513e+01
+#&gt;  -4.5013e+00 -1.6560e+00 -2.1425e+00  3.9734e+00 -9.9441e+00 -4.9897e+00
+#&gt;  -1.9045e+00 -1.0288e+01 -1.8777e-01 -1.0439e+01  7.6219e+00  4.2557e+00
+#&gt;   1.1217e+01  1.0260e+01 -5.4071e+00  9.8172e+00  2.0094e+00  8.2361e+00
+#&gt;   2.5562e+00  7.6749e+00  1.0999e+01 -1.9015e+00  1.4723e+01 -3.3837e+00
+#&gt;   1.5211e+01  2.2636e+00  6.9040e+00  4.8008e-01 -7.9932e+00  8.8856e+00
+#&gt;  -1.7622e+00 -7.6240e+00  1.9189e+00 -8.2306e+00 -3.6926e+00 -4.5966e+00
+#&gt;   9.7993e+00  1.1645e+01  7.0390e+00 -1.0689e+01  6.1414e+00  1.2007e+00
+#&gt;   1.4607e+01 -1.3078e+01 -2.0350e+00 -7.0639e+00  6.3654e+00 -1.4949e+01
+#&gt;   5.9529e-01  1.3820e+01 -5.3896e+00  3.5609e-01 -9.5186e+00  2.4723e+00
+#&gt;  -5.3721e+00  1.7624e+00 -2.6790e+00 -1.7448e-01 -4.4717e+00  9.1652e+00
+#&gt;   2.8137e+00 -1.2846e+00 -6.8347e+00  2.9087e+00 -3.4082e+00 -6.5433e-01
+#&gt;  -6.4573e-01 -6.3193e-01  6.7526e+00  1.0402e-01 -3.3774e-01  1.5402e+01
+#&gt;   4.2707e+00  6.3796e+00 -8.3490e+00 -1.5283e+00  2.7979e+00 -1.1153e+00
+#&gt;   1.5370e+00  3.4194e-01 -5.9215e+00  2.6696e+00 -2.1622e+00 -1.2943e+01
+#&gt;   8.6297e+00 -1.2615e+01 -1.2578e+01  8.5392e+00  8.5492e+00  5.3639e+00
+#&gt;  -2.3783e+00 -7.4137e+00  9.6901e+00 -6.9019e+00  9.9480e+00  9.2872e+00
+#&gt;   8.0943e-02 -6.6542e-02 -4.4073e+00 -3.1565e+00  8.1904e+00  6.8113e+00
+#&gt;  -1.2081e+01  5.3051e+00  1.1608e+01 -8.2586e+00 -1.2726e+01  3.8582e-01
+#&gt;   6.6568e+00  2.4584e+00  4.0412e+00  1.3642e+00 -1.1038e+01  4.6452e+00
+#&gt;   6.9482e-01  2.0867e+00  1.4203e+00 -7.3544e+00  9.0480e+00  2.9928e+00
+#&gt;  -1.7859e+01 -7.8506e+00  1.0674e+01  1.0165e+00 -8.9218e+00  1.1449e+01
+#&gt;   1.2400e+01  6.1530e+00 -4.2715e+00  1.5877e+01  9.0478e+00 -3.7542e+00
+#&gt;  -1.1289e+00  7.9320e-01  8.2069e+00 -4.6946e+00  1.4807e+01 -3.4348e+00
+#&gt;  -1.0215e+01 -9.0403e+00  7.6240e+00  1.4947e+00 -1.8116e+00  6.4025e+00
+#&gt;  -6.5855e+00  8.3943e+00 -4.2471e+00 -7.4104e+00 -2.8329e+00 -6.5444e+00
+#&gt;  -2.0193e+01 -5.1753e+00  7.4816e+00  8.0486e+00 -9.9913e-01 -9.3372e+00
+#&gt; 
+#&gt; Columns 43 to 48 -1.3640e+01  1.7220e+00 -4.6315e+00 -4.8305e+00 -9.9433e+00 -1.1415e+01
+#&gt;   1.2615e+01 -5.4900e+00  4.4614e+00 -7.3902e+00  1.8600e+00  4.1669e+00
+#&gt;  -7.0354e+00 -6.4858e-01  9.1743e+00 -5.2491e+00  5.0688e-02 -3.5844e+00
+#&gt;  -4.5732e+00  7.7138e+00 -5.5949e-01 -4.7766e+00 -6.5941e+00  2.2081e+00
+#&gt;  -2.4143e+00  3.9033e+00  6.1685e+00 -4.4903e+00  3.6106e+00  2.7718e+00
+#&gt;   1.2963e+01  1.6424e+01  6.5619e+00  5.3657e+00 -7.7476e+00  2.1978e+00
+#&gt;  -6.5296e+00 -8.5861e+00  6.0724e+00  1.3922e+00  9.2813e+00 -4.5972e+00
+#&gt;   9.8361e+00 -6.1249e+00  6.1533e-01  1.5170e+01 -9.7208e+00 -9.8965e+00
+#&gt;  -8.7144e+00  4.2792e+00  6.8535e+00 -1.7561e+00  3.5048e-02  6.5872e-01
+#&gt;   1.5302e+00  1.7437e+01 -1.4131e+00 -6.8379e+00 -2.9896e+00  2.4287e+00
+#&gt;  -1.0938e+01 -2.2670e-01  3.5169e+00 -3.7272e+00 -8.9138e+00 -7.5340e+00
+#&gt;  -1.4197e+01 -3.7520e+00  4.5150e+00 -9.5360e-01  4.2155e+00 -4.0695e+00
+#&gt;  -5.2665e+00 -7.5975e+00 -2.0861e+00 -6.9546e+00  1.2292e+00 -3.7878e+00
+#&gt;  -1.6940e+01 -3.8391e+00  2.2983e+01 -1.2483e-01  3.3173e+00 -4.4470e+00
+#&gt;  -7.8407e+00 -5.7469e+00 -1.6211e+01  6.9511e+00  5.4495e+00  9.2661e+00
+#&gt;   3.8092e+00 -1.0979e+00  6.3600e-01 -1.5279e+01  1.6077e+00  1.2852e+00
+#&gt;  -6.3536e-01 -2.2383e+01  8.7236e+00 -9.1232e-01  9.8138e+00 -1.6103e+00
+#&gt;  -4.8979e+00 -1.3216e+01 -2.4730e+00 -4.6399e+00 -2.1728e+00 -1.0300e+00
+#&gt;   4.0771e+00  2.8650e+00 -4.5676e+00 -1.2963e+00 -1.0398e+01 -8.8620e+00
+#&gt;   2.8616e+00  1.1801e+01 -1.4050e+00 -7.7831e+00  4.0118e+00 -1.6161e-01
+#&gt;   5.4261e+00  2.7380e+00  1.4998e+00  2.3039e+00  5.0045e+00  9.0082e+00
+#&gt;   1.7714e+01  6.0926e+00 -1.7778e+01 -1.7248e+00  1.7784e+00 -1.0243e+01
+#&gt;   7.9459e+00 -3.6603e+00  9.8580e+00 -2.7786e+00 -1.3638e+01 -9.5378e+00
+#&gt;   2.4795e+00 -2.9875e+00  1.5674e+00 -4.6055e+00  7.4356e+00  3.9743e-01
+#&gt;   7.2215e+00 -1.1131e+01 -6.4836e+00  1.4040e+01  1.1492e+00  9.9893e-01
+#&gt;   4.0891e+00  1.2133e+01  1.5029e+00  2.3619e+00  1.4673e+01  4.5757e+00
+#&gt;  -1.2461e+00  3.4615e+00 -4.2478e+00  4.4469e+00  7.1510e-01  1.7123e+00
+#&gt;  -2.6894e+00 -1.0350e+00 -7.3582e-01 -1.8254e+00  3.3137e+00  3.3876e+00
+#&gt;   2.8312e+00  3.0288e+00  8.0730e+00  3.7114e+00  8.7465e+00  1.9342e+01
+#&gt;  -8.3858e+00  1.0696e+00  8.7608e+00  3.7185e+00  3.4854e+00 -6.5794e-01
+#&gt;  -8.6587e+00 -1.4916e+01  7.9075e+00  3.2647e+00 -6.0043e+00  4.1202e+00
+#&gt;  -2.8534e+00  5.2064e+00 -2.3374e-01  2.1394e+00  3.8324e+00  5.6622e+00
+#&gt;  -5.3596e+00 -9.4697e+00 -8.4387e+00  4.1856e+00 -8.2728e-01  4.2662e+00
+#&gt; 
+#&gt; Columns 49 to 54  1.5110e+00  1.1612e+01 -8.1481e+00  7.7504e+00  4.7089e+00  8.6133e-01
+#&gt;  -6.7131e+00  4.4196e-01  5.9638e+00 -1.7034e+00 -7.0029e-01 -2.8722e-01
+#&gt;  -6.9634e+00 -7.0357e+00 -5.7691e+00 -5.0540e+00 -5.0232e+00  4.1304e+00
+#&gt;  -1.1507e+01  8.6575e+00 -8.0303e+00  1.1754e+00  4.6072e+00 -3.5357e+00
+#&gt;   4.4017e+00 -6.7497e+00 -2.4502e+00 -1.6876e+01  5.9885e-01  1.7616e+00
+#&gt;   9.3639e+00  6.6137e+00  3.8002e+00 -1.9916e+00  4.9963e+00  1.8824e+00
+#&gt;   3.8667e+00 -1.2947e+01  5.6025e+00 -2.4671e+00 -1.0458e+01  8.9027e-01
+#&gt;  -1.2818e+01  8.3827e+00 -2.0432e+00 -5.8657e-01 -1.0108e+00 -9.3442e-01
+#&gt;   3.8007e+00  1.3953e+01 -1.9088e+00  8.6154e+00 -4.4130e-01 -2.6356e+00
+#&gt;   5.7717e+00  3.0625e-01 -5.0145e+00 -5.3261e-01 -2.3341e+00 -2.8519e+00
+#&gt;  -2.1037e+01 -8.3455e+00 -5.1127e+00 -4.5669e-01 -1.0479e+01 -3.4343e+00
+#&gt;  -1.9757e+00 -1.0135e+01  3.7165e+00  7.5472e-01  6.7992e-01 -2.2342e+00
+#&gt;  -2.4433e+01  8.9641e+00  1.1905e+01  8.4401e-01  5.5836e+00 -7.9179e-01
+#&gt;   1.0468e+01  1.8752e-01 -6.2280e+00  1.1272e+01 -4.0073e+00 -3.6035e+00
+#&gt;  -3.0949e+00 -1.7943e+00 -1.4946e+00 -7.2659e+00  2.7339e+00 -3.3407e+00
+#&gt;  -5.7835e+00 -5.1514e+00 -7.1319e-01  8.4498e+00  4.2640e+00  1.7035e+00
+#&gt;   4.5562e-04 -9.2789e+00  1.7952e+01 -7.2646e+00 -1.0763e+01 -2.6272e+00
+#&gt;   5.2462e+00  2.3286e+00  9.3039e+00 -6.0350e+00 -2.9160e+00 -1.4515e+00
+#&gt;   1.0351e+01 -4.6743e+00  3.2394e+00  3.9729e+00 -6.5436e+00 -2.4057e+00
+#&gt;  -1.9072e-01 -1.0872e+01 -4.8054e+00  9.2820e+00  3.1677e+00  4.0453e+00
+#&gt;  -8.6390e+00  6.5988e+00  6.5066e+00 -2.8962e-01  2.2690e+00  1.5657e+00
+#&gt;  -5.6277e+00  7.8628e+00  4.3847e+00 -9.2744e+00 -6.1405e-01  5.3696e-01
+#&gt;  -8.5831e+00  2.5337e+00  3.5184e+00  1.5149e+01  3.6907e+00 -4.3372e+00
+#&gt;   2.0339e+01  1.5128e+01 -1.0260e+01 -5.6791e+00  6.8810e+00 -2.6907e+00
+#&gt;  -7.1959e+00 -1.7392e+00 -6.0054e+00  1.7916e+00  4.5486e+00  4.5773e+00
+#&gt;  -1.1536e+00  6.2013e+00  5.2471e-01  2.9748e-01 -2.0977e+00 -4.8847e+00
+#&gt;   1.5113e+00  3.6688e+00 -3.7778e+00 -7.6354e-01  2.2902e+00  2.3995e+00
+#&gt;   3.5179e+00  2.1436e+00  1.5282e+01 -6.9281e+00 -1.2934e+01 -1.4841e+00
+#&gt;   1.6872e+01 -1.4593e+01 -6.3487e+00  5.1606e+00  2.8223e+00 -8.5161e-01
+#&gt;   1.0120e+00  3.3539e+00 -5.2198e+00 -1.1814e+01  6.5890e+00  2.4779e+00
+#&gt;   2.9020e+00 -3.1077e+00  5.1558e+00  1.2185e+01 -3.7317e+00  6.3348e-01
+#&gt;  -7.3495e-02  3.7732e+00  2.4100e+00 -5.2214e+00  2.1536e+00  2.3925e+00
+#&gt;  -3.3776e+00 -8.3365e+00 -1.0611e+01 -8.1708e+00 -3.5078e+00 -1.1994e+00
+#&gt; 
+#&gt; (6,.,.) = 
+#&gt;  Columns 1 to 8   5.8372  -9.8998  -2.0121  -2.4662  13.5752   6.9889  -2.8107   7.1202
+#&gt;    0.6224   0.0788   4.6555  14.5333  -8.0570  -0.3515  -5.9703  -5.2597
+#&gt;    9.7019  -2.4902  -3.7452   4.8322  10.6615   3.1522   2.0184  -8.1924
+#&gt;   -0.4211  -1.8528   8.4477   1.6152  -9.3875  -4.3582   4.2066  -5.1822
+#&gt;   -5.7169   0.6433   0.5421  -0.6013  -6.9453 -10.2944  -7.6998 -11.1344
+#&gt;   -5.5389  -0.5468   7.3565  -0.8178 -10.4985  11.2354  -0.8214  -7.7982
+#&gt;    3.5369   6.8267 -10.5947  -8.4343  -6.4685   3.9466  -2.0369 -10.0967
+#&gt;   11.1584 -15.3450  10.2952   9.0181   3.0937  -9.2322  14.4363   2.1945
+#&gt;   -0.0193  -1.3542   5.8896 -14.3968   5.4872   6.0471  -7.3033  -9.9609
+#&gt;   -3.0220  -2.5323   3.9394  16.2894   8.3263   3.6074  -3.3756  -2.2061
+#&gt;   -2.8605   3.0711  -1.3183  -6.2283   4.1860  -3.6294  -3.6637 -13.1819
+#&gt;   -1.3793  -0.2396   3.0307  -7.1593  -4.5503  -1.6779   5.1447  13.8509
+#&gt;   -0.1465  -3.9010  11.4829  11.0006 -11.1987  13.0582   4.4387  -2.3655
+#&gt;   -4.7880   4.5173   9.1224   3.5623  11.7311  -7.4407   9.0906   4.6791
+#&gt;   -6.5952   0.4970  -5.9774   5.8664   6.9302  -8.0594 -10.3868  -6.9531
+#&gt;   -4.6250   7.2551   3.0804 -10.5546  -4.6623  -3.6951   0.1074  15.4016
+#&gt;   -3.5132   4.1171 -10.7817   4.3338   4.8770   0.4874   6.0347  -1.1558
+#&gt;   -3.9374   4.1109  -9.2184  -3.1122  -4.0898   0.0078 -19.8531  -3.5478
+#&gt;    1.2404   5.1677  -2.4492 -14.6120   4.9774  -1.8887   5.0211  13.0184
+#&gt;   -1.6529  -1.9162   5.9899   6.5129  -6.4674   0.0788   0.9734  -7.4534
+#&gt;    2.7855  -5.5393   7.6601  -3.4464  -9.9863   4.4274   2.6896   5.6719
+#&gt;    3.4731  -4.5157 -12.2779   4.9178   0.1459  10.9105  -7.6646 -10.4843
+#&gt;    8.3818   5.3656   2.8337 -13.5562 -15.3615  -6.3959 -11.6485  -3.5062
+#&gt;   -0.3563  10.8070  -6.9681  -4.0528  -0.5741 -15.1804 -10.4712  -1.5669
+#&gt;   -0.4883   7.6477  11.7307   1.3656  -8.3575   0.1948   9.9536  11.1918
+#&gt;    8.4912  -2.2264  -5.8630  -3.8422  -1.1351  -5.2031   5.7807  -2.7160
+#&gt;    5.5358 -10.8007  -3.4382   9.1261  -9.6091   6.6322  -1.3668  -2.2500
+#&gt;    5.9400   3.2803  -4.4874  -2.2849   2.1297  -3.7096   5.4339  -5.1589
+#&gt;  -11.3716   3.9791  10.5131   9.5946 -16.9824   7.3506 -10.2104   2.1259
+#&gt;   -6.6718  -0.0340   8.5590   2.9999  -8.5308   7.4367  -9.0804 -10.4841
+#&gt;    7.3935 -12.8008  -3.2281  -7.2737   7.6732  -2.0011  -2.2509  -7.0506
+#&gt;  -11.1762   6.9170   3.5395  -4.2980   9.7279   1.4796  -9.4957   1.8829
+#&gt;   -6.0718  -8.0331  17.0624  11.7189   0.1565 -11.3853  -3.9514 -21.4767
+#&gt; 
+#&gt; Columns 9 to 16   3.1270   4.7312  -6.4996 -12.1034   0.8866  -3.3480   2.1150  -5.1799
+#&gt;    5.7491   7.5753   9.1675  -8.3121   0.1323  -3.4256 -10.1828   2.8702
+#&gt;   -6.0724   0.3255  -4.2952   4.0437  -6.3153   6.2150   8.1903  -7.0992
+#&gt;   -6.3629  -9.1470  -3.4025  -8.4366  -4.3414  12.8227  18.4577   5.3753
+#&gt;   -9.7089  -0.6214  -1.8766   1.3886   8.7637  -0.2797  -6.8564  -4.6726
+#&gt;   11.2725   4.2084   4.9454   0.1580   5.5927  -3.0540 -13.8818  -2.9320
+#&gt;    0.8992 -13.0661  -3.6130  10.5605  -6.7307  -0.1158   5.6653 -13.4067
+#&gt;  -13.7012   6.2335   4.5332 -15.4137  -3.3848   2.2410 -13.7391  -6.5338
+#&gt;    0.3081 -13.2906  -3.5264  -8.5625  11.3408   2.2639   4.0107  -1.9744
+#&gt;   12.5743  -8.6153   6.6129   3.9813   2.6037   6.4202  -2.7709 -17.4761
+#&gt;   -2.7177   5.9604  -3.1499 -11.8414 -20.1259  13.4410   0.1031  -9.2148
+#&gt;    3.2199  11.1343   1.8144  -5.5571   0.2029   3.1182  -8.7780  -2.6389
+#&gt;    4.6146   6.8731   5.7189 -23.2510  -8.2365  13.5839  -6.9237  -9.6302
+#&gt;    6.5635   1.5082  -2.8093   2.9363   3.1272 -23.9563  -6.7154   0.5377
+#&gt;   -9.7716  -2.2921  16.4473  18.8884  -9.5087 -10.7768  -3.8763  -5.5696
+#&gt;    3.7348   4.8383  -0.4432   0.2188   2.8202  -0.5749  -7.6459   7.0642
+#&gt;   -3.1382  -0.8714   2.7118  -9.4357  13.1350   1.8498  -4.6894   6.9628
+#&gt;   -6.0351  -5.6972  15.7125   5.6265  21.5144   5.2340  -2.9256 -13.5315
+#&gt;   -0.1302  -5.8842 -10.7976 -10.0615  -1.6952  -7.3183   3.7136  15.4941
+#&gt;   -3.4020   2.0545   8.1644  14.6500  -8.0948   0.3742   6.5363  -8.3616
+#&gt;   -7.8988   4.0873   6.6725  -7.4203   9.5629  -5.1464  12.8811   1.5573
+#&gt;  -14.4934 -21.1438   2.1387   5.1099  -4.1852  10.2888  10.0368  10.2898
+#&gt;  -17.3838 -18.6930  -1.4715 -21.7694  -6.2966  -1.2640   8.6499  13.2985
+#&gt;   18.8828 -14.0267  12.0243  10.6017   9.4435 -11.3057  -4.5340  10.7612
+#&gt;   15.0840   1.0585  -6.6564  -5.3599  -4.6832  -1.4636  -6.8946   0.3874
+#&gt;   -6.4301 -13.2148 -11.5608  15.6281   6.3202   5.1255   4.1724   1.9846
+#&gt;   14.7582  -4.2892   5.4274  -5.8187 -13.8346   8.0116  13.6895  -4.7489
+#&gt;   -9.6515  -5.0152  -5.6887  -1.3467   6.7512  14.9172  -4.7750   6.3344
+#&gt;    5.3507   5.0692   9.3586  -2.2776   8.1056  10.8131   5.0828   9.8253
+#&gt;    8.9306   4.9590   6.4751  -9.1079   7.8426   1.9053  -9.8635  -1.3610
+#&gt;  -16.2635  -7.6040   7.7798   6.5987  -6.5625   6.0631  10.9673   1.2997
+#&gt;   -0.8216   3.8448  -2.8957   2.7185   0.7707  -8.1755  -1.3966  -3.3229
+#&gt;  -12.8707   5.7332   5.8443   1.1134  -0.8635   5.8408   4.2175 -16.5586
+#&gt; 
+#&gt; Columns 17 to 24   6.7434   3.6172   7.5219  -4.2430   5.7572 -18.9009  10.6751  -0.4495
+#&gt;  -10.0496   0.7910   4.8055   0.1286   2.3416  11.2289   5.8153  -1.7091
+#&gt;   -5.4093   7.5992  -8.8217   3.7474   4.3123   6.6951  -3.4955  -1.6625
+#&gt;   18.4912  -1.0653  -4.2397 -20.1113  11.7525 -15.7751   2.9362  -3.7723
+#&gt;   -9.6425  -4.3680   2.8832  -9.9571  -0.3633  -3.4885  -6.2398   8.7156
+#&gt;   31.5278   1.6027  -9.4435  19.1221  -3.8702   2.3171   3.8170  17.7132
+#&gt;    9.7651  17.3714   4.6024  -5.8386   5.7030  -6.1837   8.2561  -4.4413
+#&gt;  -16.7743   9.1330   6.2069   0.1146 -18.9357   8.0628  -8.2238  -1.9070
+#&gt;   -3.4211   7.0988  -0.4025  -6.1283   0.5604 -15.7983 -10.3986  10.9773
+#&gt;    6.9417  -2.9097  -5.0079 -14.0668   4.5167   5.3750  11.1730 -13.7184
+#&gt;   -8.5718  -1.3174  -7.9277  -3.4962   0.1227 -10.9827  -8.6096  -0.6327
+#&gt;   -1.1413  12.0654  -0.6577  -2.0581  13.2240  -2.8314 -11.7772  12.7701
+#&gt;    9.8398  10.4058  -4.5915  -5.3860  16.5096 -19.2353  -8.3984  -5.0658
+#&gt;  -19.7751  -2.7539   6.8370  -4.8043  -3.8224  -5.9301 -12.7765   4.3204
+#&gt;   -5.9741  -9.8371  -6.3898   5.3392  -8.3081   1.1014  -4.2730 -14.6473
+#&gt;    3.9865  -7.2380  15.8227  -4.1409   5.2327   4.2395   9.6288  -1.4335
+#&gt;   -7.6413  -6.5342   1.9042   1.7599   2.4410  -8.4803   4.6289  -0.5024
+#&gt;    0.0245   0.4454  -0.3680 -11.9465   6.2814   0.0516  -7.8827   3.7139
+#&gt;   13.1931   1.2556  16.9853  -3.0643   6.8853  -4.0688   2.7113   9.2518
+#&gt;    0.4569   3.6248  -0.8355  -7.7372  -2.9394   5.1682   0.0059 -10.4355
+#&gt;    4.6640   0.8229   7.5867 -23.6020   0.0220   9.3969  -5.1461  -3.7425
+#&gt;    4.1282 -10.1168 -20.9062   6.9623  -3.9451  -0.5187   2.9524 -11.3756
+#&gt;   -6.4655  -0.3596  13.1940   9.8203  -4.7303   8.6359  -9.9316  14.5752
+#&gt;   11.8410 -25.7768   2.2486  -0.9346  -7.5109 -11.0041  13.8072   1.6992
+#&gt;   11.5055  -4.6496  11.1935  13.8994  19.3569  -7.2374   3.1911  -3.5341
+#&gt;  -14.6882  15.9491  12.0762  -4.8073  -7.7098   8.6749  13.5211  11.0884
+#&gt;   15.1053  -6.8504   5.3475   1.4031  -5.4007   5.1388  -9.6504  -5.8465
+#&gt;   -6.6915  12.9552  -3.9263   3.6180  -0.2964  11.9318  -4.7163   2.9965
+#&gt;    0.8383   0.7919  -0.3834  -0.2002  -0.3010   0.7126   4.5338   3.6352
+#&gt;    2.5618  10.4896  -9.2390  -6.3396  10.5246   4.8557  -6.6497   5.2051
+#&gt;   -5.4984   0.9804  -3.3242   6.7121   7.0364   7.6747  -8.8115  -1.3950
+#&gt;   15.2412  -5.7309  11.5133   7.5772   6.1621   1.3538   3.9427  13.9237
+#&gt;  -16.7468   3.0447 -10.9687  -7.2660   0.8922  -6.3033 -12.3895  -5.5437
+#&gt; 
+#&gt; Columns 25 to 32 -12.0697  -3.5384   3.1297  -4.6535  -5.2481  15.8287   5.4154  -0.3778
+#&gt;   -2.1089   9.5972   0.8430  -4.0199  -6.0881   2.9838   6.0103   4.5010
+#&gt;   -2.6543   4.3419  -0.0420   1.9371  -3.5836   0.8798   7.6040  15.6894
+#&gt;   12.1703   0.8527 -16.7525 -14.1144  -2.8514  -1.8223  13.7236  -2.8442
+#&gt;   -2.6466  -4.5494   6.9735  14.2975  20.0328   1.3083   5.4931  -2.0729
+#&gt;   -8.6758  -8.7606  -3.5248  -6.7319 -23.9848  11.8449   2.1175   9.2487
+#&gt;   -9.3546   2.4001  11.0860   1.9609   4.1101   1.5524  -9.9594  -4.8627
+#&gt;   13.1873  -2.1725 -13.8422  12.5630  12.0236  -5.8243  16.2306  -3.0538
+#&gt;   -4.1563 -21.6641  -3.8594   2.7444   2.9823  -0.3435   3.1660  -7.2944
+#&gt;  -12.8167  -0.6253 -13.2272  10.3824   3.1855 -15.4092   0.4140   4.1161
+#&gt;    8.6379  -7.7792  -1.4905  -5.8643  -7.0079  11.6172   5.6935   8.8235
+#&gt;   14.8754   5.8787  -5.5924  13.7661  -5.5452 -24.9628   5.7362 -10.2188
+#&gt;   17.7307  -1.6486 -12.9553   5.3010  -0.9417  -3.4197  16.9908  -3.1881
+#&gt;    7.1434   6.3656 -10.4272  -0.7966  -6.9522   1.6485   7.7171 -13.7625
+#&gt;   -7.7999  14.8574   4.1914  -2.3317  10.0932   7.8385   1.0473   7.9744
+#&gt;    5.8419  -5.9148   1.6019 -17.8381  -2.7671   3.6393  -1.7876  -0.0644
+#&gt;   -7.3340  -5.3850  11.1706  -4.6075 -17.8987  -2.9685  -1.0708  -4.2589
+#&gt;   -0.5210   0.4319   1.5871  10.8863  -3.6306   1.6850   0.7711  -0.9064
+#&gt;   -2.9105  -1.4267   2.8435  -1.2618   3.9577  -4.1292   7.1147  -3.8027
+#&gt;   -4.2346   0.3152  -2.0866  -6.2390   5.7696   8.0283 -10.6504  12.0618
+#&gt;    1.4405  10.5334 -20.7627  19.0846   3.8392 -21.0015   9.1211  -6.8417
+#&gt;  -12.8519 -14.0754   9.5872   6.3090  -5.6876   5.9102  -9.6384  10.8206
+#&gt;    1.0209   3.4411  -5.6529   8.2674  17.3244   5.4750   2.9686  -3.5951
+#&gt;  -14.2953  16.2404  -8.5252   1.0102   4.7611  -0.9951 -17.7755  -9.6286
+#&gt;   12.8574  11.9751   3.7719 -13.3267  -1.1982   9.6215   5.9018  -0.5553
+#&gt;   -1.7271  20.9145   3.3571   9.7421   9.9440 -18.9676  -2.7275   8.6002
+#&gt;   -6.0209  12.4593 -10.9827 -10.5646  10.0390 -10.0646 -22.2542  17.5519
+#&gt;   -5.0658   4.7174   8.2967  -4.6677  15.4137  -6.6179  -5.3330 -11.1588
+#&gt;    2.1353 -13.7841  12.9964   4.7600  -5.2383   6.7317 -15.1759  -7.0365
+#&gt;    1.7096  11.8668  -0.9163  12.2730   1.5656   2.7579  18.2381 -12.5987
+#&gt;    4.2124   0.8708   2.3994  -9.3146  -2.9083  -7.0406   0.4310   3.3430
+#&gt;   -8.3238  10.6261  -8.8687   5.3676 -11.4817   6.0798   6.5560  -3.2692
+#&gt;   -1.8217   6.9275  -9.0067   1.5917   4.7243  15.1044  19.6905 -14.1816
+#&gt; 
+#&gt; Columns 33 to 40  18.0137   0.1437   1.0359   8.1105  -4.9806  16.9608   1.7034   0.7581
+#&gt;   -4.1803  -0.5965   0.0856   2.3563 -10.2289  -3.9149  -9.4304   1.1640
+#&gt;   10.3162   9.0257   0.7877 -10.3640  -9.9317   2.3473  -2.1994  -0.8416
+#&gt;   -6.4261  -0.0093 -10.1654  14.0540  -7.8450   6.5073  -2.0542  -8.0125
+#&gt;   12.0965  10.8590  -7.7790 -12.6560   0.0736   4.8304   1.2262   7.6016
+#&gt;   -2.1623   0.5889  18.8545  -0.2921  -7.2104  -1.2669   7.4378  -7.8752
+#&gt;    4.7243  -4.5159  11.9878 -18.0457   8.2826  13.9053  13.4480   4.5478
+#&gt;  -13.6581   2.4030  -0.3871   9.3807   3.9741  -5.1651   0.3349   5.1812
+#&gt;   11.1939  -2.9157 -11.7361  -1.6043 -22.8290   3.8562  -3.6200  -1.6497
+#&gt;  -13.1913   0.1082  -5.5334  -8.3986  -6.5263   5.5662 -11.0957   7.5131
+#&gt;   16.4945  -0.9001  -0.7629   2.2027  -5.6176   4.2583  19.4694   6.7701
+#&gt;   -9.8157   3.9220  -2.5901   3.8787   3.5791  -1.2144  -5.8429   5.8580
+#&gt;  -16.6308   5.3659  -9.2491   7.4077 -15.5380 -13.3558  -5.7481   5.4371
+#&gt;    6.2558  -3.2801  -7.3593   4.4177  -0.7377  -5.6666  -1.8812  19.0885
+#&gt;   -5.3019 -14.4732  -2.1184   9.8816   3.1071   4.0681  -9.8392   1.2120
+#&gt;   -9.6794   3.6849  -1.7766   2.8213  -5.8930  -3.7362  -5.6313   2.3740
+#&gt;    5.1394   3.5812   7.7915 -21.3930  -4.9200  -6.9383   8.8521  -1.8699
+#&gt;    1.5971  -3.1726  -2.3342  -0.0188   5.8653  -5.2357  -5.4085  11.3280
+#&gt;    3.3370   3.1580   3.7043   7.4843  -5.3311   3.5652   7.1179   7.5347
+#&gt;    4.0595  -6.2943   2.0057  -0.4389   9.6982   2.4842  -5.7146  -0.3769
+#&gt;   -6.7431   2.8492  -5.3043  -0.8100  -0.0629 -12.7998  -9.2995   9.5977
+#&gt;   11.1320  -3.0477  -1.9959  -5.0161   7.0474  -3.0932   5.2943  -1.7621
+#&gt;    4.1791  14.7743   8.0079   7.9273 -12.0689  -1.5847   9.2181   9.6022
+#&gt;   -1.2992   7.4819  -6.6553 -14.3183  -1.4715   3.8195 -13.1888  -2.3540
+#&gt;  -11.7587  -6.7343   8.6164   7.4476  -2.3062 -12.9534  -1.0827 -11.3341
+#&gt;    6.0703  -2.0472  -8.3528  -2.0073   1.6976  -6.1322 -12.9710 -12.4216
+#&gt;   11.1874  -8.0109   4.0227  10.4308   3.0215   4.3172 -17.6065  -5.2152
+#&gt;    4.0862   0.7025   2.0104 -12.1847  -5.8455  -1.7024   7.5525  -3.8789
+#&gt;  -12.4878   0.4462  10.2940  -9.2408   3.7506  -8.0383  -7.6609 -13.3497
+#&gt;   16.3533   3.2812  -9.3564  -2.9163  -4.2649   6.4444 -13.4228  -7.0914
+#&gt;    4.9011  -1.0207  -0.0596   9.3285 -10.9899  -9.2491  -0.2204   0.8386
+#&gt;    6.6736   0.9916  -1.4295  -8.5807  14.1271  -4.3453  -5.9411 -10.3486
+#&gt;    0.0912 -14.7209  -3.3929  -3.3242   1.1695  -0.8085  13.9671   9.9344
+#&gt; 
+#&gt; Columns 41 to 48  -2.8734  -8.4924 -17.5193  10.2680   2.1600   2.0287   6.5707  -2.2567
+#&gt;    5.2224 -13.3770  -2.5050   0.0954   0.4293 -13.4584   9.6534  12.0570
+#&gt;  -16.2988   2.3852  10.8625   0.2537 -10.8285  -2.8360  -2.1878 -15.1237
+#&gt;   -4.4878   7.1922   4.9887   1.6470  13.0142   4.5364  -7.0848  -1.1903
+#&gt;    1.4093  10.3102  18.3857   2.2670  10.7662  -1.6782  -8.2206   1.4979
+#&gt;    5.1478 -15.8922  -9.0215   1.8039  -6.8724   3.2032  -2.2787   4.8853
+#&gt;    2.8464  19.2271  -9.3307  -4.0319  -1.8629  23.8544   9.6340 -12.7128
+#&gt;    2.8826  -8.5982  -4.0705  -1.1010   9.4100   5.6208 -13.4208  -2.3558
+#&gt;  -15.4890   3.0345  18.6851  -0.2153  -6.2293  -2.9044   8.5082  -0.9174
+#&gt;   -3.8986   2.3894   2.2295 -18.0275   1.1315   7.5655   2.3939  -7.4495
+#&gt;    9.3855 -12.9117  -7.1190  -0.2013   4.1409   1.2911 -14.5928   9.9418
+#&gt;   -3.2735   6.9314  -3.9730  -8.8119   6.7769   6.2036  -4.9626  -4.8748
+#&gt;    8.6534  -8.0155  -9.7671  -5.3605  18.3674   1.1821  -7.3533  -3.2661
+#&gt;   -8.1285 -10.3746 -16.3919   0.2280  11.6997  -5.6910 -15.6176  -8.3501
+#&gt;   10.5599   0.8822   6.8635 -10.8261  14.3865   1.1993   0.5539   7.1540
+#&gt;   12.9604  -3.8049 -15.1851  -4.8909   9.1719   8.8168  -3.5436  13.8130
+#&gt;   -5.4091  13.2977   1.2928  10.5356  -6.0310  -0.1603   2.7894  -6.7051
+#&gt;    8.8039  17.0807   3.7103  -2.3510   4.5326  10.2308   0.0510  -8.5260
+#&gt;   -7.9950  -8.3507  -1.1604  12.6094  -5.5550 -11.0050  21.6340  13.5570
+#&gt;    7.4298  -0.0157  -5.8632   0.2383   6.6793   0.3072  -5.5691  12.1531
+#&gt;    1.8551  -2.8246  -5.6893  -1.9213  -2.7554  -3.0900   4.5301  -5.5418
+#&gt;    3.9559   1.3747   4.6716   9.8295  13.7761   0.1630  -0.0806   9.6606
+#&gt;   -4.8118 -14.7208   0.1570   3.6495   4.6368 -15.8935   4.4471  -6.7278
+#&gt;   -8.2398   8.1630  16.1575  -1.0816  12.1064 -14.7024  -9.8358  -6.6119
+#&gt;   -1.1686  -1.8082   1.2105  -4.8058  -6.6663  -1.0301  12.7177   2.1250
+#&gt;  -12.0589   8.9554  11.2514   1.9952 -10.0227   4.4163   5.1555  -2.2884
+#&gt;    2.8321 -14.5843   5.3931   6.7814  -1.2274   5.3709   7.6621   6.8095
+#&gt;   -7.6772   4.0721   3.4299  21.4104  -2.8104   1.2902   4.7023 -16.2162
+#&gt;   -1.0247  -5.2604  -3.9623   2.1954  16.4670   0.6985 -18.0196   5.2822
+#&gt;   -2.3482   2.1633 -10.7999  -6.9703   7.4139   3.7502  -2.8416 -26.7818
+#&gt;   -0.3373   2.4728   0.3189   2.5142 -15.0152   2.2724   1.5376 -10.4134
+#&gt;   -0.0889   5.8051 -13.2319  -9.9406   2.4245  12.6232  -8.6694   2.0255
+#&gt;   10.1628  13.3009   5.6029  17.3776  -4.9576  12.5435  -3.0724 -13.9567
+#&gt; 
+#&gt; Columns 49 to 54  15.6849  -5.2490   9.2349  -1.5794  -2.9792   4.6405
+#&gt;    6.9880   0.7062 -14.9495   0.4172  -3.1521   4.1999
+#&gt;   -3.7239  21.9186  -1.1966  -5.0637  -0.4306  -2.2426
+#&gt;    4.2418  -2.8615   2.6557   2.5540  -8.3940   3.1412
+#&gt;    3.4456  -4.1329 -12.0184   3.5324   4.0986  -1.4316
+#&gt;    6.0576   9.7856 -11.7726  -9.1875  -1.7548  -4.7044
+#&gt;   -7.1676 -14.7830  16.3865  -4.1484   7.2425  -0.3355
+#&gt;  -14.5263   6.9334  -5.9519  -0.1055   2.2809  -2.0225
+#&gt;   -5.8340  -8.7762   1.7046   5.7863  -7.9306   2.9383
+#&gt;    5.0428  -3.5437 -15.8749  -7.9344  -2.9875  -1.0581
+#&gt;   13.7009   1.3866  -6.1902  -4.8131  -5.1588   5.3303
+#&gt;    5.9989   2.7255   1.7380   0.7751   2.5019   0.3435
+#&gt;    2.8318   2.5967   4.5077  14.8610   3.3433   2.8488
+#&gt;   23.4690 -10.7648  16.1782   4.3626  -1.5103   4.0486
+#&gt;   -9.9930  15.5036 -14.7680   5.5554  11.9162   0.5810
+#&gt;    5.2166  -8.9599  -1.9074   3.7155  -8.1369  -4.3223
+#&gt;    6.4710   2.6931   3.9900 -18.1333   8.4946   4.3847
+#&gt;    0.7701  -3.1075  -7.9428  -3.0856  -6.7230  -2.4987
+#&gt;   -0.0960 -14.2224  10.6409  -5.5347   4.6661   7.2279
+#&gt;    3.7741 -17.6026   3.7981   3.4200  -8.1906   2.2729
+#&gt;    1.7229  -7.1844   7.6353  13.6129   6.1083  -2.6823
+#&gt;  -22.5709  -4.7772 -10.3082  10.4800  -1.8415  -3.4303
+#&gt;    1.6789   1.2973  -5.5922  17.5860  -8.7833   4.6301
+#&gt;   15.7483 -13.0379 -13.9133  -0.0562  -0.4216   3.0191
+#&gt;  -14.7516 -12.8231  -2.0598   4.1340   2.3340   4.7072
+#&gt;    7.2764   3.7421  -3.6283   7.6889  -4.8453  -0.1343
+#&gt;   -7.9484  -0.0244 -16.3767   8.4560   4.7999   0.5011
+#&gt;   -4.0384  -1.9578  -6.8265  -7.5644  -0.4945   4.0614
+#&gt;   -7.6236   9.2067   9.0229  11.9141  -2.1053   1.3307
+#&gt;    4.5215  -4.0815   0.0132  -1.5415  -0.0943  -4.1174
+#&gt;   10.0483   9.1725  16.6767   1.9959  -0.3576   1.9711
+#&gt;   -1.3797  -5.5918   5.3150   1.8638  -0.1105  -1.3726
+#&gt;   -2.3779   2.5244   5.0120 -13.8557  -0.1838  -2.6333
+#&gt; 
+#&gt; (7,.,.) = 
+#&gt;  Columns 1 to 8  -0.6127   0.8602   4.0707 -17.2720   9.1873  10.7466   4.8171  15.5534
+#&gt;   -1.6149   2.8619  -3.2587   7.1475   5.7965   7.3948  -7.9632   1.1804
+#&gt;    6.5168   6.6588  -4.2214  -4.8462   0.4125   2.0058  19.6637   2.1694
+#&gt;    0.4760 -11.3199   3.9707   1.2572  -8.4510   3.7066  -8.8305 -13.8750
+#&gt;    0.3363   1.7052   1.7680   5.5880  -1.5957  -5.1208   5.5055  -1.0847
+#&gt;   -4.0448   5.9806   7.0820   5.8893 -11.4771  -1.3977 -10.4268  -7.2437
+#&gt;   -1.6654   7.3950  12.6163  -3.9669  -3.9656  -6.1800  16.0702  -0.1868
+#&gt;   -0.0455  -4.7763  -7.0575  11.4144   6.8467   5.7396   2.8729  -6.6305
+#&gt;    1.3588  -2.1503   9.3749 -10.7310  -4.1430   0.4640  17.0588   2.8468
+#&gt;    1.7788  -8.8820 -10.5011   3.4414   2.7228 -11.7916   3.1457   3.6890
+#&gt;   -4.0497   2.2994  -7.7931   0.0908   2.9860   2.5071  -7.8457  -4.4900
+#&gt;   -2.0260  -4.7833   7.8291   7.1111   6.9172 -11.1081  -5.9024  -1.5690
+#&gt;   -0.8372  -8.0708   0.9861  10.7736  -2.1552   9.1345  -6.6132  -1.6625
+#&gt;    1.9936   7.4995   4.1519  -0.0550   7.0429  -8.7814  12.7491  16.7252
+#&gt;    4.3270   3.5657  -3.9918   4.0760   4.0217   6.6901  11.2858  11.1260
+#&gt;   -3.8955  -2.0160   1.4473   1.9261  12.7488 -12.7384 -14.8865  -7.3731
+#&gt;   -4.3515   6.3135   4.9883  -7.3654   5.7990  -1.3878   7.5930 -12.0186
+#&gt;   -1.3299  -7.6369   2.5160   6.8659  -6.8668 -12.6240  -0.9304  -3.3401
+#&gt;   -5.9669   1.4915   3.7437   2.7153  -3.4913  -1.2524 -19.4998 -14.2078
+#&gt;    1.8816   1.4189  -9.9366  -2.6553  -1.7600  -1.7476  -1.5857   3.8363
+#&gt;    5.3438   0.0244  -1.8589   3.5840  -5.2578   2.8645  -2.3510  11.4651
+#&gt;   -5.0214  -2.8464  -0.3529  -5.0528 -23.8036   7.4167  10.9668 -16.8754
+#&gt;   -6.5692  -3.0138   7.3143   2.2489  -2.2314  24.1479   2.3553  -4.3058
+#&gt;   -0.2328   9.7560   2.0405   2.7370  -4.6171  -2.4686  15.4630   0.7934
+#&gt;   -0.7612   1.0114   3.1264  13.3547   9.4779  -5.0772 -16.1384   0.3394
+#&gt;    8.0910  17.3947  10.9892  -0.2604   8.4808   1.5966  -9.6748  -0.7410
+#&gt;   -0.4330  -9.5881  -8.5733   0.4734  -1.7358   4.1283 -13.9772  -7.4636
+#&gt;    0.4772   2.9322   5.6349   2.3088  -2.3083  -5.6474  -2.5709  -4.5265
+#&gt;   -2.6507  -1.6445   3.0145  -8.5407  -0.7977   0.1677  -6.6109   2.0266
+#&gt;    3.4436  -5.2601   7.8358   2.6887  -0.9017  -2.7233  -9.6875   8.9759
+#&gt;    4.0120   1.9026   4.4819  -6.5699  -0.4820   7.8510  15.4935  -0.3251
+#&gt;   -7.5299  -1.9291   2.5879  -0.5706  -1.2503   0.8443 -11.5242   9.2105
+#&gt;    1.9418  -3.2870  -3.3924  -9.6777   1.4283  -2.1436   3.8129   5.1940
+#&gt; 
+#&gt; Columns 9 to 16  10.9571  -6.5737  -0.2077  12.5503   4.1865  -6.1791  -7.0763   0.1965
+#&gt;   -8.8695  10.7537   0.3532  20.5753   5.4241  -2.1564   6.3179  -8.9029
+#&gt;   -0.6702  16.9306  -7.0601   0.4260   3.8519   2.1401   0.1246  -5.6079
+#&gt;    8.3267  -1.0636   9.2678   1.8702  -0.2401   5.5926  13.1306   4.9335
+#&gt;   -6.5986   3.7130  -7.2328  -6.0084  -4.6307 -18.2156  -3.4626   0.3911
+#&gt;   -2.3652   1.7909  -7.1237   5.5230 -10.2107   3.4266   1.4408  -8.3406
+#&gt;    1.3943 -11.3927  -7.6842  -9.6408  -1.2621   1.1670  -5.3837 -17.8851
+#&gt;   16.2027  -5.7363  -0.7484   2.8857   1.1050  -4.5606  -5.7095   3.3818
+#&gt;   16.2908  -6.0660   5.7284   4.4618   2.3688   4.1868  -5.4237   1.1845
+#&gt;   -8.7040   8.2184  11.7077   5.5538   0.3863  10.2449   2.7922   3.8356
+#&gt;    2.8524   0.4918  -8.0683   8.1671   2.6062   7.1684  -0.7367   2.2054
+#&gt;   -0.8745   5.4079   5.7910  -2.2340  -0.7039   0.7330   0.0945   2.3333
+#&gt;    7.5403  11.2966  -4.5131   7.8712  -5.7957  -9.7941  -5.2514 -13.1442
+#&gt;   12.2324  13.0981  15.7952   1.3508  -8.5745 -14.5636 -18.6429   4.7431
+#&gt;  -21.2673  27.8406  -4.0932   5.3727   5.8858  -1.2683   3.9223  -1.1203
+#&gt;   15.6746  -7.5262   8.9094   9.7716  12.2952   6.5154  11.0744  -9.2273
+#&gt;    0.6147  -5.4507  14.8522  -5.6633  -4.0854  -2.5155  -2.8841   9.4550
+#&gt;   -6.7478  -4.5933  -4.9118  -1.4062  10.6929   3.7418  -3.6940  -4.5576
+#&gt;   -4.7530   2.7798   1.5448 -15.6911   4.4059  -4.8929  -1.6179   6.0765
+#&gt;   -1.1021  -9.2705  -1.2855  13.9661  -4.3380  -3.6592   4.4865 -19.0169
+#&gt;    3.8989  15.4263  -1.7052   7.0711  -3.0013  -1.4601  16.3679   1.8411
+#&gt;   -1.4202   9.8199 -18.3763  -4.5312   0.1934   3.7991  13.9990   0.1671
+#&gt;    4.7478  -1.3143 -16.8650   7.8815  -7.3830   6.6322  -4.8448   3.0777
+#&gt;   -8.3913   9.6030   7.5135  12.3141  -2.6286   0.3046 -13.1053  12.2796
+#&gt;   11.2419  12.2097  16.2024   2.6510   3.9965  -1.1477 -12.0345 -13.1411
+#&gt;    7.9201   5.8420  -2.7981  -9.0971   6.4303  -0.2831  -0.3092  -5.2020
+#&gt;  -10.4398  -1.1489   4.0544   2.0237  -0.5546   4.2036 -12.2154   2.8440
+#&gt;    4.2917 -13.6569   3.7762  -8.5169   0.1295   2.3418 -20.9835  -1.0567
+#&gt;   -3.6594 -13.4791  14.9113   1.0772  -1.4652  -0.1085  15.8382   1.3571
+#&gt;   -5.5677  -6.4323  10.3060   2.9423   0.3724   9.3563  -3.3977   5.6300
+#&gt;    1.9829  -4.8128  -1.3179   5.2454  11.3372  11.9656  -7.9866   1.6916
+#&gt;  -24.7450  -5.2551   3.8997  -6.1162   5.0284  -8.5934  -0.9088   2.3862
+#&gt;   -0.4460 -14.0491  -0.3221  -1.3527  -0.0903  -7.3134  -5.4660 -12.6041
+#&gt; 
+#&gt; Columns 17 to 24  -6.3862   0.6770  -2.8913   2.6446  -2.0113   2.0948  -3.9316 -10.3205
+#&gt;   -3.3042   2.7250   3.8922  12.5669  -8.8322   6.0081  -1.6474  -1.3496
+#&gt;    8.0881  -1.4549   3.3471   4.8254 -12.2232   4.5284 -10.5167  -7.2270
+#&gt;    0.8639  16.4537   6.0937  -8.1169 -13.8927 -14.5495  -6.0751   0.8588
+#&gt;   -9.5304  -5.5361  -5.8441  10.9372  -3.3628   3.0612  -0.8589   0.9682
+#&gt;    1.9300   6.7491   5.6737  -5.7373   0.1888  -2.2724  -0.9412   7.9022
+#&gt;    1.3358  12.5313 -13.9733   0.7424  -5.9798  -4.1006  16.4825  -4.5572
+#&gt;   -6.0400   3.8897  -5.4041  -4.4170   0.2472  -4.0526  -2.3943  13.3473
+#&gt;   -0.9131  -3.8272 -10.3153   9.9783   2.1318  12.0297  -3.2124   0.3001
+#&gt;    1.6257  -6.6324  14.7083   8.7235  13.7729  -7.8260 -14.8461  12.1033
+#&gt;    4.7265  -9.3282  -8.3582  -1.6361   4.0490  -7.2239   6.4747  -5.1505
+#&gt;   18.6856   1.8156  -3.3326  -2.6225  -6.8087   6.1158   4.0315   7.7480
+#&gt;   16.2540  -6.3447  -0.7650  -5.4297  -7.5572   6.6932   3.5214 -11.0778
+#&gt;   -1.4396   2.9347 -14.9760   5.6475  -0.2878   3.0462  -3.3582  -1.0588
+#&gt;    6.9936   5.8066  11.7568  -2.2827 -10.0926   7.5195  -8.8348  22.8042
+#&gt;    8.9934   0.0923   6.6329  10.4184   3.2270  -1.7795  11.4010  -7.4289
+#&gt;   12.6000  -1.9638   4.8100  -6.8164  -0.0098   1.4891   3.9932 -10.4896
+#&gt;   15.8137   0.5698   0.8974   4.4534  -8.9241   0.7093  -3.4582  -3.6150
+#&gt;    2.7426  -3.3546  -8.9759  -0.8950   3.8870   0.7215   4.1513 -12.5556
+#&gt;  -13.8795   7.3449   1.0272  12.8563  -9.1313 -11.8193  -5.6979   1.2040
+#&gt;   -4.0437  -5.4842  -6.8933  -4.2900  -4.3514   3.2097   2.6541   0.8546
+#&gt;   -3.6259  -7.0295   6.5473  -3.4971   4.0426  10.0794  -0.2139   1.7212
+#&gt;   -1.5114   4.2256  -9.2188   2.0336  -5.7193   8.5824   3.7002   6.1402
+#&gt;    4.7120   3.7413   7.9710   2.3441   2.4947  19.4809  -6.4370  12.9674
+#&gt;   -7.1538   6.5032   1.1843 -11.0555   4.7123  -6.6621  12.5625   1.8256
+#&gt;  -17.3850  -5.5194  -9.6443   6.7363   7.7781  11.0695   7.0998   8.3377
+#&gt;   -1.3867  11.3660  -4.2410  -3.2328  13.8050  -9.5354  -4.0114  14.7591
+#&gt;   -3.6691   1.7124  -3.8861  -0.6895  -1.2039  -1.8457  13.1434  -8.7010
+#&gt;   -1.3713  -1.4235   1.8947   2.0566   2.3588   0.9970  12.8145  17.3779
+#&gt;   -6.1097   6.0223 -11.1578  -0.4343  -8.3625   5.8353   8.0600   9.2822
+#&gt;   -0.2852  14.5481  -8.3150  11.4958  -0.0643   0.7529   6.3325 -15.5322
+#&gt;    0.0462   0.7017   2.5437  -5.4227  -6.8571  11.7224  -3.2721 -16.5096
+#&gt;  -16.1163  12.1808   1.3101   9.5219   1.4452  -7.4029 -15.1918  -6.0166
+#&gt; 
+#&gt; Columns 25 to 32  16.7771   2.5286   6.9042   6.2303 -15.9087  -9.4931  14.2393  -9.3278
+#&gt;   10.2568   6.5342 -17.2927  -4.2694  13.6054  -5.7999   4.6742  -1.9928
+#&gt;    0.9143  12.3806  -0.1864  -0.7706  -2.3867  -3.2355  -5.7401  16.6064
+#&gt;    8.9669  -7.7847  16.1993   4.0804   9.3619  -1.9008   9.2652  -1.0932
+#&gt;    2.3558   0.7323  -8.9165 -11.6120  -5.3710 -12.8421  -3.8472  15.6658
+#&gt;   -2.7332  -1.9911   4.0549   6.7536   4.8172   9.6795 -20.7399  -6.8654
+#&gt;    6.6537   4.6724   8.3192   2.9000 -11.0492   3.9040  -9.4077  15.2481
+#&gt;  -12.1571  -3.8718  13.3725 -11.5947   0.8376   7.8997   3.6024   2.2890
+#&gt;   -9.2799  -2.2657  12.7767  -3.7067   7.7755  -5.1627  19.9460   3.5988
+#&gt;   -9.9184  -7.2907   2.9753 -11.7184   2.7403  14.5138  -9.1724  -3.7580
+#&gt;   13.8878  -1.7623  -2.0449   2.2927   9.5829 -14.8202  -1.0320   7.1218
+#&gt;   -3.7194 -10.7002  -4.5157   1.2188  -1.3918   2.0307  -4.8687   5.4336
+#&gt;    0.7384 -15.3376 -12.5756  -1.6164  11.3294  -6.6587   4.7188  -1.1753
+#&gt;    0.6555   2.9704   3.3755   6.6369   3.8169   5.7535 -10.3770  -2.6155
+#&gt;   16.2458  15.0967   7.0097   9.3637  16.9596   3.9738  -2.3135  11.5663
+#&gt;    7.1835   0.2678  -7.9150   1.8311  24.1035  -8.0588  -1.2880   4.6781
+#&gt;    3.9928   2.4614 -14.0668  -3.9464   1.3520   6.5379 -17.3260   2.2953
+#&gt;   -9.4879  -6.3483  -6.9556   5.9862  -3.4417   4.5690   8.5713  10.0653
+#&gt;   -7.9826  -0.2856  -7.7034   1.6021  -5.2799  -7.1478  -4.6322 -11.1482
+#&gt;   15.5197  -4.0462   1.5270  -1.5469  -8.3823 -10.8340   6.9581   2.7991
+#&gt;    2.5861  -5.2750 -12.3159  12.5381   2.4441  19.5723   5.2797   4.2267
+#&gt;    0.4503   5.0235  -2.7517   5.1426  -1.4245 -18.3980   3.7672   8.4228
+#&gt;   -4.7789  10.2163 -11.9028  12.3721   5.4755 -17.3363  10.2470   4.8958
+#&gt;    6.0804  12.9226   7.1657   8.4844   6.1737  -5.6766   8.2505   9.2802
+#&gt;    2.6966  -8.2131  -1.6640  -6.8382   3.1647  -6.9614  -4.6510 -10.8113
+#&gt;    3.2803   0.5916  -4.3080  -3.4198  -1.7825  -1.4891  10.8797  -4.6491
+#&gt;   -3.5887  -6.4568   6.7569 -21.2021  -6.3740  -3.8290  -2.0275  -3.1784
+#&gt;   -9.4296   1.6017 -16.2227  -0.2107 -12.9060   1.9015  -5.7891  -2.1861
+#&gt;   15.2954  16.3788   7.4437  -9.8675   7.5977  -3.1748  16.1293   6.4717
+#&gt;    2.9967   4.0513  -4.7117   7.0075  -4.5454  -0.2602   4.6099  -0.3121
+#&gt;    9.2938   4.1807  11.1087  -2.3951   2.9823  19.5268   3.2484   8.7916
+#&gt;   -2.8504  -6.3786 -15.5947  12.2028 -11.1596   6.8691  -3.8037  -5.6914
+#&gt;    9.5168  -7.6326  10.2155  -3.2639 -18.6851  16.3554   2.1796   0.7864
+#&gt; 
+#&gt; Columns 33 to 40  -1.8424  13.4965   5.9359  -3.1670  -3.1803  -3.7731  -7.7101  -3.3640
+#&gt;   -3.0546   1.1266   4.9155  -9.1120   4.3837   1.0432   0.2036   3.4830
+#&gt;    2.7782   5.8812  11.9892  -2.7495   7.1515   9.4099   4.2019  -3.2517
+#&gt;    7.1420   6.0410  -1.8747   8.6460  -2.9767  10.0526 -11.9293   4.5738
+#&gt;   -4.2437   6.8362  -1.5379  -5.3885  -6.5491  -2.5902  -1.4827  -3.2206
+#&gt;   -6.4982   9.8482  16.1417  -2.2945  -3.6906 -18.0744   9.1594  -2.6025
+#&gt;  -16.6491 -10.8893  -2.6142   2.4836   2.5255  -8.5350  -7.0168  -8.9131
+#&gt;    1.7256  -2.5309  10.2332   3.2002  -5.4432 -16.5663  -2.3239  -8.0805
+#&gt;    1.0808   6.8372  10.3936  16.6316   2.6614   5.4583  -6.1073  11.2989
+#&gt;   -7.6508  10.4915  -2.4019  -8.2223   9.1519  -9.5589  15.4694  -0.7167
+#&gt;    2.1963   4.1809  10.1309  18.6083  -6.5981  10.4373  -5.6071   7.2492
+#&gt;   -2.0518 -14.4212  -1.8670 -14.9595  -3.0426  -6.4525  -3.3262  -2.4451
+#&gt;    4.3743   6.7467   6.6219  10.7107  -5.0255  -7.3092 -18.7796   7.1447
+#&gt;   18.3567  15.6001   6.0591  -5.3036  -2.1265   5.3208 -12.8160  17.3172
+#&gt;   -3.1986  -2.0748   8.1779  -2.4382  -8.0289  -3.9662  -6.9810 -14.4200
+#&gt;   11.4986  -8.6713  -1.0075   8.1491 -16.4517   3.4094 -16.0637   0.4579
+#&gt;    1.7948  -7.4603  -5.5256   8.1267   4.2255  -5.1880   6.3938  -6.9577
+#&gt;   -7.8557   7.8170  -7.7228   2.2092  20.7377 -13.5920   3.6758 -18.9333
+#&gt;    6.2087   1.6671   1.6227 -16.8905 -14.6488  -2.7445  -0.7253  11.5320
+#&gt;  -17.2548  14.1190   8.0960   2.3876   1.5339   2.4185 -10.6004   4.8182
+#&gt;    7.0715  -1.5290  -3.4204 -10.6872  12.8909  -1.1878   1.6240   5.4122
+#&gt;   -2.2597   9.0978  -3.9608  -5.0600   8.3489  -0.2848  10.0797 -10.7936
+#&gt;   17.7356  -1.1240   1.2717  -8.4553  -0.7845   6.9360   0.1280   0.4396
+#&gt;   -0.4524  23.6251   2.3271 -12.1734 -10.2318  -5.3682  11.4550   0.2321
+#&gt;    4.8857  -1.9313  10.8503  13.9392  -3.1913  -3.9294   8.0877  -6.1294
+#&gt;    6.5706 -16.9693  -1.9114   7.4077  10.2159   9.7724  -6.6862  20.9263
+#&gt;   -8.2047   7.2422  -1.7496  -6.9020   6.0776  -0.2092  13.3831  -4.0678
+#&gt;    6.0206 -22.3505   5.8035   3.2204  15.3749  10.4475   9.3134  -0.1864
+#&gt;   12.6119   6.7182 -16.6868  -7.9640  -5.2994  -6.0246   7.3100   0.1317
+#&gt;    4.6046  -3.8573  21.3451  -5.5336  16.4337  -4.1470  20.8281  -1.5446
+#&gt;   -2.4232  -8.9493  -9.6627   6.3923   1.3328   7.5283 -11.4277  -8.5131
+#&gt;   -5.0047  -3.0523   9.3086  -6.4050  18.0022 -12.5359  12.8243   5.6132
+#&gt;    6.9146   5.2188   7.4482  11.7864  10.6915 -15.4290 -10.6112  -4.0470
+#&gt; 
+#&gt; Columns 41 to 48  -5.5767  -3.8123  -8.3427  -5.2574   5.0344   0.8432  13.6247  16.0588
+#&gt;   11.9970   2.5541   8.5713   7.8814   5.7165  -7.7433  -3.3369  -1.9118
+#&gt;   -2.0087   1.8799  -5.0319  -9.0380  -5.0690  -8.6191  -2.0421  -7.6519
+#&gt;  -11.4042 -13.2615  -7.3124   1.6295   7.9630   0.0894  -2.9275   7.8191
+#&gt;   10.5512  -2.0984  -3.4992 -14.1445  -0.7259  -1.2510 -12.3144   0.3860
+#&gt;   17.1771   8.8067  14.6089  10.8677  -8.2794  -6.8339   0.8230  -7.2536
+#&gt;    7.1541   6.7248  -5.9598  -7.5477   5.3594   2.2800  -4.8214   0.0527
+#&gt;    7.2494  -2.9123  -0.8093 -11.4293  -9.4994  -5.3124  14.6816 -12.0252
+#&gt;   -4.0650  -3.5543  -4.3100 -15.2891  -9.2865  -5.5602   5.1895  -5.0372
+#&gt;   13.4037  10.5660  -0.0899   6.1516   9.5618  11.7577 -16.0881  -2.2386
+#&gt;   -1.2400  -0.7587  -7.2687  -2.7092   6.6366   0.8120   1.8416  10.2779
+#&gt;   -0.8043  -4.0606   2.9531   6.1974   5.7183   8.4783   2.1110  -3.3513
+#&gt;   -3.1867 -14.9174  -9.7651  -2.5045  -0.9003   2.0940   2.3022  15.0925
+#&gt;   -7.6922  21.5073  13.1315   1.7297   6.5255  -1.2053 -12.3053  -4.2204
+#&gt;    2.9455   7.5772  -3.4221   1.2700   0.1425  -2.5533 -20.6513   5.0561
+#&gt;   -2.5448  -4.2967   0.6008  -1.1660   7.9227   8.2935   5.2803   3.9738
+#&gt;   -2.4051   5.4875  11.9692  -0.4424  -0.4812  -0.7993 -12.5396 -14.4680
+#&gt;    4.4766  -1.7018   0.2177  -0.6928  -3.7054  11.1766  -0.9645  -3.0890
+#&gt;   -6.6712   4.6193  -8.4913  10.4303  -5.2905  12.2603  10.5912  11.5043
+#&gt;    1.1754   5.0128  -6.6766   3.6303  10.5086 -14.8414  -3.1029   9.7789
+#&gt;    7.3242 -15.6055  -2.2896   6.5068   0.9513  -6.9077  -7.8493  22.6716
+#&gt;   -3.7519 -14.4534   8.0234   9.4587  -7.6534 -16.7834  -2.9745   7.0931
+#&gt;    7.3508 -16.2754  -0.2349  -5.1106  -5.1751  -5.9914  17.0252  10.8574
+#&gt;   -0.5602  15.3284  -5.8678 -11.0183  12.5782  -7.7635 -13.9929  -6.6450
+#&gt;   -2.5427  -1.9955  13.5255  -9.9100   3.9117  -5.7953   4.7139   2.5794
+#&gt;    0.6945  12.8239  12.1433   6.4143  11.0303 -12.6060  -8.4101  -1.9576
+#&gt;    7.0843  -3.7882   7.6630 -10.5038 -15.1195   1.5714   1.0156  -3.3395
+#&gt;    4.4371  -1.8333   4.4307   5.6060  -1.4728  -9.9524  -7.3490  -8.8954
+#&gt;   21.9396  -0.1028  11.4260   5.6503  18.2624   4.1884  -1.6542 -13.5589
+#&gt;   16.3157  -5.6074  -0.5211  -4.8651   3.1517   1.1494   0.6412  -4.8549
+#&gt;   -7.7746   6.8673  -0.1777   3.0733   5.7553   2.9049   2.6433  -7.2542
+#&gt;    3.5829  -1.1483   2.2033   3.3943   3.8698  15.5526  -9.6710  10.7033
+#&gt;   -6.7823  -3.8312  -8.0106 -10.4973  -0.2336   9.3450   5.3332 -10.5924
+#&gt; 
+#&gt; Columns 49 to 54  -1.8343  17.7541   6.7127  -3.5064   2.3426   7.0252
+#&gt;   -3.5401  -4.8931 -10.3428  -2.6911   0.5330   0.8503
+#&gt;   15.2936  -9.6012   2.2967   1.3161  -1.4428   7.3306
+#&gt;  -21.8066  -3.0973  -1.0753  -6.1871   5.4726   1.5535
+#&gt;   17.2229  -8.8055   1.1184  -6.3699  -5.1258   0.7082
+#&gt;   -1.6950  20.6292  -4.3222   4.9447   3.9206   0.8077
+#&gt;    6.2088   8.8589   8.2650   1.5856   7.3887  -7.0106
+#&gt;   -2.7026  -5.6998   0.3055   0.6344 -12.7283   3.0237
+#&gt;   13.8986  -2.2224   1.9932   0.6777  -0.0134  -4.8253
+#&gt;  -11.7346  -3.0460 -12.0943   2.0108  -3.2044  -3.4012
+#&gt;   13.3435   7.0371  -1.1698  -0.6680  10.0373  -3.2892
+#&gt;   -8.3936  -8.6182  -0.0468   1.8136  -6.7346   1.1122
+#&gt;   -4.9654  -0.2158  -1.8528   4.9590  -1.4644  -3.4088
+#&gt;   -8.9411   8.1443   2.5521  -3.6202  -0.3143   4.8254
+#&gt;   -7.1834 -14.4674  -2.6967   8.8684  -4.1868  -1.7125
+#&gt;    1.3816 -11.9041  -8.1992  -2.8824   1.1431  -6.2098
+#&gt;    6.9124   9.4540 -11.5077   5.2963  -4.4932  -2.9735
+#&gt;    0.0991  -5.4287  -2.4734  -3.8005   1.9282  -2.7588
+#&gt;   -1.5505  11.6649   0.1514  -6.5469   3.0994   2.7394
+#&gt;    0.0905  -2.3823   8.1655  -3.4520   4.8792   3.2606
+#&gt;   -2.1946   1.3596  -0.3702   1.6147   1.9985   1.6740
+#&gt;    1.7902  -7.2148  12.3227  -1.3156  -6.2056  -5.1462
+#&gt;   12.8373   0.2384   4.3437  -7.0630   3.6899   4.9104
+#&gt;   -2.4433 -12.2084  -8.1028  -1.1702 -16.2865   5.9017
+#&gt;   -8.2896  -1.5226  -3.3016  -1.4798   1.1837   0.9620
+#&gt;   -0.2347 -23.1194   4.4594  -5.4188   2.2736  -7.9268
+#&gt;   14.0461  -2.9727  -4.4063   0.3881   1.1977  -1.7749
+#&gt;    0.9196  -1.8711  -0.8332  -5.8261   9.0320  -7.7057
+#&gt;    8.3060   5.3831  -4.5026  -6.6984 -10.2018   1.0121
+#&gt;    2.3768   3.4562  -1.0686   4.1457  -4.1291   1.1535
+#&gt;    4.4473   2.4399   0.3042  -7.1260  10.3929   0.5017
+#&gt;   -6.7038  -2.0523   9.1131  -0.5452   1.7847  -4.1554
+#&gt;   -2.2229  12.9701  -5.2224 -16.5133   9.5451  -5.2632
+#&gt; 
+#&gt; (8,.,.) = 
+#&gt;  Columns 1 to 8  -1.2386  -6.6557  -5.7359   8.7007  -1.4756 -22.2371  -9.8361   0.2464
+#&gt;   -0.2773   4.0665   8.3574   6.4503  11.8810  -6.1403  -2.8884  13.2044
+#&gt;   -0.6591   2.2262  -0.0887  15.2786  14.7186  11.5726   6.0648  -6.0864
+#&gt;    6.1982   2.9446  12.6473 -12.9558   2.9889   5.5596  -2.0329  -1.9073
+#&gt;   -4.8988   0.0376  -2.5519  11.5645 -10.0178  14.2919  12.6015   3.1491
+#&gt;   -2.3991   4.7100  -4.8162  -4.8229   7.4487   4.5662  -3.1959   4.7877
+#&gt;    0.7086  -8.6799  -9.7750  -7.1017   5.6339  -3.0132   1.6500   1.5601
+#&gt;   -5.0250   4.1333   7.9076  -1.2778  -6.0876  -1.1585   0.6839   5.3396
+#&gt;   -2.2762   4.9162   2.9937   6.2812  -6.1639  -7.0113   7.5574  -7.0249
+#&gt;    6.1409   2.9378   8.8781  -3.3653  13.8300  -3.6334   9.2394  -2.6541
+#&gt;    7.3229   0.6074   5.5275   3.7079  10.0168  -1.9350  -8.6505   3.0828
+#&gt;    2.5946   7.8824  -1.6077   1.3345  -7.2813   0.2058   6.0130  -2.6293
+#&gt;    3.1692   4.5650  10.0818  -5.4949   9.0942 -13.9313   0.3521   1.2611
+#&gt;    4.0248 -10.5574  10.4274 -10.8648 -14.1220 -14.5702   1.2047 -11.5819
+#&gt;   -4.4736   1.8697  -0.6305  11.1447  -5.4738   6.5114  -2.9410  -3.5729
+#&gt;    6.8356  10.4735  -4.9814  10.4058   7.2451   5.3009   2.6563   3.6116
+#&gt;    3.5384   1.9147  -3.3813   3.9148   4.9032   0.2468   8.6457  -2.7713
+#&gt;    4.1245   6.9455  -8.9467   7.2937  11.2478  -6.0324   7.5209   2.6760
+#&gt;    8.0597   1.4393  -4.9737  -6.3850  -4.3215 -13.1023   9.0532   0.6358
+#&gt;   -7.2548  -5.3086   8.0534   1.4170   1.6036 -11.1632  -2.8319  -4.0257
+#&gt;   -4.1577  -1.9521   1.0066 -14.9572   0.5408   2.0758   1.0115  -3.3268
+#&gt;   -5.4344  -0.0741  -7.8543   7.2897  10.7040  10.4217  -5.9500  -0.4322
+#&gt;    1.1035  -0.0304   3.1303   6.2802  -0.1111   7.2508   0.8758  13.8683
+#&gt;    6.7262   1.3074   7.9453  10.6012   0.5703 -13.6811   0.3449   1.0457
+#&gt;    3.2287   6.3162   8.3626   4.4921   1.0661  -6.6015   2.3204   7.0992
+#&gt;   -2.5365  -7.2800  -1.5505  -1.2421   0.7988  27.5398  -8.2030   6.6027
+#&gt;  -17.1901  -1.4530   0.8999   0.8717 -15.8282  -9.4943  -1.3518  21.1274
+#&gt;    6.6538  -6.2006   2.2470   2.2221   1.6375  12.5634 -13.6554   1.3106
+#&gt;   -9.9256   2.3837 -12.9811   3.4591  -5.6458   6.0142   4.4481  -3.8653
+#&gt;    1.2003   6.0700   8.0300   5.6897  10.6546   8.2393   7.9712   3.3288
+#&gt;    0.4206  -7.7476   2.0656   3.9185  13.3095   0.8433   3.4833   3.5886
+#&gt;    1.0991  -2.1457  -5.2532   0.1567   5.3771  10.0281   6.8851  23.7975
+#&gt;    0.4489  -3.3574  13.1555   4.9923  -2.8310   3.7748  -9.6098   4.9965
+#&gt; 
+#&gt; Columns 9 to 16   6.7832 -13.7821   4.4662   1.0761   3.8056  11.4756  10.8140   4.1145
+#&gt;    0.2604   5.7517 -10.9681  -4.4028   6.7957  -3.9583  14.8481   0.7897
+#&gt;   -1.1360   3.4995  -3.6651  -8.1714  -6.5169 -11.3286  -0.8255  12.1935
+#&gt;    7.9599  -2.6114  11.3199  -7.4855  -4.3361  -4.5887 -10.4610 -11.8132
+#&gt;    0.3610   2.7278   3.2638 -11.1727 -10.9999   0.5326   3.8333  -6.8610
+#&gt;   -9.0624  10.5482  -0.0374   1.7146   7.7158 -12.9160  -5.5823  -4.2765
+#&gt;   -5.8257 -20.9969  -5.1232  -9.3122   5.3073   6.8794   2.7560   0.7989
+#&gt;  -14.3975  14.6538  -8.6528  -2.1857  -1.2501   9.6379  -0.7634   0.5584
+#&gt;    0.3095   2.2708  -0.9672  20.4971   5.6079  14.8120  -1.6854  -8.5086
+#&gt;   -5.3860   0.3313   9.9838  12.0481  -3.9272  -3.0648 -13.0644  -6.4053
+#&gt;    2.2785   6.3809  -9.3108   2.6389 -14.2079  -8.5372   5.4338  -3.5765
+#&gt;    3.2861  -3.4737  -5.4502   4.8764  -4.8236  14.5205  13.1233  -2.7353
+#&gt;    2.3916  -5.9340  -6.6059   0.5346   7.0030   6.8850   9.6263  -9.5210
+#&gt;   -7.8399  -5.2241  12.6349   5.2605  -2.4723   9.4437   2.7421 -16.5902
+#&gt;    3.9001  -0.1205  18.7478  -0.5916 -10.3781  -4.7193   3.8084  10.6507
+#&gt;   13.2789  -2.6716  -8.8196  10.9410  -0.6431   6.3290  11.9756   2.2585
+#&gt;    3.6011  -4.4671   8.7177  17.1741   4.6441  -4.6869   3.0142  -6.9013
+#&gt;    0.8539   2.7775  -2.7376   3.6020   4.7656  -2.1004  -5.5815  -5.4277
+#&gt;    4.9741  -7.7645   0.3187   3.8744  -4.4129   0.7936   4.2426   1.3779
+#&gt;   -4.8911   0.5781  -0.4731 -12.0659  -7.7598  -1.6553  11.0999  -0.4283
+#&gt;   -3.3174  -2.4584  -2.6069  -3.0589   7.4794  10.2512   5.5712  -6.3497
+#&gt;  -16.9110  -0.9935  -3.6336   4.1780   8.7888   3.9658  -9.9355  -1.5366
+#&gt;    0.3330   9.3097 -19.7546 -12.5324 -13.5795  -3.2058   4.9282  -1.4063
+#&gt;   -6.1823  -8.8298  14.8820  10.5941  -6.1332 -10.5407   3.7271   1.2386
+#&gt;    7.5949 -10.4864 -13.9984 -10.9754  -6.1487   0.2152   3.8827  11.2900
+#&gt;   -2.1246  -8.4616 -14.0754  -3.6663   7.9090   9.4109  -2.5075  10.8019
+#&gt;   -7.6572   6.1259  -3.9265  -8.3555  -1.2643   9.8219   4.2961   4.2423
+#&gt;   10.1329 -11.0471 -14.4955   6.5691  10.6779   7.3595 -18.6987  -9.7182
+#&gt;    6.3881  14.8544  -2.7489  -4.0257  -2.0266 -13.0539   8.6928   2.6957
+#&gt;   10.4079  -5.2841  -2.7571  -8.0091  -3.1103  -1.0414 -11.0158  -6.1718
+#&gt;   -6.4212  11.2837  -4.5784   7.5454   5.0112  -7.1306   8.5360  -2.2496
+#&gt;   16.0291  -3.7346  -2.4386  -7.8284 -12.2543  -6.9022   3.0015  -8.1896
+#&gt;   -9.8392  11.3461  11.3539  -8.8640  -2.2912 -10.6087 -10.8046 -16.3326
+#&gt; 
+#&gt; Columns 17 to 24  -3.8181   3.5378  -7.5364   2.0519  -1.1574   9.0829  13.3129  -8.1194
+#&gt;    3.8522   9.3402  12.0454 -13.4299   0.7772 -10.0543  -1.1425 -12.0032
+#&gt;   -3.0556   7.9356  -6.3537  -1.2470  11.8061  -7.4057   3.6360   8.3525
+#&gt;    4.8905 -17.1222  -0.5280  -0.6388   7.1534  20.4932  12.6426   4.8937
+#&gt;   -1.6627  -5.0511  -2.3096   2.0789  -3.6078 -13.2165 -13.8051 -15.0447
+#&gt;    8.8540   9.7598  17.4443  -2.6986  -5.6849   3.8985   6.8530   3.5966
+#&gt;    3.2848   9.8828  -1.8873   2.7057  15.8798  26.6394 -15.2037  -1.7653
+#&gt;   -4.5540 -14.9251  -6.8075  -3.9965   9.4959  -5.1095   0.6675   2.4862
+#&gt;    0.6328 -20.5414  -8.0892  -2.2073  -5.0799   1.2374  -4.4981  -0.2458
+#&gt;    1.5963  -1.5600   5.1990 -13.6533   1.5979  -0.5885 -24.4527 -10.7978
+#&gt;    5.0742 -17.0051   1.8368  12.9870  -3.1137   8.5719   5.8553  -5.8236
+#&gt;   -3.9114   2.8226  -2.7207  -1.7210  -6.1309  -2.6114 -10.6038  -1.5694
+#&gt;  -10.4239 -23.2444  -4.6536 -11.4708  -2.7148  -4.8113   5.0560  -8.3583
+#&gt;   18.7890   2.5796   6.3800  -0.7864 -15.9076  -6.2544 -11.0225 -16.7624
+#&gt;    6.4259  -2.8455  12.5595  -7.7595   3.5839   4.7312  -3.7208  -4.2754
+#&gt;   -3.4020   4.4032  -9.9221  -3.2326 -16.1397  16.0583  -5.0237  -6.4389
+#&gt;   -7.8684   3.4457  -4.4468  -4.7244   5.6385  -0.8368  -6.4453  -8.0733
+#&gt;    0.2546   0.0812   1.4544  -7.6102  -6.1491 -15.5641  -0.9825  -4.7077
+#&gt;   -4.5914  10.1294  -0.9949   4.6124  -0.5657   3.5410   1.2348  14.0613
+#&gt;   -0.0191   5.3228   5.4258  -2.6048  -1.0057  -1.6115  -6.9707   1.0934
+#&gt;    2.6736  -6.5737   4.4893  -2.9167  -4.3483 -11.3847  15.2476 -18.3195
+#&gt;  -13.0177 -13.9211   0.8724   3.0664   6.5222  -8.6685   2.6954   7.6246
+#&gt;    1.4958 -16.9410  -1.1762   1.4130  14.5685  -4.2866  21.0710  -2.7017
+#&gt;   -0.3956  -0.8935   8.4032 -22.3429   2.6546 -13.8019  -6.5515  13.3860
+#&gt;   -4.0700  14.8472  15.5960  -8.1308  -0.5878   5.3835   8.8421  -5.2318
+#&gt;   14.7388   6.6651   4.6989  26.1218 -17.2070 -16.5160 -19.6198 -10.9307
+#&gt;   11.0064 -13.3230  11.1876  -4.2556  -8.9662  -7.0936  21.5521   6.4291
+#&gt;    7.0076  -0.7431 -11.9614  27.6689   1.0181  -7.9860  -7.1009  -2.0687
+#&gt;   -4.2808   8.2195   6.5982  -5.1092  25.5834   6.2753 -13.0036 -11.7545
+#&gt;   -1.3946  -4.6868   0.1945   4.3473  10.8548  -5.9432   0.6596  -8.7896
+#&gt;   -1.4423   2.4108  -9.0420  11.8343  -5.8388  10.1416  10.7012  -9.5838
+#&gt;   -5.4262   0.0316   7.2794   1.8373   3.5906   0.3534   3.3103   2.6197
+#&gt;   10.0691 -10.8212 -13.9875  13.7426  -0.4254   9.6408  -4.1174 -12.9087
+#&gt; 
+#&gt; Columns 25 to 32   5.7815 -11.2052 -10.1110 -15.0753  -3.7367   2.4327  14.2180   2.2737
+#&gt;   -5.2775   3.1873  -8.7778   6.7015   7.1382   8.2293 -12.5117   2.4427
+#&gt;   -7.3568   2.9960  -6.5885 -16.8982 -16.5649   3.7975  -1.6600   7.3327
+#&gt;   10.9394 -11.4489 -21.5761  -7.8549  -6.8414 -10.9404  -4.2704  -0.0477
+#&gt;  -10.3743   0.7871   3.4286   0.5672  11.4105  -1.3975   6.1030  -0.6356
+#&gt;   16.8554   5.9367  16.2723   1.7485  12.3916   7.4086  -8.5019  -0.6549
+#&gt;   11.9617 -12.5959   6.3841  -2.2748  -1.6286 -14.0480  11.3096  13.4072
+#&gt;   -7.4953   6.1567  16.8598  -0.2953 -21.0817  16.6099   8.3513 -16.7929
+#&gt;   14.8269   0.6151   2.3543   5.8286   2.2856  -3.9406  -0.9631   3.1815
+#&gt;   -3.9532   3.0284  -0.2219  10.4799  10.4760   3.7992  -6.2466 -18.3574
+#&gt;  -10.8578  -9.3689  -7.1844  -3.0032  -0.1545  11.5331  12.7631  13.6629
+#&gt;    3.5911  -0.6161  -5.9469   0.6818   2.3619  -6.1240  -7.6180  -1.3287
+#&gt;   -2.5327  10.6189 -18.5148   5.3148  -0.6584   2.6013  -9.2113  13.8353
+#&gt;    9.4187   0.5777   7.9389   0.5358  13.1063   0.7554  -1.3956   3.5146
+#&gt;  -17.2354 -12.3225  -5.8946   4.4433  -7.8367   0.8939  -0.7904  -4.1935
+#&gt;    7.2861  -2.1638  -9.3430 -11.9848  14.3549  -5.6270  -7.5111  -4.3978
+#&gt;   -3.9379  -9.9126  -5.8633   4.0363  11.0591  -9.9864   2.4630   7.3822
+#&gt;   -5.0085  15.3669  11.6356  -7.1488   9.0924   1.9527   7.3431  -3.2836
+#&gt;    2.7997 -10.5143  -3.9859  -5.4752   1.8580 -14.4903   7.8025  -1.2043
+#&gt;    4.2449  -5.1456   5.4715   0.6693   0.2564   3.2547  -8.1555   4.8945
+#&gt;    6.7695  -5.4372  13.4409  -1.6403  -4.4004   7.4706  -7.1136  -6.3188
+#&gt;  -26.4656  -9.6441   8.3506   7.7984 -15.6762  -6.2710   8.4947  -5.3092
+#&gt;   -1.5954  -4.9106  -4.4741  -5.1403 -10.9120  -6.8455   0.4099   9.0399
+#&gt;   13.4713  -5.0032   0.1034   1.3208   4.6050 -19.9842   9.5379  -8.5128
+#&gt;    9.3263   2.6677 -10.5032   8.1141   3.5864 -10.2731 -12.8021  -0.5099
+#&gt;   -6.6738  -1.6292   1.7206  10.6710  -4.8733   3.6649   9.3597  -6.3020
+#&gt;   -4.3809  -2.4609  14.0715  -9.0213  -9.2908  -0.6594   2.5404  -5.1101
+#&gt;    0.5244   9.6204   1.3933  13.4220   4.4351  -3.4956   2.2497   2.2802
+#&gt;    0.8334  -4.4792 -16.3570   0.9805   8.8349   1.6111 -10.4217  -9.5464
+#&gt;    3.4417   8.0954   1.1975   5.5218   1.2787   9.3994  -1.2515   2.7017
+#&gt;   -6.2431  -3.4588 -11.8408 -16.5218  -3.1631   6.5201  -2.6856   8.5064
+#&gt;   16.6163  -4.2587   7.4921   7.8182  16.3340  -4.9343  12.4147  -4.0190
+#&gt;    3.4008  -3.5263   6.8179   1.2807   2.9962  10.0039   7.8072  -3.8946
+#&gt; 
+#&gt; Columns 33 to 40   0.6153  12.6312  -4.5639  -1.7969  11.6116   5.4296   1.5044   8.4756
+#&gt;  -13.2160   9.4266 -15.7469  -5.2275   6.5396 -15.4728  14.4696  -3.1124
+#&gt;   -9.9255  -0.4149   2.6068  -0.1747  -5.0429   2.1270  -6.1363 -12.1138
+#&gt;    9.2543   1.2021 -14.3793 -12.1762  11.4430  -4.3578 -15.6615   5.0231
+#&gt;   -4.6041  -3.6716   9.3975  -5.8685  -2.6094  14.8090  12.1272 -16.6805
+#&gt;   11.8514  -9.1434 -10.2178   7.1378  18.5872 -19.1870  14.8325   2.1634
+#&gt;   19.5262 -24.5421   8.8771  13.1751   8.5918   4.8357   8.0893  23.1986
+#&gt;    1.4895   1.0209   4.7899   4.8026  -0.0754   1.7163   5.4794   7.5594
+#&gt;   -1.0331  -4.6197  -6.7906   9.2240  -6.8523   9.3637  -3.3565  -9.6718
+#&gt;   -5.0029   4.3012  -5.7359  -8.8524  -4.6995 -10.0583  -6.9865 -15.9948
+#&gt;    4.0009   2.2732  -8.0308   8.9335   8.5793   8.3403   6.6375  -2.2105
+#&gt;    5.6792  -4.7119  -1.6580   1.2476  -1.1267   5.3460 -12.2722   5.2688
+#&gt;   -1.8956   7.9910 -15.9089   2.4161   6.5787 -13.7194  -4.2087   0.0350
+#&gt;   -9.0922   9.5968  -2.1486   1.0909  -7.9967  16.5149   5.1405   0.6490
+#&gt;   -8.1729  10.0460  -2.0875  -0.4981   1.0082  17.0891  17.1244   8.0785
+#&gt;    5.2259 -10.8711  -2.8976  -4.4191   0.6887 -23.9850   4.5274  -5.9280
+#&gt;    4.7499 -13.6675  -0.0904   2.6866  -3.5671  -4.9347  -0.4173  13.2649
+#&gt;   -4.6781  -8.9673   0.2868 -10.3915  -4.7363  -8.2594  13.4670  -1.8958
+#&gt;   11.6591  -8.3003  -5.6046   9.3901  -3.5828  -9.1874   6.8649   1.0554
+#&gt;   13.4827   4.2361  -9.7745  -0.8909  -6.0660   4.4624   2.2054  -2.4023
+#&gt;   -1.2169  -5.2659  14.5143  -2.7991   8.5474   5.4740  -6.8762 -12.6495
+#&gt;   -3.7247  15.0822   1.4707  -0.4692  -6.2861 -14.5584   8.6142  -8.4804
+#&gt;    8.4012  -3.5130  -7.3314   6.7885  -2.0321   4.0237  -2.4397 -13.0585
+#&gt;   -7.4914  -0.0303  -9.6862  -8.3558  -4.4822   1.6017  21.4095  -8.9082
+#&gt;   15.0708  -8.1171 -10.5559   2.5879   0.9688 -20.0621  -9.6058  18.6956
+#&gt;  -22.9990   0.3863   6.2837  -8.1493   5.9334   3.0045 -18.7993  -4.2683
+#&gt;   -8.5526  11.0582   2.9008  -3.6809   0.5880  -7.3651  16.2700 -11.3598
+#&gt;   -6.4579 -19.8438  20.3426  -1.5718 -16.8870   1.8191 -19.6990  11.1823
+#&gt;   13.2661  -6.8728 -24.8098   3.3060   8.4801  -4.5746  -5.0747 -26.4260
+#&gt;   -2.9784  -7.9800  17.0551  -8.3335   0.7352   4.6203  -9.0919   0.5804
+#&gt;   -0.2750   1.6194 -12.5697   2.9655  -4.6859   7.3483  -4.8088   9.7103
+#&gt;   -0.2157 -18.7111   9.1968  -4.0598   4.2597 -13.5639  -2.9689   4.3862
+#&gt;   16.8788  -7.4737 -12.5424  -6.5635   3.4351   5.2706   2.7987   8.1112
+#&gt; 
+#&gt; Columns 41 to 48  -4.6081  -0.2186  12.4059   2.3258  -9.2978  -9.0476   7.2262   9.7289
+#&gt;    2.0818   9.9012   3.3630  -5.5130   7.9671 -11.9698 -14.7923  -0.5322
+#&gt;    4.8438  10.9454   5.9332  -4.1776  15.0645  13.8891 -10.3100  -8.9127
+#&gt;    5.3579  -7.7129  -1.3611   3.7962   3.2228 -16.7771   6.7819  12.6064
+#&gt;   -0.0313   5.1999  -8.1578  -7.9746  11.9216  20.4942 -13.9941 -10.7265
+#&gt;   -9.7730 -11.4795  11.9194   0.7846  -6.0939  -1.5018  15.0925   8.3961
+#&gt;   12.9930 -15.6832  -5.3033   4.2834 -11.0360 -11.2718 -13.6859   5.6258
+#&gt;  -17.0769   7.9962  -1.0748  -0.8639  -4.9793  12.0624  -7.1264 -14.0314
+#&gt;    5.7152   1.3865 -19.0770   3.0961   8.9462   2.2730  16.6593  16.3357
+#&gt;    8.3332  -1.5241  12.2376   0.7902  -5.8305   7.1885  -1.3968  -1.9835
+#&gt;   -4.6199   7.1669  12.7579  -0.7066 -11.9175  -6.4062  -1.7167 -15.7563
+#&gt;   11.8015 -13.2358  -8.3818   8.5178  -3.1642 -14.0368 -13.4443   2.3771
+#&gt;    6.0066 -19.1066   2.3028   7.3583  15.3317 -15.0640  -9.5922  16.5623
+#&gt;   -4.3867   9.1373   0.2435  -0.0986  -0.0762 -13.7349   3.4532   5.1368
+#&gt;   10.3933  -1.2704   2.3008 -10.6742   8.1758  -1.5208 -11.1490 -13.2506
+#&gt;   -0.0919   6.2322  -3.4364   7.1699  -6.4452  -0.6239  -1.0018 -11.6023
+#&gt;   -4.6317  -3.4427  -4.2826  -1.0602  -6.1983  12.9943  -4.2320  -5.9153
+#&gt;   10.8377  -6.5072  -2.7856  -2.4237  -2.7781  10.0801  -7.5869  -1.8684
+#&gt;   -9.9327   2.3958   5.5314  12.3773 -23.8599  -8.0391   7.7051   5.0509
+#&gt;   11.1409  -1.4190  -9.9447   1.3283  18.8076  -5.8233 -10.8330  -1.3174
+#&gt;    2.3021  -2.0881  -7.5843  -4.7476   9.9467   0.2661   6.2724  20.2826
+#&gt;   -4.0467  -7.0384  -3.1936  -4.1471   7.2308   2.3575   0.3775  -0.4381
+#&gt;    6.0766  -1.5979   1.1240  -0.4845   6.2339 -10.0734  -1.5752  -7.2063
+#&gt;   -8.8085 -14.1890   5.1767  -6.4603  -6.7137   8.9089   5.3676   6.6996
+#&gt;    2.8207  -9.9635   2.8069  11.1562  -0.2039 -12.2122  -7.7154  -4.6887
+#&gt;   18.0377   3.0167 -23.0681  -7.6777   3.5636  -2.3696  -1.9580  -7.0301
+#&gt;  -11.5130  -1.5931   4.9840 -11.5243   2.1282   7.6383   6.3256  -2.3138
+#&gt;    3.0400   0.3301  -2.2775  -0.3457 -16.3886   7.2376  -0.6263  -3.9783
+#&gt;    3.1493   2.8850  -4.0373  -8.4329  10.8766  -3.4952   5.5861  -1.1541
+#&gt;  -11.0791  -3.7675   8.2105   3.6783   4.1629   3.3627 -10.1947   2.4801
+#&gt;   -0.4216   6.0176  -4.3146   0.9449   2.6340   1.0334   0.7877  -1.3224
+#&gt;    0.0025  -6.9363   6.5819  11.5109  -4.2354  -1.0384   8.9223  -1.9641
+#&gt;   -0.9662  10.4527   5.9766   3.6725  -5.2594   7.8263  -1.5536 -16.6347
+#&gt; 
+#&gt; Columns 49 to 54   1.9588   8.3283  13.8873   8.6834   0.5259   1.1903
+#&gt;    5.2661 -12.5476  -2.8332   1.9896  -3.4706  -2.3168
+#&gt;   -5.2456  -1.0644   9.1147  -2.1190  -4.6427   5.3401
+#&gt;   11.0142   1.9411  -6.3673  -2.1707  -7.1105  -0.4202
+#&gt;   -8.6967  -5.1558  -4.2474  -5.4727   0.4504   1.1208
+#&gt;   -1.6927  -9.9452   0.6525   1.6136  -2.8117   2.7959
+#&gt;  -14.0690 -13.4435   0.4909  24.8668  -1.8938   2.2268
+#&gt;   11.2345  19.4879   3.9870   8.5541  -6.7318  -2.5259
+#&gt;   15.0291   5.8583  14.2704  -4.8200  -0.1153   1.5786
+#&gt;   -4.1208 -11.5654  10.1012   5.8516  -5.0757  -2.9495
+#&gt;   -1.6121   7.9661   1.3122   6.2015   3.9888  -5.7656
+#&gt;    5.4010  -0.3276   3.1856  -3.2439  -2.8550   0.0666
+#&gt;    7.8241   4.6211  11.1584   7.4595  -3.2768  -4.8039
+#&gt;    5.7145  13.4337   3.2486  -2.6163  -1.5198   0.9364
+#&gt;    8.8906  -9.5267  -0.3171   1.0335   3.3856  -5.0768
+#&gt;   -4.6740   8.9297   4.9648   4.0569  12.6832  -1.7116
+#&gt;   -8.5909  -1.8645  -8.9489   9.4512  -3.1062  -2.8445
+#&gt;    8.1606  -5.4371  -5.0712  -3.3699   0.1449   4.7645
+#&gt;  -10.8949   3.7928  -0.3640   0.2197  -5.7262  -1.6394
+#&gt;   12.0143  -0.9339  -9.0014   1.9697  -0.6400   0.4976
+#&gt;    6.0262   3.5343  11.7559  15.4925  -3.1998   7.0436
+#&gt;  -10.8077   6.3692   9.8305  -5.3393   0.6431  -4.7708
+#&gt;    9.3598   5.6291   7.4261   0.5866   0.7967  -6.0903
+#&gt;   18.3737  -9.8889 -11.1041  -2.4223  -4.2197   0.2418
+#&gt;    3.8259   5.1196 -15.0322  11.3117   0.2863  -2.9050
+#&gt;   -1.9559   2.2944  -4.7705  -9.1695  -9.5788  -7.3815
+#&gt;    2.6341  -8.2463  -7.3040  -1.5010   3.8757  -5.4977
+#&gt;    4.6336   5.3556 -16.7745  -1.0408 -11.2265  -8.4252
+#&gt;   -9.5540  -9.2326  11.3845   3.3105  -3.3986  -3.8443
+#&gt;    6.8270 -15.0225   8.3427   0.6549 -12.3857   6.7373
+#&gt;    2.1005   7.0748 -10.9618   9.3835   8.7323   2.2216
+#&gt;  -11.1123  -6.8023 -16.3364  14.5614  -2.2583   2.3636
+#&gt;   -7.6711  10.2272 -11.4722   6.8585  -6.0782   6.5229
+#&gt; 
+#&gt; (9,.,.) = 
+#&gt;  Columns 1 to 8   1.0735   3.9054   8.9035  -0.2565  -6.1633   8.1643  15.0834  13.5828
+#&gt;   -2.5966   2.3693  -4.3026   7.2852 -13.0546   7.3715 -10.0758   5.7012
+#&gt;   -1.4647  -1.9157   8.2975   3.0261   9.2869   6.5125  -3.8768  -3.2859
+#&gt;   -2.1843  10.5563 -12.0219   2.9543  -5.6263  -3.5726  11.8489  -3.6930
+#&gt;    1.6798  -5.6974  -3.9604   7.3593  12.9994   3.5268  -2.7130   1.0953
+#&gt;   -6.9651  -1.3729  -9.8204  -9.3804   2.2997  -1.8982 -21.3711   3.7881
+#&gt;   -2.4449 -11.3437   1.3579  -8.6287   5.0291  -2.0934   9.9355 -32.6100
+#&gt;    2.7162   4.3471 -16.5572  10.7799   4.2249   1.2279   1.6086  -8.4085
+#&gt;    0.8175   0.0933  -0.1991  -0.9655   1.8447  -3.6023   5.4509   4.7328
+#&gt;   -0.6463   5.4465  -1.1174   8.9566  17.9524 -13.4573  -8.2900  -6.3494
+#&gt;   -5.2350   3.3144 -12.4327  -2.6800  -7.5286  -5.4293 -13.3200   4.5779
+#&gt;    4.1509  -3.0731   4.4164  -3.5928  -3.4108  -6.6018   8.5740   4.6753
+#&gt;    1.0473   8.0859  -2.9306  10.5522  -1.6310  10.5982   3.5165  13.5564
+#&gt;    4.2752   3.6573  14.5529   3.3785 -13.1357  -1.0683  20.0800   6.6249
+#&gt;   -0.5407  -1.2084   4.8380   0.8793   2.2436  -4.3094 -10.9556  12.2060
+#&gt;    3.3666  -9.4404  -6.3948   4.1900   3.4935  -9.0387   7.3016  -7.2717
+#&gt;    4.3984  -5.6790  -1.5033  -7.1358   6.4276  -5.6084  15.1699 -15.7159
+#&gt;    2.7821   4.6108   3.8193  -2.7762   9.0128  -1.0443  -0.0539   0.6151
+#&gt;   -5.1694  -3.0569   1.3078  -9.5484  -3.4733  15.1284  -0.3976  -6.8847
+#&gt;   -2.5611  -3.7937   8.2898  12.8734  -6.9508   3.8760   3.1157  -4.9876
+#&gt;    3.2563   3.6606  -3.2690   9.0415   3.9909  12.7054  -8.1152   1.9505
+#&gt;    0.4398   0.0075   3.0800   2.6692   8.2081   3.8358 -15.0855  -4.9207
+#&gt;   -3.2285  11.5741  -2.5871  -3.3632 -11.5564   6.4468   3.4818  13.8326
+#&gt;   -0.0612   8.8998  -3.0649   5.3760  -7.7608 -17.2493  22.3423   4.2994
+#&gt;   -8.1860   2.6882  -5.9581   1.0583  -1.1013   3.2890  16.8306   6.0326
+#&gt;   -2.0240  -4.8830  -3.4334  12.8048  11.2242   2.5959   6.7187 -12.4128
+#&gt;   -7.1289  16.5580 -12.0429 -15.0252   1.9339  10.4103  -5.0611  17.3638
+#&gt;   -2.6788   3.8403   1.9228  -7.7660   7.7863   1.0034   0.8515  -9.2354
+#&gt;    5.6612 -10.7418  -3.1110   6.2218 -10.4262 -11.0472   6.7749  -0.8332
+#&gt;   -1.8490  -1.6039  -7.3412   8.0914  -7.9953  -9.3456  -9.5047   1.7559
+#&gt;   -5.2588   7.0208   8.2431  -9.5051  -1.8955   4.6671  -7.0597  -2.5657
+#&gt;    0.0276  -2.2944  -8.2584  -8.8120 -11.1925  -8.4706   1.7941   5.8807
+#&gt;   -2.6734   1.9046   0.4894  -0.7032   8.9083  -2.3308 -10.9878  -3.2104
+#&gt; 
+#&gt; Columns 9 to 16  13.2465   6.4433  -8.6046  -1.1845   3.8323 -12.3201   8.9943  -3.9047
+#&gt;    0.4544  -1.8620  -3.3839  -5.8816  13.0031   5.3914   8.6402  -8.9050
+#&gt;   15.6944   9.7085   9.9874  -9.6605  12.1715  -3.1628  -2.3314  -2.0266
+#&gt;    3.6023  -0.3007 -11.3339   9.9588   1.0732   6.4970  10.8542  -1.6746
+#&gt;  -16.7024   5.0472   2.0216   5.7319   5.6213 -15.9480  -5.0707  -4.7655
+#&gt;   -5.2032  -7.3914  -1.9551   7.2575 -12.0884  -1.6787 -10.0721  -5.3452
+#&gt;   -7.5331   1.3891  29.7222  17.9035 -11.3366 -12.3587 -12.5128 -10.8938
+#&gt;   -3.8445  17.0920  -9.4624   4.3973 -16.0368   8.5743   6.0565   9.5379
+#&gt;   -1.7758  -7.2182  -1.0541  -2.2164  -1.0248  -9.4553  -1.1625   3.0394
+#&gt;   -2.2983  -3.4121  14.8455   4.7460  -0.3923  10.1846  11.1070   3.5390
+#&gt;   -2.6307  16.8253  -7.9007   2.6415 -22.1009  -3.8888 -25.7525  -4.4652
+#&gt;  -14.3153  -5.0006   4.1921   3.6170  16.4159   3.9102   8.9996   5.3158
+#&gt;   -0.2249   5.0144   0.1123  -7.6480  10.6608   1.9076   6.8513  -3.9613
+#&gt;    4.6802  -1.4356  -3.0320  -0.2363   6.9620   0.6770   4.0127   3.6345
+#&gt;  -14.7316   9.5782  -4.1529  -5.5849  -9.0149  -3.2603  13.2802   0.5355
+#&gt;   -6.0926  -3.7464   0.9260   4.0663   1.2446  22.6448  -1.6881   8.9952
+#&gt;  -17.3501   3.5519   3.1421  10.5127   2.6415   0.3943 -17.9886   4.7924
+#&gt;  -15.1627 -13.0156  -6.8509  -4.7591  -5.9031   5.8196  13.7230  -8.2514
+#&gt;   -1.7240  -1.7272   3.2095  -7.4035  12.6146   4.1364   8.4938 -10.0151
+#&gt;   14.1426  -1.1944  -0.9654  -1.0893  -0.5539 -11.8718   6.8059   1.1718
+#&gt;   -7.9204   5.9217   9.0367   7.3435   8.0015  10.7738  23.7962 -17.0851
+#&gt;   -0.4257  18.5709   1.1863  -2.7962 -15.0884  -3.9665  -7.0347   8.4267
+#&gt;   -0.2908  -3.4902  -2.1145  11.7449   6.1509  -0.9811  -2.7646  -9.2497
+#&gt;   -4.2742 -12.7498  -9.6965   4.7310   8.8934  -2.1604  12.7799   1.5827
+#&gt;   15.9432  -1.9880 -11.8355  -1.5774  21.8418   3.9167   2.9111   9.0589
+#&gt;   12.1510   1.0352  25.2667   0.8655   1.5272 -12.3486   0.3810   4.9968
+#&gt;   -5.1686 -15.6784  -7.3029   3.3857  -3.1386  -1.9642   4.3806   3.8814
+#&gt;   14.6953  -1.7739  -7.0277  -1.6882   3.4467  -8.5026  -9.1406   6.5295
+#&gt;   -8.5005  -9.0115   8.9160   8.1339  12.3356  15.3479   4.7163  23.3220
+#&gt;   12.5806  -7.5174   9.0809 -12.6274   0.3153 -23.3882   8.4827  -5.6356
+#&gt;    0.3253   8.5266  -2.4614   1.2410  14.3005   5.2676   4.9897  -6.5873
+#&gt;  -14.0444  -2.9457  -9.0449   7.3549  -4.2304  -2.5845   2.6562 -16.3753
+#&gt;    9.5149  -0.0036 -15.2331  -6.5545 -14.5967  -6.3073   2.3052  -3.8767
+#&gt; 
+#&gt; Columns 17 to 24   9.1108  -5.8852   9.4106   1.7577   5.5345  14.7085  -1.5553  -5.5393
+#&gt;   -7.4435  -1.3629   3.8554  -1.8817   6.6976   6.6251   3.9899  -0.2122
+#&gt;    6.3102   3.5170 -19.0083  -5.8928   0.1021  22.7603   0.0510  -9.0458
+#&gt;   10.2538 -15.8787   7.4476  -6.0061   7.4084   1.9730 -16.2613  -4.8448
+#&gt;    1.9727  -7.3840  -6.6374  21.3157  11.4013   6.3762  16.4940   3.5851
+#&gt;  -13.9261   7.4495  15.9916 -13.7559 -13.9218 -15.0398 -13.0659   2.7139
+#&gt;   -1.7143   3.7097  12.9159  -5.4136   1.3846  10.6575  -2.6298   3.4312
+#&gt;   -0.1100   4.2187 -18.0611  15.4935  10.5499 -16.6544   1.7923 -11.7621
+#&gt;   -0.3548  -1.2753  -6.1631  11.5357   4.7380  -7.5232  -1.9752  -3.5531
+#&gt;   -7.4261   4.0613  -8.6857 -15.5452   7.2832 -11.2826   5.9104  13.5837
+#&gt;   -5.2524  -1.5555  12.7220 -12.5006  14.8736  21.9126   2.8786   0.3697
+#&gt;   -0.3842   2.0122  -0.7657  18.2707   8.8032 -15.5875   5.8973   2.2300
+#&gt;    8.5452   4.5014   0.9094  10.7083   7.2936  -6.5902  -3.5868   4.8614
+#&gt;    5.6845 -13.7795   5.7552   5.6715  -9.3158  -4.1249  14.4188  13.3217
+#&gt;    0.4612  -0.7442   3.2900  -3.0268   3.3358   5.1864   5.6845  -7.3762
+#&gt;    5.5567  17.9968  16.8128  -6.5117  -3.0693   1.3810   0.8088  11.5427
+#&gt;   -2.1269   6.0299  -6.2446   1.5503  -0.1344   3.4348   8.8951   3.4751
+#&gt;    9.8986  -2.9783  -3.4552  11.9663   3.5168  -3.1248 -12.2686   1.1864
+#&gt;    2.9204  15.7249  12.1296 -12.3881 -16.3484   3.4656  19.3450  10.9863
+#&gt;   -7.1073 -13.3275   0.8049  -3.4258   4.9472  -0.9507 -12.5504   4.2871
+#&gt;   14.6746  -0.6757   0.4590   9.5481  -5.3562   4.6719   8.3205  -6.9180
+#&gt;    2.4060  10.8216  -7.3185  -4.3943   7.1381   4.5894  -9.5015  -5.0768
+#&gt;   -2.2087  -6.6675  -7.5092   9.6980  13.7328  14.6772  10.6330  -3.2139
+#&gt;   -7.8486  -1.1354  -8.2768   5.7469 -12.4938 -20.9289  -0.2475   7.5843
+#&gt;   -0.6887  12.6767  -0.5174  -6.6744 -14.5340 -15.5469  -6.4152   2.3927
+#&gt;   -4.5653   4.9490  -0.7414  13.3096   7.2078  -0.2862  -3.0870  -6.7875
+#&gt;  -21.3772  -2.0972  -5.9350 -18.0664  16.4198 -13.3079  -0.7904  22.0595
+#&gt;   -2.2383   3.1956 -14.7318  -7.0698   6.8923  -6.2933 -18.4383 -15.0711
+#&gt;   -6.6730   1.2529  -7.0132  -0.7741   4.9305 -12.5031  13.2373   9.1619
+#&gt;    9.0291  -5.0809  -1.0876  14.5123   0.6909 -19.2040  -3.5472 -14.5042
+#&gt;    9.0016  -4.4740   1.2477  -5.7460   5.2325  27.0660  -1.1174  -0.0905
+#&gt;    8.0508 -12.4503  11.1096  12.6742  -9.1776  -3.9803  -7.4178   5.6043
+#&gt;   18.3664  -8.6539  -5.5655   0.3600   5.0420   8.9341  -8.4686 -10.3761
+#&gt; 
+#&gt; Columns 25 to 32   0.3008  -7.0571  -8.4149  -4.5962  -2.9224   3.1753 -12.6795   4.0980
+#&gt;    9.5784  -1.7830  -5.8914  -3.1915   6.3987   8.4283  -9.2462   5.7840
+#&gt;   16.1477   8.7329   3.5931 -14.8631  -9.1864  -6.5386  -1.5663  -2.1406
+#&gt;    0.7749  -1.8754  -0.1599   4.7293  -2.5122  10.4562  -8.2233  -2.8465
+#&gt;   -4.3017   5.2190   7.9096  -1.5344  -9.9853   2.4526  -7.1441  -0.3820
+#&gt;    0.0889  -4.2401  -0.9629  15.7039   9.8557  -4.1554  -1.1819 -22.0733
+#&gt;  -15.6935   8.3697   7.1618   5.1377  -3.7180 -16.9134   5.2836  12.9208
+#&gt;    6.0987  -4.7289   7.0652 -12.9729   1.3067   6.7724   8.8482  -7.8131
+#&gt;   -3.0771  -3.2627  -4.6143   6.2622  -4.1074  -3.7573 -14.7813   4.4464
+#&gt;   -9.6590   5.7455  -3.0179  18.7896 -13.9661   2.1411   7.5048   1.9581
+#&gt;   -8.6272  -4.7795   5.4977  -9.7543  -5.1203 -10.2755  -1.7329 -27.8673
+#&gt;   -4.0099  -5.9967  -3.1690  -1.3150   1.7005  -0.5414   7.4053  -0.2833
+#&gt;    3.0250 -15.8504 -12.5121   5.1994  -0.6908  14.9010   1.3966   2.9322
+#&gt;   -4.5000 -12.3745   4.7834  -2.7725  12.0570  16.1221 -12.6307  -2.4153
+#&gt;   -7.0002   1.6214  -4.7188   5.7197  -5.4331  19.3668  17.1480   2.5092
+#&gt;  -20.2407  -6.3909   5.9688  -0.5903  -8.5754   3.5485  -5.4323 -14.0733
+#&gt;  -12.6081   1.6736  11.7592   2.5367   1.7223  -1.5014   7.4325   8.1650
+#&gt;    0.6163  -9.0269 -17.1684  -3.7598 -10.7735   0.7089   0.4417  -1.9499
+#&gt;    0.3810   6.2666   3.8684  -7.2137  -3.1259 -17.9577 -11.2625   8.2531
+#&gt;    4.4174   8.1828  -3.4086 -16.0366  12.7148  -1.1848 -18.2540  13.6571
+#&gt;    1.3962  -8.3721  -7.6677   2.9592  -5.8263  -3.7404   5.6893  -1.6895
+#&gt;   12.0753  17.7868  -0.1284  -5.2200 -10.6603   7.4665   1.8309   3.6457
+#&gt;   20.6822  -0.5222  -0.2637   2.0915  -2.1368  -9.2732 -24.6116  -4.2364
+#&gt;   -1.4115  -6.7627  12.3034  13.1054  -0.8178   5.0396 -16.5100  12.1881
+#&gt;   16.7217   3.1474  -1.3767  -3.9947   8.0686  -0.7637 -22.6418   2.2609
+#&gt;   12.2405  12.9657   2.6951  -3.8284  -1.9389  -9.9976   5.2749   7.2858
+#&gt;    6.3970   0.3287 -14.7146  16.2203  -0.5022   0.4890  -4.2413   8.4292
+#&gt;   22.4388  16.0109  -8.7131  -6.3525  14.0905 -11.6210   4.3831  -8.4089
+#&gt;  -11.9730  11.7715  10.0646   3.1135  11.2826   3.3526 -11.7663  -0.8952
+#&gt;   10.7174  -5.8578  -0.9425   5.9157  -1.9214  -3.8553   6.5107 -16.4646
+#&gt;    8.4125   1.1668  -5.0757 -12.1152   4.6271   6.3382   3.3747   6.0867
+#&gt;    1.6975 -10.5447   6.3402   4.8285  -0.8840  -6.2778   5.1701  -6.7291
+#&gt;    1.0201  -0.5535  -4.0492  -6.8163   6.0372  -5.7309   7.7101 -17.4128
+#&gt; 
+#&gt; Columns 33 to 40  -8.3005  14.7272  -6.7074  12.7758   2.9899  -4.3429  -0.2968  -1.3443
+#&gt;   -2.0667  -2.5044  12.6257   9.2933  10.2412   2.6942  -7.6705   2.5916
+#&gt;    3.3653  -6.9535  -2.8776  -2.2025   5.1992   0.6193  -4.5544  -9.4456
+#&gt;   -7.3743  -9.1843   2.0995  -9.3366  -0.0310  -5.1709  -2.6634   3.5477
+#&gt;   14.6331   8.1799   7.9666  -0.2023  -9.0092   2.5689 -13.5106 -12.9327
+#&gt;   -3.8559  -1.6064  13.4314   6.0501  -8.6245   2.8513  -0.0219   6.0727
+#&gt;   -0.4318  -9.7012  -6.5943   5.8313   3.7841  -6.3746  -5.8966 -20.2121
+#&gt;    0.0934  -3.7661  10.3504  -7.8322 -10.9738  -1.6299  10.5393   6.6490
+#&gt;    6.7381  -3.8583   3.1032   6.0667   4.0452  -2.5885 -10.1868  -4.6375
+#&gt;  -15.4833  -1.4561  11.9122  13.3496   4.4545  -0.4175  12.7009  10.7143
+#&gt;    2.5562 -11.7562   4.6176   3.0168  13.1050 -11.7575 -11.8906 -17.1705
+#&gt;    6.1098   6.0384  -3.8374  -7.0562  -5.1985   5.9250   7.0644   8.5208
+#&gt;    2.9506  -6.7071  -1.8607 -11.0242  -6.2298 -13.5995   7.3248  10.3573
+#&gt;    9.7444   4.3608   5.9477  -7.8566   2.1875   3.0231  15.0895  -6.3208
+#&gt;    4.0446  -4.2066   1.3350  11.2506 -16.0540  10.1319  -4.8498   9.5998
+#&gt;    0.5336   0.2125   0.2089  -2.9279 -13.9673  10.1236   9.0002  -5.1703
+#&gt;   -0.1647  -5.5400 -12.4962   1.9435   8.9818  -3.3593   7.7355   1.7319
+#&gt;   -0.5365  -3.9592 -13.4042  -4.1858   5.8470  -4.3103  -8.1551   5.4808
+#&gt;   -5.6067   7.4605  -6.2640  -6.4502   2.1007   9.8207  -1.1358  -9.9572
+#&gt;    2.8434   0.0870  12.1987  -0.7862   8.0063  -8.2581 -11.6546 -11.8047
+#&gt;  -18.6080  -8.1193  -0.9787  -4.6226   2.4769  -0.9187  -3.9897  -1.3944
+#&gt;    5.8660  -3.8559   0.9946   5.5243  -5.7550  -1.7439 -14.3546  -7.3971
+#&gt;   11.1755   0.3875   0.5909  -6.8003   6.6283  13.9861 -12.2645   1.2621
+#&gt;    7.6756  10.7757   9.6400  -5.7359   4.5565  -0.8801   6.2724   9.9751
+#&gt;    2.9415   4.6918   1.8767   3.1535  -5.2464  -0.5474  -4.1746   5.6156
+#&gt;   -2.3543  -2.9242   6.4376   8.7504  12.2642  -1.3250  -5.3298  -4.3991
+#&gt;   18.4896  -0.2483  11.8350  13.6104  -8.5979 -12.4203 -10.6695   4.2116
+#&gt;    5.3618  -5.8365  -3.6373   7.7287  12.9988   1.9299 -19.4603  -0.8785
+#&gt;  -15.5827   5.2480  -4.9182 -14.1546  -9.8278   5.4751  -1.3194  -2.6228
+#&gt;  -10.5405   7.2224   3.9087   1.7110  -5.3455  -2.9035  -9.1984  10.6450
+#&gt;    6.5837 -14.6251 -19.5291   2.0283  17.5292   3.6927  -1.5740  -6.7273
+#&gt;   -9.8712   9.7002  -6.4679  -2.6612  -3.0343  -8.4513  -5.1446  11.2936
+#&gt;   -3.8341   1.4596  -7.8576  -4.4343   0.0754 -10.7116 -14.9492 -10.1589
+#&gt; 
+#&gt; Columns 41 to 48  -1.4469  -2.8999  -0.9630  -6.3509 -12.8624   0.1363   3.8723  10.0935
+#&gt;    1.5435   9.5251 -10.4515  -6.3777  16.9190  -8.1521  23.9701   4.9641
+#&gt;   12.7367  -7.9641  -9.8458   0.6162 -25.2348 -12.6177   0.4619  -7.2481
+#&gt;   -5.8560  -5.1255  -3.1354 -18.5729 -10.6835   6.6299 -16.9487 -13.3048
+#&gt;  -13.5108  12.6743  15.3481  -5.5595   4.2583   3.7890   2.8971   3.6391
+#&gt;   -6.3202   3.1731   2.8785   0.6849  16.7047 -12.0166   7.8555   1.0515
+#&gt;    1.3086  -4.6868  -2.1914   5.2846  -2.5489  12.8513   4.4374   9.1135
+#&gt;   -4.0261   3.8887  -1.2627   1.7281  -9.6485 -10.0991  -8.0326  12.0395
+#&gt;    2.6953  -8.3056   3.7025  -6.9717  -8.3421   0.7269  -8.8189   4.3341
+#&gt;   -7.8908 -11.2529  -6.9019   4.5368  11.9622  19.1285   5.0951   2.0622
+#&gt;   -5.8568 -11.5333 -12.7621  -7.6150   0.8706   0.5273   9.3849   0.6782
+#&gt;    1.1827  -5.4800   6.4904   8.8677  -6.3656  11.7740  -2.6136  -6.4490
+#&gt;   12.6030   0.7996  -5.3255  -7.1771 -10.4342   7.4206   0.3773  -0.1530
+#&gt;  -12.1226  -2.2457  22.0030  10.6510   2.8411  12.1533   3.6864 -10.1760
+#&gt;    1.2764   3.6508  13.9743   4.0651  21.3132 -12.1229  10.9814  12.8568
+#&gt;   15.1948   1.6840 -14.4206   5.6209  -4.1085  -0.4338  12.0710   9.5107
+#&gt;   -2.3546   4.4954  -0.8796  13.1283   6.9081   9.2268   6.1242   5.1834
+#&gt;   -2.5499  -1.8342  -3.4145   7.9242  -0.5652  -3.3177   0.7154  -6.5618
+#&gt;  -13.3003  -7.1681  -3.8071  -9.7384 -12.7232   8.6988   1.7359  -5.4667
+#&gt;    1.8086   4.9076   9.4442  -0.0852  -9.6192  -3.7186   7.6617 -10.9622
+#&gt;    1.9929   7.8155 -13.7915 -15.3899   9.9665 -11.1532  -9.2972   1.1085
+#&gt;   -6.6702 -13.3472  -9.3195  17.1402  11.5610 -10.0072   0.8832 -13.6221
+#&gt;   -9.6352 -11.9467  -9.7642 -21.7313 -13.6817  -6.7609  12.2533   3.6639
+#&gt;  -18.3198  11.0650  15.2191  14.5646   2.9968  -7.3285  14.3316   0.2506
+#&gt;    7.6122  -2.4307  -1.3098  -4.9238  -6.5340   4.9611 -14.4775   1.0199
+#&gt;    1.5326  -6.7138  -9.1889   5.1527  12.0538  -0.4903  -6.8468   8.7367
+#&gt;   -2.1124   7.9258  27.0479 -13.5470   3.2345  -1.3535 -14.3106 -11.4123
+#&gt;    4.2531 -21.5239  -5.0090   5.8895   5.3684  28.9170 -17.2063 -12.3373
+#&gt;   -7.5529  10.2042   8.8707   0.3480   1.4809 -13.5463   0.2219   0.7899
+#&gt;   -8.3035  -8.1356   0.1940  -8.0646   3.5596  -1.6872 -14.3589   4.7166
+#&gt;   14.5250  -5.2545   4.8554  -4.8012 -19.3753  15.5188   9.3791   5.3975
+#&gt;   -6.5756   7.1268   0.6758  -7.0289   8.0116  -1.6571  -6.2199  13.9036
+#&gt;   -1.1904   1.9509   7.1661  -9.9119   0.0153  12.0100  -6.9495  13.8010
+#&gt; 
+#&gt; Columns 49 to 54  -0.2883   3.9251   2.4917  -3.5285  -3.8354   1.9130
+#&gt;   -9.5924  -0.0397  -7.0266  -2.3239   2.6360  -0.8788
+#&gt;  -19.3069   3.8304  -7.6437  -2.9513  -1.9829  -1.7687
+#&gt;  -12.8549  -3.0123   0.5920  -5.8921  -6.6200   1.3946
+#&gt;   17.4575  -6.8584  -8.8831   7.2746   6.2740   1.7815
+#&gt;   -3.7614  -3.1141  -5.3400  -2.8170  -4.7686  -0.1632
+#&gt;  -11.4588  -1.9522   0.8138   6.0205   6.5438   3.7458
+#&gt;   -3.8474  10.7437  -7.9791 -11.3983   4.7973  -0.8495
+#&gt;   10.0112  -0.5624  -7.1970  12.1377   0.2231  -2.2062
+#&gt;  -10.5210 -19.2729 -10.4373   1.6629   4.1790   4.6455
+#&gt;   13.6884   0.0696  -2.0373   3.4275   8.1889   5.3210
+#&gt;    2.0698   2.7083  -6.8417  -1.9533   0.0743   0.7355
+#&gt;   21.5921   5.4718   2.4355  -2.2130  -1.0402   0.3817
+#&gt;    7.1291   2.9363  -0.4991  -0.5806   3.6087   2.5239
+#&gt;   -1.1978   2.3942  25.1930   1.8153  -4.3313   2.8886
+#&gt;    6.0419   2.7377  -6.0539  -9.7997  -4.9919  -0.0148
+#&gt;   -2.2113  -8.0248   5.7452   1.0438  15.2932  -1.4453
+#&gt;    3.3594   3.4097  -2.8406   6.9497  -1.0904  -0.4175
+#&gt;   -5.8860  -8.5307 -11.0248  -7.2802   2.6497   4.1530
+#&gt;   -2.4865   2.5076  -6.1890   2.9720   1.7406   1.3478
+#&gt;  -11.0451   1.3601  -4.5293  10.2894  -3.2070  -3.0481
+#&gt;  -13.6334  -7.3734   7.4083   5.8849  -0.1251   2.7981
+#&gt;    8.7304   7.7720  -6.5287  -8.3295  -4.6946  -0.0223
+#&gt;    3.8491  -9.9238  11.3099  -6.4363  -1.1995   2.6177
+#&gt;  -10.2489   4.1072  -0.0930 -10.1622  -7.3642  -2.6042
+#&gt;   -9.0480 -10.1765  -8.8019  11.2094  -1.5077  -2.1801
+#&gt;    3.4538   9.4010  -9.0294   0.3479   0.9695   0.7321
+#&gt;  -14.7509  -3.1005 -11.6585  11.8664   8.3125  -1.2099
+#&gt;    1.5439   5.6177  -3.1403  -5.1001   3.6383  -5.2296
+#&gt;  -11.6184  12.8553  -4.8697  11.6031  -6.0272  -5.3029
+#&gt;    8.6764  13.1203   3.5574  -5.1431  -1.3548  -0.5325
+#&gt;    6.7802   2.7706   8.1629  -2.3705 -10.0547  -1.8048
+#&gt;    7.8417  11.9808  -5.6188   0.6759   5.7834   2.2238
+#&gt; 
+#&gt; (10,.,.) = 
+#&gt;  Columns 1 to 6  9.7380e-01  1.4490e+00  1.8312e+00 -8.7913e+00  4.1246e+00  6.1416e+00
+#&gt;   4.1422e+00  1.9490e-01  7.1978e+00 -4.9095e+00  2.1002e+01 -1.1334e+01
+#&gt;   8.6002e+00  7.8669e-01 -6.0797e+00 -5.8410e+00  2.2738e+00  1.2178e+01
+#&gt;   4.5969e+00 -9.6160e+00  3.5879e-01  3.2520e+00 -4.9143e-01  8.9170e+00
+#&gt;   1.2350e+00  8.5498e+00 -3.5786e+00  4.0318e+00 -1.0806e+01  1.3644e+01
+#&gt;  -3.8857e+00  1.1034e+01 -1.2057e+00 -9.4542e+00  2.1439e+00 -2.7162e+00
+#&gt;   1.8798e+00 -5.7011e-01 -1.1796e+01  1.2672e+01 -2.6563e+01  1.3341e+01
+#&gt;  -7.6033e+00 -7.2512e+00  1.3764e+01  4.6117e+00 -4.9539e+00 -1.4034e+01
+#&gt;  -2.1579e+00  5.3434e+00  5.2073e+00  1.9832e+00 -7.5288e+00 -1.3320e+00
+#&gt;   9.1001e+00 -3.7459e+00 -1.0102e+01  4.2869e+00 -7.9098e+00  7.5913e+00
+#&gt;  -3.7879e-01 -1.5565e+00  3.5275e+00 -7.2230e+00  1.6133e+01  8.0198e+00
+#&gt;  -6.3870e-01 -4.7495e+00  8.8710e-01  8.9196e+00 -1.6305e+01 -1.6101e+00
+#&gt;   4.7581e+00 -6.6625e+00  1.9250e+01 -4.6821e+00  3.8569e+00 -9.6632e+00
+#&gt;   3.7697e+00  9.9504e-01  2.1924e+00  1.8413e+00 -3.1287e+00 -7.6354e+00
+#&gt;   1.9353e+00  4.9703e+00 -5.4919e+00  1.0535e+01  1.6755e+01  6.0989e-01
+#&gt;  -5.6916e+00  6.3663e+00 -1.2364e+00  6.8718e+00 -5.0680e+00 -1.0921e+01
+#&gt;   1.9499e+00  4.3528e+00 -4.9796e+00 -4.7218e+00 -5.9021e+00  4.2923e+00
+#&gt;  -3.9844e+00  4.7720e+00 -7.5301e+00  1.2187e+01 -1.6360e+01  1.2726e+01
+#&gt;  -6.8201e+00  3.6636e+00 -2.2617e+00  1.1609e+00 -1.4828e+01 -5.0818e+00
+#&gt;   4.9086e+00 -4.4233e+00  4.1111e+00  4.3334e-01  3.4612e+00  1.0340e+01
+#&gt;  -1.4880e+00  4.0621e+00 -4.6902e+00  1.2577e+01 -1.1600e+01 -1.2424e+00
+#&gt;  -2.1784e+00  2.9666e+00  2.6355e+00 -9.4748e+00 -7.9034e+00  4.8282e+00
+#&gt;  -1.7914e+00  7.1721e+00  2.0501e+00  1.6545e+00  6.4078e-01 -7.3620e+00
+#&gt;   7.0301e+00  1.9841e+00 -9.4659e+00  1.9471e-02 -4.0225e+00 -1.2733e+01
+#&gt;  -2.2844e+00 -4.0059e+00  7.5528e+00  5.6614e+00  1.8246e+00 -2.1709e+01
+#&gt;   7.6560e+00 -8.1628e+00 -2.3944e+00  4.1034e+00  6.9953e+00 -1.7369e+01
+#&gt;  -4.6991e+00 -3.8699e-02  1.3691e+00  8.1784e+00  8.0656e+00 -5.7873e+00
+#&gt;  -8.6688e-01  2.0912e+00 -1.1272e+01  5.5044e+00 -9.1747e+00  1.0300e+00
+#&gt;  -5.2787e+00  5.7283e-02  5.1677e+00 -1.1812e+01  7.4668e+00  7.7460e+00
+#&gt;  -1.3267e+00  5.6637e+00  5.5546e-01  5.4979e+00 -3.4629e+00  1.8040e+01
+#&gt;   4.6097e+00 -1.9614e+00  4.1431e+00 -7.8649e-01  3.3812e+00  7.5966e+00
+#&gt;  -1.1130e+01  1.0027e+01 -3.0521e-01  1.3338e+01 -7.0440e-01 -2.4646e+00
+#&gt;  -1.0834e+00  1.5344e+00 -5.0158e+00 -1.1813e+00  3.8881e+00  1.4119e+01
+#&gt; 
+#&gt; Columns 7 to 12 -3.0010e+01 -6.6165e+00  8.4032e+00 -6.8274e-01 -9.0195e+00 -2.0540e+00
+#&gt;   1.0079e+00 -1.2331e+01  1.8611e+00  1.4432e+01 -1.3957e+01 -7.3971e+00
+#&gt;   7.2369e+00 -2.1319e+01  1.7590e+01  1.7050e+00  2.9713e+00  3.7688e+00
+#&gt;  -9.5183e+00  1.8749e+01 -9.2851e+00 -1.5082e-02  3.7627e+00 -8.1922e+00
+#&gt;  -2.4509e+00  5.0913e-01 -1.2228e+00  1.5254e+01  5.9769e+00  1.4472e+01
+#&gt;  -3.7212e-01  1.0772e+01  1.8635e+00 -1.9094e+00  1.2522e+01 -7.5197e+00
+#&gt;  -2.4230e+01 -3.5736e+00  3.0001e+00 -1.2669e+01  6.1788e+00  4.0507e+00
+#&gt;   9.3861e+00 -1.6381e+00 -2.1131e+01  2.0590e+01 -1.0562e+01  1.6755e+00
+#&gt;  -3.3105e+00 -2.8660e+00  1.9779e+01 -8.6758e+00  3.0051e+00  9.7456e+00
+#&gt;   2.2840e+00  2.6228e+00  3.5843e+00 -3.2269e+01  3.0991e+00 -3.0214e+00
+#&gt;   8.8598e+00 -8.1051e+00 -5.1534e+00  1.6551e+01 -3.0966e+00 -1.5080e+01
+#&gt;  -5.3818e+00 -1.2176e+01  1.6221e+01 -9.8099e+00 -9.1781e-01  2.0126e+00
+#&gt;  -1.3203e+00 -1.9429e-01  2.7570e+01  1.0377e+01  1.0636e+01  3.3602e+00
+#&gt;   7.3243e+00  2.2125e+00 -4.5594e-01 -1.2620e+01 -3.5310e+00  2.8700e+00
+#&gt;   6.4630e+00  9.1406e-01 -3.5838e+00 -7.9412e+00  2.2477e+01 -1.5457e+01
+#&gt;   7.4491e+00 -7.1114e+00 -2.9125e+01  1.4957e+01 -4.8028e+00 -8.9383e+00
+#&gt;   3.9169e+00  7.5088e+00  8.6104e+00 -2.9066e+00  9.7379e-01  8.7434e+00
+#&gt;  -8.3075e+00 -9.7575e+00  1.8055e+01 -7.1664e+00 -3.6796e+00  8.2397e+00
+#&gt;   9.6591e+00 -1.8395e+00 -3.2242e+00  1.3317e+00 -2.2787e+01  3.2769e+00
+#&gt;  -9.0037e+00 -2.3628e+01  1.5783e-01  1.8059e+01 -8.4509e+00 -7.8330e+00
+#&gt;   3.6535e+00 -1.7096e+00  3.7125e-01 -7.2359e+00  4.1249e-03 -1.0515e+00
+#&gt;   2.7810e+00 -2.7969e+00 -9.4412e+00 -3.5242e+00  3.5175e+00 -1.1864e+01
+#&gt;  -2.5415e-03 -1.7145e+01  1.2049e+00  1.3563e+01  1.1815e+01  4.3641e+00
+#&gt;   7.5487e+00  6.6598e+00  9.3083e+00  6.4563e+00  5.5130e+00  4.9231e+00
+#&gt;  -9.7076e+00 -1.5475e+00 -7.0297e+00 -2.6467e+00 -3.9106e+00 -5.5356e+00
+#&gt;   2.4310e+00  7.1102e+00  7.9353e-01  1.7824e+00  8.2437e+00  1.1257e+01
+#&gt;  -2.0769e+01  6.4784e+00  3.8660e+00 -1.2394e+01  1.1686e+01 -9.8153e+00
+#&gt;  -1.1151e+01  6.8596e-01  1.1964e+01 -2.2998e+00 -1.0429e+01  8.1477e+00
+#&gt;   8.9297e-02 -8.8204e+00 -1.1794e+01  2.4298e+00 -3.4245e-01 -7.9011e+00
+#&gt;  -8.9061e+00 -5.5781e-01  2.3508e+01 -8.3288e+00  1.2172e+01 -7.0293e-02
+#&gt;  -2.3819e+00 -2.8189e+00  3.9560e+00  1.1461e+01 -4.3939e-02  1.8385e+01
+#&gt;  -2.6116e+00  1.0575e+01  9.6807e-01  1.3881e+01  8.0968e+00  1.3677e+01
+#&gt;  -5.0407e+00  5.9978e+00 -2.8054e+00  1.4021e+00 -4.4690e+00  8.9486e+00
+#&gt; 
+#&gt; Columns 13 to 18  8.8993e+00  4.7415e+00  4.4859e+00 -5.7668e+00 -6.9999e+00  1.5650e+00
+#&gt;   5.8629e+00  4.1877e+00  7.3453e+00  1.6745e+00 -4.1151e+00 -1.3395e+01
+#&gt;  -4.2558e+00 -1.0452e+01 -4.1373e+00  2.4526e+01  2.5696e+01 -7.6015e+00
+#&gt;  -7.3704e+00 -7.3710e+00  1.9229e+00 -3.0092e+00  1.6079e+01  7.4628e-01
+#&gt;  -5.9248e-01 -9.9398e+00 -3.7181e+00 -1.1601e+00  7.2940e+00  1.5779e+01
+#&gt;  -4.2213e+00 -8.4778e+00 -1.6147e+01  6.6524e+00 -5.1158e+00  5.8316e+00
+#&gt;  -7.3672e+00  5.7705e+00 -1.6387e+01 -8.3544e+00 -3.2935e+00 -1.5326e+01
+#&gt;   5.1255e+00  9.3415e+00 -4.2049e+00 -6.2573e+00  4.4090e+00  7.8587e+00
+#&gt;   1.2954e+01 -3.2016e+00 -3.6492e+00 -1.4351e+01  5.4966e+00 -7.0020e+00
+#&gt;  -9.6956e+00  7.3025e+00  2.6994e+00 -4.1748e+00  6.6155e+00 -4.4575e+00
+#&gt;  -4.6948e+00  1.5444e+01 -7.0963e+00  8.3528e+00 -9.0845e+00 -3.6703e+00
+#&gt;  -7.1457e+00 -6.6985e-01  5.9290e+00 -7.9600e+00  2.0777e+00 -9.9763e+00
+#&gt;   4.1814e+00  1.6123e+01  1.8267e+00  4.2746e+00  3.7384e+00 -7.2365e+00
+#&gt;  -3.4998e+00 -5.2595e+00  8.9079e+00 -1.6606e+00  2.2729e+01 -8.8141e+00
+#&gt;  -1.4404e+00  2.3608e-01  1.2236e+01  4.6271e+00 -2.7030e+00 -9.0437e+00
+#&gt;  -8.9669e+00  2.2795e+00 -6.8421e-01  2.9245e+00 -1.3561e+01 -5.5535e+00
+#&gt;  -7.2864e+00 -5.0646e+00 -1.0792e+01 -1.7618e+00 -1.7860e+00 -8.5841e+00
+#&gt;  -1.6103e+01 -3.7075e+00 -1.4694e+00  1.7613e+00  8.1363e+00 -9.0179e+00
+#&gt;  -2.6679e+00  8.3764e-01 -2.0586e+00 -1.4770e+01 -7.0641e+00  1.0958e+01
+#&gt;   6.1465e+00 -1.7301e+00  6.8926e-01  1.1042e+01 -8.0646e+00  1.3740e+01
+#&gt;   9.2186e+00  6.9219e+00  1.0736e+01 -7.6760e+00  1.1137e+01  1.2983e-01
+#&gt;   5.1655e+00  3.9911e+00 -1.1730e+01  1.6808e+01 -7.6769e+00  1.3294e+01
+#&gt;   1.7941e+01  5.9604e+00  1.6718e+01 -6.6048e+00  1.1282e+01 -5.0028e+00
+#&gt;  -9.0343e+00 -5.8421e+00  1.0260e+01 -4.2524e+00  2.6833e+00 -9.7470e+00
+#&gt;   1.3666e+01 -4.8584e+00  2.1007e+00 -4.1773e+00 -1.1972e+01  1.2956e+01
+#&gt;  -6.5014e+00 -4.6207e+00  2.1431e+00 -7.7870e+00  1.8501e+01  6.5167e-01
+#&gt;   2.2908e+01  6.1302e+00 -9.0525e+00 -4.2188e+00 -1.2936e+01  1.4321e+01
+#&gt;   4.9014e+00 -4.1631e+00  2.4287e+00 -1.7578e+01  4.6079e+00 -2.5836e+00
+#&gt;   1.4056e+01  6.7470e-01  2.8408e+00  1.6996e+01 -1.0415e+01  2.1274e+01
+#&gt;   1.2592e+01 -7.1130e+00  1.0846e+01 -1.0264e+01  2.6116e+01 -1.1069e+01
+#&gt;  -6.8943e+00  2.1540e+00  6.4012e+00  6.3127e+00  3.7900e+00 -5.0710e+00
+#&gt;  -5.2806e+00  9.2638e+00 -5.8424e+00 -3.5825e+00 -4.8384e+00  5.1157e+00
+#&gt;  -2.2761e+01  3.0809e+00 -8.2334e+00 -1.0741e+01  1.8393e+01  4.0946e+00
+#&gt; 
+#&gt; Columns 19 to 24  5.3620e+00 -2.3777e+00  4.2923e+00  1.1956e+00  8.2449e+00 -1.8528e+01
+#&gt;  -7.6953e+00 -2.6968e+00  2.1382e+00  4.4323e+00 -1.2187e+00 -2.8213e+00
+#&gt;   1.2562e+01 -1.1218e+00  6.4784e+00 -1.4275e+01  7.0761e+00  4.8327e+00
+#&gt;   8.9042e-01 -7.8073e+00 -2.0207e+01 -7.8072e+00  1.1351e+01 -5.4043e+00
+#&gt;   3.3035e+00  5.8111e+00  4.1300e+00  2.1795e+01  1.3702e+01 -1.4520e+01
+#&gt;  -1.4710e+01 -1.1862e+01  6.4156e-01  1.5063e+01 -1.4422e+01 -3.8048e-01
+#&gt;   5.6821e+00  1.1837e+01  1.7712e+01 -1.1364e+01 -2.8194e+00  1.6764e+00
+#&gt;   2.6854e+00  5.9453e+00 -1.0956e+01  9.3101e+00  1.3290e+01 -1.9139e+01
+#&gt;   8.2914e+00 -1.0784e+00  6.3122e+00  1.4631e+01 -1.5058e+01 -1.4125e+01
+#&gt;  -1.6912e-01  5.4058e+00 -3.5318e+00 -1.3410e+01  5.6009e+00  4.3664e+00
+#&gt;  -5.5236e+00  4.4297e+00 -1.2670e+01  5.6168e+00  4.8356e+00  5.6053e+00
+#&gt;  -2.4133e+00 -4.7941e+00 -5.2335e+00 -1.2109e+01  3.9621e-01  8.1410e-01
+#&gt;   1.3102e+01  1.6143e+00 -1.6402e+01 -2.1645e+00  8.7013e+00 -1.5632e+01
+#&gt;   8.1271e+00 -8.1008e+00  1.4215e+01  6.8634e+00 -5.8671e-01  8.7925e-01
+#&gt;  -1.2478e+01  4.7577e+00 -1.4711e+01 -1.2847e+01 -1.1610e+01  1.3058e+01
+#&gt;  -1.7032e+01  5.1948e+00 -8.0982e+00 -2.7467e+00 -2.2929e+01  3.9658e+00
+#&gt;  -8.0204e+00  2.0920e+00  4.1907e+00 -1.6993e+01  5.7090e+00  1.0770e+01
+#&gt;  -5.0310e+00  5.3340e+00  2.0351e+00 -1.1819e+01 -2.0917e+00 -4.9536e+00
+#&gt;   8.9459e+00  4.5408e+00 -1.2252e+00 -3.2597e+00 -1.9617e+00  6.0336e+00
+#&gt;   1.2713e+01 -1.4828e+01  9.7539e+00  8.6881e+00  5.0384e+00 -7.8791e+00
+#&gt;   3.3678e+00 -7.0126e+00  1.1724e+01  1.2602e+00 -8.2098e+00  1.9833e-02
+#&gt;   1.4143e+01  6.1607e-04 -1.0256e+01  1.0763e+01  8.9912e+00 -2.3142e+00
+#&gt;   6.3153e+00  7.2645e+00  5.8631e+00  2.3282e+01 -1.4853e+01 -2.1863e+00
+#&gt;   2.0174e+00  2.7245e+00 -1.3041e+01 -2.5928e+00 -7.1844e+00 -5.0949e+00
+#&gt;   3.6104e+00  5.2921e+00  1.2311e+00  1.1486e+01  4.3245e+00 -1.8870e+01
+#&gt;   8.1833e-02  1.3504e+01  2.0587e+01  5.9465e+00 -7.3169e+00  4.9747e+00
+#&gt;   1.2588e+01 -6.2767e+00 -2.8220e+00  1.4288e+01  6.7474e+00 -1.2098e+01
+#&gt;  -2.8981e-02  1.6053e+01  1.9742e+01 -3.1571e+00  6.6680e+00  1.9848e+00
+#&gt;  -8.9624e+00 -1.8030e+01 -1.7666e+00  1.5758e+01 -9.6462e+00 -6.6230e-01
+#&gt;   1.4211e+01 -1.2624e+01  7.8362e+00  6.6253e+00 -1.5997e+00 -5.3106e+00
+#&gt;  -4.2488e+00 -3.7679e+00 -7.5373e+00 -2.1616e+01  1.0286e+01 -8.9994e+00
+#&gt;  -1.3258e+00 -3.1426e+00 -3.0732e-01 -2.8969e-01  2.0871e+00 -6.6631e-01
+#&gt;  -4.4706e+00 -3.2235e+00 -7.1209e+00  8.5614e+00  1.0714e+01 -1.9168e+01
+#&gt; 
+#&gt; Columns 25 to 30 -1.3738e+01  1.9407e-01 -6.9228e+00  4.9592e+00  2.7284e+00 -1.3088e+01
+#&gt;   2.3503e+00 -2.0550e+00  1.1039e+01 -3.9911e+00  2.4592e+00 -7.9029e+00
+#&gt;  -1.8685e+01 -3.5048e+00 -6.9269e+00 -3.9238e+00 -2.5702e+00 -9.5877e+00
+#&gt;   1.6462e+01 -1.1015e+01  7.6716e+00 -1.1743e+01 -2.0550e+01  3.2843e+01
+#&gt;  -8.0997e+00  1.4429e+01  1.1023e+01  1.2398e+01 -1.1172e+01 -6.1405e+00
+#&gt;   1.1380e+01  2.7527e+00 -1.6173e+01  5.8441e-01 -3.5161e+00 -3.1103e-01
+#&gt;  -7.9379e+00  9.1177e+00 -1.0283e+00 -6.6229e-01 -2.5849e+01 -1.3594e+00
+#&gt;   5.2394e+00 -7.1344e-01  1.0498e+01  4.1503e+00 -1.8481e+00 -9.5384e+00
+#&gt;  -1.8064e+00 -4.9391e+00  1.1056e+01  5.7745e+00 -1.1675e+00 -9.9092e+00
+#&gt;   8.6111e+00  3.9188e+00 -8.4630e+00 -4.1664e+00 -1.0488e+00  7.1290e+00
+#&gt;  -7.8759e+00 -2.8698e+01  2.3918e+00  5.9546e+00  1.3440e+01  1.7421e+01
+#&gt;  -3.9861e+00  1.2915e+01  1.1409e+00 -1.1497e+01 -3.8018e+00  5.1922e-01
+#&gt;   7.6864e-02 -8.8953e-01  3.0814e+00 -2.0490e+01  9.5406e-01  1.6945e+01
+#&gt;   2.9916e+00  5.9803e+00  3.2879e+00  5.6387e+00  4.9739e+00  6.3383e-02
+#&gt;   8.9156e+00 -1.0299e+01  3.1609e+00 -2.2392e+00 -8.0176e+00  3.5495e+00
+#&gt;   1.2996e+00 -1.1130e+01  3.1512e+00  3.7519e-01 -1.0313e+01 -3.7287e+00
+#&gt;  -1.5403e+00 -3.7772e+00 -1.2884e+00 -2.4016e+00  6.9672e+00 -1.4478e+00
+#&gt;   7.8027e+00  2.0717e+01  1.1774e-01 -8.1338e+00 -1.1843e+01 -8.3353e-02
+#&gt;   1.1373e+00  4.8027e+00 -7.1674e+00  4.1796e+00  4.8723e+00 -1.0801e+01
+#&gt;  -4.0332e+00  1.6163e+01  1.3942e+01 -8.9178e-01 -1.2489e+01 -8.6920e+00
+#&gt;   2.5148e+00  6.1723e+00 -6.3309e+00 -7.0200e+00 -4.0136e+00  5.8757e+00
+#&gt;  -3.5399e+00 -4.7152e+00 -1.1348e+01 -4.4675e+00  1.2779e+01  5.2246e+00
+#&gt;  -9.6200e+00 -1.5830e+01 -1.3623e-01 -9.5731e-02 -1.4709e+01 -3.6802e+00
+#&gt;  -1.7013e+00  1.2616e+01 -6.2482e+00  3.0809e+00 -3.3756e+00  1.6695e+01
+#&gt;  -5.2060e+00 -1.5555e+00  7.9039e+00  2.5685e+00 -4.5289e+00 -2.1201e+01
+#&gt;  -6.4956e+00  2.5324e+00  7.6992e+00  8.3877e+00  1.7758e+00 -3.5438e+00
+#&gt;  -3.6889e+00  1.7558e+01  9.0596e+00  4.0924e+00 -6.4671e+00 -6.9760e+00
+#&gt;  -1.2708e+01  3.0308e+00  1.2044e+01  3.0528e+00 -5.0074e+00  2.5508e+00
+#&gt;   1.0672e+00 -2.1581e+01  4.4682e+00  5.3672e+00  2.0925e+01  5.4427e+00
+#&gt;  -6.6319e+00 -4.6486e+00 -3.7764e+00 -8.9514e+00 -6.2342e+00  8.3887e+00
+#&gt;   1.2911e+00 -6.7639e+00 -1.4354e+01  7.7890e-02 -1.4188e+01 -3.8556e+00
+#&gt;  -1.3275e+00 -1.9247e+00 -5.8075e+00 -6.7388e-01 -9.3005e+00  2.0396e+01
+#&gt;  -1.1306e-02 -5.8658e+00  1.1033e+01  2.3647e+01 -3.6233e+00  5.3512e+00
+#&gt; 
+#&gt; Columns 31 to 36 -1.5202e+01  2.4354e+00 -8.2399e+00 -8.1755e-01  5.9034e+00 -2.0364e+00
+#&gt;  -2.1992e+01 -1.3849e+00 -4.3722e+00  1.0039e+01  1.3087e+00  6.5794e+00
+#&gt;  -2.9016e+00  5.2726e+00 -2.3553e+01 -1.7231e+01 -2.7549e+00  2.7730e+00
+#&gt;   6.3767e+00  5.4126e+00 -2.3162e+00 -8.4837e+00  5.2359e-01 -2.2921e+00
+#&gt;  -2.0619e+00  7.2101e+00  4.2319e+00 -3.9911e+00 -7.0854e+00  3.5053e+00
+#&gt;   4.0992e+00 -1.1882e+01  7.4594e+00 -1.4278e+00 -8.3049e+00  1.9544e+00
+#&gt;   3.9721e-01  8.6233e+00  2.4166e+00 -6.9363e+00 -7.7536e+00 -1.8720e+01
+#&gt;  -5.7188e+00  9.3542e+00  1.4333e+01  1.9902e+00  1.8773e+00  6.6106e+00
+#&gt;   3.4952e+00  1.5997e+01  3.1629e+00  8.4303e+00 -6.3740e-01  1.1113e+01
+#&gt;  -2.9555e+00  1.0813e+01  2.8458e-01  4.5747e+00  2.3225e+01 -2.2558e+00
+#&gt;  -8.7068e+00 -2.2495e+01 -7.6862e-01 -5.5118e+00 -2.0754e+00  5.5314e+00
+#&gt;   7.2146e+00  2.7650e-01 -3.6630e+00  6.4178e+00  1.0216e+01 -2.0088e+00
+#&gt;  -1.5362e-01  1.3131e+01  9.7638e+00  1.5974e+01 -1.9576e+00  8.3369e+00
+#&gt;   1.3024e+01  1.2571e+01  3.7349e+00  1.2891e+00  1.4476e+01 -1.3264e+01
+#&gt;   3.6886e+00 -4.3462e+00  1.2288e+01  8.2774e+00 -5.7755e+00  1.7274e+01
+#&gt;  -5.9559e+00 -1.2825e+01  6.9343e+00  1.6892e+01  1.8917e+01  4.9605e+00
+#&gt;   4.8494e+00  1.0662e+01  3.2896e+00  5.9429e+00 -5.0767e+00 -2.1693e+00
+#&gt;   8.1237e+00  1.0483e+01 -7.8821e+00  6.5832e+00 -2.3975e+00  1.2192e+01
+#&gt;  -8.4196e+00 -3.8164e+00  2.0849e+00  6.8310e+00 -3.6229e+00 -1.9822e+01
+#&gt;  -7.6289e+00  8.9065e+00 -1.3043e-01  1.1697e+01  2.2462e-01 -1.4295e+01
+#&gt;   1.3772e+01 -4.1033e+00  1.9545e+01 -7.2149e+00 -1.1904e+01  2.1790e+01
+#&gt;  -2.8698e+00  6.3158e-01 -1.5593e-01  1.3231e+01 -8.5647e+00  6.1170e+00
+#&gt;  -1.5071e+00 -1.4404e+01 -5.2590e+00 -1.0187e+01 -2.7895e+01 -5.0782e+00
+#&gt;   2.5744e+00  2.5337e+01 -2.6079e+00 -3.4419e-01  1.3615e+01 -8.8423e+00
+#&gt;  -2.5558e+01  4.1642e-01  1.4168e+00  1.2753e+01 -1.3027e+00 -8.0492e+00
+#&gt;  -1.4020e+01 -8.7159e+00 -6.4408e+00 -5.9928e+00  2.4667e+00  1.0677e+00
+#&gt;  -1.2690e+01 -2.1022e+00  1.4398e+01 -1.0129e+00 -1.2697e+01  3.6410e-01
+#&gt;   4.2876e+00 -7.4832e+00  3.4943e+00  6.7610e-01 -4.9578e+00 -4.9512e+00
+#&gt;   4.9301e+00  1.1563e+01 -1.4322e+01  2.5729e+01 -1.6189e+00  1.6177e+00
+#&gt;   1.2431e+00  3.6739e+00 -7.3476e+00 -9.7248e+00  3.0216e+00  1.0001e+01
+#&gt;   1.0557e+01  2.9558e+00 -6.0461e+00 -7.5559e+00 -1.7653e+00 -1.2573e+01
+#&gt;  -3.5863e+00 -1.7674e+01 -7.5302e+00 -3.5161e+00 -6.9405e+00  9.9321e+00
+#&gt;   1.2192e+00 -1.2505e+00  1.4940e+01  6.5002e+00 -7.2249e+00  6.9642e+00
+#&gt; 
+#&gt; Columns 37 to 42 -2.1839e+00 -3.0819e+00  1.5255e+00 -3.0231e+00  4.7962e+00  1.2023e+01
+#&gt;   4.4927e+00 -9.4327e+00 -2.3355e+00 -9.2311e+00  1.0838e+01  2.0955e+00
+#&gt;   8.3446e+00 -1.0250e+01  3.2047e+00 -1.5100e+01 -4.5512e+00 -1.2529e+00
+#&gt;  -1.2340e+01  3.8265e+00 -1.3124e+01 -1.2238e+01 -1.1541e+01  2.7594e-01
+#&gt;   4.5085e+00 -1.2752e+00  5.7550e+00  5.1428e+00  9.8829e+00  7.7298e+00
+#&gt;  -2.5823e+00  7.6269e+00  2.2596e-01 -1.5687e+01  7.9061e-01 -5.1668e+00
+#&gt;   8.5447e+00 -2.0057e+01  1.1856e+01  2.1813e+01  7.2455e+00 -7.8119e+00
+#&gt;  -6.8094e+00 -2.2211e+00  3.8332e+00 -1.3119e+00 -7.4602e+00 -8.5137e+00
+#&gt;   2.2390e+00 -9.5517e+00 -5.3426e+00  3.6890e+00  8.5591e+00 -4.3057e+00
+#&gt;  -1.1398e+01 -2.8661e+00  1.5568e+01 -2.3049e+00 -5.6419e+00  4.3975e+00
+#&gt;   1.2040e+01  2.9522e+00 -2.0292e+00 -3.8237e+00 -6.7427e+00  1.1371e+01
+#&gt;  -3.0471e+00  9.1596e+00 -2.2951e+00  1.4278e+00  2.8581e+00  1.1450e+00
+#&gt;  -5.7798e+00  1.8992e+00  2.7155e+00 -5.4368e+00  1.6683e+00  6.8179e+00
+#&gt;   1.5552e+01  1.0856e+01 -7.4877e+00  1.7503e+01  1.1446e+01 -1.0355e+01
+#&gt;  -1.2055e+01  1.5282e+00 -5.4749e+00 -4.1974e+00 -5.9257e+00  1.0064e+01
+#&gt;   2.3358e+00  1.4353e+01  9.0634e-01 -5.1698e+00 -6.9872e+00 -5.3377e+00
+#&gt;   1.4625e+01  2.8490e+00 -7.1319e+00  1.3525e+01  1.6654e+01 -1.0162e+00
+#&gt;   1.0403e+01  4.9621e+00  7.0472e+00  1.9989e+00 -1.9969e+00 -6.8726e+00
+#&gt;   7.7782e+00  7.1451e-01 -1.0276e+01  2.5898e+00  3.3833e+00 -5.7464e+00
+#&gt;   6.3637e+00 -1.4091e+01 -6.6739e+00  3.4672e+00  2.8298e+00  4.3459e+00
+#&gt;  -1.4757e+01  6.2524e+00  5.7120e+00 -8.2872e-01  2.9873e+00 -1.1124e+00
+#&gt;  -4.5514e+00 -4.1434e+00 -6.8436e+00 -1.9198e+00  1.0507e+01  6.1106e+00
+#&gt;   4.9061e+00 -8.2704e+00 -1.0759e+01 -1.0625e+01  1.3115e+01 -1.7348e+01
+#&gt;   1.9217e+01 -1.1588e+01  6.3912e-01 -1.8243e+01  1.2293e+01 -9.5986e+00
+#&gt;  -8.5925e-01 -3.4281e+00 -1.5110e+01 -5.4068e+00  8.1443e+00 -1.1779e+01
+#&gt;   1.1450e+01 -5.6268e+00  1.5817e+01  1.0199e+01  1.4240e+01 -9.1857e+00
+#&gt;   1.2182e+00 -2.1214e+01  1.1216e+00  4.1931e-01 -7.8672e-01  9.4782e-01
+#&gt;   5.4024e+00 -3.0095e+00  1.8014e+00  1.5928e+00  9.5287e+00 -2.3651e+00
+#&gt;  -6.2293e+00  1.4186e+01  1.1173e+01 -5.3153e-01  5.2723e+00  2.2811e+00
+#&gt;  -6.3319e+00 -5.1972e+00  2.6973e+01 -1.4934e+01 -5.2616e+00 -1.1713e+01
+#&gt;   2.1004e+01  1.9954e+00 -9.3554e+00 -8.2138e-01 -4.1082e+00  3.9037e-01
+#&gt;   8.3272e+00  3.9975e+00  4.2137e+00 -7.9366e+00  1.6947e-02 -1.6094e+01
+#&gt;   1.1774e+01  9.9972e-01  5.9695e+00 -4.2308e+00  1.3514e+00  8.0235e-02
+#&gt; 
+#&gt; Columns 43 to 48  9.8296e-01 -1.1794e+01  4.6783e+00 -1.1284e+01 -6.5485e-02  2.4002e+00
+#&gt;   9.3494e+00  2.6465e-01  1.0985e+01  1.1484e+01  5.3779e+00 -6.5260e+00
+#&gt;   1.3920e+01 -5.9769e+00  8.4406e+00 -6.1062e+00 -2.4462e+00 -1.0576e+00
+#&gt;   8.5015e+00 -3.4099e+00  1.3045e+01 -2.4871e+00 -1.1143e+01  1.8200e+00
+#&gt;  -5.6810e+00 -4.6638e+00 -1.6928e+00  7.9357e+00 -9.2787e+00  1.2768e+01
+#&gt;  -2.0173e+01  1.2707e+00  1.7612e+01 -7.0284e-01 -7.2805e+00 -1.8927e+01
+#&gt;   1.3834e+01 -1.2518e+01 -1.1550e+01 -1.5144e+01 -2.0212e+01 -4.5403e-01
+#&gt;  -8.4138e+00 -9.1491e+00 -1.5514e+00  6.5946e+00 -5.7358e+00 -2.8624e+01
+#&gt;  -5.4824e+00 -1.2562e+01  1.8653e+00  9.9120e-03 -6.2146e+00 -2.3214e+01
+#&gt;   6.3677e+00 -2.5788e+00  8.7075e+00  1.0912e+01  4.8855e+00 -1.0308e+01
+#&gt;  -9.0831e+00 -2.0813e+01 -7.1917e+00  2.9565e+00  7.4505e+00 -4.5852e-01
+#&gt;   1.0393e+01  1.5779e+00  1.2805e+01  8.2504e+00 -1.0377e+01 -1.2014e+00
+#&gt;   1.6074e+00 -1.5799e+01  1.8749e+01 -2.1596e+00 -1.0772e+00 -8.7998e+00
+#&gt;   1.8281e+01  1.2651e+01  1.1182e+00 -1.0956e+01 -6.5179e+00  3.6558e+00
+#&gt;  -1.5627e+01  7.2080e+00 -1.0683e+01  1.7225e-01  8.0603e+00  2.5588e+00
+#&gt;   2.6387e+00  1.7141e+01 -3.0660e-01  1.2546e+01 -1.0772e+01 -2.4877e+00
+#&gt;   5.5422e+00 -7.0834e+00  8.2290e+00  5.2221e+00  1.4808e+01 -6.4113e+00
+#&gt;   1.8658e+01  4.4167e+00 -7.8011e+00 -3.7508e-01  2.1720e+00  2.3224e+00
+#&gt;   1.1844e+01  5.7979e+00  5.4032e+00  9.5333e+00 -1.9390e+01 -1.8636e+00
+#&gt;   1.1507e+01 -6.2315e+00  6.5892e+00 -8.4342e-01 -9.3099e+00  2.1889e+00
+#&gt;   8.2866e+00 -5.2548e+00 -7.6570e+00  2.7553e+00 -2.9758e+00 -3.7415e+00
+#&gt;  -1.2918e+01 -3.3581e+00 -1.6786e+01 -3.7029e+00  6.6405e+00 -1.8627e+01
+#&gt;   1.2714e-01 -5.9943e+00 -1.0005e+01  1.0083e+00 -1.5790e+01 -8.2264e+00
+#&gt;   1.2429e+01 -5.3445e+00  1.0344e+01 -2.0266e+01  1.7524e+00  6.4923e+00
+#&gt;   8.3809e+00  1.0603e+01  2.7851e+01  9.0080e+00 -2.4086e+00 -2.2249e+01
+#&gt;   1.9947e+01  9.2557e+00  1.0491e+01  1.5285e+01  4.0656e-02 -2.8611e+00
+#&gt;  -7.9679e+00  6.0484e+00 -1.7679e+01  2.1741e+01  2.0318e+01 -2.8798e+00
+#&gt;   1.7513e+01  4.3717e-01 -3.5861e+00  9.6215e-01  3.4398e+00  8.3210e+00
+#&gt;  -1.3057e+01 -6.2074e+00  3.6011e+00  3.1037e+00  1.8853e+01 -5.6660e+00
+#&gt;  -1.4325e+00 -1.2691e+01  1.4120e+01 -1.5470e+01 -1.8924e+00  1.4935e+01
+#&gt;   1.3133e+01 -7.3707e+00 -2.5290e+01  2.8716e+00  2.6644e+00  1.7006e+01
+#&gt;  -7.8089e+00 -6.7755e+00  2.9553e+00 -5.1522e+00  1.0546e+01  1.5615e+01
+#&gt;  -2.2052e+00 -1.7775e+01 -2.4910e+00 -7.6424e+00  4.0206e+00  1.4578e+00
+#&gt; 
+#&gt; Columns 49 to 54 -1.1930e+01  1.0408e+01  1.9932e+00  1.1867e+01 -1.5796e+00  4.7469e+00
+#&gt;  -2.5033e+01 -2.9055e+00  7.0222e+00  6.1650e+00 -7.0954e+00  2.8061e+00
+#&gt;   3.8242e+00 -1.9144e+01 -8.6292e+00 -7.0170e+00  4.1554e+00  3.1093e+00
+#&gt;  -4.9245e+00  8.9887e+00 -2.6099e+00 -1.3385e+01 -2.2275e-01 -6.9555e-01
+#&gt;  -7.4531e+00 -9.0411e+00  2.1363e+00  1.6619e+01 -9.6453e-01 -1.6231e+00
+#&gt;   2.2502e+01  5.5189e+00  4.7697e-01 -1.0418e+01  4.2871e+00 -1.4408e+00
+#&gt;   1.5951e+01  1.3212e+01 -7.5329e+00  1.0959e+00  2.7447e+00 -4.6562e+00
+#&gt;   1.1344e+01 -2.2125e+01  1.7901e+01 -4.6182e+00 -2.8106e-01 -2.3148e+00
+#&gt;   7.6376e+00  1.9672e+01  3.7193e+00  4.9079e+00  2.0155e+00 -4.4264e+00
+#&gt;  -1.6625e+01  1.1072e+01 -3.8426e+00 -1.2944e+01  5.5449e+00 -6.7104e+00
+#&gt;  -1.8381e+01 -9.1512e+00 -5.1241e+00  1.2869e+01  3.3699e+00 -5.3465e+00
+#&gt;  -3.5731e+00 -1.7432e+00 -4.5762e+00  3.4288e+00  5.0277e+00 -2.1140e+00
+#&gt;  -7.9599e+00  1.5342e+01  1.4389e+00  8.1760e+00  5.6928e-01 -3.1954e-01
+#&gt;  -1.7447e+01 -5.0924e+00 -1.7623e+00 -1.9096e+00  3.1523e+00 -2.2438e+00
+#&gt;   1.3763e+00  2.2958e-01 -8.2987e+00  8.1516e+00 -1.1614e+01 -7.8641e-02
+#&gt;  -1.3026e+00 -1.1605e+01  1.7032e+00  6.7978e+00  2.0229e+00  3.4178e+00
+#&gt;   1.3882e+01 -1.7011e+01 -1.5101e+00  2.0343e+00  3.7246e+00 -4.3124e+00
+#&gt;   6.1257e+00  3.9545e+00  2.9905e+00  2.9953e+00 -3.4676e+00 -3.5509e+00
+#&gt;  -1.2858e+00  1.5554e+01 -1.3385e+01  3.0229e+00  1.3257e+01 -1.7151e+00
+#&gt;  -1.2664e+01  7.1216e+00  2.8620e+00  5.7296e+00 -2.6791e+00 -6.0639e-01
+#&gt;  -1.8125e-01 -1.1158e+01  1.5228e+01  2.1234e+00 -5.7803e+00  1.1344e+00
+#&gt;   7.7264e+00  7.7212e+00 -3.0359e-01  6.7482e-01 -3.0877e+00 -1.5337e+00
+#&gt;   1.1560e+01 -2.1762e+00  1.0641e+01  1.1824e+01  3.9550e+00  4.4653e+00
+#&gt;   9.3274e+00  6.0953e+00 -9.0194e+00 -1.1214e+01  4.1548e+00 -2.2084e+00
+#&gt;  -9.6038e+00  2.2374e+00 -8.2138e-01 -1.1406e+01  5.8240e+00  5.5075e+00
+#&gt;  -2.2071e+01 -1.3338e-01  1.1620e+01  1.5719e+00  1.4193e+00 -3.2624e+00
+#&gt;  -1.2207e+01  4.2834e+00  2.3571e+00  8.4039e-01  6.5941e+00 -4.5320e+00
+#&gt;   1.4071e-02 -4.2638e+00  1.4311e+01 -3.6200e+00 -3.4290e+00 -3.9109e+00
+#&gt;   8.4256e+00 -8.9969e+00 -1.4901e+01 -6.7363e+00  6.0295e+00  3.4049e+00
+#&gt;  -5.4866e+00  7.0723e+00 -9.0642e+00 -2.3394e+00 -6.0919e+00  1.9601e+00
+#&gt;   1.2050e+00  5.8304e+00 -5.3384e+00  2.3990e+00  5.0838e+00  5.8618e-02
+#&gt;   1.0580e+01 -4.8685e+00  1.4148e-01 -3.3725e+00 -1.8417e-01 -5.7808e-01
+#&gt;   2.0337e-01 -5.1599e-01 -2.3296e-01 -5.5305e+00 -2.6131e+00 -2.7271e+00
+#&gt; 
+#&gt; (11,.,.) = 
+#&gt;  Columns 1 to 8  -5.4055 -12.8212 -11.4962 -11.2653  -9.8770  -7.0898 -14.1908 -16.6895
+#&gt;   -0.9588  -6.7719  -6.7740  13.1522   2.5239  -4.9201  -9.0637  -2.6464
+#&gt;    6.7203   4.0261  -3.5288   7.3960 -22.5836   2.1697  -9.1963 -16.7508
+#&gt;   -0.0230   7.3559   7.0085   0.8274   6.6417  -0.5680  -3.3451   9.1296
+#&gt;    9.2374   4.0972   1.3119   2.0917  10.2749   5.4807 -13.5052  13.4374
+#&gt;   -7.2157  -6.5169  -1.0032   8.4717   0.7223   9.3963  -8.1653   3.7773
+#&gt;   -0.0921  -1.5552  -1.2707  -7.8980 -29.2803 -13.3941   6.4451  10.5154
+#&gt;    0.6800   6.8448 -10.4146  -8.3573  16.7202  17.9789  -7.9937  -7.7566
+#&gt;   -2.7608  -7.5636   4.3386  10.7266  12.7765   2.9435  -3.0109  -2.5703
+#&gt;   -2.4705  -8.8155  15.8010  18.7013  -7.6651   4.8273 -15.2839 -22.3401
+#&gt;   -4.2251  -7.1305 -12.5061 -21.0559  -1.1047  -3.7846 -10.1375   5.1159
+#&gt;   -0.4302  -2.1175   2.2277  -0.0405  -5.3440   3.8683  18.2067   6.4389
+#&gt;    2.3190  -0.9290   2.4649   6.1844   4.2881   5.6679   5.9470  10.5908
+#&gt;   -1.1352   4.7306  -8.0197 -17.6189   4.0034  -3.6601   7.7812  -7.2113
+#&gt;    6.7854  -1.5334  -0.4881  -6.5860  -9.2010   6.2877  -3.2831   2.2807
+#&gt;   -7.1120   1.0091  -1.8908  -0.8197   8.8857  -4.6941  -2.6334  10.5442
+#&gt;    0.6511   8.3130   5.9025  -2.2319   1.8958   2.6976  16.8064  -3.7850
+#&gt;   -1.1480  -5.0506   7.3444  -0.0132 -18.8620  20.5486  -1.9485  -4.9859
+#&gt;   -9.2978  -7.1933  -0.3520   5.1499   2.6228 -13.3133  -3.5121  -7.1834
+#&gt;    5.1911  -3.7486 -10.2273   6.6342   0.1885 -15.2781 -19.7429   3.2060
+#&gt;   -1.3515  -4.5879  -3.5312   6.6493   3.3874   8.0278   9.6604   2.2411
+#&gt;    0.6587   1.2721   9.1621  -6.2055  -9.5126   9.2114  -7.2505  -4.4917
+#&gt;   -4.9576   2.3409  -3.7596  -8.3144   0.1786   1.8665  -7.6558  -1.1847
+#&gt;   -1.9648   0.0582   6.4989  16.6409  -1.1180  26.6440  -2.2385  -3.8422
+#&gt;   -1.2133  -1.6047  -3.4843   3.5029  -7.3982 -22.1989   3.1332   6.5466
+#&gt;   -1.0831   6.7521  -8.0247   3.2630   3.3820   8.8123  20.4866  -2.4224
+#&gt;   -0.4180  -5.5231  -4.5282   8.0060   4.2186   9.3785  -8.6098  -0.3633
+#&gt;    1.0353  11.5919  -1.4118  -4.8064  -1.7891  -5.2798  21.8117  -6.8129
+#&gt;    1.5290  -1.1109  -1.6289  -2.0407   3.1045  -6.7241   7.7408  32.9108
+#&gt;    4.5344   3.8848  -1.6082   0.0696 -16.1280  -5.5542  10.8213   8.0411
+#&gt;    3.7386   1.5182   0.7064  -5.4933   0.4963   7.0519   8.9854   0.6076
+#&gt;   -4.3463   3.0614   2.6529  11.4385 -10.4198   4.7534   8.7773   3.2090
+#&gt;    3.9197   6.6092  -1.9214  -7.9200  -3.8039  -8.5101  -8.0896   4.3386
+#&gt; 
+#&gt; Columns 9 to 16  -8.0963  -5.0893  11.2595 -21.7700  -0.4435   9.4434  -1.6422 -15.6053
+#&gt;   -5.4985   7.1083  16.8098  -5.3270  -2.6601   4.7787  -4.2944  -8.5968
+#&gt;  -12.8671 -21.2995  -3.0081   1.4710  -2.5656  -0.7884   6.1528  -6.6789
+#&gt;   -9.2201  -1.9824 -14.0278   0.3191   5.0258   1.0274 -13.7023  17.3186
+#&gt;   19.6774  -1.2202   9.0924   7.5304  -3.3807  -7.3085   7.3692   7.5759
+#&gt;   -9.4689   0.1457   9.1901  -8.9213 -13.8453 -13.0196 -10.9186 -13.1116
+#&gt;   17.7428  26.0072   8.6514 -14.4933  -5.2205   2.6055  -0.3344  -9.3308
+#&gt;   24.7822  -6.2983  -7.4286  18.6491  13.0411 -11.3752   7.8269  15.9172
+#&gt;   -5.1160  -1.6017  -0.5521   4.4829   4.6389   2.8735  10.8363   7.8346
+#&gt;  -11.6842  13.8607  -2.7583  -7.2021  16.9271  10.5532  -3.4277  -5.2636
+#&gt;  -14.7114  13.8395  -2.7297  14.8692 -15.5743   3.5719  -8.1930   7.6920
+#&gt;   12.9891  -0.7218   1.8757   6.5407  11.9038  -5.6843   1.6255   6.8036
+#&gt;    4.7963  -0.9979  -7.9144   2.8503  -0.1705   3.4232  -4.7482  15.9476
+#&gt;   -1.2452  -6.6892  -1.8842   3.2330  14.9785  -7.7715   3.4890   5.1187
+#&gt;   14.1618  20.5121  10.5584  -6.5404  -3.9107  -1.4618  -6.7874 -13.0442
+#&gt;   -7.0971   9.0339  -5.5109  -1.0006   6.7793   1.3089  -7.3950   4.5605
+#&gt;    4.2148   5.6966  11.1746   4.5634   4.2315 -11.5886  -0.1598  -4.9882
+#&gt;   -5.5178  -1.6864  12.7013  -0.8401   0.2114   3.2006   7.0705  -3.7734
+#&gt;    8.0506   4.4067  -2.7286  -2.3772   1.5381   3.8432  -1.5158   1.5586
+#&gt;   -2.7677  -5.3447  -0.3715  -0.4163  -7.7631  -2.1778  -4.1122  -0.5362
+#&gt;   -1.2315 -25.3957   0.4581 -13.7141  15.5662  10.6567   2.1952   5.4874
+#&gt;   -0.6417  -1.5647   0.1031  13.0846  -1.6135  -4.7646   8.8332   7.9774
+#&gt;   -9.5805  -5.6133   2.9529  16.4223   3.3296   2.4445   5.4741   2.5839
+#&gt;    6.0440  17.0827  -5.4099  -8.0291  -1.3871   7.8044  13.4143  -6.8301
+#&gt;   -2.2846  -9.5562 -11.1248 -11.6881 -12.4201   0.3416   1.3621   1.6872
+#&gt;   -6.9161   7.4641  -4.6135  -1.7430  -5.8045   0.5027  -0.4220  -4.2977
+#&gt;   11.4381   0.6050  -6.4387  -0.7690  -8.0419  -4.8361   9.1172 -19.6965
+#&gt;  -13.2769   2.1120   2.5309   5.2099  -8.7817   0.8572  -0.2561   7.0573
+#&gt;   10.1496  -4.4357  -9.8793  -6.0450  -1.9153  -5.2773  -2.9340  -4.7760
+#&gt;   14.4536 -13.1284  -1.1670 -12.4540 -18.7169  -5.2540  -2.2472   2.5345
+#&gt;   -3.1225  -5.1695  12.4998   1.8068   5.2393   9.0779  -5.4050 -11.2571
+#&gt;   19.0003   0.4353   4.1467 -12.5603   0.0177 -10.2766 -10.0077 -10.1492
+#&gt;   17.5557  -4.6641   0.6727  -1.7144   9.5760  -6.3881  -0.9979  17.6790
+#&gt; 
+#&gt; Columns 17 to 24   5.5389   9.7153  -0.8149  -9.5693  -3.4722  -2.8820  10.2517   4.9646
+#&gt;   10.1253  -7.4265  -1.9225   3.8907   5.0756  -1.5778  -9.6211   8.5616
+#&gt;    9.1293   9.3994   7.3130 -10.8699   2.4559  -6.2287   5.1936  -3.3511
+#&gt;   13.6538   8.4455  -6.5015   7.5070   3.4916  -4.4760  -3.8086   9.2538
+#&gt;    8.0192   0.4894   0.1876  -2.4902  -0.1445  -7.0434  11.4712 -12.6585
+#&gt;    1.7324 -13.9589   0.8041  13.5750  -2.5177  -6.1032 -14.0721   9.3925
+#&gt;   -8.8323  -4.9451 -11.8284   3.4365   8.6094  -7.0845  -2.0510 -26.0251
+#&gt;    3.0504  13.3794  -3.6231   7.0293   6.3882  -6.6549  -4.5141   0.6877
+#&gt;   -4.5420  10.6653   6.1909   0.1608 -12.9508  16.0592  23.2264 -10.8292
+#&gt;   12.5975  11.6704  -6.9887  14.9949  18.1805   5.4730  -2.9134  -4.9841
+#&gt;  -10.5332  12.6488   1.7304  11.6070  -0.8718  -0.6584  -6.1346  20.9687
+#&gt;   -3.5676  -0.7987  -4.9272  -5.4014   3.3703  -0.4896  -0.4138   2.9948
+#&gt;   16.2486   4.7883   1.6822  15.7260   1.9915 -16.1390  -5.9753  10.2376
+#&gt;    3.8683   5.5006   4.3829 -11.0420 -11.3496 -11.4821   5.6880  -3.6688
+#&gt;   11.9727   0.0843  -1.3996   3.0607  -0.6485   5.3883 -16.4463   8.4056
+#&gt;   -6.6803  -3.3068   1.5495  11.6893   8.4106   0.3437  -9.5375   4.6183
+#&gt;   -1.4904   5.8014   2.8704  -5.6100  20.2162  -2.6406   2.4729 -15.4521
+#&gt;   -5.3582  -5.6083   4.3181  -1.3172  -3.4599  -6.6939   1.7393 -14.3633
+#&gt;  -12.8406   2.4952   9.9817  -3.9607  -6.5257  -2.5706   0.6663   4.8252
+#&gt;    7.8893  -2.8912  -0.5334   3.5568   6.0494  -5.4785   1.5054 -10.0505
+#&gt;    9.2342  -8.0807  -2.8325   4.7177  -6.1211   2.3771   5.2189  12.9520
+#&gt;   10.0793   0.3359   8.1422   3.8272 -13.6446  11.1437  20.9021   6.6736
+#&gt;   -0.4169  -4.6307  10.1826 -13.3094 -25.1051  -1.2309  -5.1323   6.9775
+#&gt;    4.6990  -7.5735  11.3742 -18.8726  -7.4936 -14.2206  16.5621   0.0137
+#&gt;    3.8652   0.0141   2.7227   6.6565  -3.2034 -14.6458 -13.0527  -9.5219
+#&gt;   -6.1301  -5.3862 -11.2960 -16.6052 -11.7143   4.1288 -10.9636 -18.1907
+#&gt;   -1.3663   8.9263  -0.4715  11.6974 -10.0834   9.7843  20.9122   4.4779
+#&gt;  -11.5308   2.9462  -6.4476  -1.0752  -6.5345 -13.5413  -0.9235   7.4826
+#&gt;   13.1922 -17.1404 -10.6783  -1.9518   1.6250   9.2958   7.0275  -6.1738
+#&gt;    5.3413  -4.3852  -8.6940  12.9464  -8.2948  -6.5683  -0.4623  12.6793
+#&gt;  -16.6802   7.5168   0.9948  -6.2193   7.4433   1.5622   4.1176  -1.9193
+#&gt;    4.0622  -9.2806  12.1699  -0.1856   6.8562  -7.4302   0.8241 -10.5828
+#&gt;   13.5447   1.1691 -10.1185  13.4078  15.7959 -11.0607  -4.6914 -11.4070
+#&gt; 
+#&gt; Columns 25 to 32   8.6906  -4.4819 -16.0977   2.4697   3.1499   0.9833  12.7757   6.6100
+#&gt;    4.5444  -8.1830  13.3868  11.6473 -10.6073   9.0276   0.5097  -5.6914
+#&gt;    5.9988   8.6714  21.7937  -4.5504  -6.1166   6.4743   6.9227   6.7058
+#&gt;    3.8787   8.7077  -1.6324  -1.2688 -10.0218 -17.8990   6.8641  -7.0852
+#&gt;  -17.6675  -3.9856   3.3549   6.8274   9.7155 -12.3437  10.5366  -3.4925
+#&gt;    3.3215  -9.0382  16.6955  -6.3661  -7.1147  -3.3227  -9.7662  10.1416
+#&gt;   26.5915  -7.7979  -9.0020   3.4738   7.1377  -6.9711   3.3329 -25.6101
+#&gt;   -6.4348  -0.7443   2.2093   5.6551 -12.5932  12.2770  -1.8114  -0.0719
+#&gt;  -12.7800  18.4545 -14.7236 -17.5124   9.1215  -5.5512   8.3363   1.4436
+#&gt;   -4.1571   6.9203   2.7244 -13.6063  14.2651  -8.2992  -0.8229   9.0277
+#&gt;  -13.7235  -8.2734  -5.6812  -0.8279 -20.6463 -16.2376   4.5752  23.4529
+#&gt;    6.9439  14.8364  -6.2147  -4.7256   5.3776  -5.0525  -3.9604 -11.5019
+#&gt;   -1.8704   2.3293   0.2194  -5.7172  -3.9069  -0.8479  -4.6177 -16.1231
+#&gt;    6.6336   2.7141 -20.3401  10.7361   9.1753   2.2894  13.2451   7.7904
+#&gt;    6.1828  -3.8053  -0.5917   7.3192   6.3272  -9.3474  23.2893   1.4797
+#&gt;   14.5297  -5.6312   2.8111   3.6819  -8.0714 -10.8505  -8.7319  -2.6226
+#&gt;   11.4411  -7.7391  -2.6136   5.7803  -1.6828 -17.1462 -11.5075  -8.5545
+#&gt;    7.0145   5.4355   5.7301 -22.4279   0.0704   3.8067  -6.4166 -15.4869
+#&gt;    4.8561   0.9614  -1.1349   5.8904  -6.4565  -9.5521 -12.3810   8.3897
+#&gt;    1.1373  -6.8023   2.3514  14.6949  -5.0576   3.3793  17.4717 -12.4308
+#&gt;   13.4749 -21.1074   8.8062   1.9577 -16.8838   5.5613   7.6369  -5.1571
+#&gt;   -8.7234  -9.4534   4.8240  -3.3674   1.4043  -0.5429   4.8862   0.3845
+#&gt;   -0.1187   2.7708   3.0856   8.0535  -0.2191  -2.3842  11.1446  10.1312
+#&gt;   10.9804   8.7844 -10.2858  -1.8880  17.5858 -16.4390  -5.3838   4.6293
+#&gt;   -0.9628  15.2486   5.7033  -2.6914  10.7375   2.7955  -5.2543  -2.6170
+#&gt;    6.6361  22.1522   9.1405   9.1258  -3.2185   4.8683   9.2677  14.2053
+#&gt;  -24.4219  -1.0824  14.5277  -2.1023  17.2105   2.2666  -3.9664   0.9500
+#&gt;   -9.1430   2.0245  -4.2121  -3.8316   3.7740  -0.7031   3.1172   4.9890
+#&gt;    8.1631  -6.8762  -4.9908   9.3535  21.4092  -8.6858   1.6469  -2.8641
+#&gt;  -10.3839   8.7087  -8.9921  -4.4137  12.4108   2.1464  -1.1056   1.1637
+#&gt;    6.8284  10.7424  -5.6882   4.4955   1.3618  -1.4492  -7.8652   3.9328
+#&gt;   11.9747 -23.6127  -7.0290   4.9756   9.3470   9.7060  -9.9396 -14.5454
+#&gt;   -8.7183 -20.1927   4.2785  -0.6520 -12.1457   1.9948  -2.8725  -1.0328
+#&gt; 
+#&gt; Columns 33 to 40  -3.4957 -12.9007  -6.7540   5.1744  -7.6554 -20.8943   3.3641  12.3797
+#&gt;   -5.3283   2.1093   4.5289  16.1537   6.7174   2.9742  -2.6721   0.6115
+#&gt;    3.9822 -17.6992 -24.2052  -3.4368   2.3902  12.8162   1.9887   2.0391
+#&gt;    2.5299   2.6777   5.2940  -7.9351  -4.3510  -2.1907  11.8613  -8.4697
+#&gt;    5.7702  -2.1336   0.2546  10.1146  13.6231   2.1547   6.8078  -2.0359
+#&gt;   -4.9064  10.9651   0.5256  -2.2876 -21.8221   3.4748  14.1588   1.6659
+#&gt;    2.2628  15.3465   5.3890  -3.9440   5.9947  -2.6753  19.6240   9.5830
+#&gt;    2.5197   7.1930 -12.9271   3.6338   3.2426  -5.0879  -5.2884  -3.3596
+#&gt;   -0.0810   1.4495  -3.6879   2.8248  -2.8201   3.4760  -7.0414   7.5413
+#&gt;  -15.0129   1.5153   3.2359  15.2691  -7.9809   0.5467  -4.8724   2.3613
+#&gt;  -17.9237   9.4862   0.9706   5.9850  17.0496  13.8400   5.2478  -1.0264
+#&gt;  -10.5961  12.3432   5.4140  -0.9479  -8.5752 -11.7603  -0.3170  -0.0237
+#&gt;   -4.8983   7.8893  -8.8595  -5.2496 -13.3228 -11.5002  -4.2536 -11.4074
+#&gt;   18.0229  10.9709   7.3556  -4.8270 -20.2337  -4.2284 -10.4610  10.1696
+#&gt;  -11.9457   5.4662  12.8361  11.1042   1.1041  11.3119   6.4178  -6.0587
+#&gt;   -7.8864   9.9089   3.3984  -2.2397   3.6824  -5.8370   8.5485 -13.3280
+#&gt;    8.0090   4.0433   8.1660 -11.0333  11.1999   9.0627  -1.6642  -2.3552
+#&gt;    7.7223   2.9116  13.1093  13.0952   5.2031  -9.0280   6.6690   4.8306
+#&gt;    7.3031   0.5444  -0.7612 -12.9485   5.4158   0.0919   4.3915   0.8169
+#&gt;   -2.0863   0.4553   0.7929   6.1557   7.6528  -7.3692  -8.0175   5.4582
+#&gt;   11.1042   7.7931  -8.7395  -7.5686  -4.3529 -15.2286   4.3901 -14.2214
+#&gt;    6.0429  -8.2275 -21.4503   7.2640  20.0072  12.2605  -1.5984  -6.5557
+#&gt;   -4.1199  -8.7502 -12.5962  -1.9513  -2.4513  11.3904   1.8514   7.2773
+#&gt;    5.0082   1.4738   3.8043   2.4984  -6.3557  11.0754 -13.6473  18.8892
+#&gt;    3.2076   5.6957   8.5382 -12.0987  -7.0255  -5.0878 -10.5054   0.0105
+#&gt;   -2.7257   2.1655   1.7951  -1.9168   8.3308   3.9440  -6.5315   8.8702
+#&gt;  -15.6634   3.8071  -2.8284   2.8686   2.6537  -9.9190  -6.4964   8.5380
+#&gt;   -5.5322   1.5889   2.1419  -0.9648  14.0815   3.4972  -3.2159  -1.7197
+#&gt;   -4.4879  -5.7155  12.5518 -11.9951  -6.4850   0.5837   6.0140   9.7379
+#&gt;   -7.8803   3.2550   0.9981   9.3336  -2.2737  -5.6261   8.0397  11.7345
+#&gt;   -1.1274  -9.1740  -4.2339  -3.3501   1.5657  10.3702   1.9456  -2.4436
+#&gt;   -5.4884  -3.4300  12.7837 -15.8801   0.5969  -7.2572  14.8341   6.3409
+#&gt;   -4.1985  -6.2091  19.6562   8.1881   4.9050  10.5610  19.5348 -11.3853
+#&gt; 
+#&gt; Columns 41 to 48  -2.7137  -4.6608  -7.8639 -18.1423  -1.5267  -3.2773  -5.4016  -8.6433
+#&gt;    6.2280   1.0183  -1.4649  -4.1771 -10.7345  -2.6907  -8.6749   2.7132
+#&gt;    6.1075   5.7429  13.1550  13.0300  -4.9505  -5.0011 -11.9035   3.2295
+#&gt;    0.1574   0.5322  17.0278  10.7063   6.7545  12.1497  22.9725  -7.7652
+#&gt;   -1.1801 -10.1937   9.9222   4.5274   6.9176  -4.9439  -3.5044 -12.8462
+#&gt;   -2.3856   8.4841  -7.7070  -9.5067   0.0728  12.6317 -13.1261   3.8723
+#&gt;   -6.3127  -1.7740   6.6936  -0.2792  -0.6567  -3.0826   2.8149   2.7094
+#&gt;    7.1113   4.0884   6.6075  -0.5773  -0.0683  10.5518  -0.1571   7.2865
+#&gt;   -5.1004  -8.1917  -5.5682  -2.5908   0.2107  -2.4291   2.2285   8.9865
+#&gt;  -11.2097 -10.6410  10.7745  -2.4273 -21.2566   1.6016  -9.4105 -12.2471
+#&gt;    3.1427   5.7268   9.7448  -2.6100 -13.4216  -2.8819  -5.9152   3.9733
+#&gt;   -2.9482  -8.1471  12.7303  -3.7645  -2.3743  17.9188   3.1142  -8.6335
+#&gt;   -0.8742  -3.7746   8.0121  -6.9740 -11.5285   8.5837   4.2794  -8.0273
+#&gt;    6.7697 -10.8690  -4.4483  -2.4258   5.9669   0.2114  -7.8955 -11.7361
+#&gt;   -3.9469  -1.4720  -2.5445  -5.8754  -3.0648  -2.9488  10.4857  -7.1903
+#&gt;    0.6893   0.9485  -1.6990   2.3992 -11.1795  -6.2548   0.4749   5.5308
+#&gt;  -15.4657  -9.3748   0.4303   1.4809  16.5658  -0.9612  -6.9927   5.7430
+#&gt;   -2.1066  -2.3958  -3.9747  -4.9547  -4.1973   7.2521   1.0492  -3.7327
+#&gt;    8.2104  -0.2188  -2.3428  -3.5882  17.7026   1.7196  -0.1771   0.0817
+#&gt;   -1.0454  -4.1729  13.4594 -11.0697  -7.7331  -8.8572   6.0746  -0.2361
+#&gt;    0.3822  -4.0953   5.8121  20.4561  -7.5034   5.6746  10.3273 -10.2685
+#&gt;    1.3891   5.1878  -2.8041 -15.3951   1.7782   3.0298  -9.8381  -2.7373
+#&gt;   14.2780  15.8650 -13.7873  10.2324 -15.2655 -11.4930 -12.6654 -12.1412
+#&gt;   -7.0948  -9.5416 -18.0632  -4.7630  10.5022  10.3967   7.4148  -1.2079
+#&gt;    7.6191   7.2975   2.7397  -3.0366  13.8674  -3.8837   2.3324   5.9046
+#&gt;   17.4417  14.5925  10.1028  20.0136  -3.7869 -11.1534   2.0239   3.1739
+#&gt;   -1.7296 -20.4732 -15.1557   2.0850   6.5636  18.1938   0.9150   6.5254
+#&gt;   15.9038   9.1991 -13.8631   2.8205   4.4692  -5.3159  15.2506   9.2716
+#&gt;  -19.8675  -3.1060  16.0112   4.6046  26.2780   2.9539  -8.7354   7.3370
+#&gt;   -3.8077   7.6845   1.5365   5.6504 -12.1643   5.4406   6.5761  -3.1648
+#&gt;    4.3883  -2.6695  -3.8233   0.3331  -2.4336 -14.9341  -8.7418  -4.4109
+#&gt;    9.4210  -2.6092  -9.9504   1.8270  -2.1828  -3.9466   6.8509 -10.3546
+#&gt;   -4.9575   7.1540   1.8171   3.5707  -0.1473 -11.0221   3.3263   3.0011
+#&gt; 
+#&gt; Columns 49 to 54   7.7686 -19.2088  -4.3984  -6.6009   1.0931  -0.0593
+#&gt;   -0.1722  -1.0048   5.8329  -4.6133   2.5930  -4.0542
+#&gt;   -0.7772  -8.3398  -8.7232   2.4307   0.6651  -3.0323
+#&gt;    2.3375   2.2467   7.1897   4.5060   1.5466   2.6489
+#&gt;   -4.0726  -5.1446  -9.9577  -0.7306  -2.1495  -5.3092
+#&gt;    5.8799  16.1226  14.1187  -6.3258 -12.7240   5.7837
+#&gt;   17.9912   1.2103 -14.4796   5.2234   7.1612  10.9232
+#&gt;    3.1071  -8.8086   6.2567   3.1653  -4.1226  -6.4468
+#&gt;   17.6475   1.4954  -3.9971  -1.0792   4.5360  -3.3024
+#&gt;   -5.2560  -8.0136   7.9477   6.7578 -10.5123   4.1404
+#&gt;  -13.2961  12.3127   0.6827  -1.0238   8.6313   3.1152
+#&gt;   -1.6527  14.1260  -8.2222  11.8463  -0.6558  -0.6822
+#&gt;   -2.9386  -5.6103 -12.1391  -1.8200  -0.1392   3.7910
+#&gt;    2.9182   2.2694   5.4313  10.4099   4.6388  -4.1158
+#&gt;    1.2167   3.6775  -2.8355  -8.2874   0.8455   1.7383
+#&gt;  -12.7928  16.2392  -0.9791 -11.0838   1.6811   6.0138
+#&gt;   -2.2261  -2.1926  -9.0140  13.0908   8.7340   1.4901
+#&gt;    8.9703  18.2571   8.0074   8.7897  -0.9565  -3.0671
+#&gt;    2.2582   9.9103 -11.1425  -3.9471   4.5475   1.0640
+#&gt;   -1.2556  -2.3475   0.2533  -5.3723  -3.7134  -3.2432
+#&gt;    6.2705   9.3926   6.0208   4.9499  -3.7650   0.8310
+#&gt;   -4.8548  -6.7536  -6.7908  -0.0707   2.7131   4.7570
+#&gt;   -7.3806   2.5334 -10.3324 -17.7143  10.4208  -5.3948
+#&gt;    3.9395   5.7795   7.5972 -10.3655   1.4875  -2.3655
+#&gt;   -4.5489  -2.9826   4.1896  -6.3864   0.5334   3.7579
+#&gt;   -1.6332   6.7876   8.8370  11.5702  -5.1311  -2.9854
+#&gt;   -1.9556  -5.7515   0.5072  -5.4332 -13.8866   0.0639
+#&gt;  -13.4486  16.0679   5.5380  14.5054   2.6706   0.6628
+#&gt;   -7.0647  -4.9027  12.0031   0.4694   9.6411   3.3881
+#&gt;  -12.9776  -2.0363   3.5486   6.7200  -1.2885   2.0454
+#&gt;    2.2942  -9.3349  -3.9989  -6.3987   5.6489  -1.1669
+#&gt;    2.3960  12.7751  -4.8202  -5.7435   6.8233   1.0905
+#&gt;   12.0144 -14.9577   3.7293  14.0925   6.1287   1.6907
+#&gt; 
+#&gt; (12,.,.) = 
+#&gt;  Columns 1 to 8   0.6229   0.7915  -1.1087   7.8374  10.0051  -7.7150   4.9062   7.8908
+#&gt;   -0.0694  -2.8023  -4.7687  14.2162  10.5257 -10.7560 -21.2938  -9.7516
+#&gt;    1.8988   9.5241  -2.5876   0.9445  -5.8729   5.6424   4.7821   4.6663
+#&gt;    2.2769   0.1422   7.5566  -2.3968 -15.5029  -5.7022  10.8948  18.1715
+#&gt;    4.6877   5.5713  -3.5079 -18.5530  14.5777  13.3651  -1.9050  -8.8703
+#&gt;   -2.2608 -10.8032  -8.4165  12.6980  -8.4522  12.0540   3.4561 -14.1412
+#&gt;    1.4388   5.8246 -10.8205 -10.4470 -10.0608  -3.0611   0.3587   3.1356
+#&gt;   -2.9395   1.1474  -1.9937   7.4385  11.0530  11.3669   2.6307 -11.3260
+#&gt;   -0.5357   4.5709  14.3846   6.7678   4.4890   8.4158   3.3504  -8.6785
+#&gt;   -1.6078   2.4123   3.6483  17.4909  -4.5587  -9.6758  -9.2399  -9.8593
+#&gt;    1.0561  -6.8999  -6.4034   0.1626  11.3164  22.6796   5.4696   2.3235
+#&gt;    1.4055  -0.7043  -3.1966 -12.8185  -2.8923  -1.5228   3.1578  -9.0939
+#&gt;    2.8777  -2.4606   0.7928   3.4029   1.5469  -2.4470  -8.3945 -14.2637
+#&gt;   -0.6468   5.2892  -0.3504  -8.7743   0.0913  -4.0508 -11.2090  13.5111
+#&gt;    4.0296  -2.7171 -12.6160  -9.6473   1.6610  -8.9166  -1.2568   2.2622
+#&gt;   -4.3760  -3.5491   4.4934  -0.9193  17.4047  -6.2420 -19.9250  -8.4942
+#&gt;    2.4884   3.3511 -11.0656 -33.2959  -5.7327   4.2863   1.9573  -9.1797
+#&gt;    1.4906   1.8312  -0.8521  -0.3516  -6.5020   7.9463  -4.5182  -4.6547
+#&gt;   -6.8905  -4.7727  17.1723   7.4306   3.7605  -5.8834  -0.0043   3.6442
+#&gt;    5.4320  -3.3874   2.2405  10.1437   7.1875  -7.3165 -13.8895   6.6712
+#&gt;   -1.3703   0.7421  -2.6668  17.4942   6.1591   2.4394   0.1591  12.4642
+#&gt;    5.0569   0.6890  -0.6120   8.6410   5.1975   1.9946   2.6576  -0.1100
+#&gt;   -3.8598  11.1419  10.2535  17.9036  -4.7257   7.1292   2.5413   6.2846
+#&gt;   -3.0385   7.9449  -0.3607  -7.3119  -9.2193  -9.0367 -15.9032   2.4966
+#&gt;   -3.8747  -4.3002   9.7375  11.5059  -1.2934 -10.2958 -12.3196  -3.9663
+#&gt;   -2.0728   0.9591 -13.1327   1.5240  12.8185   0.8781  -4.9501  -7.9751
+#&gt;    0.2162   2.5040  -0.0823   5.0865 -10.6410  10.1654   4.6406  -3.2647
+#&gt;   -3.4860   8.0917   4.1999   6.0495 -17.4762 -15.6921   4.2501   7.8961
+#&gt;    4.7829  -8.0504  -5.5278  -8.0957   3.9923   5.9154   5.7076  -2.8182
+#&gt;    0.6827   2.1102  -6.3540  14.5539   1.0061  10.3586   8.4657   5.0889
+#&gt;    3.5737   3.4540   3.5922  -4.7371 -15.5827  -4.0762   4.4211   8.8044
+#&gt;   -3.0682   4.6046  -2.8153  -7.7425  -0.8732  21.5143  -1.8952  -6.1452
+#&gt;    2.0572   4.3809  -5.3978   7.2423 -10.0628  -3.8495  11.1717  -9.7059
+#&gt; 
+#&gt; Columns 9 to 16  13.1054  18.0277  -6.0260   0.2420   8.3980  -6.9372   6.2657 -11.9200
+#&gt;   -6.0747   7.9303   7.5229  -4.3968   2.4170  -5.7601   5.4535  -0.0501
+#&gt;    3.4723  -3.0820  -7.2816  19.9661  34.0843  -2.0608   5.7500  -3.6835
+#&gt;  -21.8542  -2.1709  14.0783  -0.0227  10.1440  -0.1063   1.5406  15.0271
+#&gt;  -10.4748  -2.7479   1.4695   0.0121  -3.5012  14.2570   4.9082  -0.4205
+#&gt;    1.2982  14.7621   1.7995 -11.6836 -11.5936   4.4736  14.8308 -10.7383
+#&gt;   13.6573  37.3630  -1.9853 -28.1744   0.2730  -9.9434  -2.2139   5.3577
+#&gt;    3.9378   2.6297  10.4705   8.9094 -15.8067  12.2414   6.9211   9.9428
+#&gt;    1.3213  11.1123 -11.5620 -11.6397   5.2031   7.7923 -11.9422   0.9900
+#&gt;   -1.8480   1.3055   3.3730  -1.1550   6.7097 -10.0045   6.1946   9.9348
+#&gt;   -9.3842  13.3287   6.3690 -11.0413  -3.1044  -6.3424   0.7110   1.8190
+#&gt;  -11.2253   7.1318   7.6301   1.5611  -2.2177   2.7896  -5.8078  10.0294
+#&gt;  -15.6639   7.0832   7.2002  -9.6245   2.4862  -0.8140  21.6638   4.0479
+#&gt;   -1.4398  -1.9538   1.5081   4.6188   5.3399  20.8294 -15.5621   5.3192
+#&gt;   -6.8321  -7.8058  -1.1027  -4.5126  -5.9816 -12.7773  -1.9567   8.2813
+#&gt;   -5.8907   2.2779  11.0943  -7.5164   0.7313   1.8247   0.0621  -8.3329
+#&gt;   -1.6861  -1.0769  -9.0422 -11.4377  -6.1250  -2.3405  -1.3732  11.3301
+#&gt;    0.2473  -4.9452   6.3063  -4.2525  -2.2493  -6.9930   0.6344 -10.9879
+#&gt;   12.7692  15.0232   4.0342 -11.8127 -12.5489   7.0898 -10.3255   8.5702
+#&gt;   -6.3658   8.5593   3.8469   8.3642   3.3234 -13.5362   5.9985   1.0681
+#&gt;    0.9440 -10.2386   9.9136  -9.4521  -3.1750  15.9229   0.0124   2.3349
+#&gt;   13.8990  -3.6652 -10.2097   2.9960   3.0371  -5.6915  18.5167 -22.6270
+#&gt;    1.1244   2.9051  13.6522   4.2208   4.2418  16.7871  -3.9917  -8.3738
+#&gt;    3.3449  -0.2137  -6.7876   9.3774  11.8340  -5.9345   5.0493   0.4011
+#&gt;   -4.8042  -1.6463  16.4893  -4.5129   3.2499   9.9832   6.8517   4.0192
+#&gt;   -8.6577  -1.8142   5.4381   2.4526   9.2979   6.0551  -6.0966   4.9886
+#&gt;    2.1910 -16.4252  -3.5771  -4.1214 -16.3212  -7.2827  19.1999  -2.1758
+#&gt;    1.8902  -2.4865  -5.9268  -3.3036  -3.1475   6.4938 -13.0891   3.0017
+#&gt;   -8.2386  -6.6439 -14.7280  -6.3897   3.1754   5.0881   7.0446 -17.3720
+#&gt;   -5.1558  -1.9583 -12.0438  -5.6105  14.7233  11.9309   4.5504  -0.2746
+#&gt;   -3.9558 -14.8110   0.2810   7.0282  -7.0585   4.4361 -15.3473   7.6275
+#&gt;  -11.7893 -20.1127   3.4704 -17.3109  -3.8947   3.1418   6.7855   6.6511
+#&gt;  -12.1893  -0.4085   6.9987   0.3152  -6.5864   8.6177 -15.5359   4.0214
+#&gt; 
+#&gt; Columns 17 to 24 -11.6615  10.5632  -2.4200 -10.2524   5.1441   2.3397  -3.9033  12.2461
+#&gt;    2.1998   6.5962  -0.9250  -0.0817   6.9914   0.3577 -10.4958   1.6117
+#&gt;   -1.8944  -3.1994  -3.6397   6.5397   2.7144   8.2007  -1.7855 -10.5797
+#&gt;   -7.7227 -15.8568  11.0396   5.2943   4.5943  10.8854  -7.5387   6.5586
+#&gt;  -14.3565   2.0211  12.9089  -8.2543  -1.8719  -2.9142   4.3158   0.0391
+#&gt;    0.3928   5.0229  -8.5872  -7.5235   3.5602  -4.4647   0.0271  -8.5625
+#&gt;   -8.8302  -3.2055  10.0607   6.3659  -5.6354 -11.9080  -4.0432  11.1333
+#&gt;   -9.8045   1.5131  11.8736   8.6078 -17.1371 -11.8324  10.9882  11.9569
+#&gt;    1.1724   4.6120   1.1067 -14.7848   6.7956   0.3228   7.3402  18.5039
+#&gt;   12.2876   1.2404  -8.5821  -2.5870   8.5460   0.5873  -6.3000   6.6839
+#&gt;  -10.5291  -5.9038   8.6065   1.9188   4.3460   9.7324   6.6274  -0.9019
+#&gt;    0.5496  -0.6834   8.8903   7.5900  -4.6946   2.9964  -1.7698  17.6238
+#&gt;  -12.5180  -0.4222  12.3620  -1.0974  -3.9157   5.5843  -3.8384  13.8410
+#&gt;   -0.0500   1.5192  -3.7752  -5.7582  -0.4553   1.1262  -9.2395  -1.7804
+#&gt;    9.1267  -1.6329  -5.5847  -4.5938   9.5350  -8.3514  -9.9696  -3.0077
+#&gt;   13.0405  -3.9405  10.3975  13.1198   0.3756  -0.4030  -1.1406   4.8598
+#&gt;    7.3404  -9.6809  -3.6912  16.5556   1.8384   3.4509   5.3169 -11.9876
+#&gt;    3.3568  15.9024   7.5228   7.8160   1.3312  -2.7920  13.0140  -1.1514
+#&gt;   -2.7608  -0.3533   1.4966   0.7164   3.5189   1.7852  -0.0909   8.2748
+#&gt;   -4.2513   3.3682   6.2583 -12.5878  -1.6890  -4.0262  -9.0551  -0.6686
+#&gt;   -7.2619  17.0131  -6.0109   3.7430  -4.9631  -0.4176   3.0952   8.8443
+#&gt;  -11.6211  -0.4046   2.2322  -3.0091  -0.4379   0.4270   5.4041  -2.0646
+#&gt;  -11.0826  -5.8817   1.5498   1.3301   2.6119  -4.7856   5.5867   6.9728
+#&gt;   -2.3084   6.0377 -10.1231 -12.9566   5.1678  -6.1464   3.0075 -19.1242
+#&gt;    4.6652  -5.2460   6.7259   9.5123  -3.3718   2.1713 -12.0838  -1.2914
+#&gt;    7.7468  -3.6890  -0.5174   0.4274  -8.2590  -1.0826  -5.3796   7.4317
+#&gt;   -9.5119   7.1720  -3.5629 -13.5772  -4.6550   0.8548 -10.8200   6.2651
+#&gt;   -3.9594  -6.8401  20.8511   4.3915 -15.2072   6.0818   2.9340   3.8214
+#&gt;    9.8847 -10.4059  -0.6240   0.4630  -4.8572 -14.2137  -1.7101  -6.0853
+#&gt;  -23.9771  12.5208   1.1088  -4.5807  -9.2331   9.0315  -0.7076   8.5631
+#&gt;   11.5185  -6.7143   0.4632   0.2514   2.8042   9.7327 -10.4660   2.3422
+#&gt;  -12.1903   2.7876   0.0121   9.4966   1.0631   2.8549  14.2169 -19.3689
+#&gt;   -9.8758   2.7902   9.1959   7.1353   3.4879   1.4194   6.2847  -2.2458
+#&gt; 
+#&gt; Columns 25 to 32   4.5306  10.0239  -0.4469   7.0473 -17.0017  -6.6273  -3.4350  -7.2549
+#&gt;    0.4246   9.9583   7.8468  -3.9488  -6.1045   4.3270  -3.0799  -0.4587
+#&gt;   -0.9743  14.8645  -2.6655  10.4335   8.9047 -17.4343   1.3187 -15.2915
+#&gt;   -1.4697  -8.5132   2.2909  -0.0960 -12.7902   0.5393   2.2986   6.8757
+#&gt;    4.7715   2.8477  -9.1590   6.4779 -10.4095  10.6809  17.5576   1.2134
+#&gt;  -19.4920  -6.9989  23.0444  -5.5358 -10.0281  -1.8228   1.1484  -9.5458
+#&gt;    9.2121 -13.8715  -5.7789 -12.9094  16.2945  -5.1009  -5.1085  -1.7312
+#&gt;   -2.0899  -5.9829  -7.7419  12.3199   4.1673  -5.6364  13.8474  -4.1620
+#&gt;   -8.1518  12.3607  -9.7095  -2.7287  -2.8512  -8.9781 -11.1845   9.7048
+#&gt;   -2.2913  -5.8673  -1.6576   5.2512  -5.2403   2.1694  -2.9328  13.1544
+#&gt;    8.3081   7.5182  14.9806 -14.0427   3.5668   0.7667   6.3285   4.8872
+#&gt;   -4.6346  -6.3342  -5.8248   4.4028   3.9935  -2.0995  -1.1211   2.5779
+#&gt;    1.9450  -7.1452   0.8038   9.2172   6.5199 -13.6754   0.1850   3.7368
+#&gt;  -10.2145   3.9818  15.6611   9.4165   0.5767   4.5900   9.6266   7.8135
+#&gt;    3.2014   7.9482   3.8777  13.7058  -8.6177  22.3753   0.8108  -2.6301
+#&gt;    2.7221  -4.7654   4.5407  -7.5669  -3.7058   1.5908  -1.1659 -14.3292
+#&gt;    7.2399  -1.6960   4.7654  -8.0337   3.6575  -7.8885   2.2866   4.2081
+#&gt;    2.7355 -12.2470 -20.2008   2.6150   7.9502  -7.0101 -17.7491   1.5312
+#&gt;   -5.4821   5.0057  -2.0815 -11.7545  -8.6805   5.6487  -7.1170   5.6992
+#&gt;   -2.8292   9.9959  -5.2379  -5.1389   3.9304 -10.2201   5.6104  -4.7453
+#&gt;    5.6196  -4.8448  -4.5968   0.8013   6.6862   0.4325  -3.1806 -11.6766
+#&gt;   -0.8924  -6.0481   1.1216   5.3271   8.6393   5.1332  -7.7949  -6.9238
+#&gt;    3.5106   6.1411   5.9817  -6.2763   2.1675  10.2647   0.0526   7.1093
+#&gt;    7.2340   7.8741  -7.8990   5.7538 -20.0345  -4.6588  12.7252   3.4780
+#&gt;    9.1655  -5.5844  -2.2182  -6.6134  -4.9100 -10.3093 -11.3365  -2.7572
+#&gt;   -2.1367  -6.0750  -7.5535  -2.4472  12.0668   3.9066 -12.2970  -6.3955
+#&gt;   11.2347   2.3724 -14.2610  -1.0497   5.8683   2.0895  -0.0487  -1.5310
+#&gt;   -3.8810 -10.0591  -2.1246 -13.0769   5.2932  -8.1686 -17.7479   8.2709
+#&gt;   -4.0192  -4.3515  16.9953  -7.0999   0.9301  16.0774  -4.8301  -0.6348
+#&gt;   -9.8164  -2.0018  -0.5420  11.5896  -1.6949   5.7029  -1.7505   9.4581
+#&gt;   -3.7871   2.9083   0.1670  11.3802  17.2895  -4.6849  -4.5711  -5.1924
+#&gt;    2.5878 -13.6899  -6.3023  10.3935   6.8784   3.7867   5.5330  -2.7283
+#&gt;   -5.1429  -3.7365  15.4687 -13.9259 -11.8810   1.5338  -5.8120  17.3488
+#&gt; 
+#&gt; Columns 33 to 40   2.5604 -15.4177 -14.6819  10.6696   6.4719  -8.2174   5.9633   3.6121
+#&gt;    3.2475   3.7884  -9.9572   9.9449   6.1172 -12.3016  -9.9045   8.6785
+#&gt;   -9.6476  13.1789 -14.3362  -5.2921   9.3703  13.5705   3.5328 -18.2553
+#&gt;    0.8200  -9.2395 -12.6426   2.1794   5.7069  -5.1984  -1.7159   8.8606
+#&gt;   12.1887  14.5776  16.0802   3.5241   4.9984   5.0564  10.0536  -6.3784
+#&gt;   -7.3819 -16.3646  -7.3752  13.1533  -9.5506  -8.5513  -2.4685   5.7979
+#&gt;  -15.7432   3.4323   4.3331 -12.1468  -9.8013  -9.0356  18.4537   1.4085
+#&gt;    8.0163  11.3554  -8.2558  14.7985   8.7950  14.3984  -3.5777   3.7516
+#&gt;    8.4761  -3.6927  15.2411   9.0781 -11.9967   2.9025  12.2549  -0.5434
+#&gt;    5.6748   8.2198   6.8690  17.7507  -2.5757  -2.1245   7.3225  15.4973
+#&gt;   13.2693   1.1514 -14.7790  -6.6366   7.2152   3.4473 -10.8023  -0.5673
+#&gt;   -3.3480  -2.7546   1.8944  -3.8607   1.4287  -1.0664  -4.3925  -4.4589
+#&gt;    5.1777   7.8544  -8.1477   9.9872  16.8814 -17.1649  -2.4407  -4.9054
+#&gt;   -4.4187 -12.4269  10.0166  -8.0165   4.2810 -17.1213  -7.2087  -3.1573
+#&gt;    2.8324  14.6614  -1.6358  -8.3364   4.7225   7.8425  -0.4754  -4.6862
+#&gt;   -0.7236 -10.6665   0.2801  -5.7126  -9.9734   3.0372   0.6493  -2.7656
+#&gt;  -10.4361  12.9258  28.0120 -16.0748   1.4014   1.9361  -4.5752 -14.5180
+#&gt;    3.4714   8.7199   7.3691   7.8173 -10.6775  -8.0766  -7.7064   0.7006
+#&gt;    1.7590 -18.1827  17.4603 -10.1387  -5.4156  -1.1636   0.2717   1.2229
+#&gt;   11.5710  -2.9663 -19.4479  11.8578  21.6067  -5.6635  14.5880  -1.5109
+#&gt;   -3.4410   0.6300  -8.2975  -1.5521  -7.4187 -22.0133   6.5697  19.8905
+#&gt;    8.2915  29.1055   7.7777   6.0414   5.3075  24.2294  14.8538 -13.4689
+#&gt;   -6.5008   1.8680  -3.2257   6.1621  -2.5149   8.2843 -17.5140   2.2492
+#&gt;    0.7379  10.8900   7.1080   0.5840  -2.1923 -16.2487   8.1567  -1.8218
+#&gt;   -9.2839 -19.8571 -21.6250  -5.2127   9.3285 -12.4685  -8.8177  12.7496
+#&gt;  -17.6580  -1.7395  -3.1409 -20.1595   7.3245  -4.6812 -18.5302   4.2510
+#&gt;   -1.6834   4.4052  -1.1025  10.2804  13.0208   7.8221   1.5916  30.9571
+#&gt;  -13.0202   0.7954   8.7631   0.6770  -3.9413  -1.1535 -30.5018  20.1708
+#&gt;   -0.9510 -24.0391  -6.5558  -4.2580 -22.9719  -8.8187   5.9489 -18.0942
+#&gt;   -5.1406   4.6596 -19.3178  11.1041 -12.9293  -8.5829  -4.6197   3.1705
+#&gt;    3.3627   0.8514  -4.8366  -6.5678   5.2303   4.3907 -20.2697  -1.9497
+#&gt;  -16.1903  -6.4804   6.0883  -5.3278  -7.6647 -18.9676   2.8304  -6.0799
+#&gt;   19.6219  -3.1933  -0.5165  -5.5669   8.8122  -0.7631  -8.4943   6.1732
+#&gt; 
+#&gt; Columns 41 to 48  15.3043   7.4350   5.2746  17.0398  10.8575  15.6638   4.7680  -8.8015
+#&gt;    3.1865 -11.5273  -1.4048   3.2386  -4.5437  18.3177  -5.1113 -11.6743
+#&gt;    4.7627  13.9291   2.1461  -5.5553   8.0767  25.1584  19.3968   8.2596
+#&gt;  -15.1059   9.4222   0.7374  15.0578  -1.9248   9.3788  -5.2342  -5.1922
+#&gt;   -4.0628   0.0799 -12.2949  -7.1987  -9.3535  -7.2319  12.0573  -1.3216
+#&gt;   -3.6261  -4.3267  12.6372 -10.7914   1.8285  -2.8429  -5.2521   9.4206
+#&gt;  -10.4351   3.0622   4.0635   8.7156  -8.4692  -3.5347  -9.3349 -11.2326
+#&gt;   -4.8819   3.1711  10.9417   4.0195 -16.2581   2.3147  -5.3727   1.5587
+#&gt;  -13.9949  20.5441  -0.1346  -5.6686   4.7856  -8.0310  -3.5242   6.1520
+#&gt;  -20.9580  -2.9451   2.6229  -6.2504   7.1897   2.8362   7.3396   6.1694
+#&gt;   -7.7526  16.0218  -6.5002  14.2846   2.0576  12.0403  -1.4440   0.0529
+#&gt;   -1.8210   3.8910  -9.4577  -9.9758   4.3448  -6.8880 -11.5471  -2.9537
+#&gt;  -13.6959  -2.1616  -1.8796   3.8157   0.2745  18.6436 -18.3162   1.1090
+#&gt;    0.6558   9.7255 -10.4935  11.2421  17.1597   1.4255   8.1980  12.9525
+#&gt;   14.8064  -6.5950   3.9824  -4.6857  12.3295 -18.0792   4.4572  -2.3019
+#&gt;   -4.3114  -1.4401  -0.2082   7.6283  -3.7553   7.0221  -1.9723  -0.4984
+#&gt;    0.1655 -16.8091  -4.4321  -4.1608 -19.3574   7.8121  -3.4068   6.5521
+#&gt;   10.3457   4.8228  -2.4552   6.0876  14.5269   2.8116 -11.4125 -11.4710
+#&gt;    2.8365   8.5408  -3.6436   6.9717 -14.5556  -4.1611  11.8448  -1.2942
+#&gt;    2.3556   6.9870  -9.0469  10.9832   4.0157  16.0751  10.8844 -12.7906
+#&gt;   -6.0826 -10.1812   9.7034  -0.3338   1.7216   3.3417  -0.5874  -5.0220
+#&gt;   -1.1600  -3.7470   2.4539   0.6351   5.5996   0.6931   5.9151   5.4411
+#&gt;   -1.4959   0.7328  -0.6596   6.0922  14.3099   1.6037   3.8605 -10.7451
+#&gt;    6.8728   8.6751 -14.6904   4.8840 -10.7428 -12.2921  -5.5567   2.1713
+#&gt;    5.4584   1.9766  -2.8809   7.7141  -2.5709  12.2849  -3.1887   6.8841
+#&gt;   -6.8739   1.4988  -0.8740 -13.1916  -9.8405   3.4185   1.2487  16.4778
+#&gt;   -4.7139  -8.2436  -6.6425 -10.3109   6.6327 -14.2698  -7.3648  -7.1846
+#&gt;    2.3693  -2.4466  -2.0346   0.3403   2.8040   1.7793 -13.2464  -0.8262
+#&gt;   -4.8966  -8.5134  11.7160 -12.8599 -11.4779  -1.5727 -10.0090   6.2925
+#&gt;  -26.8955  12.4366  -1.2455  -5.7940   9.3323   5.7103 -18.9559   5.3448
+#&gt;   -0.5664  -3.4544  -8.2387   0.9737   3.7511  -0.6067  10.4947 -12.2625
+#&gt;   -7.6955  -8.9588  -0.3107   2.4558  -5.5364 -11.5780  -8.0680  -7.8119
+#&gt;   -4.8699   2.4457  24.9365  -1.5236  -0.0761  13.6635  -6.5005  -2.1388
+#&gt; 
+#&gt; Columns 49 to 54   4.6061 -11.1767   5.2272  -7.5479   1.8996   0.5870
+#&gt;   -1.9617   0.7968  12.8098   2.1013   1.7502  -1.0937
+#&gt;   -5.9461   3.6231  -6.2823 -12.2479   2.2487   1.2517
+#&gt;    5.4913  -2.7761  -3.6395 -11.8342 -11.6724  -2.3949
+#&gt;    1.0509  -1.7664   2.2307  12.5083  12.6361   7.0670
+#&gt;   -7.9719  -1.8669 -11.6196  -8.0688   3.1259   0.5858
+#&gt;    4.6331  -0.2254  12.8652  -4.7407  -7.7812  -0.5685
+#&gt;    4.4783   0.2114   6.4664   3.2386   2.9876   6.3434
+#&gt;   -9.9895  -7.3874 -11.6368  -3.1943   2.6782   2.6706
+#&gt;    1.6126  -6.0231  14.5779   3.4581   0.1845  -5.6553
+#&gt;   -9.7551  -3.2479   3.3885  -7.4853 -10.8401   5.5263
+#&gt;    1.8967  16.1583  -1.4822   1.8835  -1.5732   1.6357
+#&gt;    4.2449  12.8940  10.7280  -7.9487  -2.8604   3.9444
+#&gt;   17.3303  -9.3588 -13.6531   0.3946  -4.3630   2.1931
+#&gt;  -20.4530   3.2279   5.8892   7.7337   3.5031  -1.0432
+#&gt;   -6.0292  -3.8491  14.4200   0.4378  -8.6521   0.0662
+#&gt;    9.4528   4.2694  -0.5314   3.2997  -1.3007  -1.9550
+#&gt;   -2.0293  -8.9622  -2.1403  -4.2731   3.6069   1.6770
+#&gt;   -5.5609   1.5505   3.7999  -6.1164  -4.1350   0.1311
+#&gt;   -7.9973  -2.6276   3.8596  -4.2382  -0.4680   3.8643
+#&gt;    0.7822  -1.0493  -2.6086  -4.1395  -5.9424  -0.1388
+#&gt;   -5.7123  -5.3117  -2.6779  -8.6652  -0.3869   3.7877
+#&gt;   -3.7720  -0.1263  -4.4734  -3.0565   0.0027  -2.2615
+#&gt;   -1.2315 -17.7116   0.8698   3.4517   8.2641  -0.6160
+#&gt;    9.4001  17.4240   5.7139 -14.3328  -4.6654  -4.0643
+#&gt;   12.4197  -5.0413  -8.1980   3.7925  -8.7958  -3.4557
+#&gt;    2.2063  -7.8742   4.9997   8.4577  -1.4558   5.6535
+#&gt;   18.6236  15.4684 -16.6984  -9.9905  -1.4535  -2.3841
+#&gt;  -18.8788   0.3210   2.9123  -2.9104  -3.4047  -8.7111
+#&gt;    6.5853   5.4339  -5.0259  -5.7721  -1.2164  -2.6667
+#&gt;    0.6133  -3.4483   2.2334  -4.8047   0.5273   1.0393
+#&gt;   10.3740  -6.3779   7.4333   2.6176   1.5118  -0.8147
+#&gt;   -2.0307  -5.6574   4.3505  -2.9951   1.9067   4.1163
+#&gt; 
+#&gt; (13,.,.) = 
+#&gt;  Columns 1 to 8   8.1685  18.1310   9.2017  -4.4406   9.0612   4.8394   8.9754   0.5965
+#&gt;    0.5417  -0.6371  -3.8832 -11.9493   2.1802   9.6493   6.6725   8.5286
+#&gt;    7.4608   1.6885  -1.8740   4.2153  -7.6569   5.2148 -18.5076  -2.2715
+#&gt;    1.2582 -15.3859  -4.0612   6.0551 -11.5922   5.6975 -10.6903   3.1235
+#&gt;   -2.3057   7.2851   4.1571  -3.7623 -13.3014  -3.3147   0.7165   8.2489
+#&gt;   -5.5216   6.5140   6.7643   0.2079  10.0632  -0.1891   9.7549 -10.0496
+#&gt;    1.9597  -3.5380  -2.9677  16.1760  -1.5035   7.2587  -4.4721  -4.2364
+#&gt;   -1.6937  -3.2525  -1.5470  -0.9722  -7.1244  -4.9299  -2.4470 -12.3289
+#&gt;    2.8852  -2.0236   1.3117  -5.7844   2.1517  -0.6345  -8.5330  -3.3713
+#&gt;    7.0658 -11.1340  -7.4566   5.4111  14.4295  -2.9644 -10.9916  11.9038
+#&gt;   -1.6794   5.2344  -0.4831  -8.2227 -15.2647  -9.2234  19.3877  -3.3697
+#&gt;   -2.2222  -8.7085   2.1459  -2.3315 -13.2460   4.9453   3.3532  16.0788
+#&gt;    1.1439  -8.1831  10.4678   0.7750 -22.9676   2.5373  -6.1360  -2.1722
+#&gt;   -0.3907  -2.4381   1.5294  -1.2772  -6.7203  10.3003   1.0238  -3.5374
+#&gt;   -3.4757  -9.6613   2.7461   4.1443   4.8124 -11.8703  -4.9022  14.1939
+#&gt;   -6.9834  -5.5026 -10.8559   1.4985 -12.0526   5.3668   7.6786  -5.4803
+#&gt;   -5.3383   0.7282  -2.7798   3.8661  -4.1999   1.9224  -4.2842  -8.8528
+#&gt;   -0.7675  -5.1321  -7.6997   0.7224  -0.1155  -4.1549 -11.6683  -7.6208
+#&gt;   -5.0463   7.2115   6.3373  -5.5542 -10.0361  -4.2997  12.2951   7.4486
+#&gt;    0.3448   2.9213   9.9393   6.2153  -2.4870  12.5195  -0.9724   1.7917
+#&gt;   -1.5647  -8.2731  -4.5555  -5.7891  -4.1437   1.8244   8.3772 -14.1058
+#&gt;    1.0482   6.5136  10.4520   0.4397  -1.7507  -0.6993   3.9861 -14.4631
+#&gt;    1.7088   6.1604  11.0710  -2.2001  -5.1874  -5.5833  14.4653 -10.1261
+#&gt;   -2.5775  -0.5083  -2.2582   5.3694 -12.5665 -21.2035 -10.8536  14.9236
+#&gt;    1.8758  -0.3528   1.5673  -4.7510   0.3847   4.9639  -1.8656  -3.3787
+#&gt;    8.9301  -5.1093 -18.5290  -3.7455  -2.5514   6.0338  -1.9197   5.8923
+#&gt;    8.1802  11.1580   9.9619  -4.8865   6.8277  -0.0333  11.4610   7.7824
+#&gt;    6.9339  -2.1000 -15.4039   3.2951  10.8199   3.8083 -12.4813 -13.0707
+#&gt;   -2.0960   0.3521  -4.1971 -19.7384 -12.2238 -11.8048  -3.5601   8.3752
+#&gt;    7.3922  -1.0737  -1.1850  -8.5491   3.3542  -6.1760 -12.4863  11.1563
+#&gt;   -0.7960  -8.5561  -0.7178   6.4331  -0.5254   4.7416   0.1240   4.5459
+#&gt;   -8.6579   2.2964   4.8503   6.0717 -10.4698  -5.1670   9.0755  -1.3557
+#&gt;    1.8304  -3.5696 -11.9794  -7.5583  -3.1501  -8.3338 -10.3645  -9.4198
+#&gt; 
+#&gt; Columns 9 to 16 -11.0021 -10.9249  16.2891   0.0882  -7.9128   1.6974   6.3797   8.1230
+#&gt;   -9.5374  15.8626  -2.4815   4.6046  -0.0928 -18.1945  11.8253 -12.4632
+#&gt;    7.2605   1.1474  -2.1147   2.8611  -3.9746   0.3914  -6.6777  -7.2535
+#&gt;  -11.2044  -6.1130  -1.3746   6.5575   6.7507   9.4469  13.0109 -14.4164
+#&gt;   -2.6247   7.0394   8.3947  -4.6857  -5.6457 -15.7065   3.2095   5.1134
+#&gt;  -19.3815   7.9436   6.1093 -28.0882   4.2993  13.2899  15.6173  -9.4412
+#&gt;   11.9219  -3.2553   8.1343  -7.4777  -9.2920  -4.5519  -8.7648  16.9167
+#&gt;    3.1517 -12.3206   9.5224  21.0205  10.3701 -24.0904  18.2913  -1.6430
+#&gt;   -8.9871  10.5288   4.0401  -2.1487  -9.5816   2.8172  13.7086  -6.8393
+#&gt;   -3.0137   5.1247  -0.1836  -3.1610  -2.9165  13.0244  -2.2387   7.7961
+#&gt;  -10.6710 -10.3327  -6.4834 -16.2986   3.8466  -2.9872  13.5495  12.1297
+#&gt;    5.5083  -3.4488   1.6611  11.6190 -10.9689   0.0111 -14.8758  -1.9930
+#&gt;  -16.4534  -7.1903  -6.3189  10.8443  -9.7576 -17.8507  -3.2521  -8.2236
+#&gt;   -7.0884 -10.9956   4.9712   0.1294   0.2673   5.7678   6.1968  -2.6220
+#&gt;  -10.1659   8.9873  -6.8182  -5.1344   4.3459  -1.7587 -12.4333   0.7112
+#&gt;    7.1221   3.1867 -19.9492  14.4134  12.6258   8.8062 -18.7293  -9.2968
+#&gt;    4.5233   2.1924  -9.6172   1.4641 -11.6853 -19.2012   3.7209  11.5915
+#&gt;   -3.3990  19.1853  -7.7865   9.2036  10.0295  -7.4047 -23.1191  10.2670
+#&gt;   -1.6671   1.8350  15.1094  -3.0537   1.0734  13.0679  -0.2235  -6.1474
+#&gt;   -5.3390 -11.6550   4.9796   4.2657   4.0214   4.8109   4.5774  -9.8083
+#&gt;    0.2785   6.9181   6.7965  -3.8192   0.7373  -3.0569   0.4019 -17.1030
+#&gt;   -8.4861   9.6221  -1.0142   7.5091   4.8052   5.6862   4.8217  17.0577
+#&gt;   -2.6712  16.7904  18.2559  16.1520   9.7135 -10.6487  -2.1274 -23.2701
+#&gt;    5.7470   7.7881   1.3139   0.0961   3.6937 -12.6210  19.0965 -12.0381
+#&gt;    4.9706 -16.2171   4.3602  -0.7338  17.7089  -4.6464  14.7318 -20.7157
+#&gt;   14.8004   7.6476  -3.8669   3.5837 -12.6696  -5.5832  -1.7650 -10.2432
+#&gt;  -12.3197  -2.9730  10.3361  -0.9796  16.5506  -3.5573   7.3058  -7.3455
+#&gt;    9.4243  -4.7248  11.3604   6.2869 -12.8884  -7.6743  -2.1170  15.0787
+#&gt;  -10.0851  10.8309   3.4603  13.8976  -2.3457  -1.3532   9.0514 -23.9656
+#&gt;   -4.2319  -7.7085  -1.6451 -11.6734 -15.2542  -1.3529  13.8607  -8.0336
+#&gt;   11.2010  -5.1881  12.3673  -4.0243   0.9423 -17.6126 -17.8881  -5.3914
+#&gt;   -0.3544 -13.0569   1.1551 -14.3875   3.6698  -3.7429 -14.1105  -0.5932
+#&gt;   -7.5711 -11.6290  10.6177 -19.8359   4.3088  -1.8451  17.8137   8.1614
+#&gt; 
+#&gt; Columns 17 to 24  -5.7535 -13.6507   0.4260  -4.9204  -1.4511 -10.2812  -4.0653  17.3489
+#&gt;   -1.5493  -4.7359  -2.2354   8.0775   8.8627 -14.0385  17.9817  11.3177
+#&gt;   -2.4823   4.7306  -7.4366   8.9477  -6.2620   5.2805   0.9624   7.8016
+#&gt;    3.8917  -6.2281  21.1442 -13.2944  -3.2937   1.6714  -6.0299 -12.0161
+#&gt;    0.6774 -12.5701  15.6722   9.4217  27.2130   9.8320   1.2353   1.8094
+#&gt;   -8.0426  -4.4288  -5.4305  11.8428  -5.9798 -18.7180  -6.1792  -8.1874
+#&gt;   14.3977  18.3625   1.1329   1.8208  -2.0881  -6.5014  -8.1138   0.0873
+#&gt;    2.8809   5.2091   5.4588  -8.6967   1.8709  23.3601   2.2067  -0.6979
+#&gt;    9.8144 -22.9668   2.9467 -10.7592  -1.2527   9.8109   1.8020   1.9310
+#&gt;  -22.1668  -1.8032   2.5275  12.0921  -4.3896   5.9487   4.6041   7.8023
+#&gt;   23.0005 -12.2541  -4.5206   9.9436   6.1314  -3.0797  -6.7752  -8.7213
+#&gt;    8.4727   0.6582  -3.8563   1.4148   5.1803  10.1021   3.4280  -5.6509
+#&gt;   14.2992  -4.1243 -12.9224  -3.7907   1.6134   6.6839   9.2004 -10.9704
+#&gt;    8.9007 -23.2353   7.6862  -2.5331  15.6309   1.3322   7.4129  -0.5102
+#&gt;   -0.0274   6.6441   9.6948  10.7106   3.1750  -4.4436  13.4575   9.3859
+#&gt;   -0.3707   6.7767  -9.8070  -1.7111  -6.5149  -0.4291   4.1224   7.2699
+#&gt;   -2.0091  -2.5312 -16.6773   8.6435  15.3238   3.9757   7.3876  -2.8835
+#&gt;   10.5156   1.8706   1.0083  12.6324  -0.5944   6.9651  16.1475  -4.3249
+#&gt;    1.0385  -6.5673   2.5462  -8.8037  13.5781  -4.3920   3.0261  -8.5648
+#&gt;    4.4140  -1.1340   9.8787  -5.3499   0.1318  -1.2426 -11.1290   7.4819
+#&gt;   11.5681 -11.5965  -0.8896  -5.5384  -7.4320 -17.9907   9.9755  -9.3822
+#&gt;    2.2034   9.9652  -6.9261   4.8134 -18.4113   1.1571  -3.1118  -9.8003
+#&gt;   22.7320  -3.3941   2.9400 -10.2855  -4.6363 -11.2379   1.9989  -7.3464
+#&gt;    0.6234 -20.8203  15.4633 -10.6786   8.0310  -8.2472   4.6602   2.7410
+#&gt;   -3.0226   0.0176  -2.9008 -12.2696  -3.8482  10.7570  -1.9761   9.2274
+#&gt;   -5.5389  -1.7155   3.1498   8.2490  -1.4926   4.6617   4.5536   9.5355
+#&gt;   -0.8106 -11.7087  -5.0014  -2.1781 -14.5540  -3.3661  -2.6836  -3.9334
+#&gt;    9.2194   4.3412 -14.0290   3.1135  -1.8577  11.5905   6.6218   8.0127
+#&gt;  -20.2410   4.2235 -15.7262 -15.3850 -13.5639 -10.2291 -23.9093  -3.0371
+#&gt;   12.4885  -8.3624   5.0920  -2.9361   7.3489  -8.7218   0.7167  -6.9985
+#&gt;   10.8382   4.0552  -8.6123  -8.4467  -8.0870  -0.8843  -1.4157   0.9252
+#&gt;    5.3873  -3.4927  -6.0538   0.9851   0.1654  -6.2779  -1.2633  -6.9509
+#&gt;    6.1580   4.8961   5.5143   6.6595  11.4430  10.1527  12.1372  -1.0388
+#&gt; 
+#&gt; Columns 25 to 32   2.8042  -9.5249 -21.0267   3.1264  28.3109   7.7618  20.7447  -0.9716
+#&gt;    7.8744   4.5833  -1.6712  -8.9966   7.8448  -1.4948  -7.0991 -11.6277
+#&gt;   -2.0453  15.4764  -5.8538 -13.4746 -10.1985  -1.1669 -12.3960   0.4297
+#&gt;  -10.7893 -18.3605   1.7355   1.0931   0.4702  -1.5314  -2.9485  -2.6575
+#&gt;   13.5892  14.6822   7.2090  -1.4291   3.9263  -4.9238 -16.3571  -0.7424
+#&gt;    4.6905  -1.6928  -2.2440  13.4444  -0.6285  -7.8857  -5.8058  -6.2666
+#&gt;  -22.5014   2.3784   2.5228 -18.4715   3.8635  16.3372   6.3797  -8.2014
+#&gt;    0.2979  -5.9161   0.2947  -8.9482  -6.3781  15.1396  -8.7327 -13.6224
+#&gt;    0.3215   4.5580 -19.0640   6.0653  14.5959 -10.1248   1.4517  -3.2915
+#&gt;   -1.2405  -9.2978  16.2120  -5.6459  -4.1797 -13.9344 -22.0786 -11.3252
+#&gt;   16.5517   3.8473  -8.1317 -13.4332  11.2736  12.7668   0.5516   1.3029
+#&gt;    0.3796   3.6115   0.7702  -1.5182   7.6267   0.9081  -5.1293   3.2503
+#&gt;    3.2618   7.6959  -5.6787 -17.6102  27.0427   2.3862   5.0714   2.3728
+#&gt;    4.6810  10.6034  -4.4923   3.2739   7.9195 -11.9655   2.0914   0.0989
+#&gt;   -3.7336  -0.2368   4.6183   5.5902  -2.8246   9.2920  -1.8576  -4.1132
+#&gt;    5.9530  14.4178  -3.1727  -5.7668  -5.4502   7.9786   9.4296  -4.6057
+#&gt;    8.0693   8.1994  13.0564   7.0040   0.9989   2.0506   5.1272 -18.1135
+#&gt;    6.8487  15.7204  -2.5249  -1.2499  10.0834  -0.1315  -2.4905 -10.5584
+#&gt;   -6.0915 -10.6941   4.1259   2.0105  -4.0860  -4.5871  -1.0034   1.3747
+#&gt;   -6.5917  -6.1863  -7.9351  -6.8489   3.4930  -1.7812   0.4904  -0.2508
+#&gt;   -6.7602   9.1604  -0.6079  -6.8637   7.7246  -9.3047   3.5791  -0.6862
+#&gt;   -8.0429  -3.5321 -12.3105  -4.5122   1.9469 -12.7310 -10.9645   2.5651
+#&gt;   -2.5565  -2.9323 -22.4190 -11.5246   6.6864   9.3873   2.8537   7.9164
+#&gt;   10.2207 -16.9300   9.5375  15.9803  -0.1843   2.0617 -14.3775 -12.2400
+#&gt;   -0.5469  -6.7575   9.1287  -3.6532 -12.9932  -1.9317  13.0984 -11.7560
+#&gt;    9.0637  23.6112  11.9632   7.5023  -8.7263   0.4869  -5.7155  -3.3102
+#&gt;   -4.2799 -16.6950   0.7203   0.1279   5.6116   5.3393   2.3024  -7.4505
+#&gt;   -2.9759   4.2560  -3.2889  -4.5652   6.1786  10.2252 -19.2386  -1.8180
+#&gt;   -3.2682   2.3879   6.9372  11.7385  -5.2903  -8.7105   4.0837   4.8753
+#&gt;    3.3292   2.4731   0.3064  -8.8113  12.3164  -5.2932 -15.7692   9.8857
+#&gt;   -7.0072   9.2697   0.0230 -11.5711   7.2454   9.4541  12.2295  -0.9957
+#&gt;    2.7625   3.5537   4.5743  10.5819  -5.3057  -3.4204  -0.4771   4.1003
+#&gt;   -2.6315  13.3455   1.7102  -2.5734  -3.7750  -0.4142   4.5067  -1.6987
+#&gt; 
+#&gt; Columns 33 to 40   1.4249 -11.0755   9.3542  -9.7008  11.8526 -11.6143   0.1438 -14.9416
+#&gt;   -2.4695   4.4213  -3.3442   2.8386 -14.5500  -0.3050  11.4525  -7.3751
+#&gt;    5.4534  -8.3487  -1.4633   3.3697  -7.5637   3.0802  -5.3609  -8.5319
+#&gt;   -2.5340  -6.6086  -9.7978  -1.6956  12.7237  -0.1676   3.0869  11.5888
+#&gt;   -7.3901   4.0227  -5.8422   9.6897 -13.2719 -12.9970   4.8614  -7.8807
+#&gt;    4.9316  15.0560  -5.8030  -2.5815  10.2874  -9.3993   1.8211  10.2510
+#&gt;    5.4881  21.0625  -0.2542   9.2211   1.0045  -9.9962  12.1078   5.7644
+#&gt;   -1.5557  -0.0376   4.1277   4.8585  -5.5478 -11.3342   8.2986   4.6142
+#&gt;    2.7084   4.6693  -3.2897   0.1316   4.3207  -3.9370  -4.8066  -2.3084
+#&gt;    5.2748   9.7305  -2.2019  -7.5334  -5.4114  -3.4872   7.7262   5.5723
+#&gt;  -10.0755  10.0485 -12.7962  -3.9888  -7.6950  -9.0356  -2.9581  -7.5477
+#&gt;   -3.5278  -2.2127   0.6009   0.7558   3.3095  10.8913   3.8950   1.8363
+#&gt;    5.5640   0.7430  -7.4437  -4.0137  -9.1372   9.7445   5.1849  -7.8112
+#&gt;   -1.9517  -6.1608   3.3622  -6.0094   5.6073  15.3582  -7.3265 -20.5902
+#&gt;    0.4649   4.9782 -13.2930  -6.8730 -15.8075   3.3621   9.3135  -0.8069
+#&gt;   -1.1151  -8.3262  -5.1569   6.7165  -7.5948  -5.2884  10.2331   7.2735
+#&gt;    2.7024   5.0846  -1.0947   4.6324  -2.7163  -9.5111   5.6135 -11.6155
+#&gt;  -11.6737  19.5294  -7.8315  -6.9095 -10.4567  -4.9886  12.0837   6.6195
+#&gt;    3.3153   1.8701   5.9375  -8.0286  15.6053  -0.2567  -3.6559   8.8092
+#&gt;   -0.3040   1.5502   2.1421  10.8078  -6.6668  -3.3936   7.5686  -4.8653
+#&gt;    7.9557  -1.2807  10.9573   1.5703  -2.0374  16.7048   1.0751   6.6447
+#&gt;   -3.0448  -1.1016  -8.6984   4.9987   4.5195   5.4928  -5.8908  -6.9696
+#&gt;   -3.3984  -7.4228  -8.7317   0.8089   6.2309  -2.4708 -22.0646  -0.3850
+#&gt;   10.7299 -10.4042  -7.0590  -2.2802   1.7907   1.3410   3.3008 -21.8833
+#&gt;   -0.2072  -2.3352   0.9942  -8.1299  14.3783   2.8593   3.1763  -0.2166
+#&gt;  -11.1050  -9.6336  19.6276   4.7868 -11.1617   1.8055 -11.9346   8.4321
+#&gt;   -5.1588  12.5703   5.5086   2.5203   6.0757  -9.1832  -0.7057  10.9606
+#&gt;  -14.4499   6.5319  10.3255   0.5957   2.7447  -0.0765  -7.4166  12.5941
+#&gt;    3.9534 -10.0733  -5.7243  22.2901   7.5241   4.0727   9.5097 -16.5037
+#&gt;   -5.4545  -4.5379  -3.2065  -6.8075  -2.8386   0.5619   6.8654   5.8550
+#&gt;   -8.9576  16.0609   1.6307 -14.9022  14.0983  -9.1671  -4.7498  -2.9699
+#&gt;    6.6537  -0.2906  -0.8716  -9.0849   4.8815  -7.6360   8.5255  -0.1695
+#&gt;  -12.8973   5.7070   0.6085 -14.0956   0.0914 -15.6002   3.5593   6.6064
+#&gt; 
+#&gt; Columns 41 to 48   5.9311  17.7299  -0.7229   3.7374  -3.6511  -8.2053 -11.5240 -10.7492
+#&gt;   -1.0067  14.8023  -1.3072  -3.9467 -10.1321   4.0677 -17.3582   0.7563
+#&gt;   -9.5202  14.1758   3.8432   6.1191  27.2309   7.0681  20.0783   5.9590
+#&gt;    2.0325  -2.8585  -2.0056  14.6161   2.2878  21.0311   3.6127   7.5252
+#&gt;    6.1734  -6.7330 -13.4892   3.8152 -10.2857  10.5506  -6.4547   7.0024
+#&gt;   -9.4539   1.1894  -1.2883   6.1256   6.6439   3.5010  12.5788  -8.8679
+#&gt;  -12.1847 -12.4230   3.1144 -10.3928   2.5866   0.0955 -19.3389   3.0412
+#&gt;    3.0109  -8.4414   9.7856  -2.2481  -9.3414 -11.8433   7.1392   5.5796
+#&gt;   12.8713   7.8733   3.9543  -5.5824  -6.0260  -3.0953  -2.6589  22.8629
+#&gt;    8.1692  -2.7973  -6.5048  -8.6637 -18.1423  -0.4985  -1.7051  -2.7297
+#&gt;  -20.6182  17.1865  14.7506   8.9323 -11.6332   6.9394 -22.2232 -10.3575
+#&gt;   -5.2529  -1.0713   0.7465 -10.0037   2.9579  -7.1691  -1.6837   1.9132
+#&gt;  -11.3145   8.4803   1.1995 -10.2422   2.6584  12.0492 -10.1743  24.5186
+#&gt;    5.4599 -10.2661 -10.5127  -4.8045 -12.3273  16.6059  -2.9793   6.9405
+#&gt;   -8.5731  -6.3193  -4.3652  -6.5149  -5.7321  -5.4401  -1.6743   3.4636
+#&gt;   -3.3849   0.0514   7.9724  -0.9905  -9.1071   7.6889 -18.4658 -21.2365
+#&gt;   -3.2851  -0.4853   4.5171 -23.1480   7.8174 -10.5611  -2.3376  15.2831
+#&gt;   -0.1535   6.2131   5.2625 -10.2457  -3.8658  12.7566  -9.2627   7.7939
+#&gt;   13.5917   4.3739   8.1545   2.8746 -13.6187   1.2221 -14.5068  -9.8078
+#&gt;    6.1881  -2.6243 -12.2629  17.5239  -0.4198   1.4195  -4.3870   2.8471
+#&gt;    1.9563  -2.1754   4.7194  -1.2826  -2.6734   7.6652  -6.2243  16.0889
+#&gt;    3.8896   2.5679   3.6495  -5.7284   0.7490 -12.1440  -8.4993  12.5337
+#&gt;   -7.6442   7.6055  16.1955  -4.6797   3.1885  12.9011  -9.0566   0.3462
+#&gt;    9.0367  -6.5953 -11.5499  10.1211   1.5265  12.2219   2.2632   4.6006
+#&gt;    7.8655  -6.4106  -4.2319   2.4144   5.5798  -8.8956  18.1477   3.4162
+#&gt;   -5.3442  -6.6473  -5.4697   7.3366   3.0652   3.7171  13.7536   8.7985
+#&gt;   -5.7443  14.4374 -12.6321  16.9814 -13.5365 -13.4930  -2.4854   4.0077
+#&gt;   -9.0537   2.4328   7.1245 -15.6735   0.5791   4.4094  12.9868  -3.7366
+#&gt;    6.3340  -9.7715  -4.4469  -6.6100  14.0820  -7.9638   6.5021   3.6925
+#&gt;  -17.9757   6.9878  -3.4021  -1.7645  18.2453   3.7194  17.6361   1.1105
+#&gt;  -10.8246   8.4444  -1.6945   6.7459  -9.3670   4.0811  -4.6410  -2.5993
+#&gt;    1.5727   1.3147  -8.7397   0.7923  -0.0714   6.5934  -2.3473   1.3001
+#&gt;   -3.1788  -9.4588   2.2271  -0.1755  -4.4474   6.2041  -7.9666   5.3066
+#&gt; 
+#&gt; Columns 49 to 54 -13.6941   0.6945  -8.7885  -3.4986   0.6053   0.8150
+#&gt;   15.7277  -3.5094   2.8476  -0.4773   2.1591   0.0238
+#&gt;   -7.3607  -3.1223   1.6915  -0.1196  -4.0571   0.0635
+#&gt;    3.2340   3.6792   5.0335   0.1379   6.2757   1.4164
+#&gt;    9.3465  -4.3897   5.2178  -2.6181  -5.4572  -1.8842
+#&gt;   -2.8565   2.0266  -2.8396  -3.6875   3.7526  -0.8052
+#&gt;   -5.4079   0.0992  -5.1323  -0.5866  -0.4208  -3.3939
+#&gt;   -2.3491 -11.4709  -3.6558   0.2687   1.3869   0.0414
+#&gt;    1.4125 -11.3997   4.4783   7.2514  -6.7433   5.2444
+#&gt;   -2.3227   8.8233  -7.0617  -4.7002   2.7407  -2.8848
+#&gt;    6.9253 -14.1175  -2.3262  -1.7634  -0.6409  -2.0952
+#&gt;   -5.7669   6.4380  -4.6598   3.6529   3.1694  -2.0667
+#&gt;    0.7696 -15.2733   3.0434  -7.0144   5.7703  -0.1181
+#&gt;   -0.8130  -9.3739 -11.8269   5.4196  -3.4397   1.5477
+#&gt;   10.2240   4.9246  -7.7297  -1.6301   7.3280  -1.0768
+#&gt;    4.0419   1.0818   0.3424  -1.6101  -2.8325   1.2382
+#&gt;    1.8596   7.9131  -9.2704   4.2190   5.3003  -3.8579
+#&gt;    7.9187   0.9981  11.1683   1.7615  -1.0957   1.5316
+#&gt;   -4.3368  -6.7063   4.0030   4.1844   1.0063  -1.8471
+#&gt;    3.1494  -2.0101   8.1739 -11.6163  -5.7066   3.9029
+#&gt;    0.1406   0.1161   2.9006  -3.4985  -0.5075   0.4991
+#&gt;    2.2324 -17.4610   7.3931  -1.7045  -5.1395   1.1228
+#&gt;    0.1736 -15.9067  -1.4389  11.9064   6.6558   2.6835
+#&gt;    7.8509   9.9993   7.3451   0.5591   4.1469  -2.1620
+#&gt;    3.6479  10.3813   2.7129  -5.0629   3.0056   0.9193
+#&gt;   23.9034   8.7242  10.9977  15.4263   1.3593   5.5134
+#&gt;   15.2637   6.9468   3.1952   2.5030  -9.6888   4.5583
+#&gt;   22.8260  17.4871  -4.4741  13.0648   7.0827  -2.1707
+#&gt;   -4.6060   7.0370   6.4077   0.5699   7.1098   0.0612
+#&gt;    0.2928  11.7133  -0.3120   6.8789  -1.7140   0.3324
+#&gt;    1.3951   4.0383  -3.9130  -1.4298  -1.6821   0.7301
+#&gt;    8.7975   1.5632   4.8095  -4.0370   2.2302  -1.8490
+#&gt;   -0.4435  -6.9148  -6.4755  -1.8175  -3.8154  -3.3834
+#&gt; 
+#&gt; (14,.,.) = 
+#&gt;  Columns 1 to 8   1.0682  -1.3317  10.5563  -3.9496  -1.6608  -2.0113   5.8337   8.4680
+#&gt;   -3.2087   1.5790   4.2984   2.6121 -17.9662   2.5845  14.2544   3.7102
+#&gt;   -0.4351  10.0113   2.6389  -6.1302  -5.6138  -0.0838  -8.4350  -3.6393
+#&gt;    6.3955   8.0218  -2.8163  -2.2577   3.2324   4.7032 -11.1047  -3.5908
+#&gt;   -2.1790  -0.9942 -13.1480   1.7856   6.9983 -16.8467   0.9652   3.0140
+#&gt;   -0.5052   0.7405   0.7574   0.6794  -6.4720  16.3848   9.6637   3.8627
+#&gt;   -0.1264   2.3876   3.4571 -18.3981 -11.0937  -3.9366  18.5412 -13.5563
+#&gt;    1.1594   2.6859  -1.6409   1.0886  -1.2411  -3.9106 -13.6028   2.3904
+#&gt;    0.2962   7.5492   2.3383   5.2418  -3.0762   1.7393   5.4344   4.5620
+#&gt;   -0.2984  11.7696   9.0801 -11.2731  -6.9323  -2.9416  -2.6688   7.9035
+#&gt;   -3.8964  -1.7891  -0.6085   4.3640  -6.1822  -4.1137   4.5777  14.6381
+#&gt;    0.2409  -0.4794  -2.9572   2.9849   4.1751   6.1592  -6.7069  -1.0201
+#&gt;    0.3693   8.7138   1.5158   3.0562  -0.5071   6.5347  -1.6752   1.6722
+#&gt;    1.5698   1.9870  -6.4410  -1.9750   1.5633 -14.7235  -8.7555  -3.6683
+#&gt;   -8.1786   3.7620 -12.4598  -6.2602 -14.8774  -2.4304   8.0524  10.7301
+#&gt;   -0.9267  -4.2559   5.2403   5.8458   2.2824  -5.8456 -12.7765   2.6378
+#&gt;    6.1738  -1.8667   5.3856 -15.2022  -8.4619   1.5997   4.0490 -15.1732
+#&gt;   -6.8491  -7.0006   0.5218  -2.4846  -4.0683   5.9917   3.5972   3.7189
+#&gt;    3.2201  -5.7486   0.9012   6.3388   7.8480   4.0562   2.0984 -10.8311
+#&gt;   -0.0576  -1.3288   6.5422   4.0030 -10.3604 -10.7180  12.8008  -0.8465
+#&gt;   -5.3274   0.1229   2.4954  14.3851  -9.0433  12.9836   0.7213 -10.0064
+#&gt;    0.2998  -1.8280  -3.5392   1.4372   0.8775   2.6480 -10.5243 -21.2429
+#&gt;   -1.7309   0.6944 -10.0847   9.0442  -7.0992   3.3640   4.8158   2.7516
+#&gt;    2.3049  -4.1089  -6.4137  -7.9334  12.5217  -1.3651   2.1152 -11.6989
+#&gt;    1.7183  -5.0437   4.7943   1.9089  13.7122  -7.5609  -2.8849  -7.4888
+#&gt;   -1.6922  -8.6862   1.9557   2.0456  -5.3209  -4.4141  -8.6267  17.2118
+#&gt;   -4.2077   0.8600   0.3819  13.0846   6.3533  11.3750 -18.7503  11.9948
+#&gt;    1.5235   0.0820  -3.9783  -6.3998   3.6557  -4.6387   1.2233   1.1708
+#&gt;    2.1953  -7.2120   3.6478   0.3455  -4.2720 -14.0556  -3.8233 -15.1683
+#&gt;   -4.2392  -0.3903  -8.1206   1.7471  14.4427  14.8464   8.3121  10.3957
+#&gt;   -0.2266   2.9230   3.3701  -2.4312  -7.6215  -9.7092   5.6057   4.0541
+#&gt;    4.3938 -11.2120   2.3082  -2.9799  11.9298  16.6256  11.6223   3.0445
+#&gt;    0.3378   1.6645   3.0569 -10.4494  -6.3165 -21.0461  -0.7557   3.9742
+#&gt; 
+#&gt; Columns 9 to 16  -7.6308  -1.3117   0.1137  20.8397   7.0400  -9.9571 -17.8718  -3.0070
+#&gt;   -2.8706  -6.0722 -11.1877  17.9593   5.9154  -2.6710  -2.9567  13.1148
+#&gt;   13.6812   9.1494  -7.8987  -5.7887  -7.9125  25.3422  -8.0291   7.5238
+#&gt;  -14.5406   6.1440 -13.5509  -2.1922 -15.3191  -0.6309   6.7139  15.0202
+#&gt;    5.3562   3.3114  12.1822  -0.7365   9.0712   1.3674   1.7546   1.8026
+#&gt;   -7.2997   8.5337  -2.2627  -3.7999   2.4415 -14.1935   1.6391  -0.1001
+#&gt;    1.5942 -18.7663  10.4612  -8.4393  11.6695  23.0163  -7.2793 -11.3540
+#&gt;    9.3155 -10.0385  -9.5278   8.8083  -3.5831   1.7618 -19.5998  20.3251
+#&gt;  -14.9647   4.1962  14.4394   2.8140  -2.5513  -5.0547  -2.9139  -3.6931
+#&gt;  -15.9188  -3.1156   2.7211  -5.6836  -4.6138   7.5781  -0.8871   3.2482
+#&gt;    0.7135   3.3366  -9.3664   0.4163   6.8589  17.8568  -8.4108  -9.6010
+#&gt;    2.3402   1.7981   3.7933 -10.4290   0.1447   3.2183  -0.8672  -3.2393
+#&gt;   -3.8269   3.1614  -7.9295   5.2880  -3.0958  -4.5372  -7.8706  -1.2308
+#&gt;    0.1324   1.2515  11.7791   8.0497 -14.5167  10.0783  -2.2692  -6.0042
+#&gt;  -17.3841  14.1928  -3.1225 -11.1902  13.3952  -1.6488   2.6244 -10.8511
+#&gt;    4.1539  -3.1791  -5.2044 -11.6293   5.5238   8.6782   2.5033  -9.2838
+#&gt;   19.0114 -19.9130   7.7452  -2.1882   1.4135   9.3925   1.7562 -12.0515
+#&gt;   11.9768  -1.7584  -1.6531  -1.7584   4.1544  -6.2675   6.3657 -10.0731
+#&gt;   -4.8516   8.9139   4.9415  -9.2595   5.2237  -2.3670  10.0588   7.0271
+#&gt;   -0.8574  -6.4313   5.1848   3.0898  -1.3450   7.3532 -19.2539  18.3999
+#&gt;   -3.0916  11.4739  -3.0554   6.7343   9.1936  -8.3684  15.6775  -8.5683
+#&gt;  -14.9063  -0.4541   5.5153   4.6044  13.4720   0.0900  -5.5785   3.7305
+#&gt;   -2.5716  20.6654  -4.4617  10.7399  11.0044   1.8849  14.4940   3.9620
+#&gt;  -19.3237  -0.6214  12.6412   7.4655 -25.5257  -2.9017  -6.9305   4.6988
+#&gt;  -12.5764  -8.2220   5.2123  -4.9763  -8.8428  -7.6355   4.7707  15.4390
+#&gt;    7.6054   5.1530 -12.9802   4.9046   7.6365  16.9651  -0.5894  -6.7338
+#&gt;  -29.2327   4.0586  16.4308   4.3100  -6.9708 -13.1766  -3.3198  19.1684
+#&gt;   -2.3237 -15.2326  -5.9195   8.1093  17.5122  -9.4210  -9.0656   4.5830
+#&gt;   -2.2120  -0.1880  22.6787  -8.3586  -3.1750  -3.4459  10.6379   8.6725
+#&gt;   -8.5308   3.8757   9.2415   4.8788  -1.5141 -20.8685   9.7998  -3.5139
+#&gt;   12.9533   4.5847   0.4892   2.3813  -1.0236  25.3592  -9.8416   4.0650
+#&gt;    2.0021   1.8158  -0.2541   7.7000 -12.5841 -10.3021   5.7402 -15.0306
+#&gt;    3.8260 -10.2455  -4.4622   1.6092 -10.3928 -13.6844   4.8724   9.0436
+#&gt; 
+#&gt; Columns 17 to 24   4.9842   4.5259  -5.3421  -7.0754   4.2468  27.1351   5.8376  -2.6273
+#&gt;   -2.5972   8.0906 -20.6761   7.8792   3.4047   3.8539  -7.9565  -6.5043
+#&gt;    2.2919  -3.5523  -4.3128  -2.2364 -15.5910   1.1588  10.5466  -1.7738
+#&gt;   11.0194   7.4519  -1.3531   3.9708   2.2232 -19.5060 -22.0468  11.5362
+#&gt;   -2.5365  -7.4888 -12.3445 -11.3400  12.4142   7.3685  11.5560 -10.5030
+#&gt;    1.3197  13.2005   6.1211  -1.6043  -4.0554   3.0017 -14.1070   6.0636
+#&gt;   -2.3864  -9.5677  13.8623  15.4929  15.0028  -3.3659   7.7919   6.9288
+#&gt;   -8.0286   4.1282  -0.8025   8.0914 -11.6516  -2.5156  -8.0730 -16.7765
+#&gt;   -6.5890   3.8453  -7.0928  -6.1299 -13.2545 -10.3965   5.8059  14.0835
+#&gt;   -6.3167  -5.8134 -15.1776   1.3905   0.8991  16.8256 -16.6784   5.6563
+#&gt;    9.6508  12.3354  -0.2637  11.7067   1.4065  15.8512  -1.9023   8.7481
+#&gt;   -0.7064 -10.0190  17.2492   7.3368   9.0122  -9.2554  -3.8186  -3.2544
+#&gt;   -6.9695  -1.8119  -2.1708  10.2541 -10.4514  -8.4371 -17.7197   3.9706
+#&gt;   -2.3627  -1.8745 -12.0485  -6.3383  -6.4900  -5.7979  -3.4634   0.3086
+#&gt;   14.6418  -1.3350  -1.4662  -6.0756  11.8717  -8.0031 -12.4007 -11.8013
+#&gt;    5.4393  -3.9346  -5.6344  -1.2853  16.2014  -6.3482  -7.3916   5.0740
+#&gt;   -1.5497   4.3571   3.4982  -3.3196  -1.4032  -2.8695  -7.8070  -0.7173
+#&gt;   -9.4201   1.3658   8.0585  13.4278  -0.5700  -9.2102   4.5890  -8.8514
+#&gt;    2.9911  10.9693   2.2764  -0.8898  -1.1485   4.6408   9.7968   6.6684
+#&gt;    4.4646 -22.2244  -8.8300   8.5027  -0.8602   3.4577   9.9833  -6.1529
+#&gt;   -9.2974  -5.5403 -14.5111  -2.1462  -0.7886  -6.1526  -7.9484   0.8473
+#&gt;    0.6953 -14.0376   8.5477   7.7921 -12.2017  -3.9912  15.7656   7.1820
+#&gt;  -11.1236   4.4102 -13.2990   7.4666   2.3334   0.6001  16.0495  16.5304
+#&gt;   10.4941  16.9373 -18.3478   0.3175 -28.8111   1.0789  -3.1643   0.9006
+#&gt;    7.7730  -1.0303   6.1447   2.4639  -6.5774  -7.5510 -12.0587   8.7854
+#&gt;    1.3077   9.6216  -6.6384   1.9178   0.8710 -14.0336  -3.8990   0.5428
+#&gt;    1.1580  -9.1767  -0.7260   0.9959  -2.3034  17.3495   8.5480 -14.1308
+#&gt;   -7.5084   7.7580  12.0035  -5.3429   8.8252 -15.2600  -7.1186  13.1812
+#&gt;    0.8627 -12.7228   6.1457 -12.3317   9.1124  10.1002   0.2276   3.5845
+#&gt;   -2.9476   5.2360  11.2360  -1.3000   8.2432  -1.1090  -1.5226  -0.3381
+#&gt;  -18.6024   7.1351  -3.4096  -5.2039  -7.7222   2.7480   2.3723  10.0306
+#&gt;    6.5559  17.5489   0.9696  15.6342   9.4531  -2.8979  12.5495  -7.5411
+#&gt;   -5.6435  12.0125  -5.4215  -0.3082  13.3172  -1.2821  -7.2426   2.4742
+#&gt; 
+#&gt; Columns 25 to 32  -2.8955   7.1952   0.4607   6.0146 -12.1159   3.3703   0.6064  -1.3046
+#&gt;    8.7800  -7.3266  -0.5357  -0.1866  -6.1014  -2.6579   7.6631   3.9382
+#&gt;    8.4078  -2.3964 -12.6419   2.2767  -0.9326  -1.5690   0.6915  -8.7711
+#&gt;   -4.3703  -5.7726   4.9765  -7.1075  -9.7550  10.5853 -17.2975  -0.1327
+#&gt;    6.9166  -9.1436   3.0721  -1.5284  -3.4600   4.8331  -3.9877   3.6732
+#&gt;   16.3105  10.2621  -3.2205   3.4961  10.3559   7.3969  20.6431  -2.7180
+#&gt;   -8.0747  -5.0873  18.4055  -1.8955 -15.4231   6.9047  14.0930   3.5841
+#&gt;   -9.8793   8.3792  -3.1968  -6.2075   0.7911   5.5061  -4.9761  -3.1818
+#&gt;  -20.4612   5.3775   1.3401   2.0823  -3.2006   8.1444  12.6512   1.8527
+#&gt;    4.7339   7.5866   1.1259  -2.0918  -3.8679   9.9544   0.8422 -17.0946
+#&gt;    0.2804 -14.4982  -2.4195  -9.1474  -5.9318   2.0370   7.8673  -5.7676
+#&gt;   -3.9432   0.0240   7.7274 -10.3056   0.2360 -12.6807  -3.2647 -10.5063
+#&gt;  -17.0528 -10.7467   7.3681 -19.9432  -3.5717   8.4128  -3.0684  -5.3229
+#&gt;   -0.9222   7.1850  -5.4588  -6.6059   0.1661  -3.7781  -5.0199   8.9783
+#&gt;   -5.9012 -11.0385 -13.6209 -11.6550  -2.5110  -6.9044   0.9909 -10.7109
+#&gt;    3.8815  -6.4445  12.4120   3.8269  -1.4892   5.8873   9.9911  -0.1741
+#&gt;   -3.9271  -4.3991 -12.8010  -1.7023   0.5467  -6.6148   5.8343  -4.4812
+#&gt;   -2.5616   3.7171   5.4630   1.0476  -3.4670  -1.6932   4.6626  16.5280
+#&gt;   11.6511  11.2792   9.2341   6.6704   6.2150  -3.8874  -9.1205   0.5537
+#&gt;    0.2453  -0.6578  16.1115  -1.1198 -12.6914   7.2857   0.5511  12.9376
+#&gt;    5.8119   4.3004   4.9701  -9.4542  -2.1813  -4.8785  -4.7907  11.4160
+#&gt;    1.6442   6.0653   5.3555  -6.3213  -2.0651  -4.6171 -18.4665  -4.2377
+#&gt;   -2.1608   8.1038  13.6519  11.5492   5.7398  -7.7747 -11.3077  -1.8447
+#&gt;   -6.8095   5.6028   4.2391  10.5294  -5.9611   8.1898 -10.3368   4.4342
+#&gt;   -4.6152  -4.8975   4.4741  21.4409   1.9309   2.1857   3.7726  14.2797
+#&gt;    3.6013  -0.1528  12.8854   6.7936  -3.4744  -5.9519   0.9275   5.6669
+#&gt;   -1.1392  16.3384  -0.1937   1.1042 -18.2986  -4.5185   6.4076   0.7562
+#&gt;   -0.9276  -5.9927  12.3906   6.0865  -1.2403  -1.9945   2.0729   3.6452
+#&gt;   17.5002   2.8927   1.7038   6.9912   0.2463   5.0752   7.1471  -1.2791
+#&gt;   -1.9058   7.4053   0.8129  -4.0243   0.3111   6.3161   9.8653  -4.0330
+#&gt;    2.0178  -4.8426  -5.6489   2.9212 -10.6190 -14.3120  -2.7817  14.7493
+#&gt;   -7.8906  -1.6224   4.1810  -6.9609  -3.4207  14.0193   4.9421  18.7300
+#&gt;   -7.0843 -17.3787  -9.4141  -7.5089   8.1533  14.0384   5.7030  10.8777
+#&gt; 
+#&gt; Columns 33 to 40 -16.5827 -12.5344  -6.7852  -2.2524   7.8292   5.2382   6.7258   5.9336
+#&gt;    1.4704   4.5821  -6.9088   1.2618   3.5617   2.7313   1.0961   3.7388
+#&gt;   13.6906  14.3084  -1.3718 -24.1896  17.0772  -3.1286   7.3575   2.5939
+#&gt;    9.4861  11.3426  26.4221  -0.5686 -14.8742 -12.4572   0.2607   4.7503
+#&gt;   -5.2708  -9.8033 -21.5549  15.9707  11.0396  12.1904 -13.9649 -16.2996
+#&gt;   16.0063  20.1136  -4.2239  -8.2431 -21.9482  -2.3409  -2.5736  -1.9143
+#&gt;  -18.4170   5.4266   9.0387  -4.4576  -6.8005   1.2502   6.4913  -2.3464
+#&gt;  -13.7385  -5.3310   6.6542  -8.7270  -5.9253  17.1585   2.1584 -12.1461
+#&gt;   -8.7030 -16.2517   5.4788   3.5066   6.6224   6.0847  10.2736  -6.5104
+#&gt;   -3.6481  -2.3599   7.1428  -5.8620  -2.7469 -13.4954   2.0505   7.0197
+#&gt;    6.2611  -0.0059  -9.7529  -1.6357 -19.3187   1.0854   8.6793   4.1513
+#&gt;   -0.8434   3.5754  12.8127  12.8768   6.8505  -9.1610  -5.4927  -1.1818
+#&gt;   -7.6068 -14.1712  15.0055  -0.3405   7.2821   4.8980  -6.6924  -7.0257
+#&gt;   -1.5353  -3.3516  -2.6371  11.8108   8.1527  -6.9623  -0.6607  -6.7928
+#&gt;    3.2217   1.1136 -16.4745   7.1084  20.2841  -2.2466   4.4244  -2.5931
+#&gt;    3.3643   0.9780   1.4271  -1.3384 -12.3832   3.2823 -10.0730  -2.7894
+#&gt;   -1.5428  18.5557   7.3113   3.3495  -0.7591  12.8430 -14.5960  11.3541
+#&gt;    8.1762   0.6542  13.8176  -5.7693  -5.9172  12.2618  -6.7907  -1.1713
+#&gt;  -10.6165   3.9859  -4.0476  -1.4235 -18.0125  -1.2142   9.1134  -2.0178
+#&gt;    2.5308  -9.1204  -7.9342   3.4713  -2.3418 -13.8876  11.5349 -11.4684
+#&gt;   -6.4398  -2.5942   6.9882   4.8880  -5.1512  -6.3376   7.0864  -6.1208
+#&gt;   16.7980  -0.3647  -5.7067 -18.8361  -7.5711   0.7869   1.5912   8.9501
+#&gt;   -5.9780  -9.7641   0.6641  -5.6192  -9.6519   3.2004   7.3110   5.6151
+#&gt;   18.7451  -5.4171  -6.4070  10.7518   5.8211   4.1964 -17.7327   8.1009
+#&gt;   -1.7654  -2.6971   5.8547   0.4250  10.4314  -4.4139  13.0077   5.2814
+#&gt;   14.0373   1.9799  29.1636  12.4340   5.0169  -9.8093  14.9295  10.0356
+#&gt;    4.1476 -31.1325  -8.0388   5.3732   7.0390   5.1446   7.6234  11.2519
+#&gt;   20.0538  16.0889   4.1456  -5.3208  -1.8056  11.5560  14.9791   4.1244
+#&gt;   -6.1252   1.8963   1.2880   6.1788   8.8352 -20.9866 -13.8905  15.2825
+#&gt;   10.6668   8.3772   2.6539  -1.4027   9.9779  -4.1143 -16.2849   0.2596
+#&gt;   -4.1236   7.5642  -6.4608  -8.4928  -6.1387  -2.4295  14.2868   1.8288
+#&gt;   -6.2461  -5.8768  -3.1784  -1.2247   5.6756   6.0421 -15.8626   4.6951
+#&gt;  -11.0868  -0.1595  -8.4995  -0.1629   3.8455   2.6679 -10.0015  -1.1356
+#&gt; 
+#&gt; Columns 41 to 48  -4.9705  -6.9927  10.1139   3.4636  -1.5541   1.2467   5.5312 -14.4339
+#&gt;    4.6962 -12.0791  -2.2345  -3.6285   4.2757   0.7847   6.3379   6.9498
+#&gt;    1.5080   5.6067  -5.4619   7.0293  -3.1296   7.5616   6.0792  -5.6423
+#&gt;    9.2366  13.5901   5.0306 -12.9975   5.4999 -14.5889   9.4264  15.8695
+#&gt;    4.0591 -11.1299 -11.1461  -6.2648   5.6930   5.6508  -9.9906   0.1710
+#&gt;   -1.0060   1.7579   5.8371  -0.7452  -6.3264  12.1475   6.9528  -4.9701
+#&gt;  -10.5107  15.4747 -10.0460  -7.3576  -5.8720   9.6989   8.1239  -1.2736
+#&gt;   -4.2612  11.4808 -14.3660   3.1476  11.9350   9.9523  -8.1508  -0.3578
+#&gt;  -11.9177  -4.7341   4.1306   1.3987   3.5440   6.7682  14.0690  -9.0625
+#&gt;   -6.8600  20.3472  12.2617  -9.3304  -0.8672   4.3437  -3.5301  -4.2093
+#&gt;   -4.2244 -14.5919  -4.8511 -16.7665 -13.4159  -5.3748  23.7413   7.7159
+#&gt;   -3.3487   6.8655  -1.0013   5.5959  -2.9073 -14.2582  -8.0837   1.0351
+#&gt;   -9.8738  -2.1356  -1.4778  -1.2914  10.0358 -13.8298  -0.5817 -10.4311
+#&gt;   -8.2221  -3.3398   2.1981   9.4470   5.8187 -17.8509   0.5108   3.4584
+#&gt;   -4.5577 -12.6593   5.5724  -8.0537   0.3736  -0.3577 -10.3494   5.8468
+#&gt;    3.4860   4.8631  -5.7422  -2.5935  11.3737   3.8019   3.3831  -4.5654
+#&gt;  -10.5457   3.7662  -3.1607  -7.1903 -14.8307  -2.6427  -3.8135   0.3609
+#&gt;  -11.6211   5.3634  11.7782  -0.4783  -2.8875   7.1719  -2.3906  -2.8584
+#&gt;    0.3633  -1.1711   8.9728  -2.4439   1.2721  -8.4528  -2.3368   0.9054
+#&gt;   14.0391   4.2863 -13.3439   8.7040  14.9639  11.8481   1.8799   4.7166
+#&gt;    5.0128   8.1895  -9.9560   2.8282   0.5387 -10.2402   4.5993   5.8128
+#&gt;    4.8419   5.1989 -14.9392  -3.4054   6.8341  25.0790  -4.6841  -3.6823
+#&gt;    5.1218  -2.8226  -2.2997  -2.3698   4.2732  -7.5474   6.3006   3.3641
+#&gt;   -5.4215   6.4601  17.6487 -12.7035  16.7165   0.1454 -11.3924  -9.9702
+#&gt;  -13.2645  -0.5894   4.8821  15.6570   7.0288  16.7542  -1.0207   7.3767
+#&gt;  -17.8001 -10.1375  -6.2899  -8.7367   6.7968  -2.1168   8.8565  -0.2674
+#&gt;   -7.5626   1.9409  14.6316  -3.6666   1.8705  14.5881  -3.9448 -12.1121
+#&gt;   -5.1335  -5.7657  -1.2603   9.2793  -1.8534  10.0799   4.4282   5.4261
+#&gt;   -0.2153   6.1714 -11.2568   2.3641  -9.5128  -2.5122 -13.7613  11.6866
+#&gt;    4.5939 -13.0538  -4.5407   1.1675   3.1414  -3.1486   1.8305  -0.2885
+#&gt;   -2.8329  -5.6187   4.7904  -6.8152 -13.0753  -8.1461   1.1446   2.6098
+#&gt;   -8.2667   2.6419  -9.3701  -7.1938  -6.6125  -3.5960   4.9195  -4.8958
+#&gt;  -16.2781  -4.4159  -1.8804   4.3588   0.6405  -3.2376  10.0507   6.2739
+#&gt; 
+#&gt; Columns 49 to 54 -16.4997 -13.3547   0.6912   3.6431  -1.4873  -3.1410
+#&gt;    2.5134  -2.9944   8.1467   4.3814  -0.7110  -4.7856
+#&gt;   12.7381  -3.0205   2.8836   5.3029   4.5130   1.2538
+#&gt;   15.3212  12.3674   7.7015  -6.7129   5.2488   2.6366
+#&gt;  -17.6317   3.2241   3.8934  -5.0880   5.5373   0.3936
+#&gt;    3.5317 -12.8655 -10.1067  -3.1195  -0.2586   4.7912
+#&gt;   -7.6769   3.9488  -2.1534  -8.5644  -0.4721   6.4967
+#&gt;    6.1252  -9.3189   6.0349   8.3829   3.6738   0.5524
+#&gt;  -15.6094   2.5884   2.3341  -5.9232   5.1800  -2.2496
+#&gt;   11.3329   9.9988  17.8980  -1.8807   2.2449   5.1792
+#&gt;    1.4664  -3.3432   0.2860   9.7708   8.2604   1.2490
+#&gt;    4.3415   7.2991   3.2315  -2.9154  -1.1743  -3.6001
+#&gt;  -13.3036  -7.5742  -1.3056  -1.8536   5.0821  -2.2994
+#&gt;   -1.0885   8.8465   6.3945   1.2256  -3.3178   0.6133
+#&gt;    4.1804   6.2278 -13.8247   7.8920   5.8814  -3.2915
+#&gt;    5.2694  -5.8170  -2.8530  -2.9755   5.5307   0.6773
+#&gt;    8.6352  -2.7823   2.2828   6.4963  -7.5428   1.7999
+#&gt;    6.4909   5.7304   2.9439 -10.6418  -5.4520   0.5230
+#&gt;  -10.5764   2.2096  10.7326   0.1552   2.6700  -1.2733
+#&gt;   -3.2994  -7.0886   0.7244  -8.0308   1.0865   0.5060
+#&gt;   -7.9574  13.5639  -2.9610  -7.7068  -4.5694   0.7560
+#&gt;   10.4968   4.1418  -4.4844   5.5859  10.6809   3.4893
+#&gt;  -13.6542  -4.1204  -4.9947   3.6366   1.1129  -3.9829
+#&gt;    5.5567  -8.0876  10.1639   0.7135  -2.9387   2.1173
+#&gt;   -2.1073  -8.7430   7.6505   0.4635  -3.9520  -0.1823
+#&gt;    7.7646  -0.2450   1.3579  -4.9050   0.8377  -2.6740
+#&gt;    2.3609 -13.5276  11.8893  -1.8041  -9.8533   4.7342
+#&gt;    1.7955   5.5517   6.6732   4.2278  -6.5946  -4.1777
+#&gt;    0.4811  -5.9701  12.9905  -2.8380   3.6038  -0.1904
+#&gt;   -3.0507   3.8007  -0.4866  -2.0904  -2.5750   3.9200
+#&gt;   -3.2328  -8.9425  -3.3577  -3.9753  -0.8461   1.2268
+#&gt;   -4.9758   0.4337  -2.5192  -4.6356  -1.4582   3.7282
+#&gt;   -6.2956   4.1150   6.8176   7.8445   8.7865   0.9325
+#&gt; 
+#&gt; (15,.,.) = 
+#&gt;  Columns 1 to 8  -4.9328   7.3178   8.7430  -6.3928  14.8703   2.7630  -1.9050 -16.3873
+#&gt;   -4.4680  -8.4466  -4.8983  -3.4391  -0.8101  -4.0713 -11.7264   4.9084
+#&gt;   -4.1179 -10.0010  -5.9395   7.2514 -17.3568  -1.8234  -4.8319 -20.7847
+#&gt;    2.2961   4.5832   3.0554   0.2390  -1.8471 -24.6781  -7.9666  -8.7934
+#&gt;   -0.8332  -6.1430   5.9178 -10.0756   1.5199  -1.4757  12.6072  -3.2561
+#&gt;    7.4522   6.4549  -6.3038  -0.6804   9.8932   3.8696 -10.3272  24.3162
+#&gt;   -4.8750  -2.7677   4.6232   9.1124  -3.2398  -7.2643   7.7384   4.6930
+#&gt;    7.4665  -1.6288   0.7243  -2.3744   9.5925  -8.8214   7.9525 -16.3618
+#&gt;   -2.7288  -0.4732   7.1707   0.2121   0.3897   8.0427  13.7533  -3.5827
+#&gt;   -5.5571   4.5583  -2.2729   6.6749   2.5241  -6.6153  -0.9368   2.0178
+#&gt;    4.4070   1.5552   0.2017  -1.6898  -0.9701   1.5116  -6.8111  21.3446
+#&gt;   -0.0993  -1.7742   4.8599  -5.0896  -4.1081   1.9309  -0.3863  -4.4506
+#&gt;   -3.7298   3.9316   8.0563  10.9570   2.0475  -2.7074   0.8421  -7.7736
+#&gt;   -6.3895  -3.6186  -2.0591  -5.1573   3.4262   7.0903  -6.5710 -20.4886
+#&gt;   -3.7893   1.9785  -5.9091  -0.0369  -5.3028  -4.9728  -4.5592   6.6721
+#&gt;    2.7202  -4.1592  -7.3281   7.4225   4.4771  -1.7763  -5.4908  20.4185
+#&gt;   -0.8111  -1.2126   6.8951   1.8754  -7.7215   0.2131   0.0820   1.1217
+#&gt;   -5.7301  -3.2006  -6.7083  12.5226   3.4311 -15.6035  -6.8852   3.8276
+#&gt;   10.8132  -8.7679   0.2336 -10.7230  18.5749  -2.7675  -2.7558  -1.7553
+#&gt;   -1.6653  -3.6512   2.1612  -6.1718   2.4873  13.2646  -2.3671  -6.2557
+#&gt;   -5.6501  -1.0080   1.2501  -5.7407  17.0261 -14.0837   6.4080   5.3224
+#&gt;   -1.0114   4.1990   0.0805  -2.0892  -4.8267  10.3077   6.1778   2.3833
+#&gt;    2.9098  -7.3345  -0.5344   4.5190   1.2796  -1.1394  -6.0879  -3.1857
+#&gt;    2.0842  -0.9095   0.7107 -10.9939  -7.0167  -2.0472  11.8109  -2.2504
+#&gt;    6.7794 -12.7292  -9.2754 -12.2863   1.4173  -2.6130  -1.5276  -7.3488
+#&gt;   -4.4658  -9.8398 -13.5225 -24.7894 -16.0531  -5.1242  -5.1884  -3.1845
+#&gt;    5.3420  -2.8360   2.9838  -8.0145  15.0321  24.4878  -9.8807  -5.3889
+#&gt;   -2.4023 -13.0096   1.1745  -1.5041  -6.9856 -17.0877  -3.4722   8.7882
+#&gt;   -2.2276   6.6563   6.1868 -10.8852 -22.2603  12.8244  -7.3980  12.2693
+#&gt;   -3.4111  -0.7189   6.5161 -13.8551  -9.5669  -5.3583   9.4225   2.8161
+#&gt;   -1.0182  -7.9810   0.7640   9.7088   6.9861  -2.4327  -1.9906 -11.7918
+#&gt;   -3.2752   4.9139  14.3108   0.0637   2.2092  -8.4217   8.9753  -1.8305
+#&gt;   -2.2435   5.7426   2.2143   2.2549   7.5511 -19.5528   5.8062  -2.4257
+#&gt; 
+#&gt; Columns 9 to 16   1.7784 -10.6116  18.8916  11.7382  12.8056   1.5768   3.2779  14.9432
+#&gt;   -0.3574  -5.0433  20.9862  -3.2176   4.7611  -4.2310   0.6011  22.4073
+#&gt;    0.5763   5.0978  -3.7787  10.0012   9.2696  12.6251   3.1758  -9.6194
+#&gt;   18.8368   1.2991   2.2419 -10.4459 -19.3857   5.2887  -3.4947  -2.6045
+#&gt;   -8.9263  -1.4035   3.6362   9.1014  17.1883  -7.3270 -11.6537  -4.9035
+#&gt;   -2.6145  -9.2262   8.3823   0.8489   3.1518   2.6679  -1.2571   0.4608
+#&gt;    4.9115   3.8220   7.5343  19.4945 -11.7214  -3.4566   5.6069 -14.0102
+#&gt;    3.9337   1.5707   9.7323  -9.5611  -6.0542   1.7548  -8.7745  -0.8593
+#&gt;   -2.5963   3.3522   1.1015 -11.6833   9.1430  -1.4195  -2.1441   5.3349
+#&gt;    0.9231 -11.3075  -0.3966  -1.1279  -8.6557  -6.7683  -6.6452   8.2509
+#&gt;   -9.1589  -1.5744   6.8670  13.6930  14.8558 -12.5839  -3.4082  -3.4675
+#&gt;    2.1363   9.0875  -2.7776   5.6743  -8.4215  -1.8386  -1.2358   4.5936
+#&gt;   -2.3384  -4.6296  -0.8728   6.9245  -4.3669   2.1953  -0.1426  -0.2619
+#&gt;   -2.9725  -2.5162  -2.4488   1.5525  10.9119   1.2826  -2.0064   3.9925
+#&gt;    1.7356   4.2003  15.6750   8.2792  -1.8962   5.0725  12.8496   4.5140
+#&gt;  -11.8334  -3.4519   2.7947  14.1890  -2.7292 -12.2159   0.8604   1.2984
+#&gt;   -2.8984   3.9930 -10.9699   5.5955  -4.4798  -5.7629  -2.2829  -7.4244
+#&gt;   -0.4230   3.7897  -0.3485  -3.5940  -5.9810 -12.7383  11.2135   3.9844
+#&gt;    1.0031   1.5830   2.1796 -15.4505  -0.0103  -1.5564  -2.1125  -4.8409
+#&gt;   -7.0784  -5.9262   1.5270   3.7862  -3.0427  -5.6054   1.9523  11.0924
+#&gt;    6.2898  -9.2303   1.3076 -11.7546  -4.2247  -6.3058   8.3821   8.8879
+#&gt;   -2.4704  10.8537   1.3857  -2.9049 -14.8050  -1.2383   5.0545 -14.2544
+#&gt;   -0.7749   7.1873  19.4923   2.8166  15.6717   5.1621   7.2092   4.2178
+#&gt;    5.9891  13.2824   6.7528   1.0629   3.5650  -5.6318   7.6846  -4.9949
+#&gt;    6.3304  -9.5514  -5.3183  -4.4670  -4.6939   8.8137   7.1928   1.4077
+#&gt;    1.8776   4.4449   0.4581  16.0566   6.6165   2.0124   3.8151  13.4180
+#&gt;   -1.4359 -13.0809   4.7537  -9.6230  -0.2089  -0.8786  -3.1419  14.1345
+#&gt;    7.4082   3.5545 -11.8935  -7.2739 -17.5130  -2.1501  -1.1007  -4.4789
+#&gt;   -6.2560   4.3406   1.7099   4.4764   5.2993  -2.5860 -11.3778  10.5995
+#&gt;    8.7505  -0.2493  13.1371   4.6845   8.3199   7.9976   0.5115   2.5394
+#&gt;   24.2112  -4.6620   4.5530  -4.3868   7.0348  17.4049  -0.5706   3.9495
+#&gt;   -5.2599  -6.5792   1.3911   7.3624   7.7792  -3.5900   7.5302 -16.5536
+#&gt;    4.5886  -9.5352   7.9796  -7.5769   3.3832   0.0814 -21.4389 -18.7231
+#&gt; 
+#&gt; Columns 17 to 24   9.2787   0.1982   1.5504  11.3399   1.2111  14.6673  18.7854   6.3625
+#&gt;   -0.9752  -7.3254  -2.1699  -3.3196   2.0386 -11.0122  -5.8307   4.4358
+#&gt;    5.4092   6.9548   4.1863  -5.8647  -6.8616  -0.6640  10.1477   1.6399
+#&gt;    5.0527   0.3074  11.8740  -0.2983  -1.7315   8.2427   8.3338   4.6236
+#&gt;   -3.4113 -10.7663 -14.8191  -1.3755 -12.7231 -12.0317 -18.3439 -12.3815
+#&gt;   -5.6351  -7.0939   6.8910  -3.4747   3.9538   8.7547   2.5367  12.3079
+#&gt;   -4.6032 -23.8156  12.8856  21.8855  -2.4887   4.4766  11.6141  -9.9143
+#&gt;  -12.9748  11.3994  -6.9040  -0.3635  -1.1072  -2.9807  -3.8582  12.4080
+#&gt;  -14.7694   4.5977  -2.3043   1.8589  -6.9520   7.2736  17.1332  -2.7067
+#&gt;   -4.8280  -1.0101   8.8881   7.0700   3.3483   6.6308 -14.4445   9.7119
+#&gt;    6.3701  -3.4438   0.4236  18.1360   4.6127  -6.6543   9.0146  12.5606
+#&gt;    1.7462  -0.4416 -10.2707  -3.3285  -3.2200  -4.6096   0.3415  -5.3734
+#&gt;   -4.3973 -19.4633   1.0211  -7.1705  -8.5736  -6.6903  -2.4908   4.6847
+#&gt;   12.3077  -2.1865  -2.0518  -5.8797  -3.8338   0.0074  24.1812   5.9451
+#&gt;    1.8784  -4.9494  -8.1918 -12.5618  -3.1634 -13.8377 -19.1328  -6.2817
+#&gt;   -0.0792   2.2887  -3.3162   8.2582 -11.9628   0.4759   8.1665  -9.7636
+#&gt;    2.4773 -12.4838 -14.7030   2.7567   0.0552 -16.9608 -10.3523  -8.7655
+#&gt;    0.5989  -5.3416 -10.7168   0.0302  -5.5756 -10.4459  -0.5413  -3.3216
+#&gt;   -2.9938  12.8954  -2.1675  18.0611  10.9736  15.1048  -5.6207   7.0488
+#&gt;    8.7778  -6.6210   9.0462   6.4141   0.8643   2.4889  14.0606 -10.2601
+#&gt;    2.4058 -10.6825   9.5511  10.2986   8.7942  -1.7521  -4.2356  28.5379
+#&gt;  -11.8222   0.0615  10.6875  12.5234  12.5557   4.9664  -0.2542   0.5580
+#&gt;    6.1884   9.7078  -7.8526   1.0346  10.0836   8.7839   8.3358   1.7056
+#&gt;    3.4453   5.6992 -21.9889 -13.8549   3.0866   6.2960 -14.1334  -2.9023
+#&gt;    0.8797   1.2786   9.0333  -5.1301 -10.4399  -2.3050  -6.3290  17.2964
+#&gt;    5.8477  -0.5743  -2.7718 -12.4145 -14.6122 -14.8226 -10.7020  -1.3154
+#&gt;    7.8803  -6.6331   8.2859   8.1343   7.1675  10.8932 -20.0518   0.5796
+#&gt;   -3.8860  -1.0948   0.3296  -4.5012 -10.3061   6.1479  -7.2847  -9.4322
+#&gt;   10.6928   9.4802  15.4376   4.3896  17.0611 -13.1377   4.1840 -11.3105
+#&gt;   -9.6850  -0.8327   3.5414  -2.6485 -11.2932  -6.9164  -2.7452   9.1715
+#&gt;    3.8276   3.4628  -6.0453  -2.5660  -2.6078  -5.2282   4.0372  -8.0659
+#&gt;    7.0145  -7.9692 -12.5756  -1.9500   4.4520  -7.0035 -13.7542  -1.6138
+#&gt;   -5.0200  -7.9610   6.8528  -3.0502  -5.0376 -11.2978 -10.0984   7.5152
+#&gt; 
+#&gt; Columns 25 to 32  -4.0404  -1.4430   0.3607   3.3906  -2.3900  10.1742   5.5135  25.0277
+#&gt;    3.5052  -1.4220  -8.9032   2.5179  -2.9835  -3.5487   9.3304   1.5378
+#&gt;    6.6237  -5.5332   8.3167  -0.7853  -3.9757 -19.1286  -0.8038  -3.4982
+#&gt;   -3.5841  19.2283   4.6492  -5.5086 -23.7843  -8.8377   2.3797   2.3257
+#&gt;  -16.0309  -2.5382  -0.5452  -1.9870  11.8249  -5.2754  -9.2668  14.0315
+#&gt;    5.3959  -3.6825   2.3861 -15.0529 -27.1974   4.1309   0.8618 -13.8103
+#&gt;   -6.2427  11.3191  10.2869  19.2107   7.5176  -1.7367  -8.1956  -1.5833
+#&gt;   -6.7464   2.4417   4.1475  -6.0524   0.8928  -7.8421   3.1080  15.0934
+#&gt;  -14.8396 -18.4550  -4.8427 -20.0457   1.0476  -9.0142   0.0407   5.3435
+#&gt;   -7.7599  -8.8913  25.1247  -3.6969  -7.0626  13.1309 -10.4188   0.6810
+#&gt;   11.4706   3.3378 -10.5876  -8.8050  -9.6707 -12.6866 -11.1414  14.9977
+#&gt;    5.0862   9.7291   8.7763   3.7778  -1.1664  13.5353   6.8519  12.9135
+#&gt;   -3.3324   5.6444  -3.9762  -3.5315 -10.8808   1.7029   8.5269  12.2964
+#&gt;    8.2909  14.5977  -7.5689  -5.2819  13.6479  -1.1449   6.1938  11.2496
+#&gt;   12.5643  -8.4485  13.2345   6.2915   5.4052 -13.3637 -21.9951   3.2774
+#&gt;   -2.0334   7.6593 -13.9139 -11.0922   4.6272   5.0187  -9.6147   6.7881
+#&gt;   -7.2256  10.9059  -0.0457  12.3354  10.9111  -5.3706  -7.8584  -9.1651
+#&gt;  -17.2781   5.7023   7.1502  -6.2450  -1.9618  -2.8856 -20.4779  -8.7634
+#&gt;    0.5739  12.3096 -20.4771   4.7135   3.8365  30.0124   5.4050   4.8705
+#&gt;   11.3993   6.0502  -5.1046   2.5172  -2.3657 -18.9375   5.3492   7.0289
+#&gt;   -3.5367   3.0282 -16.2368  13.8093  -6.2384   4.6840   0.7767   0.3309
+#&gt;    6.4043  -1.9743  -6.5194 -12.9549   3.8220   3.7224 -10.7915  -6.2787
+#&gt;   -6.5494  -8.4102 -21.8307 -23.1294   2.5929 -10.0229  10.7691   8.3459
+#&gt;  -21.3921  -1.3958  -1.6219 -39.8597   2.5127  -4.2193  -2.6451  -4.0861
+#&gt;    2.0386   8.3716  -8.0288  -4.9455  -5.6660   4.3270   7.3855   0.0502
+#&gt;    7.3334  -5.0550  -1.1481   4.3261   4.3941  -2.4642   1.9597 -13.8305
+#&gt;    0.0530 -17.9078   2.2163   1.3784  -7.7722  21.9329  -5.6291  -5.8106
+#&gt;   -0.2091  -1.8925  -6.6149   1.6091   0.5615   3.1125   2.0563 -12.6482
+#&gt;   -1.7814  -1.8443   4.3231 -10.0351  -6.6395 -11.3759  13.0064   9.0888
+#&gt;   -9.4602   2.8972   3.3182  -9.6499 -27.7990 -12.7712  -8.8141  -6.8202
+#&gt;   -4.4265   0.1465  -1.8701   9.6701   3.4133  -7.3095 -11.8970   1.9456
+#&gt;  -14.8678  11.2420  -0.5675   2.2864 -15.3030   6.6797  -7.3339  -2.2994
+#&gt;   -4.6842  -2.7975   5.7364  13.4220 -10.1075 -16.4725 -10.5969   6.2391
+#&gt; 
+#&gt; Columns 33 to 40 -13.6806   0.0062   2.7024   0.1656  17.1923  10.9281   7.4626  11.8520
+#&gt;    9.3688 -18.2322   7.8515  -6.6231   9.8467  -2.5199  -8.2668  -3.7490
+#&gt;   -1.2868 -11.7083   1.7489  -4.1703 -11.6923   3.7311  10.0102   7.9855
+#&gt;  -11.4488 -10.5165  -2.1662 -20.7559  -7.5795 -11.6906 -15.0227   5.8678
+#&gt;    0.9158   1.0827   6.6132  10.6499  -4.5283  -5.1345  -9.6351   7.3192
+#&gt;    0.0054  -0.5887  10.9239   5.1057   2.9807  -4.8446   4.4155   0.9054
+#&gt;   -9.0131  19.0571   6.2585   8.1577   3.4832  -1.4605 -10.9881   1.1069
+#&gt;    6.0856  -0.1204 -19.5616  17.0739   1.2550  -7.4597   7.1107  -7.6856
+#&gt;  -14.5574  -7.5907   6.1311   2.5264   6.0238   5.5392  -4.5329  -1.7663
+#&gt;    0.1306   2.3647   8.1794   3.8633   1.4221 -15.7879  -5.0071  -9.0464
+#&gt;   11.7398 -15.2417   6.7309  -6.2415  14.7766   0.5277 -15.6454  -2.6727
+#&gt;   -3.5572  -5.6365  -2.0427   2.3924 -11.7509  -1.9761  -4.6798 -16.5507
+#&gt;    7.4429 -22.6910   5.0999  -7.8902  13.8731   4.2361  -7.2994   4.6956
+#&gt;    6.8706   8.3718   8.0796  -3.4826   2.1241  16.3710 -24.5550   6.4867
+#&gt;    8.3631   3.0281  -4.2836  -5.7600   4.3311   3.8684   3.4780  -3.1230
+#&gt;    9.3325  -3.9323   2.3066  -3.1985   1.0924  -3.1014  -7.3856  -8.0613
+#&gt;   -5.1521  -7.0339   4.8373   8.2819  -5.5091   0.0910 -13.6147  -5.9339
+#&gt;   -0.3512  -7.7056  -2.1338  -8.1271  -1.3670   4.7521   6.2824   0.4692
+#&gt;   -0.0775   7.7405  -1.4074  10.9758   9.1070   1.8745 -16.8018 -17.3561
+#&gt;   -1.3179  11.8846  -0.2873 -20.0243  14.6354  -4.1182   1.5336   9.4046
+#&gt;   10.9130   3.9092 -13.3798  17.8067  -6.8405  -5.6669  -1.3556   8.0783
+#&gt;   -1.5835   7.0228   9.7448  -9.0786   0.5883 -16.2685   3.6657   7.2246
+#&gt;    4.0283 -12.8571 -11.8117  -5.7793  12.8297   5.8284  -1.6986  -3.5658
+#&gt;   -5.2016   1.1295   3.1815 -29.1516  15.5328   1.6484  -6.0560   4.8481
+#&gt;  -11.6748   6.3841   0.1354 -12.1163   2.2596  10.0080  -7.1320  -4.4598
+#&gt;   -6.7141  -7.8233   8.7967  -9.3728  -8.1989  -3.9306  -1.3272  10.5867
+#&gt;   17.8738  -6.6065  -3.1639 -12.6698  14.8907  -2.1204 -20.8855  -1.2224
+#&gt;  -24.5303  -0.5188  -1.6418   6.9470 -12.8942   2.1682  -2.4463  -3.8339
+#&gt;    4.0891   4.7385  13.1690  -3.4484   0.2950 -26.1274   5.2645 -10.7306
+#&gt;   -7.0131  -1.0200   7.1370   9.9407 -16.1058  -1.9401   8.9331  11.7031
+#&gt;    7.1123   2.6941  -6.9144  -7.9629  -0.4457  23.3039  -3.6105  -9.2034
+#&gt;   -3.7213  -0.0295  -2.0509   0.4072   7.5788   1.3633 -11.1139   6.5488
+#&gt;    1.5411   9.3479  -3.3330   9.7347 -10.2704   6.7816   2.0851   2.7570
+#&gt; 
+#&gt; Columns 41 to 48   2.8567  -1.2509   0.7831   0.8194   2.7188   8.6329  -9.1868  -8.1310
+#&gt;  -10.5539   0.3902  -5.4374   3.0626  10.5527  -3.2135  -8.0609   5.6467
+#&gt;   17.3678   4.4211 -13.4232  11.7988  12.3921   7.7966  -2.1892   0.1657
+#&gt;  -14.3177  18.2527   8.0008   5.4604  -0.2969  -3.3893   9.6372   6.0779
+#&gt;   17.2199 -11.9583  -3.8224  -4.7957  -2.8627   2.7558  -0.9646 -19.3567
+#&gt;    0.5162   8.6835  -0.2533 -19.4177   5.4450  13.9350   1.8988   2.7825
+#&gt;    6.9616   3.6158 -22.6991  -0.9634   5.5848   1.2298 -11.3165   0.6343
+#&gt;   -6.6978  12.4381   5.6340   1.4762  -9.8032  11.8252  19.2159  -9.1805
+#&gt;    3.5207  -8.3466  -5.4355  -1.2235  -2.9561   2.4640   3.4395   2.7065
+#&gt;  -12.9215 -10.0668   0.5002  14.1226   6.6489 -13.0293  15.9507  25.0442
+#&gt;  -10.7093  -8.4703  -9.3923  -7.2155  -9.0077   8.0308 -16.2839   6.2839
+#&gt;    3.2378  -5.2749  -5.6435   2.7889 -13.7544  -2.0767  15.1048   2.6578
+#&gt;    7.3178   9.6251  -1.1023  -1.0402  -5.3451  -8.2557  14.3928   7.5724
+#&gt;    3.8133  -1.8312  -8.7755   4.6773   6.2228  14.8890   4.6655  11.3970
+#&gt;   -2.3716  -9.3234  13.5430  -8.2350  12.4191  -9.2580  -4.3517 -10.8570
+#&gt;   -3.6950  -7.4019   4.1029  -6.4903  -4.2991   5.4390   6.6195  25.1670
+#&gt;    6.7634   0.2952 -15.1368  -8.9548   8.9557   1.9366   2.4054  -6.7388
+#&gt;    3.7285  -1.9482  -3.5839   4.9337  -2.0387 -12.1164   8.1546   5.6810
+#&gt;   -1.9431   7.5485  -1.7385  -5.2008  -4.8101  10.8890   4.2470  -6.5374
+#&gt;   -4.1856  10.4930  10.8537  13.1265   1.2434  10.1904 -12.1159  -1.9328
+#&gt;   -1.4070   2.7280   4.4063  -2.0554  -4.2765  -5.4015  -0.9147   7.3790
+#&gt;   -0.6859 -18.6484   4.5034  -4.0111   5.4810  -5.7982  22.5001  -1.1958
+#&gt;    7.4635  10.7947 -11.7759 -13.4256 -14.8836  -8.6435   4.0845 -16.3680
+#&gt;  -16.4932   1.9518  -8.3171   3.2632   5.2895  -7.0985   7.4600 -14.5847
+#&gt;   -2.4035  22.0945  17.4568  -1.2950   7.9909  -0.7241  15.2463  -0.7937
+#&gt;   -6.5223  -4.7757  -4.5651  -5.2934  -7.2794   0.2040  -2.0507   2.6962
+#&gt;    1.7143   6.3054  13.2956   6.2607   3.7199 -29.8309   2.6897  -2.4614
+#&gt;    3.4529   4.6949  -7.0161   7.2513   8.2711   3.9631  -0.0563   7.0867
+#&gt;  -12.2865  -5.8011   4.6900  -1.8933  -1.1135  -6.4920   5.6744 -16.6353
+#&gt;   -5.8497   0.6882 -14.7212  10.2791  -5.5395  -6.1162  -1.8134  -5.8064
+#&gt;    3.7475   3.9480 -15.1052   0.0570 -11.1260   3.7976 -13.5154  -0.3392
+#&gt;    3.8720  -1.8354  -1.9172 -14.5821  -1.8462  -5.6563   1.7059   0.3672
+#&gt;  -16.0087   0.7300   1.3261   3.0401   8.6503  15.3800 -11.1643  -4.5400
+#&gt; 
+#&gt; Columns 49 to 54   6.5568  -9.7026  -7.1953   6.3538  -7.4264  -0.9708
+#&gt;   30.9744  -6.4547  12.6697   3.6079   0.0469  -0.7767
+#&gt;    9.4024 -16.0238   8.9534  -0.2179   5.3167   1.5216
+#&gt;    1.2299  -3.9890  -6.1065  -1.6290   0.9819   1.2173
+#&gt;    2.6867  15.8257  -2.8357  -2.2088  -2.5515   8.3031
+#&gt;   -6.9879  -6.5174 -16.0876  -5.6866   5.9698  -3.6638
+#&gt;   -1.0844 -14.0817  -9.1045  11.2855  -6.7743  -1.6082
+#&gt;   -2.5941   8.7188   6.2116  -7.3284   0.8581   5.6757
+#&gt;   -4.8610  16.6501  -9.9816  -4.9624   3.7696  -0.2728
+#&gt;    1.7523  11.3379   1.6448  13.4161   1.0309   2.6940
+#&gt;    4.0764   2.5950   4.9161   2.0570   7.1652  -0.3929
+#&gt;    2.9331   6.7424  -0.0478   8.2542   1.1336  -2.3051
+#&gt;   -1.6343  -7.1790   6.0740 -15.0653   2.8794  -1.6636
+#&gt;    3.9545   5.8668   1.1831   1.5058  -8.0106   6.4086
+#&gt;   -3.7696   5.5248  -7.8983   7.8554   3.2396  -3.2321
+#&gt;   10.0122   6.1376   7.4421  10.4932   2.8359   3.4674
+#&gt;   18.6560  -3.6614   0.6000   4.3321   3.1300   0.5418
+#&gt;   -8.0462  -2.4664   6.4827   2.6765  -7.8352   1.4117
+#&gt;   -7.2277   8.3355  -2.8573   2.8742   3.5099   2.4450
+#&gt;    3.2092 -14.4512   8.0362 -14.1675  -9.4150  -0.8199
+#&gt;    8.4226  13.4240  -4.0751   1.4271  -1.3003  -2.6394
+#&gt;  -20.1595  -0.7043  20.5986  -1.4484  -1.9009   4.3301
+#&gt;   -0.9838   0.8683  -8.6424   0.5942   5.8201   1.5375
+#&gt;   -4.6741  -4.9432  11.1699  -1.8994  -1.3315   5.7283
+#&gt;   15.8932  -5.5368   6.4147 -10.6884   2.2649  -5.7028
+#&gt;    4.7088  -8.3024   2.7415   0.9052   8.9913  -0.3258
+#&gt;  -22.7436  15.3307   1.5748 -10.1957 -14.2503   3.2781
+#&gt;   -4.2070  -8.0638   2.4944   4.2893  -3.7130 -11.7386
+#&gt;   16.2969   2.3844   9.4157   4.1409  15.5271  -0.2734
+#&gt;    0.3184   2.1784  -1.5733   9.0323   0.8671  -2.6642
+#&gt;    2.9546   7.9019   2.1216   2.3385  -1.4722   2.2566
+#&gt;    7.2053  -3.8532   3.4720  -1.0322  -2.0542  -0.4322
+#&gt;    1.9949  14.8634   0.7372  -6.6369   1.6521   3.1908
+#&gt; 
+#&gt; (16,.,.) = 
+#&gt;  Columns 1 to 8   1.7697   7.8231   8.6120   5.2963  -1.2343  -0.9777  -4.0528  -7.1771
+#&gt;    2.3795  -3.7493  11.5089  12.0890  -9.3660   7.1136 -12.0514   4.4451
+#&gt;   -1.8789  -1.2572  -0.6798 -13.7703  24.1168   5.1803  -3.9050   0.9457
+#&gt;    1.9866  -5.0244 -22.3673  -3.2447 -34.5358 -21.4472  -0.6213  -8.1068
+#&gt;    2.6026   4.2723   1.5155   0.1620   9.4152  12.7744   3.3296  12.9299
+#&gt;   -0.3071  -4.6883  -0.1982  -2.4439   1.0588   8.3292   8.6379   0.2811
+#&gt;   -2.8710   4.8084  -2.9594 -28.5942  -4.3785  -3.7145   1.0101  -3.0829
+#&gt;   -0.3498  -8.4707   1.7360   9.8888  -6.2180   3.2786   2.4448  -1.2863
+#&gt;   -4.0379   5.9845   6.6338  -5.8816   4.7582  14.4587  -8.1834  -6.8522
+#&gt;   -8.8423   8.8415  14.1958   8.3647  12.2898  -8.2332   3.3496  -2.9632
+#&gt;    2.0142  -1.0245  -7.4184  16.0781  -3.5417   0.5323  -8.1766   0.3534
+#&gt;   -2.4849   4.1325  -2.9216 -17.0117   3.2954 -12.2175  -9.8271  -2.0682
+#&gt;    0.9173   4.2991   6.8886  11.4014  -2.7774  -1.0993  -6.8784  -2.8886
+#&gt;   13.4337  11.5280  -9.8062  15.3525  13.9176   0.1049  -3.4438  -5.9171
+#&gt;   -1.0894  -7.1077  -9.6651  -9.9595   2.7730 -16.9990 -22.5391   5.3471
+#&gt;   -4.5623  -7.1814   5.0093   0.2806  -5.7331 -14.1492  -6.9815  -8.1291
+#&gt;    1.2719   1.1986 -14.1326 -14.5914   8.7864   5.2089  -0.7267 -10.3378
+#&gt;   -7.0493  -2.1834   4.9225 -16.6646  12.1307 -15.3796 -12.8936  -4.1587
+#&gt;   -6.9274   0.5319  13.3562   0.6256  -5.4126   2.4206  -0.0722  -7.8487
+#&gt;   -1.1814  -2.7907   7.6144   6.9151  -9.7289   8.8656   7.7155   7.1346
+#&gt;   -6.4354  -4.7063   3.8487  -4.7140   4.7482 -13.2276  10.2124  -2.0034
+#&gt;   -0.5469  -4.1568   2.5225 -10.3752  -3.8306   2.1754  14.3554   8.1533
+#&gt;    3.1296   4.0774  10.4607   8.8223  11.7213   5.8240  -3.9445  11.0766
+#&gt;   -2.0549  -1.4588  -7.5864  -6.6973  -6.0404  -0.5402  -4.5653   4.3114
+#&gt;   -2.2802  -1.8898  -5.0546   5.5354 -11.9697  10.7120  14.6425 -12.6019
+#&gt;    5.8921  -4.1813 -14.2185   2.9851   9.3341   2.8864   7.5513   0.0464
+#&gt;    0.6838  -0.4889   4.8951   4.5487 -10.2015   2.1203  11.6653   4.5425
+#&gt;    4.1227   1.1392  -9.5394  -2.0351   2.2566  -7.8231  11.2073   6.9540
+#&gt;    2.3322  -5.5290  -1.9318 -17.9380 -17.8237  16.7737   0.6159   4.8332
+#&gt;    4.7504   8.8325   0.5344  -3.4972  -0.5738  13.3032  -4.2240   8.0981
+#&gt;    0.2960   2.7964  -8.7055   2.4322   2.3106   4.6955  -8.5344   4.2836
+#&gt;    6.4717   8.9236  14.2096  -9.3419   7.2685  -7.3669  16.0820   2.5748
+#&gt;    3.4450   5.1718  -7.1637  -7.9680  -3.8327  -0.6678  -1.3635  -7.9201
+#&gt; 
+#&gt; Columns 9 to 16   9.9000  -1.9064  13.9795  15.4671  -7.0296   4.4961   1.0969   4.8526
+#&gt;  -10.1480   6.5909  -9.2743   6.3157  -1.2375   5.5422  -2.0735  -7.6841
+#&gt;   -1.6440 -20.5329  -8.7478  13.6704   5.3678  -5.4746   0.1420 -10.4349
+#&gt;   -4.6557  -5.9320  -7.3280  -2.6079  -5.7878   9.3410 -24.6938  17.9438
+#&gt;   -7.7938   8.5064  -3.1349   5.0849   0.6603   7.5690  18.2352 -13.2141
+#&gt;   16.4708   3.4304   1.5029 -13.6260   6.8327  12.9616   4.4406 -11.4976
+#&gt;   37.2328  -3.8313  -6.8786  -3.6172   6.9452  -2.2848   9.2737  -0.4529
+#&gt;   -8.7894   3.5348   6.9157  -2.5667 -13.7301  -8.0534   1.5875  -2.3443
+#&gt;   -8.4161   3.5718  -1.2136  12.7011  -5.9564  -4.3919  -1.4307  17.0527
+#&gt;    9.6242   3.7935  -0.7158 -14.5228   0.2585  20.8162  -6.8776 -16.3709
+#&gt;   -1.3885  -4.4480 -11.0857  12.8871   8.8989   8.2894   3.1028  15.7340
+#&gt;   -7.8462   3.0626  -5.0693  -1.3555  -4.7493 -18.0875  -5.3732  -8.5544
+#&gt;  -12.8403   2.9894  -4.3974  17.3711  -6.7865  11.2650  -7.1493  19.7333
+#&gt;   -4.5397  23.2233  13.6957  11.3891   1.4535  -0.5644   0.8427  10.4301
+#&gt;   -9.1539   4.0060   3.8074  -1.1349  -0.0091  -2.1499   0.4519  -3.8254
+#&gt;    8.5675   0.6465 -17.8590  -6.1994  14.4108  11.8678 -13.6367  -8.6303
+#&gt;   -0.3827   1.0060 -11.8104  12.2828  -1.2360   1.9369   0.4711   3.2054
+#&gt;    7.9029   4.0724  -6.1550   7.3966  -8.7446 -16.2476   8.1946  -2.3873
+#&gt;    3.5166   4.5060  -9.2269  -4.2032   6.4045  -7.9896  -4.8831   5.1163
+#&gt;    6.5437  -4.3654   0.4464  13.0827  -0.4307  -0.9888   3.0257   7.4889
+#&gt;    3.5640  14.2778   0.9009 -16.2148   3.9189   0.6202  10.9532  -6.6318
+#&gt;   -4.4477 -14.7847  -6.1820  14.1875   5.7793  -1.8452  -3.8695  -0.0626
+#&gt;   -6.1742   0.6028   5.8470   9.2874   8.1991 -11.7732   7.5792  14.1381
+#&gt;   -7.0286  -3.3844  -2.7282   1.9353 -10.3101   4.4140  -6.2162   7.4030
+#&gt;   -2.8200  -5.8922   5.0646   3.0160   3.3355  -3.6240 -19.4316  16.2253
+#&gt;   -8.2071 -10.2448   4.0068  -5.9674  -0.0127  -8.8853 -10.3026   1.2651
+#&gt;   -3.6192  -9.8168   6.5678  -5.7630 -16.5598   9.7662  10.2760   0.4595
+#&gt;    0.3560 -17.6844   8.4908   0.4143   2.9337 -25.1112  -3.2149  11.9771
+#&gt;    6.3447   1.0973  -5.5224 -16.2662   6.2528  12.0775 -12.4180  -1.4815
+#&gt;   -0.0916  -4.3156  -1.3490 -16.8514  -0.0790  -6.3456   7.8934  -4.1172
+#&gt;   -2.3289  -6.3002  -4.8253  14.1059   5.3633  -1.3675   3.6250  19.4321
+#&gt;    7.8740   1.9631   1.2724 -21.1095  12.1956  -3.0687  15.0578   7.6955
+#&gt;   -1.9299  -1.7296  14.4124  -1.6739  12.2284   0.4329   4.1399   8.2577
+#&gt; 
+#&gt; Columns 17 to 24 -11.6009  22.0213   6.6196   2.7686  -6.7935   6.6405  -1.8731   1.5987
+#&gt;   -4.8475   1.4497  -6.1142  -9.1211   2.8824 -14.6762   4.0170   0.5676
+#&gt;   17.7661 -10.7091 -12.5137   3.4202   4.1326   2.1232  -7.6068  -7.5023
+#&gt;   -7.5991  15.5823  -1.7418  -1.3845   8.1397  12.8041   9.0708 -10.6008
+#&gt;   -9.1976  -2.8274   8.0926 -30.0434  -4.1656  -9.8114   3.7626 -14.7293
+#&gt;    3.9396   8.3693  -2.1834 -13.3237   6.9885   3.0003  -5.2317  -0.4947
+#&gt;    9.4872  10.7169   5.8791 -25.3369  -1.0772  -4.1301   3.7120  -0.2123
+#&gt;    3.6405  -4.1067  -3.4298 -12.0492  -7.5760   3.1582  -6.9279  -4.5208
+#&gt;   -6.1974  15.0876  -1.0535  -4.2255   5.1222  10.2420   2.5771  10.4022
+#&gt;   -8.4490  -0.8669  -1.6400   7.2100 -10.0771  10.1309  -4.0657  -3.3534
+#&gt;   -8.3783  -0.1408  18.0774 -11.6504  -4.8912   2.9820   9.5248 -12.6447
+#&gt;    2.4244 -12.6826   5.0148   1.4782  -8.0102  -2.8640   1.2667   4.6894
+#&gt;   -6.8496   7.6288   3.1449  -1.7850  -7.3471   0.8432   8.6055  -3.6856
+#&gt;  -18.5910  -6.8910   9.3741  10.0406   6.3631  -4.0381   7.5384  12.6679
+#&gt;    6.9114  10.3067  -8.0363   1.8523  -9.8395  -6.1725 -10.5238  -4.5921
+#&gt;   -5.7217   0.4141  -5.1321   1.9550  -6.6907  -4.6277   2.3827  -7.2143
+#&gt;    1.7261  -6.2427   5.9918  -0.0032  -9.7840  -1.1118  -1.0200  -4.4638
+#&gt;  -12.6195  -2.7205  18.5832   2.2899  -3.9882  -4.4527  -0.1273  -6.8444
+#&gt;   -6.8270  -7.2222   1.3957  -3.1436  -0.5386  -2.2124  12.1577   8.2202
+#&gt;   -8.0268  15.0382   7.8973 -13.2213  17.1631  -0.8632   0.3795   9.1084
+#&gt;   -8.8132   5.8229   4.5916  -5.5550  22.7646  -0.7858  16.6488   2.4779
+#&gt;    4.4191  -4.5787  -1.5911   3.2636   2.8622   9.6346   0.7014 -13.4927
+#&gt;    0.3287 -13.3972   3.8340 -10.3846  -5.9126  -6.8613  -0.2202   2.4956
+#&gt;   -7.6616  -7.8870   3.6699  -0.7547 -11.8913  -1.4331   1.9404  -3.7107
+#&gt;    9.2290  -8.7490   0.2571  -7.0671  -5.8319   3.5638  -4.6722   6.5871
+#&gt;    4.9291 -17.3877 -14.8163  -6.3332   3.4669  -3.2231  -2.3084   1.8338
+#&gt;   -7.1738  -0.2332  31.5866  -2.3907  -1.3109   1.2148  10.8057  -8.3530
+#&gt;   14.8927 -31.3649   9.4766  -0.0843   7.7952   7.6698  -2.7628   0.2231
+#&gt;    6.4920  15.8902  -7.7696  -0.1625  -9.2543   4.4658  -4.4855  13.4814
+#&gt;    5.9749  -7.7058   4.5334 -22.2708   7.1025   4.3723   2.0585  -1.6211
+#&gt;    1.4198 -14.7659   9.4616  -2.1611   0.1507  -4.8649  -4.4704  -3.7856
+#&gt;   -2.4949   0.9292  17.9165  -4.6141 -15.8654 -16.5336   9.6175  -8.8211
+#&gt;   10.4338   4.0037   6.6796 -19.1662  -4.6070   7.0008  -3.3524  -6.1062
+#&gt; 
+#&gt; Columns 25 to 32   9.6566  -9.1663  10.9344  11.7395   4.9549  -7.5276  -1.2756  18.8765
+#&gt;    7.4470  -8.4346   0.2678   5.7281  -5.2281  -9.0784 -12.0160   8.6164
+#&gt;   -6.7503   6.9754  -8.3045  18.7376   3.1970   4.1477   8.0606  -6.6611
+#&gt;    5.8657 -14.5795   3.7240   1.0550  -1.9824  12.4239  -9.2815  -6.5168
+#&gt;    7.8076   0.5792  -5.2520   7.5196   3.5677 -12.2524   7.8021  -7.0846
+#&gt;   -0.0678  -7.5979   8.7863  -5.6260 -11.0602  -6.0723  -0.7457   3.0615
+#&gt;   -8.5058  -9.4856  -1.8653   2.1144  17.7533  -2.5741  -5.2134   7.3999
+#&gt;   13.6945   5.5516 -14.6413  -4.7243  -4.3727  -4.4692  -5.5395   2.4296
+#&gt;    3.5659   1.1249   5.7436  -0.4159 -13.8032  -4.7807  -7.3343   6.7135
+#&gt;   12.9533  -0.0736  -4.9114 -10.2170  -3.7267  -5.5711   7.0701   6.4046
+#&gt;    3.2389  15.2308   1.3515   8.2620  -2.8910  -9.5465 -19.5893   1.4798
+#&gt;    1.1973   1.3824  -8.8070   3.6789  -2.8311  -9.6534  10.1234  -7.3253
+#&gt;   12.5387 -10.9293 -11.7844  15.2538 -11.6515  -9.2029 -10.9647   1.2111
+#&gt;   -5.8873   0.6263  13.7267  -3.3958 -10.3882  11.6040  10.8817   6.1875
+#&gt;   17.4590  -0.6513  -9.4424   7.7948   2.0112  -3.4352   9.7776 -17.2679
+#&gt;   -0.0230   3.7101 -14.3744 -18.1747   2.7047  -7.3458 -16.5721  11.0432
+#&gt;   -6.6831   2.1461   5.1203   3.1521   2.2618  -8.8729  -7.5402 -10.5199
+#&gt;   19.7619   6.8662   1.3187 -12.0551  14.0766   1.9216   2.7810   5.9483
+#&gt;   -6.1166  -5.5280   6.5886 -12.0131  -1.1430  -5.3102  12.8459  18.9011
+#&gt;   -0.1965  -6.7035   1.8048   9.9410  15.8474   1.5910 -11.0978  12.7264
+#&gt;   -3.9838   3.8904   1.1062  -1.3114  -0.2945   0.6846  -4.8257  -6.9986
+#&gt;   10.7371  -5.5756   2.1448   7.2605   2.5549   3.2154  -9.1792  -8.2455
+#&gt;   -5.9145 -12.5473   8.1026  -2.6770  -0.5885   7.0260  -6.5885  -1.2770
+#&gt;   14.0670 -12.5548   0.9392  -2.0448  -4.4855  -4.5305  -3.8521  -1.4823
+#&gt;   -8.0152 -11.9022  -3.7449  -0.0058  -7.5993   2.7174   6.7479  10.2567
+#&gt;    0.0129  12.9285   6.7327  -2.9286  -6.9996  19.0981   0.8962  -0.3266
+#&gt;    3.8237   0.1796   6.7072   7.8696  -0.5336   3.4412  -1.9806   7.1151
+#&gt;   -5.1513   0.2888   1.4149   1.7265   5.0009   6.4119  11.0597   4.7295
+#&gt;   -9.3078 -17.7354   1.2000  -5.0176   5.0675  -7.7543  -4.4655 -12.6498
+#&gt;    1.6458  -2.4406  -4.0469   9.3308  -3.2241 -12.6094   5.7571   5.8206
+#&gt;   -6.1657  -9.3138   1.2429   2.0299   4.8295   9.0674 -14.2895 -15.3708
+#&gt;   -0.8646 -10.1943   1.4619 -20.2956   9.6826  -5.8096  -6.9881   0.2323
+#&gt;    2.6498  -9.5218   2.3828  -1.3531  -0.6577  -3.0825   0.4045  -1.4742
+#&gt; 
+#&gt; Columns 33 to 40 -10.6229   8.6789   2.2178  -6.7040  -0.6326   1.5334   9.2213  -3.3000
+#&gt;   14.4014 -11.2622  23.1654 -12.8932  -8.0920  -7.5030  -5.5508   5.0168
+#&gt;  -11.0422  -2.7758  -2.8666   4.5146   1.9365   0.2622  -8.9088   1.2884
+#&gt;   -5.3691 -15.7819   1.7578   0.9424  13.8666   3.3970   5.4180 -18.4514
+#&gt;   12.3635  -1.1468  11.3830  -7.2679   1.0156 -11.4125  -4.8787  -7.9729
+#&gt;   12.3441   4.6422   5.6238  -7.8218 -10.3204   3.0823  -5.5093  13.1061
+#&gt;   -8.0159   8.0071  -5.8348  -8.1469   9.9367  11.5474   6.5032  -0.6570
+#&gt;   16.8167 -11.1660  -9.0735  13.9219  -9.8278   1.4944  22.7669 -12.5160
+#&gt;  -14.1331  17.7856   3.6391 -12.4763   5.1527  -6.3651   2.3149  -0.9128
+#&gt;  -11.7482  18.8823  -3.9689   4.8419  -9.8544   0.2651  12.1470  -5.7802
+#&gt;    5.8595  -2.0600  23.0567  -6.0460   4.7197  -6.5538 -12.0873  -0.9905
+#&gt;   -8.3227   7.2505 -13.9033   0.2445   6.9149  -1.6440   2.3829  -5.6416
+#&gt;   -0.1194 -12.8388  -5.2210 -14.3280  11.0063  -2.5582  -2.0446  -4.6459
+#&gt;   -1.6576  -1.8032  -2.9412  -2.9725  -0.7047 -12.1007 -11.6922  -1.1728
+#&gt;   -0.5854  -6.4267 -13.1453 -11.3883  -5.0637  -4.0518  -6.3786   1.9079
+#&gt;   -2.5605   5.2524  -0.3209 -11.5121  -6.4272   3.4591   8.9157  -4.7580
+#&gt;   -4.8789   5.7178  -0.9700  16.5166   1.4804  -9.6791   0.1136   1.4191
+#&gt;   -6.7359  17.0078   4.4160  -5.6875   6.0253 -11.5817 -11.0020   0.6016
+#&gt;    5.3858   1.8812  -4.8278  -7.9088   5.7006   7.6922   6.1906  -2.4290
+#&gt;   13.8951  -2.6366  13.2971 -22.1817   3.2779  11.0970  -5.5572  -4.3547
+#&gt;   -7.1838  -8.4953  -0.3669   0.8697   0.1441  -0.4647  -0.1208  -1.3417
+#&gt;   -2.5223   1.0208  -1.9705  -1.4863   6.8423   3.6362   1.7609   4.1067
+#&gt;    1.7234  -0.5239   2.8915 -12.4322   9.1356  -5.0871  -7.3245  -5.7226
+#&gt;    1.5437  -9.9038   6.0135 -14.2809  -2.2871 -10.2505   5.6736  -1.9195
+#&gt;   27.0936  -7.8168  -6.7540   3.1978 -13.1858   9.4741   3.8702   5.3423
+#&gt;   10.7769   8.8273   6.0504   5.1611 -13.6625 -21.3920  -7.8566  -6.6555
+#&gt;    9.3609  -9.2878  -9.9304  -4.2579   7.2767   4.6373  10.4448  24.5626
+#&gt;    3.6608  11.5316  -0.4860  17.9653   2.0688  -7.4523  -3.3428  -0.0631
+#&gt;  -19.8172 -10.8198   5.3163  -8.2135  -5.1607  18.4298   1.3843  -3.0208
+#&gt;   -6.4828  -8.7025   5.3616  -7.4629  -3.3489   7.0791  -8.1782 -11.8469
+#&gt;  -17.0357  -9.2986  -3.1121  -0.6257   9.9659 -12.5118 -10.7899 -11.4824
+#&gt;    0.7937  -2.6290  -2.4406  -1.4290  -1.6814   1.5998  -6.5598  -6.3476
+#&gt;   -0.0380   5.8284   2.9693   6.8694   3.4494  -0.5668 -10.5894 -10.2823
+#&gt; 
+#&gt; Columns 41 to 48   6.2774 -18.1864  -3.4018 -11.2180  -3.5322   2.7174 -10.5623 -10.6231
+#&gt;  -12.7863  18.0507  10.6248  -0.3488  -6.7826   3.7352  11.3845  -5.4283
+#&gt;   -0.1122   9.5195  -6.2941  -7.7556   4.4018  -4.6933  10.8892   1.8100
+#&gt;   10.9189   5.0866   7.9907  18.7885  -5.5178  -6.2315   8.2512 -14.7725
+#&gt;   -8.2085 -16.0672  -7.0914  -2.8034  -7.5503 -17.5078   0.2551  17.3002
+#&gt;   -8.2185  27.4550  26.0083   8.5154 -17.4318  -0.4562  -6.6915  -8.3391
+#&gt;   -6.8927  -0.3459   7.1382 -11.8649  31.5663  22.8106   9.1958  -2.2901
+#&gt;   14.8486 -15.4098  -0.3975   6.1668   5.2021 -15.0225   5.3419   8.6112
+#&gt;    5.8912  -4.2721   1.5269 -18.8994  -4.8116 -13.1604 -11.2970  -6.8411
+#&gt;   -3.7090   6.0151  15.8139   0.2310  10.5546   3.2455  -5.8534   3.2205
+#&gt;   -8.9412   5.9837  -4.2832  10.5690  -9.1906  21.0550 -10.0617  -5.7039
+#&gt;    8.3613  -2.2182  -9.7109   3.6916  15.4845   7.2722   9.6880   3.2922
+#&gt;  -12.5781   1.7351 -29.7383  -1.7174 -17.1644  -4.0509   0.9297 -28.8874
+#&gt;    3.8587  -6.1355  10.8727   3.1289  13.5693 -12.7812 -10.7436  -2.7971
+#&gt;  -15.7318   5.4135  -1.1823   3.3775   0.0919  15.9588  -5.8201   7.5614
+#&gt;   -2.0112  15.7379   5.4613  -6.2413  -1.9344  10.4081  10.6265  14.4889
+#&gt;  -10.7885 -11.0621   3.3423  -2.7723   4.6190   5.9750   4.7556  -9.2367
+#&gt;   -2.8487   6.3066  -1.2910   1.2042   6.7052  -5.2055  -3.4929 -15.4715
+#&gt;   13.9751  -8.3139  -9.1452  -6.8789  10.8781   5.6326  12.5587  -0.6621
+#&gt;    1.7425  25.6232   3.9080  -5.4210  -3.4463  -2.2130  -8.6763  -4.5002
+#&gt;   -3.9253   2.7579  -6.7523   3.3445  -3.5237 -19.9601   2.2407 -15.6260
+#&gt;   -3.2751  14.7127  -2.1673   8.7985   4.7725  11.2860  -6.2253   1.1196
+#&gt;    2.0084   1.3282 -24.1468 -17.8029 -15.3072  -0.8156  22.5892   0.8694
+#&gt;   12.2622  29.0844   0.7350 -10.3071  -4.3900  -9.1008  -5.3611  -5.9090
+#&gt;    6.2074   8.0331  10.4998  -5.2353  -4.3860  12.3646  20.1491   7.3873
+#&gt;  -17.4049  -6.4294  13.3101   3.0213  12.9921  -0.2051  11.9756   8.7426
+#&gt;    0.6374   4.9446  -6.3065  -7.0719  -2.7146  -2.9173 -19.1332  -1.9017
+#&gt;   10.5367 -12.9605  14.5342   0.4513  14.8723   1.2068  14.6907  -1.3696
+#&gt;  -10.9197  18.3182  14.4147   7.8424  -5.6563   6.5287  -7.8349  20.1241
+#&gt;   -1.4181  -6.9705  10.2070   2.5901  13.3057  -3.2291   9.3065 -12.6322
+#&gt;    1.2362 -22.9517   3.5291  -4.7567   5.9149  -3.3526   5.4651  -8.5231
+#&gt;   -3.6754 -11.2693  -1.1005  11.3768  -3.2981  -3.3885   0.6195 -25.9140
+#&gt;    0.4240 -21.1537  29.8423   9.7752  -2.7528 -16.3139   8.5193  -2.7063
+#&gt; 
+#&gt; Columns 49 to 54  18.1912 -15.7907  -3.9558  -2.0804  -5.8474  -2.1283
+#&gt;    8.8668   3.5290  12.7925   0.1398   2.6789  -1.2222
+#&gt;    3.2999   0.0110 -12.6241 -16.9354  10.1889  -2.9761
+#&gt;   -2.1456  -8.0910  15.6478   3.4534  -9.4753   1.2913
+#&gt;    5.5167  12.1132 -19.4272  -2.5755   3.7057   3.4676
+#&gt;    8.5158  18.9967  17.4010 -14.7757  -5.0401  -5.8380
+#&gt;   -1.0111   2.4076   7.3687  -0.7984  -4.9909   6.1201
+#&gt;   -0.6987   2.1589  18.6585  -1.3616   2.5712   2.5530
+#&gt;    4.8704  11.3662 -10.0319   1.9212   8.7550   1.0719
+#&gt;    6.1440 -16.3357  22.2800   0.1487   8.5740   2.0471
+#&gt;   -1.8990  12.0370   9.7612  -6.0730  -9.3985  10.1700
+#&gt;   -6.6664   2.8795   1.7469  -0.2079   8.2883  -4.1957
+#&gt;  -18.5809  -8.5180   2.4861 -19.8535  -1.6639  -0.7353
+#&gt;    6.4558  -3.1731  -3.2707  18.1044  -1.8837   0.5426
+#&gt;  -21.2803  11.4748   5.1035   7.9882   4.7134   4.2060
+#&gt;    2.1991  -5.9666  11.6436   0.5247  -2.6853   3.9402
+#&gt;    2.3018  -1.2973   0.7850  -8.8660   1.2988   3.6674
+#&gt;   -2.0062 -11.9106  11.7197  13.1610   2.9937   0.8289
+#&gt;   11.3765   0.3339 -11.8986 -17.0903  -5.6438   5.4151
+#&gt;   -7.2570 -10.3062  -0.8249   9.2584  -5.8272  -8.5365
+#&gt;   -5.0818 -12.0503   3.3193   5.4731  -1.8446  -0.5912
+#&gt;   -2.4373   6.1826  -4.4394  -2.8937   5.4018  -0.3870
+#&gt;    2.6183  16.8474  -4.9494   3.4744  -5.2207  -4.3346
+#&gt;   38.2951   8.5367   6.6771   0.6938   3.2265   1.0787
+#&gt;    2.4264  -6.7126   0.3540 -19.4928  -0.4715  -5.2072
+#&gt;    5.8695 -15.1944  -6.8595  13.3478   0.6535  -1.8124
+#&gt;   13.5172  -4.1587  -6.7230  -9.0951 -11.7814   0.6975
+#&gt;  -17.8280   8.9426   2.4428  -5.3610  -5.7832  -4.6514
+#&gt;    2.4178  -4.2723   2.7156  -7.5423   7.7660  -9.2237
+#&gt;    8.1569   2.9548   3.8302  -5.8128   3.1560  -6.0940
+#&gt;  -14.6825  -0.2284  -0.3681   9.4919  -3.2106   3.3711
+#&gt;   25.3513  -7.2872  -0.9155  -0.2299 -12.7788   6.3386
+#&gt;   -8.1845  -6.2757  14.2294   5.2468  -3.6520  14.6932
+#&gt; 
+#&gt; (17,.,.) = 
+#&gt;  Columns 1 to 8  -1.1945   5.9357  13.7610   4.6001  -2.9968   0.1567  -0.4438  -2.4103
+#&gt;    0.0367   2.4763   6.6312  -9.5617   0.1143  14.2438  -4.9430  -0.7515
+#&gt;   -3.9668  -6.6500  -2.5517  -3.6693 -11.0968   6.0578   6.2461   0.8728
+#&gt;   -2.1622  -2.7587   3.6359   0.1597   1.3649   7.5670  -0.8458  -4.0292
+#&gt;    3.9699  -7.5345  -7.1817  -5.2912  -6.8139  -1.3103  -1.3937  -9.7703
+#&gt;    4.0056  10.3494   2.6678  -5.7951  -4.0568   2.8811   1.1952   2.0518
+#&gt;    1.7394  -5.4003   0.6521  23.1251 -11.7972   6.4599  10.9959   9.3620
+#&gt;   -6.8429   0.3808   3.2818  -4.4718  13.9160 -13.1619  16.5356   1.2451
+#&gt;    6.0260   0.2662  -1.4141  -0.8238  -3.1741  -7.5774  -3.0280   1.9594
+#&gt;    3.9875   5.0174 -13.7977  -3.6185 -17.4418  10.5346  11.7027   9.1794
+#&gt;    1.0173   3.9696  11.9721  12.7129   4.3783  12.9305  18.7580   3.0929
+#&gt;    1.5908  -6.0321   4.2819  12.1846  10.0166  -6.2988  -2.7936  -3.6806
+#&gt;   -3.1961  -4.4246   7.6744  -2.4911  -1.4316   7.3917 -25.5858  -9.7842
+#&gt;    5.0093  -6.0207  -0.7671  -1.3993  -1.7692  -5.8252  -1.3073  -9.7046
+#&gt;    5.7459  -2.7283   1.3508  -0.5482  -1.6587  19.6017  14.6458  -5.2798
+#&gt;    3.4183   1.9121  -1.4290  12.7108   4.8154   9.7358   0.5929   7.0476
+#&gt;    1.2533  -1.4050   0.5945  -2.2962  -3.5722  -3.1145   7.9074   4.0017
+#&gt;   -1.0276   0.0731  -2.5617  17.7163 -11.6945 -15.4622   0.1691   9.3550
+#&gt;   -1.3410   6.6033   6.5680   3.6091  12.2945  -8.3083  -2.0391  -3.1811
+#&gt;    2.6979  -2.8586  -7.0698  -5.1492   5.1086   7.7824  -8.8721 -12.3567
+#&gt;    0.1352   7.9279  -4.4972  -3.0158   2.8392  -2.8841  -9.2191   9.6129
+#&gt;   -1.3872  -2.4786   2.5221  -2.5535  -3.2631   4.3959   0.7497  14.8393
+#&gt;   -4.4588  -6.8535   6.2479  11.1136  15.6840   3.7984  -7.0598  -0.9730
+#&gt;    2.2904   2.8873  -5.5605  -4.1421  -1.0114  -2.0932  -6.3533  -1.0628
+#&gt;   -0.5695  -0.2034  -5.6579  -1.3840  13.2703   1.9438 -14.6659 -11.4732
+#&gt;   -0.7225   1.3376   3.2180  -7.6824 -12.6814 -13.5173  -9.2295 -10.0886
+#&gt;    1.0159  -1.2425  -4.1128   3.7774  -4.8689  -2.7273 -13.7697  -3.0257
+#&gt;   -6.4470  -3.9544   7.4392  -5.6479 -15.8026 -12.1384   3.7016  -9.1124
+#&gt;    7.2432  -2.0111  -5.3657  -8.0843   6.2781  12.4000   2.3539  14.0012
+#&gt;    2.7117  -4.0745  -1.3105   5.0903  -3.0864  -1.7051  -1.0388  -4.4434
+#&gt;   -3.8594   1.1344   8.7419   2.6828  -2.1251  -4.7349  16.1477  -1.9756
+#&gt;   -1.4308   9.6469 -11.2050   2.1474   6.9698 -12.3214  -4.3573   7.7483
+#&gt;   -0.9032   3.6116  -7.7650 -11.3682  -2.1276   3.8079  21.5545   2.0709
+#&gt; 
+#&gt; Columns 9 to 16  -4.2809  14.9856   0.9747 -18.6864   2.5562   7.4461   9.4287  -1.2786
+#&gt;    0.3906   5.4501  -6.8274 -16.3989  -7.6286  -2.4832 -10.1324 -10.4796
+#&gt;  -13.6979   8.8202  11.5512 -11.5892   4.5668   2.0810  -6.2758  -7.3325
+#&gt;    8.7636   9.2025 -17.0848   9.3263  25.3252  -7.1282   7.0646  -5.9105
+#&gt;   -1.7513  -2.8084   6.3440  -6.4785  -7.0918   8.5817   2.3478  -2.9963
+#&gt;   -4.5858  -5.9181  11.9489  12.4078  -5.9416  -6.4059   2.3872  15.6740
+#&gt;    4.1593  -0.3638  -7.1120   7.1704  -1.6941  -3.3962  -1.1642  14.1100
+#&gt;    0.6987  -7.6049 -14.0736  -0.2314   2.1962  -3.3736  -9.1508  -4.7658
+#&gt;    8.9245 -12.9431 -13.4310  -3.2654   3.9478  -2.9020   2.0256   1.8051
+#&gt;    4.2866   0.0715  -0.9944   4.4333  11.7606 -16.5368  -6.2418   9.0348
+#&gt;    7.3539  17.6790   4.9204  -5.0699 -14.3741   3.3521 -10.7943  -7.3542
+#&gt;    5.8408  -6.8705  -2.8739   1.8558  -5.2406 -14.0624  -0.7518   5.0516
+#&gt;    2.8608   0.7189  -7.4459  14.6039  28.6982 -10.6808  13.5832  13.6354
+#&gt;   -0.6098  21.8898  12.0036   2.4947 -16.7648  -7.0569  13.2104  -4.4672
+#&gt;    1.3674   3.4912  24.4712 -12.4146  -8.7437   2.9366   2.0321 -11.8064
+#&gt;    9.3356  -1.2168   3.2995   5.5203  -1.2169  -8.3922   2.6478   6.3334
+#&gt;  -14.8857   3.8096  10.0236   2.4527  -4.6751   6.5519   7.0608   8.0162
+#&gt;  -10.1171  -3.0784  10.7235   1.3285   1.6605  -4.9907  -9.7527   7.2168
+#&gt;    7.3572 -10.3371   1.2387  10.0666 -13.4021   4.9186   5.3739  -8.2916
+#&gt;    6.2000   3.4103 -20.5083   4.8153  10.2720   5.9020   1.8455   1.5598
+#&gt;   17.3297  -6.9410  -2.1290  -2.1530  -1.3592  -0.9819 -13.4255   5.8479
+#&gt;    1.3249  -0.8255  11.2061   3.9923  14.4966   3.2033   3.7043  -0.4251
+#&gt;   -2.3475   3.0203  -5.2621  -8.8402  -8.4170   7.4889   4.8786 -14.2834
+#&gt;   -7.8473  14.9303  12.7534  -6.0639 -10.7028  -6.6792  14.0166  -0.6058
+#&gt;   -2.3551  -7.5074 -22.6458  10.5946   9.2316 -12.8380  -3.5219   2.8540
+#&gt;  -10.7704  -5.4070 -16.1130 -13.0789  10.1141  -7.5616 -20.6803  -5.3971
+#&gt;    7.5679   0.4259 -18.7126 -12.0772  13.6099   7.5732   2.4727  11.0583
+#&gt;  -11.5572  -4.3637 -16.0857  -1.8149   4.9713   4.0778 -12.0961   5.3785
+#&gt;   23.8781  24.2907   4.8381  15.3205   0.9698  -0.8144   1.6659 -15.7205
+#&gt;   -4.8986  -8.1382   5.5597   7.2077  -3.0750  -6.8760 -10.6915  -0.8411
+#&gt;   -4.2023  21.3992  -3.1833   3.3113   5.4178   5.1335   1.7809   0.5336
+#&gt;    1.9082  -7.2875   7.3810  16.7604 -11.3049   5.3751   8.5039  -4.0526
+#&gt;    5.5594  18.7720   2.0388  12.1433   7.7461  10.1617 -10.5559  -6.4869
+#&gt; 
+#&gt; Columns 17 to 24  -8.5176   4.6507  -9.0314   0.4318   1.9225  13.1487  11.7611   4.7002
+#&gt;   12.5557  -1.8317   5.9481  -0.7826   7.5287   0.4098   0.8948  -0.3997
+#&gt;   -8.0780 -18.8625  -0.6382  16.9047 -16.6192 -15.9150   7.9157   6.4388
+#&gt;  -26.3069 -11.9827  -5.4375  -9.6564  -1.5852 -17.9336  10.7353  -5.6754
+#&gt;   -2.2539   8.7048  17.4703  -4.4870  -5.1263 -12.5946   2.6317  -0.5057
+#&gt;    1.2671  -4.6918  -4.9520 -12.1233   6.5261  17.5858   4.7685   5.8081
+#&gt;   -4.2971  14.6401 -10.6889  10.6989   8.7278  -2.0912  13.6802  11.9321
+#&gt;   -4.6551  -2.1508  -2.8937   4.3977  -1.0729 -12.3336  -1.6079   1.8039
+#&gt;    0.3912  14.8130  -7.1374   2.1708  -5.1387  -4.8654   9.8697   3.2350
+#&gt;   -1.0918   3.1655  -2.2974  -0.7077   2.9168   2.1394 -18.2311 -10.4767
+#&gt;  -13.6448 -13.4811   2.7296 -11.3237   7.4976  -7.3000   7.8088   5.3485
+#&gt;   -6.1547   9.0813  10.3575  -5.3045 -12.4711  -1.0880   0.5393  -5.2169
+#&gt;   -1.6821   1.9627  10.5608 -11.9120  -5.2021 -14.8731   8.8732  -3.4069
+#&gt;    2.2960  15.6631   2.4661   3.0519  -4.9549  14.3640  14.9355  -8.5279
+#&gt;    5.4191 -11.8065   7.4382  -3.3906   8.3078 -12.8723   0.2156   3.8655
+#&gt;   -6.5227  10.8106 -13.6598   5.6528  22.9678  -3.7721  -0.2000 -17.8468
+#&gt;    0.0332  12.2625   4.8987  -6.1812   1.5143  -9.2063   7.6447  13.9360
+#&gt;   -2.2296  -1.8215   1.2683  -7.6731   8.4074 -11.6097  -6.7154  -9.6227
+#&gt;    4.4651  -3.6364  -7.6862   7.4514  14.5848  10.4993 -20.5772  -8.4878
+#&gt;    4.3600  -3.5558  -0.0910   5.6951  -2.6760  -1.8089   0.3184  -3.0931
+#&gt;    9.7662  -1.6603  -1.6957   8.0732   1.1176   1.7891  -6.3230 -10.8002
+#&gt;   -5.6969 -12.4185  -5.5132  -2.5430  10.8658  -2.3153  -1.1399   5.8108
+#&gt;  -12.8339 -12.8540  -9.6225   9.9798  -2.3513  -4.6996  12.3909  -0.0322
+#&gt;  -14.1234   2.4385  -4.9102   1.7481   5.9121   9.2784   4.1968  -7.3774
+#&gt;    1.9370   8.2565   3.5238   6.7143  13.6739  15.9826 -10.4484   6.0355
+#&gt;   17.2101  20.1894  -2.3342  13.6302 -14.2520  12.1984  13.2722  -0.9451
+#&gt;   -3.0642   0.4862   6.2188   2.4881   6.0619   4.6073 -15.5296  -3.1794
+#&gt;    7.6234   4.4858   7.1091  16.2194  -0.1533  -2.2753   7.1786   6.2531
+#&gt;   -7.7554  10.8494  12.2926   3.0223  15.5357  14.5266  -0.4013   5.4663
+#&gt;  -16.4785 -11.1094  -2.8604  -4.8357 -11.6945   8.6168   3.2778   6.6541
+#&gt;   -4.5555  -4.6189   5.9754   8.4901   0.5280 -12.8330  -8.3715  13.2930
+#&gt;    1.0469   3.2885   7.1255  -8.8634  -5.9635   6.3313   0.9104   3.4169
+#&gt;   -1.7300   3.2286   0.9228  10.1687  -4.2517  -6.2419  -3.2846   6.9897
+#&gt; 
+#&gt; Columns 25 to 32   0.2320  -0.8547 -13.1887 -11.1736   3.8571   1.2408  -9.5766 -11.6119
+#&gt;   -7.2324   9.9934 -15.2830   4.2745  -0.4072  -0.1736   5.0742 -10.5743
+#&gt;    6.5299  -4.3027  -3.8422  -3.3640 -11.7661 -10.7601  -0.1278  -8.2473
+#&gt;    4.7748  -1.7510  -0.2738  18.1343   8.4248 -17.7706  -4.5318  16.2630
+#&gt;  -20.4072   3.7203  -9.6475   9.2674  -4.5089  -0.8554  -3.5872   5.7413
+#&gt;   -8.1475  -7.7532  10.6418   9.7816  10.1368 -13.1655   6.8676   2.8375
+#&gt;   16.0354 -19.0811   3.4356 -14.2017   3.9377   5.8120   3.5010   4.3098
+#&gt;  -18.6464  -0.4056 -19.4190  15.3488   3.1117  -0.5276 -14.3700  12.9940
+#&gt;    0.8612 -24.8122  -5.7517   2.6817  -7.5426  11.6442  -5.1525   6.2859
+#&gt;   15.2032 -18.9056  13.6918  -2.7647   9.2866   4.0309  -8.9129  -1.4976
+#&gt;   -5.2809   2.5317 -22.6318 -15.5670  -4.7408   8.9304  -6.7400  -0.7977
+#&gt;   -6.2725  -3.5081  11.7261   0.8719 -10.7490   7.3407   0.5090  -1.5701
+#&gt;   -4.3321   1.6710  -5.4596   0.9024  -1.9139  -6.0424  -3.3059  -4.8631
+#&gt;   12.5325  -5.2914  10.0675 -16.3548 -12.4664  -4.1947  -6.9445  -3.6123
+#&gt;   -3.3057   6.3641  -4.2429   1.2611   7.7674   3.9772  10.2070   1.2617
+#&gt;   -1.9652   7.4931  -6.2278  -8.0131  -6.4442   4.2234   8.4705   6.0585
+#&gt;   -8.4888  -9.3521   5.2108  -6.9133  11.9634   8.7817  -5.8179  -5.5310
+#&gt;   -5.5910  -0.0267   8.8629 -18.6715   0.6947  12.2113 -10.0464  14.0775
+#&gt;   -9.0078  -3.8097  15.6286   0.3785 -14.1525   3.8843  -5.5465 -10.3125
+#&gt;   10.5508  -2.7362  -4.8545  10.0730  -0.1848  -5.6251  10.6128  -6.6135
+#&gt;    8.7989   2.4052   6.6470 -22.2156   2.5028  -5.0765  -5.0022   0.1233
+#&gt;   -8.0055  -0.8764  -0.4251   5.8127   0.2807   6.1428  -1.8243  -1.4170
+#&gt;   -6.6094   4.8514  -0.1901  -1.8807 -10.5834   5.3967  -2.5112   1.3605
+#&gt;  -10.4232  -0.9690  13.3882  13.4259   1.3262  -4.5520  16.7237  13.7532
+#&gt;    0.7603   8.2806   1.0988   8.3490   2.1919 -21.3010  -5.5930   2.4877
+#&gt;    3.8935   0.7023   0.4993 -11.9143 -13.2605   1.3567  -7.3537  -3.7524
+#&gt;   -1.2562   0.1787  15.5072   6.8030   7.4880  10.4575  -2.0694  -4.9916
+#&gt;   -1.1330  -6.2391   2.7930  -0.4653  -1.3610   9.2366 -14.2203   9.8848
+#&gt;    2.3531  -2.8806   0.5263  10.1168  -8.4596 -15.0307  10.7694  -5.4531
+#&gt;   -8.8112  -0.9047   3.3837   5.9525   1.9664 -15.2173  -4.6034   8.1945
+#&gt;    9.3376   7.3662 -14.0258  -2.9666  -8.7932  15.8481  -0.9324   0.4922
+#&gt;   -3.4387  -4.7718   9.6222  -4.2471   3.8059  -0.5909 -10.4423   7.4008
+#&gt;    6.1939 -15.2302 -20.2640  -6.2330   5.4920   0.0324 -20.5632  25.6258
+#&gt; 
+#&gt; Columns 33 to 40  -1.0949  -7.8015   4.9329 -10.5205 -10.3315  12.0000 -12.0384 -20.9387
+#&gt;    7.9118  13.2554  -7.8025   4.6869  -9.0013  -2.5157 -10.2050 -13.2897
+#&gt;   -3.2332  14.7656  -2.8396  -3.7716  15.0232  -2.0743  -0.9805  -3.6461
+#&gt;    9.3389  12.4186  -3.4417  14.6364   6.3534  15.4567  19.1659  -4.8524
+#&gt;   -5.4858  -3.1153   1.8488  -8.9521 -10.9912 -13.4338  -0.0593   0.2635
+#&gt;    6.9337  -5.0296 -19.6420 -10.6511   1.0063   1.9995  -8.8161   1.7152
+#&gt;   -7.4369   5.1616  -3.3007  -3.6334 -23.3955  -8.8335   1.3518 -12.2084
+#&gt;   -3.9604   5.2947  11.3275 -12.1789  22.6017  -0.0752  -3.1207  21.3796
+#&gt;    4.2955   3.7270  16.5392  -9.9468 -15.8815   2.2264   3.8298 -17.7371
+#&gt;    7.9218  -5.6900   3.6449  -0.7947   3.4458   3.9923  -4.3017   2.7656
+#&gt;   -8.9517   1.2676   5.6155  -3.7716 -18.7268   9.9223   1.8376 -12.6411
+#&gt;    6.1272  -3.6295   8.5606   0.7275   2.0534   5.0195   3.3077   1.5170
+#&gt;    0.2332   5.2794   5.4327   7.4953  -2.5189  23.3859   5.7894  -7.0571
+#&gt;   -7.9007  -3.8526  -7.6889  15.2641  -7.2014  -0.7732   0.3606 -11.3231
+#&gt;    2.9877   4.7735   3.1117 -17.0739   5.6423  -9.8650  -5.8380   1.7340
+#&gt;    1.3905  -4.4132   4.2730   8.1252  -2.9131  -8.0850  -9.9162   5.2451
+#&gt;   -4.5849   8.1466   1.9840  -6.6218  -9.1559   6.9800  -7.7543   4.5452
+#&gt;    4.2496 -10.5153  14.9882   2.6485 -16.4099  -6.6727  -2.2787 -11.8236
+#&gt;    0.6055  -6.5124  -2.4144   4.3632  -4.4317   5.2460   4.3530   0.1038
+#&gt;   -1.6034   7.5475 -15.9108   7.9887 -13.8177  -2.0681  13.7637  -5.0010
+#&gt;   -2.4930   2.5469   0.3014  15.2680  -0.1038  -5.5995   3.5746  -8.8897
+#&gt;    2.2502  -0.8856   9.9862  -2.3436   4.9548   1.1025  14.5543  16.1863
+#&gt;   -3.4679  -2.5738  11.4560   5.6415  -4.5213  -6.7063  10.9575 -25.7627
+#&gt;   11.4552   3.7832  -1.3785   9.8109  -6.2845   3.4034  -4.8431   2.4609
+#&gt;   -6.5703  10.3562 -16.6455   2.4634  17.5233  -5.4211   8.5169  -2.4641
+#&gt;    3.5048  10.3812   5.9781   7.5783  -9.5489  -7.4988  -2.9599 -22.6984
+#&gt;   -5.9605  -5.2253   9.9344   0.6568   1.5765   1.0338  21.8366   4.3084
+#&gt;   -1.6509  -1.5457   4.3108   6.4686  -0.2890 -15.7786   7.9581  -8.5936
+#&gt;    1.6220   3.7935   4.0385  15.6555  12.9142   5.7641   7.5789   7.7209
+#&gt;   -0.3752  -8.6618  -5.5172   4.5217  -0.9188  -0.7832   6.9243  -8.6669
+#&gt;   -0.8226   4.9436   7.2000  -0.8491 -11.6900 -10.9037   0.3746  -3.7334
+#&gt;   -5.4069  -6.9325  -1.7711   6.9012  -6.2216  -6.6870  13.4777 -16.5412
+#&gt;    6.3956  -0.3344   1.6789  -5.1011  -7.0512  -1.5941   0.6538  -6.6800
+#&gt; 
+#&gt; Columns 41 to 48  -0.1698   8.8217  -6.4694 -16.4661   0.9077  12.7058 -13.3564  -1.2277
+#&gt;    3.7345  -5.9974  11.6953   1.0140   3.1550  -3.2640 -13.4529  -8.2532
+#&gt;  -16.7333   0.4663  -3.3279  -6.2335   0.1770   2.2374  -5.7612   6.6991
+#&gt;   -1.6576 -18.9939 -14.9989   9.8988 -16.7891  -3.0302  -0.8575  27.8761
+#&gt;   -1.7592  18.6416  -3.0356   3.2867   1.1347  -9.6340   8.6015  -2.7213
+#&gt;    9.4523   4.5226  16.7468  -4.9462 -12.1722  -9.1386  17.1996  -4.6083
+#&gt;   17.1410  28.4998 -10.0920  -3.5216 -16.8519   3.3198   6.7380   3.4705
+#&gt;   -8.2911 -11.6910  14.6342   3.9526   5.8166   1.7158 -10.0800   6.0359
+#&gt;  -17.9142  18.2124   0.7111   5.0680  -8.1894  -3.4014   2.0671   4.9197
+#&gt;   23.5714   1.1053 -11.3645   8.0493   8.3621   5.3639  -5.2680  16.4519
+#&gt;   -3.6381   4.2138   3.3700 -10.1134 -11.1222  -9.9525   7.9240 -12.9872
+#&gt;   -6.6284   4.8647   5.2983  -2.0913   0.3208 -10.4628   2.8716   3.9991
+#&gt;   -2.4523  -6.0604  18.8198   7.1272  -9.5356  -4.8851  -5.0653  11.6148
+#&gt;    1.1664  15.2290  -9.0593   6.8798  -5.4856  -5.0677  -4.3022   1.2298
+#&gt;    8.2731  -2.9381  -9.1734  15.1833  16.7573 -15.4167   6.4589  -8.6401
+#&gt;   -7.6856  -2.5541   1.3450  -2.9230   1.5550  -8.9173   2.4712   6.8891
+#&gt;   -1.8132  11.4218  16.3257 -15.0904  -3.5487   2.8636  -3.5318  -0.5746
+#&gt;   -4.9779  -4.7626  -3.1841   0.6262   2.6685   6.0077 -11.4513 -11.3537
+#&gt;   -2.5424   1.6647  -0.8174 -10.5674 -14.6828  -6.7312   3.1601   1.0290
+#&gt;    0.4273   4.3375  -3.4777   8.0603  -3.9064   6.5420  -0.0892   1.4283
+#&gt;   15.4705 -23.4476 -12.6628   4.8800  -5.8321   5.8370  -2.1516  -1.2398
+#&gt;    0.8174 -23.1288   3.5473   8.7841   6.1238  12.9360  16.1962  -5.8630
+#&gt;  -25.2451  -5.5789   5.3848   3.3651 -11.0691  -5.4782  16.2179  -4.5155
+#&gt;    5.0774  12.7844   2.1877   6.6938   4.1463   8.7491   2.1690   7.4667
+#&gt;  -13.9223 -12.1191  19.2083  -2.9883  -4.8362 -11.0929 -12.7274  13.0493
+#&gt;    2.3783   0.7724 -16.6332   0.5814  -3.9713   5.5427 -11.2599  -4.7913
+#&gt;   11.5428  -2.7389  -9.3639   9.0854   7.7848  12.2623  -9.0551 -10.2595
+#&gt;   -4.1513  13.5519  -5.7473  -5.3084   6.0087  -3.3954  -1.2762  -6.7904
+#&gt;  -10.5682  -6.6513  14.7823  -6.8701   5.6471   9.8141  10.5264   8.1733
+#&gt;    6.7891   3.8397  -4.5906 -11.2489   6.0019   0.4279  -3.0140  -3.6645
+#&gt;  -28.8394   0.0399  -1.1060  11.3085   6.8042  -0.6700 -11.8443   3.7786
+#&gt;    9.6303  -6.1626   2.5019   4.5818  -7.1691   6.1651   2.4477 -15.4298
+#&gt;   -0.5048   3.2294   3.5276  18.2456  -7.6242 -10.3284  -5.7173   3.2867
+#&gt; 
+#&gt; Columns 49 to 54  -5.5995  20.9823  -9.8211  -9.4221   1.9872   0.7064
+#&gt;    5.9381  -0.5587  -4.5614  -4.6571   0.8054  -0.5948
+#&gt;   -7.1636  -8.5008  -3.2620  -2.0975   0.3987  -0.3976
+#&gt;   -0.5686   7.8110   6.2780  -7.0534   5.8295  -2.0320
+#&gt;    0.1019 -16.0085  -6.3111 -13.1460   3.6006   1.1852
+#&gt;   17.0260  -3.7308  -1.9848   1.1072   0.3429  -0.8318
+#&gt;   -8.3555  -7.5393  15.5764 -12.1047   2.5861  -0.0350
+#&gt;    8.4494   1.5198  -5.0936   1.4265   1.9363   2.4409
+#&gt;   -6.7050   8.3492  -2.0682   3.9060  -0.0488   0.7735
+#&gt;    6.5935  -6.0620   0.4721   2.7401   2.4293  -2.0944
+#&gt;   -3.3023   3.2847 -11.2997  -8.9486  -5.7318   3.3426
+#&gt;  -16.5071   0.3623  12.6809  -3.7546   2.9133   0.0275
+#&gt;   -2.5453   9.7901  11.8695  -5.8115   1.5117   4.0224
+#&gt;    2.5855   6.5631  -2.9538  -7.3839   1.6488  -1.3814
+#&gt;   -4.9437   1.9161   9.5159   6.1644  -2.0306   3.5429
+#&gt;   -2.6007  -4.1000   4.9058   5.2294  -4.0530   1.1769
+#&gt;    8.1945  -8.5164   5.9058  -7.5355  -4.9895   0.8525
+#&gt;   -2.6536 -17.9847   8.9036   0.1987   3.6190  -4.3432
+#&gt;   -7.9242   8.8606  -6.5877 -10.6735  -1.0000   3.9231
+#&gt;    1.9386  12.0055   2.3776  -7.1784   5.6855  -0.8002
+#&gt;    7.5407  -1.4188   0.4098  -2.5373  10.0966  -3.4645
+#&gt;    6.3016   1.0639  11.3004   0.9768  -2.7151   3.1043
+#&gt;  -10.8548  -6.7497  -2.5480 -21.0140  -2.1168   1.2054
+#&gt;   -0.5643  -6.0690  21.8681  -8.9487   0.3526   1.5624
+#&gt;   -3.4643  -1.0701   3.3123  -2.6660  -6.1458   3.3124
+#&gt;  -19.0319 -18.3858 -10.2737   1.1562   0.5357  -0.9052
+#&gt;   33.2400   1.4011   2.3428   0.2940   0.0104   4.2698
+#&gt;   -4.7071  -8.8811  -4.3700   0.9346  -1.0509  -1.3634
+#&gt;    1.1192  -1.0273   5.7608  -7.0857  -1.6421   0.4937
+#&gt;   -8.4233  -7.4025  -2.0837  -2.1768   4.4988  -3.8958
+#&gt;  -12.1766   7.6390   6.8675  -0.8565  -4.2443   1.1196
+#&gt;  -10.1526  -0.8164  10.6687  -9.9186   4.3158  -0.8867
+#&gt;    3.5572   6.2757 -10.8123  -3.5706   7.7994  -2.3397
+#&gt; 
+#&gt; (18,.,.) = 
+#&gt;  Columns 1 to 8  -0.6402   4.7434   1.8131  -9.0814   0.1055   3.5166 -11.0354  -2.1385
+#&gt;    0.9856   0.9196  -4.9516   1.2091   2.9464  -3.9340  -7.0973   7.7832
+#&gt;   -6.5080  10.8607  -0.2128 -12.7948   7.3569 -14.3323  -2.0781  -9.3965
+#&gt;    1.2588  -7.7663  -5.2737   3.5688 -13.4894  11.3071 -16.0923  17.9381
+#&gt;   -3.1761  16.1444  -0.6368 -11.7985  -1.2194 -10.7200   4.5110   6.6089
+#&gt;    3.7938  -1.5923 -10.7022  14.1368  18.4437   2.1778  -9.3515   0.0366
+#&gt;   -0.1702   4.4401  -6.6548 -17.5698  -0.2235  -1.2114  -1.4631 -13.3428
+#&gt;   -5.4591  -5.0729  -0.5420  -9.9977  -6.6662   7.9867  -4.6333  19.9847
+#&gt;    1.6030  13.0850   0.9668  -3.9323  -1.7781  18.4845  15.0948  -1.3705
+#&gt;    3.0230   2.9641   5.2274   2.8294  16.3949  -0.6543 -24.5857  -7.1291
+#&gt;   -1.9360   4.2210  -0.5944 -10.0358 -25.5254  -7.8593 -10.5588  -0.0213
+#&gt;    1.3608  -1.3583  -0.1132  -7.8611  -2.0944   2.9602   8.2347   5.0018
+#&gt;   -5.5325   1.0469  -2.9028  -4.2451  -0.5880  11.7759  -4.8940  19.1387
+#&gt;    6.5882   0.2508  -2.5480   7.8370   3.2671  -9.5231   7.6839  -6.2263
+#&gt;   -2.2181   2.4591 -10.2505   6.4494  16.4529 -11.5110 -11.5513   6.8777
+#&gt;    3.2458  -7.4024  -2.8479  11.7129   4.5454   1.8381   9.9420  12.1778
+#&gt;    3.8067   5.1511   3.4393  -5.6485  -3.5249  14.0147   1.5736 -14.7018
+#&gt;   -6.8008  -4.4231  -1.5175   1.5062   6.7506  15.2381   6.2466  -3.5652
+#&gt;   -0.3280 -14.0092   8.7571   6.0134 -16.9417  -5.9196  -0.6036  -0.2575
+#&gt;   -2.3694   1.2576  -2.4105   0.9188  -0.9218 -10.2164  -5.5485  -4.6254
+#&gt;   -3.3375  -2.5219   0.7902   6.2528   4.9509   9.7387  -5.9530   5.3763
+#&gt;    0.4704  -5.1322  -8.4724  20.5485  -5.4800 -23.7240  -1.6934   1.7389
+#&gt;   -1.6665  -7.4155  -0.8382  11.1109 -16.3196  -5.5543   6.7446   9.9557
+#&gt;    2.0163   3.3944   4.8981  13.9078  22.7312   1.0781  -9.8206   7.9088
+#&gt;    1.4904  -3.2828   1.4835  -3.8563 -14.0991  12.7025  -3.7869  11.3769
+#&gt;    8.7064  -2.1175  -2.8878 -10.3032  -4.7273  26.4450  21.6295 -14.9229
+#&gt;   -1.1672   6.5164   2.7089  10.4096 -24.9721  -6.9781  -7.6589  12.5868
+#&gt;    1.1866  -7.2541   9.2835  -0.6601  -8.6966  13.7798   8.5238 -13.9082
+#&gt;    8.0989   7.6595   3.9582  10.4038 -13.3495  -2.4347  11.5073  -1.2542
+#&gt;    1.5925   3.5610   5.5849  -9.6810  10.0129   4.2686  -6.1855   9.4021
+#&gt;   -5.8902   1.9189   6.9174   1.1192   2.6001  11.5549   8.3715 -14.1585
+#&gt;    5.2909  -1.3217  -0.8316  -0.4801  -3.0564  12.0386  -9.8129   8.7052
+#&gt;   -3.8948   3.9388   6.3068 -14.6661 -17.3926   5.9122  -1.6922  -6.1795
+#&gt; 
+#&gt; Columns 9 to 16   3.1212   8.9768  -7.7829  -9.9287   4.3650  12.1186   7.9969   1.3515
+#&gt;    9.5904  12.7234 -14.7654 -15.4517 -12.6266 -10.1074 -11.2457 -10.5228
+#&gt;  -13.0193   6.0127  21.2913  -9.2166   4.6633   2.2295  -1.5320   0.1976
+#&gt;    2.0440  -6.1785 -15.1383  19.7634   6.1511   2.2815  -2.1816   6.6448
+#&gt;  -15.7721  -4.8728   2.1662   8.0321   2.3490   2.7814 -10.1976 -22.7731
+#&gt;   -8.3340 -21.6577   3.5734  16.7013   9.3389   8.5474 -10.7842   5.6079
+#&gt;   15.8511 -31.5892 -11.5088  14.5114  10.3812  13.1984   5.2820  -7.6642
+#&gt;   -8.3634   5.4411  -3.0961  15.4396  -0.9041  -4.5280 -11.5267   9.0417
+#&gt;   -7.1770   0.3140  -1.6628 -13.4240  -7.4003   8.8183  -1.2335   1.8166
+#&gt;   18.6558   1.3587   0.0706 -10.5494   5.1193 -11.0114 -21.1990  -3.8616
+#&gt;   -7.9893  12.3873   5.9942   1.0123 -20.4534  13.4816   2.2847 -23.4193
+#&gt;    9.4019  -7.7280   7.4527   4.6136  -5.3764  -4.3965 -11.1285   1.8470
+#&gt;    3.3213  -5.8639  -8.4869   5.0989  -6.5155  -0.3605  -2.4638  -0.0107
+#&gt;  -10.8851  -2.7753  -1.8128  -0.7322 -23.6905 -12.3329  -0.3330 -13.0412
+#&gt;   -3.3664  -7.8045   3.2785  -9.6358  -6.8970  -3.7473 -12.7874 -11.3062
+#&gt;    8.1392  -2.1360  -2.6379   4.2500  -6.8766   2.0015 -13.1190  -3.9774
+#&gt;    9.4907  -6.7738  -7.0910  -3.6525  -0.7717  13.0721  -1.0126 -11.7119
+#&gt;    6.4099  -0.3371  14.4211   5.5252  -1.3406   7.2345   9.8366  10.8173
+#&gt;    2.8487   4.7465  -6.5586  12.2626   2.7087  -3.4645   5.4999   4.5418
+#&gt;    5.4154   4.8134 -13.5437   4.3640  19.4612  -8.3526  -3.4889   5.8875
+#&gt;    2.8817  -0.8727  -7.1474  20.0984 -20.1178  -4.7040   2.7801   4.0371
+#&gt;   -8.1304  -2.1947  15.4954   2.0142   0.1754 -12.3287  -6.0262   8.5394
+#&gt;   -3.7817  -2.7384  -1.3907  -0.9739 -21.9563 -14.3583  11.7999   4.1277
+#&gt;    2.8614   7.4567  -4.6579  -4.1701  16.0878 -17.3606  -0.2404  13.1254
+#&gt;    4.7443   7.0819 -21.0819  -2.9417  30.7228  -7.2210  -0.3566  -1.2270
+#&gt;    0.9078   5.4075   7.3044  -7.1971   5.0863 -11.6226  -1.8456 -17.5967
+#&gt;    8.3709  -5.9648  -1.5901  -4.8041  13.5493  11.9323  -1.9895  16.9380
+#&gt;    8.6526  -2.6055  -1.0883   0.3579  -1.2297   1.8231  18.8077   9.1044
+#&gt;    2.6350   8.6512   4.1492  -0.4413  13.7071 -13.4789 -10.8800 -14.2305
+#&gt;   -0.2720 -15.9998  12.1914  12.9956  -1.3823  -6.7339  -1.2898 -13.3419
+#&gt;    0.2440   5.4391 -13.1262 -13.8534 -21.1040  -0.1105  -1.2542  -6.6261
+#&gt;    5.1576 -13.9171 -11.1951  10.1678   0.4347  -7.4268   7.3335 -10.2450
+#&gt;    0.3775  -5.3758   1.2710  15.0254  -7.6080  11.5949   9.1129 -16.9504
+#&gt; 
+#&gt; Columns 17 to 24  -1.2359  -8.5151  -4.9691  -5.5363  -5.6185   4.5126  -7.5885  -3.2411
+#&gt;   -3.0516  -7.7003  -0.3205   3.8185   3.8513  11.8521  -8.0834   6.7628
+#&gt;    9.4162   6.3903   8.9985 -11.4852 -10.1576   9.2670  -9.4125  -1.6675
+#&gt;   25.6122  24.6035   4.7898  20.4474  -5.1089 -10.1887  -2.2117 -12.9580
+#&gt;  -18.4575   6.6532   0.2662 -10.4893  -7.9176   5.3809  -8.9158   4.7225
+#&gt;    4.6109   4.3092   2.5403   4.2719   7.7647  -4.2787  -8.0528  -9.2415
+#&gt;    9.8034  -9.4504   4.8232  -0.6465  -7.5265  11.5893  31.0453  -5.8467
+#&gt;  -11.1272   4.5555   2.3503   2.5335  -0.5362  -4.4438   8.6267  -4.2531
+#&gt;    4.1193   5.8930  -8.2347  -3.1109  -6.0583  -6.9705  -6.4738   0.6213
+#&gt;  -16.0153   3.3135  -6.8481   9.7210  23.4779  -1.8885 -10.3987 -17.9696
+#&gt;    1.2457   1.9422 -16.2943   2.0579   1.9381   5.8277  -2.7959  -7.1186
+#&gt;   -1.6487   4.1090   3.8216  11.1703   2.5012   0.9400  14.8761  13.8019
+#&gt;   10.3919  -4.5696   2.9751   5.1873   0.8022   7.1395   1.5344   4.1708
+#&gt;  -10.1221  13.4091   1.8450   5.3546  -2.6772  -3.0008   5.9746  11.0633
+#&gt;   -5.6692   3.6746 -12.2909  -2.4199  14.6603  10.3287  -6.0446   9.5920
+#&gt;    3.7258  -7.4862 -10.1744   8.5243   8.7851  -1.8077  -1.1021  11.8224
+#&gt;   -8.8759  -2.3468  17.9357  -2.0293   1.8067  10.5534  16.8988  -7.4985
+#&gt;    4.7142   9.1845   4.3939 -13.8710  -4.2234   2.8338   8.8917  -1.5247
+#&gt;  -13.8352 -14.6098   2.2798   9.4814  -8.9881 -10.2032  10.2493   4.7158
+#&gt;   26.2432 -14.3499   5.1706  20.6862 -15.3507  -8.9687 -10.3074  -0.6704
+#&gt;   -1.6121  12.9632  -7.4375  -8.1558   8.8426  -3.1457   4.9443  -9.4114
+#&gt;    2.8822  -5.1626   5.2782   6.7536  -0.0838 -23.7936 -17.3393  -5.1881
+#&gt;   -9.0059   1.3427 -11.4110 -13.6968 -18.9035 -20.0758   4.5333   6.1590
+#&gt;    8.7138   4.2739   5.6557   4.5342  -4.8090  -6.1795 -18.1063   4.2191
+#&gt;   17.8636  -0.9109   8.3921  21.8324  -4.3993 -14.8443  12.3992   6.9068
+#&gt;   -0.1226  18.6954  -7.6301   3.9097  14.0524   9.0352   1.4781  12.1794
+#&gt;  -29.0194  10.7366   3.0024   7.8518  -3.8900 -10.1120  -8.9764  -0.1042
+#&gt;    8.4657  11.0135  11.5666  -2.2558   2.2921   2.1164  15.4396 -15.8703
+#&gt;   -1.2208 -13.4990  -0.2787   4.6179  -1.0195 -15.3200  -3.7176   3.3242
+#&gt;   -2.9548   9.0405   1.3908  -6.8472  -4.7532   3.4735  -7.2340 -14.3944
+#&gt;   -7.2153  -5.7233 -13.0185 -11.4077  -9.7393  13.1545  15.8776  12.9083
+#&gt;  -15.9679 -10.7577  -3.8447  -3.7532   9.8389  -0.5559  15.9604  -9.6028
+#&gt;   -3.6659  19.6096   2.7075 -13.8219   3.1756   1.9758   9.0128  -8.8238
+#&gt; 
+#&gt; Columns 25 to 32   1.9274   4.3896 -16.3643 -14.6990   1.1926 -15.0499   9.9802 -11.7104
+#&gt;   13.4793  -5.2875   2.4070  -4.4473  11.9843   3.9622   3.4315   5.6026
+#&gt;   -1.6660 -13.3265  10.1604   7.5670  -8.5662  -9.7122  -2.3879  -5.5062
+#&gt;  -18.9636  -2.5495  -1.1489  -1.4347   6.8116  -8.6081 -18.9034  -2.2259
+#&gt;    6.5778  14.9713  10.6284   1.7543   8.4570  16.1060   6.5723  14.5249
+#&gt;   -0.4853 -24.5913  -0.1650  -0.0331  -0.1665   6.9419   3.9479 -17.4175
+#&gt;    2.4660  11.4405  -8.8133   7.7053  -9.1260 -14.7865   4.3316   9.1293
+#&gt;    5.7723  -8.3636   2.9328  -4.2591   1.0561   3.5199  -9.3543 -11.1262
+#&gt;  -20.3206  -3.8134 -14.7849   0.2517  -4.0225   6.7378   1.7420  15.8689
+#&gt;   -2.8622  -8.0222  -4.0324  10.5067  -0.0821   4.7956   8.8798  -7.8659
+#&gt;   -4.1033  12.2578  -4.9912   1.0899   3.9773  14.7161  13.1326  -5.5376
+#&gt;    2.0499   1.9164  -0.7168  -0.7590  -1.1822  -1.7127   2.7421   8.0052
+#&gt;    6.0050  -5.1090  -1.5934  -3.4421   2.2375   2.0690   3.0761   3.4515
+#&gt;  -12.2081   5.1257   2.8837   5.8534  12.9221   4.3078   3.1548  17.0783
+#&gt;   18.8153  -2.1232  10.3712   4.8582 -15.2536  -0.3799   0.2904   5.2387
+#&gt;   -5.5080   2.8503  -7.7855  -4.1919   3.1368   2.7440  -6.9105  -9.5721
+#&gt;   -6.4521   3.8363   2.2905  10.3506   9.9592   4.5875  22.8841  11.6125
+#&gt;   -2.0568  -1.2586   8.8603   8.2651  -5.8694   5.7861  -0.9998 -11.3552
+#&gt;   -9.7005   3.9304  -1.3246   5.5093  19.2795  -3.1123  -4.8202 -11.9433
+#&gt;   -0.3071   4.8476 -10.4747 -15.6811   8.1133  -6.9384  -6.0605   8.4081
+#&gt;   15.5502 -13.5337   4.5394 -12.1722  -1.1972  -2.7866 -20.4364  -2.3818
+#&gt;    1.1061 -11.9509   5.4021  -7.8925 -13.8391   3.4655   1.6950  -2.7135
+#&gt;   -4.9492  -2.3996   6.1579  -5.8705  -2.0081   3.6366 -16.8255  -3.9599
+#&gt;  -16.4392  -4.0470  -8.5074  -8.2516   3.8233   4.8957  26.2796  -5.7294
+#&gt;    9.3205  -9.2771  -3.7460  -3.1085   7.7708  -5.1837  -1.6613  -5.6988
+#&gt;   -2.9244  13.5807  -3.2159   2.7173   7.8858  -0.8228  -0.0907   4.4152
+#&gt;   13.6970 -11.8831 -12.5843  -7.5293  -3.8181  15.1901  -1.6164 -14.6948
+#&gt;   -3.5478   7.2534   0.3166  11.3995  -0.3518   3.3743   3.1063   9.8545
+#&gt;    6.1717  12.0053   1.4703 -15.5365  -7.1714  -1.5570   3.9972  13.3000
+#&gt;   15.7730  -0.7918  -1.1586  -0.6822 -14.0889   5.5660   3.1862  12.7262
+#&gt;  -10.5246   3.7932  -1.7341   1.0231  -6.9974  -8.8409  -3.7959   3.0137
+#&gt;   13.8169  10.6955  10.6924  -2.5440   6.9646 -12.5453  13.7020  -7.6772
+#&gt;    1.2037  15.3911  -4.1420  14.5617  -0.5829   3.2137  13.7592   5.5065
+#&gt; 
+#&gt; Columns 33 to 40 -15.9017  19.8289  -1.4494   9.9546   2.8381  -5.8092   5.5306  -7.9625
+#&gt;   14.3514  -2.3809   4.0669   4.0744   6.5918  17.5361 -14.1913  11.9106
+#&gt;   -1.2500  -2.0659   4.4168   4.7728 -10.4990  -4.8201  10.7299  16.5050
+#&gt;   -8.0709 -12.7665  -5.8409  -8.0910 -14.6648   2.8312  23.0723  11.7831
+#&gt;   11.9143  -3.4812  -3.1850   0.9683  -1.1069  13.8365   3.2718   4.3104
+#&gt;   17.6026 -11.7299  -2.9446  -5.8498   2.4150   0.4797 -11.2809 -21.7138
+#&gt;   -5.2596   4.1238  -6.4967   5.0332   7.0039 -14.9075  -6.3568 -13.5012
+#&gt;   14.1926  -1.8025  -1.2939   6.0462 -18.2979   9.9267  -1.0225 -14.1316
+#&gt;   -6.2897  -4.5259  10.7730   1.4662   1.6391  -6.3149   1.2990  12.0719
+#&gt;    9.1414  11.6358   7.1335  -7.0918   1.3331  -5.2896   3.1283  -7.8965
+#&gt;    2.2127  -2.7778   0.1279  11.5026  -4.0410   6.9783   2.6770   6.4022
+#&gt;   -9.8901   3.9378  -0.1162  -8.3718   8.0727  -0.5122   8.9128   2.3976
+#&gt;    2.4732  -9.5704   0.8080  -9.4388  -7.1319   2.6326  -3.1099   7.3057
+#&gt;    0.9238   8.8140  -5.6856   2.7701   4.9751   3.5825 -13.0656  15.8158
+#&gt;   32.9573   7.1917  -2.9788  13.9394   9.2468  -2.3492   2.4536  12.7421
+#&gt;    7.8021   6.3202   6.1492  -2.3438  18.9970   3.2881  -4.6176 -18.2991
+#&gt;    7.3711  -0.2389  -0.5569  -6.8879   9.2659 -11.5070  -0.5789  18.5673
+#&gt;   11.0779   6.9295  -1.4665  -9.7184  14.9254   6.7255  14.8203   0.0350
+#&gt;  -19.2333  -0.7846  -0.8613  -6.6446   5.6036  -9.2336 -11.9312  -7.2170
+#&gt;   -0.7900  -1.2667  -1.1898  -7.0447  -7.1265  13.5867 -14.4753  -0.5253
+#&gt;   -8.0133  -9.5562  -1.3595  -1.2104  11.8182   5.1204  -3.0254  -6.4768
+#&gt;   12.4020   1.6835   3.7220  -1.0084  -4.9357  -8.5555  -7.4911  -8.1255
+#&gt;    0.3477 -13.4256   4.1321  14.8491   8.1742  -3.2309   0.9943  11.5252
+#&gt;   22.8293 -10.3870   0.1999  -6.3627   9.3321   6.4844   4.9202   5.6303
+#&gt;    2.0806  -3.3848   2.9210  -7.0609  -4.3613   0.6444  -6.2517  -1.3243
+#&gt;   -4.6048  12.2416   2.6795   9.1540  14.7008   6.9532  13.4680   0.9090
+#&gt;   -2.1774   2.6063   0.4495  -6.9727 -16.6756  -8.9110   3.8136 -12.1270
+#&gt;    2.7138  14.7726 -10.0168   0.7281  -3.8214 -14.2197   6.1618  -2.6406
+#&gt;   -3.7193 -18.9873  13.9520 -18.3534  -1.0845  -2.3399 -13.4829  11.1438
+#&gt;   -1.7673   6.2762   1.2490   2.7316  -1.2734  -2.9658  11.2157  -3.0200
+#&gt;    0.2754  18.0402  -5.0929  16.9264  -7.5677   3.8953  -4.5697   8.6893
+#&gt;    2.8513   1.8195  -5.0325  -2.3912  -1.8182  -9.1630  -5.6628  -2.9472
+#&gt;   14.6407   4.0824 -20.4510   9.0786 -16.7662  -4.4722   9.2136  12.7086
+#&gt; 
+#&gt; Columns 41 to 48  19.9825  -2.4135  -1.8140  -0.2352  -8.4371  21.2284  -4.9427  10.3563
+#&gt;    2.0929  -0.9596 -14.4055   2.7796 -11.4302   1.7215  -3.0949 -11.9220
+#&gt;   12.7472   7.7430  -5.8433 -14.4004   0.4688  -6.2496   1.2925   1.5456
+#&gt;   10.4949  -8.6562 -15.1366  11.1817  -4.6283  16.3292   8.4729   6.7488
+#&gt;    1.8578  -1.8055  -9.5686   0.0959   9.1349 -14.8557   5.6090 -11.5172
+#&gt;   -7.6768   5.3956   7.2504   4.2450  11.0339  -0.2446   5.0597  13.7156
+#&gt;  -12.0703 -14.2829  18.7942  -5.3005   4.3003   6.6919   8.5263 -22.2124
+#&gt;   -1.9022  13.3941  -9.9091  -7.1722  -6.5594  -8.5446 -14.1859  16.5809
+#&gt;   -0.4991   3.2186   7.6311   2.3051  -5.0829  10.1925  -6.0801  -3.8085
+#&gt;    1.7771   9.6630   1.1029 -15.7105 -14.8743   0.8401  -6.0027 -10.3531
+#&gt;  -10.5784  11.8372   1.8579   1.2497   3.6849  14.5035   5.7341   2.8824
+#&gt;  -13.3272  -8.8119  -1.6545  -6.4224   3.8017  -4.2546  -2.4515 -11.1848
+#&gt;    5.7604   7.8640 -12.3966   4.7883 -22.8609  10.9887 -15.5800  -6.8996
+#&gt;    7.8910 -17.5010  -4.3003 -18.9640 -23.4317  -7.0928 -11.3561  -5.0840
+#&gt;   -4.0275  12.1429  -9.6797 -22.7276  10.5789  -2.0026   6.0124  -1.1752
+#&gt;   -0.7158   1.9147  11.6325   0.8597 -11.9986  -2.2259   5.3061  -5.9046
+#&gt;   -7.2563  -1.3592 -14.8967  -5.0000  -4.2093  -2.1025  -1.3454  -7.8412
+#&gt;   -7.1904   3.4257  12.1356   0.2198  -4.3924  10.8561  21.8964 -15.5202
+#&gt;    4.5209   5.0271   9.2597   1.4535   9.2988  -0.3062  -4.4441  11.4083
+#&gt;    8.9135   1.1426   3.0396  -2.5395   2.1145   5.0658  -2.5784  -5.2599
+#&gt;    6.4097  -9.5186  10.7536  10.1055   1.1736   1.4161   3.7388   7.5245
+#&gt;   -4.9861  11.8011   0.3431   3.1095   1.2019   5.8417   1.8617   0.6218
+#&gt;    8.2233  -1.3984  -9.1638  10.5929   4.4425  13.0148  18.2417  -2.9129
+#&gt;   -2.0693  12.6648 -17.6013  -2.1148  -0.3509   0.5956  -1.9759 -18.9371
+#&gt;   -0.4969   2.0451   7.4661  10.5700  -2.3554 -11.6705 -19.2058 -13.2703
+#&gt;   -5.5057  -5.0098  19.5467  11.8053 -13.8865  -5.5786   3.8843  -9.5756
+#&gt;    4.5929  16.1116   8.9349  11.0129   0.6013  -5.7657  -1.2153  -3.3872
+#&gt;   -1.7534  -1.4609   2.9706   7.0784   5.2399  -9.4168   9.5644 -14.8966
+#&gt;    0.7387  -6.1535   0.3570   3.8876  12.7630  10.4211   2.3205   2.2860
+#&gt;   -4.2484  -2.8584   4.3622  19.2862  14.6254   1.7646  12.2513 -19.7506
+#&gt;   11.3878  -2.6585   3.4115  -8.2136 -10.2696  10.6788   0.3415  11.3967
+#&gt;    1.0095 -11.3156  18.4836   4.8709  12.9354   1.7132   3.9450  -8.4643
+#&gt;   10.3810 -15.1599   4.7001 -13.6059  -2.1802   0.8466  -5.1479   0.7696
+#&gt; 
+#&gt; Columns 49 to 54  14.9333   8.5446  -1.0856   4.8676   0.7913   0.9684
+#&gt;   -3.1436 -10.6986   0.5190   2.6541   1.0925   1.4596
+#&gt;    4.7135  -2.7721  -3.4758   0.7220  10.0513   1.3868
+#&gt;   11.6558   4.3242  -4.5343   5.4663   1.6779   4.5546
+#&gt;    0.3374  -9.0572  -6.2981   5.8576  -3.7875  -5.6278
+#&gt;    7.7066   1.7292  -0.3229  -5.5232   0.9591  -3.4107
+#&gt;   29.2129  14.5294   6.8547  -4.1036   3.9797  -0.9070
+#&gt;  -19.2131   3.3668 -10.1499   3.9380  -2.8376   0.8068
+#&gt;    0.6124  -0.6532  -2.5873  -1.4719  -7.6453  -1.0898
+#&gt;   -7.1441 -20.8443 -18.0205   0.6258  -1.4782   1.0112
+#&gt;    7.0443  10.3443 -11.6343   2.3147   8.1567   1.6857
+#&gt;    1.1138   1.2715   7.1518   2.8545   4.1725  -3.4115
+#&gt;   12.5523  -3.5992   1.3982   1.9025  -1.2296  -4.2767
+#&gt;   -1.3358  -0.6525   5.2423   1.4915  -0.3749   1.5280
+#&gt;   -0.4321   4.0747  -6.9063   8.2032   2.2917   4.3343
+#&gt;   -0.7368   1.2588  -2.3436  -1.7070  -0.6109  -3.8114
+#&gt;    2.1002  -1.0872   1.4160  -8.6263   0.3481   1.6243
+#&gt;    9.9057  -6.9949  -5.9608  -4.5947   0.3429   0.4212
+#&gt;    7.8891  13.0376   3.5967   5.6461  -0.1403   2.4187
+#&gt;    8.1190   0.5203   6.9234   4.0052  -1.9999  -6.9853
+#&gt;   -0.4321  -6.9285   7.7258  -8.5372  -1.8424   0.0092
+#&gt;    4.8562  -6.1752  -1.0074   2.5207  -4.3907  -4.0113
+#&gt;    3.7989   3.2774   6.0602  -1.6650  -0.5464   1.9183
+#&gt;   -0.1145 -12.4350  -7.0779   3.4605  -2.6369  -1.3820
+#&gt;  -14.1670   3.0106   8.0427   6.0119   2.7458   1.2553
+#&gt;   -7.0025  -7.1132  14.1941  -4.1907  -3.0654   1.6230
+#&gt;   11.8094   3.1970  -4.0947   3.3817  -1.8617  -4.6954
+#&gt;  -15.1024  -5.3801   1.4814  -0.4673   3.5067   2.9342
+#&gt;    1.9082 -13.1175   9.2756  -1.4339  -4.9287  -1.5384
+#&gt;    6.1539 -12.8605  -1.7919  -7.1876   1.3803  -0.5260
+#&gt;    3.0840  16.2391 -10.2064  -3.4088   1.3223   1.3707
+#&gt;   -3.4470  -0.0689  -7.6676  -4.6356   0.6760   1.2200
+#&gt;   -2.4014  -6.9472  -5.3587   0.0275   3.0329   4.4052
+#&gt; 
+#&gt; (19,.,.) = 
+#&gt;  Columns 1 to 8   6.1934   0.6016   8.3598   3.0326 -12.9455  13.6546  -8.3350   6.5919
+#&gt;   -1.4887   2.2354   4.6899   4.0643 -19.0120   0.7482  -4.1261   2.9749
+#&gt;    0.4922  -1.0899  -2.7840   4.2196   6.3346 -19.8369  -3.7838   1.9757
+#&gt;   -1.9319  -2.3496  -5.1289   4.6528  -5.8942  -3.7496   4.4994  -6.2430
+#&gt;    0.0337  -4.0425   2.3811   0.5730   0.2054   9.6694   2.5225  -9.5001
+#&gt;   -0.5876   0.3160   7.7939  -4.7752   5.7192  -5.6494  -8.0888  14.6691
+#&gt;    4.3229  -6.3642  -8.4702  -2.1014  23.2589   7.3972 -15.1733 -11.3082
+#&gt;    2.0543   0.6571  -2.5315  -0.3325   8.6183 -16.6163   0.1893  -2.7989
+#&gt;    5.6083   2.1832  -2.4867  -7.9341   3.1974  11.9689   3.1765  -2.5647
+#&gt;   -2.2926   9.2332  -1.2313  -3.1631  -1.5512  -1.3260  -6.3034 -12.3526
+#&gt;    1.6317   2.5714  12.9366   5.4867  -6.9424  -3.9743   0.9728  -0.6109
+#&gt;    1.2038   4.1512  -1.5282  -6.5023   2.6542  -1.2755   6.3453 -14.6680
+#&gt;   -1.4641  -4.0733  -0.2566   5.3166 -16.6637   9.4802   0.5710  -9.4650
+#&gt;    9.4367   9.7752   6.1162 -12.1200 -26.9064   6.3087  -0.2352   2.9740
+#&gt;   -4.3341   0.9391   4.9173  -8.7431   2.4471  -8.0176   2.8032   2.0200
+#&gt;    2.4573  -0.8673  -0.4458  -1.1450   4.6255  -9.2203   2.7095 -14.0133
+#&gt;    3.1340  -1.0957  -2.5156  -6.6083  10.6748  -3.6518  -4.4568  -1.1938
+#&gt;   -0.6760  -4.3519   1.9990  -3.1414   3.1890  12.4459   3.0494   2.1602
+#&gt;    4.5522  -1.8339  -7.4528   6.1832  -8.1795  -3.1925  -3.7697   5.9783
+#&gt;   -2.7287  -3.6379   6.2073   7.2695  -8.6929  -0.7868  -2.8261   6.8385
+#&gt;   -4.0738  -8.2125   7.5363  -4.8496 -15.8754  22.5223   0.6181   1.3733
+#&gt;    0.1273  -2.9454   2.6994   5.5410   7.5915 -21.8249 -14.0528   3.2240
+#&gt;    2.8974  -1.9770   2.5353   7.7352 -10.8922   1.1035  14.8167  -0.9914
+#&gt;    1.3719   4.5675   0.5142   1.9600  -3.9910  -5.8259   3.5965  14.5469
+#&gt;    0.4437   6.4980  -3.4437   2.7937  -7.6009 -15.2242  -7.1377   8.5971
+#&gt;   -3.7725   2.4433  -0.8972   6.8068  -1.3289  -3.0030   4.5202 -14.2860
+#&gt;   -0.9888   2.9218  -2.4031   0.1627  -7.3088   6.3008  12.4894  11.9645
+#&gt;    2.1441  -2.5996  -0.6631  12.1015 -11.5187 -22.2022  20.2998  -8.2719
+#&gt;   -1.2663  -0.1324  -4.9035 -15.9993   6.3059  -4.5996  -7.5443  -4.8725
+#&gt;   -1.6626   2.6081   4.2534  11.0306 -10.0911   4.7944  -0.9095 -12.9522
+#&gt;   -0.1362  -7.1845  -4.4547  13.0362  -4.1170  -3.1437  -2.7968   3.4724
+#&gt;   -0.0237  -8.6747   3.7257  -3.5996  -2.3078  22.0702  -9.9110  -4.6093
+#&gt;   -0.6613  -6.0696   7.6438   3.0433  -6.6383   4.2396 -15.0798  -0.0339
+#&gt; 
+#&gt; Columns 9 to 16   2.8798  -3.0725   7.2669  -8.6678   8.8890   3.7638  -2.5844  -2.5241
+#&gt;  -13.7307   4.4794  -8.3669  -1.0329   1.4814  14.0255  -1.4771  -2.6008
+#&gt;   -6.5771   3.8326  -7.9315  -5.0395 -12.6048  12.8029   2.0019   0.1064
+#&gt;    9.3538   3.9234  -9.3982  10.8300  21.5176  -9.6451   9.2925   7.6096
+#&gt;    4.0361  -3.8203 -14.2969  -0.8630  -6.6651  -3.7881 -11.5364  -0.5598
+#&gt;   -4.0482   2.1511   6.9734  13.0590 -14.5905  -1.1738  13.3416 -10.6066
+#&gt;  -12.1073  17.4167  14.8756   5.2775   8.3533   8.6792   5.9096  -1.4173
+#&gt;   10.3553   0.7710   0.4817  -9.9571  26.4267 -10.9394   3.2493   4.1104
+#&gt;   24.8736  -5.9802   4.3403   8.4823   0.4159  -7.9480  15.0445  -3.4468
+#&gt;   -2.6414  -3.3396   0.0889  -5.3915   4.5256   3.4420  -5.5165 -15.8534
+#&gt;   -6.8405  18.8525  -0.2873  -4.2911  16.0573  16.1792   9.1864   9.1881
+#&gt;    2.4798  -1.5783  -2.8959   5.1518   6.6068  -2.5040  -3.4740  19.6131
+#&gt;   -2.0984  12.7616 -21.5559   4.9098   0.7599 -17.0955  -8.8220   8.5591
+#&gt;    0.2830  -2.2745  -5.4048  -5.3338   4.7524 -19.1735  -0.4606  13.3997
+#&gt;   -1.6081   7.7972  -5.1667   7.6012 -19.7526  13.4538 -15.0041 -12.9088
+#&gt;  -21.4450  11.7592   3.6115 -16.0658  16.4340   8.3671  -2.4403  -8.5778
+#&gt;  -15.7357  12.9648  12.6815 -21.2259 -10.4463  -0.5693  -2.0798   4.5000
+#&gt;  -12.8018   1.3293   1.4460   2.2941  -1.3528 -11.2727 -12.6531   0.4794
+#&gt;    4.7367 -13.4382   5.8000  -1.8316   5.4515   6.7628  15.5774   2.2675
+#&gt;    7.1600   7.1958 -22.9738  10.3199  10.3020   2.9799 -18.5614   0.5556
+#&gt;   -5.8958   2.2755  -1.4859   6.7113   6.9451  -5.7261   6.3672   8.5693
+#&gt;    7.0025   6.8126   6.2316  15.4182 -14.0714  -0.4964  -6.2274   4.6494
+#&gt;   -6.4367  -2.6352   3.9081  13.2540   1.7440   3.5604   5.1332  13.8318
+#&gt;    0.3749   6.7699  -4.3908  -5.0820  -7.0969   4.2889 -10.1144   5.3642
+#&gt;   -2.9467  -3.1925 -12.3471  -6.1830   1.3040  -1.9060  -2.6016  -0.4002
+#&gt;   -1.0362  -3.1978   8.8118 -15.3453  -0.7546   7.7332   3.7634  -1.8564
+#&gt;    3.3061  -4.8062  -6.5058  13.5938  -4.2818  -4.0861   2.0229  -9.4537
+#&gt;    4.1389   0.2342  10.9445 -12.8379   6.9789 -18.8211  -9.9630   6.3284
+#&gt;   -7.2165   8.0571  -8.4737  10.5222 -18.7037   3.8124 -10.1854   9.0345
+#&gt;   14.6915   1.0380  -6.3960   2.5288   5.0942 -15.4381   1.9265  -5.4321
+#&gt;   -6.6489  -5.3268  16.5260 -11.1882  12.1356  -8.4580   4.7768   0.6481
+#&gt;  -20.7960   3.2741   2.8290   4.4515  -3.2537 -11.0028  -5.7387  -2.4862
+#&gt;    7.2241  -2.5927  -5.9032 -15.7293  17.8961 -17.7844   4.1726 -12.0396
+#&gt; 
+#&gt; Columns 17 to 24   6.5391 -14.5864 -11.8604 -11.4786   5.6443  -2.0073   7.6965  -4.6677
+#&gt;   12.0979  -3.9987  -3.4949   2.3190   2.4423  -4.2993   6.1381 -11.4657
+#&gt;   14.2456  -0.9163  11.1421  -5.7223  -9.5826 -10.5583  -3.8580   1.5903
+#&gt;    5.4065   6.3033   1.3012  -0.4432  -2.1939 -14.5521  -0.0028 -14.0889
+#&gt;  -14.2170  -4.8922  -0.8307  -9.2438  14.3694  12.5264  12.7776  -3.5967
+#&gt;   -9.3725 -11.6499  -8.6719   9.9445   0.9808   0.2336  -1.5833  -4.2600
+#&gt;    6.6962  -4.6848  -6.4189   0.4120   2.8630  -1.5822  -4.0069   1.0265
+#&gt;   -1.1279   4.5786  11.1326  -8.0944   0.6882  12.2893 -11.5990 -12.9291
+#&gt;   -3.4631 -13.0946  -7.7095 -18.1560   1.5465   4.0599  -1.8186  14.4567
+#&gt;   12.4410 -14.4470  20.0901   9.7878   5.4741   4.2032   3.8252  -2.6984
+#&gt;   -9.9990  -1.9192 -11.0305   5.3239 -11.1249  -3.8732   6.7137  -4.0402
+#&gt;   11.8593   1.5556   9.8289  -2.1987   9.6795   4.5857 -15.0401   1.5829
+#&gt;    0.3604  -8.2160  -3.8773  -3.5271  -2.3932   0.1904   2.8119  -8.4704
+#&gt;   12.1340   4.8606   4.2701   1.8484  11.7156   5.0856  -0.6180  14.4383
+#&gt;    5.7874  12.1384   2.4820  11.4998   1.8744  -4.7238  13.7177  -6.9019
+#&gt;   -8.9404  -7.7889  -2.8964  -4.5992  -6.6008   5.2246  -4.6712  -5.9672
+#&gt;   -9.4088   4.1162  -3.4417   0.5993   0.2595  -2.6834  -0.2342   9.4726
+#&gt;   -0.0734 -17.9673  -2.7938 -16.2442   0.6266   2.7935   6.5794   2.4333
+#&gt;  -16.2170  -4.8645  13.6569   8.2434  -0.7888 -12.5193  -3.6816  -0.5647
+#&gt;    0.9639  -5.7729  -6.6615 -10.3802  10.3718   8.1148  -9.2690 -12.9491
+#&gt;   11.4655   0.1740   4.9162  -4.9980  -5.1379   1.7215   3.7164   4.6842
+#&gt;    1.5508  -1.6158   3.3389  -4.1350  -9.3359   0.6012  12.8650   1.7450
+#&gt;    6.7992  -4.6559  -9.1753 -15.7167  -2.8661   3.0046   8.1778  14.1793
+#&gt;    3.0958 -13.1580   2.8024   0.7439   6.5187   8.3216  14.8557  -3.0691
+#&gt;   -9.6449  10.5255   7.8832  -4.6325  -5.7371 -18.1773  -8.8166 -23.2309
+#&gt;    4.8762   6.5577   4.0182   6.7272  -7.4877  -4.3655  -1.9526   5.2064
+#&gt;   14.4899 -16.7208   2.1138   6.7385   6.8778  -5.1295   7.8338  -2.0964
+#&gt;   -1.7491   4.9707  -9.4929  -6.8305  -8.2027  -8.2111  -3.6427   8.0782
+#&gt;   14.1475  11.4035  -1.4518   3.5647  20.2029   4.8265 -14.0111 -11.1388
+#&gt;   13.8582   0.3056  -6.1752  -3.6393  11.9486  -7.3676 -11.8621  -3.0034
+#&gt;    2.6802  -0.6858   3.7664 -10.4697  -8.1922   4.6575  12.2959  19.7323
+#&gt;   -2.8445  -0.1341  -6.7498  -0.5399  11.9542  -0.7129  -1.4025  -9.9363
+#&gt;  -15.2275   0.5037  -9.9558  -2.0779  -2.0914   4.1467  -0.4205  -8.2044
+#&gt; 
+#&gt; Columns 25 to 32   3.7903   3.3255  -5.7292   7.5276  11.8205 -10.4675 -10.6192   8.0113
+#&gt;    0.8361  -3.3669  -9.5519   8.3768   4.9518 -12.9102  -0.0174  15.1387
+#&gt;  -12.5670 -11.0598  -3.1942  -8.3689   8.9384  -2.3949   8.8404  -4.6961
+#&gt;    7.4176   5.2798  -1.0288  -5.2463 -11.3578   2.2115  22.9316   7.1917
+#&gt;    2.5144   8.4124   2.3300 -14.5787   5.1187   1.9948 -15.7565 -10.7584
+#&gt;   -7.4503   3.3198 -10.1121  -5.9335  -9.8042   2.6150  10.7930   9.4060
+#&gt;   -6.3741   0.5055  23.4130  -2.1110  -3.5391  -7.4406   7.4490 -14.4898
+#&gt;    6.8932  13.4604  12.8060  -3.6329   3.5193  -3.8410   4.0571   2.0357
+#&gt;   14.1691   7.2257  10.0986   7.1218  -6.5157  -0.3897   0.8214   0.8872
+#&gt;  -11.4461   5.3822   1.2093  16.0759  -0.7038  -1.4869   8.4618  -0.1718
+#&gt;   -0.3760   2.2350  -1.7025  -2.6248  -6.2334  16.0488 -24.6371  -6.3375
+#&gt;   -7.2315   1.0800   7.2978  -1.4302  -3.2127   9.0249  11.0530   3.6445
+#&gt;   15.7921  16.7880  14.4549   2.2096  -4.1307   3.5315  12.6594  15.9640
+#&gt;    9.4974 -10.4409   1.4475  -4.9661  -8.8074   2.5021  -0.4174   4.7382
+#&gt;  -16.1161   7.1551  10.5302  -6.1067 -11.0190 -15.3492 -11.5276 -11.3453
+#&gt;   -9.3822   6.8743  -5.8723   5.6408   3.4993  15.7824   6.1130  10.4046
+#&gt;   -7.6319  -7.5774  11.2611  -3.8455   0.3930  -7.2326   4.4891  -2.6157
+#&gt;  -16.4360 -14.7686  -2.5789   5.0919  -0.7319  -6.2399   4.4602  -2.1547
+#&gt;    4.6968   0.0927  -0.9485   0.7130   6.5895  22.6459   7.2589  -8.4118
+#&gt;   14.9886  -4.0438  -2.1775  -2.1337   2.5141  -5.9813  -4.9089   5.9291
+#&gt;   11.7019  -3.9686   2.4360   4.3078  20.4404   2.6189   9.3113   8.9625
+#&gt;    2.2853   2.7111  -1.9807   0.6896   5.7212  -3.6727  -6.6924  -7.7792
+#&gt;   18.7836   1.7695   0.3937   3.3705  -5.8818   3.6156   6.1784  -2.5669
+#&gt;    9.2455   0.3481 -10.9108  -9.4289  -9.6590   0.4839  11.9312 -20.5037
+#&gt;    2.4875  -0.6515  -1.7652  -1.9367  -3.9500  -8.1281  19.0556   8.8230
+#&gt;    1.8250  -4.2213 -14.2542   7.0002  -2.8151 -18.4079  -6.9226  -0.1910
+#&gt;  -21.7549   4.9411  -1.1866   7.9180   0.4587  -7.5399 -10.5217  -1.3080
+#&gt;   -1.8189 -15.6254  -8.5672   0.2147   0.6852 -11.7538  -1.6671  -3.6402
+#&gt;   14.1308   4.6025  -3.9681   0.4679   0.1882  -2.8152   2.7897   9.2692
+#&gt;    2.5538   2.5195  -7.7524  -7.8544 -13.9627  -4.6951   4.4365  -3.7481
+#&gt;    2.0063  -3.2778   9.1413   9.2929   4.3252   0.3978  -1.4988   0.4056
+#&gt;   -1.6680  -2.3542  12.1117  -1.9918   2.7421  -5.0232  13.8558 -10.7903
+#&gt;    6.0628   5.7619   4.5081  -5.2663  -9.3721 -12.9506   5.4327   0.4773
+#&gt; 
+#&gt; Columns 33 to 40   8.1017 -16.3518 -11.8488   5.2833 -17.5721  -1.9173   1.8891 -15.4496
+#&gt;    0.1731  -9.0688  -3.7495  -7.1022   4.3007  13.1694  -6.0692  19.0576
+#&gt;   -0.9493   5.5288  -3.4587   0.5222   2.1423  -6.1549  -0.7781  -4.1996
+#&gt;    1.0215   2.3486  -7.8628   2.9591 -17.3152 -12.6207 -16.5884   3.6502
+#&gt;    9.1096   1.8355  -2.9040   7.4276  16.7771   0.4230   8.0398 -10.2031
+#&gt;  -10.2698  -7.1725  -5.1960  -5.5190   6.6967   2.1429 -15.5684   9.2121
+#&gt;  -19.3767  -5.6862   7.8534  10.6132  18.2067  -1.9074   9.7766 -15.5770
+#&gt;    5.2263  -4.2951  -5.4397  -8.1393   3.7078  18.5770 -12.5704   4.8184
+#&gt;   -1.7836   3.0403 -18.9434   6.4766  -3.4476 -14.9199   9.2449  -2.9303
+#&gt;   -1.2301  -2.2305  -7.8244  -5.0573   6.3950  -6.3923  10.4575   6.5719
+#&gt;    9.8506  11.4765  -9.2351  -3.0002 -13.7599   2.9845 -22.4761   5.3977
+#&gt;    5.6193   6.1970  -3.7173   3.0055   6.9053 -11.9419  17.4443  -8.0083
+#&gt;    3.6494  16.5555  -3.1813   6.6481  15.9151   0.2748  -0.3932  12.0858
+#&gt;   -2.9139   2.6803  -3.0415  16.0457  -4.9883   5.8753  -0.3729 -12.7299
+#&gt;   -8.3219   8.4769  -4.8462   5.1220   9.1838   3.4023  -2.8892 -11.2306
+#&gt;    7.6885  -3.5387  -3.7079 -10.4078  -3.1272  -8.8266   4.0314  13.4915
+#&gt;    1.0996  -2.5958  20.0192  -1.6561  15.9806  15.0182  10.3297 -25.8490
+#&gt;    6.3978  -3.5960  -9.7776  -8.5038  -1.5981 -13.7985   3.0721  -6.3714
+#&gt;    0.4850  -5.7412   9.6595   0.3586   2.6632  -0.3682  14.7224  -7.9278
+#&gt;   -0.5118 -11.5803  -8.1301   2.5937  -4.3635   3.2610 -15.2348   9.8542
+#&gt;    7.7489  15.6425  -5.5420  -5.6919   2.3951 -13.3311 -11.1449  -9.0787
+#&gt;    2.9989  -4.4263  -6.8323  -5.8438   4.0403   9.1415  -6.7349 -13.6461
+#&gt;   -1.7004  -3.3287 -21.2722  -2.5126  -7.3674   0.3515   2.0060   2.1176
+#&gt;   -5.3685  -5.4785  -6.8406  -0.1505  -9.5887 -10.1640   8.3817  -5.8131
+#&gt;   -6.4718 -12.4564   6.9947  -6.8727  10.5639   0.6349   1.7956  14.2546
+#&gt;    1.1731   3.1685   2.2902  -6.1751   4.7103   8.4293  -5.0037  -1.2424
+#&gt;   -2.4388  14.7543  -3.9616  -8.4939  -0.0702  14.5129  -8.7235   6.5275
+#&gt;  -10.0653 -12.1855   9.9079  -1.9125  13.2866   4.6751  14.6080 -10.8142
+#&gt;   10.3276  -0.5984  13.2652   1.9736   2.5442  -6.9664 -11.3229   5.9947
+#&gt;   -4.5489   4.9185  -3.0494   9.0023   9.6750 -27.3298   5.2614  -5.3131
+#&gt;   -2.3094  13.9642   3.0953   0.2910  -5.1558  10.8093   1.1577  -2.9486
+#&gt;   21.2301   4.4138   4.4121   2.4246   1.7106   4.2483  18.2669   8.2993
+#&gt;   -6.8699   2.8283   7.2359  -1.4976   3.9263   0.8536 -11.3219  -6.3654
+#&gt; 
+#&gt; Columns 41 to 48   6.3987   5.3726 -15.1102 -19.9172  12.5064   6.9965  -5.7315 -17.1862
+#&gt;    3.4244  -8.5284 -10.4238  -9.4293   2.1006  -5.2414 -12.7033  11.0285
+#&gt;   -6.0304   8.8814   0.9639  -2.4080  -5.6089  12.8117  -3.3538  -6.5833
+#&gt;   -9.0917 -10.3516   7.4486   2.0141  18.1599   3.7454  -1.9926  -8.6860
+#&gt;    7.3703  11.7580  -1.4917 -13.5822   8.8879   9.4748  -4.8432  -1.1621
+#&gt;    4.4972 -13.0873   9.3680  -7.3819  11.5981  -2.5588 -11.3400   6.7135
+#&gt;  -11.8895 -35.7564  22.0851  -3.2676   0.4339   9.9938  16.2517  -3.3763
+#&gt;    6.5308  18.2512  -0.8322  10.9775  -5.0022  12.3768  -0.0811   8.4797
+#&gt;   -4.3165  11.7174   3.9955  -3.6485   2.5013   2.1386  -0.2413 -10.2440
+#&gt;    1.9551  -6.7131   6.7738 -14.6548  -0.1942 -12.0407  -0.7043  -5.1429
+#&gt;  -16.1036  15.5812   5.1011 -16.5777  -1.5173   0.9820   3.3362   3.3592
+#&gt;   -4.8309  -0.3282   0.6207   7.7402  -2.8567   6.1089   4.6141   7.5731
+#&gt;  -11.8528   7.9254   9.5656  -1.0380   3.1958  -0.3382   2.8357  -2.7504
+#&gt;    7.3604  -4.7194  -5.1374   8.3832   1.6657  -0.5481  16.1025  -8.5649
+#&gt;  -17.7274  -5.9748 -10.2013  -0.8172  -4.6582   0.6032  -5.7292  14.5913
+#&gt;   -4.3346 -13.7389  -1.5869 -10.6060   7.0140 -15.5251   1.0579   7.8580
+#&gt;   -3.9522 -11.9852  12.5604  -0.1477  -7.7052  -7.5955  12.1044   0.1048
+#&gt;   -2.9733 -22.9579  14.3785  -6.0695  -6.9459  -3.2409   9.0416   2.9197
+#&gt;    1.1180   6.9788  -3.5675 -10.0713 -11.9052   7.0531   5.3110   8.9956
+#&gt;    2.3332  -3.0973 -14.2154   0.4811   9.7985   2.5278 -10.1085  -8.1604
+#&gt;   19.4557 -18.9490  19.7723  15.0608   9.4433   3.3345  -3.1479   6.6671
+#&gt;  -15.0291  14.3806   7.9204  -0.8433  -3.0652  -4.9490   6.3862   2.0887
+#&gt;    3.3946   8.2103  -2.2330  -1.1686  -6.4077  -1.8844  -0.6360   1.9359
+#&gt;   -7.2017  10.6838   9.3675 -10.5559  14.7724  -3.2499   6.3603  -1.1350
+#&gt;    5.2416 -14.1291  -5.9369   6.3695  -8.0808   4.9498  -1.0099   4.0686
+#&gt;    7.8008  -3.9301 -14.1962  16.6963  -2.1643   2.7470   7.2891   7.1268
+#&gt;    9.7406 -13.0360   3.2398  -7.9581   4.9944   3.2405  -3.2256  -5.0415
+#&gt;    0.3682  -7.9787  -5.2600  17.1641 -10.2795   1.7377   3.9875   0.1306
+#&gt;   -9.7142   8.1789  -4.8007   6.2437  17.5034   1.1674  -3.3046  -7.7050
+#&gt;    7.5847  -7.4089  14.3233  -1.2958   7.2360   9.0604 -10.8416   1.0312
+#&gt;   -6.2819  -8.5516   1.6586   4.2253 -17.5011   9.3042   3.1412 -14.8686
+#&gt;    8.2997  -8.1064   4.9181  -1.0426  -7.9551   4.1494  -9.7840   3.8240
+#&gt;   18.1633 -14.6371  -4.4313   1.8154  -3.6519   5.9923  -5.1153  -9.8723
+#&gt; 
+#&gt; Columns 49 to 54  -8.4769  -5.9151  -6.4813  -7.0201   6.2623  -8.4037
+#&gt;    0.3882  15.9138   5.6105  -3.8608  -2.2757  -5.6603
+#&gt;  -14.5014   2.0358   6.1023   2.8811  -5.4190  -6.7584
+#&gt;    1.1881  -2.5143  -3.3320 -10.2318  -4.7125  -1.7250
+#&gt;    2.3052  13.8123   5.7191  -0.2945   9.1147   4.2297
+#&gt;    2.8621  -0.2156  -4.8806   6.0030  -1.1765   7.7670
+#&gt;   20.1996  -7.7344  -6.2379  -7.5948   1.6836  15.4909
+#&gt;   -9.3062   3.6242   8.6981  -1.2253   0.9231  -2.3114
+#&gt;   -1.9740  -8.3923  -2.0443  -3.2118  12.0007   3.0823
+#&gt;   14.1085   3.3791   6.9403   9.6675   2.2356  -2.4772
+#&gt;  -14.2471   6.0709   0.3183  11.1711  -1.1398  11.7890
+#&gt;    7.6216   3.8623  -3.1473 -12.5835  -7.7868   2.6106
+#&gt;   -1.7931   6.3723  -3.8766  -7.6933  -2.7478   8.7902
+#&gt;    6.1837 -12.2718  -0.4701  -4.2918   0.0584  -4.6319
+#&gt;   -1.9340  -6.0971  -3.5143   1.3975  -3.3757  -7.9841
+#&gt;   -1.1598   9.0673   6.4370  -2.2928  -5.7345   2.5671
+#&gt;    9.1795   3.8481   3.3573  -3.7878   4.0010   6.8017
+#&gt;    0.2499  15.8674   8.1792 -10.0088  -4.9935   0.4390
+#&gt;    4.9046  -3.8502  -4.8795  -3.0779  -2.4735  -2.3404
+#&gt;    1.5899   0.0293   0.9283  -1.2674  -3.9225   0.4987
+#&gt;   -9.3770 -15.8860  -5.1442  -6.6900  -3.4330   0.5659
+#&gt;  -16.0483  -9.8292  -3.6364  16.3431   8.5265   7.9262
+#&gt;   -9.0845   3.6114  10.0347   2.8926  -1.9010  -1.4543
+#&gt;    8.0890   3.4206 -10.5650  -0.3379   5.8540  -4.7952
+#&gt;   16.3089   9.6797  -6.8557  -5.6673  -7.9154   1.0721
+#&gt;    2.1187  -0.1974 -12.1989   0.4059 -10.0289  -3.6241
+#&gt;   -6.3076   0.5004  -1.2712   2.3650  -2.7705   0.4133
+#&gt;    6.9401  10.5418   2.2367  -1.7524  -5.1332   9.8441
+#&gt;   -2.2532  -6.0632   1.6978   8.2168   3.2946  -4.0279
+#&gt;   -2.4332   1.6308  -0.5258  -3.4941  11.7292   3.0047
+#&gt;   -1.8863  -7.4249  10.6301  -0.2827  -3.7962  -0.5907
+#&gt;    4.2639  13.4959   4.5675   7.7005   6.4981   6.6331
+#&gt;    8.1390   8.6622  13.0797  11.7492   6.6612   4.1266
+#&gt; 
+#&gt; (20,.,.) = 
+#&gt;  Columns 1 to 8  -2.2653  -0.2992  -8.0840 -13.1240   2.8674   6.6295   6.5126   6.3446
+#&gt;   -1.8972   0.3906  11.1849  -3.2554  -8.5344  -9.8407  -0.4684  -9.0555
+#&gt;    3.3541   0.8391   6.3411   6.5816 -18.8454   2.7702 -11.9616  -4.1944
+#&gt;    1.4189   1.1001   5.9307  10.4461  20.3536   1.1633   4.4568  -6.3664
+#&gt;    7.0339   1.1404 -16.9503  -9.7155  -2.3246  -8.8742   5.7853   4.3189
+#&gt;   -3.0903  -0.8244   7.8981  10.2077  10.2137  -8.9271  -8.1074  -3.7657
+#&gt;    5.5382  -0.7625 -15.4976   1.2459  -8.3574   7.7494 -10.5614   9.5826
+#&gt;    0.2973  -1.9852   6.5622   3.2223   7.0269  -2.3723   5.8789 -12.0903
+#&gt;    9.5609  10.7393  -5.2497  -8.8528   7.8993  -0.9968  10.9202  -4.5327
+#&gt;    3.0976   5.1308   3.9697   7.3310  11.5743 -14.8499 -11.5704  16.6631
+#&gt;   -0.0480  -1.9001  -2.3730  -3.6587   4.0397  -9.3742  -1.6229  10.8680
+#&gt;   -0.2145  -9.9279  -3.6742   0.0782  10.3799  12.2620  -3.7961  -0.7784
+#&gt;    0.8431  -3.5641   3.2876  -1.1760   8.3219   5.5639  11.7674   1.0064
+#&gt;   -5.2871  -0.5497  -7.7351 -10.3605   2.8745   6.0945 -14.8624  18.0661
+#&gt;   -0.7404  -3.3939  -7.2218   4.7577  -4.2778 -16.8384  17.6664   3.1392
+#&gt;    0.5205  -5.2389   7.6788   9.0223   4.5100  -5.2292  -2.7248  -7.0030
+#&gt;    0.5769  -3.3444  10.3431  -8.5299  -8.7415   8.7767  -4.7396   1.2269
+#&gt;   -5.8526  -7.4778 -10.2137   2.3334   5.3831  -1.5980  -8.9971  -7.5469
+#&gt;   -0.8442   0.2740   2.3468  -2.5717  -0.1457  -2.9135  -0.8418   5.4692
+#&gt;    2.7965   5.7155  -3.7094  -8.7235  -4.5975   5.8828  -9.3944  -0.2782
+#&gt;   -2.6803  -4.8377   4.3721  17.9544   4.5883   9.8608  -1.4583   3.1014
+#&gt;   -2.8660   4.3645  18.4897   4.4416  -9.6468  -6.2421  20.4134  -8.2255
+#&gt;    4.7237   5.2169  -1.3295   3.3900  -6.6240   5.5033   7.2892 -21.3770
+#&gt;    7.1605   3.9767  -3.0323  -3.4185   0.6512   0.6174   0.5952  -3.9720
+#&gt;    1.5655   6.6524   3.6685 -19.1833  -4.1078  -0.0181  -5.7368  -7.9393
+#&gt;   -5.0347  -8.6797   0.3316  -4.0256  -9.5080  -6.6997 -16.6795  -8.4676
+#&gt;    2.8142   6.4528  -7.4576 -16.9667  21.4351   1.9185  -1.7165  14.0511
+#&gt;   -3.0426   2.9307   3.2615  -4.5143 -14.9627  -9.9714 -17.2782   0.8737
+#&gt;    0.1046   5.6565   4.9881  -0.9736  12.6404   7.8123  14.4654 -15.1957
+#&gt;    1.5976   2.0808 -10.7211  -5.2283   8.0706  -1.1106  -9.6745  -2.7221
+#&gt;   -2.1869   4.1961   8.8017   1.3681  -0.4700   9.7436  -4.8551  11.5580
+#&gt;   -3.5422  -2.7089   0.2502   9.7823   0.3797  16.4799  -4.6434  -8.0544
+#&gt;   -0.3372   7.9501  -8.4571  -9.9149  -5.7148   5.2510  -0.9710   9.2152
+#&gt; 
+#&gt; Columns 9 to 16  22.7046   8.8656  -1.8227   8.9593  14.7723   8.0486  -1.0943   0.0352
+#&gt;   -1.1169   2.5402 -12.4899  21.1558  -9.4494  -7.9042  -2.7939  28.6017
+#&gt;    4.3768 -11.2553  -4.1071  18.4578  14.5702  12.0320   5.6829  -0.9867
+#&gt;  -28.0625   2.9737   4.5000  -0.2179   5.5182  -3.9749  -0.8898   4.2535
+#&gt;   -6.4355   1.6308   1.7177  -4.6168   9.9663   4.1929 -15.4372 -12.3801
+#&gt;    8.3310   1.3950   5.3045  -9.5887 -12.1274   1.9325  -1.2257   4.4975
+#&gt;   11.3182  32.6717   0.3343 -12.0492   8.5118  -7.2801  -9.5235 -11.3916
+#&gt;    4.5921  -5.1121   0.4439 -18.4692  -2.2870   8.2574  -5.6966  -0.1372
+#&gt;   -3.4784   2.0897  -0.4041  -9.9791   9.7519   3.8283  14.5008 -10.6872
+#&gt;  -16.7427  -5.4824  10.4802 -18.8204  -8.0625 -12.5081   4.8563   1.9332
+#&gt;   -1.7032  12.5403   5.8618   6.3167  -9.1933  -3.0000 -11.1713  -2.9417
+#&gt;   -2.2482  -0.7298   7.2539  -7.5992   6.2598  -1.0015   4.3402  -5.9344
+#&gt;   -3.7315  20.7157 -14.7432  13.1493  -4.9645   3.1082  10.1389   5.6530
+#&gt;    2.2658  -8.5603 -12.8248  -1.9801   7.9876 -10.0319  -6.5442  -6.7262
+#&gt;    5.8323  -6.0048   9.5594   6.6087   4.1530  -4.4811  -4.9229  -2.6157
+#&gt;    0.2485   9.5105   4.0412  -8.6230   1.1599   3.0516   8.0232  -1.2285
+#&gt;   -1.9085  -0.6954 -15.3894  11.5232  -2.8657  -1.1888  -5.0973  -6.7306
+#&gt;   -7.8041   2.1794  -1.9183  -6.6390   9.9993   8.2957  11.5401  -0.0086
+#&gt;    6.1306  12.6493   3.2182  -6.5494   3.9135   1.9680   3.3683   2.6004
+#&gt;    1.6508  11.0516  -3.1102   1.1376  -0.2661  -7.1742 -13.7552  14.2504
+#&gt;    0.6556   0.7736   7.4219  -3.2018  -6.1690  -7.4612   7.6728  24.6016
+#&gt;   13.6035  11.7464 -13.2721 -14.8095 -16.9787  -2.3197   0.8223  -4.9328
+#&gt;   12.9162   8.4816   4.4873   2.0722   6.2033  12.1520   6.3933  17.6712
+#&gt;  -16.2263  -7.0064  -7.0218  -5.2966   4.6059  16.4592 -12.8893  -4.5726
+#&gt;  -13.9331   4.3118   5.4921   8.3261 -11.1891  19.0333  10.3206  19.8337
+#&gt;   -6.0739  -7.3151   8.1623   1.1320  -2.0046  -1.4451   8.7414  20.3453
+#&gt;    5.2604 -10.8343  -6.7325  -5.9068 -16.6203   3.7952   2.1398   2.1272
+#&gt;   -3.0736 -13.2078   1.0946 -11.4924  -0.7304   4.2453   8.1450  16.6810
+#&gt;   -3.9020  -5.9777 -14.1405   1.5603 -17.3142  -2.6642 -16.0191  -2.4234
+#&gt;   -4.2214  -0.4177  -3.9181   1.2628   6.8582   5.5985   3.2752   1.6840
+#&gt;    7.0367  -0.9933   8.5887   9.4574  14.0041  -0.1052  -2.3672   6.7918
+#&gt;   -0.5503  -0.7571   3.0058  -5.6141   0.0150  11.2386  -7.4648  -2.9740
+#&gt;  -10.6669 -13.3201   6.4182   7.0915   9.1428 -11.3971 -16.9469   2.8021
+#&gt; 
+#&gt; Columns 17 to 24  17.6642  -0.6387   7.9046  15.2093  -7.7815   7.4469   4.1875  -1.5077
+#&gt;   -0.9758  -0.9738 -12.1521   2.7148 -15.3814 -10.5035   3.7223  -1.2998
+#&gt;    5.9875   2.2168   6.1767  11.0110  14.6629 -17.7826  -4.9001   0.8982
+#&gt;   -5.6393   5.3944   5.7311   5.5054  10.0760  15.5363   5.9889   7.3000
+#&gt;    8.7352   6.1695  -3.2813 -14.6480 -19.0425   3.1577  -2.7995   3.5504
+#&gt;    2.0529  21.4071 -18.3231 -10.7602  18.3895   0.0733  -9.8752  -6.8944
+#&gt;   -6.2217   3.2915  13.7572 -13.1856   7.4382  20.4022  -8.1393  -5.9567
+#&gt;   -6.5613   9.1675   8.0228 -19.1505  -3.6138  -6.6123  -0.6475   5.2600
+#&gt;   -2.3261   3.1672  -1.2693 -19.5586 -17.0771   4.5576   5.1650   5.3382
+#&gt;    5.9462  -1.1929  -3.4518  -9.9776 -20.9776   5.0989  -2.3550 -15.8299
+#&gt;    1.6283   5.6687   0.4092  10.9363 -10.4706 -13.9946 -16.0593  -8.0922
+#&gt;    0.7369   5.6484   6.7144  -0.5455  -8.0493  13.8250  14.5871   5.5704
+#&gt;  -19.5043   0.7672   7.2171  -8.3151  -8.2818  15.7777  15.2274  15.9179
+#&gt;   -9.1341 -10.4634  -5.9890  -6.3053 -18.1182   0.9232  -7.3188  -7.4505
+#&gt;    1.3812  -2.8491  -8.0542  -2.8909   1.9746 -10.2501 -13.2893  15.5367
+#&gt;   15.0537  -0.8085  -4.4413  13.9893  11.6323  -9.4141  -8.2112   1.9273
+#&gt;    3.3577  -2.5148  12.3265 -10.9512  -9.5564   8.2188   4.5326  -1.3718
+#&gt;   -6.1836  -1.9051   0.1954  -4.0796 -14.3666  17.7900   1.1285  -1.3318
+#&gt;   13.3311 -10.3902 -10.0682  19.8027   7.1195   5.7822  -5.8048  -7.8914
+#&gt;    6.4710  -7.7272  -3.0857  -3.4119 -13.4485  -7.9478   1.1953   8.5252
+#&gt;  -25.9261   8.1285   9.1884  -4.5096  12.1478   7.2600  -5.9244   3.5302
+#&gt;    6.2438   5.0811 -10.1497 -11.7140   0.6813  -5.9037 -10.0189   3.9940
+#&gt;  -12.0951   4.6454   2.8733  18.5339   1.2435  -5.9668   4.1898  11.5805
+#&gt;    8.4386 -12.2192 -22.5463  -6.2082  -2.5395  17.9824   6.2090  -5.6919
+#&gt;    1.7823 -14.1368   2.3466   7.0545   2.1329  -6.9889   0.1930  -0.9751
+#&gt;   -1.1099  -1.4668  10.2118  -9.3422  -9.2612 -10.6328   4.2368 -21.1498
+#&gt;   -8.8242   5.1813  -1.1785   5.1899 -12.8420  -7.4009  13.8923  -0.1505
+#&gt;  -11.2040 -23.8364  10.4214   5.6040 -19.1460  -7.8605   7.8769  -7.5557
+#&gt;    6.6239   1.6977   4.0559   1.4558   1.4975   2.3042  -0.5022 -10.7446
+#&gt;   -9.0102  18.5619  -8.8101 -10.7224  -7.9344  12.2323  -3.2101 -10.8233
+#&gt;   -3.2937  -8.6577   9.1005  26.2489   3.7980  -4.6835  -0.8302   2.7777
+#&gt;   -9.8487  16.4674   6.7647  -3.2271  -4.0097  12.5370  -4.5381  -8.1880
+#&gt;  -20.0576  -6.8400  10.2333 -15.7089  -9.9337   3.1535  -6.7635  -3.4640
+#&gt; 
+#&gt; Columns 25 to 32   5.9104   4.8221  -8.1394  -9.6691   9.3055  -5.1001 -12.2185   2.6398
+#&gt;    3.4581   4.4536  -8.3125   2.5345  -6.5927  -0.0154  -3.3810   4.1154
+#&gt;   -6.6569   8.5043  11.9527  -9.7875   0.0155   4.9674   5.8879  -4.6753
+#&gt;   -3.0395   0.5927  -5.4095  11.2886   0.4358   4.0293 -17.5789  13.0740
+#&gt;    2.7918  -2.7854  -7.4394   1.4016  -5.1818  10.5699   4.5737  -3.1707
+#&gt;   -7.4011   0.1835  -3.2074  10.7163  -3.9187 -10.3619   7.1848   0.9578
+#&gt;  -15.1819 -12.0398  -3.1670   6.0243   9.0537  -7.4634  -4.8999   9.0450
+#&gt;   -5.5812   6.5045  -5.0183   8.6197   2.2698   1.6468 -10.2231   5.6208
+#&gt;    7.5617 -10.9167   1.3932   1.0610 -17.3475   3.6534   3.8007  10.5845
+#&gt;    7.8356   5.9945 -17.7030   1.9506 -11.7852   0.6386  -1.0682  14.8160
+#&gt;   -5.4559 -11.3433  -1.6827   1.9712  -3.0699   0.2285 -10.0801   2.3636
+#&gt;    2.3210  -6.3367   8.0976   1.6282   4.3854   6.5529  -7.7830  -0.8202
+#&gt;   12.3213   2.0599   1.5219  11.5187  -4.3801  -4.2886  -6.0968  13.6755
+#&gt;    2.3666 -12.0247 -12.8151  -4.8653  -2.4643  -1.0182 -13.3706  -1.2856
+#&gt;   10.3363  -5.3233   1.4460   4.1572  -6.3407  -2.9956 -10.2585 -32.6818
+#&gt;    4.8838  -3.7452   4.0146   7.0686  -1.9888  -4.5866  -4.6609  -1.3247
+#&gt;   -8.7365   4.3888  -0.0985 -21.9485  -1.5032  13.5189   3.0245  11.4382
+#&gt;   -2.0524 -11.0958 -10.1485  13.3686   1.0175  -2.8466  -5.0082   1.0060
+#&gt;    8.1681  -9.2563   9.5008   8.1686  -1.9951   1.2165  10.2728  11.6361
+#&gt;    6.4259   0.9784  -6.1052   0.4424   6.7311 -19.1240  -2.6960   6.5045
+#&gt;   -2.6689   1.0665   0.9865  20.0187 -18.4775  -4.7755  -0.6665  -4.9734
+#&gt;    4.6048   1.9818   0.0268  -8.6312 -12.6262  -3.2561   8.2110   6.7139
+#&gt;   -2.6962 -15.7499   0.1256  13.0784  -8.2836  10.8653  -0.3269   5.2771
+#&gt;    9.0601   0.5579 -16.0441  -2.1294  -3.0186  -0.6771  -7.0197   3.3308
+#&gt;    6.5242   4.0100  -5.3352   2.7399   9.5822  -3.0179  -1.2517   7.2918
+#&gt;    6.9063   5.6368   6.3425  -8.1189  11.9978   1.3311  -0.7957  -8.6222
+#&gt;   14.0419  19.9841 -19.2937  15.6144   6.7469 -10.1073  -3.2629  20.3081
+#&gt;  -12.2120   1.5340   3.9908 -11.4030   5.8836   5.1645   1.5510  14.7949
+#&gt;   -4.5906  -0.6133   2.1705   5.4995   4.3333   0.2632   7.6452  -1.3775
+#&gt;   -3.9061   0.9274  -1.6277   6.9139   7.2659   7.1171  -7.6749  -6.3526
+#&gt;    4.8205   0.5059   3.3250   7.3435  -0.2742 -10.0117  -5.7437   3.8513
+#&gt;  -11.4412   1.5989   7.2549  12.2832   6.6201   4.0207  18.6231  -2.6978
+#&gt;   -3.2150 -10.5552 -14.6349   1.0026   2.9579  -3.5328   1.9846  -3.9082
+#&gt; 
+#&gt; Columns 33 to 40 -13.2848  -0.5988  -1.2989   5.8212  11.2562  -6.6643   5.2632  -3.5389
+#&gt;   -8.8260 -10.5065  -0.1619  -0.9349 -14.6161  -1.1251   1.5410   0.6819
+#&gt;   -6.9181 -12.1762 -15.0838 -13.6559  13.2063   2.2151  -5.2201   5.0383
+#&gt;   -8.2575  -2.5460  -8.3200  11.6442   0.8071  -4.8722  17.0862   3.3237
+#&gt;    1.2646 -15.8315  -5.4005  -8.6578   2.9465   0.6985  -7.5534   5.4819
+#&gt;   11.0173   5.3423  -3.4031  21.6489 -10.3451   0.3455  -1.3004   8.8321
+#&gt;   -8.0202   2.6883  13.6055 -13.8843  14.5995  19.4407   8.8667   7.2221
+#&gt;    7.3830   5.3822 -10.6590  26.2000  -5.7342   2.1643  -0.0664 -12.4417
+#&gt;  -10.7809   5.5687   7.8680  -3.7356   8.9632   8.1361  -6.8870   0.5630
+#&gt;    6.5460   5.0822   5.1806  -2.6952  -8.1342   2.4732  -2.8841   1.8029
+#&gt;   -3.0723 -19.3079 -19.4181  -3.8867   2.4883  -0.6227  -5.4244  -0.7217
+#&gt;    6.1192   6.0294   9.8505  -2.7279   8.4087  -0.0448 -10.4724   7.0981
+#&gt;  -21.8484  -6.8548   1.1474 -17.4633  10.1407  -5.7662   1.6476  -0.5942
+#&gt;  -18.0175   1.7014  -2.3008 -11.6641   3.7682   2.2228   7.7583  -7.2064
+#&gt;   -7.5446 -16.4989 -20.7692  -6.7358  -0.1978 -10.4529  -4.2468  -3.2102
+#&gt;    4.4813   3.3473   5.4832  -0.1446   6.8409  -1.3990  -4.6832  10.3867
+#&gt;   -2.8606   2.7559 -14.8896 -19.1378  -5.5776   7.3396  -5.2749  -8.6807
+#&gt;   -1.9968  -0.7495  -0.2332  -8.4584   5.3272   3.6040  -5.3820  14.3607
+#&gt;   10.6130   6.5070   2.7207  -4.3841  -2.1949  -0.3277  -1.9849  -0.0598
+#&gt;   -8.6159  -6.3805  17.1809   3.6410  -2.8060  -6.9909   2.4218  16.9963
+#&gt;   -9.5896  14.2145   6.6624   2.5391   7.3797   5.5521  10.5887  -3.8019
+#&gt;   -0.7171   4.9104   9.8122   4.7194  12.8769  -1.7959  -4.3849   1.9492
+#&gt;   -6.2699  -4.2470  -3.6704  11.3357   3.8254  -1.7273   4.2892  -0.9200
+#&gt;    1.2836  11.5547   6.2253  12.6112  -4.6704   1.1384  -7.0601  -1.2174
+#&gt;   -6.0212   2.1315  -2.0683  10.7270  -4.7864  -5.4257   4.4478  -0.7735
+#&gt;   -6.9576  -8.7218   0.2272  -5.9443   2.5234   6.0720  -1.6406  -1.9135
+#&gt;    9.6933   9.6212   9.9820  -1.4911 -18.6658   1.1614   1.1155  -2.1856
+#&gt;    3.0013  -2.0622   3.7523  10.9132  -7.4350  -6.8037   6.1280  -5.5437
+#&gt;    0.1733   2.5096  14.0835   6.6632 -10.4960  20.6756  -5.3487  -2.0246
+#&gt;  -14.5584   1.0116  -0.9558   3.5640 -12.4739   4.6679  -6.5922  -4.0250
+#&gt;   -1.1797  -8.4165 -13.5125 -15.1991   5.7454  -2.8517   2.0499  -4.1946
+#&gt;   -5.8201   5.6230 -12.1709 -12.6365  -7.3619  -1.7180  15.8541  -5.4316
+#&gt;   -8.1388 -15.1476 -14.7358   5.4903  -1.3170   0.5399  13.2027  -5.8415
+#&gt; 
+#&gt; Columns 41 to 48  -5.8575  -1.1832  -1.1470   0.8476   5.0894  -7.0232  -5.8470   0.0351
+#&gt;    0.7060  -6.4848   4.5100 -12.3964  -7.9843  -2.6963   2.8206  -0.0505
+#&gt;   -1.3362  13.0885  13.4296  -2.2460  10.5508  10.7067 -12.4676 -11.7519
+#&gt;   19.4782   2.0082   7.1231  -6.6430  10.8793   5.2294   0.3712  10.6722
+#&gt;  -12.4520  -7.5845  -8.5223  -2.7500  -9.8489   0.0859  -7.9954   6.8262
+#&gt;    1.4447 -11.1030 -12.5502  12.8197   7.5714   3.8164  14.2554   6.3675
+#&gt;  -14.6389 -15.3754  10.2145 -14.5279 -10.8230   8.4901  15.4390  -2.5918
+#&gt;    0.1149 -12.9130   7.2866   8.6505   0.4887   5.5779  -9.7180  -0.8259
+#&gt;   -6.8633   8.7274   3.1915  -1.6969   2.7831  -8.8030   4.1106  -8.4376
+#&gt;   22.3276  -3.8989   2.7296 -11.7009  -3.6822  -7.7497  10.0564   2.5126
+#&gt;    4.9275 -16.9212 -16.3241 -19.8715  -5.4912   1.6888  -5.5892  -2.8319
+#&gt;   -1.2967   3.0875  -5.0835   7.5323  -2.7911 -12.0536  -2.9674   5.9111
+#&gt;    0.2800   4.3716   9.9010 -11.1197  14.2852  -4.9287  -8.5188   3.6311
+#&gt;  -13.7508 -13.0850  -1.6860  -3.5667  -6.7858  -9.7164   1.1143  -4.1267
+#&gt;  -13.2925  -8.6209 -12.0692  -3.1955  -2.8567   2.6562   4.9640  -6.7264
+#&gt;   -4.2125   4.9015  -9.4357   9.5524   3.9941   0.3523  -3.1834  -0.7954
+#&gt;    0.3652   2.4108   5.1524  -1.3808 -15.2275   4.4895   4.9816 -14.0834
+#&gt;    1.5387  -1.4155  15.2860  -1.2077 -16.7041 -10.3103  16.2057  -7.0775
+#&gt;   18.3921  10.9614  -7.6098   6.7147  -8.5978  -4.9138 -15.3389  -5.3784
+#&gt;    1.4735  -8.5954  -0.7906   1.1221   8.3126  -5.6129  -1.0612  21.9490
+#&gt;    7.4943   1.1357   9.9221  -5.0446  -0.3586  -8.5198   0.8268   0.6990
+#&gt;  -10.2649   9.8347   1.0324  -5.3509   8.6235  11.2996  -2.2346  -1.9970
+#&gt;   -9.4363   4.5089   9.2187   5.0147  -3.4025  -8.3976  -7.9586 -11.1828
+#&gt;    1.3212   0.6170   5.6923   9.9970  -6.6220  13.6253  -7.1009  -0.0962
+#&gt;   23.5576  -2.1115   2.2221  27.7146  22.0444   2.3116  -4.4454   4.0914
+#&gt;   -7.8340   6.7754  -4.6155   3.6454  -8.7927  12.3246  -5.2933  -3.2191
+#&gt;    6.2377   3.3940  -1.4850  10.7535   7.7656  -7.3435  11.8374   7.3073
+#&gt;    0.3927   5.3734  21.5026  10.2683  -4.4226   6.6118   4.6513  -5.3856
+#&gt;   -9.4217   1.2088  -3.5455  11.8381   8.1124   2.5137  10.4823   4.9352
+#&gt;   -2.2640 -10.7916  10.9737  -6.8278   8.6452   1.7453  10.1867   2.6673
+#&gt;    5.7317  -0.8226   2.8983 -13.4839  -3.5434 -12.2212  -1.9737   0.7393
+#&gt;    0.4864  -7.3695  -7.6554   6.3468 -11.5907   9.2358   8.0288   4.5832
+#&gt;    6.4283 -12.5552  -8.4994  -3.4785  -3.2836   2.6417   6.6195  11.7416
+#&gt; 
+#&gt; Columns 49 to 54   9.1007  -3.0177  -1.8838   7.2480   0.1200   3.8837
+#&gt;   -0.5773  -1.0676 -11.7548   4.0879  -0.9830   1.8452
+#&gt;    6.2033  -3.8315  -7.3474   1.4246  -4.2984  -1.4961
+#&gt;   -4.4075 -11.2964   4.7264  -0.3367   2.2216  -2.9164
+#&gt;   12.0234  -2.5118  -5.9832  -6.1021   1.3594  -0.4622
+#&gt;    1.9878   7.5738   8.7366   2.0166  -6.1659  -2.0650
+#&gt;   -3.9390  11.0900  10.9211  -1.2368   6.4134   0.9693
+#&gt;   -8.5893  -4.2376 -12.2887   5.3077  -0.9279  -4.4723
+#&gt;   -0.8214   3.1198  -1.0422   7.3185  -0.3208  -1.2252
+#&gt;  -10.6750  -4.2415  -9.7995  -6.6033  -2.4468  -3.4383
+#&gt;   12.7180  -0.8569  -3.2433  -3.1250   3.1652   4.3897
+#&gt;  -16.0551  -1.9551   2.3425  -2.2168  -1.2255  -1.9735
+#&gt;  -12.2473  -3.5930  -0.2202   5.3557  -1.5723   2.4245
+#&gt;  -12.9533  12.3988  -0.7214  -0.0127   0.2706   0.4035
+#&gt;   10.6038  -2.0731   1.2670  -7.3337   9.1796   4.5962
+#&gt;    0.9354 -14.4622  -1.1519  -7.8692  -1.0576  -0.0993
+#&gt;  -12.8159  20.0985  -5.1880  -4.4215  -4.0017   4.3007
+#&gt;    1.0518   4.3241   0.5598  -0.7126  -1.0072  -2.8076
+#&gt;   -1.9028  16.1889   0.6727  -5.7127  -2.5740   1.3590
+#&gt;    6.2794 -16.1370   3.9592   7.2018  -0.9700  -2.1768
+#&gt;   -1.6357   0.3465  -2.6915  -0.4033   2.5698  -2.8284
+#&gt;   15.0365  -9.8127  -5.0431  -6.2516   0.8636  -1.0819
+#&gt;    1.7100   5.7835  10.4537   5.7782   7.6487   3.6805
+#&gt;   -0.0082   9.7222  -5.5707   0.4330 -10.3284   4.2040
+#&gt;  -17.7295  -8.6692   3.4524   4.9592  -0.3906   2.4489
+#&gt;   -1.5549   8.2899   1.8337   0.1230   5.6002  -5.3337
+#&gt;   20.2444  -7.2078  -2.7447  -4.5550  -1.5866   0.3140
+#&gt;  -15.8512   9.4708  10.1017  -3.2339   6.9926  -2.8168
+#&gt;   -4.4492 -17.7566   3.1178   1.5984  -5.2506   1.2193
+#&gt;   -4.2897  -1.4693  -6.3019   8.7731  -5.6519   0.7179
+#&gt;   -6.3805  13.5619   3.4258   1.9243   2.3275   4.2455
+#&gt;    1.6612   4.9800   5.4945  -6.7546   4.0067   0.8389
+#&gt;  -13.7101  -0.5703  -8.4804   3.0112   7.8513  -3.5254
+#&gt; [ CPUFloatType{20,33,54} ]</div></pre>
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+    <h1>Conv_transpose2d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_conv_transpose2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Conv_transpose2d</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_conv_transpose2d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>weight</span>,
+  bias = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(),
+  stride = <span class='fl'>1L</span>,
+  padding = <span class='fl'>0L</span>,
+  output_padding = <span class='fl'>0L</span>,
+  groups = <span class='fl'>1L</span>,
+  dilation = <span class='fl'>1L</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iH , iW)\)</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>filters of shape \((\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kH , kW)\)</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>optional bias of shape \((\mbox{out\_channels})\). Default: NULL</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the convolving kernel. Can be a single number or a      tuple <code>(sH, sW)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p><code>dilation * (kernel_size - 1) - padding</code> zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      <code>(padH, padW)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>output_padding</th>
+      <td><p>additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple <code>(out_padH, out_padW)</code>.      Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>the spacing between kernel elements. Can be a single number or      a tuple <code>(dH, dW)</code>. Default: 1</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-"><a class="anchor" href="#conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-"></a>conv_transpose2d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -&gt; Tensor </h2>
+
+    
+
+
+<p>Applies a 2D transposed convolution operator over an input image
+composed of several input planes, sometimes also called "deconvolution".</p>
+<p>See <code><a href='nn_conv_transpose2d.html'>nn_conv_transpose2d()</a></code> for details and output shape.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='co'># With square kernels and equal stride</span>
+<span class='kw'>inputs</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>4</span>, <span class='fl'>5</span>, <span class='fl'>5</span>))
+<span class='kw'>weights</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>8</span>, <span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='fu'><a href='nnf_conv_transpose2d.html'>nnf_conv_transpose2d</a></span>(<span class='kw'>inputs</span>, <span class='kw'>weights</span>, padding=<span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,1,.,.) = 
+#&gt;  -2.9663 -1.2733 -3.9435 -0.5276  2.6750
+#&gt;  -1.2722  1.0069 -5.7263  2.7787 -0.1404
+#&gt;  -1.7860 -3.8682  4.8713  1.1316  3.8511
+#&gt;  -0.0010  5.3181 -0.6754  2.5730 -2.4711
+#&gt;   0.5885  4.9659 -3.5166  4.5335 -3.7058
+#&gt; 
+#&gt; (1,2,.,.) = 
+#&gt;  -5.2250  0.2726  8.2119 -6.4099  5.9053
+#&gt;   4.6700  5.1411 -1.5878  3.8120  0.1009
+#&gt;  -3.5450 -5.0390  9.2086 -2.9578 -0.5509
+#&gt;   8.0991 -14.7839 -3.2309 -12.1295  2.1448
+#&gt;   0.1715  2.9083 -1.4596 -1.0306 -2.7095
+#&gt; 
+#&gt; (1,3,.,.) = 
+#&gt;   3.1749  2.3139 -8.2462 -6.3721  0.0100
+#&gt;   0.8951 -3.3390  0.3912 -8.3079  1.3706
+#&gt;   3.5525  3.0387  6.1074  6.3115 -2.0431
+#&gt;  -3.2812  2.3111  2.1700  3.3244  4.8020
+#&gt;   5.2995 -1.5489 -1.4167 -6.5637 -0.9624
+#&gt; 
+#&gt; (1,4,.,.) = 
+#&gt;  -10.0481   7.0235  10.5055  -2.0748  -3.7513
+#&gt;    8.1387  -0.4205  -3.0138 -10.8628   5.5135
+#&gt;    2.9024  -5.1426  -1.6595   4.3741  -5.7961
+#&gt;    4.4890 -14.6718   5.5729   4.6866  -0.7137
+#&gt;    1.1518   3.0874   0.7841   0.3746   0.9620
+#&gt; 
+#&gt; (1,5,.,.) = 
+#&gt;    0.9123  -8.0814   1.0808   7.4471   0.6151
+#&gt;    2.1226   2.7764  11.3909   4.8650  -4.3402
+#&gt;  -11.2861   4.5080  -7.8865   5.5343  -9.9356
+#&gt;   -1.9028  -1.0153  -3.6493  -7.0338  -8.3456
+#&gt;   -3.5633  -8.6804  -7.8108   6.3765   2.8930
+#&gt; 
+#&gt; (1,6,.,.) = 
+#&gt;   -0.5542   1.5379   3.6290  -4.7070  -6.1971
+#&gt;   15.2333   1.0645   0.0866  -3.6305   0.7299
+#&gt;   -0.0833 -10.8658  -6.3612   0.5463   2.2777
+#&gt;   -4.8246   2.8323  -2.6610  -1.3371  -0.6676
+#&gt;   -5.5904   6.6461   2.8359   5.6594  -0.5057
+#&gt; 
+#&gt; (1,7,.,.) = 
+#&gt;   2.0289 -0.2122  4.9581  0.2454  1.7928
+#&gt;  -4.9431 -0.3061  5.8549  0.7861 -2.7473
+#&gt;  -3.8568 -3.2374 -1.5201 -1.2617 -7.8991
+#&gt;   1.0549  1.0301  3.8253  1.0461 -6.5575
+#&gt;  -3.1756  2.8057 -2.0838  7.5251  3.8319
+#&gt; 
+#&gt; (1,8,.,.) = 
+#&gt;   7.6274  4.6864 -5.2624  0.0808 -3.7774
+#&gt;  -0.0753 -18.2773 -7.6814 -6.3614  1.1808
+#&gt;  -0.9235  9.3308 -1.1801 -6.3923  2.6811
+#&gt;  -0.3068  9.7232  2.0825  5.2834  8.0793
+#&gt;   5.7741  8.8292  2.7251 -3.5073  0.5215
+#&gt; [ CPUFloatType{1,8,5,5} ]</div></pre>
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+    <div class="page-header">
+    <h1>Conv_transpose3d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_conv_transpose3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Conv_transpose3d</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_conv_transpose3d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>weight</span>,
+  bias = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(),
+  stride = <span class='fl'>1L</span>,
+  padding = <span class='fl'>0L</span>,
+  output_padding = <span class='fl'>0L</span>,
+  groups = <span class='fl'>1L</span>,
+  dilation = <span class='fl'>1L</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)\)</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>filters of shape \((\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kT , kH , kW)\)</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>optional bias of shape \((\mbox{out\_channels})\). Default: NULL</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the convolving kernel. Can be a single number or a      tuple <code>(sT, sH, sW)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p><code>dilation * (kernel_size - 1) - padding</code> zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      <code>(padT, padH, padW)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>output_padding</th>
+      <td><p>additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple      <code>(out_padT, out_padH, out_padW)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>the spacing between kernel elements. Can be a single number or      a tuple <code>(dT, dH, dW)</code>. Default: 1</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-"><a class="anchor" href="#conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-"></a>conv_transpose3d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -&gt; Tensor </h2>
+
+    
+
+
+<p>Applies a 3D transposed convolution operator over an input image
+composed of several input planes, sometimes also called "deconvolution"</p>
+<p>See <code><a href='nn_conv_transpose3d.html'>nn_conv_transpose3d()</a></code> for details and output shape.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='kw'>inputs</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>10</span>, <span class='fl'>20</span>))
+<span class='kw'>weights</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>3</span>, <span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='fu'><a href='nnf_conv_transpose3d.html'>nnf_conv_transpose3d</a></span>(<span class='kw'>inputs</span>, <span class='kw'>weights</span>)
+}
+}
+</div></pre>
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+      
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+</div><!--/.navbar -->
+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Cos</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_cos.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Cos</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_cos</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="cos-input-out-null-gt-tensor-"><a class="anchor" href="#cos-input-out-null-gt-tensor-"></a>cos(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the cosine  of the elements of <code>input</code>.</p>
+<p>$$
+    \mbox{out}_{i} = \cos(\mbox{input}_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_cos</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0.7828
+#&gt;  0.9242
+#&gt;  0.9376
+#&gt;  0.9922
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_cosh.html b/static/docs/dev/reference/torch_cosh.html
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+
+      
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Cosh</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_cosh.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Cosh</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_cosh</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="cosh-input-out-null-gt-tensor-"><a class="anchor" href="#cosh-input-out-null-gt-tensor-"></a>cosh(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the hyperbolic cosine  of the elements of
+<code>input</code>.</p>
+<p>$$
+    \mbox{out}_{i} = \cosh(\mbox{input}_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_cosh</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  2.4203
+#&gt;  1.7086
+#&gt;  1.1125
+#&gt;  1.1147
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/torch_cosine_similarity.html b/static/docs/dev/reference/torch_cosine_similarity.html
new file mode 100644
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--- /dev/null
+++ b/static/docs/dev/reference/torch_cosine_similarity.html
@@ -0,0 +1,368 @@
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+<div class="row">
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+    <div class="page-header">
+    <h1>Cosine_similarity</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_cosine_similarity.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Cosine_similarity</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_cosine_similarity</span>(<span class='kw'>x1</span>, <span class='kw'>x2</span>, dim = <span class='fl'>2L</span>, eps = <span class='fl'>0</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>x1</th>
+      <td><p>(Tensor) First input.</p></td>
+    </tr>
+    <tr>
+      <th>x2</th>
+      <td><p>(Tensor) Second input (of size matching x1).</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int, optional) Dimension of vectors. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>eps</th>
+      <td><p>(float, optional) Small value to avoid division by zero.        Default: 1e-8</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="cosine-similarity-x-x-dim-eps-e-gt-tensor-"><a class="anchor" href="#cosine-similarity-x-x-dim-eps-e-gt-tensor-"></a>cosine_similarity(x1, x2, dim=1, eps=1e-8) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns cosine similarity between x1 and x2, computed along dim.</p>
+<p>$$
+    \mbox{similarity} = \frac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert _2 \cdot \Vert x_2 \Vert _2, \epsilon)}
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>input1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>100</span>, <span class='fl'>128</span>))
+<span class='kw'>input2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>100</span>, <span class='fl'>128</span>))
+<span class='kw'>output</span> <span class='op'>=</span> <span class='fu'>torch_cosine_similarity</span>(<span class='kw'>input1</span>, <span class='kw'>input2</span>)
+<span class='kw'>output</span>
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0.0857
+#&gt;  0.0101
+#&gt;  0.1649
+#&gt;  0.0623
+#&gt;  0.0738
+#&gt; -0.0599
+#&gt; -0.0418
+#&gt;  0.0788
+#&gt;  0.0598
+#&gt;  0.0059
+#&gt; -0.0343
+#&gt; -0.0889
+#&gt;  0.0976
+#&gt;  0.1223
+#&gt;  0.1000
+#&gt; -0.0479
+#&gt; -0.1086
+#&gt;  0.2057
+#&gt;  0.1539
+#&gt;  0.1357
+#&gt;  0.0878
+#&gt;  0.0818
+#&gt;  0.0544
+#&gt; -0.0295
+#&gt;  0.0689
+#&gt; -0.0774
+#&gt;  0.0074
+#&gt; -0.0211
+#&gt; -0.0021
+#&gt;  0.0976
+#&gt;  0.0913
+#&gt; -0.0176
+#&gt;  0.2034
+#&gt; -0.0049
+#&gt; -0.0285
+#&gt;  0.1157
+#&gt; -0.1759
+#&gt;  0.0079
+#&gt;  0.0375
+#&gt; -0.0873
+#&gt;  0.0755
+#&gt; -0.0989
+#&gt;  0.0888
+#&gt;  0.0649
+#&gt; -0.0739
+#&gt;  0.0348
+#&gt; -0.0271
+#&gt;  0.0272
+#&gt; -0.1020
+#&gt; -0.0533
+#&gt; -0.1703
+#&gt; -0.0898
+#&gt;  0.1356
+#&gt; -0.0408
+#&gt;  0.0232
+#&gt;  0.1387
+#&gt; -0.0054
+#&gt; -0.0062
+#&gt; -0.0278
+#&gt; -0.0370
+#&gt; -0.1010
+#&gt; -0.0419
+#&gt; -0.1620
+#&gt;  0.0823
+#&gt;  0.0396
+#&gt;  0.1143
+#&gt; -0.0720
+#&gt; -0.0441
+#&gt;  0.0365
+#&gt;  0.1570
+#&gt;  0.0785
+#&gt; -0.1786
+#&gt;  0.0428
+#&gt;  0.1420
+#&gt;  0.0014
+#&gt;  0.0639
+#&gt; -0.0082
+#&gt; -0.0694
+#&gt;  0.0665
+#&gt; -0.0191
+#&gt;  0.0376
+#&gt;  0.0624
+#&gt;  0.1519
+#&gt;  0.0230
+#&gt;  0.0474
+#&gt;  0.0567
+#&gt; -0.0727
+#&gt;  0.0950
+#&gt;  0.1371
+#&gt; -0.0143
+#&gt; -0.0568
+#&gt;  0.0634
+#&gt;  0.1218
+#&gt; -0.1276
+#&gt;  0.0446
+#&gt; -0.0189
+#&gt; -0.0732
+#&gt; -0.0915
+#&gt; -0.1336
+#&gt; -0.0561
+#&gt; [ CPUFloatType{100} ]</div></pre>
+  </div>
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diff --git a/static/docs/dev/reference/torch_cross.html b/static/docs/dev/reference/torch_cross.html
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--- /dev/null
+++ b/static/docs/dev/reference/torch_cross.html
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+
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+    <div class="page-header">
+    <h1>Cross</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_cross.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Cross</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_cross</span>(<span class='kw'>self</span>, <span class='kw'>other</span>, dim = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor) the second input tensor</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int, optional) the dimension to take the cross-product in.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="cross-input-other-dim-out-null-gt-tensor-"><a class="anchor" href="#cross-input-other-dim-out-null-gt-tensor-"></a>cross(input, other, dim=-1, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the cross product of vectors in dimension <code>dim</code> of <code>input</code>
+and <code>other</code>.</p>
+<p><code>input</code> and <code>other</code> must have the same size, and the size of their
+<code>dim</code> dimension should be 3.</p>
+<p>If <code>dim</code> is not given, it defaults to the first dimension found with the
+size 3.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span>
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>3</span>))
+<span class='kw'>b</span>
+<span class='fu'>torch_cross</span>(<span class='kw'>a</span>, <span class='kw'>b</span>, dim=<span class='fl'>2</span>)
+<span class='fu'>torch_cross</span>(<span class='kw'>a</span>, <span class='kw'>b</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.1652  0.3135  0.0509
+#&gt; -0.4376  0.1250 -0.1171
+#&gt;  1.4616 -1.2569  1.0052
+#&gt;  0.1030  0.3297 -0.5130
+#&gt; [ CPUFloatType{4,3} ]</div></pre>
+  </div>
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+    <div class="page-header">
+    <h1>Cummax</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_cummax.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Cummax</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_cummax</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the dimension to do the operation over</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="cummax-input-dim-gt-tensor-longtensor-"><a class="anchor" href="#cummax-input-dim-gt-tensor-longtensor-"></a>cummax(input, dim) -&gt; (Tensor, LongTensor) </h2>
+
+    
+
+
+<p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the cumulative maximum of
+elements of <code>input</code> in the dimension <code>dim</code>. And <code>indices</code> is the index
+location of each maximum value found in the dimension <code>dim</code>.</p>
+<p>$$
+    y_i = max(x_1, x_2, x_3, \dots, x_i)
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_cummax</span>(<span class='kw'>a</span>, dim=<span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt; -1.8418
+#&gt; -1.2347
+#&gt; -0.2324
+#&gt;  0.8768
+#&gt;  0.8768
+#&gt;  0.8768
+#&gt;  0.8768
+#&gt;  0.8768
+#&gt;  0.8768
+#&gt;  1.0924
+#&gt; [ CPUFloatType{10} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt;  0
+#&gt;  1
+#&gt;  2
+#&gt;  3
+#&gt;  3
+#&gt;  3
+#&gt;  3
+#&gt;  3
+#&gt;  3
+#&gt;  9
+#&gt; [ CPULongType{10} ]
+#&gt; </div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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+    <div class="page-header">
+    <h1>Cummin</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_cummin.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Cummin</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_cummin</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the dimension to do the operation over</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="cummin-input-dim-gt-tensor-longtensor-"><a class="anchor" href="#cummin-input-dim-gt-tensor-longtensor-"></a>cummin(input, dim) -&gt; (Tensor, LongTensor) </h2>
+
+    
+
+
+<p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the cumulative minimum of
+elements of <code>input</code> in the dimension <code>dim</code>. And <code>indices</code> is the index
+location of each maximum value found in the dimension <code>dim</code>.</p>
+<p>$$
+    y_i = min(x_1, x_2, x_3, \dots, x_i)
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_cummin</span>(<span class='kw'>a</span>, dim=<span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt;  1.6268
+#&gt;  1.0973
+#&gt;  1.0858
+#&gt; -0.5395
+#&gt; -0.5395
+#&gt; -1.0003
+#&gt; -1.0003
+#&gt; -1.0003
+#&gt; -1.8734
+#&gt; -1.8734
+#&gt; [ CPUFloatType{10} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt;  0
+#&gt;  1
+#&gt;  2
+#&gt;  3
+#&gt;  3
+#&gt;  5
+#&gt;  5
+#&gt;  5
+#&gt;  8
+#&gt;  8
+#&gt; [ CPULongType{10} ]
+#&gt; </div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Cumprod</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_cumprod.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Cumprod</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_cumprod</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, dtype = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the dimension to do the operation over</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to <code>dtype</code> before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="cumprod-input-dim-out-null-dtype-null-gt-tensor-"><a class="anchor" href="#cumprod-input-dim-out-null-dtype-null-gt-tensor-"></a>cumprod(input, dim, out=NULL, dtype=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the cumulative product of elements of <code>input</code> in the dimension
+<code>dim</code>.</p>
+<p>For example, if <code>input</code> is a vector of size N, the result will also be
+a vector of size N, with elements.</p>
+<p>$$
+    y_i = x_1 \times x_2\times x_3\times \dots \times x_i
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_cumprod</span>(<span class='kw'>a</span>, dim=<span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.5149e+00
+#&gt; -6.5782e-01
+#&gt; -2.5308e-02
+#&gt;  9.9330e-03
+#&gt; -9.4889e-03
+#&gt; -8.0739e-03
+#&gt;  4.0003e-05
+#&gt; -2.7845e-05
+#&gt; -3.9059e-05
+#&gt; -1.1986e-05
+#&gt; [ CPUFloatType{10} ]</div></pre>
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+      
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+    <div class="page-header">
+    <h1>Cumsum</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_cumsum.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Cumsum</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_cumsum</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, dtype = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the dimension to do the operation over</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to <code>dtype</code> before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="cumsum-input-dim-out-null-dtype-null-gt-tensor-"><a class="anchor" href="#cumsum-input-dim-out-null-dtype-null-gt-tensor-"></a>cumsum(input, dim, out=NULL, dtype=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the cumulative sum of elements of <code>input</code> in the dimension
+<code>dim</code>.</p>
+<p>For example, if <code>input</code> is a vector of size N, the result will also be
+a vector of size N, with elements.</p>
+<p>$$
+    y_i = x_1 + x_2 + x_3 + \dots + x_i
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_cumsum</span>(<span class='kw'>a</span>, dim=<span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0.6645
+#&gt; -1.4518
+#&gt; -2.8377
+#&gt; -3.4089
+#&gt; -2.3082
+#&gt; -4.4183
+#&gt; -3.9439
+#&gt; -2.9651
+#&gt; -2.3419
+#&gt; -3.3175
+#&gt; [ CPUFloatType{10} ]</div></pre>
+  </div>
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+    <div class="page-header">
+    <h1>Det</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_det.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Det</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_det</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor of size <code>(*, n, n)</code> where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is zero or more                batch dimensions.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>Backward through `det` internally uses SVD results when `input` is
+not invertible. In this case, double backward through `det` will be
+unstable in when `input` doesn't have distinct singular values. See
+`~torch.svd` for details.
+</pre>
+
+    <h2 class="hasAnchor" id="det-input-gt-tensor-"><a class="anchor" href="#det-input-gt-tensor-"></a>det(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>Calculates determinant of a square matrix or batches of square matrices.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='fu'>torch_det</span>(<span class='kw'>A</span>)
+<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>, <span class='fl'>2</span>))
+<span class='kw'>A</span>
+<span class='kw'>A</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/det.html'>det</a></span>()
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.6546
+#&gt; -0.7505
+#&gt; -0.6785
+#&gt; [ CPUFloatType{3} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
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+    <div class="page-header">
+    <h1>Create a Device object</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/device.R'><code>R/device.R</code></a></small>
+    <div class="hidden name"><code>torch_device.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>A <code>torch_device</code>  is an object representing the device on which a <code>torch_tensor</code>
+is or will be allocated.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_device</span>(<span class='kw'>type</span>, index = <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'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='co'># Via string</span>
+<span class='fu'>torch_device</span>(<span class='st'>"cuda:1"</span>)
+<span class='fu'>torch_device</span>(<span class='st'>"cpu"</span>)
+<span class='fu'>torch_device</span>(<span class='st'>"cuda"</span>) <span class='co'># current cuda device</span>
+
+<span class='co'># Via string and device ordinal</span>
+<span class='fu'>torch_device</span>(<span class='st'>"cuda"</span>, <span class='fl'>0</span>)
+<span class='fu'>torch_device</span>(<span class='st'>"cpu"</span>, <span class='fl'>0</span>)
+
+}
+</div><div class='output co'>#&gt; torch_device(type='cpu', index=0)</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+    <div class="page-header">
+    <h1>Diag</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_diag.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Diag</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_diag</span>(<span class='kw'>self</span>, diagonal = <span class='fl'>0L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>diagonal</th>
+      <td><p>(int, optional) the diagonal to consider</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="diag-input-diagonal-out-null-gt-tensor-"><a class="anchor" href="#diag-input-diagonal-out-null-gt-tensor-"></a>diag(input, diagonal=0, out=NULL) -&gt; Tensor </h2>
+
+    
+
+<ul>
+<li><p>If <code>input</code> is a vector (1-D tensor), then returns a 2-D square tensor
+with the elements of <code>input</code> as the diagonal.</p></li>
+<li><p>If <code>input</code> is a matrix (2-D tensor), then returns a 1-D tensor with
+the diagonal elements of <code>input</code>.</p></li>
+</ul>
+
+<p>The argument <code>diagonal</code> controls which diagonal to consider:</p><ul>
+<li><p>If <code>diagonal</code> = 0, it is the main diagonal.</p></li>
+<li><p>If <code>diagonal</code> &gt; 0, it is above the main diagonal.</p></li>
+<li><p>If <code>diagonal</code> &lt; 0, it is below the main diagonal.</p></li>
+</ul>
+
+
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+    <div class="page-header">
+    <h1>Diag_embed</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_diag_embed.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Diag_embed</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_diag_embed</span>(<span class='kw'>self</span>, offset = <span class='fl'>0L</span>, dim1 = <span class='op'>-</span><span class='fl'>2L</span>, dim2 = <span class='op'>-</span><span class='fl'>1L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor. Must be at least 1-dimensional.</p></td>
+    </tr>
+    <tr>
+      <th>offset</th>
+      <td><p>(int, optional) which diagonal to consider. Default: 0        (main diagonal).</p></td>
+    </tr>
+    <tr>
+      <th>dim1</th>
+      <td><p>(int, optional) first dimension with respect to which to        take diagonal. Default: -2.</p></td>
+    </tr>
+    <tr>
+      <th>dim2</th>
+      <td><p>(int, optional) second dimension with respect to which to        take diagonal. Default: -1.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="diag-embed-input-offset-dim-dim-gt-tensor-"><a class="anchor" href="#diag-embed-input-offset-dim-dim-gt-tensor-"></a>diag_embed(input, offset=0, dim1=-2, dim2=-1) -&gt; Tensor </h2>
+
+    
+
+
+<p>Creates a tensor whose diagonals of certain 2D planes (specified by
+<code>dim1</code> and <code>dim2</code>) are filled by <code>input</code>.
+To facilitate creating batched diagonal matrices, the 2D planes formed by
+the last two dimensions of the returned tensor are chosen by default.</p>
+<p>The argument <code>offset</code> controls which diagonal to consider:</p><ul>
+<li><p>If <code>offset</code> = 0, it is the main diagonal.</p></li>
+<li><p>If <code>offset</code> &gt; 0, it is above the main diagonal.</p></li>
+<li><p>If <code>offset</code> &lt; 0, it is below the main diagonal.</p></li>
+</ul>
+
+<p>The size of the new matrix will be calculated to make the specified diagonal
+of the size of the last input dimension.
+Note that for <code>offset</code> other than \(0\), the order of <code>dim1</code>
+and <code>dim2</code> matters. Exchanging them is equivalent to changing the
+sign of <code>offset</code>.</p>
+<p>Applying <code>torch_diagonal</code> to the output of this function with
+the same arguments yields a matrix identical to input. However,
+<code>torch_diagonal</code> has different default dimensions, so those
+need to be explicitly specified.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+<span class='fu'>torch_diag_embed</span>(<span class='kw'>a</span>)
+<span class='fu'>torch_diag_embed</span>(<span class='kw'>a</span>, offset=<span class='fl'>1</span>, dim1=<span class='fl'>1</span>, dim2=<span class='fl'>3</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;   0.0000  0.2012  0.0000  0.0000
+#&gt;   0.0000 -1.0725  0.0000  0.0000
+#&gt; 
+#&gt; (2,.,.) = 
+#&gt;   0.0000  0.0000 -0.1869  0.0000
+#&gt;   0.0000  0.0000 -0.9137  0.0000
+#&gt; 
+#&gt; (3,.,.) = 
+#&gt;   0.0000  0.0000  0.0000 -0.1624
+#&gt;   0.0000  0.0000  0.0000  0.7809
+#&gt; 
+#&gt; (4,.,.) = 
+#&gt;   0  0  0  0
+#&gt;   0  0  0  0
+#&gt; [ CPUFloatType{4,2,4} ]</div></pre>
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+    <div class="page-header">
+    <h1>Diagflat</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_diagflat.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Diagflat</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_diagflat</span>(<span class='kw'>self</span>, offset = <span class='fl'>0L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>offset</th>
+      <td><p>(int, optional) the diagonal to consider. Default: 0 (main        diagonal).</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="diagflat-input-offset-gt-tensor-"><a class="anchor" href="#diagflat-input-offset-gt-tensor-"></a>diagflat(input, offset=0) -&gt; Tensor </h2>
+
+    
+
+<ul>
+<li><p>If <code>input</code> is a vector (1-D tensor), then returns a 2-D square tensor
+with the elements of <code>input</code> as the diagonal.</p></li>
+<li><p>If <code>input</code> is a tensor with more than one dimension, then returns a
+2-D tensor with diagonal elements equal to a flattened <code>input</code>.</p></li>
+</ul>
+
+<p>The argument <code>offset</code> controls which diagonal to consider:</p><ul>
+<li><p>If <code>offset</code> = 0, it is the main diagonal.</p></li>
+<li><p>If <code>offset</code> &gt; 0, it is above the main diagonal.</p></li>
+<li><p>If <code>offset</code> &lt; 0, it is below the main diagonal.</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_diagflat</span>(<span class='kw'>a</span>)
+<span class='fu'>torch_diagflat</span>(<span class='kw'>a</span>, <span class='fl'>1</span>)
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_diagflat</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.1689  0.0000  0.0000  0.0000
+#&gt;  0.0000 -0.1269  0.0000  0.0000
+#&gt;  0.0000  0.0000  0.7075  0.0000
+#&gt;  0.0000  0.0000  0.0000  0.7293
+#&gt; [ CPUFloatType{4,4} ]</div></pre>
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+    <div class="page-header">
+    <h1>Diagonal</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_diagonal.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Diagonal</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_diagonal</span>(<span class='kw'>self</span>, <span class='kw'>outdim</span>, dim1 = <span class='fl'>1L</span>, dim2 = <span class='fl'>2L</span>, offset = <span class='fl'>0L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor. Must be at least 2-dimensional.</p></td>
+    </tr>
+    <tr>
+      <th>outdim</th>
+      <td><p>dimension name if <code>self</code> is a named tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim1</th>
+      <td><p>(int, optional) first dimension with respect to which to        take diagonal. Default: 0.</p></td>
+    </tr>
+    <tr>
+      <th>dim2</th>
+      <td><p>(int, optional) second dimension with respect to which to        take diagonal. Default: 1.</p></td>
+    </tr>
+    <tr>
+      <th>offset</th>
+      <td><p>(int, optional) which diagonal to consider. Default: 0        (main diagonal).</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="diagonal-input-offset-dim-dim-gt-tensor-"><a class="anchor" href="#diagonal-input-offset-dim-dim-gt-tensor-"></a>diagonal(input, offset=0, dim1=0, dim2=1) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a partial view of <code>input</code> with the its diagonal elements
+with respect to <code>dim1</code> and <code>dim2</code> appended as a dimension
+at the end of the shape.</p>
+<p>The argument <code>offset</code> controls which diagonal to consider:</p><ul>
+<li><p>If <code>offset</code> = 0, it is the main diagonal.</p></li>
+<li><p>If <code>offset</code> &gt; 0, it is above the main diagonal.</p></li>
+<li><p>If <code>offset</code> &lt; 0, it is below the main diagonal.</p></li>
+</ul>
+
+<p>Applying <code>torch_diag_embed</code> to the output of this function with
+the same arguments yields a diagonal matrix with the diagonal entries
+of the input. However, <code>torch_diag_embed</code> has different default
+dimensions, so those need to be explicitly specified.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_diagonal</span>(<span class='kw'>a</span>, offset = <span class='fl'>0</span>)
+<span class='fu'>torch_diagonal</span>(<span class='kw'>a</span>, offset = <span class='fl'>1</span>)
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>5</span>, <span class='fl'>4</span>, <span class='fl'>2</span>))
+<span class='fu'>torch_diagonal</span>(<span class='kw'>x</span>, offset=<span class='op'>-</span><span class='fl'>1</span>, dim1=<span class='fl'>1</span>, dim2=<span class='fl'>2</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;   1.7990
+#&gt;  -0.4819
+#&gt; 
+#&gt; (2,.,.) = 
+#&gt;  -0.8694
+#&gt;  -1.2228
+#&gt; 
+#&gt; (3,.,.) = 
+#&gt;  -0.2355
+#&gt;   1.2403
+#&gt; 
+#&gt; (4,.,.) = 
+#&gt;  -1.7326
+#&gt;  -0.4145
+#&gt; [ CPUFloatType{4,2,1} ]</div></pre>
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diff --git a/static/docs/dev/reference/torch_digamma.html b/static/docs/dev/reference/torch_digamma.html
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+    <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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_digamma.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Digamma</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_digamma</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the tensor to compute the digamma function on</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="digamma-input-out-null-gt-tensor-"><a class="anchor" href="#digamma-input-out-null-gt-tensor-"></a>digamma(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the logarithmic derivative of the gamma function on <code>input</code>.</p>
+<p>$$
+    \psi(x) = \frac{d}{dx} \ln\left(\Gamma\left(x\right)\right) = \frac{\Gamma'(x)}{\Gamma(x)}
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>0.5</span>))
+<span class='fu'>torch_digamma</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0.5772
+#&gt; -1.9635
+#&gt; [ CPUFloatType{2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+
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+    <div class="page-header">
+    <h1>Dist</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_dist.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Dist</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_dist</span>(<span class='kw'>self</span>, <span class='kw'>other</span>, p = <span class='fl'>2L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor) the Right-hand-side input tensor</p></td>
+    </tr>
+    <tr>
+      <th>p</th>
+      <td><p>(float, optional) the norm to be computed</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="dist-input-other-p-gt-tensor-"><a class="anchor" href="#dist-input-other-p-gt-tensor-"></a>dist(input, other, p=2) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the p-norm of (<code>input</code> - <code>other</code>)</p>
+<p>The shapes of <code>input</code> and <code>other</code> must be
+broadcastable .</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>x</span>
+<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>y</span>
+<span class='fu'>torch_dist</span>(<span class='kw'>x</span>, <span class='kw'>y</span>, <span class='fl'>3.5</span>)
+<span class='fu'>torch_dist</span>(<span class='kw'>x</span>, <span class='kw'>y</span>, <span class='fl'>3</span>)
+<span class='fu'>torch_dist</span>(<span class='kw'>x</span>, <span class='kw'>y</span>, <span class='fl'>0</span>)
+<span class='fu'>torch_dist</span>(<span class='kw'>x</span>, <span class='kw'>y</span>, <span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; 3.8911
+#&gt; [ CPUFloatType{} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
+  </div>
+</div>
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+
+      
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Div</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_div.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Div</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_div</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Number) the number to be divided to each element of <code>input</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="div-input-other-out-null-gt-tensor-"><a class="anchor" href="#div-input-other-out-null-gt-tensor-"></a>div(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Divides each element of the input <code>input</code> with the scalar <code>other</code> and
+returns a new resulting tensor.</p>
+
+
+<p>Each element of the tensor <code>input</code> is divided by each element of the tensor
+<code>other</code>. The resulting tensor is returned.</p>
+<p>$$
+    \mbox{out}_i = \frac{\mbox{input}_i}{\mbox{other}_i}
+$$
+The shapes of <code>input</code> and <code>other</code> must be broadcastable
+. If the <code>torch_dtype</code> of <code>input</code> and
+<code>other</code> differ, the <code>torch_dtype</code> of the result tensor is determined
+following rules described in the type promotion documentation
+. If <code>out</code> is specified, the result must be
+castable  to the <code>torch_dtype</code> of the
+specified output tensor. Integral division by zero leads to undefined behavior.</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p>Integer division using div is deprecated, and in a future release div will
+perform true division like <code><a href='torch_true_divide.html'>torch_true_divide()</a></code>.
+Use <code><a href='torch_floor_divide.html'>torch_floor_divide()</a></code> to perform integer division,
+instead.</p>
+<p>$$
+    \mbox{out}_i = \frac{\mbox{input}_i}{\mbox{other}}
+$$
+If the <code>torch_dtype</code> of <code>input</code> and <code>other</code> differ, the
+<code>torch_dtype</code> of the result tensor is determined following rules
+described in the type promotion documentation . If
+<code>out</code> is specified, the result must be castable
+to the <code>torch_dtype</code> of the specified output tensor. Integral division
+by zero leads to undefined behavior.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_div</span>(<span class='kw'>a</span>, <span class='fl'>0.5</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>b</span>
+<span class='fu'>torch_div</span>(<span class='kw'>a</span>, <span class='kw'>b</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;   -2.0561  -18.6148    0.3729   -0.3667
+#&gt;   -2.1486    5.5361    0.6578   -0.3014
+#&gt;    4.5507 -138.1689   -1.8987   -0.0247
+#&gt;   -0.9767  101.7390   -0.3603   -0.0525
+#&gt; [ CPUFloatType{4,4} ]</div></pre>
+  </div>
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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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_dot.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Dot</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_dot</span>(<span class='kw'>self</span>, <span class='kw'>tensor</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>tensor</th>
+      <td><p>the other input tensor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>This function does not broadcast .</p>
+    <h2 class="hasAnchor" id="dot-input-tensor-gt-tensor-"><a class="anchor" href="#dot-input-tensor-gt-tensor-"></a>dot(input, tensor) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the dot product (inner product) of two tensors.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_dot</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; 7
+#&gt; [ CPUFloatType{} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Torch data types</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/dtype.R'><code>R/dtype.R</code></a></small>
+    <div class="hidden name"><code>torch_dtype.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Returns the correspondent data type.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_float32</span>()
+
+<span class='fu'>torch_float</span>()
+
+<span class='fu'>torch_float64</span>()
+
+<span class='fu'>torch_double</span>()
+
+<span class='fu'>torch_float16</span>()
+
+<span class='fu'>torch_half</span>()
+
+<span class='fu'>torch_uint8</span>()
+
+<span class='fu'>torch_int8</span>()
+
+<span class='fu'>torch_int16</span>()
+
+<span class='fu'>torch_short</span>()
+
+<span class='fu'>torch_int32</span>()
+
+<span class='fu'>torch_int</span>()
+
+<span class='fu'>torch_int64</span>()
+
+<span class='fu'>torch_long</span>()
+
+<span class='fu'>torch_bool</span>()
+
+<span class='fu'>torch_quint8</span>()
+
+<span class='fu'>torch_qint8</span>()
+
+<span class='fu'>torch_qint32</span>()</pre>
+
+
+
+  </div>
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diff --git a/static/docs/dev/reference/torch_eig.html b/static/docs/dev/reference/torch_eig.html
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+    <div class="page-header">
+    <h1>Eig</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_eig.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Eig</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_eig</span>(<span class='kw'>self</span>, eigenvectors = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the square matrix of shape \((n \times n)\) for which the eigenvalues and eigenvectors        will be computed</p></td>
+    </tr>
+    <tr>
+      <th>eigenvectors</th>
+      <td><p>(bool) <code>TRUE</code> to compute both eigenvalues and eigenvectors;        otherwise, only eigenvalues will be computed</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>Since eigenvalues and eigenvectors might be complex, backward pass is supported only
+for [`torch_symeig`]
+</pre>
+
+    <h2 class="hasAnchor" id="eig-input-eigenvectors-false-out-null-gt-tensor-tensor-"><a class="anchor" href="#eig-input-eigenvectors-false-out-null-gt-tensor-tensor-"></a>eig(input, eigenvectors=False, out=NULL) -&gt; (Tensor, Tensor) </h2>
+
+    
+
+
+<p>Computes the eigenvalues and eigenvectors of a real square matrix.</p>
+
+  </div>
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+    <div class="page-header">
+    <h1>Einsum</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_einsum.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Einsum</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_einsum</span>(<span class='kw'>equation</span>, <span class='kw'>tensors</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>equation</th>
+      <td><p>(string) The equation is given in terms of lower case letters (indices) to be associated           with each dimension of the operands and result. The left hand side lists the operands           dimensions, separated by commas. There should be one index letter per tensor dimension.           The right hand side follows after <code>-&gt;</code> and gives the indices for the output.           If the <code>-&gt;</code> and right hand side are omitted, it implicitly defined as the alphabetically           sorted list of all indices appearing exactly once in the left hand side.           The indices not apprearing in the output are summed over after multiplying the operands           entries.           If an index appears several times for the same operand, a diagonal is taken.           Ellipses <code>...</code> represent a fixed number of dimensions. If the right hand side is inferred,           the ellipsis dimensions are at the beginning of the output.</p></td>
+    </tr>
+    <tr>
+      <th>tensors</th>
+      <td><p>(Tensor) The operands to compute the Einstein sum of.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="einsum-equation-operands-gt-tensor-"><a class="anchor" href="#einsum-equation-operands-gt-tensor-"></a>einsum(equation, *operands) -&gt; Tensor </h2>
+
+    
+
+
+<p>This function provides a way of computing multilinear expressions (i.e. sums of products) using the
+Einstein summation convention.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>))
+<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='fu'>torch_einsum</span>(<span class='st'>'i,j-&gt;ij'</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>x</span>, <span class='kw'>y</span>))  <span class='co'># outer product</span>
+<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>,<span class='fl'>5</span>,<span class='fl'>4</span>))
+<span class='kw'>l</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>,<span class='fl'>5</span>))
+<span class='kw'>r</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>,<span class='fl'>4</span>))
+<span class='fu'>torch_einsum</span>(<span class='st'>'bn,anm,bm-&gt;ba'</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>l</span>, <span class='kw'>A</span>, <span class='kw'>r</span>)) <span class='co'># compare torch_nn$functional$bilinear</span>
+<span class='kw'>As</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>,<span class='fl'>2</span>,<span class='fl'>5</span>))
+<span class='kw'>Bs</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>,<span class='fl'>5</span>,<span class='fl'>4</span>))
+<span class='fu'>torch_einsum</span>(<span class='st'>'bij,bjk-&gt;bik'</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>As</span>, <span class='kw'>Bs</span>)) <span class='co'># batch matrix multiplication</span>
+<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='fu'>torch_einsum</span>(<span class='st'>'ii-&gt;i'</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>A</span>)) <span class='co'># diagonal</span>
+<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='fu'>torch_einsum</span>(<span class='st'>'...ii-&gt;...i'</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>A</span>)) <span class='co'># batch diagonal</span>
+<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span>))
+<span class='fu'>torch_einsum</span>(<span class='st'>'...ij-&gt;...ji'</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>A</span>))<span class='op'>$</span><span class='kw'>shape</span> <span class='co'># batch permute</span>
+
+}
+}
+</div></pre>
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+</li>
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+</li>
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Empty</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_empty.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Empty</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_empty</span>(
+  <span class='kw'>...</span>,
+  names = <span class='kw'>NULL</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>...</th>
+      <td><p>a sequence of integers defining the shape of the output tensor.</p></td>
+    </tr>
+    <tr>
+      <th>names</th>
+      <td><p>optional character vector naming each dimension.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="empty-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-pin-memory-false-gt-tensor-"><a class="anchor" href="#empty-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-pin-memory-false-gt-tensor-"></a>empty(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, pin_memory=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a tensor filled with uninitialized data. The shape of the tensor is
+defined by the variable argument <code>size</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_empty</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.9205e+31  1.8891e+31  6.3375e-10
+#&gt;  1.8169e+31  4.4726e+21  8.4843e+26
+#&gt; [ CPUFloatType{2,3} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/torch_empty_like.html b/static/docs/dev/reference/torch_empty_like.html
new file mode 100644
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+++ b/static/docs/dev/reference/torch_empty_like.html
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Empty_like</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_empty_like.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Empty_like</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_empty_like</span>(
+  <span class='kw'>input</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>,
+  memory_format = <span class='fu'><a href='torch_memory_format.html'>torch_preserve_format</a></span>()
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>memory_format</th>
+      <td><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="empty-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"><a class="anchor" href="#empty-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a>empty_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns an uninitialized tensor with the same size as <code>input</code>.
+<code>torch_empty_like(input)</code> is equivalent to
+<code><a href='torch_empty.html'>torch_empty(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)</a></code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>,<span class='fl'>3</span>), dtype = <span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>())
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.2885e+10  0.0000e+00  0.0000e+00
+#&gt;  0.0000e+00  1.7180e+10  1.3700e+02
+#&gt; [ CPULongType{2,3} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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+
+
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+
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+
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+
diff --git a/static/docs/dev/reference/torch_empty_strided.html b/static/docs/dev/reference/torch_empty_strided.html
new file mode 100644
index 000000000..91aeecf0a
--- /dev/null
+++ b/static/docs/dev/reference/torch_empty_strided.html
@@ -0,0 +1,295 @@
+<!-- Generated by pkgdown: do not edit by hand -->
+<!DOCTYPE html>
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Empty_strided</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_empty_strided.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Empty_strided</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_empty_strided</span>(
+  <span class='kw'>size</span>,
+  <span class='kw'>stride</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>,
+  pin_memory = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>size</th>
+      <td><p>(tuple of ints) the shape of the output tensor</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>(tuple of ints) the strides of the output tensor</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>pin_memory</th>
+      <td><p>(bool, optional) If set, returned tensor would be allocated in        the pinned memory. Works only for CPU tensors. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="empty-strided-size-stride-dtype-null-layout-null-device-null-requires-grad-false-pin-memory-false-gt-tensor-"><a class="anchor" href="#empty-strided-size-stride-dtype-null-layout-null-device-null-requires-grad-false-pin-memory-false-gt-tensor-"></a>empty_strided(size, stride, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, pin_memory=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a tensor filled with uninitialized data. The shape and strides of the tensor is
+defined by the variable argument <code>size</code> and <code>stride</code> respectively.
+<code>torch_empty_strided(size, stride)</code> is equivalent to
+<code>torch_empty(size).as_strided(size, stride)</code>.</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p>More than one element of the created tensor may refer to a single memory
+location. As a result, in-place operations (especially ones that are
+vectorized) may result in incorrect behavior. If you need to write to
+the tensors, please clone them first.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'>torch_empty_strided</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>), <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>))
+<span class='kw'>a</span>
+<span class='kw'>a</span><span class='op'>$</span><span class='fu'>stride</span>(<span class='fl'>1</span>)
+<span class='kw'>a</span><span class='op'>$</span><span class='fu'>size</span>(<span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; [1] 2</div></pre>
+  </div>
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+    <div class="page-header">
+    <h1>Eq</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_eq.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Eq</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_eq</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the tensor to compare</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor or float) the tensor or value to compare
+Must be a <code>ByteTensor</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="eq-input-other-out-null-gt-tensor-"><a class="anchor" href="#eq-input-other-out-null-gt-tensor-"></a>eq(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes element-wise equality</p>
+<p>The second argument can be a number or a tensor whose shape is
+broadcastable  with the first argument.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_eq</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>3</span>,<span class='fl'>4</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>, <span class='fl'>2</span>, <span class='fl'>4</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1
+#&gt;  0
+#&gt;  0
+#&gt;  1
+#&gt; [ CPUBoolType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+
+      
+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Equal</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_equal.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Equal</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_equal</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>the other input tensor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="equal-input-other-gt-bool-"><a class="anchor" href="#equal-input-other-gt-bool-"></a>equal(input, other) -&gt; bool </h2>
+
+    
+
+
+<p><code>TRUE</code> if two tensors have the same size and elements, <code>FALSE</code> otherwise.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_equal</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>)))
+}
+</div><div class='output co'>#&gt; [1] TRUE</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/torch_erf.html b/static/docs/dev/reference/torch_erf.html
new file mode 100644
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+++ b/static/docs/dev/reference/torch_erf.html
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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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_erf.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Erf</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_erf</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="erf-input-out-null-gt-tensor-"><a class="anchor" href="#erf-input-out-null-gt-tensor-"></a>erf(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the error function of each element. The error function is defined as follows:</p>
+<p>$$
+    \mathrm{erf}(x) = \frac{2}{\sqrt{\pi}} \int_{0}^{x} e^{-t^2} dt
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_erf</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='op'>-</span><span class='fl'>1.</span>, <span class='fl'>10.</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.0000
+#&gt; -0.8427
+#&gt;  1.0000
+#&gt; [ CPUFloatType{3} ]</div></pre>
+  </div>
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+    <div class="page-header">
+    <h1>Erfc</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_erfc.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Erfc</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_erfc</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="erfc-input-out-null-gt-tensor-"><a class="anchor" href="#erfc-input-out-null-gt-tensor-"></a>erfc(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the complementary error function of each element of <code>input</code>.
+The complementary error function is defined as follows:</p>
+<p>$$
+    \mathrm{erfc}(x) = 1 - \frac{2}{\sqrt{\pi}} \int_{0}^{x} e^{-t^2} dt
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_erfc</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='op'>-</span><span class='fl'>1.</span>, <span class='fl'>10.</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.0000e+00
+#&gt;  1.8427e+00
+#&gt;  1.4013e-45
+#&gt; [ CPUFloatType{3} ]</div></pre>
+  </div>
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+    <div class="page-header">
+    <h1>Erfinv</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_erfinv.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Erfinv</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_erfinv</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="erfinv-input-out-null-gt-tensor-"><a class="anchor" href="#erfinv-input-out-null-gt-tensor-"></a>erfinv(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the inverse error function of each element of <code>input</code>.
+The inverse error function is defined in the range \((-1, 1)\) as:</p>
+<p>$$
+    \mathrm{erfinv}(\mathrm{erf}(x)) = x
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_erfinv</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>0.5</span>, <span class='op'>-</span><span class='fl'>1.</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.0000
+#&gt;  0.4769
+#&gt;    -inf
+#&gt; [ CPUFloatType{3} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Exp</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_exp.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Exp</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_exp</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="exp-input-out-null-gt-tensor-"><a class="anchor" href="#exp-input-out-null-gt-tensor-"></a>exp(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the exponential of the elements
+of the input tensor <code>input</code>.</p>
+<p>$$
+    y_{i} = e^{x_{i}}
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_exp</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fu'><a href='https://rdrr.io/r/base/Log.html'>log</a></span>(<span class='fl'>2</span>))))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1
+#&gt;  2
+#&gt; [ CPUFloatType{2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+    <div class="page-header">
+    <h1>Expm1</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_expm1.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Expm1</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_expm1</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="expm-input-out-null-gt-tensor-"><a class="anchor" href="#expm-input-out-null-gt-tensor-"></a>expm1(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the exponential of the elements minus 1
+of <code>input</code>.</p>
+<p>$$
+    y_{i} = e^{x_{i}} - 1
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_expm1</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fu'><a href='https://rdrr.io/r/base/Log.html'>log</a></span>(<span class='fl'>2</span>))))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0
+#&gt;  1
+#&gt; [ CPUFloatType{2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
+  </div>
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+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
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+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Eye</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_eye.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Eye</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_eye</span>(
+  <span class='kw'>n</span>,
+  m = <span class='kw'>n</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>n</th>
+      <td><p>(int) the number of rows</p></td>
+    </tr>
+    <tr>
+      <th>m</th>
+      <td><p>(int, optional) the number of columns with default being <code>n</code></p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="eye-n-m-null-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#eye-n-m-null-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>eye(n, m=NULL, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a 2-D tensor with ones on the diagonal and zeros elsewhere.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_eye</span>(<span class='fl'>3</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1  0  0
+#&gt;  0  1  0
+#&gt;  0  0  1
+#&gt; [ CPUFloatType{3,3} ]</div></pre>
+  </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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+      <a href="../articles/tensor/index.html">Tensor class</a>
+    </li>
+    <li class="dropdown-header">Datasets</li>
+    <li>
+      <a href="../articles/loading-data.html">Loading Data</a>
+    </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-header">Examples</li>
+<li>
+  <a href="../reference/index.html">Reference</a>
+</li>
+<li>
+  <a href="../news/index.html">Changelog</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>Fft</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_fft.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Fft</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_fft</span>(<span class='kw'>self</span>, <span class='kw'>signal_ndim</span>, normalized = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor of at least <code>signal_ndim</code> <code>+ 1</code>        dimensions</p></td>
+    </tr>
+    <tr>
+      <th>signal_ndim</th>
+      <td><p>(int) the number of dimensions in each signal.        <code>signal_ndim</code> can only be 1, 2 or 3</p></td>
+    </tr>
+    <tr>
+      <th>normalized</th>
+      <td><p>(bool, optional) controls whether to return normalized results.        Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>For CUDA tensors, an LRU cache is used for cuFFT plans to speed up
+repeatedly running FFT methods on tensors of same geometry with same
+configuration. See cufft-plan-cache for more details on how to
+monitor and control the cache.
+</pre>
+
+    <h2 class="hasAnchor" id="fft-input-signal-ndim-normalized-false-gt-tensor-"><a class="anchor" href="#fft-input-signal-ndim-normalized-false-gt-tensor-"></a>fft(input, signal_ndim, normalized=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Complex-to-complex Discrete Fourier Transform</p>
+<p>This method computes the complex-to-complex discrete Fourier transform.
+Ignoring the batch dimensions, it computes the following expression:</p>
+<p>$$
+    X[\omega_1, \dots, \omega_d] =
+        \sum_{n_1=0}^{N_1-1} \dots \sum_{n_d=0}^{N_d-1} x[n_1, \dots, n_d]
+         e^{-j\ 2 \pi \sum_{i=0}^d \frac{\omega_i n_i}{N_i}},
+$$
+where \(d\) = <code>signal_ndim</code> is number of dimensions for the
+signal, and \(N_i\) is the size of signal dimension \(i\).</p>
+<p>This method supports 1D, 2D and 3D complex-to-complex transforms, indicated
+by <code>signal_ndim</code>. <code>input</code> must be a tensor with last dimension
+of size 2, representing the real and imaginary components of complex
+numbers, and should have at least <code>signal_ndim + 1</code> dimensions with optionally
+arbitrary number of leading batch dimensions. If <code>normalized</code> is set to
+<code>TRUE</code>, this normalizes the result by dividing it with
+\(\sqrt{\prod_{i=1}^K N_i}\) so that the operator is unitary.</p>
+<p>Returns the real and the imaginary parts together as one tensor of the same
+shape of <code>input</code>.</p>
+<p>The inverse of this function is <code><a href='torch_ifft.html'>torch_ifft</a></code>.</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p>For CPU tensors, this method is currently only available with MKL. Use
+<code>torch_backends.mkl.is_available</code> to check if MKL is installed.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='co'># unbatched 2D FFT</span>
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>3</span>, <span class='fl'>2</span>))
+<span class='fu'>torch_fft</span>(<span class='kw'>x</span>, <span class='fl'>2</span>)
+<span class='co'># batched 1D FFT</span>
+<span class='fu'>torch_fft</span>(<span class='kw'>x</span>, <span class='fl'>1</span>)
+<span class='co'># arbitrary number of batch dimensions, 2D FFT</span>
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>, <span class='fl'>5</span>, <span class='fl'>5</span>, <span class='fl'>2</span>))
+<span class='fu'>torch_fft</span>(<span class='kw'>x</span>, <span class='fl'>2</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,1,1,.,.) = 
+#&gt;  -3.8906 -0.2521
+#&gt;  -0.6301  7.3887
+#&gt;  -0.1569 -3.4600
+#&gt;   6.8864  4.7697
+#&gt;  -4.4483 -4.8385
+#&gt; 
+#&gt; (2,1,1,.,.) = 
+#&gt;    8.1809  -9.1632
+#&gt;   13.5965  10.6084
+#&gt;    7.5879   1.5757
+#&gt;   -2.9251   9.8662
+#&gt;   -3.5436  -0.0408
+#&gt; 
+#&gt; (3,1,1,.,.) = 
+#&gt;  -6.8657  0.6179
+#&gt;  -2.1867 -0.9080
+#&gt;   3.0976  1.3844
+#&gt;  -4.6698  4.5027
+#&gt;  -4.1710 -1.5937
+#&gt; 
+#&gt; (1,2,1,.,.) = 
+#&gt;  -4.5069 -1.0340
+#&gt;  -1.7006  5.2808
+#&gt;  -1.1689 -2.7179
+#&gt;   8.6645  5.0827
+#&gt;  -4.1420  2.3250
+#&gt; 
+#&gt; (2,2,1,.,.) = 
+#&gt;   7.5556 -1.3444
+#&gt;   2.5145 -2.9687
+#&gt;   0.8374  1.2748
+#&gt;   7.4609  1.1490
+#&gt;  -2.5103  6.5001
+#&gt; 
+#&gt; (3,2,1,.,.) = 
+#&gt;   -3.1516  10.3080
+#&gt;    5.4505   7.7295
+#&gt;    1.9037   0.0331
+#&gt;   -2.4931   2.9686
+#&gt;    3.7238  -2.8382
+#&gt; 
+#&gt; (1,3,1,.,.) = 
+#&gt;  -1.8638  4.4898
+#&gt;   6.2693 -3.0226
+#&gt;  -3.7461 -0.5782
+#&gt;  -4.6917  4.8592
+#&gt;  -7.3360  7.1371
+#&gt; 
+#&gt; (2,3,1,.,.) = 
+#&gt;  -6.3858  2.2408
+#&gt;  -5.9384 -2.9792
+#&gt;   2.7604  1.3434
+#&gt;  -7.1528  0.3610
+#&gt;  -1.0356  3.4105
+#&gt; 
+#&gt; (3,3,1,.,.) = 
+#&gt;   8.0636  4.3733
+#&gt;  -6.8172  4.5569
+#&gt;  -8.2686  9.6735
+#&gt;  -6.1795  8.9975
+#&gt;   3.4867  1.4089
+#&gt; 
+#&gt; (1,1,2,.,.) = 
+#&gt;    4.0458   1.1211
+#&gt;    6.7274   0.8219
+#&gt;    3.2647  -8.1180
+#&gt;   -4.3900   8.7062
+#&gt;   -6.9060  12.5835
+#&gt; 
+#&gt; (2,1,2,.,.) = 
+#&gt;  -14.9166  -0.4563
+#&gt;   -8.9484   3.7034
+#&gt;    7.5586   0.5212
+#&gt;   -6.2767  -7.2894
+#&gt;   -2.6380   0.9689
+#&gt; 
+#&gt; (3,1,2,.,.) = 
+#&gt;  -5.0481  4.8095
+#&gt;  -0.1282 -0.0783
+#&gt;  -3.2317 -2.6416
+#&gt;  -0.9148  3.6559
+#&gt;  -5.0307  3.9920
+#&gt; 
+#&gt; (1,2,2,.,.) = 
+#&gt;  -1.9028  2.8111
+#&gt;  -8.5834 -3.5393
+#&gt;   6.6530  5.8489
+#&gt;  -4.2728  8.3334
+#&gt;   0.6024 -1.7717
+#&gt; 
+#&gt; (2,2,2,.,.) = 
+#&gt;   5.4114 -5.5515
+#&gt;   4.1821  5.1946
+#&gt;   1.0588  8.8242
+#&gt;  -0.0587  2.2180
+#&gt;  -11.7108  2.6790
+#&gt; 
+#&gt; (3,2,2,.,.) = 
+#&gt;   2.8153  8.5202
+#&gt;   0.0949  1.5582
+#&gt;  -0.9742 -6.8452
+#&gt;  -11.5933  1.6363
+#&gt;  -2.0684  5.5530
+#&gt; 
+#&gt; (1,3,2,.,.) = 
+#&gt;   9.8351  0.5795
+#&gt;  -1.6079 -6.4322
+#&gt;  -2.0161 -7.3104
+#&gt;   1.4246 -2.9077
+#&gt;   4.1010 -1.4020
+#&gt; 
+#&gt; (2,3,2,.,.) = 
+#&gt;  -4.0648 -2.0372
+#&gt;   6.0770  6.2051
+#&gt;  -7.0857  3.3361
+#&gt;  -3.6015 -0.8916
+#&gt;   8.3811  3.9670
+#&gt; 
+#&gt; (3,3,2,.,.) = 
+#&gt;   0.1586  2.9111
+#&gt;  -10.1217 -0.9805
+#&gt;   5.0078  0.2680
+#&gt;   0.1776  2.0732
+#&gt;  -1.6860  0.8195
+#&gt; 
+#&gt; (1,1,3,.,.) = 
+#&gt;  -9.8876 -1.3592
+#&gt;   3.7233 -8.8646
+#&gt;  -0.5858  2.5747
+#&gt;  -0.6867 -0.9947
+#&gt;  -7.1288 -6.7674
+#&gt; 
+#&gt; (2,1,3,.,.) = 
+#&gt;  -0.2221 -2.7402
+#&gt;  -4.0179  0.0893
+#&gt;   7.6763 -6.2895
+#&gt;   3.8782  6.3572
+#&gt;   0.5241 -0.7963
+#&gt; 
+#&gt; (3,1,3,.,.) = 
+#&gt;  -1.7983  1.1807
+#&gt;   2.8927  2.6928
+#&gt;  -2.2799  0.1756
+#&gt;   6.7964 -8.9340
+#&gt;   0.0358  1.1838
+#&gt; 
+#&gt; (1,2,3,.,.) = 
+#&gt;   2.4838 -2.3196
+#&gt;  -0.8169  0.6886
+#&gt;  -5.2591 -5.7622
+#&gt;   0.9913  4.7963
+#&gt;  -1.9588  5.2229
+#&gt; 
+#&gt; (2,2,3,.,.) = 
+#&gt;  -1.7353  5.1988
+#&gt;   2.5770 -3.1907
+#&gt;  -1.6854 -3.0060
+#&gt;   1.8460 -0.4476
+#&gt;   3.2443 -3.0184
+#&gt; 
+#&gt; (3,2,3,.,.) = 
+#&gt;  -3.8917 -4.1796
+#&gt;   4.3798 -2.0535
+#&gt;  -5.1279  3.5968
+#&gt;   9.4164  3.9474
+#&gt;  -1.9752  5.4251
+#&gt; 
+#&gt; (1,3,3,.,.) = 
+#&gt;   0.4361 -5.6941
+#&gt;   1.8463 -3.5674
+#&gt;   9.1693  1.7494
+#&gt;  -6.2192  1.9202
+#&gt;   4.6439 -1.7834
+#&gt; 
+#&gt; (2,3,3,.,.) = 
+#&gt;  -10.0798  -1.5540
+#&gt;    3.2683   6.1536
+#&gt;    5.1260   7.6848
+#&gt;   -1.8577  -3.3287
+#&gt;    6.9663   1.9440
+#&gt; 
+#&gt; (3,3,3,.,.) = 
+#&gt;  -3.6094  3.3084
+#&gt;   4.7373 -5.0252
+#&gt;  -3.1775  3.2782
+#&gt;  -1.7631  0.7487
+#&gt;   5.1359 -5.6054
+#&gt; 
+#&gt; (1,1,4,.,.) = 
+#&gt;    6.2109  -5.7290
+#&gt;   -3.2070  -3.6565
+#&gt;   10.3394   2.8397
+#&gt;   -5.9428   3.2633
+#&gt;   -2.0450  -2.4186
+#&gt; 
+#&gt; (2,1,4,.,.) = 
+#&gt;   4.7595  4.1469
+#&gt;   0.7921  4.8637
+#&gt;  -1.0122 -4.4003
+#&gt;  -11.7296  1.0495
+#&gt;  -3.6038 -9.4859
+#&gt; 
+#&gt; (3,1,4,.,.) = 
+#&gt;   2.5091  0.5782
+#&gt;   1.8351 -4.7400
+#&gt;  -4.6407 -2.1848
+#&gt;  -2.8872 -3.2071
+#&gt;   3.2865 -3.3006
+#&gt; 
+#&gt; (1,2,4,.,.) = 
+#&gt;  -0.7310  6.7275
+#&gt;   1.1613 -2.5606
+#&gt;   3.1072 -3.6625
+#&gt;   0.1855  3.8225
+#&gt;   0.3006  1.0869
+#&gt; 
+#&gt; (2,2,4,.,.) = 
+#&gt;   3.9528  3.7990
+#&gt;   1.7951 -3.5298
+#&gt;  -7.1744 -0.6763
+#&gt;   2.3892  4.9950
+#&gt;   8.5449 -6.1794
+#&gt; 
+#&gt; (3,2,4,.,.) = 
+#&gt;   5.5085  2.2022
+#&gt;  -4.3377  3.1459
+#&gt;   7.6732 -3.6306
+#&gt;   1.8619  7.0566
+#&gt;   7.4051 -7.7637
+#&gt; 
+#&gt; (1,3,4,.,.) = 
+#&gt;  -1.2423  1.1820
+#&gt;  -0.4600  1.5733
+#&gt;  -8.5312 -2.3545
+#&gt;   0.4854 -0.7314
+#&gt;  -2.3858 -4.7297
+#&gt; 
+#&gt; (2,3,4,.,.) = 
+#&gt;  -3.8325 -0.5313
+#&gt;   0.6302  5.9889
+#&gt;  -6.4304  8.7342
+#&gt;  -5.5349 -1.8498
+#&gt;  -2.5480 -0.2350
+#&gt; 
+#&gt; (3,3,4,.,.) = 
+#&gt;  -5.6014 -5.7887
+#&gt;  -5.6342 -0.1735
+#&gt;  -8.3357 -0.1609
+#&gt;  -3.4009  4.3665
+#&gt;  -1.2554 -2.9821
+#&gt; 
+#&gt; (1,1,5,.,.) = 
+#&gt;   -4.1281   1.7054
+#&gt;   -4.5965   1.2471
+#&gt;    8.7289  12.5479
+#&gt;   -1.2868   9.5942
+#&gt;    1.1662   0.0020
+#&gt; 
+#&gt; (2,1,5,.,.) = 
+#&gt;   4.1019  3.2576
+#&gt;   3.4793  0.8775
+#&gt;   2.5433  4.6935
+#&gt;   5.2767  2.7942
+#&gt;  -0.5224 -2.5172
+#&gt; 
+#&gt; (3,1,5,.,.) = 
+#&gt;  -3.0225  0.2177
+#&gt;   6.4263  0.8066
+#&gt;  -3.0760  1.7617
+#&gt;   6.6881 -2.1407
+#&gt;  -8.4150  3.4503
+#&gt; 
+#&gt; (1,2,5,.,.) = 
+#&gt;   0.3347 -9.9952
+#&gt;   1.0171 -1.1604
+#&gt;   1.1945  3.9782
+#&gt;  -1.1903  3.4292
+#&gt;   3.8634  1.9562
+#&gt; 
+#&gt; (2,2,5,.,.) = 
+#&gt;   6.2246  5.1168
+#&gt;   1.6311 -0.1134
+#&gt;   3.6844 -3.9802
+#&gt;  -4.8940 -4.1660
+#&gt;   5.7609  4.0242
+#&gt; 
+#&gt; (3,2,5,.,.) = 
+#&gt;   4.8045 -1.2544
+#&gt;   0.6540 -6.0830
+#&gt;  -6.5560  0.9239
+#&gt;  -7.5844 -4.3400
+#&gt;   2.4527 -0.6081
+#&gt; 
+#&gt; (1,3,5,.,.) = 
+#&gt;   0.0012  1.4379
+#&gt;   2.3320 -0.5475
+#&gt;   3.0302 -1.6159
+#&gt;  -8.4547 -7.8288
+#&gt;  -0.9196 -3.0474
+#&gt; 
+#&gt; (2,3,5,.,.) = 
+#&gt;  -1.0915 -0.7531
+#&gt;   7.7590  2.9822
+#&gt;   1.4004  2.6508
+#&gt;   5.4837 -4.4701
+#&gt;   0.8624  5.0485
+#&gt; 
+#&gt; (3,3,5,.,.) = 
+#&gt;   1.2067  2.4785
+#&gt;  -3.7972  2.4715
+#&gt;   1.6085  4.8943
+#&gt;   1.5530 -0.9489
+#&gt;  -0.9469 -2.9451
+#&gt; [ CPUFloatType{3,3,5,5,2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
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+
+
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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> 1.6.1.</p>
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diff --git a/static/docs/dev/reference/torch_finfo.html b/static/docs/dev/reference/torch_finfo.html
new file mode 100644
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--- /dev/null
+++ b/static/docs/dev/reference/torch_finfo.html
@@ -0,0 +1,239 @@
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Floating point type info</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/type-info.R'><code>R/type-info.R</code></a></small>
+    <div class="hidden name"><code>torch_finfo.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>A list that represents the numerical properties of a
+floating point torch.dtype</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_finfo</span>(<span class='kw'>dtype</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>dtype</th>
+      <td><p>dtype to check information</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/torch_flatten.html b/static/docs/dev/reference/torch_flatten.html
new 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>Flatten</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_flatten.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Flatten</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_flatten</span>(<span class='kw'>self</span>, <span class='kw'>dims</span>, start_dim = <span class='fl'>1L</span>, end_dim = <span class='op'>-</span><span class='fl'>1L</span>, <span class='kw'>out_dim</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dims</th>
+      <td><p>if tensor is named you can pass the name of the dimensions to
+flatten</p></td>
+    </tr>
+    <tr>
+      <th>start_dim</th>
+      <td><p>(int) the first dim to flatten</p></td>
+    </tr>
+    <tr>
+      <th>end_dim</th>
+      <td><p>(int) the last dim to flatten</p></td>
+    </tr>
+    <tr>
+      <th>out_dim</th>
+      <td><p>the name of the resulting dimension if a named tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="flatten-input-start-dim-end-dim-gt-tensor-"><a class="anchor" href="#flatten-input-start-dim-end-dim-gt-tensor-"></a>flatten(input, start_dim=0, end_dim=-1) -&gt; Tensor </h2>
+
+    
+
+
+<p>Flattens a contiguous range of dims in a tensor.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>t</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>), ncol = <span class='fl'>2</span>))
+<span class='fu'>torch_flatten</span>(<span class='kw'>t</span>)
+<span class='fu'>torch_flatten</span>(<span class='kw'>t</span>, start_dim=<span class='fl'>2</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1  2
+#&gt; [ CPUFloatType{1,2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/torch_flip.html b/static/docs/dev/reference/torch_flip.html
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+    <h1>Flip</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_flip.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Flip</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_flip</span>(<span class='kw'>self</span>, <span class='kw'>dims</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dims</th>
+      <td><p>(a list or tuple) axis to flip on</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="flip-input-dims-gt-tensor-"><a class="anchor" href="#flip-input-dims-gt-tensor-"></a>flip(input, dims) -&gt; Tensor </h2>
+
+    
+
+
+<p>Reverse the order of a n-D tensor along given axis in dims.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0</span>, <span class='fl'>8</span>)<span class='op'>$</span><span class='fu'>view</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>, <span class='fl'>2</span>))
+<span class='kw'>x</span>
+<span class='fu'>torch_flip</span>(<span class='kw'>x</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;   6  7
+#&gt;   4  5
+#&gt; 
+#&gt; (2,.,.) = 
+#&gt;   2  3
+#&gt;   0  1
+#&gt; [ CPUFloatType{2,2,2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_floor.html b/static/docs/dev/reference/torch_floor.html
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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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_floor.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Floor</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_floor</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="floor-input-out-null-gt-tensor-"><a class="anchor" href="#floor-input-out-null-gt-tensor-"></a>floor(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the floor of the elements of <code>input</code>,
+the largest integer less than or equal to each element.</p>
+<p>$$
+    \mbox{out}_{i} = \left\lfloor \mbox{input}_{i} \right\rfloor
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_floor</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -1
+#&gt;  0
+#&gt; -1
+#&gt; -3
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+
diff --git a/static/docs/dev/reference/torch_floor_divide.html b/static/docs/dev/reference/torch_floor_divide.html
new file mode 100644
index 000000000..fae2c3a29
--- /dev/null
+++ b/static/docs/dev/reference/torch_floor_divide.html
@@ -0,0 +1,263 @@
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Floor_divide</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_floor_divide.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Floor_divide</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_floor_divide</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the numerator tensor</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor or Scalar) the denominator</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="floor-divide-input-other-out-null-gt-tensor-"><a class="anchor" href="#floor-divide-input-other-out-null-gt-tensor-"></a>floor_divide(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Return the division of the inputs rounded down to the nearest integer. See <code><a href='torch_div.html'>torch_div</a></code>
+for type promotion and broadcasting rules.</p>
+<p>$$
+    \mbox{{out}}_i = \left\lfloor \frac{{\mbox{{input}}_i}}{{\mbox{{other}}_i}} \right\rfloor
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4.0</span>, <span class='fl'>3.0</span>))
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2.0</span>, <span class='fl'>2.0</span>))
+<span class='fu'>torch_floor_divide</span>(<span class='kw'>a</span>, <span class='kw'>b</span>)
+<span class='fu'>torch_floor_divide</span>(<span class='kw'>a</span>, <span class='fl'>1.4</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  2
+#&gt;  2
+#&gt; [ CPUFloatType{2} ]</div></pre>
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+    <h1>Fmod</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_fmod.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Fmod</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_fmod</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the dividend</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor or float) the divisor, which may be either a number or a tensor of the same shape as the dividend</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="fmod-input-other-out-null-gt-tensor-"><a class="anchor" href="#fmod-input-other-out-null-gt-tensor-"></a>fmod(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the element-wise remainder of division.</p>
+<p>The dividend and divisor may contain both for integer and floating point
+numbers. The remainder has the same sign as the dividend <code>input</code>.</p>
+<p>When <code>other</code> is a tensor, the shapes of <code>input</code> and
+<code>other</code> must be broadcastable .</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_fmod</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>3.</span>, <span class='op'>-</span><span class='fl'>2</span>, <span class='op'>-</span><span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>)), <span class='fl'>2</span>)
+<span class='fu'>torch_fmod</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1.</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span>)), <span class='fl'>1.5</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.0000
+#&gt;  0.5000
+#&gt;  0.0000
+#&gt;  1.0000
+#&gt;  0.5000
+#&gt; [ CPUFloatType{5} ]</div></pre>
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+      <a href="../articles/using-autograd.html">Using autograd</a>
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+
+      
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Frac</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_frac.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Frac</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_frac</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="frac-input-out-null-gt-tensor-"><a class="anchor" href="#frac-input-out-null-gt-tensor-"></a>frac(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the fractional portion of each element in <code>input</code>.</p>
+<p>$$
+    \mbox{out}_{i} = \mbox{input}_{i} - \left\lfloor |\mbox{input}_{i}| \right\rfloor * \mbox{sgn}(\mbox{input}_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_frac</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2.5</span>, <span class='op'>-</span><span class='fl'>3.2</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.0000
+#&gt;  0.5000
+#&gt; -0.2000
+#&gt; [ CPUFloatType{3} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Full</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_full.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Full</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_full</span>(
+  <span class='kw'>size</span>,
+  <span class='kw'>fill_value</span>,
+  names = <span class='kw'>NULL</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>size</th>
+      <td><p>(int...) a list, tuple, or <code>torch_Size</code> of integers defining the        shape of the output tensor.</p></td>
+    </tr>
+    <tr>
+      <th>fill_value</th>
+      <td><p>NA the number to fill the output tensor with.</p></td>
+    </tr>
+    <tr>
+      <th>names</th>
+      <td><p>optional names of the dimensions</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="full-size-fill-value-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#full-size-fill-value-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>full(size, fill_value, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a tensor of size <code>size</code> filled with <code>fill_value</code>.</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p>In PyTorch 1.5 a bool or integral <code>fill_value</code> will produce a warning if
+<code>dtype</code> or <code>out</code> are not set.
+In a future PyTorch release, when <code>dtype</code> and <code>out</code> are not set
+a bool <code>fill_value</code> will return a tensor of torch.bool dtype,
+and an integral <code>fill_value</code> will return a tensor of torch.long dtype.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_full</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>), <span class='fl'>3.141592</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  3.1416  3.1416  3.1416
+#&gt;  3.1416  3.1416  3.1416
+#&gt; [ CPUFloatType{2,3} ]</div></pre>
+  </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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+
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+</div>
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Full_like</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_full_like.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Full_like</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_full_like</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>fill_value</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>,
+  memory_format = <span class='fu'><a href='torch_memory_format.html'>torch_preserve_format</a></span>()
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
+    </tr>
+    <tr>
+      <th>fill_value</th>
+      <td><p>the number to fill the output tensor with.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>memory_format</th>
+      <td><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="full-like-input-fill-value-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-"><a class="anchor" href="#full-like-input-fill-value-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-"></a>full_like(input, fill_value, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, </h2>
+
+    
+
+
+<p>memory_format=torch.preserve_format) -&gt; Tensor</p>
+<p>Returns a tensor with the same size as <code>input</code> filled with <code>fill_value</code>.
+<code>torch_full_like(input, fill_value)</code> is equivalent to
+<code><a href='torch_full.html'>torch_full(input.size(), fill_value, dtype=input.dtype, layout=input.layout, device=input.device)</a></code>.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_gather.html b/static/docs/dev/reference/torch_gather.html
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+    <li>
+      <a href="../articles/getting-started/control-flow-and-weight-sharing.html">Control flow &amp; Weight sharing</a>
+    </li>
+    <li class="dropdown-header">Torch Mechanics</li>
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+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
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+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
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+</li>
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Gather</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_gather.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Gather</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_gather</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, <span class='kw'>index</span>, sparse_grad = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the source tensor</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the axis along which to index</p></td>
+    </tr>
+    <tr>
+      <th>index</th>
+      <td><p>(LongTensor) the indices of elements to gather</p></td>
+    </tr>
+    <tr>
+      <th>sparse_grad</th>
+      <td><p>(bool,optional) If <code>TRUE</code>, gradient w.r.t. <code>input</code> will be a sparse tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="gather-input-dim-index-sparse-grad-false-gt-tensor-"><a class="anchor" href="#gather-input-dim-index-sparse-grad-false-gt-tensor-"></a>gather(input, dim, index, sparse_grad=FALSE) -&gt; Tensor </h2>
+
+    
+
+
+<p>Gathers values along an axis specified by <code>dim</code>.</p>
+<p>For a 3-D tensor the output is specified by::</p><pre><span class='kw'>out</span>[<span class='kw'>i</span>][<span class='kw'>j</span>][<span class='kw'>k</span>] <span class='op'>=</span> <span class='kw'>input</span>[<span class='kw'>index</span>[<span class='kw'>i</span>][<span class='kw'>j</span>][<span class='kw'>k</span>]][<span class='kw'>j</span>][<span class='kw'>k</span>]  <span class='co'># if dim == 0</span>
+<span class='kw'>out</span>[<span class='kw'>i</span>][<span class='kw'>j</span>][<span class='kw'>k</span>] <span class='op'>=</span> <span class='kw'>input</span>[<span class='kw'>i</span>][<span class='kw'>index</span>[<span class='kw'>i</span>][<span class='kw'>j</span>][<span class='kw'>k</span>]][<span class='kw'>k</span>]  <span class='co'># if dim == 1</span>
+<span class='kw'>out</span>[<span class='kw'>i</span>][<span class='kw'>j</span>][<span class='kw'>k</span>] <span class='op'>=</span> <span class='kw'>input</span>[<span class='kw'>i</span>][<span class='kw'>j</span>][<span class='kw'>index</span>[<span class='kw'>i</span>][<span class='kw'>j</span>][<span class='kw'>k</span>]]  <span class='co'># if dim == 2</span>
+</pre>
+
+<p>If <code>input</code> is an n-dimensional tensor with size
+\((x_0, x_1..., x_{i-1}, x_i, x_{i+1}, ..., x_{n-1})\)
+and <code>dim = i</code>, then <code>index</code> must be an \(n\)-dimensional tensor with
+size \((x_0, x_1, ..., x_{i-1}, y, x_{i+1}, ..., x_{n-1})\) where \(y \geq 1\)
+and <code>out</code> will have the same size as <code>index</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>t</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>3</span>,<span class='fl'>4</span>), ncol = <span class='fl'>2</span>, byrow = <span class='fl'>TRUE</span>))
+<span class='fu'>torch_gather</span>(<span class='kw'>t</span>, <span class='fl'>2</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>1</span>), ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>), dtype = <span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>()))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1  1
+#&gt;  4  3
+#&gt; [ CPUFloatType{2,2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
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+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
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+    <div class="page-header">
+    <h1>Ge</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_ge.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Ge</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_ge</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the tensor to compare</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor or float) the tensor or value to compare</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="ge-input-other-out-null-gt-tensor-"><a class="anchor" href="#ge-input-other-out-null-gt-tensor-"></a>ge(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes \(\mbox{input} \geq \mbox{other}\) element-wise.</p>
+<p>The second argument can be a number or a tensor whose shape is
+broadcastable  with the first argument.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_ge</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fl'>1</span><span class='op'>:</span><span class='fl'>4</span>, ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>)), 
+         <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>4</span>,<span class='fl'>4</span>), ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1  1
+#&gt;  0  1
+#&gt; [ CPUBoolType{2,2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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diff --git a/static/docs/dev/reference/torch_generator.html b/static/docs/dev/reference/torch_generator.html
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+
+      
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+
+<div class="row">
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+    <div class="page-header">
+    <h1>Create a Generator object</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/generator.R'><code>R/generator.R</code></a></small>
+    <div class="hidden name"><code>torch_generator.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>A <code>torch_generator</code>  is an object which manages the state of the algorithm
+that produces pseudo random numbers. Used as a keyword argument in many
+In-place random sampling functions.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_generator</span>()</pre>
+
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='co'># Via string</span>
+<span class='kw'>generator</span> <span class='op'>&lt;-</span> <span class='fu'>torch_generator</span>()
+<span class='kw'>generator</span><span class='op'>$</span><span class='fu'>current_seed</span>()
+<span class='kw'>generator</span><span class='op'>$</span><span class='fu'>set_current_seed</span>(<span class='fl'>1234567L</span>)
+<span class='kw'>generator</span><span class='op'>$</span><span class='fu'>current_seed</span>()
+
+
+}
+</div><div class='output co'>#&gt; integer64
+#&gt; [1] 1234567</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Geqrf</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_geqrf.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Geqrf</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_geqrf</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input matrix</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="geqrf-input-out-null-gt-tensor-tensor-"><a class="anchor" href="#geqrf-input-out-null-gt-tensor-tensor-"></a>geqrf(input, out=NULL) -&gt; (Tensor, Tensor) </h2>
+
+    
+
+
+<p>This is a low-level function for calling LAPACK directly. This function
+returns a namedtuple (a, tau) as defined in <code>LAPACK documentation for geqrf</code>_ .</p>
+<p>You'll generally want to use <code><a href='torch_qr.html'>torch_qr</a></code> instead.</p>
+<p>Computes a QR decomposition of <code>input</code>, but without constructing
+\(Q\) and \(R\) as explicit separate matrices.</p>
+<p>Rather, this directly calls the underlying LAPACK function <code>?geqrf</code>
+which produces a sequence of 'elementary reflectors'.</p>
+<p>See <code>LAPACK documentation for geqrf</code>_ for further details.</p>
+
+  </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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+    <li class="dropdown-header">Beginers guide</li>
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+    </li>
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+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Ger</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_ger.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Ger</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_ger</span>(<span class='kw'>self</span>, <span class='kw'>vec2</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) 1-D input vector</p></td>
+    </tr>
+    <tr>
+      <th>vec2</th>
+      <td><p>(Tensor) 1-D input vector</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>This function does not broadcast .</p>
+    <h2 class="hasAnchor" id="ger-input-vec-out-null-gt-tensor-"><a class="anchor" href="#ger-input-vec-out-null-gt-tensor-"></a>ger(input, vec2, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Outer product of <code>input</code> and <code>vec2</code>.
+If <code>input</code> is a vector of size \(n\) and <code>vec2</code> is a vector of
+size \(m\), then <code>out</code> must be a matrix of size \((n \times m)\).</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>v1</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>5.</span>)
+<span class='kw'>v2</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>4.</span>)
+<span class='fu'>torch_ger</span>(<span class='kw'>v1</span>, <span class='kw'>v2</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;   1   2   3
+#&gt;   2   4   6
+#&gt;   3   6   9
+#&gt;   4   8  12
+#&gt; [ CPUFloatType{4,3} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <li>
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+      <a href="../articles/getting-started/tensors-and-autograd.html">Tensors and autograd</a>
+    </li>
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+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/optim.html">optim: optimizers in torch</a>
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+      <a href="../articles/getting-started/custom-nn.html">Custom nn modules</a>
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+    </li>
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+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
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+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
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+
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+    <div class="page-header">
+    <h1>Gt</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_gt.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Gt</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_gt</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the tensor to compare</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor or float) the tensor or value to compare</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="gt-input-other-out-null-gt-tensor-"><a class="anchor" href="#gt-input-other-out-null-gt-tensor-"></a>gt(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes \(\mbox{input} &gt; \mbox{other}\) element-wise.</p>
+<p>The second argument can be a number or a tensor whose shape is
+broadcastable  with the first argument.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_gt</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fl'>1</span><span class='op'>:</span><span class='fl'>4</span>, ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>)), 
+         <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>4</span>,<span class='fl'>4</span>), ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0  1
+#&gt;  0  0
+#&gt; [ CPUBoolType{2,2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
+  </div>
+</div>
+
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diff --git a/static/docs/dev/reference/torch_hamming_window.html b/static/docs/dev/reference/torch_hamming_window.html
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+    <h1>Hamming_window</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
+    <div class="hidden name"><code>torch_hamming_window.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Hamming_window</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_hamming_window</span>(
+  <span class='kw'>window_length</span>,
+  periodic = <span class='fl'>TRUE</span>,
+  alpha = <span class='fl'>0.54</span>,
+  beta = <span class='fl'>0.46</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>window_length</th>
+      <td><p>(int) the size of returned window</p></td>
+    </tr>
+    <tr>
+      <th>periodic</th>
+      <td><p>(bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.</p></td>
+    </tr>
+    <tr>
+      <th>alpha</th>
+      <td><p>(float, optional) The coefficient \(\alpha\) in the equation above</p></td>
+    </tr>
+    <tr>
+      <th>beta</th>
+      <td><p>(float, optional) The coefficient \(\beta\) in the equation above</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). Only floating point types are supported.</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned window tensor. Only          <code>torch_strided</code> (dense layout) is supported.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>If `window_length` \eqn{=1}, the returned window contains a single value 1.
+</pre>
+
+<pre>This is a generalized version of `torch_hann_window`.
+</pre>
+
+    <h2 class="hasAnchor" id="hamming-window-window-length-periodic-true-alpha-beta-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#hamming-window-window-length-periodic-true-alpha-beta-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>hamming_window(window_length, periodic=TRUE, alpha=0.54, beta=0.46, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Hamming window function.</p>
+<p>$$
+    w[n] = \alpha - \beta\ \cos \left( \frac{2 \pi n}{N - 1} \right),
+$$
+where \(N\) is the full window size.</p>
+<p>The input <code>window_length</code> is a positive integer controlling the
+returned window size. <code>periodic</code> flag determines whether the returned
+window trims off the last duplicate value from the symmetric window and is
+ready to be used as a periodic window with functions like
+<code>torch_stft</code>. Therefore, if <code>periodic</code> is true, the \(N\) in
+above formula is in fact \(\mbox{window\_length} + 1\). Also, we always have
+<code>torch_hamming_window(L, periodic=TRUE)</code> equal to
+<code>torch_hamming_window(L + 1, periodic=False)[:-1])</code>.</p>
+
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+    <div class="page-header">
+    <h1>Hann_window</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
+    <div class="hidden name"><code>torch_hann_window.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Hann_window</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_hann_window</span>(
+  <span class='kw'>window_length</span>,
+  periodic = <span class='fl'>TRUE</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>window_length</th>
+      <td><p>(int) the size of returned window</p></td>
+    </tr>
+    <tr>
+      <th>periodic</th>
+      <td><p>(bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). Only floating point types are supported.</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned window tensor. Only          <code>torch_strided</code> (dense layout) is supported.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>If `window_length` \eqn{=1}, the returned window contains a single value 1.
+</pre>
+
+    <h2 class="hasAnchor" id="hann-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#hann-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>hann_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Hann window function.</p>
+<p>$$
+    w[n] = \frac{1}{2}\ \left[1 - \cos \left( \frac{2 \pi n}{N - 1} \right)\right] =
+            \sin^2 \left( \frac{\pi n}{N - 1} \right),
+$$
+where \(N\) is the full window size.</p>
+<p>The input <code>window_length</code> is a positive integer controlling the
+returned window size. <code>periodic</code> flag determines whether the returned
+window trims off the last duplicate value from the symmetric window and is
+ready to be used as a periodic window with functions like
+<code>torch_stft</code>. Therefore, if <code>periodic</code> is true, the \(N\) in
+above formula is in fact \(\mbox{window\_length} + 1\). Also, we always have
+<code>torch_hann_window(L, periodic=TRUE)</code> equal to
+<code>torch_hann_window(L + 1, periodic=False)[:-1])</code>.</p>
+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Histc</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_histc.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Histc</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_histc</span>(<span class='kw'>self</span>, bins = <span class='fl'>100L</span>, min = <span class='fl'>0L</span>, max = <span class='fl'>0L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>bins</th>
+      <td><p>(int) number of histogram bins</p></td>
+    </tr>
+    <tr>
+      <th>min</th>
+      <td><p>(int) lower end of the range (inclusive)</p></td>
+    </tr>
+    <tr>
+      <th>max</th>
+      <td><p>(int) upper end of the range (inclusive)</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="histc-input-bins-min-max-out-null-gt-tensor-"><a class="anchor" href="#histc-input-bins-min-max-out-null-gt-tensor-"></a>histc(input, bins=100, min=0, max=0, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the histogram of a tensor.</p>
+<p>The elements are sorted into equal width bins between <code>min</code> and
+<code>max</code>. If <code>min</code> and <code>max</code> are both zero, the minimum and
+maximum values of the data are used.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_histc</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1.</span>, <span class='fl'>2</span>, <span class='fl'>1</span>)), bins=<span class='fl'>4</span>, min=<span class='fl'>0</span>, max=<span class='fl'>3</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0
+#&gt;  2
+#&gt;  1
+#&gt;  0
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
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+
+      
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_ifft.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Ifft</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_ifft</span>(<span class='kw'>self</span>, <span class='kw'>signal_ndim</span>, normalized = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor of at least <code>signal_ndim</code> <code>+ 1</code>        dimensions</p></td>
+    </tr>
+    <tr>
+      <th>signal_ndim</th>
+      <td><p>(int) the number of dimensions in each signal.        <code>signal_ndim</code> can only be 1, 2 or 3</p></td>
+    </tr>
+    <tr>
+      <th>normalized</th>
+      <td><p>(bool, optional) controls whether to return normalized results.        Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>For CUDA tensors, an LRU cache is used for cuFFT plans to speed up
+repeatedly running FFT methods on tensors of same geometry with same
+configuration. See cufft-plan-cache for more details on how to
+monitor and control the cache.
+</pre>
+
+    <h2 class="hasAnchor" id="ifft-input-signal-ndim-normalized-false-gt-tensor-"><a class="anchor" href="#ifft-input-signal-ndim-normalized-false-gt-tensor-"></a>ifft(input, signal_ndim, normalized=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Complex-to-complex Inverse Discrete Fourier Transform</p>
+<p>This method computes the complex-to-complex inverse discrete Fourier
+transform. Ignoring the batch dimensions, it computes the following
+expression:</p>
+<p>$$
+    X[\omega_1, \dots, \omega_d] =
+        \frac{1}{\prod_{i=1}^d N_i} \sum_{n_1=0}^{N_1-1} \dots \sum_{n_d=0}^{N_d-1} x[n_1, \dots, n_d]
+         e^{\ j\ 2 \pi \sum_{i=0}^d \frac{\omega_i n_i}{N_i}},
+$$
+where \(d\) = <code>signal_ndim</code> is number of dimensions for the
+signal, and \(N_i\) is the size of signal dimension \(i\).</p>
+<p>The argument specifications are almost identical with <code><a href='torch_fft.html'>torch_fft</a></code>.
+However, if <code>normalized</code> is set to <code>TRUE</code>, this instead returns the
+results multiplied by \(\sqrt{\prod_{i=1}^d N_i}\), to become a unitary
+operator. Therefore, to invert a <code><a href='torch_fft.html'>torch_fft</a></code>, the <code>normalized</code>
+argument should be set identically for <code><a href='torch_fft.html'>torch_fft</a></code>.</p>
+<p>Returns the real and the imaginary parts together as one tensor of the same
+shape of <code>input</code>.</p>
+<p>The inverse of this function is <code><a href='torch_fft.html'>torch_fft</a></code>.</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p>For CPU tensors, this method is currently only available with MKL. Use
+<code>torch_backends.mkl.is_available</code> to check if MKL is installed.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>, <span class='fl'>2</span>))
+<span class='kw'>x</span>
+<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_fft.html'>torch_fft</a></span>(<span class='kw'>x</span>, <span class='fl'>2</span>)
+<span class='fu'>torch_ifft</span>(<span class='kw'>y</span>, <span class='fl'>2</span>)  <span class='co'># recover x</span>
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;  -0.1187  0.4043
+#&gt;   1.4305  0.6184
+#&gt;   0.0020 -1.6723
+#&gt; 
+#&gt; (2,.,.) = 
+#&gt;   0.6435 -0.1685
+#&gt;  -1.8431  0.5859
+#&gt;  -2.5527 -0.6183
+#&gt; 
+#&gt; (3,.,.) = 
+#&gt;  -0.5707  1.3996
+#&gt;   0.3492 -0.7337
+#&gt;   0.4699  0.5105
+#&gt; [ CPUFloatType{3,3,2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Integer type info</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/type-info.R'><code>R/type-info.R</code></a></small>
+    <div class="hidden name"><code>torch_iinfo.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>A list that represents the numerical properties of a integer
+type.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_iinfo</span>(<span class='kw'>dtype</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>dtype</th>
+      <td><p>dtype to get information from.</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Imag</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_imag.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Imag</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_imag</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="imag-input-gt-tensor-"><a class="anchor" href="#imag-input-gt-tensor-"></a>imag(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the imaginary part of the <code>input</code> tensor.</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p>Not yet implemented.</p>
+<p>$$
+    \mbox{out}_{i} = imag(\mbox{input}_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='fu'>torch_imag</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>1</span> <span class='op'>+</span> <span class='fl'>1i</span>, <span class='op'>-</span><span class='fl'>2</span> <span class='op'>+</span> <span class='fl'>2i</span>, <span class='fl'>3</span> <span class='op'>-</span> <span class='fl'>3i</span>)))
+}
+}
+</div></pre>
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+    <div class="page-header">
+    <h1>Index_select</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_index_select.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Index_select</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_index_select</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, <span class='kw'>index</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the dimension in which we index</p></td>
+    </tr>
+    <tr>
+      <th>index</th>
+      <td><p>(LongTensor) the 1-D tensor containing the indices to index</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>The returned tensor does <strong>not</strong> use the same storage as the original
+tensor.  If <code>out</code> has a different shape than expected, we
+silently change it to the correct shape, reallocating the underlying
+storage if necessary.</p>
+    <h2 class="hasAnchor" id="index-select-input-dim-index-out-null-gt-tensor-"><a class="anchor" href="#index-select-input-dim-index-out-null-gt-tensor-"></a>index_select(input, dim, index, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor which indexes the <code>input</code> tensor along dimension
+<code>dim</code> using the entries in <code>index</code> which is a <code>LongTensor</code>.</p>
+<p>The returned tensor has the same number of dimensions as the original tensor
+(<code>input</code>).  The <code>dim</code>\ th dimension has the same size as the length
+of <code>index</code>; other dimensions have the same size as in the original tensor.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>4</span>))
+<span class='kw'>x</span>
+<span class='kw'>indices</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>), dtype = <span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>())
+<span class='fu'>torch_index_select</span>(<span class='kw'>x</span>, <span class='fl'>1</span>, <span class='kw'>indices</span>)
+<span class='fu'>torch_index_select</span>(<span class='kw'>x</span>, <span class='fl'>2</span>, <span class='kw'>indices</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.8498 -0.2319
+#&gt;  0.0647 -1.1608
+#&gt;  0.0595  0.9246
+#&gt; [ CPUFloatType{3,2} ]</div></pre>
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Inverse</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_inverse.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Inverse</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_inverse</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor of size \((*, n, n)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is zero or more                    batch dimensions</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>Irrespective of the original strides, the returned tensors will be
+transposed, i.e. with strides like `input.contiguous().transpose(-2, -1).stride()`
+</pre>
+
+    <h2 class="hasAnchor" id="inverse-input-out-null-gt-tensor-"><a class="anchor" href="#inverse-input-out-null-gt-tensor-"></a>inverse(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Takes the inverse of the square matrix <code>input</code>. <code>input</code> can be batches
+of 2D square tensors, in which case this function would return a tensor composed of
+individual inverses.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_rand.html'>torch_rand</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'>torch_inverse</span>(<span class='kw'>x</span>)
+<span class='kw'>z</span> <span class='op'>=</span> <span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='kw'>x</span>, <span class='kw'>y</span>)
+<span class='kw'>z</span>
+<span class='fu'><a href='torch_max.html'>torch_max</a></span>(<span class='fu'><a href='torch_abs.html'>torch_abs</a></span>(<span class='kw'>z</span> <span class='op'>-</span> <span class='fu'><a href='torch_eye.html'>torch_eye</a></span>(<span class='fl'>4</span>))) <span class='co'># Max non-zero</span>
+<span class='co'># Batched inverse example</span>
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'>torch_inverse</span>(<span class='kw'>x</span>)
+<span class='kw'>z</span> <span class='op'>=</span> <span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span>(<span class='kw'>x</span>, <span class='kw'>y</span>)
+<span class='fu'><a href='torch_max.html'>torch_max</a></span>(<span class='fu'><a href='torch_abs.html'>torch_abs</a></span>(<span class='kw'>z</span> <span class='op'>-</span> <span class='fu'><a href='torch_eye.html'>torch_eye</a></span>(<span class='fl'>4</span>)<span class='op'>$</span><span class='fu'>expand_as</span>(<span class='kw'>x</span>))) <span class='co'># Max non-zero</span>
+}
+}
+</div></pre>
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+    <div class="page-header">
+    <h1>Irfft</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_irfft.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Irfft</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_irfft</span>(
+  <span class='kw'>self</span>,
+  <span class='kw'>signal_ndim</span>,
+  normalized = <span class='fl'>FALSE</span>,
+  onesided = <span class='fl'>TRUE</span>,
+  signal_sizes = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>()
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor of at least <code>signal_ndim</code> <code>+ 1</code>        dimensions</p></td>
+    </tr>
+    <tr>
+      <th>signal_ndim</th>
+      <td><p>(int) the number of dimensions in each signal.        <code>signal_ndim</code> can only be 1, 2 or 3</p></td>
+    </tr>
+    <tr>
+      <th>normalized</th>
+      <td><p>(bool, optional) controls whether to return normalized results.        Default: <code>FALSE</code></p></td>
+    </tr>
+    <tr>
+      <th>onesided</th>
+      <td><p>(bool, optional) controls whether <code>input</code> was halfed to avoid        redundancy, e.g., by <code><a href='torch_rfft.html'>torch_rfft()</a></code>. Default: <code>TRUE</code></p></td>
+    </tr>
+    <tr>
+      <th>signal_sizes</th>
+      <td><p>(list or <code>torch.Size</code>, optional) the size of the original        signal (without batch dimension). Default: <code>NULL</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>Due to the conjugate symmetry, `input` do not need to contain the full
+complex frequency values. Roughly half of the values will be sufficient, as
+is the case when `input` is given by [`~torch.rfft`] with
+`rfft(signal, onesided=TRUE)`. In such case, set the `onesided`
+argument of this method to `TRUE`. Moreover, the original signal shape
+information can sometimes be lost, optionally set `signal_sizes` to be
+the size of the original signal (without the batch dimensions if in batched
+mode) to recover it with correct shape.
+
+Therefore, to invert an [torch_rfft()], the `normalized` and
+`onesided` arguments should be set identically for [torch_irfft()],
+and preferably a `signal_sizes` is given to avoid size mismatch. See the
+example below for a case of size mismatch.
+
+See [torch_rfft()] for details on conjugate symmetry.
+</pre>
+
+<p>The inverse of this function is <code><a href='torch_rfft.html'>torch_rfft()</a></code>.</p>
+<pre>For CUDA tensors, an LRU cache is used for cuFFT plans to speed up
+repeatedly running FFT methods on tensors of same geometry with same
+configuration. See cufft-plan-cache for more details on how to
+monitor and control the cache.
+</pre>
+
+    <h2 class="hasAnchor" id="irfft-input-signal-ndim-normalized-false-onesided-true-signal-sizes-null-gt-tensor-"><a class="anchor" href="#irfft-input-signal-ndim-normalized-false-onesided-true-signal-sizes-null-gt-tensor-"></a>irfft(input, signal_ndim, normalized=False, onesided=TRUE, signal_sizes=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Complex-to-real Inverse Discrete Fourier Transform</p>
+<p>This method computes the complex-to-real inverse discrete Fourier transform.
+It is mathematically equivalent with <code><a href='torch_ifft.html'>torch_ifft</a></code> with differences only in
+formats of the input and output.</p>
+<p>The argument specifications are almost identical with <code><a href='torch_ifft.html'>torch_ifft</a></code>.
+Similar to <code><a href='torch_ifft.html'>torch_ifft</a></code>, if <code>normalized</code> is set to <code>TRUE</code>,
+this normalizes the result by multiplying it with
+\(\sqrt{\prod_{i=1}^K N_i}\) so that the operator is unitary, where
+\(N_i\) is the size of signal dimension \(i\).</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p>Generally speaking, input to this function should contain values
+following conjugate symmetry. Note that even if <code>onesided</code> is
+<code>TRUE</code>, often symmetry on some part is still needed. When this
+requirement is not satisfied, the behavior of <code>torch_irfft</code> is
+undefined. Since <code>torch_autograd.gradcheck</code> estimates numerical
+Jacobian with point perturbations, <code>torch_irfft</code> will almost
+certainly fail the check.</p>
+
+<p>For CPU tensors, this method is currently only available with MKL. Use
+<code>torch_backends.mkl.is_available</code> to check if MKL is installed.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='fu'><a href='torch_rfft.html'>torch_rfft</a></span>(<span class='kw'>x</span>, <span class='fl'>2</span>, onesided=<span class='fl'>TRUE</span>)
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>))
+<span class='fu'><a href='torch_rfft.html'>torch_rfft</a></span>(<span class='kw'>x</span>, <span class='fl'>2</span>, onesided=<span class='fl'>TRUE</span>)
+<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_rfft.html'>torch_rfft</a></span>(<span class='kw'>x</span>, <span class='fl'>2</span>, onesided=<span class='fl'>TRUE</span>)
+<span class='fu'>torch_irfft</span>(<span class='kw'>y</span>, <span class='fl'>2</span>, onesided=<span class='fl'>TRUE</span>, signal_sizes=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>,<span class='fl'>5</span>))  <span class='co'># recover x</span>
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.4783  0.0485 -1.0355 -0.9623  0.9389
+#&gt; -1.4649  0.3731 -1.4910 -0.1119  0.8593
+#&gt;  0.0232  0.6348  0.4739 -0.6755  0.1265
+#&gt; -1.1202 -0.9652  0.6908 -1.8474  1.7297
+#&gt; [ CPUFloatType{4,5} ]</div></pre>
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+    <div class="page-header">
+    <h1>Is_complex</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_is_complex.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Is_complex</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_is_complex</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the PyTorch tensor to test</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="is-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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+    <div class="page-header">
+    <h1>Is_floating_point</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_is_floating_point.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Is_floating_point</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_is_floating_point</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the PyTorch tensor to test</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="is-floating-point-input-gt-bool-"><a class="anchor" href="#is-floating-point-input-gt-bool-"></a>is_floating_point(input) -&gt; (bool) </h2>
+
+    
+
+
+<p>Returns TRUE if the data type of <code>input</code> is a floating point data type i.e.,
+one of <code>torch_float64</code>, <code>torch.float32</code> and <code>torch.float16</code>.</p>
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+    <h1>Verifies if torch is installed</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>torch_is_installed.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Verifies if torch is installed</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_is_installed</span>()</pre>
+
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+    <div class="page-header">
+    <h1>Isfinite</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_isfinite.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Isfinite</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_isfinite</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) A tensor to check</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
+
+    
+
+
+<p>Returns a new tensor with boolean elements representing if each element is <code>Finite</code> or not.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_isfinite</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>Inf</span>, <span class='fl'>2</span>, <span class='op'>-</span><span class='fl'>Inf</span>, <span class='fl'>NaN</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1
+#&gt;  0
+#&gt;  1
+#&gt;  0
+#&gt;  0
+#&gt; [ CPUBoolType{5} ]</div></pre>
+  </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>
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diff --git a/static/docs/dev/reference/torch_isinf.html b/static/docs/dev/reference/torch_isinf.html
new file mode 100644
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Isinf</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_isinf.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Isinf</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_isinf</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) A tensor to check</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
+
+    
+
+
+<p>Returns a new tensor with boolean elements representing if each element is <code>+/-INF</code> or not.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_isinf</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>Inf</span>, <span class='fl'>2</span>, <span class='op'>-</span><span class='fl'>Inf</span>, <span class='fl'>NaN</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0
+#&gt;  1
+#&gt;  0
+#&gt;  1
+#&gt;  0
+#&gt; [ CPUBoolType{5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_isnan.html b/static/docs/dev/reference/torch_isnan.html
new file mode 100644
index 000000000..82db099ce
--- /dev/null
+++ b/static/docs/dev/reference/torch_isnan.html
@@ -0,0 +1,253 @@
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+    <div class="page-header">
+    <h1>Isnan</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_isnan.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Isnan</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_isnan</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) A tensor to check</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
+
+    
+
+
+<p>Returns a new tensor with boolean elements representing if each element is <code>NaN</code> or not.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_isnan</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>NaN</span>, <span class='fl'>2</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0
+#&gt;  1
+#&gt;  0
+#&gt; [ CPUBoolType{3} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <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/static/docs/dev/reference/torch_kthvalue.html b/static/docs/dev/reference/torch_kthvalue.html
new file mode 100644
index 000000000..2ad65c95e
--- /dev/null
+++ b/static/docs/dev/reference/torch_kthvalue.html
@@ -0,0 +1,283 @@
+<!-- Generated by pkgdown: do not edit by hand -->
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+    <h1>Kthvalue</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_kthvalue.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Kthvalue</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_kthvalue</span>(<span class='kw'>self</span>, <span class='kw'>k</span>, dim = <span class='op'>-</span><span class='fl'>1L</span>, keepdim = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>k</th>
+      <td><p>(int) k for the k-th smallest element</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int, optional) the dimension to find the kth value along</p></td>
+    </tr>
+    <tr>
+      <th>keepdim</th>
+      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="kthvalue-input-k-dim-null-keepdim-false-out-null-gt-tensor-longtensor-"><a class="anchor" href="#kthvalue-input-k-dim-null-keepdim-false-out-null-gt-tensor-longtensor-"></a>kthvalue(input, k, dim=NULL, keepdim=False, out=NULL) -&gt; (Tensor, LongTensor) </h2>
+
+    
+
+
+<p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the <code>k</code> th
+smallest element of each row of the <code>input</code> tensor in the given dimension
+<code>dim</code>. And <code>indices</code> is the index location of each element found.</p>
+<p>If <code>dim</code> is not given, the last dimension of the <code>input</code> is chosen.</p>
+<p>If <code>keepdim</code> is <code>TRUE</code>, both the <code>values</code> and <code>indices</code> tensors
+are the same size as <code>input</code>, except in the dimension <code>dim</code> where
+they are of size 1. Otherwise, <code>dim</code> is squeezed
+(see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in both the <code>values</code> and
+<code>indices</code> tensors having 1 fewer dimension than the <code>input</code> tensor.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>6.</span>)
+<span class='kw'>x</span>
+<span class='fu'>torch_kthvalue</span>(<span class='kw'>x</span>, <span class='fl'>4</span>)
+<span class='kw'>x</span><span class='op'>=</span><span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>,<span class='fl'>7.</span>)<span class='op'>$</span><span class='fu'>resize_</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>,<span class='fl'>3</span>))
+<span class='kw'>x</span>
+<span class='fu'>torch_kthvalue</span>(<span class='kw'>x</span>, <span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>TRUE</span>)
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt;  4  5  6
+#&gt; [ CPUFloatType{1,3} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt;  1  1  1
+#&gt; [ CPULongType{1,3} ]
+#&gt; </div></pre>
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+    <h1>Creates the corresponding layout</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/layout.R'><code>R/layout.R</code></a></small>
+    <div class="hidden name"><code>torch_layout.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Creates the corresponding layout</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_strided</span>()
+
+<span class='fu'>torch_sparse_coo</span>()</pre>
+
+
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+    Getting started
+     
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+    <li class="dropdown-header">Beginers guide</li>
+    <li>
+      <a href="../articles/getting-started/warmup.html">Warm-up</a>
+    </li>
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+      <a href="../articles/getting-started/tensors.html">Tensors</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/tensors-and-autograd.html">Tensors and autograd</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/optim.html">optim: optimizers in torch</a>
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+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
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+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_le.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Le</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_le</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the tensor to compare</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor or float) the tensor or value to compare</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="le-input-other-out-null-gt-tensor-"><a class="anchor" href="#le-input-other-out-null-gt-tensor-"></a>le(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes \(\mbox{input} \leq \mbox{other}\) element-wise.</p>
+<p>The second argument can be a number or a tensor whose shape is
+broadcastable  with the first argument.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_le</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fl'>1</span><span class='op'>:</span><span class='fl'>4</span>, ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>)), 
+         <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>4</span>,<span class='fl'>4</span>), ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1  0
+#&gt;  1  1
+#&gt; [ CPUBoolType{2,2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_lerp.html b/static/docs/dev/reference/torch_lerp.html
new file mode 100644
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+    <li class="dropdown-header">Beginers guide</li>
+    <li>
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+      <a href="../articles/getting-started/tensors.html">Tensors</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/tensors-and-autograd.html">Tensors and autograd</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/optim.html">optim: optimizers in torch</a>
+    </li>
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+      <a href="../articles/getting-started/custom-nn.html">Custom nn modules</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/control-flow-and-weight-sharing.html">Control flow &amp; Weight sharing</a>
+    </li>
+    <li class="dropdown-header">Torch Mechanics</li>
+    <li>
+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
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+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
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+      <a href="../articles/getting-started/neural-networks.html">Neural networks</a>
+    </li>
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Lerp</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_lerp.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Lerp</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_lerp</span>(<span class='kw'>self</span>, <span class='kw'>end</span>, <span class='kw'>weight</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the tensor with the starting points</p></td>
+    </tr>
+    <tr>
+      <th>end</th>
+      <td><p>(Tensor) the tensor with the ending points</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>(float or tensor) the weight for the interpolation formula</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="lerp-input-end-weight-out-null-"><a class="anchor" href="#lerp-input-end-weight-out-null-"></a>lerp(input, end, weight, out=NULL) </h2>
+
+    
+
+
+<p>Does a linear interpolation of two tensors <code>start</code> (given by <code>input</code>) and <code>end</code> based
+on a scalar or tensor <code>weight</code> and returns the resulting <code>out</code> tensor.</p>
+<p>$$
+    \mbox{out}_i = \mbox{start}_i + \mbox{weight}_i \times (\mbox{end}_i - \mbox{start}_i)
+$$
+The shapes of <code>start</code> and <code>end</code> must be
+broadcastable . If <code>weight</code> is a tensor, then
+the shapes of <code>weight</code>, <code>start</code>, and <code>end</code> must be broadcastable .</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>start</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>5.</span>)
+<span class='kw'>end</span> <span class='op'>=</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>4</span>)<span class='op'>$</span><span class='fu'>fill_</span>(<span class='fl'>10</span>)
+<span class='kw'>start</span>
+<span class='kw'>end</span>
+<span class='fu'>torch_lerp</span>(<span class='kw'>start</span>, <span class='kw'>end</span>, <span class='fl'>0.5</span>)
+<span class='fu'>torch_lerp</span>(<span class='kw'>start</span>, <span class='kw'>end</span>, <span class='fu'><a href='torch_full_like.html'>torch_full_like</a></span>(<span class='kw'>start</span>, <span class='fl'>0.5</span>))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  5.5000
+#&gt;  6.0000
+#&gt;  6.5000
+#&gt;  7.0000
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
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diff --git a/static/docs/dev/reference/torch_lgamma.html b/static/docs/dev/reference/torch_lgamma.html
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+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Lgamma</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_lgamma.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Lgamma</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_lgamma</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="lgamma-input-out-null-gt-tensor-"><a class="anchor" href="#lgamma-input-out-null-gt-tensor-"></a>lgamma(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the logarithm of the gamma function on <code>input</code>.</p>
+<p>$$
+    \mbox{out}_{i} = \log \Gamma(\mbox{input}_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0.5</span>, <span class='fl'>2</span>, <span class='fl'>0.5</span>)
+<span class='fu'>torch_lgamma</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.5724
+#&gt;  0.0000
+#&gt; -0.1208
+#&gt; [ CPUFloatType{3} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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+  </div>
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+
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diff --git a/static/docs/dev/reference/torch_linspace.html b/static/docs/dev/reference/torch_linspace.html
new file mode 100644
index 000000000..4ca88db33
--- /dev/null
+++ b/static/docs/dev/reference/torch_linspace.html
@@ -0,0 +1,288 @@
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Linspace</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_linspace.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Linspace</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_linspace</span>(
+  <span class='kw'>start</span>,
+  <span class='kw'>end</span>,
+  steps = <span class='fl'>100</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>start</th>
+      <td><p>(float) the starting value for the set of points</p></td>
+    </tr>
+    <tr>
+      <th>end</th>
+      <td><p>(float) the ending value for the set of points</p></td>
+    </tr>
+    <tr>
+      <th>steps</th>
+      <td><p>(int) number of points to sample between <code>start</code>        and <code>end</code>. Default: <code>100</code>.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="linspace-start-end-steps-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#linspace-start-end-steps-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>linspace(start, end, steps=100, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a one-dimensional tensor of <code>steps</code>
+equally spaced points between <code>start</code> and <code>end</code>.</p>
+<p>The output tensor is 1-D of size <code>steps</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_linspace</span>(<span class='fl'>3</span>, <span class='fl'>10</span>, steps=<span class='fl'>5</span>)
+<span class='fu'>torch_linspace</span>(<span class='op'>-</span><span class='fl'>10</span>, <span class='fl'>10</span>, steps=<span class='fl'>5</span>)
+<span class='fu'>torch_linspace</span>(start=<span class='op'>-</span><span class='fl'>10</span>, end=<span class='fl'>10</span>, steps=<span class='fl'>5</span>)
+<span class='fu'>torch_linspace</span>(start=<span class='op'>-</span><span class='fl'>10</span>, end=<span class='fl'>10</span>, steps=<span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -10
+#&gt; [ CPUFloatType{1} ]</div></pre>
+  </div>
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+    <div class="page-header">
+    <h1>Loads a saved object</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/save.R'><code>R/save.R</code></a></small>
+    <div class="hidden name"><code>torch_load.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Loads a saved object</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_load</span>(<span class='kw'>path</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>path</th>
+      <td><p>a path to the saved object</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
+
+    <div class='dont-index'><p>Other torch_save: 
+<code><a href='torch_save.html'>torch_save</a>()</code></p></div>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_log.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Log</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_log</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="log-input-out-null-gt-tensor-"><a class="anchor" href="#log-input-out-null-gt-tensor-"></a>log(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the natural logarithm of the elements
+of <code>input</code>.</p>
+<p>$$
+    y_{i} = \log_{e} (x_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_log</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;     nan
+#&gt;     nan
+#&gt; -0.7203
+#&gt;     nan
+#&gt; -1.1992
+#&gt; [ CPUFloatType{5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+
+      
+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Log10</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_log10.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Log10</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_log10</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="log-input-out-null-gt-tensor-"><a class="anchor" href="#log-input-out-null-gt-tensor-"></a>log10(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the logarithm to the base 10 of the elements
+of <code>input</code>.</p>
+<p>$$
+    y_{i} = \log_{10} (x_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_rand.html'>torch_rand</a></span>(<span class='fl'>5</span>)
+<span class='kw'>a</span>
+<span class='fu'>torch_log10</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -1.0370
+#&gt; -0.6577
+#&gt; -0.1037
+#&gt; -0.7685
+#&gt; -0.8610
+#&gt; [ CPUFloatType{5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/torch_log1p.html b/static/docs/dev/reference/torch_log1p.html
new file mode 100644
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+
+      
+
+      </header>
+
+<div class="row">
+  <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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_log1p.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Log1p</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_log1p</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>This function is more accurate than <code><a href='torch_log.html'>torch_log</a></code> for small
+values of <code>input</code></p>
+    <h2 class="hasAnchor" id="log-p-input-out-null-gt-tensor-"><a class="anchor" href="#log-p-input-out-null-gt-tensor-"></a>log1p(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the natural logarithm of (1 + <code>input</code>).</p>
+<p>$$
+    y_i = \log_{e} (x_i + 1)
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_log1p</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.4854
+#&gt; -0.3607
+#&gt;  1.1316
+#&gt; -0.0408
+#&gt; -0.7326
+#&gt; [ CPUFloatType{5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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+
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+    <div class="page-header">
+    <h1>Log2</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_log2.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Log2</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_log2</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="log-input-out-null-gt-tensor-"><a class="anchor" href="#log-input-out-null-gt-tensor-"></a>log2(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the logarithm to the base 2 of the elements
+of <code>input</code>.</p>
+<p>$$
+    y_{i} = \log_{2} (x_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_rand.html'>torch_rand</a></span>(<span class='fl'>5</span>)
+<span class='kw'>a</span>
+<span class='fu'>torch_log2</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0.1392
+#&gt; -0.0448
+#&gt; -1.5715
+#&gt; -1.7101
+#&gt; -0.0220
+#&gt; [ CPUFloatType{5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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+  <a href="../news/index.html">Changelog</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>
+     
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_logdet.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Logdet</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_logdet</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor of size <code>(*, n, n)</code> where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is zero or more                batch dimensions.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>Result is `-inf` if `input` has zero log determinant, and is `NaN` if
+`input` has negative determinant.
+</pre>
+
+<pre>Backward through `logdet` internally uses SVD results when `input`
+is not invertible. In this case, double backward through `logdet` will
+be unstable in when `input` doesn't have distinct singular values. See
+`~torch.svd` for details.
+</pre>
+
+    <h2 class="hasAnchor" id="logdet-input-gt-tensor-"><a class="anchor" href="#logdet-input-gt-tensor-"></a>logdet(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>Calculates log determinant of a square matrix or batches of square matrices.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='fu'><a href='torch_det.html'>torch_det</a></span>(<span class='kw'>A</span>)
+<span class='fu'>torch_logdet</span>(<span class='kw'>A</span>)
+<span class='kw'>A</span>
+<span class='kw'>A</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/det.html'>det</a></span>()
+<span class='kw'>A</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/det.html'>det</a></span>()<span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/Log.html'>log</a></span>()
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; 1.25629
+#&gt; [ CPUFloatType{} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+<div class="row">
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+    <div class="page-header">
+    <h1>Logical_and</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_logical_and.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Logical_and</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_logical_and</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor) the tensor to compute AND with</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="logical-and-input-other-out-null-gt-tensor-"><a class="anchor" href="#logical-and-input-other-out-null-gt-tensor-"></a>logical_and(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the element-wise logical AND of the given input tensors. Zeros are treated as <code>FALSE</code> and nonzeros are
+treated as <code>TRUE</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_logical_and</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>TRUE</span>, <span class='fl'>FALSE</span>, <span class='fl'>TRUE</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>TRUE</span>, <span class='fl'>FALSE</span>, <span class='fl'>FALSE</span>)))
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>10</span>, <span class='fl'>0</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int8</a></span>())
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>0</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int8</a></span>())
+<span class='fu'>torch_logical_and</span>(<span class='kw'>a</span>, <span class='kw'>b</span>)
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='fu'>torch_logical_and</span>(<span class='kw'>a</span>, <span class='kw'>b</span>, out=<span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>4</span>, dtype=<span class='fu'><a href='torch_dtype.html'>torch_bool</a></span>()))
+}
+}
+</div></pre>
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+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Logical_not</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_logical_not.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Logical_not</p>
+    </div>
+
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="logical-not-input-out-null-gt-tensor-"><a class="anchor" href="#logical-not-input-out-null-gt-tensor-"></a>logical_not(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the element-wise logical NOT of the given input tensor. If not specified, the output tensor will have the bool
+dtype. If the input tensor is not a bool tensor, zeros are treated as <code>FALSE</code> and non-zeros are treated as <code>TRUE</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_logical_not</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>TRUE</span>, <span class='fl'>FALSE</span>)))
+<span class='fu'>torch_logical_not</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>1</span>, <span class='op'>-</span><span class='fl'>10</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int8</a></span>()))
+<span class='fu'>torch_logical_not</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0.</span>, <span class='fl'>1.5</span>, <span class='op'>-</span><span class='fl'>10.</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_double</a></span>()))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1
+#&gt;  0
+#&gt;  0
+#&gt; [ CPUBoolType{3} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Logical_or</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_logical_or.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Logical_or</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_logical_or</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor) the tensor to compute OR with</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="logical-or-input-other-out-null-gt-tensor-"><a class="anchor" href="#logical-or-input-other-out-null-gt-tensor-"></a>logical_or(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the element-wise logical OR of the given input tensors. Zeros are treated as <code>FALSE</code> and nonzeros are
+treated as <code>TRUE</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_logical_or</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>TRUE</span>, <span class='fl'>FALSE</span>, <span class='fl'>TRUE</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>TRUE</span>, <span class='fl'>FALSE</span>, <span class='fl'>FALSE</span>)))
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>10</span>, <span class='fl'>0</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int8</a></span>())
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>0</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int8</a></span>())
+<span class='fu'>torch_logical_or</span>(<span class='kw'>a</span>, <span class='kw'>b</span>)
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='fu'>torch_logical_or</span>(<span class='kw'>a</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/double.html'>double</a></span>(), <span class='kw'>b</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/double.html'>double</a></span>())
+<span class='fu'>torch_logical_or</span>(<span class='kw'>a</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/double.html'>double</a></span>(), <span class='kw'>b</span>)
+<span class='fu'>torch_logical_or</span>(<span class='kw'>a</span>, <span class='kw'>b</span>, out=<span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>4</span>, dtype=<span class='fu'><a href='torch_dtype.html'>torch_bool</a></span>()))
+}
+}
+</div></pre>
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diff --git a/static/docs/dev/reference/torch_logical_xor.html b/static/docs/dev/reference/torch_logical_xor.html
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+    <div class="page-header">
+    <h1>Logical_xor</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_logical_xor.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Logical_xor</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_logical_xor</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor) the tensor to compute XOR with</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="logical-xor-input-other-out-null-gt-tensor-"><a class="anchor" href="#logical-xor-input-other-out-null-gt-tensor-"></a>logical_xor(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the element-wise logical XOR of the given input tensors. Zeros are treated as <code>FALSE</code> and nonzeros are
+treated as <code>TRUE</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_logical_xor</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>TRUE</span>, <span class='fl'>FALSE</span>, <span class='fl'>TRUE</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>TRUE</span>, <span class='fl'>FALSE</span>, <span class='fl'>FALSE</span>)))
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>10</span>, <span class='fl'>0</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int8</a></span>())
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>0</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int8</a></span>())
+<span class='fu'>torch_logical_xor</span>(<span class='kw'>a</span>, <span class='kw'>b</span>)
+<span class='fu'>torch_logical_xor</span>(<span class='kw'>a</span><span class='op'>$</span><span class='fu'>to</span>(dtype=<span class='fu'><a href='torch_dtype.html'>torch_double</a></span>()), <span class='kw'>b</span><span class='op'>$</span><span class='fu'>to</span>(dtype=<span class='fu'><a href='torch_dtype.html'>torch_double</a></span>()))
+<span class='fu'>torch_logical_xor</span>(<span class='kw'>a</span><span class='op'>$</span><span class='fu'>to</span>(dtype=<span class='fu'><a href='torch_dtype.html'>torch_double</a></span>()), <span class='kw'>b</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1
+#&gt;  1
+#&gt;  0
+#&gt;  0
+#&gt; [ CPUBoolType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+<div class="row">
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+    <div class="page-header">
+    <h1>Logspace</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_logspace.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Logspace</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_logspace</span>(
+  <span class='kw'>start</span>,
+  <span class='kw'>end</span>,
+  steps = <span class='fl'>100</span>,
+  base = <span class='fl'>10</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>start</th>
+      <td><p>(float) the starting value for the set of points</p></td>
+    </tr>
+    <tr>
+      <th>end</th>
+      <td><p>(float) the ending value for the set of points</p></td>
+    </tr>
+    <tr>
+      <th>steps</th>
+      <td><p>(int) number of points to sample between <code>start</code>        and <code>end</code>. Default: <code>100</code>.</p></td>
+    </tr>
+    <tr>
+      <th>base</th>
+      <td><p>(float) base of the logarithm function. Default: <code>10.0</code>.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="logspace-start-end-steps-base-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#logspace-start-end-steps-base-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>logspace(start, end, steps=100, base=10.0, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a one-dimensional tensor of <code>steps</code> points
+logarithmically spaced with base <code>base</code> between
+\({\mbox{base}}^{\mbox{start}}\) and \({\mbox{base}}^{\mbox{end}}\).</p>
+<p>The output tensor is 1-D of size <code>steps</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_logspace</span>(start=<span class='op'>-</span><span class='fl'>10</span>, end=<span class='fl'>10</span>, steps=<span class='fl'>5</span>)
+<span class='fu'>torch_logspace</span>(start=<span class='fl'>0.1</span>, end=<span class='fl'>1.0</span>, steps=<span class='fl'>5</span>)
+<span class='fu'>torch_logspace</span>(start=<span class='fl'>0.1</span>, end=<span class='fl'>1.0</span>, steps=<span class='fl'>1</span>)
+<span class='fu'>torch_logspace</span>(start=<span class='fl'>2</span>, end=<span class='fl'>2</span>, steps=<span class='fl'>1</span>, base=<span class='fl'>2</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  4
+#&gt; [ CPUFloatType{1} ]</div></pre>
+  </div>
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Logsumexp</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_logsumexp.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Logsumexp</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_logsumexp</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, keepdim = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
+    </tr>
+    <tr>
+      <th>keepdim</th>
+      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="logsumexp-input-dim-keepdim-false-out-null-"><a class="anchor" href="#logsumexp-input-dim-keepdim-false-out-null-"></a>logsumexp(input, dim, keepdim=False, out=NULL) </h2>
+
+    
+
+
+<p>Returns the log of summed exponentials of each row of the <code>input</code>
+tensor in the given dimension <code>dim</code>. The computation is numerically
+stabilized.</p>
+<p>For summation index \(j\) given by <code>dim</code> and other indices \(i\), the result is</p>
+<p>$$
+        \mbox{logsumexp}(x)_{i} = \log \sum_j \exp(x_{ij})
+$$</p>
+<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size
+as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
+Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
+output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='fu'>torch_logsumexp</span>(<span class='kw'>a</span>, <span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.3320
+#&gt;  1.7755
+#&gt;  2.0098
+#&gt; [ CPUFloatType{3} ]</div></pre>
+  </div>
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diff --git a/static/docs/dev/reference/torch_lstsq.html b/static/docs/dev/reference/torch_lstsq.html
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+  <a href="../news/index.html">Changelog</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>
+     
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+
+      
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Lstsq</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_lstsq.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Lstsq</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_lstsq</span>(<span class='kw'>self</span>, <span class='kw'>A</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the matrix \(B\)</p></td>
+    </tr>
+    <tr>
+      <th>A</th>
+      <td><p>(Tensor) the \(m\) by \(n\) matrix \(A\)</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>The case when \eqn{m &lt; n} is not supported on the GPU.
+</pre>
+
+    <h2 class="hasAnchor" id="lstsq-input-a-out-null-gt-tensor-"><a class="anchor" href="#lstsq-input-a-out-null-gt-tensor-"></a>lstsq(input, A, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the solution to the least squares and least norm problems for a full
+rank matrix \(A\) of size \((m \times n)\) and a matrix \(B\) of
+size \((m \times k)\).</p>
+<p>If \(m \geq n\), <code>torch_lstsq()</code> solves the least-squares problem:</p>
+<p>$$
+   \begin{array}{ll}
+   \min_X &amp; \|AX-B\|_2.
+   \end{array}
+$$
+If \(m &lt; n\), <code>torch_lstsq()</code> solves the least-norm problem:</p>
+<p>$$
+   \begin{array}{llll}
+   \min_X &amp; \|X\|_2 &amp; \mbox{subject to} &amp; AX = B.
+   \end{array}
+$$
+Returned tensor \(X\) has shape \((\mbox{max}(m, n) \times k)\). The first \(n\)
+rows of \(X\) contains the solution. If \(m \geq n\), the residual sum of squares
+for the solution in each column is given by the sum of squares of elements in the
+remaining \(m - n\) rows of that column.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span>(
+ <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>1</span>),
+ <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>,<span class='fl'>3</span>,<span class='fl'>4</span>),
+ <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>,<span class='fl'>5</span>,<span class='fl'>2</span>),
+ <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>,<span class='fl'>2</span>,<span class='fl'>5</span>),
+ <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>,<span class='fl'>4</span>,<span class='fl'>3</span>)
+))
+<span class='kw'>B</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span>(
+ <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>10</span>, <span class='op'>-</span><span class='fl'>3</span>),
+ <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>12</span>, <span class='fl'>14</span>),
+ <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>14</span>, <span class='fl'>12</span>),
+ <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>16</span>, <span class='fl'>16</span>),
+ <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>18</span>, <span class='fl'>16</span>)
+))
+<span class='kw'>out</span> <span class='op'>=</span> <span class='fu'>torch_lstsq</span>(<span class='kw'>B</span>, <span class='kw'>A</span>)
+<span class='kw'>out</span>[[<span class='fl'>1</span>]]
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;   2.0000   1.0000
+#&gt;   1.0000   1.0000
+#&gt;   1.0000   2.0000
+#&gt;  10.9635   4.8501
+#&gt;   8.9332   5.2418
+#&gt; [ CPUFloatType{5,2} ]</div></pre>
+  </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>
+
+<div class="pkgdown">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+</div>
+
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diff --git a/static/docs/dev/reference/torch_lt.html b/static/docs/dev/reference/torch_lt.html
new file mode 100644
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+        <a class="navbar-link" href="../index.html">torch</a>
+        <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.0.3.9000</span>
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+     
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+  <ul class="dropdown-menu" role="menu">
+    <li class="dropdown-header">Beginers guide</li>
+    <li>
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+      <a href="../articles/getting-started/tensors.html">Tensors</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/tensors-and-autograd.html">Tensors and autograd</a>
+    </li>
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+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
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+      <a href="../articles/getting-started/custom-nn.html">Custom nn modules</a>
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+    </li>
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+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
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+      <a href="../articles/getting-started/neural-networks.html">Neural networks</a>
+    </li>
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+    <li class="dropdown-header">Tensors</li>
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+      <a href="../articles/tensor-creation.html">Creating tensors</a>
+    </li>
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+      <a href="../articles/using-autograd.html">Using autograd</a>
+    </li>
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+      <a href="../articles/extending-autograd.html">Extending autograd</a>
+    </li>
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+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_lt.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Lt</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_lt</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the tensor to compare</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor or float) the tensor or value to compare</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="lt-input-other-out-null-gt-tensor-"><a class="anchor" href="#lt-input-other-out-null-gt-tensor-"></a>lt(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes \(\mbox{input} &lt; \mbox{other}\) element-wise.</p>
+<p>The second argument can be a number or a tensor whose shape is
+broadcastable  with the first argument.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_lt</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fl'>1</span><span class='op'>:</span><span class='fl'>4</span>, ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>)), 
+         <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>4</span>,<span class='fl'>4</span>), ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0  0
+#&gt;  1  0
+#&gt; [ CPUBoolType{2,2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
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+
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diff --git a/static/docs/dev/reference/torch_lu.html b/static/docs/dev/reference/torch_lu.html
new file mode 100644
index 000000000..dea17909a
--- /dev/null
+++ b/static/docs/dev/reference/torch_lu.html
@@ -0,0 +1,281 @@
+<!-- Generated by pkgdown: do not edit by hand -->
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+<li class="dropdown-header">Examples</li>
+<li>
+  <a href="../reference/index.html">Reference</a>
+</li>
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+  <a href="../news/index.html">Changelog</a>
+</li>
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>LU</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
+    <div class="hidden name"><code>torch_lu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Computes the LU factorization of a matrix or batches of matrices A. Returns a
+tuple containing the LU factorization and pivots of A. Pivoting is done if pivot
+is set to True.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_lu</span>(<span class='kw'>A</span>, pivot = <span class='fl'>TRUE</span>, get_infos = <span class='fl'>FALSE</span>, out = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>A</th>
+      <td><p>(Tensor) the tensor to factor of size (<em>, m, n)(</em>,m,n)</p></td>
+    </tr>
+    <tr>
+      <th>pivot</th>
+      <td><p>(bool, optional) – controls whether pivoting is done. Default: TRUE</p></td>
+    </tr>
+    <tr>
+      <th>get_infos</th>
+      <td><p>(bool, optional) – if set to True, returns an info IntTensor. Default: FALSE</p></td>
+    </tr>
+    <tr>
+      <th>out</th>
+      <td><p>(tuple, optional) – optional output tuple. If get_infos is True, then the elements
+in the tuple are Tensor, IntTensor, and IntTensor. If get_infos is False, then the
+elements in the tuple are Tensor, IntTensor. Default: NULL</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='fu'>torch_lu</span>(<span class='kw'>A</span>)
+
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;   1.9995  1.3835  0.1162
+#&gt;   0.0141 -0.6562 -0.2773
+#&gt;  -0.3905 -0.7564  0.1848
+#&gt; 
+#&gt; (2,.,.) = 
+#&gt;  -0.6658 -0.4938  0.4441
+#&gt;  -0.3991 -0.6220  0.0216
+#&gt;   0.8804 -0.9766  1.1155
+#&gt; [ CPUFloatType{2,3,3} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt;  2  2  3
+#&gt;  1  2  3
+#&gt; [ CPUIntType{2,3} ]
+#&gt; </div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+<div class="row">
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+    <div class="page-header">
+    <h1>Lu_solve</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_lu_solve.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Lu_solve</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_lu_solve</span>(<span class='kw'>self</span>, <span class='kw'>LU_data</span>, <span class='kw'>LU_pivots</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the RHS tensor of size \((*, m, k)\), where \(*\)                is zero or more batch dimensions.</p></td>
+    </tr>
+    <tr>
+      <th>LU_data</th>
+      <td><p>(Tensor) the pivoted LU factorization of A from <code>torch_lu</code> of size \((*, m, m)\),                       where \(*\) is zero or more batch dimensions.</p></td>
+    </tr>
+    <tr>
+      <th>LU_pivots</th>
+      <td><p>(IntTensor) the pivots of the LU factorization from <code>torch_lu</code> of size \((*, m)\),                           where \(*\) is zero or more batch dimensions.                           The batch dimensions of <code>LU_pivots</code> must be equal to the batch dimensions of                           <code>LU_data</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="lu-solve-input-lu-data-lu-pivots-out-null-gt-tensor-"><a class="anchor" href="#lu-solve-input-lu-data-lu-pivots-out-null-gt-tensor-"></a>lu_solve(input, LU_data, LU_pivots, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the LU solve of the linear system \(Ax = b\) using the partially pivoted
+LU factorization of A from <code>torch_lu</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>1</span>))
+<span class='kw'>out</span> <span class='op'>=</span> <span class='fu'><a href='torch_lu.html'>torch_lu</a></span>(<span class='kw'>A</span>)
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'>torch_lu_solve</span>(<span class='kw'>b</span>, <span class='kw'>out</span>[[<span class='fl'>1</span>]], <span class='kw'>out</span>[[<span class='fl'>2</span>]])
+<span class='fu'><a href='torch_norm.html'>torch_norm</a></span>(<span class='fu'><a href='torch_bmm.html'>torch_bmm</a></span>(<span class='kw'>A</span>, <span class='kw'>x</span>) <span class='op'>-</span> <span class='kw'>b</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; 1.68587e-07
+#&gt; [ CPUFloatType{} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/torch_manual_seed.html b/static/docs/dev/reference/torch_manual_seed.html
new file mode 100644
index 000000000..3afb06de7
--- /dev/null
+++ b/static/docs/dev/reference/torch_manual_seed.html
@@ -0,0 +1,237 @@
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+    <p>Sets the seed for generating random numbers.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_manual_seed</span>(<span class='kw'>seed</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>seed</th>
+      <td><p>integer seed.</p></td>
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diff --git a/static/docs/dev/reference/torch_masked_select.html b/static/docs/dev/reference/torch_masked_select.html
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+    <h1>Masked_select</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_masked_select.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Masked_select</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_masked_select</span>(<span class='kw'>self</span>, <span class='kw'>mask</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>mask</th>
+      <td><p>(BoolTensor) the tensor containing the binary mask to index with</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>The returned tensor does <strong>not</strong> use the same storage
+as the original tensor</p>
+    <h2 class="hasAnchor" id="masked-select-input-mask-out-null-gt-tensor-"><a class="anchor" href="#masked-select-input-mask-out-null-gt-tensor-"></a>masked_select(input, mask, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new 1-D tensor which indexes the <code>input</code> tensor according to
+the boolean mask <code>mask</code> which is a <code>BoolTensor</code>.</p>
+<p>The shapes of the <code>mask</code> tensor and the <code>input</code> tensor don't need
+to match, but they must be broadcastable .</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>4</span>))
+<span class='kw'>x</span>
+<span class='kw'>mask</span> <span class='op'>=</span> <span class='kw'>x</span><span class='op'>$</span><span class='fu'>ge</span>(<span class='fl'>0.5</span>)
+<span class='kw'>mask</span>
+<span class='fu'>torch_masked_select</span>(<span class='kw'>x</span>, <span class='kw'>mask</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.0696
+#&gt;  0.6496
+#&gt;  2.4253
+#&gt;  1.3309
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <h1>Matmul</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_matmul.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Matmul</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_matmul</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the first tensor to be multiplied</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor) the second tensor to be multiplied</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>The 1-dimensional dot product version of this function does not support an `out` parameter.
+</pre>
+
+    <h2 class="hasAnchor" id="matmul-input-other-out-null-gt-tensor-"><a class="anchor" href="#matmul-input-other-out-null-gt-tensor-"></a>matmul(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Matrix product of two tensors.</p>
+<p>The behavior depends on the dimensionality of the tensors as follows:</p><ul>
+<li><p>If both tensors are 1-dimensional, the dot product (scalar) is returned.</p></li>
+<li><p>If both arguments are 2-dimensional, the matrix-matrix product is returned.</p></li>
+<li><p>If the first argument is 1-dimensional and the second argument is 2-dimensional,
+a 1 is prepended to its dimension for the purpose of the matrix multiply.
+After the matrix multiply, the prepended dimension is removed.</p></li>
+<li><p>If the first argument is 2-dimensional and the second argument is 1-dimensional,
+the matrix-vector product is returned.</p></li>
+<li><p>If both arguments are at least 1-dimensional and at least one argument is
+N-dimensional (where N &gt; 2), then a batched matrix multiply is returned.  If the first
+argument is 1-dimensional, a 1 is prepended to its dimension for the purpose of the
+batched matrix multiply and removed after.  If the second argument is 1-dimensional, a
+1 is appended to its dimension for the purpose of the batched matrix multiple and removed after.
+The non-matrix (i.e. batch) dimensions are broadcasted  (and thus
+must be broadcastable).  For example, if <code>input</code> is a
+\((j \times 1 \times n \times m)\) tensor and <code>other</code> is a \((k \times m \times p)\)
+tensor, <code>out</code> will be an \((j \times k \times n \times p)\) tensor.</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='co'># vector x vector</span>
+<span class='kw'>tensor1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>))
+<span class='kw'>tensor2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>))
+<span class='fu'>torch_matmul</span>(<span class='kw'>tensor1</span>, <span class='kw'>tensor2</span>)
+<span class='co'># matrix x vector</span>
+<span class='kw'>tensor1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>4</span>))
+<span class='kw'>tensor2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='fu'>torch_matmul</span>(<span class='kw'>tensor1</span>, <span class='kw'>tensor2</span>)
+<span class='co'># batched matrix x broadcasted vector</span>
+<span class='kw'>tensor1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>4</span>))
+<span class='kw'>tensor2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='fu'>torch_matmul</span>(<span class='kw'>tensor1</span>, <span class='kw'>tensor2</span>)
+<span class='co'># batched matrix x batched matrix</span>
+<span class='kw'>tensor1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>4</span>))
+<span class='kw'>tensor2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>4</span>, <span class='fl'>5</span>))
+<span class='fu'>torch_matmul</span>(<span class='kw'>tensor1</span>, <span class='kw'>tensor2</span>)
+<span class='co'># batched matrix x broadcasted matrix</span>
+<span class='kw'>tensor1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>4</span>))
+<span class='kw'>tensor2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>))
+<span class='fu'>torch_matmul</span>(<span class='kw'>tensor1</span>, <span class='kw'>tensor2</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;   2.9089  0.9845 -1.9209  0.7565 -0.8089
+#&gt;   1.5602 -0.1355 -0.5663 -0.7256 -0.3005
+#&gt;  -5.1681  0.8439 -0.2311  4.0118  0.6029
+#&gt; 
+#&gt; (2,.,.) = 
+#&gt;  -3.2413  0.6460 -0.0018  2.4108 -0.3839
+#&gt;   1.1368  0.6532  1.2959 -0.8586 -1.9191
+#&gt;   1.8520 -0.0187  0.1186 -0.9161  0.4118
+#&gt; 
+#&gt; (3,.,.) = 
+#&gt;   2.9787  0.2050  0.3688 -1.8496 -1.7943
+#&gt;  -2.8292  0.7724  0.4590  2.3608  0.5896
+#&gt;   2.1940  0.3648  0.9281 -1.0902  0.0810
+#&gt; 
+#&gt; (4,.,.) = 
+#&gt;  -2.1130  0.3511 -0.6688  2.3332  1.7527
+#&gt;   3.4641  0.3747  1.2832 -2.0061 -0.3228
+#&gt;   3.3574 -0.2530  0.2699 -2.4027 -0.9421
+#&gt; 
+#&gt; (5,.,.) = 
+#&gt;  -0.7329 -0.3116 -0.9270  0.1775 -1.1252
+#&gt;  -0.5466  0.5188 -0.3335  1.2077  0.1667
+#&gt;  -2.5609 -0.2285 -1.5218  2.0231  0.8828
+#&gt; 
+#&gt; (6,.,.) = 
+#&gt;  -2.6296  1.0644 -0.3949  3.1285  0.5754
+#&gt;   0.6244 -1.2722  0.0847 -2.0712  0.1138
+#&gt;  -1.6715  0.1444 -2.2961  2.2244 -0.3139
+#&gt; 
+#&gt; (7,.,.) = 
+#&gt;   2.3106 -1.2625  1.1150 -3.0800  1.7108
+#&gt;  -1.4338  0.6178  0.9726  1.0354  0.0449
+#&gt;   0.5690 -1.0606 -0.5211 -1.1207  1.3386
+#&gt; 
+#&gt; (8,.,.) = 
+#&gt;  -0.7904  0.7064  1.5994  0.5113  0.1367
+#&gt;  -0.6828 -0.1212 -2.8078  1.8834  0.6446
+#&gt;   1.6149 -0.3797 -0.5884 -0.9357  0.2698
+#&gt; 
+#&gt; (9,.,.) = 
+#&gt;   2.2742 -1.5073 -0.8308 -2.4203  1.3077
+#&gt;  -3.3323 -1.0881 -2.3815  2.2911  3.2742
+#&gt;  -1.8565 -0.3452 -0.5353  0.8325  0.3332
+#&gt; 
+#&gt; (10,.,.) = 
+#&gt;   2.3585 -0.4239  0.6924 -2.1180  0.0934
+#&gt;   0.9073  1.1976 -0.0628  0.8384 -1.5615
+#&gt;  -1.1092 -0.2548  0.6868 -0.3382 -0.6751
+#&gt; [ CPUFloatType{10,3,5} ]</div></pre>
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+    <div class="page-header">
+    <h1>Matrix_power</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_matrix_power.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Matrix_power</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_matrix_power</span>(<span class='kw'>self</span>, <span class='kw'>n</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>n</th>
+      <td><p>(int) the power to raise the matrix to</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="matrix-power-input-n-gt-tensor-"><a class="anchor" href="#matrix-power-input-n-gt-tensor-"></a>matrix_power(input, n) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the matrix raised to the power <code>n</code> for square matrices.
+For batch of matrices, each individual matrix is raised to the power <code>n</code>.</p>
+<p>If <code>n</code> is negative, then the inverse of the matrix (if invertible) is
+raised to the power <code>n</code>.  For a batch of matrices, the batched inverse
+(if invertible) is raised to the power <code>n</code>. If <code>n</code> is 0, then an identity matrix
+is returned.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>, <span class='fl'>2</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_matrix_power</span>(<span class='kw'>a</span>, <span class='fl'>3</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;   0.1113  0.3742
+#&gt;   1.4282  4.8037
+#&gt; 
+#&gt; (2,.,.) = 
+#&gt;  -6.5694 -8.0205
+#&gt;  -5.1387 -6.1698
+#&gt; [ CPUFloatType{2,2,2} ]</div></pre>
+  </div>
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Matrix_rank</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_matrix_rank.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Matrix_rank</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_matrix_rank</span>(<span class='kw'>self</span>, <span class='kw'>tol</span>, symmetric = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input 2-D tensor</p></td>
+    </tr>
+    <tr>
+      <th>tol</th>
+      <td><p>(float, optional) the tolerance value. Default: <code>NULL</code></p></td>
+    </tr>
+    <tr>
+      <th>symmetric</th>
+      <td><p>(bool, optional) indicates whether <code>input</code> is symmetric.                               Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="matrix-rank-input-tol-null-symmetric-false-gt-tensor-"><a class="anchor" href="#matrix-rank-input-tol-null-symmetric-false-gt-tensor-"></a>matrix_rank(input, tol=NULL, symmetric=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the numerical rank of a 2-D tensor. The method to compute the
+matrix rank is done using SVD by default. If <code>symmetric</code> is <code>TRUE</code>,
+then <code>input</code> is assumed to be symmetric, and the computation of the
+rank is done by obtaining the eigenvalues.</p>
+<p><code>tol</code> is the threshold below which the singular values (or the eigenvalues
+when <code>symmetric</code> is <code>TRUE</code>) are considered to be 0. If <code>tol</code> is not
+specified, <code>tol</code> is set to <code>S.max() * max(S.size()) * eps</code> where <code>S</code> is the
+singular values (or the eigenvalues when <code>symmetric</code> is <code>TRUE</code>), and <code>eps</code>
+is the epsilon value for the datatype of <code>input</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_eye.html'>torch_eye</a></span>(<span class='fl'>10</span>)
+<span class='fu'>torch_matrix_rank</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; 10
+#&gt; [ CPULongType{} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <li class="dropdown-header">Beginers guide</li>
+    <li>
+      <a href="../articles/getting-started/warmup.html">Warm-up</a>
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+      <a href="../articles/getting-started/tensors.html">Tensors</a>
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+    <li>
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+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Max</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_max.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Max</p>
+    </div>
+
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the dimension to reduce.</p></td>
+    </tr>
+    <tr>
+      <th>keepdim</th>
+      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>out</th>
+      <td><p>(tuple, optional) the result tuple of two output tensors (max, max_indices)</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor) the second input tensor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>When the shapes do not match, the shape of the returned output tensor
+follows the broadcasting rules .</p>
+    <h2 class="hasAnchor" id="max-input-gt-tensor-"><a class="anchor" href="#max-input-gt-tensor-"></a>max(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the maximum value of all elements in the <code>input</code> tensor.</p>
+    <h2 class="hasAnchor" id="max-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"><a class="anchor" href="#max-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"></a>max(input, dim, keepdim=False, out=NULL) -&gt; (Tensor, LongTensor) </h2>
+
+    
+
+
+<p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the maximum
+value of each row of the <code>input</code> tensor in the given dimension
+<code>dim</code>. And <code>indices</code> is the index location of each maximum value found
+(argmax).</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p><code>indices</code> does not necessarily contain the first occurrence of each
+maximal value found, unless it is unique.
+The exact implementation details are device-specific.
+Do not expect the same result when run on CPU and GPU in general.</p>
+<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensors are of the same size
+as <code>input</code> except in the dimension <code>dim</code> where they are of size 1.
+Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting
+in the output tensors having 1 fewer dimension than <code>input</code>.</p>
+    <h2 class="hasAnchor" id="max-input-other-out-null-gt-tensor-"><a class="anchor" href="#max-input-other-out-null-gt-tensor-"></a>max(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Each element of the tensor <code>input</code> is compared with the corresponding
+element of the tensor <code>other</code> and an element-wise maximum is taken.</p>
+<p>The shapes of <code>input</code> and <code>other</code> don't need to match,
+but they must be broadcastable .</p>
+<p>$$
+    \mbox{out}_i = \max(\mbox{tensor}_i, \mbox{other}_i)
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_max</span>(<span class='kw'>a</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_max</span>(<span class='kw'>a</span>, dim = <span class='fl'>1</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>b</span>
+<span class='fu'>torch_max</span>(<span class='kw'>a</span>, other = <span class='kw'>b</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0.0453
+#&gt;  0.1631
+#&gt; -0.6085
+#&gt;  2.0054
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
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diff --git a/static/docs/dev/reference/torch_mean.html b/static/docs/dev/reference/torch_mean.html
new file mode 100644
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+    </li>
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+
+      
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Mean</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_mean.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Mean</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_mean</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, keepdim = <span class='fl'>FALSE</span>, dtype = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
+    </tr>
+    <tr>
+      <th>keepdim</th>
+      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>the resulting data type.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="mean-input-gt-tensor-"><a class="anchor" href="#mean-input-gt-tensor-"></a>mean(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the mean value of all elements in the <code>input</code> tensor.</p>
+    <h2 class="hasAnchor" id="mean-input-dim-keepdim-false-out-null-gt-tensor-"><a class="anchor" href="#mean-input-dim-keepdim-false-out-null-gt-tensor-"></a>mean(input, dim, keepdim=False, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the mean value of each row of the <code>input</code> tensor in the given
+dimension <code>dim</code>. If <code>dim</code> is a list of dimensions,
+reduce over all of them.</p>
+<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size
+as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
+Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
+output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_mean</span>(<span class='kw'>a</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_mean</span>(<span class='kw'>a</span>, <span class='fl'>1</span>)
+<span class='fu'>torch_mean</span>(<span class='kw'>a</span>, <span class='fl'>1</span>, <span class='fl'>TRUE</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0.2487 -0.7366 -0.5339 -1.1544
+#&gt; [ CPUFloatType{1,4} ]</div></pre>
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+    <div class="page-header">
+    <h1>Median</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_median.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Median</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_median</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, keepdim = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the dimension to reduce.</p></td>
+    </tr>
+    <tr>
+      <th>keepdim</th>
+      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="median-input-gt-tensor-"><a class="anchor" href="#median-input-gt-tensor-"></a>median(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the median value of all elements in the <code>input</code> tensor.</p>
+    <h2 class="hasAnchor" id="median-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"><a class="anchor" href="#median-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"></a>median(input, dim=-1, keepdim=False, out=NULL) -&gt; (Tensor, LongTensor) </h2>
+
+    
+
+
+<p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the median
+value of each row of the <code>input</code> tensor in the given dimension
+<code>dim</code>. And <code>indices</code> is the index location of each median value found.</p>
+<p>By default, <code>dim</code> is the last dimension of the <code>input</code> tensor.</p>
+<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensors are of the same size
+as <code>input</code> except in the dimension <code>dim</code> where they are of size 1.
+Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in
+the outputs tensor having 1 fewer dimension than <code>input</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_median</span>(<span class='kw'>a</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_median</span>(<span class='kw'>a</span>, <span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt; -0.4870
+#&gt; -1.3090
+#&gt; -0.2144
+#&gt;  1.1017
+#&gt; -0.6253
+#&gt; [ CPUFloatType{5} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt;  2
+#&gt;  1
+#&gt;  1
+#&gt;  3
+#&gt;  1
+#&gt; [ CPULongType{5} ]
+#&gt; </div></pre>
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diff --git a/static/docs/dev/reference/torch_memory_format.html b/static/docs/dev/reference/torch_memory_format.html
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+    </li>
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+      
+    </div><!--/.nav-collapse -->
+  </div><!--/.container -->
+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Memory format</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/memory_format.R'><code>R/memory_format.R</code></a></small>
+    <div class="hidden name"><code>torch_memory_format.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <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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diff --git a/static/docs/dev/reference/torch_meshgrid.html b/static/docs/dev/reference/torch_meshgrid.html
new file mode 100644
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@@ -0,0 +1,268 @@
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+      <a href="../articles/using-autograd.html">Using autograd</a>
+    </li>
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+    </li>
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+</li>
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+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Meshgrid</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_meshgrid.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Meshgrid</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_meshgrid</span>(<span class='kw'>tensors</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensors</th>
+      <td><p>(list of Tensor) list of scalars or 1 dimensional tensors. Scalars will be
+treated (1,).</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
+
+    
+
+
+<p>Take \(N\) tensors, each of which can be either scalar or 1-dimensional
+vector, and create \(N\) N-dimensional grids, where the \(i\) <code>th</code> grid is defined by
+expanding the \(i\) <code>th</code> input over dimensions defined by other inputs.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>))
+<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>, <span class='fl'>6</span>))
+<span class='kw'>out</span> <span class='op'>=</span> <span class='fu'>torch_meshgrid</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>x</span>, <span class='kw'>y</span>))
+<span class='kw'>out</span>
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt;  1  1  1
+#&gt;  2  2  2
+#&gt;  3  3  3
+#&gt; [ CPUFloatType{3,3} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt;  4  5  6
+#&gt;  4  5  6
+#&gt;  4  5  6
+#&gt; [ CPUFloatType{3,3} ]
+#&gt; </div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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+<li class="dropdown-header">Examples</li>
+<li>
+  <a href="../reference/index.html">Reference</a>
+</li>
+<li>
+  <a href="../news/index.html">Changelog</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>
+     
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+</li>
+      </ul>
+      
+    </div><!--/.nav-collapse -->
+  </div><!--/.container -->
+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Min</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_min.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Min</p>
+    </div>
+
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the dimension to reduce.</p></td>
+    </tr>
+    <tr>
+      <th>keepdim</th>
+      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
+    </tr>
+    <tr>
+      <th>out</th>
+      <td><p>(tuple, optional) the tuple of two output tensors (min, min_indices)</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor) the second input tensor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>When the shapes do not match, the shape of the returned output tensor
+follows the broadcasting rules .</p>
+    <h2 class="hasAnchor" id="min-input-gt-tensor-"><a class="anchor" href="#min-input-gt-tensor-"></a>min(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the minimum value of all elements in the <code>input</code> tensor.</p>
+    <h2 class="hasAnchor" id="min-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"><a class="anchor" href="#min-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"></a>min(input, dim, keepdim=False, out=NULL) -&gt; (Tensor, LongTensor) </h2>
+
+    
+
+
+<p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the minimum
+value of each row of the <code>input</code> tensor in the given dimension
+<code>dim</code>. And <code>indices</code> is the index location of each minimum value found
+(argmin).</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p><code>indices</code> does not necessarily contain the first occurrence of each
+minimal value found, unless it is unique.
+The exact implementation details are device-specific.
+Do not expect the same result when run on CPU and GPU in general.</p>
+<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensors are of the same size as
+<code>input</code> except in the dimension <code>dim</code> where they are of size 1.
+Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in
+the output tensors having 1 fewer dimension than <code>input</code>.</p>
+    <h2 class="hasAnchor" id="min-input-other-out-null-gt-tensor-"><a class="anchor" href="#min-input-other-out-null-gt-tensor-"></a>min(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Each element of the tensor <code>input</code> is compared with the corresponding
+element of the tensor <code>other</code> and an element-wise minimum is taken.
+The resulting tensor is returned.</p>
+<p>The shapes of <code>input</code> and <code>other</code> don't need to match,
+but they must be broadcastable .</p>
+<p>$$
+    \mbox{out}_i = \min(\mbox{tensor}_i, \mbox{other}_i)
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_min</span>(<span class='kw'>a</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_min</span>(<span class='kw'>a</span>, dim = <span class='fl'>1</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>b</span>
+<span class='fu'>torch_min</span>(<span class='kw'>a</span>, other = <span class='kw'>b</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -1.0568
+#&gt; -0.9262
+#&gt;  0.8762
+#&gt; -0.0916
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
+  </div>
+</div>
+
+
+      <footer>
+      <div class="copyright">
+  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+</div>
+
+<div class="pkgdown">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+</div>
+
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+
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+
diff --git a/static/docs/dev/reference/torch_mm.html b/static/docs/dev/reference/torch_mm.html
new file mode 100644
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+        <a class="navbar-link" href="../index.html">torch</a>
+        <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.0.3.9000</span>
+      </span>
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+     
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+</li>
+<li class="dropdown">
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+    Getting started
+     
+    <span class="caret"></span>
+  </a>
+  <ul class="dropdown-menu" role="menu">
+    <li class="dropdown-header">Beginers guide</li>
+    <li>
+      <a href="../articles/getting-started/warmup.html">Warm-up</a>
+    </li>
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+      <a href="../articles/getting-started/tensors.html">Tensors</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/tensors-and-autograd.html">Tensors and autograd</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/optim.html">optim: optimizers in torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/custom-nn.html">Custom nn modules</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/control-flow-and-weight-sharing.html">Control flow &amp; Weight sharing</a>
+    </li>
+    <li class="dropdown-header">Torch Mechanics</li>
+    <li>
+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/neural-networks.html">Neural networks</a>
+    </li>
+  </ul>
+</li>
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+     
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+  </a>
+  <ul class="dropdown-menu" role="menu">
+    <li class="dropdown-header">Tensors</li>
+    <li>
+      <a href="../articles/tensor-creation.html">Creating tensors</a>
+    </li>
+    <li>
+      <a href="../articles/indexing.html">Indexing</a>
+    </li>
+    <li>
+      <a href="../articles/tensor/index.html">Tensor class</a>
+    </li>
+    <li class="dropdown-header">Datasets</li>
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+      <a href="../articles/loading-data.html">Loading Data</a>
+    </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-header">Examples</li>
+<li>
+  <a href="../reference/index.html">Reference</a>
+</li>
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+
+      
+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_mm.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Mm</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_mm</span>(<span class='kw'>self</span>, <span class='kw'>mat2</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the first matrix to be multiplied</p></td>
+    </tr>
+    <tr>
+      <th>mat2</th>
+      <td><p>(Tensor) the second matrix to be multiplied</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>This function does not broadcast .
+For broadcasting matrix products, see <code><a href='torch_matmul.html'>torch_matmul</a></code>.</p>
+    <h2 class="hasAnchor" id="mm-input-mat-out-null-gt-tensor-"><a class="anchor" href="#mm-input-mat-out-null-gt-tensor-"></a>mm(input, mat2, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Performs a matrix multiplication of the matrices <code>input</code> and <code>mat2</code>.</p>
+<p>If <code>input</code> is a \((n \times m)\) tensor, <code>mat2</code> is a
+\((m \times p)\) tensor, <code>out</code> will be a \((n \times p)\) tensor.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>mat1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+<span class='kw'>mat2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='fu'>torch_mm</span>(<span class='kw'>mat1</span>, <span class='kw'>mat2</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.7778 -1.7284 -0.0374
+#&gt; -1.1219  3.0849  0.3441
+#&gt; [ CPUFloatType{2,3} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
+  </div>
+</div>
+
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diff --git a/static/docs/dev/reference/torch_mode.html b/static/docs/dev/reference/torch_mode.html
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+</div><!--/.navbar -->
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Mode</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_mode.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Mode</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_mode</span>(<span class='kw'>self</span>, dim = <span class='op'>-</span><span class='fl'>1L</span>, keepdim = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the dimension to reduce.</p></td>
+    </tr>
+    <tr>
+      <th>keepdim</th>
+      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>This function is not defined for <code>torch_cuda.Tensor</code> yet.</p>
+    <h2 class="hasAnchor" id="mode-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"><a class="anchor" href="#mode-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"></a>mode(input, dim=-1, keepdim=False, out=NULL) -&gt; (Tensor, LongTensor) </h2>
+
+    
+
+
+<p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the mode
+value of each row of the <code>input</code> tensor in the given dimension
+<code>dim</code>, i.e. a value which appears most often
+in that row, and <code>indices</code> is the index location of each mode value found.</p>
+<p>By default, <code>dim</code> is the last dimension of the <code>input</code> tensor.</p>
+<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensors are of the same size as
+<code>input</code> except in the dimension <code>dim</code> where they are of size 1.
+Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting
+in the output tensors having 1 fewer dimension than <code>input</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randint.html'>torch_randint</a></span>(<span class='fl'>0</span>, <span class='fl'>50</span>, size = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>5</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_mode</span>(<span class='kw'>a</span>, <span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt; 11
+#&gt; [ CPUFloatType{} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt; 3
+#&gt; [ CPULongType{} ]
+#&gt; </div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    </li>
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Mul</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_mul.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Mul</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_mul</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the first multiplicand tensor</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor) the second multiplicand tensor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="mul-input-other-out-null-"><a class="anchor" href="#mul-input-other-out-null-"></a>mul(input, other, out=NULL) </h2>
+
+    
+
+
+<p>Multiplies each element of the input <code>input</code> with the scalar
+<code>other</code> and returns a new resulting tensor.</p>
+<p>$$
+    \mbox{out}_i = \mbox{other} \times \mbox{input}_i
+$$
+If <code>input</code> is of type <code>FloatTensor</code> or <code>DoubleTensor</code>, <code>other</code>
+should be a real number, otherwise it should be an integer</p>
+
+
+<p>Each element of the tensor <code>input</code> is multiplied by the corresponding
+element of the Tensor <code>other</code>. The resulting tensor is returned.</p>
+<p>The shapes of <code>input</code> and <code>other</code> must be
+broadcastable .</p>
+<p>$$
+    \mbox{out}_i = \mbox{input}_i \times \mbox{other}_i
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_mul</span>(<span class='kw'>a</span>, <span class='fl'>100</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>1</span>))
+<span class='kw'>a</span>
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>4</span>))
+<span class='kw'>b</span>
+<span class='fu'>torch_mul</span>(<span class='kw'>a</span>, <span class='kw'>b</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.1073 -0.3304  0.0649  0.2010
+#&gt; -0.3764  1.1592 -0.2277 -0.7051
+#&gt; -0.2627  0.8090 -0.1589 -0.4921
+#&gt; -0.3030  0.9332 -0.1833 -0.5676
+#&gt; [ CPUFloatType{4,4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </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> 1.6.1.</p>
+</div>
+
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+
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+    <h1>Multinomial</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_multinomial.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Multinomial</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_multinomial</span>(<span class='kw'>self</span>, <span class='kw'>num_samples</span>, replacement = <span class='fl'>FALSE</span>, generator = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor containing probabilities</p></td>
+    </tr>
+    <tr>
+      <th>num_samples</th>
+      <td><p>(int) number of samples to draw</p></td>
+    </tr>
+    <tr>
+      <th>replacement</th>
+      <td><p>(bool, optional) whether to draw with replacement or not</p></td>
+    </tr>
+    <tr>
+      <th>generator</th>
+      <td><p>(<code>torch.Generator</code>, optional) a pseudorandom number generator for sampling</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>The rows of `input` do not need to sum to one (in which case we use
+the values as weights), but must be non-negative, finite and have
+a non-zero sum.
+</pre>
+
+<p>Indices are ordered from left to right according to when each was sampled
+(first samples are placed in first column).</p>
+<p>If <code>input</code> is a vector, <code>out</code> is a vector of size <code>num_samples</code>.</p>
+<p>If <code>input</code> is a matrix with <code>m</code> rows, <code>out</code> is an matrix of shape
+\((m \times \mbox{num\_samples})\).</p>
+<p>If replacement is <code>TRUE</code>, samples are drawn with replacement.</p>
+<p>If not, they are drawn without replacement, which means that when a
+sample index is drawn for a row, it cannot be drawn again for that row.</p>
+<pre>When drawn without replacement, `num_samples` must be lower than
+number of non-zero elements in `input` (or the min number of non-zero
+elements in each row of `input` if it is a matrix).
+</pre>
+
+    <h2 class="hasAnchor" id="multinomial-input-num-samples-replacement-false-generator-null-out-null-gt-longtensor-"><a class="anchor" href="#multinomial-input-num-samples-replacement-false-generator-null-out-null-gt-longtensor-"></a>multinomial(input, num_samples, replacement=False, *, generator=NULL, out=NULL) -&gt; LongTensor </h2>
+
+    
+
+
+<p>Returns a tensor where each row contains <code>num_samples</code> indices sampled
+from the multinomial probability distribution located in the corresponding row
+of tensor <code>input</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>weights</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>0</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_float</a></span>()) <span class='co'># create a tensor of weights</span>
+<span class='fu'>torch_multinomial</span>(<span class='kw'>weights</span>, <span class='fl'>2</span>)
+<span class='fu'>torch_multinomial</span>(<span class='kw'>weights</span>, <span class='fl'>4</span>, replacement=<span class='fl'>TRUE</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1
+#&gt;  1
+#&gt;  2
+#&gt;  1
+#&gt; [ CPULongType{4} ]</div></pre>
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+    <div class="page-header">
+    <h1>Mv</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_mv.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Mv</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_mv</span>(<span class='kw'>self</span>, <span class='kw'>vec</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) matrix to be multiplied</p></td>
+    </tr>
+    <tr>
+      <th>vec</th>
+      <td><p>(Tensor) vector to be multiplied</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>This function does not broadcast .</p>
+    <h2 class="hasAnchor" id="mv-input-vec-out-null-gt-tensor-"><a class="anchor" href="#mv-input-vec-out-null-gt-tensor-"></a>mv(input, vec, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Performs a matrix-vector product of the matrix <code>input</code> and the vector
+<code>vec</code>.</p>
+<p>If <code>input</code> is a \((n \times m)\) tensor, <code>vec</code> is a 1-D tensor of
+size \(m\), <code>out</code> will be 1-D of size \(n\).</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>mat</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+<span class='kw'>vec</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>))
+<span class='fu'>torch_mv</span>(<span class='kw'>mat</span>, <span class='kw'>vec</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.3571
+#&gt; -0.0321
+#&gt; [ CPUFloatType{2} ]</div></pre>
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+    <div class="page-header">
+    <h1>Mvlgamma</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_mvlgamma.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Mvlgamma</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_mvlgamma</span>(<span class='kw'>self</span>, <span class='kw'>p</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the tensor to compute the multivariate log-gamma function</p></td>
+    </tr>
+    <tr>
+      <th>p</th>
+      <td><p>(int) the number of dimensions</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="mvlgamma-input-p-gt-tensor-"><a class="anchor" href="#mvlgamma-input-p-gt-tensor-"></a>mvlgamma(input, p) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the <code>multivariate log-gamma function &lt;https://en.wikipedia.org/wiki/Multivariate_gamma_function&gt;</code>_) with dimension
+\(p\) element-wise, given by</p>
+<p>$$
+    \log(\Gamma_{p}(a)) = C + \displaystyle \sum_{i=1}^{p} \log\left(\Gamma\left(a - \frac{i - 1}{2}\right)\right)
+$$
+where \(C = \log(\pi) \times \frac{p (p - 1)}{4}\) and \(\Gamma(\cdot)\) is the Gamma function.</p>
+<p>All elements must be greater than \(\frac{p - 1}{2}\), otherwise an error would be thrown.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))<span class='op'>$</span><span class='fu'>uniform_</span>(<span class='fl'>1</span>, <span class='fl'>2</span>)
+<span class='kw'>a</span>
+<span class='fu'>torch_mvlgamma</span>(<span class='kw'>a</span>, <span class='fl'>2</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.4040  0.4059  0.7450
+#&gt;  0.3997  0.8720  0.4162
+#&gt; [ CPUFloatType{2,3} ]</div></pre>
+  </div>
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+  <a href="../reference/index.html">Reference</a>
+</li>
+<li>
+  <a href="../news/index.html">Changelog</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>
+     
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+</li>
+      </ul>
+      
+    </div><!--/.nav-collapse -->
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+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Narrow</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_narrow.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Narrow</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_narrow</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, <span class='kw'>start</span>, <span class='kw'>length</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the tensor to narrow</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the dimension along which to narrow</p></td>
+    </tr>
+    <tr>
+      <th>start</th>
+      <td><p>(int) the starting dimension</p></td>
+    </tr>
+    <tr>
+      <th>length</th>
+      <td><p>(int) the distance to the ending dimension</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="narrow-input-dim-start-length-gt-tensor-"><a class="anchor" href="#narrow-input-dim-start-length-gt-tensor-"></a>narrow(input, dim, start, length) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor that is a narrowed version of <code>input</code> tensor. The
+dimension <code>dim</code> is input from <code>start</code> to <code>start + length</code>. The
+returned tensor and <code>input</code> tensor share the same underlying storage.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span><span class='op'>:</span><span class='fl'>9</span>), ncol = <span class='fl'>3</span>, byrow= <span class='fl'>TRUE</span>))
+<span class='fu'>torch_narrow</span>(<span class='kw'>x</span>, <span class='fl'>1</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fl'>0L</span>)<span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/sum.html'>sum</a></span>(dim = <span class='fl'>1</span>), <span class='fl'>2</span>)
+<span class='fu'>torch_narrow</span>(<span class='kw'>x</span>, <span class='fl'>2</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fl'>1L</span>)<span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/sum.html'>sum</a></span>(dim = <span class='fl'>1</span>), <span class='fl'>2</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  2  3
+#&gt;  5  6
+#&gt;  8  9
+#&gt; [ CPULongType{3,2} ]</div></pre>
+  </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>
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+    <li class="dropdown-header">Beginers guide</li>
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+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
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+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
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+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Ne</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_ne.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Ne</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_ne</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the tensor to compare</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor or float) the tensor or value to compare</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="ne-input-other-out-null-gt-tensor-"><a class="anchor" href="#ne-input-other-out-null-gt-tensor-"></a>ne(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes \(input \neq other\) element-wise.</p>
+<p>The second argument can be a number or a tensor whose shape is
+broadcastable  with the first argument.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_ne</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fl'>1</span><span class='op'>:</span><span class='fl'>4</span>, ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>)), 
+         <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/rep.html'>rep</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>4</span>), each = <span class='fl'>2</span>), ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0  1
+#&gt;  1  0
+#&gt; [ CPUBoolType{2,2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
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+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/optim.html">optim: optimizers in torch</a>
+    </li>
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+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Neg</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_neg.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Neg</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_neg</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="neg-input-out-null-gt-tensor-"><a class="anchor" href="#neg-input-out-null-gt-tensor-"></a>neg(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the negative of the elements of <code>input</code>.</p>
+<p>$$
+    \mbox{out} = -1 \times \mbox{input}
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_neg</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0.6722
+#&gt; -0.2995
+#&gt;  0.8433
+#&gt; -0.6480
+#&gt;  1.4570
+#&gt; [ CPUFloatType{5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Nonzero</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_nonzero.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Nonzero</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_nonzero</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>[`torch_nonzero(..., as_tuple=False) &lt;torch.nonzero&gt;`] (default) returns a
+2-D tensor where each row is the index for a nonzero value.
+
+[`torch_nonzero(..., as_tuple=TRUE) &lt;torch.nonzero&gt;`] returns a tuple of 1-D
+index tensors, allowing for advanced indexing, so `x[x.nonzero(as_tuple=TRUE)]`
+gives all nonzero values of tensor `x`. Of the returned tuple, each index tensor
+contains nonzero indices for a certain dimension.
+
+See below for more details on the two behaviors.
+</pre>
+
+    <h2 class="hasAnchor" id="nonzero-input-out-null-as-tuple-false-gt-longtensor-or-tuple-of-longtensors-"><a class="anchor" href="#nonzero-input-out-null-as-tuple-false-gt-longtensor-or-tuple-of-longtensors-"></a>nonzero(input, *, out=NULL, as_tuple=False) -&gt; LongTensor or tuple of LongTensors </h2>
+
+    
+
+
+<p><strong>When</strong> <code>as_tuple</code> <strong>is <code>FALSE</code> (default)</strong>:</p>
+<p>Returns a tensor containing the indices of all non-zero elements of
+<code>input</code>.  Each row in the result contains the indices of a non-zero
+element in <code>input</code>. The result is sorted lexicographically, with
+the last index changing the fastest (C-style).</p>
+<p>If <code>input</code> has \(n\) dimensions, then the resulting indices tensor
+<code>out</code> is of size \((z \times n)\), where \(z\) is the total number of
+non-zero elements in the <code>input</code> tensor.</p>
+<p><strong>When</strong> <code>as_tuple</code> <strong>is <code>TRUE</code></strong>:</p>
+<p>Returns a tuple of 1-D tensors, one for each dimension in <code>input</code>,
+each containing the indices (in that dimension) of all non-zero elements of
+<code>input</code> .</p>
+<p>If <code>input</code> has \(n\) dimensions, then the resulting tuple contains \(n\)
+tensors of size \(z\), where \(z\) is the total number of
+non-zero elements in the <code>input</code> tensor.</p>
+<p>As a special case, when <code>input</code> has zero dimensions and a nonzero scalar
+value, it is treated as a one-dimensional tensor with one element.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_nonzero</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>0</span>, <span class='fl'>1</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0
+#&gt;  1
+#&gt;  2
+#&gt;  4
+#&gt; [ CPULongType{4,1} ]</div></pre>
+  </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="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Norm</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_norm.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Norm</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_norm</span>(<span class='kw'>self</span>, p = <span class='fl'>2L</span>, <span class='kw'>dim</span>, keepdim = <span class='fl'>FALSE</span>, <span class='kw'>dtype</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>p</th>
+      <td><p>(int, float, inf, -inf, 'fro', 'nuc', optional) the order of norm. Default: <code>'fro'</code>        The following norms can be calculated:        =====  ============================  ==========================        ord    matrix norm                   vector norm        =====  ============================  ==========================        NULL   Frobenius norm                2-norm        'fro'  Frobenius norm                --        'nuc'  nuclear norm                  --        Other  as vec norm when dim is NULL  sum(abs(x)<strong>ord)</strong>(1./ord)        =====  ============================  ==========================</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int, 2-tuple of ints, 2-list of ints, optional) If it is an int,        vector norm will be calculated, if it is 2-tuple of ints, matrix norm        will be calculated. If the value is NULL, matrix norm will be calculated        when the input tensor only has two dimensions, vector norm will be        calculated when the input tensor only has one dimension. If the input        tensor has more than two dimensions, the vector norm will be applied to        last dimension.</p></td>
+    </tr>
+    <tr>
+      <th>keepdim</th>
+      <td><p>(bool, optional) whether the output tensors have <code>dim</code>        retained or not. Ignored if <code>dim</code> = <code>NULL</code> and        <code>out</code> = <code>NULL</code>. Default: <code>FALSE</code>
+Ignored if        <code>dim</code> = <code>NULL</code> and <code>out</code> = <code>NULL</code>.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of        returned tensor. If specified, the input tensor is casted to        'dtype' while performing the operation. Default: NULL.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
+
+    
+
+
+<p>Returns the matrix norm or vector norm of a given tensor.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0</span>, <span class='fl'>9</span>, dtype = <span class='fu'><a href='torch_dtype.html'>torch_float</a></span>())
+<span class='kw'>b</span> <span class='op'>=</span> <span class='kw'>a</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/stats/reshape.html'>reshape</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='fu'>torch_norm</span>(<span class='kw'>a</span>)
+<span class='fu'>torch_norm</span>(<span class='kw'>b</span>)
+<span class='fu'>torch_norm</span>(<span class='kw'>a</span>, <span class='fl'>Inf</span>)
+<span class='fu'>torch_norm</span>(<span class='kw'>b</span>, <span class='fl'>Inf</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; 8
+#&gt; [ CPUFloatType{} ]</div></pre>
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+    <div class="page-header">
+    <h1>Normal</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
+    <div class="hidden name"><code>torch_normal.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Normal</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_normal</span>(<span class='kw'>mean</span>, std = <span class='fl'>1L</span>, <span class='kw'>size</span>, generator = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>mean</th>
+      <td><p>(Tensor) the tensor of per-element means</p></td>
+    </tr>
+    <tr>
+      <th>std</th>
+      <td><p>(Tensor) the tensor of per-element standard deviations</p></td>
+    </tr>
+    <tr>
+      <th>size</th>
+      <td><p>(int...) a sequence of integers defining the shape of the output tensor.</p></td>
+    </tr>
+    <tr>
+      <th>generator</th>
+      <td><p>(<code>torch.Generator</code>, optional) a pseudorandom number generator for sampling</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>When the shapes do not match, the shape of <code>mean</code>
+is used as the shape for the returned output tensor</p>
+    <h2 class="hasAnchor" id="normal-mean-std-generator-null-out-null-gt-tensor-"><a class="anchor" href="#normal-mean-std-generator-null-out-null-gt-tensor-"></a>normal(mean, std, *, generator=NULL, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a tensor of random numbers drawn from separate normal distributions
+whose mean and standard deviation are given.</p>
+<p>The <code>mean</code> is a tensor with the mean of
+each output element's normal distribution</p>
+<p>The <code>std</code> is a tensor with the standard deviation of
+each output element's normal distribution</p>
+<p>The shapes of <code>mean</code> and <code>std</code> don't need to match, but the
+total number of elements in each tensor need to be the same.</p>
+    <h2 class="hasAnchor" id="normal-mean-std-out-null-gt-tensor-"><a class="anchor" href="#normal-mean-std-out-null-gt-tensor-"></a>normal(mean=0.0, std, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Similar to the function above, but the means are shared among all drawn
+elements.</p>
+    <h2 class="hasAnchor" id="normal-mean-std-out-null-gt-tensor-"><a class="anchor" href="#normal-mean-std-out-null-gt-tensor-"></a>normal(mean, std=1.0, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Similar to the function above, but the standard-deviations are shared among
+all drawn elements.</p>
+    <h2 class="hasAnchor" id="normal-mean-std-size-out-null-gt-tensor-"><a class="anchor" href="#normal-mean-std-size-out-null-gt-tensor-"></a>normal(mean, std, size, *, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Similar to the function above, but the means and standard deviations are shared
+among all drawn elements. The resulting tensor has size given by <code>size</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='fu'>torch_normal</span>(mean=<span class='fl'>0</span>, std=<span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1</span>, <span class='fl'>0</span>, <span class='op'>-</span><span class='fl'>0.1</span>))
+
+
+<span class='fu'>torch_normal</span>(mean=<span class='fl'>0.5</span>, std=<span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>6.</span>))
+
+
+<span class='fu'>torch_normal</span>(mean=<span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>6.</span>))
+
+
+<span class='fu'>torch_normal</span>(<span class='fl'>2</span>, <span class='fl'>3</span>, size=<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>1</span>, <span class='fl'>4</span>))
+}
+}
+</div></pre>
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+    <div class="page-header">
+    <h1>Ones</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_ones.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Ones</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_ones</span>(
+  <span class='kw'>...</span>,
+  names = <span class='kw'>NULL</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>...</th>
+      <td><p>(int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.</p></td>
+    </tr>
+    <tr>
+      <th>names</th>
+      <td><p>optional names for the dimensions</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="ones-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#ones-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>ones(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a tensor filled with the scalar value <code>1</code>, with the shape defined
+by the variable argument <code>size</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_ones</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+<span class='fu'>torch_ones</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1
+#&gt;  1
+#&gt;  1
+#&gt;  1
+#&gt;  1
+#&gt; [ CPUFloatType{5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Ones_like</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_ones_like.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Ones_like</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_ones_like</span>(
+  <span class='kw'>input</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>,
+  memory_format = <span class='fu'><a href='torch_memory_format.html'>torch_preserve_format</a></span>()
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>memory_format</th>
+      <td><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="ones-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"><a class="anchor" href="#ones-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a>ones_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a tensor filled with the scalar value <code>1</code>, with the same size as
+<code>input</code>. <code>torch_ones_like(input)</code> is equivalent to
+<code><a href='torch_ones.html'>torch_ones(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)</a></code>.</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p>As of 0.4, this function does not support an <code>out</code> keyword. As an alternative,
+the old <code>torch_ones_like(input, out=output)</code> is equivalent to
+<code><a href='torch_ones.html'>torch_ones(input.size(), out=output)</a></code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+<span class='fu'>torch_ones_like</span>(<span class='kw'>input</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1  1  1
+#&gt;  1  1  1
+#&gt; [ CPUFloatType{2,3} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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+</div>
+
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diff --git a/static/docs/dev/reference/torch_orgqr.html b/static/docs/dev/reference/torch_orgqr.html
new file mode 100644
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+    </li>
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Orgqr</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_orgqr.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Orgqr</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_orgqr</span>(<span class='kw'>self</span>, <span class='kw'>input2</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the <code>a</code> from <code><a href='torch_geqrf.html'>torch_geqrf</a></code>.</p></td>
+    </tr>
+    <tr>
+      <th>input2</th>
+      <td><p>(Tensor) the <code>tau</code> from <code><a href='torch_geqrf.html'>torch_geqrf</a></code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="orgqr-input-input-gt-tensor-"><a class="anchor" href="#orgqr-input-input-gt-tensor-"></a>orgqr(input, input2) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the orthogonal matrix <code>Q</code> of a QR factorization, from the <code>(input, input2)</code>
+tuple returned by <code><a href='torch_geqrf.html'>torch_geqrf</a></code>.</p>
+<p>This directly calls the underlying LAPACK function <code>?orgqr</code>.
+See <code>LAPACK documentation for orgqr</code>_ for further details.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/torch_ormqr.html b/static/docs/dev/reference/torch_ormqr.html
new file mode 100644
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+      <a href="../articles/getting-started/neural-networks.html">Neural networks</a>
+    </li>
+  </ul>
+</li>
+<li class="dropdown">
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
+    Articles
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+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Ormqr</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_ormqr.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Ormqr</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_ormqr</span>(<span class='kw'>self</span>, <span class='kw'>input2</span>, <span class='kw'>input3</span>, left = <span class='fl'>TRUE</span>, transpose = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the <code>a</code> from <code><a href='torch_geqrf.html'>torch_geqrf</a></code>.</p></td>
+    </tr>
+    <tr>
+      <th>input2</th>
+      <td><p>(Tensor) the <code>tau</code> from <code><a href='torch_geqrf.html'>torch_geqrf</a></code>.</p></td>
+    </tr>
+    <tr>
+      <th>input3</th>
+      <td><p>(Tensor) the matrix to be multiplied.</p></td>
+    </tr>
+    <tr>
+      <th>left</th>
+      <td><p>see LAPACK documentation</p></td>
+    </tr>
+    <tr>
+      <th>transpose</th>
+      <td><p>see LAPACK documentation</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="ormqr-input-input-input-left-true-transpose-false-gt-tensor-"><a class="anchor" href="#ormqr-input-input-input-left-true-transpose-false-gt-tensor-"></a>ormqr(input, input2, input3, left=TRUE, transpose=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Multiplies <code>mat</code> (given by <code>input3</code>) by the orthogonal <code>Q</code> matrix of the QR factorization
+formed by <code><a href='torch_geqrf.html'>torch_geqrf()</a></code> that is represented by <code>(a, tau)</code> (given by (<code>input</code>, <code>input2</code>)).</p>
+<p>This directly calls the underlying LAPACK function <code>?ormqr</code>.
+See <a href='https://software.intel.com/content/www/us/en/develop/documentation/mkl-developer-reference-c/top/scalapack-routines/scalapack-computational-routines/orthogonal-factorizations-scalapack-computational-routines/p-ormqr.html'>LAPACK documentation for ormqr</a> for further details.</p>
+
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+    <div class="page-header">
+    <h1>Pdist</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_pdist.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Pdist</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_pdist</span>(<span class='kw'>self</span>, p = <span class='fl'>2L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>NA input tensor of shape \(N \times M\).</p></td>
+    </tr>
+    <tr>
+      <th>p</th>
+      <td><p>NA p value for the p-norm distance to calculate between each vector pair        \(\in [0, \infty]\).</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="pdist-input-p-gt-tensor-"><a class="anchor" href="#pdist-input-p-gt-tensor-"></a>pdist(input, p=2) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the p-norm distance between every pair of row vectors in the input.
+This is identical to the upper triangular portion, excluding the diagonal, of
+<code>torch_norm(input[:, NULL] - input, dim=2, p=p)</code>. This function will be faster
+if the rows are contiguous.</p>
+<p>If input has shape \(N \times M\) then the output will have shape
+\(\frac{1}{2} N (N - 1)\).</p>
+<p>This function is equivalent to <code>scipy.spatial.distance.pdist(input, 'minkowski', p=p)</code> if \(p \in (0, \infty)\). When \(p = 0\) it is
+equivalent to <code>scipy.spatial.distance.pdist(input, 'hamming') * M</code>.
+When \(p = \infty\), the closest scipy function is
+<code>scipy.spatial.distance.pdist(xn, lambda x, y: np.abs(x - y).max())</code>.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    </li>
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+</div><!--/.navbar -->
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Pinverse</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_pinverse.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Pinverse</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_pinverse</span>(<span class='kw'>self</span>, rcond = <span class='fl'>0</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) The input tensor of size \((*, m, n)\) where \(*\) is zero or more batch dimensions</p></td>
+    </tr>
+    <tr>
+      <th>rcond</th>
+      <td><p>(float) A floating point value to determine the cutoff for small singular values.                   Default: 1e-15</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>This method is implemented using the Singular Value Decomposition.
+</pre>
+
+<pre>The pseudo-inverse is not necessarily a continuous function in the elements of the matrix `[1]`_.
+Therefore, derivatives are not always existent, and exist for a constant rank only `[2]`_.
+However, this method is backprop-able due to the implementation by using SVD results, and
+could be unstable. Double-backward will also be unstable due to the usage of SVD internally.
+See `~torch.svd` for more details.
+</pre>
+
+    <h2 class="hasAnchor" id="pinverse-input-rcond-e-gt-tensor-"><a class="anchor" href="#pinverse-input-rcond-e-gt-tensor-"></a>pinverse(input, rcond=1e-15) -&gt; Tensor </h2>
+
+    
+
+
+<p>Calculates the pseudo-inverse (also known as the Moore-Penrose inverse) of a 2D tensor.
+Please look at <code>Moore-Penrose inverse</code>_ for more details</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>))
+<span class='kw'>input</span>
+<span class='fu'>torch_pinverse</span>(<span class='kw'>input</span>)
+<span class='co'># Batched pinverse example</span>
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>,<span class='fl'>6</span>,<span class='fl'>3</span>))
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'>torch_pinverse</span>(<span class='kw'>a</span>)
+<span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span>(<span class='kw'>b</span>, <span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;   1.0000e+00  4.4703e-08 -1.4901e-08
+#&gt;   1.3411e-07  1.0000e+00  4.4703e-08
+#&gt;  -1.7881e-07  1.1921e-07  1.0000e+00
+#&gt; 
+#&gt; (2,.,.) = 
+#&gt;   1.0000 -0.0000  0.0000
+#&gt;  -0.0000  1.0000  0.0000
+#&gt;   0.0000  0.0000  1.0000
+#&gt; [ CPUFloatType{2,3,3} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
+  </div>
+</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> 1.6.1.</p>
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diff --git a/static/docs/dev/reference/torch_pixel_shuffle.html b/static/docs/dev/reference/torch_pixel_shuffle.html
new file mode 100644
index 000000000..1538a6e83
--- /dev/null
+++ b/static/docs/dev/reference/torch_pixel_shuffle.html
@@ -0,0 +1,255 @@
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+</div><!--/.navbar -->
+
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Pixel_shuffle</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_pixel_shuffle.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Pixel_shuffle</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_pixel_shuffle</span>(<span class='kw'>self</span>, <span class='kw'>upscale_factor</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>upscale_factor</th>
+      <td><p>(int) factor to increase spatial resolution by</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="rearranges-elements-in-a-tensor-of-shape-"><a class="anchor" href="#rearranges-elements-in-a-tensor-of-shape-"></a>Rearranges elements in a tensor of shape </h2>
+
+    <p>math:<code>(*, C \times r^2, H, W)</code> to a :</p>
+<p>Rearranges elements in a tensor of shape \((*, C \times r^2, H, W)\) to a
+tensor of shape \((*, C, H \times r, W \times r)\).</p>
+<p>See <code>~torch.nn.PixelShuffle</code> for details.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>9</span>, <span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>output</span> <span class='op'>=</span> <span class='fu'><a href='nnf_pixel_shuffle.html'>nnf_pixel_shuffle</a></span>(<span class='kw'>input</span>, <span class='fl'>3</span>)
+<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='kw'>output</span><span class='op'>$</span><span class='fu'>size</span>())
+}
+</div><div class='output co'>#&gt; [1]  1  1 12 12</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/torch_poisson.html b/static/docs/dev/reference/torch_poisson.html
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+    <div class="page-header">
+    <h1>Poisson</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_poisson.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Poisson</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_poisson</span>(<span class='kw'>self</span>, generator = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor containing the rates of the Poisson distribution</p></td>
+    </tr>
+    <tr>
+      <th>generator</th>
+      <td><p>(<code>torch.Generator</code>, optional) a pseudorandom number generator for sampling</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="poisson-input-generator-null-gt-tensor-"><a class="anchor" href="#poisson-input-generator-null-gt-tensor-"></a>poisson(input *, generator=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a tensor of the same size as <code>input</code> with each element
+sampled from a Poisson distribution with rate parameter given by the corresponding
+element in <code>input</code> i.e.,</p>
+<p>$$
+    \mbox{out}_i \sim \mbox{Poisson}(\mbox{input}_i)
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>rates</span> <span class='op'>=</span> <span class='fu'><a href='torch_rand.html'>torch_rand</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>)) <span class='op'>*</span> <span class='fl'>5</span>  <span class='co'># rate parameter between 0 and 5</span>
+<span class='fu'>torch_poisson</span>(<span class='kw'>rates</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  2  4  3  2
+#&gt;  1  4  1  5
+#&gt;  1  2  2  0
+#&gt;  4  5  7  0
+#&gt; [ CPUFloatType{4,4} ]</div></pre>
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+    <div class="page-header">
+    <h1>Polygamma</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_polygamma.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Polygamma</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_polygamma</span>(<span class='kw'>n</span>, <span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>n</th>
+      <td><p>(int) the order of the polygamma function</p></td>
+    </tr>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>This function is not implemented for \eqn{n \geq 2}.
+</pre>
+
+    <h2 class="hasAnchor" id="polygamma-n-input-out-null-gt-tensor-"><a class="anchor" href="#polygamma-n-input-out-null-gt-tensor-"></a>polygamma(n, input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the \(n^{th}\) derivative of the digamma function on <code>input</code>.
+\(n \geq 0\) is called the order of the polygamma function.</p>
+<p>$$
+    \psi^{(n)}(x) = \frac{d^{(n)}}{dx^{(n)}} \psi(x)
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>0.5</span>))
+<span class='fu'>torch_polygamma</span>(<span class='fl'>1</span>, <span class='kw'>a</span>)
+}
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_pow.html b/static/docs/dev/reference/torch_pow.html
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+    <div class="page-header">
+    <h1>Pow</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_pow.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Pow</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_pow</span>(<span class='kw'>self</span>, <span class='kw'>exponent</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(float) the scalar base value for the power operation</p></td>
+    </tr>
+    <tr>
+      <th>exponent</th>
+      <td><p>(float or tensor) the exponent value</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="pow-input-exponent-out-null-gt-tensor-"><a class="anchor" href="#pow-input-exponent-out-null-gt-tensor-"></a>pow(input, exponent, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Takes the power of each element in <code>input</code> with <code>exponent</code> and
+returns a tensor with the result.</p>
+<p><code>exponent</code> can be either a single <code>float</code> number or a <code>Tensor</code>
+with the same number of elements as <code>input</code>.</p>
+<p>When <code>exponent</code> is a scalar value, the operation applied is:</p>
+<p>$$
+    \mbox{out}_i = x_i^{\mbox{exponent}}
+$$
+When <code>exponent</code> is a tensor, the operation applied is:</p>
+<p>$$
+    \mbox{out}_i = x_i^{\mbox{exponent}_i}
+$$
+When <code>exponent</code> is a tensor, the shapes of <code>input</code>
+and <code>exponent</code> must be broadcastable .</p>
+    <h2 class="hasAnchor" id="pow-self-exponent-out-null-gt-tensor-"><a class="anchor" href="#pow-self-exponent-out-null-gt-tensor-"></a>pow(self, exponent, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p><code>self</code> is a scalar <code>float</code> value, and <code>exponent</code> is a tensor.
+The returned tensor <code>out</code> is of the same shape as <code>exponent</code></p>
+<p>The operation applied is:</p>
+<p>$$
+    \mbox{out}_i = \mbox{self} ^ {\mbox{exponent}_i}
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_pow</span>(<span class='kw'>a</span>, <span class='fl'>2</span>)
+<span class='kw'>exp</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>5.</span>)
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>5.</span>)
+<span class='kw'>a</span>
+<span class='kw'>exp</span>
+<span class='fu'>torch_pow</span>(<span class='kw'>a</span>, <span class='kw'>exp</span>)
+
+
+<span class='kw'>exp</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>5.</span>)
+<span class='kw'>base</span> <span class='op'>=</span> <span class='fl'>2</span>
+<span class='fu'>torch_pow</span>(<span class='kw'>base</span>, <span class='kw'>exp</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;   2
+#&gt;   4
+#&gt;   8
+#&gt;  16
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
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+    <div class="page-header">
+    <h1>Prod</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_prod.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Prod</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_prod</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, keepdim = <span class='fl'>FALSE</span>, dtype = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the dimension to reduce.</p></td>
+    </tr>
+    <tr>
+      <th>keepdim</th>
+      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to <code>dtype</code> before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="prod-input-dtype-null-gt-tensor-"><a class="anchor" href="#prod-input-dtype-null-gt-tensor-"></a>prod(input, dtype=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the product of all elements in the <code>input</code> tensor.</p>
+    <h2 class="hasAnchor" id="prod-input-dim-keepdim-false-dtype-null-gt-tensor-"><a class="anchor" href="#prod-input-dim-keepdim-false-dtype-null-gt-tensor-"></a>prod(input, dim, keepdim=False, dtype=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the product of each row of the <code>input</code> tensor in the given
+dimension <code>dim</code>.</p>
+<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size
+as <code>input</code> except in the dimension <code>dim</code> where it is of size 1.
+Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in
+the output tensor having 1 fewer dimension than <code>input</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_prod</span>(<span class='kw'>a</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>2</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_prod</span>(<span class='kw'>a</span>, <span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; 0.01 *
+#&gt; -9.4219
+#&gt; -0.0763
+#&gt; [ CPUFloatType{2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+</li>
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+     
+  </a>
+</li>
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+      
+    </div><!--/.nav-collapse -->
+  </div><!--/.container -->
+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_promote_types.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Promote_types</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_promote_types</span>(<span class='kw'>type1</span>, <span class='kw'>type2</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>type1</th>
+      <td><p>(<code>torch.dtype</code>)</p></td>
+    </tr>
+    <tr>
+      <th>type2</th>
+      <td><p>(<code>torch.dtype</code>)</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="promote-types-type-type-gt-dtype-"><a class="anchor" href="#promote-types-type-type-gt-dtype-"></a>promote_types(type1, type2) -&gt; dtype </h2>
+
+    
+
+
+<p>Returns the <code>torch_dtype</code> with the smallest size and scalar kind that is
+not smaller nor of lower kind than either <code>type1</code> or <code>type2</code>. See type promotion
+documentation  for more information on the type
+promotion logic.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_promote_types</span>(<span class='fu'><a href='torch_dtype.html'>torch_int32</a></span>(), <span class='fu'><a href='torch_dtype.html'>torch_float32</a></span>())
+<span class='fu'>torch_promote_types</span>(<span class='fu'><a href='torch_dtype.html'>torch_uint8</a></span>(), <span class='fu'><a href='torch_dtype.html'>torch_long</a></span>())
+}
+</div><div class='output co'>#&gt; torch_Long</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+    <div class="page-header">
+    <h1>Qr</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_qr.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Qr</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_qr</span>(<span class='kw'>self</span>, some = <span class='fl'>TRUE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor of size \((*, m, n)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is zero or more                batch dimensions consisting of matrices of dimension \(m \times n\).</p></td>
+    </tr>
+    <tr>
+      <th>some</th>
+      <td><p>(bool, optional) Set to <code>TRUE</code> for reduced QR decomposition and <code>FALSE</code> for                complete QR decomposition.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>precision may be lost if the magnitudes of the elements of <code>input</code>
+are large</p>
+<p>While it should always give you a valid decomposition, it may not
+give you the same one across platforms - it will depend on your
+LAPACK implementation.</p>
+    <h2 class="hasAnchor" id="qr-input-some-true-out-null-gt-tensor-tensor-"><a class="anchor" href="#qr-input-some-true-out-null-gt-tensor-tensor-"></a>qr(input, some=TRUE, out=NULL) -&gt; (Tensor, Tensor) </h2>
+
+    
+
+
+<p>Computes the QR decomposition of a matrix or a batch of matrices <code>input</code>,
+and returns a namedtuple (Q, R) of tensors such that \(\mbox{input} = Q R\)
+with \(Q\) being an orthogonal matrix or batch of orthogonal matrices and
+\(R\) being an upper triangular matrix or batch of upper triangular matrices.</p>
+<p>If <code>some</code> is <code>TRUE</code>, then this function returns the thin (reduced) QR factorization.
+Otherwise, if <code>some</code> is <code>FALSE</code>, this function returns the complete QR factorization.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>12.</span>, <span class='op'>-</span><span class='fl'>51</span>, <span class='fl'>4</span>, <span class='fl'>6</span>, <span class='fl'>167</span>, <span class='op'>-</span><span class='fl'>68</span>, <span class='op'>-</span><span class='fl'>4</span>, <span class='fl'>24</span>, <span class='op'>-</span><span class='fl'>41</span>), ncol = <span class='fl'>3</span>, byrow = <span class='fl'>TRUE</span>))
+<span class='kw'>out</span> <span class='op'>=</span> <span class='fu'>torch_qr</span>(<span class='kw'>a</span>)
+<span class='kw'>q</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>1</span>]]
+<span class='kw'>r</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>2</span>]]
+<span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='kw'>q</span>, <span class='kw'>r</span>)<span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/Round.html'>round</a></span>()
+<span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='kw'>q</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>(), <span class='kw'>q</span>)<span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/Round.html'>round</a></span>()
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1  0  0
+#&gt;  0  1  0
+#&gt;  0  0  1
+#&gt; [ CPUFloatType{3,3} ]</div></pre>
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+    <h1>Creates the corresponding Scheme object</h1>
+    <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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+    <h1>Quantize_per_channel</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_quantize_per_channel.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Quantize_per_channel</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_quantize_per_channel</span>(<span class='kw'>self</span>, <span class='kw'>scales</span>, <span class='kw'>zero_points</span>, <span class='kw'>axis</span>, <span class='kw'>dtype</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) float tensor to quantize</p></td>
+    </tr>
+    <tr>
+      <th>scales</th>
+      <td><p>(Tensor) float 1D tensor of scales to use, size should match <code>input.size(axis)</code></p></td>
+    </tr>
+    <tr>
+      <th>zero_points</th>
+      <td><p>(int) integer 1D tensor of offset to use, size should match <code>input.size(axis)</code></p></td>
+    </tr>
+    <tr>
+      <th>axis</th>
+      <td><p>(int) dimension on which apply per-channel quantization</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>) the desired data type of returned tensor.        Has to be one of the quantized dtypes: <code>torch_quint8</code>, <code>torch.qint8</code>, <code>torch.qint32</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="quantize-per-channel-input-scales-zero-points-axis-dtype-gt-tensor-"><a class="anchor" href="#quantize-per-channel-input-scales-zero-points-axis-dtype-gt-tensor-"></a>quantize_per_channel(input, scales, zero_points, axis, dtype) -&gt; Tensor </h2>
+
+    
+
+
+<p>Converts a float tensor to per-channel quantized tensor with given scales and zero points.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>1.0</span>, <span class='fl'>0.0</span>, <span class='fl'>1.0</span>, <span class='fl'>2.0</span>), ncol = <span class='fl'>2</span>, byrow = <span class='fl'>TRUE</span>))
+<span class='fu'>torch_quantize_per_channel</span>(<span class='kw'>x</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0.1</span>, <span class='fl'>0.01</span>)), 
+                           <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10L</span>, <span class='fl'>0L</span>)), <span class='fl'>0</span>, <span class='fu'><a href='torch_dtype.html'>torch_quint8</a></span>())
+<span class='fu'>torch_quantize_per_channel</span>(<span class='kw'>x</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0.1</span>, <span class='fl'>0.01</span>)), 
+                           <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10L</span>, <span class='fl'>0L</span>)), <span class='fl'>0</span>, <span class='fu'><a href='torch_dtype.html'>torch_quint8</a></span>())<span class='op'>$</span><span class='fu'>int_repr</span>()
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;    0   10
+#&gt;  100  200
+#&gt; [ CPUByteType{2,2} ]</div></pre>
+  </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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@@ -0,0 +1,266 @@
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+    <div class="page-header">
+    <h1>Quantize_per_tensor</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_quantize_per_tensor.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Quantize_per_tensor</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_quantize_per_tensor</span>(<span class='kw'>self</span>, <span class='kw'>scale</span>, <span class='kw'>zero_point</span>, <span class='kw'>dtype</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) float tensor to quantize</p></td>
+    </tr>
+    <tr>
+      <th>scale</th>
+      <td><p>(float) scale to apply in quantization formula</p></td>
+    </tr>
+    <tr>
+      <th>zero_point</th>
+      <td><p>(int) offset in integer value that maps to float zero</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>) the desired data type of returned tensor.        Has to be one of the quantized dtypes: <code>torch_quint8</code>, <code>torch.qint8</code>, <code>torch.qint32</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="quantize-per-tensor-input-scale-zero-point-dtype-gt-tensor-"><a class="anchor" href="#quantize-per-tensor-input-scale-zero-point-dtype-gt-tensor-"></a>quantize_per_tensor(input, scale, zero_point, dtype) -&gt; Tensor </h2>
+
+    
+
+
+<p>Converts a float tensor to quantized tensor with given scale and zero point.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='fu'>torch_quantize_per_tensor</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>1.0</span>, <span class='fl'>0.0</span>, <span class='fl'>1.0</span>, <span class='fl'>2.0</span>)), <span class='fl'>0.1</span>, <span class='fl'>10</span>, <span class='fu'><a href='torch_dtype.html'>torch_quint8</a></span>())
+<span class='fu'>torch_quantize_per_tensor</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>1.0</span>, <span class='fl'>0.0</span>, <span class='fl'>1.0</span>, <span class='fl'>2.0</span>)), <span class='fl'>0.1</span>, <span class='fl'>10</span>, <span class='fu'><a href='torch_dtype.html'>torch_quint8</a></span>())<span class='op'>$</span><span class='fu'>int_repr</span>()
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;   0
+#&gt;  10
+#&gt;  20
+#&gt;  30
+#&gt; [ CPUByteType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_rand.html b/static/docs/dev/reference/torch_rand.html
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+++ b/static/docs/dev/reference/torch_rand.html
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+
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+    <div class="page-header">
+    <h1>Rand</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_rand.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Rand</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_rand</span>(
+  <span class='kw'>...</span>,
+  names = <span class='kw'>NULL</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>...</th>
+      <td><p>(int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.</p></td>
+    </tr>
+    <tr>
+      <th>names</th>
+      <td><p>optional dimension names</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="rand-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#rand-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>rand(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a tensor filled with random numbers from a uniform distribution
+on the interval \([0, 1)\)</p>
+<p>The shape of the tensor is defined by the variable argument <code>size</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_rand</span>(<span class='fl'>4</span>)
+<span class='fu'>torch_rand</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.8149  0.6329  0.7243
+#&gt;  0.7835  0.8208  0.7484
+#&gt; [ CPUFloatType{2,3} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Rand_like</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_rand_like.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Rand_like</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_rand_like</span>(
+  <span class='kw'>input</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>,
+  memory_format = <span class='fu'><a href='torch_memory_format.html'>torch_preserve_format</a></span>()
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>memory_format</th>
+      <td><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="rand-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"><a class="anchor" href="#rand-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a>rand_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a tensor with the same size as <code>input</code> that is filled with
+random numbers from a uniform distribution on the interval \([0, 1)\).
+<code>torch_rand_like(input)</code> is equivalent to
+<code><a href='torch_rand.html'>torch_rand(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)</a></code>.</p>
+
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+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <h1>Randint</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_randint.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Randint</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_randint</span>(
+  <span class='kw'>low</span>,
+  <span class='kw'>high</span>,
+  <span class='kw'>size</span>,
+  generator = <span class='kw'>NULL</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>,
+  memory_format = <span class='fu'><a href='torch_memory_format.html'>torch_preserve_format</a></span>()
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>low</th>
+      <td><p>(int, optional) Lowest integer to be drawn from the distribution. Default: 0.</p></td>
+    </tr>
+    <tr>
+      <th>high</th>
+      <td><p>(int) One above the highest integer to be drawn from the distribution.</p></td>
+    </tr>
+    <tr>
+      <th>size</th>
+      <td><p>(tuple) a tuple defining the shape of the output tensor.</p></td>
+    </tr>
+    <tr>
+      <th>generator</th>
+      <td><p>(<code>torch.Generator</code>, optional) a pseudorandom number generator for sampling</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>memory_format</th>
+      <td><p>memory format for the resulting tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="randint-low-high-size-generator-null-out-null-"><a class="anchor" href="#randint-low-high-size-generator-null-out-null-"></a>randint(low=0, high, size, *, generator=NULL, out=NULL, \ </h2>
+
+    
+
+
+<p>dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor</p>
+<p>Returns a tensor filled with random integers generated uniformly
+between <code>low</code> (inclusive) and <code>high</code> (exclusive).</p>
+<p>The shape of the tensor is defined by the variable argument <code>size</code>.</p>
+<p>.. note:
+With the global dtype default (<code>torch_float32</code>), this function returns
+a tensor with dtype <code>torch_int64</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_randint</span>(<span class='fl'>3</span>, <span class='fl'>5</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>3</span>))
+<span class='fu'>torch_randint</span>(<span class='fl'>0</span>, <span class='fl'>10</span>, size = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>))
+<span class='fu'>torch_randint</span>(<span class='fl'>3</span>, <span class='fl'>10</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  8  5
+#&gt;  4  5
+#&gt; [ CPUFloatType{2,2} ]</div></pre>
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+    <div class="page-header">
+    <h1>Randint_like</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_randint_like.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Randint_like</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_randint_like</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>low</span>,
+  <span class='kw'>high</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
+    </tr>
+    <tr>
+      <th>low</th>
+      <td><p>(int, optional) Lowest integer to be drawn from the distribution. Default: 0.</p></td>
+    </tr>
+    <tr>
+      <th>high</th>
+      <td><p>(int) One above the highest integer to be drawn from the distribution.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="randint-like-input-low-high-dtype-null-layout-torch-strided-device-null-requires-grad-false-"><a class="anchor" href="#randint-like-input-low-high-dtype-null-layout-torch-strided-device-null-requires-grad-false-"></a>randint_like(input, low=0, high, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, </h2>
+
+    
+
+
+<p>memory_format=torch.preserve_format) -&gt; Tensor</p>
+<p>Returns a tensor with the same shape as Tensor <code>input</code> filled with
+random integers generated uniformly between <code>low</code> (inclusive) and
+<code>high</code> (exclusive).</p>
+<p>.. note:
+With the global dtype default (<code>torch_float32</code>), this function returns
+a tensor with dtype <code>torch_int64</code>.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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+    <div class="page-header">
+    <h1>Randn</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_randn.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Randn</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_randn</span>(
+  <span class='kw'>...</span>,
+  names = <span class='kw'>NULL</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>...</th>
+      <td><p>(int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.</p></td>
+    </tr>
+    <tr>
+      <th>names</th>
+      <td><p>optional names for the dimensions</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="randn-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#randn-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>randn(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a tensor filled with random numbers from a normal distribution
+with mean <code>0</code> and variance <code>1</code> (also called the standard normal
+distribution).</p>
+<p>$$
+    \mbox{out}_{i} \sim \mathcal{N}(0, 1)
+$$
+The shape of the tensor is defined by the variable argument <code>size</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_randn</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='fu'>torch_randn</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0.2629  1.1612 -0.3210
+#&gt;  0.7943  0.8465 -0.2583
+#&gt; [ CPUFloatType{2,3} ]</div></pre>
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+    </li>
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+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Randn_like</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_randn_like.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Randn_like</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_randn_like</span>(
+  <span class='kw'>input</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>,
+  memory_format = <span class='fu'><a href='torch_memory_format.html'>torch_preserve_format</a></span>()
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>memory_format</th>
+      <td><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="randn-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"><a class="anchor" href="#randn-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a>randn_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a tensor with the same size as <code>input</code> that is filled with
+random numbers from a normal distribution with mean 0 and variance 1.
+<code>torch_randn_like(input)</code> is equivalent to
+<code><a href='torch_randn.html'>torch_randn(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)</a></code>.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
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+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Randperm</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_randperm.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Randperm</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_randperm</span>(
+  <span class='kw'>n</span>,
+  dtype = <span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>(),
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>n</th>
+      <td><p>(int) the upper bound (exclusive)</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: <code>torch_int64</code>.</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="randperm-n-out-null-dtype-torch-int-layout-torch-strided-device-null-requires-grad-false-gt-longtensor-"><a class="anchor" href="#randperm-n-out-null-dtype-torch-int-layout-torch-strided-device-null-requires-grad-false-gt-longtensor-"></a>randperm(n, out=NULL, dtype=torch.int64, layout=torch.strided, device=NULL, requires_grad=False) -&gt; LongTensor </h2>
+
+    
+
+
+<p>Returns a random permutation of integers from <code>0</code> to <code>n - 1</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_randperm</span>(<span class='fl'>4</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  2
+#&gt;  1
+#&gt;  3
+#&gt;  0
+#&gt; [ CPULongType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+
diff --git a/static/docs/dev/reference/torch_range.html b/static/docs/dev/reference/torch_range.html
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+    <li class="dropdown-header">Beginers guide</li>
+    <li>
+      <a href="../articles/getting-started/warmup.html">Warm-up</a>
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+      <a href="../articles/getting-started/tensors.html">Tensors</a>
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+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
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+    </li>
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+      <a href="../articles/using-autograd.html">Using autograd</a>
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Range</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_range.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Range</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_range</span>(
+  <span class='kw'>start</span>,
+  <span class='kw'>end</span>,
+  step = <span class='fl'>1</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>start</th>
+      <td><p>(float) the starting value for the set of points. Default: <code>0</code>.</p></td>
+    </tr>
+    <tr>
+      <th>end</th>
+      <td><p>(float) the ending value for the set of points</p></td>
+    </tr>
+    <tr>
+      <th>step</th>
+      <td><p>(float) the gap between each pair of adjacent points. Default: <code>1</code>.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). If <code>dtype</code> is not given, infer the data type from the other input        arguments. If any of <code>start</code>, <code>end</code>, or <code>stop</code> are floating-point, the        <code>dtype</code> is inferred to be the default dtype, see        <code>~torch.get_default_dtype</code>. Otherwise, the <code>dtype</code> is inferred to        be <code>torch.int64</code>.</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="range-start-end-step-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#range-start-end-step-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>range(start=0, end, step=1, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a 1-D tensor of size \(\left\lfloor \frac{\mbox{end} - \mbox{start}}{\mbox{step}} \right\rfloor + 1\)
+with values from <code>start</code> to <code>end</code> with step <code>step</code>. Step is
+the gap between two values in the tensor.</p>
+<p>$$
+    \mbox{out}_{i+1} = \mbox{out}_i + \mbox{step}.
+$$</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p>This function is deprecated in favor of <code><a href='torch_arange.html'>torch_arange</a></code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_range</span>(<span class='fl'>1</span>, <span class='fl'>4</span>)
+<span class='fu'>torch_range</span>(<span class='fl'>1</span>, <span class='fl'>4</span>, <span class='fl'>0.5</span>)
+}
+</div><div class='output co'>#&gt; <span class='warning'>Warning: This function is deprecated in favor of torch_arange.</span></div><div class='output co'>#&gt; <span class='warning'>Warning: This function is deprecated in favor of torch_arange.</span></div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.0000
+#&gt;  1.5000
+#&gt;  2.0000
+#&gt;  2.5000
+#&gt;  3.0000
+#&gt;  3.5000
+#&gt; [ CPUFloatType{6} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <li class="dropdown-header">Beginers guide</li>
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+    <div class="page-header">
+    <h1>Real</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_real.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Real</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_real</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="real-input-gt-tensor-"><a class="anchor" href="#real-input-gt-tensor-"></a>real(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the real part of the <code>input</code> tensor. If
+<code>input</code> is a real (non-complex) tensor, this function just
+returns it.</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p>Not yet implemented for complex tensors.</p>
+<p>$$
+    \mbox{out}_{i} = real(\mbox{input}_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='fu'>torch_real</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>1</span> <span class='op'>+</span> <span class='fl'>1i</span>, <span class='op'>-</span><span class='fl'>2</span> <span class='op'>+</span> <span class='fl'>2i</span>, <span class='fl'>3</span> <span class='op'>-</span> <span class='fl'>3i</span>)))
+}
+}
+</div></pre>
+  </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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+
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+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Reciprocal</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_reciprocal.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Reciprocal</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_reciprocal</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="reciprocal-input-out-null-gt-tensor-"><a class="anchor" href="#reciprocal-input-out-null-gt-tensor-"></a>reciprocal(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the reciprocal of the elements of <code>input</code></p>
+<p>$$
+    \mbox{out}_{i} = \frac{1}{\mbox{input}_{i}}
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_reciprocal</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -21.5189
+#&gt;   2.1018
+#&gt;   0.6203
+#&gt;   0.9015
+#&gt; [ CPUFloatType{4} ]</div></pre>
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+    <h1>Creates the reduction objet</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/reduction.R'><code>R/reduction.R</code></a></small>
+    <div class="hidden name"><code>torch_reduction.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Creates the reduction objet</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_reduction_sum</span>()
+
+<span class='fu'>torch_reduction_mean</span>()
+
+<span class='fu'>torch_reduction_none</span>()</pre>
+
+
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diff --git a/static/docs/dev/reference/torch_relu.html b/static/docs/dev/reference/torch_relu.html
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+    <h1>Relu</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_relu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Relu</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_relu</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="relu-input-gt-tensor-"><a class="anchor" href="#relu-input-gt-tensor-"></a>relu(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the relu tranformation.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <h1>Relu_</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_relu_.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Relu_</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_relu_</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="relu-input-gt-tensor-"><a class="anchor" href="#relu-input-gt-tensor-"></a>relu_(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>In-place version of <code><a href='torch_relu.html'>torch_relu()</a></code>.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Remainder</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_remainder.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Remainder</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_remainder</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the dividend</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor or float) the divisor that may be either a number or a                               Tensor of the same shape as the dividend</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="remainder-input-other-out-null-gt-tensor-"><a class="anchor" href="#remainder-input-other-out-null-gt-tensor-"></a>remainder(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the element-wise remainder of division.</p>
+<p>The divisor and dividend may contain both for integer and floating point
+numbers. The remainder has the same sign as the divisor.</p>
+<p>When <code>other</code> is a tensor, the shapes of <code>input</code> and
+<code>other</code> must be broadcastable .</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_remainder</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>3.</span>, <span class='op'>-</span><span class='fl'>2</span>, <span class='op'>-</span><span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>)), <span class='fl'>2</span>)
+<span class='fu'>torch_remainder</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1.</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span>)), <span class='fl'>1.5</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.0000
+#&gt;  0.5000
+#&gt;  0.0000
+#&gt;  1.0000
+#&gt;  0.5000
+#&gt; [ CPUFloatType{5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+<div class="row">
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+    <div class="page-header">
+    <h1>Renorm</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_renorm.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Renorm</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_renorm</span>(<span class='kw'>self</span>, <span class='kw'>p</span>, <span class='kw'>dim</span>, <span class='kw'>maxnorm</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>p</th>
+      <td><p>(float) the power for the norm computation</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the dimension to slice over to get the sub-tensors</p></td>
+    </tr>
+    <tr>
+      <th>maxnorm</th>
+      <td><p>(float) the maximum norm to keep each sub-tensor under</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>If the norm of a row is lower than <code>maxnorm</code>, the row is unchanged</p>
+    <h2 class="hasAnchor" id="renorm-input-p-dim-maxnorm-out-null-gt-tensor-"><a class="anchor" href="#renorm-input-p-dim-maxnorm-out-null-gt-tensor-"></a>renorm(input, p, dim, maxnorm, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a tensor where each sub-tensor of <code>input</code> along dimension
+<code>dim</code> is normalized such that the <code>p</code>-norm of the sub-tensor is lower
+than the value <code>maxnorm</code></p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_ones.html'>torch_ones</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='kw'>x</span>[<span class='fl'>2</span>,]<span class='op'>$</span><span class='fu'>fill_</span>(<span class='fl'>2</span>)
+<span class='kw'>x</span>[<span class='fl'>3</span>,]<span class='op'>$</span><span class='fu'>fill_</span>(<span class='fl'>3</span>)
+<span class='kw'>x</span>
+<span class='fu'>torch_renorm</span>(<span class='kw'>x</span>, <span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>5</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.0000  1.0000  1.0000
+#&gt;  1.6667  1.6667  1.6667
+#&gt;  1.6667  1.6667  1.6667
+#&gt; [ CPUFloatType{3,3} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_repeat_interleave.html b/static/docs/dev/reference/torch_repeat_interleave.html
new file mode 100644
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+    <div class="page-header">
+    <h1>Repeat_interleave</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_repeat_interleave.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Repeat_interleave</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_repeat_interleave</span>(<span class='kw'>self</span>, <span class='kw'>repeats</span>, dim = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>repeats</th>
+      <td><p>(Tensor or int) The number of repetitions for each element.        repeats is broadcasted to fit the shape of the given axis.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int, optional) The dimension along which to repeat values.        By default, use the flattened input array, and return a flat output        array.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="repeat-interleave-input-repeats-dim-null-gt-tensor-"><a class="anchor" href="#repeat-interleave-input-repeats-dim-null-gt-tensor-"></a>repeat_interleave(input, repeats, dim=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Repeat elements of a tensor.</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+<pre>This is different from `torch_Tensor.repeat` but similar to `numpy.repeat`.
+</pre>
+
+    <h2 class="hasAnchor" id="repeat-interleave-repeats-gt-tensor-"><a class="anchor" href="#repeat-interleave-repeats-gt-tensor-"></a>repeat_interleave(repeats) -&gt; Tensor </h2>
+
+    
+
+
+<p>If the <code>repeats</code> is <code>tensor([n1, n2, n3, ...])</code>, then the output will be
+<code>tensor([0, 0, ..., 1, 1, ..., 2, 2, ..., ...])</code> where <code>0</code> appears <code>n1</code> times,
+<code>1</code> appears <code>n2</code> times, <code>2</code> appears <code>n3</code> times, etc.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>))
+<span class='kw'>x</span><span class='op'>$</span><span class='fu'>repeat_interleave</span>(<span class='fl'>2</span>)
+<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span>), ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>))
+<span class='fu'>torch_repeat_interleave</span>(<span class='kw'>y</span>, <span class='fl'>2</span>)
+<span class='fu'>torch_repeat_interleave</span>(<span class='kw'>y</span>, <span class='fl'>3</span>, dim=<span class='fl'>1</span>)
+<span class='fu'>torch_repeat_interleave</span>(<span class='kw'>y</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>)), dim=<span class='fl'>1</span>)
+}
+}
+</div></pre>
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+    <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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_reshape.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Reshape</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_reshape</span>(<span class='kw'>self</span>, <span class='kw'>shape</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the tensor to be reshaped</p></td>
+    </tr>
+    <tr>
+      <th>shape</th>
+      <td><p>(tuple of ints) the new shape</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="reshape-input-shape-gt-tensor-"><a class="anchor" href="#reshape-input-shape-gt-tensor-"></a>reshape(input, shape) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a tensor with the same data and number of elements as <code>input</code>,
+but with the specified shape. When possible, the returned tensor will be a view
+of <code>input</code>. Otherwise, it will be a copy. Contiguous inputs and inputs
+with compatible strides can be reshaped without copying, but you should not
+depend on the copying vs. viewing behavior.</p>
+<p>See <code>torch_Tensor.view</code> on when it is possible to return a view.</p>
+<p>A single dimension may be -1, in which case it's inferred from the remaining
+dimensions and the number of elements in <code>input</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0</span>, <span class='fl'>4</span>)
+<span class='fu'>torch_reshape</span>(<span class='kw'>a</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>))
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>), ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>))
+<span class='fu'>torch_reshape</span>(<span class='kw'>b</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='op'>-</span><span class='fl'>1</span>))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0
+#&gt;  1
+#&gt;  2
+#&gt;  3
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_result_type.html b/static/docs/dev/reference/torch_result_type.html
new file mode 100644
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+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Result_type</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
+    <div class="hidden name"><code>torch_result_type.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Result_type</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_result_type</span>(<span class='kw'>tensor1</span>, <span class='kw'>tensor2</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensor1</th>
+      <td><p>(Tensor or Number) an input tensor or number</p></td>
+    </tr>
+    <tr>
+      <th>tensor2</th>
+      <td><p>(Tensor or Number) an input tensor or number</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="result-type-tensor-tensor-gt-dtype-"><a class="anchor" href="#result-type-tensor-tensor-gt-dtype-"></a>result_type(tensor1, tensor2) -&gt; dtype </h2>
+
+    
+
+
+<p>Returns the <code>torch_dtype</code> that would result from performing an arithmetic
+operation on the provided input tensors. See type promotion documentation
+for more information on the type promotion logic.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_result_type</span>(tensor1 = <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int</a></span>()), tensor2 = <span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; torch_Float</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/torch_rfft.html b/static/docs/dev/reference/torch_rfft.html
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+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Rfft</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_rfft.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Rfft</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_rfft</span>(<span class='kw'>self</span>, <span class='kw'>signal_ndim</span>, normalized = <span class='fl'>FALSE</span>, onesided = <span class='fl'>TRUE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor of at least <code>signal_ndim</code> dimensions</p></td>
+    </tr>
+    <tr>
+      <th>signal_ndim</th>
+      <td><p>(int) the number of dimensions in each signal.        <code>signal_ndim</code> can only be 1, 2 or 3</p></td>
+    </tr>
+    <tr>
+      <th>normalized</th>
+      <td><p>(bool, optional) controls whether to return normalized results.        Default: <code>FALSE</code></p></td>
+    </tr>
+    <tr>
+      <th>onesided</th>
+      <td><p>(bool, optional) controls whether to return half of results to        avoid redundancy. Default: <code>TRUE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>For CUDA tensors, an LRU cache is used for cuFFT plans to speed up
+repeatedly running FFT methods on tensors of same geometry with same
+configuration. See cufft-plan-cache for more details on how to
+monitor and control the cache.
+</pre>
+
+    <h2 class="hasAnchor" id="rfft-input-signal-ndim-normalized-false-onesided-true-gt-tensor-"><a class="anchor" href="#rfft-input-signal-ndim-normalized-false-onesided-true-gt-tensor-"></a>rfft(input, signal_ndim, normalized=False, onesided=TRUE) -&gt; Tensor </h2>
+
+    
+
+
+<p>Real-to-complex Discrete Fourier Transform</p>
+<p>This method computes the real-to-complex discrete Fourier transform. It is
+mathematically equivalent with <code><a href='torch_fft.html'>torch_fft</a></code> with differences only in
+formats of the input and output.</p>
+<p>This method supports 1D, 2D and 3D real-to-complex transforms, indicated
+by <code>signal_ndim</code>. <code>input</code> must be a tensor with at least
+<code>signal_ndim</code> dimensions with optionally arbitrary number of leading batch
+dimensions. If <code>normalized</code> is set to <code>TRUE</code>, this normalizes the result
+by dividing it with \(\sqrt{\prod_{i=1}^K N_i}\) so that the operator is
+unitary, where \(N_i\) is the size of signal dimension \(i\).</p>
+<p>The real-to-complex Fourier transform results follow conjugate symmetry:</p>
+<p>$$
+    X[\omega_1, \dots, \omega_d] = X^*[N_1 - \omega_1, \dots, N_d - \omega_d],
+$$
+where the index arithmetic is computed modulus the size of the corresponding
+dimension, \(\ ^*\) is the conjugate operator, and
+\(d\) = <code>signal_ndim</code>. <code>onesided</code> flag controls whether to avoid
+redundancy in the output results. If set to <code>TRUE</code> (default), the output will
+not be full complex result of shape \((*, 2)\), where \(*\) is the shape
+of <code>input</code>, but instead the last dimension will be halfed as of size
+\(\lfloor \frac{N_d}{2} \rfloor + 1\).</p>
+<p>The inverse of this function is <code><a href='torch_irfft.html'>torch_irfft</a></code>.</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p>For CPU tensors, this method is currently only available with MKL. Use
+<code>torch_backends.mkl.is_available</code> to check if MKL is installed.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>, <span class='fl'>5</span>))
+<span class='fu'>torch_rfft</span>(<span class='kw'>x</span>, <span class='fl'>2</span>)
+<span class='fu'>torch_rfft</span>(<span class='kw'>x</span>, <span class='fl'>2</span>, onesided=<span class='fl'>FALSE</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;   1.1392  0.0000
+#&gt;   5.9699 -6.3502
+#&gt;  -2.2049  5.4613
+#&gt;  -2.2049 -5.4613
+#&gt;   5.9699  6.3502
+#&gt; 
+#&gt; (2,.,.) = 
+#&gt;   3.6573  0.2440
+#&gt;  -2.9131  4.3144
+#&gt;  -2.5946 -4.3514
+#&gt;  -0.0998 -3.1859
+#&gt;  -3.6787  1.2527
+#&gt; 
+#&gt; (3,.,.) = 
+#&gt;   1.9928 -4.5021
+#&gt;  -1.6164  0.4800
+#&gt;  -0.7325 -0.2080
+#&gt;   2.5052  1.7202
+#&gt;  -3.2780 -0.2895
+#&gt; 
+#&gt; (4,.,.) = 
+#&gt;   1.9928  4.5021
+#&gt;  -3.2780  0.2895
+#&gt;   2.5052 -1.7202
+#&gt;  -0.7325  0.2080
+#&gt;  -1.6164 -0.4800
+#&gt; 
+#&gt; (5,.,.) = 
+#&gt;   3.6573 -0.2440
+#&gt;  -3.6787 -1.2527
+#&gt;  -0.0998  3.1859
+#&gt;  -2.5946  4.3514
+#&gt;  -2.9131 -4.3144
+#&gt; [ CPUFloatType{5,5,2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
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+        <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.0.3.9000</span>
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+    <li class="dropdown-header">Beginers guide</li>
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+      <a href="../articles/getting-started/tensors.html">Tensors</a>
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+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
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+
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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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_roll.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Roll</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_roll</span>(<span class='kw'>self</span>, <span class='kw'>shifts</span>, dims = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>())</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>shifts</th>
+      <td><p>(int or tuple of ints) The number of places by which the elements        of the tensor are shifted. If shifts is a tuple, dims must be a tuple of        the same size, and each dimension will be rolled by the corresponding        value</p></td>
+    </tr>
+    <tr>
+      <th>dims</th>
+      <td><p>(int or tuple of ints) Axis along which to roll</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="roll-input-shifts-dims-null-gt-tensor-"><a class="anchor" href="#roll-input-shifts-dims-null-gt-tensor-"></a>roll(input, shifts, dims=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Roll the tensor along the given dimension(s). Elements that are shifted beyond the
+last position are re-introduced at the first position. If a dimension is not
+specified, the tensor will be flattened before rolling and then restored
+to the original shape.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span>, <span class='fl'>6</span>, <span class='fl'>7</span>, <span class='fl'>8</span>))<span class='op'>$</span><span class='fu'>view</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>2</span>))
+<span class='kw'>x</span>
+<span class='fu'>torch_roll</span>(<span class='kw'>x</span>, <span class='fl'>1</span>, <span class='fl'>1</span>)
+<span class='fu'>torch_roll</span>(<span class='kw'>x</span>, <span class='op'>-</span><span class='fl'>1</span>, <span class='fl'>1</span>)
+<span class='fu'>torch_roll</span>(<span class='kw'>x</span>, shifts=<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>), dims=<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  6  5
+#&gt;  8  7
+#&gt;  2  1
+#&gt;  4  3
+#&gt; [ CPUFloatType{4,2} ]</div></pre>
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diff --git a/static/docs/dev/reference/torch_rot90.html b/static/docs/dev/reference/torch_rot90.html
new file mode 100644
index 000000000..d63f3b0e5
--- /dev/null
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+    <div class="page-header">
+    <h1>Rot90</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_rot90.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Rot90</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_rot90</span>(<span class='kw'>self</span>, k = <span class='fl'>1L</span>, dims = <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>1</span>))</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>k</th>
+      <td><p>(int) number of times to rotate</p></td>
+    </tr>
+    <tr>
+      <th>dims</th>
+      <td><p>(a list or tuple) axis to rotate</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="rot-input-k-dims-gt-tensor-"><a class="anchor" href="#rot-input-k-dims-gt-tensor-"></a>rot90(input, k, dims) -&gt; Tensor </h2>
+
+    
+
+
+<p>Rotate a n-D tensor by 90 degrees in the plane specified by dims axis.
+Rotation direction is from the first towards the second axis if k &gt; 0, and from the second towards the first for k &lt; 0.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0</span>, <span class='fl'>4</span>)<span class='op'>$</span><span class='fu'>view</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>))
+<span class='kw'>x</span>
+<span class='fu'>torch_rot90</span>(<span class='kw'>x</span>, <span class='fl'>1</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>))
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0</span>, <span class='fl'>8</span>)<span class='op'>$</span><span class='fu'>view</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>, <span class='fl'>2</span>))
+<span class='kw'>x</span>
+<span class='fu'>torch_rot90</span>(<span class='kw'>x</span>, <span class='fl'>1</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;   2  3
+#&gt;   6  7
+#&gt; 
+#&gt; (2,.,.) = 
+#&gt;   0  1
+#&gt;   4  5
+#&gt; [ CPUFloatType{2,2,2} ]</div></pre>
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+    <div class="page-header">
+    <h1>Round</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_round.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Round</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_round</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="round-input-out-null-gt-tensor-"><a class="anchor" href="#round-input-out-null-gt-tensor-"></a>round(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with each of the elements of <code>input</code> rounded
+to the closest integer.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_round</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -1
+#&gt; -0
+#&gt;  2
+#&gt;  0
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/dev/reference/torch_rrelu_.html b/static/docs/dev/reference/torch_rrelu_.html
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+    <h1>Rrelu_</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_rrelu_.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Rrelu_</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_rrelu_</span>(
+  <span class='kw'>self</span>,
+  lower = <span class='fl'>0.125</span>,
+  upper = <span class='fl'>0.333333</span>,
+  training = <span class='fl'>FALSE</span>,
+  generator = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>lower</th>
+      <td><p>lower bound of the uniform distribution. Default: 1/8</p></td>
+    </tr>
+    <tr>
+      <th>upper</th>
+      <td><p>upper bound of the uniform distribution. Default: 1/3</p></td>
+    </tr>
+    <tr>
+      <th>training</th>
+      <td><p>bool wether it's a training pass. DEfault: FALSE</p></td>
+    </tr>
+    <tr>
+      <th>generator</th>
+      <td><p>random number generator</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="rrelu-input-lower-upper-training-false-gt-tensor-"><a class="anchor" href="#rrelu-input-lower-upper-training-false-gt-tensor-"></a>rrelu_(input, lower=1./8, upper=1./3, training=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>In-place version of <code>torch_rrelu</code>.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_rsqrt.html b/static/docs/dev/reference/torch_rsqrt.html
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+    <div class="page-header">
+    <h1>Rsqrt</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_rsqrt.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Rsqrt</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_rsqrt</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="rsqrt-input-out-null-gt-tensor-"><a class="anchor" href="#rsqrt-input-out-null-gt-tensor-"></a>rsqrt(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the reciprocal of the square-root of each of
+the elements of <code>input</code>.</p>
+<p>$$
+    \mbox{out}_{i} = \frac{1}{\sqrt{\mbox{input}_{i}}}
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_rsqrt</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.7920
+#&gt;     nan
+#&gt;     nan
+#&gt;     nan
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
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diff --git a/static/docs/dev/reference/torch_save.html b/static/docs/dev/reference/torch_save.html
new file mode 100644
index 000000000..a79c333d0
--- /dev/null
+++ b/static/docs/dev/reference/torch_save.html
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+<!-- Generated by pkgdown: do not edit by hand -->
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+    <h1>Saves an object to a disk file.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/save.R'><code>R/save.R</code></a></small>
+    <div class="hidden name"><code>torch_save.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>This function is experimental, don't use for long
+term storage.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_save</span>(<span class='kw'>obj</span>, <span class='kw'>path</span>, <span class='kw'>...</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>obj</th>
+      <td><p>the saved object</p></td>
+    </tr>
+    <tr>
+      <th>path</th>
+      <td><p>a connection or the name of the file to save.</p></td>
+    </tr>
+    <tr>
+      <th>...</th>
+      <td><p>not currently used.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
+
+    <div class='dont-index'><p>Other torch_save: 
+<code><a href='torch_load.html'>torch_load</a>()</code></p></div>
+
+  </div>
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diff --git a/static/docs/dev/reference/torch_selu.html b/static/docs/dev/reference/torch_selu.html
new file mode 100644
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+
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Selu</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_selu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Selu</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_selu</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="selu-input-gt-tensor-"><a class="anchor" href="#selu-input-gt-tensor-"></a>selu(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the selu transformation.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+
+
diff --git a/static/docs/dev/reference/torch_selu_.html b/static/docs/dev/reference/torch_selu_.html
new file mode 100644
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--- /dev/null
+++ b/static/docs/dev/reference/torch_selu_.html
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+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
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+    <p>Selu_</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_selu_</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="selu-input-gt-tensor-"><a class="anchor" href="#selu-input-gt-tensor-"></a>selu_(input) -&gt; Tensor </h2>
+
+    
+
+
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+      <a href="../articles/using-autograd.html">Using autograd</a>
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+    <div class="page-header">
+    <h1>Sigmoid</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_sigmoid.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Sigmoid</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_sigmoid</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="sigmoid-input-out-null-gt-tensor-"><a class="anchor" href="#sigmoid-input-out-null-gt-tensor-"></a>sigmoid(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the sigmoid of the elements of <code>input</code>.</p>
+<p>$$
+    \mbox{out}_{i} = \frac{1}{1 + e^{-\mbox{input}_{i}}}
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_sigmoid</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.5045
+#&gt;  0.6762
+#&gt;  0.6376
+#&gt;  0.2477
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+    <div class="page-header">
+    <h1>Sign</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_sign.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Sign</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_sign</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="sign-input-out-null-gt-tensor-"><a class="anchor" href="#sign-input-out-null-gt-tensor-"></a>sign(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the signs of the elements of <code>input</code>.</p>
+<p>$$
+    \mbox{out}_{i} = \mbox{sgn}(\mbox{input}_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0.7</span>, <span class='op'>-</span><span class='fl'>1.2</span>, <span class='fl'>0.</span>, <span class='fl'>2.3</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_sign</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1
+#&gt; -1
+#&gt;  0
+#&gt;  1
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_sin.html b/static/docs/dev/reference/torch_sin.html
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+    </li>
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+
+      
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+<div class="row">
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+    <div class="page-header">
+    <h1>Sin</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_sin.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Sin</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_sin</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="sin-input-out-null-gt-tensor-"><a class="anchor" href="#sin-input-out-null-gt-tensor-"></a>sin(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the sine of the elements of <code>input</code>.</p>
+<p>$$
+    \mbox{out}_{i} = \sin(\mbox{input}_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_sin</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.9321
+#&gt;  0.3476
+#&gt;  0.3708
+#&gt; -0.2301
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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+
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+
diff --git a/static/docs/dev/reference/torch_sinh.html b/static/docs/dev/reference/torch_sinh.html
new file mode 100644
index 000000000..ef98c937e
--- /dev/null
+++ b/static/docs/dev/reference/torch_sinh.html
@@ -0,0 +1,260 @@
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+    <h1>Sinh</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_sinh.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Sinh</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_sinh</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="sinh-input-out-null-gt-tensor-"><a class="anchor" href="#sinh-input-out-null-gt-tensor-"></a>sinh(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the hyperbolic sine of the elements of
+<code>input</code>.</p>
+<p>$$
+    \mbox{out}_{i} = \sinh(\mbox{input}_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_sinh</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.7845
+#&gt; -0.4508
+#&gt;  0.2873
+#&gt; -2.0906
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
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+
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+    <div class="page-header">
+    <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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_slogdet.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Slogdet</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_slogdet</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor of size <code>(*, n, n)</code> where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is zero or more                batch dimensions.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>If `input` has zero determinant, this returns `(0, -inf)`.
+</pre>
+
+<pre>Backward through `slogdet` internally uses SVD results when `input`
+is not invertible. In this case, double backward through `slogdet`
+will be unstable in when `input` doesn't have distinct singular values.
+See `~torch.svd` for details.
+</pre>
+
+    <h2 class="hasAnchor" id="slogdet-input-gt-tensor-tensor-"><a class="anchor" href="#slogdet-input-gt-tensor-tensor-"></a>slogdet(input) -&gt; (Tensor, Tensor) </h2>
+
+    
+
+
+<p>Calculates the sign and log absolute value of the determinant(s) of a square matrix or batches of square matrices.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='kw'>A</span>
+<span class='fu'><a href='torch_det.html'>torch_det</a></span>(<span class='kw'>A</span>)
+<span class='fu'><a href='torch_logdet.html'>torch_logdet</a></span>(<span class='kw'>A</span>)
+<span class='fu'>torch_slogdet</span>(<span class='kw'>A</span>)
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt; -1
+#&gt; [ CPUFloatType{} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt; -0.634866
+#&gt; [ CPUFloatType{} ]
+#&gt; </div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+    <h1>Solve</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_solve.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Solve</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_solve</span>(<span class='kw'>self</span>, <span class='kw'>A</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) input matrix \(B\) of size \((*, m, k)\) , where \(*\)                is zero or more batch dimensions.</p></td>
+    </tr>
+    <tr>
+      <th>A</th>
+      <td><p>(Tensor) input square matrix of size \((*, m, m)\), where                \(*\) is zero or more batch dimensions.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>Irrespective of the original strides, the returned matrices
+`solution` and `LU` will be transposed, i.e. with strides like
+`B$contiguous()$transpose(-1, -2)$stride()` and
+`A$contiguous()$transpose(-1, -2)$stride()` respectively.
+</pre>
+
+    <h2 class="hasAnchor" id="solve-input-a-gt-tensor-tensor-"><a class="anchor" href="#solve-input-a-gt-tensor-tensor-"></a>solve(input, A) -&gt; (Tensor, Tensor) </h2>
+
+    
+
+
+<p>This function returns the solution to the system of linear
+equations represented by \(AX = B\) and the LU factorization of
+A, in order as a namedtuple <code>solution, LU</code>.</p>
+<p><code>LU</code> contains <code>L</code> and <code>U</code> factors for LU factorization of <code>A</code>.</p>
+<p><code>torch_solve(B, A)</code> can take in 2D inputs <code>B, A</code> or inputs that are
+batches of 2D matrices. If the inputs are batches, then returns
+batched outputs <code>solution, LU</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>6.80</span>, <span class='op'>-</span><span class='fl'>2.11</span>,  <span class='fl'>5.66</span>,  <span class='fl'>5.97</span>,  <span class='fl'>8.23</span>),
+                      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>6.05</span>, <span class='op'>-</span><span class='fl'>3.30</span>,  <span class='fl'>5.36</span>, <span class='op'>-</span><span class='fl'>4.44</span>,  <span class='fl'>1.08</span>),
+                      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>0.45</span>,  <span class='fl'>2.58</span>, <span class='op'>-</span><span class='fl'>2.70</span>,  <span class='fl'>0.27</span>,  <span class='fl'>9.04</span>),
+                      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>8.32</span>,  <span class='fl'>2.71</span>,  <span class='fl'>4.35</span>,  <span class='op'>-</span><span class='fl'>7.17</span>,  <span class='fl'>2.14</span>),
+                      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>9.67</span>, <span class='op'>-</span><span class='fl'>5.14</span>, <span class='op'>-</span><span class='fl'>7.26</span>,  <span class='fl'>6.08</span>, <span class='op'>-</span><span class='fl'>6.87</span>)))<span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>()
+<span class='kw'>B</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4.02</span>,  <span class='fl'>6.19</span>, <span class='op'>-</span><span class='fl'>8.22</span>, <span class='op'>-</span><span class='fl'>7.57</span>, <span class='op'>-</span><span class='fl'>3.03</span>),
+                      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>1.56</span>,  <span class='fl'>4.00</span>, <span class='op'>-</span><span class='fl'>8.67</span>,  <span class='fl'>1.75</span>,  <span class='fl'>2.86</span>),
+                      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>9.81</span>, <span class='op'>-</span><span class='fl'>4.09</span>, <span class='op'>-</span><span class='fl'>4.57</span>, <span class='op'>-</span><span class='fl'>8.61</span>,  <span class='fl'>8.99</span>)))<span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>()
+<span class='kw'>out</span> <span class='op'>=</span> <span class='fu'>torch_solve</span>(<span class='kw'>B</span>, <span class='kw'>A</span>)
+<span class='kw'>X</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>1</span>]]
+<span class='kw'>LU</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>2</span>]]
+<span class='fu'><a href='torch_dist.html'>torch_dist</a></span>(<span class='kw'>B</span>, <span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='kw'>A</span>, <span class='kw'>X</span>))
+<span class='co'># Batched solver example</span>
+<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>1</span>, <span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>B</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>1</span>, <span class='fl'>4</span>, <span class='fl'>6</span>))
+<span class='kw'>out</span> <span class='op'>=</span> <span class='fu'>torch_solve</span>(<span class='kw'>B</span>, <span class='kw'>A</span>)
+<span class='kw'>X</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>1</span>]]
+<span class='kw'>LU</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>2</span>]]
+<span class='fu'><a href='torch_dist.html'>torch_dist</a></span>(<span class='kw'>B</span>, <span class='kw'>A</span><span class='op'>$</span><span class='fu'>matmul</span>(<span class='kw'>X</span>))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; 2.21788e-06
+#&gt; [ CPUFloatType{} ]</div></pre>
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Sort</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_sort.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Sort</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_sort</span>(<span class='kw'>self</span>, dim = <span class='op'>-</span><span class='fl'>1L</span>, descending = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int, optional) the dimension to sort along</p></td>
+    </tr>
+    <tr>
+      <th>descending</th>
+      <td><p>(bool, optional) controls the sorting order (ascending or descending)</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="sort-input-dim-descending-false-gt-tensor-longtensor-"><a class="anchor" href="#sort-input-dim-descending-false-gt-tensor-longtensor-"></a>sort(input, dim=-1, descending=FALSE) -&gt; (Tensor, LongTensor) </h2>
+
+    
+
+
+<p>Sorts the elements of the <code>input</code> tensor along a given dimension
+in ascending order by value.</p>
+<p>If <code>dim</code> is not given, the last dimension of the <code>input</code> is chosen.</p>
+<p>If <code>descending</code> is <code>TRUE</code> then the elements are sorted in descending
+order by value.</p>
+<p>A namedtuple of (values, indices) is returned, where the <code>values</code> are the
+sorted values and <code>indices</code> are the indices of the elements in the original
+<code>input</code> tensor.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>4</span>))
+<span class='kw'>out</span> <span class='op'>=</span> <span class='fu'>torch_sort</span>(<span class='kw'>x</span>)
+<span class='kw'>out</span>
+<span class='kw'>out</span> <span class='op'>=</span> <span class='fu'>torch_sort</span>(<span class='kw'>x</span>, <span class='fl'>1</span>)
+<span class='kw'>out</span>
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt; -1.7778 -1.6086 -1.3155 -1.1151
+#&gt;  1.0098 -0.2044 -1.2104 -0.8225
+#&gt;  1.2042  2.1714  1.5548  0.3414
+#&gt; [ CPUFloatType{3,4} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt;  0  2  1  1
+#&gt;  1  1  2  0
+#&gt;  2  0  0  2
+#&gt; [ CPULongType{3,4} ]
+#&gt; </div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Sparse_coo_tensor</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
+    <div class="hidden name"><code>torch_sparse_coo_tensor.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Sparse_coo_tensor</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_sparse_coo_tensor</span>(
+  <span class='kw'>indices</span>,
+  <span class='kw'>values</span>,
+  size = <span class='kw'>NULL</span>,
+  dtype = <span class='kw'>NULL</span>,
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>indices</th>
+      <td><p>(array_like) Initial data for the tensor. Can be a list, tuple,        NumPy <code>ndarray</code>, scalar, and other types. Will be cast to a <code>torch_LongTensor</code>        internally. The indices are the coordinates of the non-zero values in the matrix, and thus        should be two-dimensional where the first dimension is the number of tensor dimensions and        the second dimension is the number of non-zero values.</p></td>
+    </tr>
+    <tr>
+      <th>values</th>
+      <td><p>(array_like) Initial values for the tensor. Can be a list, tuple,        NumPy <code>ndarray</code>, scalar, and other types.</p></td>
+    </tr>
+    <tr>
+      <th>size</th>
+      <td><p>(list, tuple, or <code>torch.Size</code>, optional) Size of the sparse tensor. If not        provided the size will be inferred as the minimum size big enough to hold all non-zero        elements.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if NULL, infers data type from <code>values</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if NULL, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="sparse-coo-tensor-indices-values-size-null-dtype-null-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#sparse-coo-tensor-indices-values-size-null-dtype-null-device-null-requires-grad-false-gt-tensor-"></a>sparse_coo_tensor(indices, values, size=NULL, dtype=NULL, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Constructs a sparse tensors in COO(rdinate) format with non-zero elements at the given <code>indices</code>
+with the given <code>values</code>. A sparse tensor can be <code>uncoalesced</code>, in that case, there are duplicate
+coordinates in the indices, and the value at that index is the sum of all duplicate value entries:
+<code>torch_sparse</code>_.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>i</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>1</span>, <span class='fl'>3</span>), ncol = <span class='fl'>3</span>, byrow = <span class='fl'>TRUE</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>())
+<span class='kw'>v</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_float32</a></span>())
+<span class='fu'>torch_sparse_coo_tensor</span>(<span class='kw'>i</span>, <span class='kw'>v</span>)
+<span class='fu'>torch_sparse_coo_tensor</span>(<span class='kw'>i</span>, <span class='kw'>v</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>4</span>))
+
+<span class='co'># create empty sparse tensors</span>
+<span class='kw'>S</span> <span class='op'>=</span> <span class='fu'>torch_sparse_coo_tensor</span>(
+  <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>0</span>), dtype = <span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>()), 
+  <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/numeric.html'>numeric</a></span>(), dtype = <span class='fu'><a href='torch_dtype.html'>torch_float32</a></span>()), 
+  <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>)
+)
+<span class='kw'>S</span> <span class='op'>=</span> <span class='fu'>torch_sparse_coo_tensor</span>(
+  <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>0</span>), dtype = <span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>()), 
+  <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>2</span>)), 
+  <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>)
+)
+}
+</div></pre>
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diff --git a/static/docs/dev/reference/torch_split.html b/static/docs/dev/reference/torch_split.html
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index 000000000..fe9a27bd5
--- /dev/null
+++ b/static/docs/dev/reference/torch_split.html
@@ -0,0 +1,260 @@
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+    <div class="page-header">
+    <h1>Split</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_split.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Split</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_split</span>(<span class='kw'>self</span>, <span class='kw'>split_size</span>, dim = <span class='fl'>1L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) tensor to split.</p></td>
+    </tr>
+    <tr>
+      <th>split_size</th>
+      <td><p>(int) size of a single chunk or        list of sizes for each chunk</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) dimension along which to split the tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
+
+    
+
+
+<p>Splits the tensor into chunks. Each chunk is a view of the original tensor.</p><pre>If `split_size_or_sections` is an integer type, then `tensor` will
+be split into equally sized chunks (if possible). Last chunk will be smaller if
+the tensor size along the given dimension `dim` is not divisible by
+`split_size`.
+
+If `split_size_or_sections` is a list, then `tensor` will be split
+into `len(split_size_or_sections)` chunks with sizes in `dim` according
+to `split_size_or_sections`.
+</pre>
+
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+    <div class="page-header">
+    <h1>Sqrt</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_sqrt.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Sqrt</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_sqrt</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="sqrt-input-out-null-gt-tensor-"><a class="anchor" href="#sqrt-input-out-null-gt-tensor-"></a>sqrt(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the square-root of the elements of <code>input</code>.</p>
+<p>$$
+    \mbox{out}_{i} = \sqrt{\mbox{input}_{i}}
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_sqrt</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.4630
+#&gt;  0.8983
+#&gt;     nan
+#&gt;  0.6670
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+    <div class="page-header">
+    <h1>Square</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_square.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Square</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_square</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="square-input-out-null-gt-tensor-"><a class="anchor" href="#square-input-out-null-gt-tensor-"></a>square(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the square of the elements of <code>input</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_square</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.1779
+#&gt;  0.0218
+#&gt;  0.0193
+#&gt;  0.7057
+#&gt; [ CPUFloatType{4} ]</div></pre>
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+    <div class="page-header">
+    <h1>Squeeze</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_squeeze.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Squeeze</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_squeeze</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int, optional) if given, the input will be squeezed only in           this dimension</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>The returned tensor shares the storage with the input tensor,
+so changing the contents of one will change the contents of the other.</p>
+    <h2 class="hasAnchor" id="squeeze-input-dim-null-out-null-gt-tensor-"><a class="anchor" href="#squeeze-input-dim-null-out-null-gt-tensor-"></a>squeeze(input, dim=NULL, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a tensor with all the dimensions of <code>input</code> of size <code>1</code> removed.</p>
+<p>For example, if <code>input</code> is of shape:
+\((A \times 1 \times B \times C \times 1 \times D)\) then the <code>out</code> tensor
+will be of shape: \((A \times B \times C \times D)\).</p>
+<p>When <code>dim</code> is given, a squeeze operation is done only in the given
+dimension. If <code>input</code> is of shape: \((A \times 1 \times B)\),
+<code>squeeze(input, 0)</code> leaves the tensor unchanged, but <code>squeeze(input, 1)</code>
+will squeeze the tensor to the shape \((A \times B)\).</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_zeros.html'>torch_zeros</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>2</span>))
+<span class='kw'>x</span>
+<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'>torch_squeeze</span>(<span class='kw'>x</span>)
+<span class='kw'>y</span>
+<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'>torch_squeeze</span>(<span class='kw'>x</span>, <span class='fl'>1</span>)
+<span class='kw'>y</span>
+<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'>torch_squeeze</span>(<span class='kw'>x</span>, <span class='fl'>2</span>)
+<span class='kw'>y</span>
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,1,.,.) = 
+#&gt;   0  0
+#&gt; 
+#&gt; (2,1,.,.) = 
+#&gt;   0  0
+#&gt; 
+#&gt; (1,2,.,.) = 
+#&gt;   0  0
+#&gt; 
+#&gt; (2,2,.,.) = 
+#&gt;   0  0
+#&gt; [ CPUFloatType{2,2,1,2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+
+      
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Stack</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_stack.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Stack</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_stack</span>(<span class='kw'>tensors</span>, dim = <span class='fl'>1L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensors</th>
+      <td><p>(sequence of Tensors) sequence of tensors to concatenate</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) dimension to insert. Has to be between 0 and the number        of dimensions of concatenated tensors (inclusive)</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="stack-tensors-dim-out-null-gt-tensor-"><a class="anchor" href="#stack-tensors-dim-out-null-gt-tensor-"></a>stack(tensors, dim=0, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Concatenates sequence of tensors along a new dimension.</p>
+<p>All tensors need to be of the same size.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
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+
+      
+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Std</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_std.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Std</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_std</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, unbiased = <span class='fl'>TRUE</span>, keepdim = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
+    </tr>
+    <tr>
+      <th>unbiased</th>
+      <td><p>(bool) whether to use the unbiased estimation or not</p></td>
+    </tr>
+    <tr>
+      <th>keepdim</th>
+      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="std-input-unbiased-true-gt-tensor-"><a class="anchor" href="#std-input-unbiased-true-gt-tensor-"></a>std(input, unbiased=TRUE) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the standard-deviation of all elements in the <code>input</code> tensor.</p>
+<p>If <code>unbiased</code> is <code>FALSE</code>, then the standard-deviation will be calculated
+via the biased estimator. Otherwise, Bessel's correction will be used.</p>
+    <h2 class="hasAnchor" id="std-input-dim-unbiased-true-keepdim-false-out-null-gt-tensor-"><a class="anchor" href="#std-input-dim-unbiased-true-keepdim-false-out-null-gt-tensor-"></a>std(input, dim, unbiased=TRUE, keepdim=False, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the standard-deviation of each row of the <code>input</code> tensor in the
+dimension <code>dim</code>. If <code>dim</code> is a list of dimensions,
+reduce over all of them.</p>
+<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size
+as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
+Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
+output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
+<p>If <code>unbiased</code> is <code>FALSE</code>, then the standard-deviation will be calculated
+via the biased estimator. Otherwise, Bessel's correction will be used.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_std</span>(<span class='kw'>a</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_std</span>(<span class='kw'>a</span>, dim=<span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.1757
+#&gt;  1.2405
+#&gt;  0.6001
+#&gt;  2.1534
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Std_mean</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_std_mean.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Std_mean</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_std_mean</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, unbiased = <span class='fl'>TRUE</span>, keepdim = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
+    </tr>
+    <tr>
+      <th>unbiased</th>
+      <td><p>(bool) whether to use the unbiased estimation or not</p></td>
+    </tr>
+    <tr>
+      <th>keepdim</th>
+      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="std-mean-input-unbiased-true-gt-tensor-tensor-"><a class="anchor" href="#std-mean-input-unbiased-true-gt-tensor-tensor-"></a>std_mean(input, unbiased=TRUE) -&gt; (Tensor, Tensor) </h2>
+
+    
+
+
+<p>Returns the standard-deviation and mean of all elements in the <code>input</code> tensor.</p>
+<p>If <code>unbiased</code> is <code>FALSE</code>, then the standard-deviation will be calculated
+via the biased estimator. Otherwise, Bessel's correction will be used.</p>
+    <h2 class="hasAnchor" id="std-mean-input-dim-unbiased-true-keepdim-false-gt-tensor-tensor-"><a class="anchor" href="#std-mean-input-dim-unbiased-true-keepdim-false-gt-tensor-tensor-"></a>std_mean(input, dim, unbiased=TRUE, keepdim=False) -&gt; (Tensor, Tensor) </h2>
+
+    
+
+
+<p>Returns the standard-deviation and mean of each row of the <code>input</code> tensor in the
+dimension <code>dim</code>. If <code>dim</code> is a list of dimensions,
+reduce over all of them.</p>
+<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size
+as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
+Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
+output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
+<p>If <code>unbiased</code> is <code>FALSE</code>, then the standard-deviation will be calculated
+via the biased estimator. Otherwise, Bessel's correction will be used.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_std_mean</span>(<span class='kw'>a</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_std_mean</span>(<span class='kw'>a</span>, <span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt;  1.5486
+#&gt;  0.8768
+#&gt;  0.3827
+#&gt;  2.0388
+#&gt; [ CPUFloatType{4} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt; -0.5692
+#&gt;  0.2571
+#&gt;  0.0071
+#&gt; -0.3322
+#&gt; [ CPUFloatType{4} ]
+#&gt; </div></pre>
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Stft</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
+    <div class="hidden name"><code>torch_stft.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Stft</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_stft</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>n_fft</span>,
+  hop_length = <span class='kw'>NULL</span>,
+  win_length = <span class='kw'>NULL</span>,
+  window = <span class='kw'>NULL</span>,
+  center = <span class='fl'>TRUE</span>,
+  pad_mode = <span class='st'>"reflect"</span>,
+  normalized = <span class='fl'>FALSE</span>,
+  onesided = <span class='fl'>TRUE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(Tensor) the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>n_fft</th>
+      <td><p>(int) size of Fourier transform</p></td>
+    </tr>
+    <tr>
+      <th>hop_length</th>
+      <td><p>(int, optional) the distance between neighboring sliding window        frames. Default: <code>NULL</code> (treated as equal to <code><a href='https://rdrr.io/r/base/Round.html'>floor(n_fft / 4)</a></code>)</p></td>
+    </tr>
+    <tr>
+      <th>win_length</th>
+      <td><p>(int, optional) the size of window frame and STFT filter.        Default: <code>NULL</code>  (treated as equal to <code>n_fft</code>)</p></td>
+    </tr>
+    <tr>
+      <th>window</th>
+      <td><p>(Tensor, optional) the optional window function.        Default: <code>NULL</code> (treated as window of all \(1\) s)</p></td>
+    </tr>
+    <tr>
+      <th>center</th>
+      <td><p>(bool, optional) whether to pad <code>input</code> on both sides so        that the \(t\)-th frame is centered at time \(t \times \mbox{hop\_length}\).        Default: <code>TRUE</code></p></td>
+    </tr>
+    <tr>
+      <th>pad_mode</th>
+      <td><p>(string, optional) controls the padding method used when        <code>center</code> is <code>TRUE</code>. Default: <code>"reflect"</code></p></td>
+    </tr>
+    <tr>
+      <th>normalized</th>
+      <td><p>(bool, optional) controls whether to return the normalized STFT results         Default: <code>FALSE</code></p></td>
+    </tr>
+    <tr>
+      <th>onesided</th>
+      <td><p>(bool, optional) controls whether to return half of results to        avoid redundancy Default: <code>TRUE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="short-time-fourier-transform-stft-"><a class="anchor" href="#short-time-fourier-transform-stft-"></a>Short-time Fourier transform (STFT). </h2>
+
+    
+
+
+<p>Short-time Fourier transform (STFT).</p><pre>Ignoring the optional batch dimension, this method computes the following
+expression:
+</pre>
+
+<p>$$
+        X[m, \omega] = \sum_{k = 0}^{\mbox{win\_length-1}}%
+                            \mbox{window}[k]\ \mbox{input}[m \times \mbox{hop\_length} + k]\ %
+                            \exp\left(- j \frac{2 \pi \cdot \omega k}{\mbox{win\_length}}\right),
+$$
+where \(m\) is the index of the sliding window, and \(\omega\) is
+the frequency that \(0 \leq \omega &lt; \mbox{n\_fft}\). When
+<code>onesided</code> is the default value <code>TRUE</code>,</p><pre>* `input` must be either a 1-D time sequence or a 2-D batch of time
+  sequences.
+
+* If `hop_length` is `NULL` (default), it is treated as equal to
+  `floor(n_fft / 4)`.
+
+* If `win_length` is `NULL` (default), it is treated as equal to
+  `n_fft`.
+
+* `window` can be a 1-D tensor of size `win_length`, e.g., from
+  `torch_hann_window`. If `window` is `NULL` (default), it is
+  treated as if having \eqn{1} everywhere in the window. If
+  \eqn{\mbox{win\_length} &lt; \mbox{n\_fft}}, `window` will be padded on
+  both sides to length `n_fft` before being applied.
+
+* If `center` is `TRUE` (default), `input` will be padded on
+  both sides so that the \eqn{t}-th frame is centered at time
+  \eqn{t \times \mbox{hop\_length}}. Otherwise, the \eqn{t}-th frame
+  begins at time  \eqn{t \times \mbox{hop\_length}}.
+
+* `pad_mode` determines the padding method used on `input` when
+  `center` is `TRUE`. See `torch_nn.functional.pad` for
+  all available options. Default is `"reflect"`.
+
+* If `onesided` is `TRUE` (default), only values for \eqn{\omega}
+  in \eqn{\left[0, 1, 2, \dots, \left\lfloor \frac{\mbox{n\_fft}}{2} \right\rfloor + 1\right]}
+  are returned because the real-to-complex Fourier transform satisfies the
+  conjugate symmetry, i.e., \eqn{X[m, \omega] = X[m, \mbox{n\_fft} - \omega]^*}.
+
+* If `normalized` is `TRUE` (default is `FALSE`), the function
+  returns the normalized STFT results, i.e., multiplied by \eqn{(\mbox{frame\_length})^{-0.5}}.
+
+Returns the real and the imaginary parts together as one tensor of size
+\eqn{(* \times N \times T \times 2)}, where \eqn{*} is the optional
+batch size of `input`, \eqn{N} is the number of frequencies where
+STFT is applied, \eqn{T} is the total number of frames used, and each pair
+in the last dimension represents a complex number as the real part and the
+imaginary part.
+</pre>
+
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p>This function changed signature at version 0.4.1. Calling with the
+previous signature may cause error or return incorrect result.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Sum</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_sum.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Sum</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_sum</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, keepdim = <span class='fl'>FALSE</span>, dtype = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
+    </tr>
+    <tr>
+      <th>keepdim</th>
+      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to <code>dtype</code> before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="sum-input-dtype-null-gt-tensor-"><a class="anchor" href="#sum-input-dtype-null-gt-tensor-"></a>sum(input, dtype=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the sum of all elements in the <code>input</code> tensor.</p>
+    <h2 class="hasAnchor" id="sum-input-dim-keepdim-false-dtype-null-gt-tensor-"><a class="anchor" href="#sum-input-dim-keepdim-false-dtype-null-gt-tensor-"></a>sum(input, dim, keepdim=False, dtype=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the sum of each row of the <code>input</code> tensor in the given
+dimension <code>dim</code>. If <code>dim</code> is a list of dimensions,
+reduce over all of them.</p>
+<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size
+as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
+Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
+output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_sum</span>(<span class='kw'>a</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_sum</span>(<span class='kw'>a</span>, <span class='fl'>1</span>)
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0</span>, <span class='fl'>4</span> <span class='op'>*</span> <span class='fl'>5</span> <span class='op'>*</span> <span class='fl'>6</span>)<span class='op'>$</span><span class='fu'>view</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>, <span class='fl'>6</span>))
+<span class='fu'>torch_sum</span>(<span class='kw'>b</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;   435
+#&gt;  1335
+#&gt;  2235
+#&gt;  3135
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </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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+
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+    <h1>Svd</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_svd.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Svd</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_svd</span>(<span class='kw'>self</span>, some = <span class='fl'>TRUE</span>, compute_uv = <span class='fl'>TRUE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor of size \((*, m, n)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is zero or more                    batch dimensions consisting of \(m \times n\) matrices.</p></td>
+    </tr>
+    <tr>
+      <th>some</th>
+      <td><p>(bool, optional) controls the shape of returned <code>U</code> and <code>V</code></p></td>
+    </tr>
+    <tr>
+      <th>compute_uv</th>
+      <td><p>(bool, optional) option whether to compute <code>U</code> and <code>V</code> or not</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>The singular values are returned in descending order. If <code>input</code> is a batch of matrices,
+then the singular values of each matrix in the batch is returned in descending order.</p>
+<p>The implementation of SVD on CPU uses the LAPACK routine <code>?gesdd</code> (a divide-and-conquer
+algorithm) instead of <code>?gesvd</code> for speed. Analogously, the SVD on GPU uses the MAGMA routine
+<code>gesdd</code> as well.</p>
+<p>Irrespective of the original strides, the returned matrix <code>U</code>
+will be transposed, i.e. with strides <code>U.contiguous().transpose(-2, -1).stride()</code></p>
+<p>Extra care needs to be taken when backward through <code>U</code> and <code>V</code>
+outputs. Such operation is really only stable when <code>input</code> is
+full rank with all distinct singular values. Otherwise, <code>NaN</code> can
+appear as the gradients are not properly defined. Also, notice that
+double backward will usually do an additional backward through <code>U</code> and
+<code>V</code> even if the original backward is only on <code>S</code>.</p>
+<p>When <code>some</code> = <code>FALSE</code>, the gradients on <code>U[..., :, min(m, n):]</code>
+and <code>V[..., :, min(m, n):]</code> will be ignored in backward as those vectors
+can be arbitrary bases of the subspaces.</p>
+<p>When <code>compute_uv</code> = <code>FALSE</code>, backward cannot be performed since <code>U</code> and <code>V</code>
+from the forward pass is required for the backward operation.</p>
+    <h2 class="hasAnchor" id="svd-input-some-true-compute-uv-true-gt-tensor-tensor-tensor-"><a class="anchor" href="#svd-input-some-true-compute-uv-true-gt-tensor-tensor-tensor-"></a>svd(input, some=TRUE, compute_uv=TRUE) -&gt; (Tensor, Tensor, Tensor) </h2>
+
+    
+
+
+<p>This function returns a namedtuple <code>(U, S, V)</code> which is the singular value
+decomposition of a input real matrix or batches of real matrices <code>input</code> such that
+\(input = U \times diag(S) \times V^T\).</p>
+<p>If <code>some</code> is <code>TRUE</code> (default), the method returns the reduced singular value decomposition
+i.e., if the last two dimensions of <code>input</code> are <code>m</code> and <code>n</code>, then the returned
+<code>U</code> and <code>V</code> matrices will contain only \(min(n, m)\) orthonormal columns.</p>
+<p>If <code>compute_uv</code> is <code>FALSE</code>, the returned <code>U</code> and <code>V</code> matrices will be zero matrices
+of shape \((m \times m)\) and \((n \times n)\) respectively. <code>some</code> will be ignored here.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span>
+<span class='kw'>out</span> <span class='op'>=</span> <span class='fu'>torch_svd</span>(<span class='kw'>a</span>)
+<span class='kw'>u</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>1</span>]]
+<span class='kw'>s</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>2</span>]]
+<span class='kw'>v</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>3</span>]]
+<span class='fu'><a href='torch_dist.html'>torch_dist</a></span>(<span class='kw'>a</span>, <span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='kw'>u</span>, <span class='fu'><a href='torch_diag.html'>torch_diag</a></span>(<span class='kw'>s</span>)), <span class='kw'>v</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>()))
+<span class='kw'>a_big</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>7</span>, <span class='fl'>5</span>, <span class='fl'>3</span>))
+<span class='kw'>out</span> <span class='op'>=</span> <span class='fu'>torch_svd</span>(<span class='kw'>a_big</span>)
+<span class='kw'>u</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>1</span>]]
+<span class='kw'>s</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>2</span>]]
+<span class='kw'>v</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>3</span>]]
+<span class='fu'><a href='torch_dist.html'>torch_dist</a></span>(<span class='kw'>a_big</span>, <span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span>(<span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span>(<span class='kw'>u</span>, <span class='fu'><a href='torch_diag_embed.html'>torch_diag_embed</a></span>(<span class='kw'>s</span>)), <span class='kw'>v</span><span class='op'>$</span><span class='fu'>transpose</span>(<span class='op'>-</span><span class='fl'>2</span>, <span class='op'>-</span><span class='fl'>1</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; 3.34915e-06
+#&gt; [ CPUFloatType{} ]</div></pre>
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+      <a href="../articles/getting-started/custom-nn.html">Custom nn modules</a>
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+    </li>
+    <li class="dropdown-header">Torch Mechanics</li>
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+    </li>
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+    </li>
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Symeig</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_symeig.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Symeig</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_symeig</span>(<span class='kw'>self</span>, eigenvectors = <span class='fl'>FALSE</span>, upper = <span class='fl'>TRUE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor of size \((*, n, n)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is zero or more                    batch dimensions consisting of symmetric matrices.</p></td>
+    </tr>
+    <tr>
+      <th>eigenvectors</th>
+      <td><p>(boolean, optional) controls whether eigenvectors have to be computed</p></td>
+    </tr>
+    <tr>
+      <th>upper</th>
+      <td><p>(boolean, optional) controls whether to consider upper-triangular or lower-triangular region</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>The eigenvalues are returned in ascending order. If <code>input</code> is a batch of matrices,
+then the eigenvalues of each matrix in the batch is returned in ascending order.</p>
+<p>Irrespective of the original strides, the returned matrix <code>V</code> will
+be transposed, i.e. with strides <code>V.contiguous().transpose(-1, -2).stride()</code>.</p>
+<p>Extra care needs to be taken when backward through outputs. Such
+operation is really only stable when all eigenvalues are distinct.
+Otherwise, <code>NaN</code> can appear as the gradients are not properly defined.</p>
+    <h2 class="hasAnchor" id="symeig-input-eigenvectors-false-upper-true-gt-tensor-tensor-"><a class="anchor" href="#symeig-input-eigenvectors-false-upper-true-gt-tensor-tensor-"></a>symeig(input, eigenvectors=False, upper=TRUE) -&gt; (Tensor, Tensor) </h2>
+
+    
+
+
+<p>This function returns eigenvalues and eigenvectors
+of a real symmetric matrix <code>input</code> or a batch of real symmetric matrices,
+represented by a namedtuple (eigenvalues, eigenvectors).</p>
+<p>This function calculates all eigenvalues (and vectors) of <code>input</code>
+such that \(\mbox{input} = V \mbox{diag}(e) V^T\).</p>
+<p>The boolean argument <code>eigenvectors</code> defines computation of
+both eigenvectors and eigenvalues or eigenvalues only.</p>
+<p>If it is <code>FALSE</code>, only eigenvalues are computed. If it is <code>TRUE</code>,
+both eigenvalues and eigenvectors are computed.</p>
+<p>Since the input matrix <code>input</code> is supposed to be symmetric,
+only the upper triangular portion is used by default.</p>
+<p>If <code>upper</code> is <code>FALSE</code>, then lower triangular portion is used.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>, <span class='fl'>5</span>))
+<span class='kw'>a</span> <span class='op'>=</span> <span class='kw'>a</span> <span class='op'>+</span> <span class='kw'>a</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>()  <span class='co'># To make a symmetric</span>
+<span class='kw'>a</span>
+<span class='kw'>o</span> <span class='op'>=</span> <span class='fu'>torch_symeig</span>(<span class='kw'>a</span>, eigenvectors=<span class='fl'>TRUE</span>)
+<span class='kw'>e</span> <span class='op'>=</span> <span class='kw'>o</span>[[<span class='fl'>1</span>]]
+<span class='kw'>v</span> <span class='op'>=</span> <span class='kw'>o</span>[[<span class='fl'>2</span>]]
+<span class='kw'>e</span>
+<span class='kw'>v</span>
+<span class='kw'>a_big</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>, <span class='fl'>2</span>, <span class='fl'>2</span>))
+<span class='kw'>a_big</span> <span class='op'>=</span> <span class='kw'>a_big</span> <span class='op'>+</span> <span class='kw'>a_big</span><span class='op'>$</span><span class='fu'>transpose</span>(<span class='op'>-</span><span class='fl'>2</span>, <span class='op'>-</span><span class='fl'>1</span>)  <span class='co'># To make a_big symmetric</span>
+<span class='kw'>o</span> <span class='op'>=</span> <span class='kw'>a_big</span><span class='op'>$</span><span class='fu'>symeig</span>(eigenvectors=<span class='fl'>TRUE</span>)
+<span class='kw'>e</span> <span class='op'>=</span> <span class='kw'>o</span>[[<span class='fl'>1</span>]]
+<span class='kw'>v</span> <span class='op'>=</span> <span class='kw'>o</span>[[<span class='fl'>2</span>]]
+<span class='fu'><a href='torch_allclose.html'>torch_allclose</a></span>(<span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span>(<span class='kw'>v</span>, <span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span>(<span class='kw'>e</span><span class='op'>$</span><span class='fu'>diag_embed</span>(), <span class='kw'>v</span><span class='op'>$</span><span class='fu'>transpose</span>(<span class='op'>-</span><span class='fl'>2</span>, <span class='op'>-</span><span class='fl'>1</span>))), <span class='kw'>a_big</span>)
+}
+</div><div class='output co'>#&gt; [1] TRUE</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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+    <li class="dropdown-header">Beginers guide</li>
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+    </li>
+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
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+    </li>
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+      <a href="../articles/getting-started/control-flow-and-weight-sharing.html">Control flow &amp; Weight sharing</a>
+    </li>
+    <li class="dropdown-header">Torch Mechanics</li>
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+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
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+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
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+      <a href="../articles/getting-started/neural-networks.html">Neural networks</a>
+    </li>
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+
+      
+
+      </header>
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+<div class="row">
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+    <div class="page-header">
+    <h1>T</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_t.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>T</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_t</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="t-input-gt-tensor-"><a class="anchor" href="#t-input-gt-tensor-"></a>t(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>Expects <code>input</code> to be &lt;= 2-D tensor and transposes dimensions 0
+and 1.</p>
+<p>0-D and 1-D tensors are returned as is. When input is a 2-D tensor this
+is equivalent to <code>transpose(input, 0, 1)</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>,<span class='fl'>3</span>))
+<span class='kw'>x</span>
+<span class='fu'>torch_t</span>(<span class='kw'>x</span>)
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>))
+<span class='kw'>x</span>
+<span class='fu'>torch_t</span>(<span class='kw'>x</span>)
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+<span class='kw'>x</span>
+<span class='fu'>torch_t</span>(<span class='kw'>x</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -1.0611 -1.1550
+#&gt; -1.1566  0.8038
+#&gt; -0.2224  0.2330
+#&gt; [ CPUFloatType{3,2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/torch_take.html b/static/docs/dev/reference/torch_take.html
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+    <div class="page-header">
+    <h1>Take</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_take.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Take</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_take</span>(<span class='kw'>self</span>, <span class='kw'>index</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>index</th>
+      <td><p>(LongTensor) the indices into tensor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="take-input-index-gt-tensor-"><a class="anchor" href="#take-input-index-gt-tensor-"></a>take(input, index) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the elements of <code>input</code> at the given indices.
+The input tensor is treated as if it were viewed as a 1-D tensor. The result
+takes the same shape as the indices.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>src</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>,<span class='fl'>3</span>,<span class='fl'>5</span>,<span class='fl'>6</span>,<span class='fl'>7</span>,<span class='fl'>8</span>), ncol = <span class='fl'>3</span>, byrow = <span class='fl'>TRUE</span>))
+<span class='fu'>torch_take</span>(<span class='kw'>src</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>5</span>), dtype = <span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>()))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  4
+#&gt;  3
+#&gt;  7
+#&gt; [ CPUFloatType{3} ]</div></pre>
+  </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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+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
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+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Tan</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_tan.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Tan</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_tan</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="tan-input-out-null-gt-tensor-"><a class="anchor" href="#tan-input-out-null-gt-tensor-"></a>tan(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the tangent of the elements of <code>input</code>.</p>
+<p>$$
+    \mbox{out}_{i} = \tan(\mbox{input}_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_tan</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -1.9736
+#&gt; -1.0167
+#&gt; -0.7853
+#&gt;  0.9984
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
+  </div>
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+
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+</div>
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+    <div class="page-header">
+    <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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_tanh.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Tanh</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_tanh</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="tanh-input-out-null-gt-tensor-"><a class="anchor" href="#tanh-input-out-null-gt-tensor-"></a>tanh(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the hyperbolic tangent of the elements
+of <code>input</code>.</p>
+<p>$$
+    \mbox{out}_{i} = \tanh(\mbox{input}_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_tanh</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0.4951
+#&gt;  0.0453
+#&gt;  0.9080
+#&gt; -0.8097
+#&gt; [ CPUFloatType{4} ]</div></pre>
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+    <div class="page-header">
+    <h1>Converts R objects to a torch tensor</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/tensor.R'><code>R/tensor.R</code></a></small>
+    <div class="hidden name"><code>torch_tensor.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Converts R objects to a torch tensor</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_tensor</span>(
+  <span class='kw'>data</span>,
+  dtype = <span class='kw'>NULL</span>,
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>,
+  pin_memory = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>data</th>
+      <td><p>an R atomic vector, matrix or array</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>a <a href='torch_dtype.html'>torch_dtype</a> instance</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>a device creted with <code><a href='torch_device.html'>torch_device()</a></code></p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>if autograd should record operations on the returned tensor.</p></td>
+    </tr>
+    <tr>
+      <th>pin_memory</th>
+      <td><p>If set, returned tensor would be allocated in the pinned memory.</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='fu'>torch_tensor</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>3</span>,<span class='fl'>4</span>))
+<span class='fu'>torch_tensor</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>3</span>,<span class='fl'>4</span>), dtype = <span class='fu'><a href='torch_dtype.html'>torch_int</a></span>())
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1
+#&gt;  2
+#&gt;  3
+#&gt;  4
+#&gt; [ CPUIntType{4} ]</div></pre>
+  </div>
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+    <div class="page-header">
+    <h1>Tensordot</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
+    <div class="hidden name"><code>torch_tensordot.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Returns a contraction of a and b over multiple dimensions.
+<code>tensordot</code> implements a generalized matrix product.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_tensordot</span>(<span class='kw'>a</span>, <span class='kw'>b</span>, dims = <span class='fl'>2</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>a</th>
+      <td><p>(Tensor) Left tensor to contract</p></td>
+    </tr>
+    <tr>
+      <th>b</th>
+      <td><p>(Tensor) Right tensor to contract</p></td>
+    </tr>
+    <tr>
+      <th>dims</th>
+      <td><p>(int or tuple of two lists of integers) number of dimensions to     contract or explicit lists of dimensions for <code>a</code> and     <code>b</code> respectively</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(start = <span class='fl'>0</span>, end = <span class='fl'>60.</span>)<span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/stats/reshape.html'>reshape</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span>))
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(start = <span class='fl'>0</span>, end = <span class='fl'>24.</span>)<span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/stats/reshape.html'>reshape</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>3</span>, <span class='fl'>2</span>))
+<span class='fu'>torch_tensordot</span>(<span class='kw'>a</span>, <span class='kw'>b</span>, dims = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>), <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>)))
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span>, device=<span class='st'>'cuda'</span>)
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>, <span class='fl'>6</span>, device=<span class='st'>'cuda'</span>)
+<span class='kw'>c</span> <span class='op'>=</span> <span class='fu'>torch_tensordot</span>(<span class='kw'>a</span>, <span class='kw'>b</span>, dims=<span class='fl'>2</span>)<span class='op'>$</span><span class='fu'>cpu</span>()
+}
+}
+</div></pre>
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+    <div class="page-header">
+    <h1>Threshold_</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_threshold_.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Threshold_</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_threshold_</span>(<span class='kw'>self</span>, <span class='kw'>threshold</span>, <span class='kw'>value</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>input tensor</p></td>
+    </tr>
+    <tr>
+      <th>threshold</th>
+      <td><p>The value to threshold at</p></td>
+    </tr>
+    <tr>
+      <th>value</th>
+      <td><p>The value to replace with</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="threshold-input-threshold-value-gt-tensor-"><a class="anchor" href="#threshold-input-threshold-value-gt-tensor-"></a>threshold_(input, threshold, value) -&gt; Tensor </h2>
+
+    
+
+
+<p>In-place version of <code>torch_threshold</code>.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+    <div class="page-header">
+    <h1>Topk</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_topk.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Topk</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_topk</span>(<span class='kw'>self</span>, <span class='kw'>k</span>, dim = <span class='op'>-</span><span class='fl'>1L</span>, largest = <span class='fl'>TRUE</span>, sorted = <span class='fl'>TRUE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>k</th>
+      <td><p>(int) the k in "top-k"</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int, optional) the dimension to sort along</p></td>
+    </tr>
+    <tr>
+      <th>largest</th>
+      <td><p>(bool, optional) controls whether to return largest or           smallest elements</p></td>
+    </tr>
+    <tr>
+      <th>sorted</th>
+      <td><p>(bool, optional) controls whether to return the elements           in sorted order</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="topk-input-k-dim-null-largest-true-sorted-true-gt-tensor-longtensor-"><a class="anchor" href="#topk-input-k-dim-null-largest-true-sorted-true-gt-tensor-longtensor-"></a>topk(input, k, dim=NULL, largest=TRUE, sorted=TRUE) -&gt; (Tensor, LongTensor) </h2>
+
+    
+
+
+<p>Returns the <code>k</code> largest elements of the given <code>input</code> tensor along
+a given dimension.</p>
+<p>If <code>dim</code> is not given, the last dimension of the <code>input</code> is chosen.</p>
+<p>If <code>largest</code> is <code>FALSE</code> then the <code>k</code> smallest elements are returned.</p>
+<p>A namedtuple of <code>(values, indices)</code> is returned, where the <code>indices</code> are the indices
+of the elements in the original <code>input</code> tensor.</p>
+<p>The boolean option <code>sorted</code> if <code>TRUE</code>, will make sure that the returned
+<code>k</code> elements are themselves sorted</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>6.</span>)
+<span class='kw'>x</span>
+<span class='fu'>torch_topk</span>(<span class='kw'>x</span>, <span class='fl'>3</span>)
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt;  5
+#&gt;  4
+#&gt;  3
+#&gt; [ CPUFloatType{3} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt;  4
+#&gt;  3
+#&gt;  2
+#&gt; [ CPULongType{3} ]
+#&gt; </div></pre>
+  </div>
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+      <a href="../articles/extending-autograd.html">Extending autograd</a>
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+<li class="dropdown-header">Examples</li>
+<li>
+  <a href="../reference/index.html">Reference</a>
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Trace</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_trace.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Trace</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_trace</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="trace-input-gt-tensor-"><a class="anchor" href="#trace-input-gt-tensor-"></a>trace(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the sum of the elements of the diagonal of the input 2-D matrix.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>10.</span>)<span class='op'>$</span><span class='fu'>view</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='kw'>x</span>
+<span class='fu'>torch_trace</span>(<span class='kw'>x</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; 15
+#&gt; [ CPUFloatType{} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+
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+    <div class="page-header">
+    <h1>Transpose</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_transpose.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Transpose</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_transpose</span>(<span class='kw'>self</span>, <span class='kw'>dim0</span>, <span class='kw'>dim1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim0</th>
+      <td><p>(int) the first dimension to be transposed</p></td>
+    </tr>
+    <tr>
+      <th>dim1</th>
+      <td><p>(int) the second dimension to be transposed</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="transpose-input-dim-dim-gt-tensor-"><a class="anchor" href="#transpose-input-dim-dim-gt-tensor-"></a>transpose(input, dim0, dim1) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a tensor that is a transposed version of <code>input</code>.
+The given dimensions <code>dim0</code> and <code>dim1</code> are swapped.</p>
+<p>The resulting <code>out</code> tensor shares it's underlying storage with the
+<code>input</code> tensor, so changing the content of one would change the content
+of the other.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+<span class='kw'>x</span>
+<span class='fu'>torch_transpose</span>(<span class='kw'>x</span>, <span class='fl'>1</span>, <span class='fl'>2</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.0214 -0.8931
+#&gt; -1.7985  0.4838
+#&gt; -0.6487  0.5333
+#&gt; [ CPUFloatType{3,2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    </div><!--/.nav-collapse -->
+  </div><!--/.container -->
+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Trapz</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_trapz.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Trapz</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_trapz</span>(<span class='kw'>y</span>, dx = <span class='fl'>1L</span>, <span class='kw'>x</span>, dim = <span class='op'>-</span><span class='fl'>1L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>y</th>
+      <td><p>(Tensor) The values of the function to integrate</p></td>
+    </tr>
+    <tr>
+      <th>dx</th>
+      <td><p>(float) The distance between points at which <code>y</code> is sampled.</p></td>
+    </tr>
+    <tr>
+      <th>x</th>
+      <td><p>(Tensor) The points at which the function <code>y</code> is sampled.        If <code>x</code> is not in ascending order, intervals on which it is decreasing        contribute negatively to the estimated integral (i.e., the convention        \(\int_a^b f = -\int_b^a f\) is followed).</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) The dimension along which to integrate.        By default, use the last dimension.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="trapz-y-x-dim-gt-tensor-"><a class="anchor" href="#trapz-y-x-dim-gt-tensor-"></a>trapz(y, x, *, dim=-1) -&gt; Tensor </h2>
+
+    
+
+
+<p>Estimate \(\int y\,dx\) along <code>dim</code>, using the trapezoid rule.</p>
+    <h2 class="hasAnchor" id="trapz-y-dx-dim-gt-tensor-"><a class="anchor" href="#trapz-y-dx-dim-gt-tensor-"></a>trapz(y, *, dx=1, dim=-1) -&gt; Tensor </h2>
+
+    
+
+
+<p>As above, but the sample points are spaced uniformly at a distance of <code>dx</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+<span class='kw'>y</span>
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>), ncol = <span class='fl'>3</span>, byrow=<span class='fl'>TRUE</span>))
+<span class='fu'>torch_trapz</span>(<span class='kw'>y</span>, x = <span class='kw'>x</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  2.8381
+#&gt;  0.2725
+#&gt; [ CPUFloatType{2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Triangular_solve</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_triangular_solve.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Triangular_solve</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_triangular_solve</span>(
+  <span class='kw'>self</span>,
+  <span class='kw'>A</span>,
+  upper = <span class='fl'>TRUE</span>,
+  transpose = <span class='fl'>FALSE</span>,
+  unitriangular = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) multiple right-hand sides of size \((*, m, k)\) where                \(*\) is zero of more batch dimensions (\(b\))</p></td>
+    </tr>
+    <tr>
+      <th>A</th>
+      <td><p>(Tensor) the input triangular coefficient matrix of size \((*, m, m)\)                where \(*\) is zero or more batch dimensions</p></td>
+    </tr>
+    <tr>
+      <th>upper</th>
+      <td><p>(bool, optional) whether to solve the upper-triangular system        of equations (default) or the lower-triangular system of equations. Default: <code>TRUE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>transpose</th>
+      <td><p>(bool, optional) whether \(A\) should be transposed before        being sent into the solver. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>unitriangular</th>
+      <td><p>(bool, optional) whether \(A\) is unit triangular.        If TRUE, the diagonal elements of \(A\) are assumed to be        1 and not referenced from \(A\). Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="triangular-solve-input-a-upper-true-transpose-false-unitriangular-false-gt-tensor-tensor-"><a class="anchor" href="#triangular-solve-input-a-upper-true-transpose-false-unitriangular-false-gt-tensor-tensor-"></a>triangular_solve(input, A, upper=TRUE, transpose=False, unitriangular=False) -&gt; (Tensor, Tensor) </h2>
+
+    
+
+
+<p>Solves a system of equations with a triangular coefficient matrix \(A\)
+and multiple right-hand sides \(b\).</p>
+<p>In particular, solves \(AX = b\) and assumes \(A\) is upper-triangular
+with the default keyword arguments.</p>
+<p><code>torch_triangular_solve(b, A)</code> can take in 2D inputs <code>b, A</code> or inputs that are
+batches of 2D matrices. If the inputs are batches, then returns
+batched outputs <code>X</code></p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>))<span class='op'>$</span><span class='fu'>triu</span>()
+<span class='kw'>A</span>
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+<span class='kw'>b</span>
+<span class='fu'>torch_triangular_solve</span>(<span class='kw'>b</span>, <span class='kw'>A</span>)
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt;  2.4651  2.4966 -0.8156
+#&gt; -1.9076 -0.5801  2.0013
+#&gt; [ CPUFloatType{2,3} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt; -0.8972 -0.6839
+#&gt;  0.0000 -0.6066
+#&gt; [ CPUFloatType{2,2} ]
+#&gt; </div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Tril</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_tril.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Tril</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_tril</span>(<span class='kw'>self</span>, diagonal = <span class='fl'>0L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>diagonal</th>
+      <td><p>(int, optional) the diagonal to consider</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="tril-input-diagonal-out-null-gt-tensor-"><a class="anchor" href="#tril-input-diagonal-out-null-gt-tensor-"></a>tril(input, diagonal=0, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the lower triangular part of the matrix (2-D tensor) or batch of matrices
+<code>input</code>, the other elements of the result tensor <code>out</code> are set to 0.</p>
+<p>The lower triangular part of the matrix is defined as the elements on and
+below the diagonal.</p>
+<p>The argument <code>diagonal</code> controls which diagonal to consider. If
+<code>diagonal</code> = 0, all elements on and below the main diagonal are
+retained. A positive value includes just as many diagonals above the main
+diagonal, and similarly a negative value excludes just as many diagonals below
+the main diagonal. The main diagonal are the set of indices
+\(\lbrace (i, i) \rbrace\) for \(i \in [0, \min\{d_{1}, d_{2}\} - 1]\) where
+\(d_{1}, d_{2}\) are the dimensions of the matrix.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_tril</span>(<span class='kw'>a</span>)
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>6</span>))
+<span class='kw'>b</span>
+<span class='fu'>torch_tril</span>(<span class='kw'>b</span>, diagonal=<span class='fl'>1</span>)
+<span class='fu'>torch_tril</span>(<span class='kw'>b</span>, diagonal=<span class='op'>-</span><span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000
+#&gt; -1.0780  0.0000  0.0000  0.0000  0.0000  0.0000
+#&gt;  0.2396 -1.1575  0.0000  0.0000  0.0000  0.0000
+#&gt;  0.6936 -0.7147  0.3855  0.0000  0.0000  0.0000
+#&gt; [ CPUFloatType{4,6} ]</div></pre>
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+    <div class="page-header">
+    <h1>Tril_indices</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
+    <div class="hidden name"><code>torch_tril_indices.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Tril_indices</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_tril_indices</span>(
+  <span class='kw'>row</span>,
+  <span class='kw'>col</span>,
+  offset = <span class='fl'>0</span>,
+  dtype = <span class='fu'><a href='torch_dtype.html'>torch_long</a></span>(),
+  device = <span class='st'>"cpu"</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>()
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>row</th>
+      <td><p>(<code>int</code>) number of rows in the 2-D matrix.</p></td>
+    </tr>
+    <tr>
+      <th>col</th>
+      <td><p>(<code>int</code>) number of columns in the 2-D matrix.</p></td>
+    </tr>
+    <tr>
+      <th>offset</th>
+      <td><p>(<code>int</code>) diagonal offset from the main diagonal.        Default: if not provided, 0.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, <code>torch_long</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) currently only support <code>torch_strided</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>When running on CUDA, `row * col` must be less than \eqn{2^{59}} to
+prevent overflow during calculation.
+</pre>
+
+    <h2 class="hasAnchor" id="tril-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-strided-gt-tensor-"><a class="anchor" href="#tril-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-strided-gt-tensor-"></a>tril_indices(row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the indices of the lower triangular part of a <code>row</code>-by-
+<code>col</code> matrix in a 2-by-N Tensor, where the first row contains row
+coordinates of all indices and the second row contains column coordinates.
+Indices are ordered based on rows and then columns.</p>
+<p>The lower triangular part of the matrix is defined as the elements on and
+below the diagonal.</p>
+<p>The argument <code>offset</code> controls which diagonal to consider. If
+<code>offset</code> = 0, all elements on and below the main diagonal are
+retained. A positive value includes just as many diagonals above the main
+diagonal, and similarly a negative value excludes just as many diagonals below
+the main diagonal. The main diagonal are the set of indices
+\(\lbrace (i, i) \rbrace\) for \(i \in [0, \min\{d_{1}, d_{2}\} - 1]\)
+where \(d_{1}, d_{2}\) are the dimensions of the matrix.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'>torch_tril_indices</span>(<span class='fl'>3</span>, <span class='fl'>3</span>)
+<span class='kw'>a</span>
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'>torch_tril_indices</span>(<span class='fl'>4</span>, <span class='fl'>3</span>, <span class='op'>-</span><span class='fl'>1</span>)
+<span class='kw'>a</span>
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'>torch_tril_indices</span>(<span class='fl'>4</span>, <span class='fl'>3</span>, <span class='fl'>1</span>)
+<span class='kw'>a</span>
+}
+}
+</div></pre>
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+    <div class="page-header">
+    <h1>Triu</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_triu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Triu</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_triu</span>(<span class='kw'>self</span>, diagonal = <span class='fl'>0L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>diagonal</th>
+      <td><p>(int, optional) the diagonal to consider</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="triu-input-diagonal-out-null-gt-tensor-"><a class="anchor" href="#triu-input-diagonal-out-null-gt-tensor-"></a>triu(input, diagonal=0, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the upper triangular part of a matrix (2-D tensor) or batch of matrices
+<code>input</code>, the other elements of the result tensor <code>out</code> are set to 0.</p>
+<p>The upper triangular part of the matrix is defined as the elements on and
+above the diagonal.</p>
+<p>The argument <code>diagonal</code> controls which diagonal to consider. If
+<code>diagonal</code> = 0, all elements on and above the main diagonal are
+retained. A positive value excludes just as many diagonals above the main
+diagonal, and similarly a negative value includes just as many diagonals below
+the main diagonal. The main diagonal are the set of indices
+\(\lbrace (i, i) \rbrace\) for \(i \in [0, \min\{d_{1}, d_{2}\} - 1]\) where
+\(d_{1}, d_{2}\) are the dimensions of the matrix.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_triu</span>(<span class='kw'>a</span>)
+<span class='fu'>torch_triu</span>(<span class='kw'>a</span>, diagonal=<span class='fl'>1</span>)
+<span class='fu'>torch_triu</span>(<span class='kw'>a</span>, diagonal=<span class='op'>-</span><span class='fl'>1</span>)
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>6</span>))
+<span class='kw'>b</span>
+<span class='fu'>torch_triu</span>(<span class='kw'>b</span>, diagonal=<span class='fl'>1</span>)
+<span class='fu'>torch_triu</span>(<span class='kw'>b</span>, diagonal=<span class='op'>-</span><span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.0762  1.2123  1.0293  1.3295 -0.9118  1.0105
+#&gt; -2.1972 -0.5764  1.0395  0.6328  1.9152  0.7976
+#&gt;  0.0000  0.7375 -0.2437 -0.6842 -1.2824  0.0754
+#&gt;  0.0000  0.0000  1.0719  1.1795 -0.2865  0.4529
+#&gt; [ CPUFloatType{4,6} ]</div></pre>
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+    <h1>Triu_indices</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
+    <div class="hidden name"><code>torch_triu_indices.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Triu_indices</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_triu_indices</span>(
+  <span class='kw'>row</span>,
+  <span class='kw'>col</span>,
+  offset = <span class='fl'>0</span>,
+  dtype = <span class='fu'><a href='torch_dtype.html'>torch_long</a></span>(),
+  device = <span class='st'>"cpu"</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>()
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>row</th>
+      <td><p>(<code>int</code>) number of rows in the 2-D matrix.</p></td>
+    </tr>
+    <tr>
+      <th>col</th>
+      <td><p>(<code>int</code>) number of columns in the 2-D matrix.</p></td>
+    </tr>
+    <tr>
+      <th>offset</th>
+      <td><p>(<code>int</code>) diagonal offset from the main diagonal.        Default: if not provided, 0.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, <code>torch_long</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) currently only support <code>torch_strided</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>When running on CUDA, `row * col` must be less than \eqn{2^{59}} to
+prevent overflow during calculation.
+</pre>
+
+    <h2 class="hasAnchor" id="triu-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-strided-gt-tensor-"><a class="anchor" href="#triu-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-strided-gt-tensor-"></a>triu_indices(row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the indices of the upper triangular part of a <code>row</code> by
+<code>col</code> matrix in a 2-by-N Tensor, where the first row contains row
+coordinates of all indices and the second row contains column coordinates.
+Indices are ordered based on rows and then columns.</p>
+<p>The upper triangular part of the matrix is defined as the elements on and
+above the diagonal.</p>
+<p>The argument <code>offset</code> controls which diagonal to consider. If
+<code>offset</code> = 0, all elements on and above the main diagonal are
+retained. A positive value excludes just as many diagonals above the main
+diagonal, and similarly a negative value includes just as many diagonals below
+the main diagonal. The main diagonal are the set of indices
+\(\lbrace (i, i) \rbrace\) for \(i \in [0, \min\{d_{1}, d_{2}\} - 1]\)
+where \(d_{1}, d_{2}\) are the dimensions of the matrix.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'>torch_triu_indices</span>(<span class='fl'>3</span>, <span class='fl'>3</span>)
+<span class='kw'>a</span>
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'>torch_triu_indices</span>(<span class='fl'>4</span>, <span class='fl'>3</span>, <span class='op'>-</span><span class='fl'>1</span>)
+<span class='kw'>a</span>
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'>torch_triu_indices</span>(<span class='fl'>4</span>, <span class='fl'>3</span>, <span class='fl'>1</span>)
+<span class='kw'>a</span>
+}
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
+  </div>
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+
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diff --git a/static/docs/dev/reference/torch_true_divide.html b/static/docs/dev/reference/torch_true_divide.html
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+    <div class="page-header">
+    <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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_true_divide.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>TRUE_divide</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_true_divide</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the dividend</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor or Scalar) the divisor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="true-divide-dividend-divisor-gt-tensor-"><a class="anchor" href="#true-divide-dividend-divisor-gt-tensor-"></a>true_divide(dividend, divisor) -&gt; Tensor </h2>
+
+    
+
+
+<p>Performs "true division" that always computes the division
+in floating point. Analogous to division in Python 3 and equivalent to
+<code><a href='torch_div.html'>torch_div</a></code> except when both inputs have bool or integer scalar types,
+in which case they are cast to the default (floating) scalar type before the division.</p>
+<p>$$
+    \mbox{out}_i = \frac{\mbox{dividend}_i}{\mbox{divisor}}
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>dividend</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>, <span class='fl'>3</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int</a></span>())
+<span class='kw'>divisor</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int</a></span>())
+<span class='fu'>torch_true_divide</span>(<span class='kw'>dividend</span>, <span class='kw'>divisor</span>)
+<span class='fu'>torch_true_divide</span>(<span class='kw'>dividend</span>, <span class='fl'>2</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  2.5000
+#&gt;  1.5000
+#&gt; [ CPUFloatType{2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Trunc</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_trunc.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Trunc</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_trunc</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="trunc-input-out-null-gt-tensor-"><a class="anchor" href="#trunc-input-out-null-gt-tensor-"></a>trunc(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the truncated integer values of
+the elements of <code>input</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_trunc</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1
+#&gt;  1
+#&gt; -0
+#&gt; -0
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Unbind</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_unbind.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Unbind</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_unbind</span>(<span class='kw'>self</span>, dim = <span class='fl'>1L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the tensor to 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'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_unbind</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fl'>1</span><span class='op'>:</span><span class='fl'>9</span>, ncol = <span class='fl'>3</span>, byrow=<span class='fl'>TRUE</span>)))
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt;  1
+#&gt;  2
+#&gt;  3
+#&gt; [ CPULongType{3} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt;  4
+#&gt;  5
+#&gt;  6
+#&gt; [ CPULongType{3} ]
+#&gt; 
+#&gt; [[3]]
+#&gt; torch_tensor 
+#&gt;  7
+#&gt;  8
+#&gt;  9
+#&gt; [ CPULongType{3} ]
+#&gt; </div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
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+</div>
+
+
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+</div>
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+    <div class="page-header">
+    <h1>Unique_consecutive</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_unique_consecutive.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Unique_consecutive</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_unique_consecutive</span>(
+  <span class='kw'>self</span>,
+  return_inverse = <span class='fl'>FALSE</span>,
+  return_counts = <span class='fl'>FALSE</span>,
+  dim = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>return_inverse</th>
+      <td><p>(bool) Whether to also return the indices for where        elements in the original input ended up in the returned unique list.</p></td>
+    </tr>
+    <tr>
+      <th>return_counts</th>
+      <td><p>(bool) Whether to also return the counts for each unique        element.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the dimension to apply unique. If <code>NULL</code>, the unique of the        flattened input is returned. default: <code>NULL</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
+
+    
+
+
+<p>Eliminates all but the first element from every consecutive group of equivalent elements.</p><pre>.. note:: This function is different from [`torch_unique`] in the sense that this function
+    only eliminates consecutive duplicate values. This semantics is similar to `std::unique`
+    in C++.
+</pre>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>2</span>))
+<span class='kw'>output</span> <span class='op'>=</span> <span class='fu'>torch_unique_consecutive</span>(<span class='kw'>x</span>)
+<span class='kw'>output</span>
+<span class='fu'>torch_unique_consecutive</span>(<span class='kw'>x</span>, return_inverse=<span class='fl'>TRUE</span>)
+<span class='fu'>torch_unique_consecutive</span>(<span class='kw'>x</span>, return_counts=<span class='fl'>TRUE</span>)
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt;  1
+#&gt;  2
+#&gt;  3
+#&gt;  1
+#&gt;  2
+#&gt; [ CPUFloatType{5} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt; [ CPULongType{0} ]
+#&gt; 
+#&gt; [[3]]
+#&gt; torch_tensor 
+#&gt;  2
+#&gt;  2
+#&gt;  1
+#&gt;  2
+#&gt;  1
+#&gt; [ CPULongType{5} ]
+#&gt; </div></pre>
+  </div>
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+    <div class="page-header">
+    <h1>Unsqueeze</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_unsqueeze.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Unsqueeze</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_unsqueeze</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the index at which to insert the singleton dimension</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="unsqueeze-input-dim-gt-tensor-"><a class="anchor" href="#unsqueeze-input-dim-gt-tensor-"></a>unsqueeze(input, dim) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with a dimension of size one inserted at the
+specified position.</p>
+<p>The returned tensor shares the same underlying data with this tensor.</p>
+<p>A <code>dim</code> value within the range <code>[-input.dim() - 1, input.dim() + 1)</code>
+can be used. Negative <code>dim</code> will correspond to <code>unsqueeze</code>
+applied at <code>dim</code> = <code>dim + input.dim() + 1</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span>))
+<span class='fu'>torch_unsqueeze</span>(<span class='kw'>x</span>, <span class='fl'>1</span>)
+<span class='fu'>torch_unsqueeze</span>(<span class='kw'>x</span>, <span class='fl'>2</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1
+#&gt;  2
+#&gt;  3
+#&gt;  4
+#&gt; [ CPUFloatType{4,1} ]</div></pre>
+  </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>Var</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_var.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Var</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_var</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, unbiased = <span class='fl'>TRUE</span>, keepdim = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
+    </tr>
+    <tr>
+      <th>unbiased</th>
+      <td><p>(bool) whether to use the unbiased estimation or not</p></td>
+    </tr>
+    <tr>
+      <th>keepdim</th>
+      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="var-input-unbiased-true-gt-tensor-"><a class="anchor" href="#var-input-unbiased-true-gt-tensor-"></a>var(input, unbiased=TRUE) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the variance of all elements in the <code>input</code> tensor.</p>
+<p>If <code>unbiased</code> is <code>FALSE</code>, then the variance will be calculated via the
+biased estimator. Otherwise, Bessel's correction will be used.</p>
+    <h2 class="hasAnchor" id="var-input-dim-keepdim-false-unbiased-true-out-null-gt-tensor-"><a class="anchor" href="#var-input-dim-keepdim-false-unbiased-true-out-null-gt-tensor-"></a>var(input, dim, keepdim=False, unbiased=TRUE, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the variance of each row of the <code>input</code> tensor in the given
+dimension <code>dim</code>.</p>
+<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size
+as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
+Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
+output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
+<p>If <code>unbiased</code> is <code>FALSE</code>, then the variance will be calculated via the
+biased estimator. Otherwise, Bessel's correction will be used.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_var</span>(<span class='kw'>a</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_var</span>(<span class='kw'>a</span>, <span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  2.2094
+#&gt;  1.3878
+#&gt;  0.3613
+#&gt;  0.4530
+#&gt; [ CPUFloatType{4} ]</div></pre>
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+    <div class="page-header">
+    <h1>Var_mean</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_var_mean.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Var_mean</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_var_mean</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, unbiased = <span class='fl'>TRUE</span>, keepdim = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
+    </tr>
+    <tr>
+      <th>unbiased</th>
+      <td><p>(bool) whether to use the unbiased estimation or not</p></td>
+    </tr>
+    <tr>
+      <th>keepdim</th>
+      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="var-mean-input-unbiased-true-gt-tensor-tensor-"><a class="anchor" href="#var-mean-input-unbiased-true-gt-tensor-tensor-"></a>var_mean(input, unbiased=TRUE) -&gt; (Tensor, Tensor) </h2>
+
+    
+
+
+<p>Returns the variance and mean of all elements in the <code>input</code> tensor.</p>
+<p>If <code>unbiased</code> is <code>FALSE</code>, then the variance will be calculated via the
+biased estimator. Otherwise, Bessel's correction will be used.</p>
+    <h2 class="hasAnchor" id="var-mean-input-dim-keepdim-false-unbiased-true-gt-tensor-tensor-"><a class="anchor" href="#var-mean-input-dim-keepdim-false-unbiased-true-gt-tensor-tensor-"></a>var_mean(input, dim, keepdim=False, unbiased=TRUE) -&gt; (Tensor, Tensor) </h2>
+
+    
+
+
+<p>Returns the variance and mean of each row of the <code>input</code> tensor in the given
+dimension <code>dim</code>.</p>
+<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size
+as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
+Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
+output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
+<p>If <code>unbiased</code> is <code>FALSE</code>, then the variance will be calculated via the
+biased estimator. Otherwise, Bessel's correction will be used.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_var_mean</span>(<span class='kw'>a</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_var_mean</span>(<span class='kw'>a</span>, <span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt;  0.8131
+#&gt;  2.9003
+#&gt;  0.3918
+#&gt;  1.6492
+#&gt; [ CPUFloatType{4} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt;  0.5266
+#&gt;  0.3362
+#&gt;  0.2158
+#&gt;  0.1401
+#&gt; [ CPUFloatType{4} ]
+#&gt; </div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+
+      
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Where</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_where.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Where</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_where</span>(<span class='kw'>condition</span>, <span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>condition</th>
+      <td><p>(BoolTensor) When TRUE (nonzero), yield x, otherwise yield y</p></td>
+    </tr>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) values selected at indices where <code>condition</code> is <code>TRUE</code></p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor) values selected at indices where <code>condition</code> is <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>The tensors `condition`, `x`, `y` must be broadcastable .
+</pre>
+
+<p>See also <code><a href='torch_nonzero.html'>torch_nonzero()</a></code>.</p>
+    <h2 class="hasAnchor" id="where-condition-x-y-gt-tensor-"><a class="anchor" href="#where-condition-x-y-gt-tensor-"></a>where(condition, x, y) -&gt; Tensor </h2>
+
+    
+
+
+<p>Return a tensor of elements selected from either <code>x</code> or <code>y</code>, depending on <code>condition</code>.</p>
+<p>The operation is defined as:</p>
+<p>$$
+    \mbox{out}_i = \left\{ \begin{array}{ll}
+        \mbox{x}_i &amp; \mbox{if } \mbox{condition}_i \\
+        \mbox{y}_i &amp; \mbox{otherwise} \\
+    \end{array}
+    \right.
+$$</p>
+    <h2 class="hasAnchor" id="where-condition-gt-tuple-of-longtensor-"><a class="anchor" href="#where-condition-gt-tuple-of-longtensor-"></a>where(condition) -&gt; tuple of LongTensor </h2>
+
+    
+
+
+<p><code>torch_where(condition)</code> is identical to
+<code><a href='torch_nonzero.html'>torch_nonzero(condition, as_tuple=TRUE)</a></code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>))
+<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_ones.html'>torch_ones</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>))
+<span class='kw'>x</span>
+<span class='fu'>torch_where</span>(<span class='kw'>x</span> <span class='op'>&gt;</span> <span class='fl'>0</span>, <span class='kw'>x</span>, <span class='kw'>y</span>)
+}
+
+
+
+}
+</div></pre>
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diff --git a/static/docs/dev/reference/torch_zeros.html b/static/docs/dev/reference/torch_zeros.html
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+    </li>
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+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
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+</li>
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+     
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+      
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Zeros</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_zeros.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Zeros</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_zeros</span>(
+  <span class='kw'>...</span>,
+  names = <span class='kw'>NULL</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>...</th>
+      <td><p>a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.</p></td>
+    </tr>
+    <tr>
+      <th>names</th>
+      <td><p>optional dimension names</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="zeros-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#zeros-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>zeros(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a tensor filled with the scalar value <code>0</code>, with the shape defined
+by the variable argument <code>size</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_zeros</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+<span class='fu'>torch_zeros</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0
+#&gt;  0
+#&gt;  0
+#&gt;  0
+#&gt;  0
+#&gt; [ CPUFloatType{5} ]</div></pre>
+  </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="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Zeros_like</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_zeros_like.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Zeros_like</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_zeros_like</span>(
+  <span class='kw'>input</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>,
+  memory_format = <span class='fu'><a href='torch_memory_format.html'>torch_preserve_format</a></span>()
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>memory_format</th>
+      <td><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="zeros-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"><a class="anchor" href="#zeros-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a>zeros_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a tensor filled with the scalar value <code>0</code>, with the same size as
+<code>input</code>. <code>torch_zeros_like(input)</code> is equivalent to
+<code><a href='torch_zeros.html'>torch_zeros(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)</a></code>.</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p>As of 0.4, this function does not support an <code>out</code> keyword. As an alternative,
+the old <code>torch_zeros_like(input, out=output)</code> is equivalent to
+<code><a href='torch_zeros.html'>torch_zeros(input.size(), out=output)</a></code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+<span class='fu'>torch_zeros_like</span>(<span class='kw'>input</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0  0  0
+#&gt;  0  0  0
+#&gt; [ CPUFloatType{2,3} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/dev/reference/with_enable_grad.html b/static/docs/dev/reference/with_enable_grad.html
new file mode 100644
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+++ b/static/docs/dev/reference/with_enable_grad.html
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+<!-- Generated by pkgdown: do not edit by hand -->
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+
+      
+
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+
+<div class="row">
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+    <div class="page-header">
+    <h1>Enable grad</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/autograd.R'><code>R/autograd.R</code></a></small>
+    <div class="hidden name"><code>with_enable_grad.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Context-manager that enables gradient calculation.
+Enables gradient calculation, if it has been disabled via <a href='with_no_grad.html'>with_no_grad</a>.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>with_enable_grad</span>(<span class='kw'>code</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>code</th>
+      <td><p>code to be executed with gradient recording.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>This context manager is thread local; it will not affect computation in
+other threads.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fl'>1</span>, requires_grad=<span class='fl'>TRUE</span>)
+<span class='fu'><a href='with_no_grad.html'>with_no_grad</a></span>({
+  <span class='fu'>with_enable_grad</span>({
+    <span class='kw'>y</span> <span class='op'>=</span> <span class='kw'>x</span> <span class='op'>*</span> <span class='fl'>2</span>
+  })
+})
+<span class='kw'>y</span><span class='op'>$</span><span class='fu'>backward</span>()
+<span class='kw'>x</span><span class='op'>$</span><span class='kw'>grad</span>
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  2
+#&gt; [ CPUFloatType{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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new file mode 100644
index 000000000..20200cbf3
--- /dev/null
+++ b/static/docs/dev/reference/with_no_grad.html
@@ -0,0 +1,249 @@
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+    </li>
+    <li>
+      <a href="../articles/getting-started/optim.html">optim: optimizers in torch</a>
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+    </li>
+    <li class="dropdown-header">Torch Mechanics</li>
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+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
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+      <a href="../articles/getting-started/neural-networks.html">Neural networks</a>
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+      <a href="../articles/using-autograd.html">Using autograd</a>
+    </li>
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+</li>
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+      
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+</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='kw'>code</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>code</th>
+      <td><p>code to be executed with no gradient recording.</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/stats/Uniform.html'>runif</a></span>(<span class='fl'>5</span>), requires_grad = <span class='fl'>TRUE</span>)
+<span class='fu'>with_no_grad</span>({
+  <span class='kw'>x</span><span class='op'>$</span><span class='fu'>sub_</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/numeric.html'>as.numeric</a></span>(<span class='fl'>1</span><span class='op'>:</span><span class='fl'>5</span>)))
+})
+<span class='kw'>x</span>
+<span class='kw'>x</span><span class='op'>$</span><span class='kw'>grad</span>
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; [ Tensor (undefined) ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
+  </div>
+</div>
+
+
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+      <div class="copyright">
+  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+</div>
+
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new file mode 100644
index 000000000..e5f1fe1df
--- /dev/null
+++ b/static/docs/docsearch.css
@@ -0,0 +1,148 @@
+/* Docsearch -------------------------------------------------------------- */
+/*
+  Source: https://github.com/algolia/docsearch/
+  License: MIT
+*/
+
+.algolia-autocomplete {
+  display: block;
+  -webkit-box-flex: 1;
+  -ms-flex: 1;
+  flex: 1
+}
+
+.algolia-autocomplete .ds-dropdown-menu {
+  width: 100%;
+  min-width: none;
+  max-width: none;
+  padding: .75rem 0;
+  background-color: #fff;
+  background-clip: padding-box;
+  border: 1px solid rgba(0, 0, 0, .1);
+  box-shadow: 0 .5rem 1rem rgba(0, 0, 0, .175);
+}
+
+@media (min-width:768px) {
+  .algolia-autocomplete .ds-dropdown-menu {
+      width: 175%
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+.algolia-autocomplete .ds-dropdown-menu::before {
+  display: none
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+  padding: 0;
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+  border: 0;
+  max-height: 80vh;
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+.algolia-autocomplete .ds-dropdown-menu .ds-suggestions {
+  margin-top: 0
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+  padding: 0;
+  overflow: visible
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+.algolia-autocomplete .algolia-docsearch-suggestion--category-header {
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+      $("#search-input").focus();
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+  $(document).ready(function() {
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diff --git a/static/docs/index.html b/static/docs/index.html
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index 000000000..881a7c951
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+<!DOCTYPE html>
+<!-- Generated by pkgdown: do not edit by hand --><html lang="en">
+<head>
+<meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
+<meta charset="utf-8">
+<meta http-equiv="X-UA-Compatible" content="IE=edge">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>Tensors and Neural Networks with GPU Acceleration • torch</title>
+<!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link href="https://cdnjs.cloudflare.com/ajax/libs/bootswatch/3.4.0/united/bootstrap.min.css" rel="stylesheet" crossorigin="anonymous">
+<script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="bootstrap-toc.css">
+<script src="bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous">
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+<!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="pkgdown.css" rel="stylesheet">
+<script src="pkgdown.js"></script><meta property="og:title" content="Tensors and Neural Networks with GPU Acceleration">
+<meta property="og:description" content="Provides functionality to define and train neural networks similar to 
+    PyTorch by Paszke et al (2019) &lt;arXiv:1912.01703&gt; but written entirely in R 
+    using the libtorch library. Also supports low-level tensor operations and 
+    GPU acceleration.">
+<!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]>
+<script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script>
+<script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script>
+<![endif]-->
+</head>
+<body data-spy="scroll" data-target="#toc">
+    <div class="container template-home">
+      <header><div class="navbar navbar-default navbar-fixed-top" role="navigation">
+  <div class="container">
+    <div class="navbar-header">
+      <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false">
+        <span class="sr-only">Toggle navigation</span>
+        <span class="icon-bar"></span>
+        <span class="icon-bar"></span>
+        <span class="icon-bar"></span>
+      </button>
+      <span class="navbar-brand">
+        <a class="navbar-link" href="index.html">torch</a>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.3</span>
+      </span>
+    </div>
+
+    <div id="navbar" class="navbar-collapse collapse">
+      <ul class="nav navbar-nav">
+<li>
+  <a href="index.html">
+    <span class="fas fa fas fa-home fa-lg"></span>
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+  </a>
+</li>
+<li class="dropdown">
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
+    Getting started
+     
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+<li class="dropdown-header">Tensors</li>
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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://torch.mlverse.org"><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="sourceCode R"><code class="sourceCode r"><span id="cb1-1"><a href="#cb1-1"></a>remotes<span class="op">::</span><span class="kw">install_github</span>(<span class="st">"mlverse/torch"</span>)</span></code></pre></div>
+<p>At the first package load additional software will be installed.</p>
+</div>
+<div id="example" class="section level2">
+<h2 class="hasAnchor">
+<a href="#example" class="anchor"></a>Example</h2>
+<p>Currently this package is only a proof of concept and you can only create a Torch Tensor from an R object. And then convert back from a torch Tensor to an R object.</p>
+<div class="sourceCode" id="cb2"><pre class="sourceCode R"><code class="sourceCode r"><span id="cb2-1"><a href="#cb2-1"></a><span class="kw">library</span>(torch)</span>
+<span id="cb2-2"><a href="#cb2-2"></a>x &lt;-<span class="st"> </span><span class="kw">array</span>(<span class="kw">runif</span>(<span class="dv">8</span>), <span class="dt">dim =</span> <span class="kw">c</span>(<span class="dv">2</span>, <span class="dv">2</span>, <span class="dv">2</span>))</span>
+<span id="cb2-3"><a href="#cb2-3"></a>y &lt;-<span class="st"> </span><span class="kw">torch_tensor</span>(x, <span class="dt">dtype =</span> <span class="kw">torch_float64</span>())</span>
+<span id="cb2-4"><a href="#cb2-4"></a>y</span>
+<span id="cb2-5"><a href="#cb2-5"></a><span class="co">#&gt; torch_tensor </span></span>
+<span id="cb2-6"><a href="#cb2-6"></a><span class="co">#&gt; (1,.,.) = </span></span>
+<span id="cb2-7"><a href="#cb2-7"></a><span class="co">#&gt;   0.5406  0.8648</span></span>
+<span id="cb2-8"><a href="#cb2-8"></a><span class="co">#&gt;   0.3097  0.9715</span></span>
+<span id="cb2-9"><a href="#cb2-9"></a><span class="co">#&gt; </span></span>
+<span id="cb2-10"><a href="#cb2-10"></a><span class="co">#&gt; (2,.,.) = </span></span>
+<span id="cb2-11"><a href="#cb2-11"></a><span class="co">#&gt;   0.1309  0.8992</span></span>
+<span id="cb2-12"><a href="#cb2-12"></a><span class="co">#&gt;   0.4849  0.1902</span></span>
+<span id="cb2-13"><a href="#cb2-13"></a><span class="co">#&gt; [ CPUDoubleType{2,2,2} ]</span></span>
+<span id="cb2-14"><a href="#cb2-14"></a><span class="kw">identical</span>(x, <span class="kw">as_array</span>(y))</span>
+<span id="cb2-15"><a href="#cb2-15"></a><span class="co">#&gt; [1] TRUE</span></span></code></pre></div>
+<div id="simple-autograd-example" class="section level3">
+<h3 class="hasAnchor">
+<a href="#simple-autograd-example" class="anchor"></a>Simple Autograd Example</h3>
+<p>In the following snippet we let torch, using the autograd feature, calculate the derivatives:</p>
+<div class="sourceCode" id="cb3"><pre class="sourceCode R"><code class="sourceCode r"><span id="cb3-1"><a href="#cb3-1"></a>x &lt;-<span class="st"> </span><span class="kw">torch_tensor</span>(<span class="dv">1</span>, <span class="dt">requires_grad =</span> <span class="ot">TRUE</span>)</span>
+<span id="cb3-2"><a href="#cb3-2"></a>w &lt;-<span class="st"> </span><span class="kw">torch_tensor</span>(<span class="dv">2</span>, <span class="dt">requires_grad =</span> <span class="ot">TRUE</span>)</span>
+<span id="cb3-3"><a href="#cb3-3"></a>b &lt;-<span class="st"> </span><span class="kw">torch_tensor</span>(<span class="dv">3</span>, <span class="dt">requires_grad =</span> <span class="ot">TRUE</span>)</span>
+<span id="cb3-4"><a href="#cb3-4"></a>y &lt;-<span class="st"> </span>w <span class="op">*</span><span class="st"> </span>x <span class="op">+</span><span class="st"> </span>b</span>
+<span id="cb3-5"><a href="#cb3-5"></a>y<span class="op">$</span><span class="kw">backward</span>()</span>
+<span id="cb3-6"><a href="#cb3-6"></a>x<span class="op">$</span>grad</span>
+<span id="cb3-7"><a href="#cb3-7"></a><span class="co">#&gt; torch_tensor </span></span>
+<span id="cb3-8"><a href="#cb3-8"></a><span class="co">#&gt;  2</span></span>
+<span id="cb3-9"><a href="#cb3-9"></a><span class="co">#&gt; [ CPUFloatType{1} ]</span></span>
+<span id="cb3-10"><a href="#cb3-10"></a>w<span class="op">$</span>grad</span>
+<span id="cb3-11"><a href="#cb3-11"></a><span class="co">#&gt; torch_tensor </span></span>
+<span id="cb3-12"><a href="#cb3-12"></a><span class="co">#&gt;  1</span></span>
+<span id="cb3-13"><a href="#cb3-13"></a><span class="co">#&gt; [ CPUFloatType{1} ]</span></span>
+<span id="cb3-14"><a href="#cb3-14"></a>b<span class="op">$</span>grad</span>
+<span id="cb3-15"><a href="#cb3-15"></a><span class="co">#&gt; torch_tensor </span></span>
+<span id="cb3-16"><a href="#cb3-16"></a><span class="co">#&gt;  1</span></span>
+<span id="cb3-17"><a href="#cb3-17"></a><span class="co">#&gt; [ CPUFloatType{1} ]</span></span></code></pre></div>
+</div>
+<div id="linear-regression" class="section level3">
+<h3 class="hasAnchor">
+<a href="#linear-regression" class="anchor"></a>Linear Regression</h3>
+<p>In the following example we are going to fit a linear regression from scratch using torch’s Autograd.</p>
+<p><strong>Note</strong> all methods that end with <code>_</code> (eg. <code>sub_</code>), will modify the tensors in place.</p>
+<div class="sourceCode" id="cb4"><pre class="sourceCode R"><code class="sourceCode r"><span id="cb4-1"><a href="#cb4-1"></a>x &lt;-<span class="st"> </span><span class="kw">torch_randn</span>(<span class="dv">100</span>, <span class="dv">2</span>)</span>
+<span id="cb4-2"><a href="#cb4-2"></a>y &lt;-<span class="st"> </span><span class="fl">0.1</span> <span class="op">+</span><span class="st"> </span><span class="fl">0.5</span><span class="op">*</span>x[,<span class="dv">1</span>] <span class="op">-</span><span class="st"> </span><span class="fl">0.7</span><span class="op">*</span>x[,<span class="dv">2</span>]</span>
+<span id="cb4-3"><a href="#cb4-3"></a></span>
+<span id="cb4-4"><a href="#cb4-4"></a>w &lt;-<span class="st"> </span><span class="kw">torch_randn</span>(<span class="dv">2</span>, <span class="dv">1</span>, <span class="dt">requires_grad =</span> <span class="ot">TRUE</span>)</span>
+<span id="cb4-5"><a href="#cb4-5"></a>b &lt;-<span class="st"> </span><span class="kw">torch_zeros</span>(<span class="dv">1</span>, <span class="dt">requires_grad =</span> <span class="ot">TRUE</span>)</span>
+<span id="cb4-6"><a href="#cb4-6"></a></span>
+<span id="cb4-7"><a href="#cb4-7"></a>lr &lt;-<span class="st"> </span><span class="fl">0.5</span></span>
+<span id="cb4-8"><a href="#cb4-8"></a><span class="cf">for</span> (i <span class="cf">in</span> <span class="dv">1</span><span class="op">:</span><span class="dv">100</span>) {</span>
+<span id="cb4-9"><a href="#cb4-9"></a>  y_hat &lt;-<span class="st"> </span><span class="kw">torch_mm</span>(x, w) <span class="op">+</span><span class="st"> </span>b</span>
+<span id="cb4-10"><a href="#cb4-10"></a>  loss &lt;-<span class="st"> </span><span class="kw">torch_mean</span>((y <span class="op">-</span><span class="st"> </span>y_hat<span class="op">$</span><span class="kw">squeeze</span>(<span class="dv">1</span>))<span class="op">^</span><span class="dv">2</span>)</span>
+<span id="cb4-11"><a href="#cb4-11"></a>  </span>
+<span id="cb4-12"><a href="#cb4-12"></a>  loss<span class="op">$</span><span class="kw">backward</span>()</span>
+<span id="cb4-13"><a href="#cb4-13"></a>  </span>
+<span id="cb4-14"><a href="#cb4-14"></a>  <span class="kw">with_no_grad</span>({</span>
+<span id="cb4-15"><a href="#cb4-15"></a>    w<span class="op">$</span><span class="kw">sub_</span>(w<span class="op">$</span>grad<span class="op">*</span>lr)</span>
+<span id="cb4-16"><a href="#cb4-16"></a>    b<span class="op">$</span><span class="kw">sub_</span>(b<span class="op">$</span>grad<span class="op">*</span>lr)   </span>
+<span id="cb4-17"><a href="#cb4-17"></a>    </span>
+<span id="cb4-18"><a href="#cb4-18"></a>    w<span class="op">$</span>grad<span class="op">$</span><span class="kw">zero_</span>()</span>
+<span id="cb4-19"><a href="#cb4-19"></a>    b<span class="op">$</span>grad<span class="op">$</span><span class="kw">zero_</span>()</span>
+<span id="cb4-20"><a href="#cb4-20"></a>  })</span>
+<span id="cb4-21"><a href="#cb4-21"></a>}</span>
+<span id="cb4-22"><a href="#cb4-22"></a><span class="kw">print</span>(w)</span>
+<span id="cb4-23"><a href="#cb4-23"></a><span class="co">#&gt; torch_tensor </span></span>
+<span id="cb4-24"><a href="#cb4-24"></a><span class="co">#&gt; 1e-09 *</span></span>
+<span id="cb4-25"><a href="#cb4-25"></a><span class="co">#&gt;  5.2672</span></span>
+<span id="cb4-26"><a href="#cb4-26"></a><span class="co">#&gt;  -6.7969</span></span>
+<span id="cb4-27"><a href="#cb4-27"></a><span class="co">#&gt; [ CPUFloatType{2,1} ]</span></span>
+<span id="cb4-28"><a href="#cb4-28"></a><span class="kw">print</span>(b) </span>
+<span id="cb4-29"><a href="#cb4-29"></a><span class="co">#&gt; torch_tensor </span></span>
+<span id="cb4-30"><a href="#cb4-30"></a><span class="co">#&gt; 0.01 *</span></span>
+<span id="cb4-31"><a href="#cb4-31"></a><span class="co">#&gt; -9.6802</span></span>
+<span id="cb4-32"><a href="#cb4-32"></a><span class="co">#&gt; [ CPUFloatType{1} ]</span></span></code></pre></div>
+</div>
+</div>
+<div id="contributing" class="section level2">
+<h2 class="hasAnchor">
+<a href="#contributing" class="anchor"></a>Contributing</h2>
+<p>No matter your current skills it’s possible to contribute to <code>torch</code> development. See the contributing guide for more information.</p>
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+<li>Daniel Falbel <br><small class="roles"> Author, maintainer, copyright holder </small>  </li>
+<li>Javier Luraschi <br><small class="roles"> Author, copyright holder </small>  </li>
+<li><a href="authors.html">All authors...</a></li>
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+        <a class="navbar-link" href="../index.html">torch</a>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.3</span>
+      </span>
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+
+    <div id="navbar" class="navbar-collapse collapse">
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+</li>
+<li class="dropdown">
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
+    Getting started
+     
+    <span class="caret"></span>
+  </a>
+  <ul class="dropdown-menu" role="menu">
+    <li class="dropdown-header">Beginers guide</li>
+    <li>
+      <a href="../articles/getting-started/warmup.html">Warm-up</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/tensors.html">Tensors</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/tensors-and-autograd.html">Tensors and autograd</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/optim.html">optim: optimizers in torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/custom-nn.html">Custom nn modules</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/control-flow-and-weight-sharing.html">Control flow &amp; Weight sharing</a>
+    </li>
+    <li class="dropdown-header">Torch Mechanics</li>
+    <li>
+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/neural-networks.html">Neural networks</a>
+    </li>
+  </ul>
+</li>
+<li class="dropdown">
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
+    Articles
+     
+    <span class="caret"></span>
+  </a>
+  <ul class="dropdown-menu" role="menu">
+    <li class="dropdown-header">Tensors</li>
+    <li>
+      <a href="../articles/tensor-creation.html">Creating tensors</a>
+    </li>
+    <li>
+      <a href="../articles/indexing.html">Indexing</a>
+    </li>
+    <li>
+      <a href="../articles/tensor/index.html">Tensor class</a>
+    </li>
+    <li class="dropdown-header">Datasets</li>
+    <li>
+      <a href="../articles/loading-data.html">Loading Data</a>
+    </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-header">Examples</li>
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+</li>
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+</li>
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+    <span class="fab fa fab fa-github fa-lg"></span>
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+      
+    </div><!--/.nav-collapse -->
+  </div><!--/.container -->
+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+      <h1 data-toc-skip>Changelog <small></small></h1>
+      <small>Source: <a href='https://github.com/mlverse/torch/blob/master/NEWS.md'><code>NEWS.md</code></a></small>
+    </div>
+
+    <div id="torch-development-version" class="section level1">
+<h1 class="page-header" data-toc-text="development version">
+<a href="#torch-development-version" class="anchor"></a>torch (development version)<small> Unreleased </small>
+</h1>
+</div>
+    <div id="torch-002" class="section level1">
+<h1 class="page-header" data-toc-text="0.0.2">
+<a href="#torch-002" class="anchor"></a>torch 0.0.2<small> 2020-08-31 </small>
+</h1>
+<ul>
+<li>Added a <code>NEWS.md</code> file to track changes to the package.</li>
+<li>Auto install when loading the package for the first time.</li>
+</ul>
+</div>
+  </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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+
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+      <footer>
+      <div class="copyright">
+  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+</div>
+
+<div class="pkgdown">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+</div>
+
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+   </div>
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+ * .footer -> footer
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+/* Fix bug in bootstrap (only seen in firefox) */
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+  border-bottom: 2px solid rgba(255, 153, 51, 0.3);
+  padding: 1px;
+}
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+/* vertical spacing after htmlwidgets */
+.html-widget {
+  margin-bottom: 10px;
+}
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+/* fontawesome ------------------------ */
+
+.fab {
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diff --git a/static/docs/pkgdown.yml b/static/docs/pkgdown.yml
new file mode 100644
index 000000000..2d96c50e4
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+  getting-started/control-flow-and-weight-sharing: control-flow-and-weight-sharing.html
+  getting-started/custom-nn: custom-nn.html
+  getting-started/neural-networks: neural-networks.html
+  getting-started/new-autograd-functions: new-autograd-functions.html
+  getting-started/nn: nn.html
+  getting-started/optim: optim.html
+  getting-started/tensors-and-autograd: tensors-and-autograd.html
+  getting-started/tensors: tensors.html
+  getting-started/warmup: warmup.html
+  getting-started/what-is-torch: what-is-torch.html
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diff --git a/static/docs/reference/AutogradContext.html b/static/docs/reference/AutogradContext.html
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+
+
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+    <li class="dropdown-header">Beginers guide</li>
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+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
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+<div class="row">
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+    <div class="page-header">
+    <h1>Class representing the context.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/autograd.R'><code>R/autograd.R</code></a></small>
+    <div class="hidden name"><code>AutogradContext.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Class representing the context.</p>
+<p>Class representing the context.</p>
+    </div>
+
+
+
+    <h2 class="hasAnchor" id="public-fields"><a class="anchor" href="#public-fields"></a>Public fields</h2>
+
+    <p><div class="r6-fields"></p><dl>
+<dt><code>ptr</code></dt><dd><p>(Dev related) pointer to the context c++ object.</p></dd>
+
+</dl><p></div></p>
+    <h2 class="hasAnchor" id="active-bindings"><a class="anchor" href="#active-bindings"></a>Active bindings</h2>
+
+    <p><div class="r6-active-bindings"></p><dl>
+<dt><code>needs_input_grad</code></dt><dd><p>boolean listing arguments of <code>forward</code> and whether they require_grad.</p></dd>
+
+<dt><code>saved_variables</code></dt><dd><p>list of objects that were saved for backward via <code>save_for_backward</code>.</p></dd>
+
+</dl><p></div></p>
+    <h2 class="hasAnchor" id="methods"><a class="anchor" href="#methods"></a>Methods</h2>
+
+    
+<h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Public methods</h3>
+
+<ul>
+<li><p><a href='#method-new'><code>AutogradContext$new()</code></a></p></li>
+<li><p><a href='#method-save_for_backward'><code>AutogradContext$save_for_backward()</code></a></p></li>
+<li><p><a href='#method-mark_non_differentiable'><code>AutogradContext$mark_non_differentiable()</code></a></p></li>
+<li><p><a href='#method-mark_dirty'><code>AutogradContext$mark_dirty()</code></a></p></li>
+<li><p><a href='#method-clone'><code>AutogradContext$clone()</code></a></p></li>
+</ul>
+<p><hr>
+<a id="method-new"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code>new()</code></h3>
+<p>(Dev related) Initializes the context. Not user related.</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
+<p><div class="r"></p><pre><span class='kw'>AutogradContext</span><span class='op'>$</span><span class='fu'>new</span>(
+  <span class='kw'>ptr</span>,
+  <span class='kw'>env</span>,
+  argument_names = <span class='kw'>NULL</span>,
+  argument_needs_grad = <span class='kw'>NULL</span>
+)</pre><p></div></p>
+
+<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Arguments</h4>
+<p><div class="arguments"></p><dl>
+<dt><code>ptr</code></dt><dd><p>pointer to the c++ object</p></dd>
+
+<dt><code>env</code></dt><dd><p>environment that encloses both forward and backward</p></dd>
+
+<dt><code>argument_names</code></dt><dd><p>names of forward arguments</p></dd>
+
+<dt><code>argument_needs_grad</code></dt><dd><p>whether each argument in forward needs grad.</p></dd>
+
+</dl><p></div></p>
+<p><hr>
+<a id="method-save_for_backward"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code>save_for_backward()</code></h3>
+<p>Saves given objects for a future call to backward().</p>
+<p>This should be called at most once, and only from inside the <code>forward()</code>
+method.</p>
+<p>Later, saved objects can be accessed through the <code>saved_variables</code> attribute.
+Before returning them to the user, a check is made to ensure they weren’t used
+in any in-place operation that modified their content.</p>
+<p>Arguments can also be any kind of R object.</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
+<p><div class="r"></p><pre><span class='kw'>AutogradContext</span><span class='op'>$</span><span class='fu'>save_for_backward</span>(<span class='kw'>...</span>)</pre><p></div></p>
+
+<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Arguments</h4>
+<p><div class="arguments"></p><dl>
+<dt><code>...</code></dt><dd><p>any kind of R object that will be saved for the backward pass.
+It's common to pass named arguments.</p></dd>
+
+</dl><p></div></p>
+<p><hr>
+<a id="method-mark_non_differentiable"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code>mark_non_differentiable()</code></h3>
+<p>Marks outputs as non-differentiable.</p>
+<p>This should be called at most once, only from inside the <code>forward()</code> method,
+and all arguments should be outputs.</p>
+<p>This will mark outputs as not requiring gradients, increasing the efficiency
+of backward computation. You still need to accept a gradient for each output
+in <code>backward()</code>, but it’s always going to be a zero tensor with the same
+shape as the shape of a corresponding output.</p>
+<p>This is used e.g. for indices returned from a max Function.</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
+<p><div class="r"></p><pre><span class='kw'>AutogradContext</span><span class='op'>$</span><span class='fu'>mark_non_differentiable</span>(<span class='kw'>...</span>)</pre><p></div></p>
+
+<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Arguments</h4>
+<p><div class="arguments"></p><dl>
+<dt><code>...</code></dt><dd><p>non-differentiable outputs.</p></dd>
+
+</dl><p></div></p>
+<p><hr>
+<a id="method-mark_dirty"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code>mark_dirty()</code></h3>
+<p>Marks given tensors as modified in an in-place operation.</p>
+<p>This should be called at most once, only from inside the <code>forward()</code> method,
+and all arguments should be inputs.</p>
+<p>Every tensor that’s been modified in-place in a call to <code>forward()</code> should
+be given to this function, to ensure correctness of our checks. It doesn’t
+matter whether the function is called before or after modification.</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
+<p><div class="r"></p><pre><span class='kw'>AutogradContext</span><span class='op'>$</span><span class='fu'>mark_dirty</span>(<span class='kw'>...</span>)</pre><p></div></p>
+
+<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Arguments</h4>
+<p><div class="arguments"></p><dl>
+<dt><code>...</code></dt><dd><p>tensors that are modified in-place.</p></dd>
+
+</dl><p></div></p>
+<p><hr>
+<a id="method-clone"></a></p><h3 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Method <code>clone()</code></h3>
+<p>The objects of this class are cloneable with this method.</p><h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Usage</h4>
+<p><div class="r"></p><pre><span class='kw'>AutogradContext</span><span class='op'>$</span><span class='fu'>clone</span>(deep = <span class='fl'>FALSE</span>)</pre><p></div></p>
+
+<h4 class='hasAnchor' id='arguments'><a class='anchor' href='#arguments'></a>Arguments</h4>
+<p><div class="arguments"></p><dl>
+<dt><code>deep</code></dt><dd><p>Whether to make a deep clone.</p></dd>
+
+</dl><p></div></p>
+
+
+
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+    <div class="page-header">
+    <h1>Converts to array</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/tensor.R'><code>R/tensor.R</code></a></small>
+    <div class="hidden name"><code>as_array.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Converts to array</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>as_array</span>(<span class='kw'>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>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Computes the sum of gradients of given tensors w.r.t. graph leaves.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/autograd.R'><code>R/autograd.R</code></a></small>
+    <div class="hidden name"><code>autograd_backward.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>The graph is differentiated using the chain rule. If any of tensors are
+non-scalar (i.e. their data has more than one element) and require gradient,
+then the Jacobian-vector product would be computed, in this case the function
+additionally requires specifying <code>grad_tensors</code>. It should be a sequence of
+matching length, that contains the “vector” in the Jacobian-vector product,
+usually the gradient of the differentiated function w.r.t. corresponding
+tensors (None is an acceptable value for all tensors that don’t need gradient
+tensors).</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>autograd_backward</span>(
+  <span class='kw'>tensors</span>,
+  grad_tensors = <span class='kw'>NULL</span>,
+  retain_graph = <span class='kw'>create_graph</span>,
+  create_graph = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensors</th>
+      <td><p>(list of Tensor) – Tensors of which the derivative will
+be computed.</p></td>
+    </tr>
+    <tr>
+      <th>grad_tensors</th>
+      <td><p>(list of (Tensor or <code>NULL)) – The “vector” in the  Jacobian-vector product, usually gradients w.r.t. each element of  corresponding tensors. </code>NULL<code>values can be specified for scalar Tensors or  ones that don’t require grad. If a</code>NULL` value would be acceptable for all
+grad_tensors, then this argument is optional.</p></td>
+    </tr>
+    <tr>
+      <th>retain_graph</th>
+      <td><p>(bool, optional) – If <code>FALSE</code>, the graph used to compute
+the grad will be freed. Note that in nearly all cases setting this option to
+<code>TRUE</code> is not needed and often can be worked around in a much more efficient
+way. Defaults to the value of <code>create_graph</code>.</p></td>
+    </tr>
+    <tr>
+      <th>create_graph</th>
+      <td><p>(bool, optional) – If <code>TRUE</code>, graph of the derivative will
+be constructed, allowing to compute higher order derivative products.
+Defaults to <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>This function accumulates gradients in the leaves - you might need to zero
+them before calling it.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fl'>1</span>, requires_grad = <span class='fl'>TRUE</span>)
+<span class='kw'>y</span> <span class='op'>&lt;-</span> <span class='fl'>2</span> <span class='op'>*</span> <span class='kw'>x</span>
+
+<span class='kw'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fl'>1</span>, requires_grad = <span class='fl'>TRUE</span>)
+<span class='kw'>b</span> <span class='op'>&lt;-</span> <span class='fl'>3</span> <span class='op'>*</span> <span class='kw'>a</span>
+
+<span class='fu'>autograd_backward</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>y</span>, <span class='kw'>b</span>))
+
+}
+</div></pre>
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diff --git a/static/docs/reference/autograd_function.html b/static/docs/reference/autograd_function.html
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+</li>
+<li class="dropdown-header">Examples</li>
+<li>
+  <a href="../reference/index.html">Reference</a>
+</li>
+<li>
+  <a href="../news/index.html">Changelog</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>Records operation history and defines formulas for differentiating ops.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/autograd.R'><code>R/autograd.R</code></a></small>
+    <div class="hidden name"><code>autograd_function.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Every operation performed on Tensor's creates a new function object, that
+performs the computation, and records that it happened. The history is
+retained in the form of a DAG of functions, with edges denoting data
+dependencies (input &lt;- output). Then, when backward is called, the graph is
+processed in the topological ordering, by calling <code>backward()</code> methods of each
+Function object, and passing returned gradients on to next Function's.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>autograd_function</span>(<span class='kw'>forward</span>, <span class='kw'>backward</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>forward</th>
+      <td><p>Performs the operation. It must accept a context <code>ctx</code> as the first argument,
+followed by any number of arguments (tensors or other types). The context can be
+used to store tensors that can be then retrieved during the backward pass.
+See <a href='AutogradContext.html'>AutogradContext</a> for more information about context methods.</p></td>
+    </tr>
+    <tr>
+      <th>backward</th>
+      <td><p>Defines a formula for differentiating the operation. It must accept
+a context <code>ctx</code> as the first argument, followed by as many outputs did <code>forward()</code>
+return, and it should return a named list. Each argument is the gradient w.r.t
+the given output, and each element in the returned list should be the gradient
+w.r.t. the corresponding input. The context can be used to retrieve tensors saved
+during the forward pass. It also has an attribute <code>ctx$needs_input_grad</code> as a
+named list of booleans representing whether each input needs gradient.
+E.g., <code>backward()</code> will have <code>ctx$needs_input_grad$input = TRUE</code> if the <code>input</code>
+argument to <code>forward()</code> needs gradient computated w.r.t. the output.
+See <a href='AutogradContext.html'>AutogradContext</a> for more information about context methods.</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>exp2</span> <span class='op'>&lt;-</span> <span class='fu'>autograd_function</span>(
+  forward = <span class='fu'>function</span>(<span class='kw'>ctx</span>, <span class='kw'>i</span>) {
+    <span class='kw'>result</span> <span class='op'>&lt;-</span> <span class='kw'>i</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/Log.html'>exp</a></span>()
+    <span class='kw'>ctx</span><span class='op'>$</span><span class='fu'>save_for_backward</span>(result = <span class='kw'>result</span>)
+    <span class='kw'>result</span>
+  },
+  backward = <span class='fu'>function</span>(<span class='kw'>ctx</span>, <span class='kw'>grad_output</span>) {
+    <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(i = <span class='kw'>grad_output</span> <span class='op'>*</span> <span class='kw'>ctx</span><span class='op'>$</span><span class='kw'>saved_variable</span><span class='op'>$</span><span class='kw'>result</span>)
+  }
+)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
+  </div>
+</div>
+
+
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+      <div class="copyright">
+  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+</div>
+
+<div class="pkgdown">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+</div>
+
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diff --git a/static/docs/reference/autograd_grad.html b/static/docs/reference/autograd_grad.html
new file mode 100644
index 000000000..0ed040254
--- /dev/null
+++ b/static/docs/reference/autograd_grad.html
@@ -0,0 +1,305 @@
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+<!DOCTYPE html>
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+
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+<meta property="og:description" content="grad_outputs 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)." />
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+     
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+    <li class="dropdown-header">Beginers guide</li>
+    <li>
+      <a href="../articles/getting-started/warmup.html">Warm-up</a>
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+    <li>
+      <a href="../articles/getting-started/tensors.html">Tensors</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/tensors-and-autograd.html">Tensors and autograd</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
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+    </li>
+    <li class="dropdown-header">Torch Mechanics</li>
+    <li>
+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
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+      <a href="../articles/getting-started/neural-networks.html">Neural networks</a>
+    </li>
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+</li>
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+    <span class="caret"></span>
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+    <li class="dropdown-header">Tensors</li>
+    <li>
+      <a href="../articles/tensor-creation.html">Creating tensors</a>
+    </li>
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+      <a href="../articles/indexing.html">Indexing</a>
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+    <li>
+      <a href="../articles/tensor/index.html">Tensor class</a>
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+      <a href="../articles/loading-data.html">Loading Data</a>
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+    <li class="dropdown-header">Autograd</li>
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+      <a href="../articles/using-autograd.html">Using autograd</a>
+    </li>
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+      <a href="../articles/extending-autograd.html">Extending autograd</a>
+    </li>
+  </ul>
+</li>
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+<li>
+  <a href="../reference/index.html">Reference</a>
+</li>
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+  <a href="../news/index.html">Changelog</a>
+</li>
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+  <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>Computes and returns the sum of gradients of outputs w.r.t. the inputs.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/autograd.R'><code>R/autograd.R</code></a></small>
+    <div class="hidden name"><code>autograd_grad.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p><code>grad_outputs</code> should be a list of length matching output containing the “vector”
+in Jacobian-vector product, usually the pre-computed gradients w.r.t. each of
+the outputs. If an output doesn’t require_grad, then the gradient can be None).</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>autograd_grad</span>(
+  <span class='kw'>outputs</span>,
+  <span class='kw'>inputs</span>,
+  grad_outputs = <span class='kw'>NULL</span>,
+  retain_graph = <span class='kw'>create_graph</span>,
+  create_graph = <span class='fl'>FALSE</span>,
+  allow_unused = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>outputs</th>
+      <td><p>(sequence of Tensor) – outputs of the differentiated function.</p></td>
+    </tr>
+    <tr>
+      <th>inputs</th>
+      <td><p>(sequence of Tensor) – Inputs w.r.t. which the gradient will be
+returned (and not accumulated into .grad).</p></td>
+    </tr>
+    <tr>
+      <th>grad_outputs</th>
+      <td><p>(sequence of Tensor) – The “vector” in the Jacobian-vector
+product. Usually gradients w.r.t. each output. None values can be specified for
+scalar Tensors or ones that don’t require grad. If a None value would be acceptable
+for all <code>grad_tensors</code>, then this argument is optional. Default: None.</p></td>
+    </tr>
+    <tr>
+      <th>retain_graph</th>
+      <td><p>(bool, optional) – If <code>FALSE</code>, the graph used to compute the
+grad will be freed. Note that in nearly all cases setting this option to <code>TRUE</code> is
+not needed and often can be worked around in a much more efficient way.
+Defaults to the value of <code>create_graph</code>.</p></td>
+    </tr>
+    <tr>
+      <th>create_graph</th>
+      <td><p>(bool, optional) – If <code>TRUE, graph of the derivative will  be constructed, allowing to compute higher order derivative products.  Default: </code>FALSE`.</p></td>
+    </tr>
+    <tr>
+      <th>allow_unused</th>
+      <td><p>(bool, optional) – If <code>FALSE</code>, specifying inputs that were
+not used when computing outputs (and therefore their grad is always zero) is an
+error. Defaults to <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>If only_inputs is <code>TRUE</code>, the function will only return a list of gradients w.r.t
+the specified inputs. If it’s <code>FALSE</code>, then gradient w.r.t. all remaining leaves
+will still be computed, and will be accumulated into their <code>.grad</code> attribute.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fl'>0.5</span>, requires_grad = <span class='fl'>TRUE</span>)
+<span class='kw'>b</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fl'>0.9</span>, requires_grad = <span class='fl'>TRUE</span>)
+<span class='kw'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/stats/Uniform.html'>runif</a></span>(<span class='fl'>100</span>))
+<span class='kw'>y</span> <span class='op'>&lt;-</span> <span class='fl'>2</span> <span class='op'>*</span> <span class='kw'>x</span> <span class='op'>+</span> <span class='fl'>1</span>
+<span class='kw'>loss</span> <span class='op'>&lt;-</span> (<span class='kw'>y</span> <span class='op'>-</span> (<span class='kw'>w</span><span class='op'>*</span><span class='kw'>x</span> <span class='op'>+</span> <span class='kw'>b</span>))<span class='op'>^</span><span class='fl'>2</span>
+<span class='kw'>loss</span> <span class='op'>&lt;-</span> <span class='kw'>loss</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/mean.html'>mean</a></span>()
+
+<span class='kw'>o</span> <span class='op'>&lt;-</span> <span class='fu'>autograd_grad</span>(<span class='kw'>loss</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>w</span>, <span class='kw'>b</span>))
+<span class='kw'>o</span>
+ 
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt; -0.9935
+#&gt; [ CPUFloatType{1} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt; -1.6206
+#&gt; [ CPUFloatType{1} ]
+#&gt; </div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
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+</div>
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+
diff --git a/static/docs/reference/autograd_set_grad_mode.html b/static/docs/reference/autograd_set_grad_mode.html
new file mode 100644
index 000000000..485d2028f
--- /dev/null
+++ b/static/docs/reference/autograd_set_grad_mode.html
@@ -0,0 +1,237 @@
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+<title>Set grad mode — autograd_set_grad_mode • torch</title>
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+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/cuda.R'><code>R/cuda.R</code></a></small>
+    <div class="hidden name"><code>cuda_current_device.Rd</code></div>
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+
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+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/cuda.R'><code>R/cuda.R</code></a></small>
+    <div class="hidden name"><code>cuda_device_count.Rd</code></div>
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+
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+    <p>Returns the number of GPUs available.</p>
+    </div>
+
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+
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+    <h1>Returns a bool indicating if CUDA is currently available.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/cuda.R'><code>R/cuda.R</code></a></small>
+    <div class="hidden name"><code>cuda_is_available.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Returns a bool indicating if CUDA is currently available.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>cuda_is_available</span>()</pre>
+
+
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diff --git a/static/docs/reference/dataloader.html b/static/docs/reference/dataloader.html
new file mode 100644
index 000000000..5f20021f4
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+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Data loader. Combines a dataset and a sampler, and provides
+single- or multi-process iterators over the dataset.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/utils-data-dataloader.R'><code>R/utils-data-dataloader.R</code></a></small>
+    <div class="hidden name"><code>dataloader.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Data loader. Combines a dataset and a sampler, and provides
+single- or multi-process iterators over the dataset.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>dataloader</span>(
+  <span class='kw'>dataset</span>,
+  batch_size = <span class='fl'>1</span>,
+  shuffle = <span class='fl'>FALSE</span>,
+  sampler = <span class='kw'>NULL</span>,
+  batch_sampler = <span class='kw'>NULL</span>,
+  num_workers = <span class='fl'>0</span>,
+  collate_fn = <span class='kw'>NULL</span>,
+  pin_memory = <span class='fl'>FALSE</span>,
+  drop_last = <span class='fl'>FALSE</span>,
+  timeout = <span class='fl'>0</span>,
+  worker_init_fn = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>dataset</th>
+      <td><p>(Dataset): dataset from which to load the data.</p></td>
+    </tr>
+    <tr>
+      <th>batch_size</th>
+      <td><p>(int, optional): how many samples per batch to load
+(default: <code>1</code>).</p></td>
+    </tr>
+    <tr>
+      <th>shuffle</th>
+      <td><p>(bool, optional): set to <code>TRUE</code> to have the data reshuffled
+at every epoch (default: <code>FALSE</code>).</p></td>
+    </tr>
+    <tr>
+      <th>sampler</th>
+      <td><p>(Sampler, optional): defines the strategy to draw samples from
+the dataset. If specified, <code>shuffle</code> must be False.</p></td>
+    </tr>
+    <tr>
+      <th>batch_sampler</th>
+      <td><p>(Sampler, optional): like sampler, but returns a batch of
+indices at a time. Mutually exclusive with <code>batch_size</code>,
+<code>shuffle</code>, <code>sampler</code>, and <code>drop_last</code>.</p></td>
+    </tr>
+    <tr>
+      <th>num_workers</th>
+      <td><p>(int, optional): how many subprocesses to use for data
+loading. 0 means that the data will be loaded in the main process.
+(default: <code>0</code>)</p></td>
+    </tr>
+    <tr>
+      <th>collate_fn</th>
+      <td><p>(callable, optional): merges a list of samples to form a mini-batch.</p></td>
+    </tr>
+    <tr>
+      <th>pin_memory</th>
+      <td><p>(bool, optional): If <code>TRUE</code>, the data loader will copy tensors
+into CUDA pinned memory before returning them.  If your data elements
+are a custom type, or your <code>collate_fn</code> returns a batch that is a custom type
+see the example below.</p></td>
+    </tr>
+    <tr>
+      <th>drop_last</th>
+      <td><p>(bool, optional): set to <code>TRUE</code> to drop the last incomplete batch,
+if the dataset size is not divisible by the batch size. If <code>FALSE</code> and
+the size of dataset is not divisible by the batch size, then the last batch
+will be smaller. (default: <code>FALSE</code>)</p></td>
+    </tr>
+    <tr>
+      <th>timeout</th>
+      <td><p>(numeric, optional): if positive, the timeout value for collecting a batch
+from workers. Should always be non-negative. (default: <code>0</code>)</p></td>
+    </tr>
+    <tr>
+      <th>worker_init_fn</th>
+      <td><p>(callable, optional): If not <code>NULL</code>, this will be called on each
+worker subprocess with the worker id (an int in <code>[0, num_workers - 1]</code>) as
+input, after seeding and before data loading. (default: <code>NULL</code>)</p></td>
+    </tr>
+    </table>
+
+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Creates an iterator from a DataLoader</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/utils-data-dataloader.R'><code>R/utils-data-dataloader.R</code></a></small>
+    <div class="hidden name"><code>dataloader_make_iter.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Creates an iterator from a DataLoader</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>dataloader_make_iter</span>(<span class='kw'>dataloader</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>dataloader</th>
+      <td><p>a dataloader object.</p></td>
+    </tr>
+    </table>
+
+
+  </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/static/docs/reference/dataloader_next.html b/static/docs/reference/dataloader_next.html
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Get the next element of a dataloader iterator</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_next.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Get the next element of a dataloader iterator</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>dataloader_next</span>(<span class='kw'>iter</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
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diff --git a/static/docs/reference/dataset.html b/static/docs/reference/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
+lenght, which is expected to return the size of the dataset by many
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+    <div class="page-header">
+    <h1>An abstract class representing a <code>Dataset</code>.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/utils-data.R'><code>R/utils-data.R</code></a></small>
+    <div class="hidden name"><code>dataset.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>All datasets that represent a map from keys to data samples should subclass
+it. All subclasses should overwrite <code>get_item</code>, supporting fetching a
+data sample for a given key. Subclasses could also optionally overwrite
+<code>lenght</code>, which is expected to return the size of the dataset by many
+<code>~torch.utils.data.Sampler</code> implementations and the default options
+of <code>~torch.utils.data.DataLoader</code>.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>dataset</span>(name = <span class='kw'>NULL</span>, inherit = <span class='kw'>Dataset</span>, <span class='kw'>...</span>, parent_env = <span class='fu'><a href='https://rdrr.io/r/base/sys.parent.html'>parent.frame</a></span>())</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>name</th>
+      <td><p>a name for the dataset. It it's also used as the class
+for it.</p></td>
+    </tr>
+    <tr>
+      <th>inherit</th>
+      <td><p>you can optionally inherit from a dataset when creating a
+new dataset.</p></td>
+    </tr>
+    <tr>
+      <th>...</th>
+      <td><p>public methods for the dataset class</p></td>
+    </tr>
+    <tr>
+      <th>parent_env</th>
+      <td><p>An environment to use as the parent of newly-created
+objects.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p><code>~torch.utils.data.DataLoader</code> by default constructs a index
+sampler that yields integral indices.  To make it work with a map-style
+dataset with non-integral indices/keys, a custom sampler must be provided.</p>
+
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diff --git a/static/docs/reference/default_dtype.html b/static/docs/reference/default_dtype.html
new file mode 100644
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+    <div class="page-header">
+    <h1>Gets and sets the default floating point dtype.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/dtype.R'><code>R/dtype.R</code></a></small>
+    <div class="hidden name"><code>default_dtype.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Gets and sets the default floating point dtype.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_set_default_dtype</span>(<span class='kw'>d</span>)
+
+<span class='fu'>torch_get_default_dtype</span>()</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>d</th>
+      <td><p>The default floating point dtype to set. Initially set to
+<code><a href='torch_dtype.html'>torch_float()</a></code>.</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
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diff --git a/static/docs/reference/enumerate.dataloader.html b/static/docs/reference/enumerate.dataloader.html
new file mode 100644
index 000000000..cdace84a5
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+    <h1>Enumerate an iterator</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/utils-data-enum.R'><code>R/utils-data-enum.R</code></a></small>
+    <div class="hidden name"><code>enumerate.dataloader.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Enumerate an iterator</p>
+    </div>
+
+    <pre class="usage"><span class='co'># S3 method for dataloader</span>
+<span class='fu'><a href='enumerate.html'>enumerate</a></span>(<span class='kw'>x</span>, max_len = <span class='fl'>1e+06</span>, <span class='kw'>...</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>x</th>
+      <td><p>the generator to enumerate.</p></td>
+    </tr>
+    <tr>
+      <th>max_len</th>
+      <td><p>maximum number of iterations.</p></td>
+    </tr>
+    <tr>
+      <th>...</th>
+      <td><p>passed to specific methods.</p></td>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/reference/enumerate.html b/static/docs/reference/enumerate.html
new file mode 100644
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@@ -0,0 +1,241 @@
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+
+      
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+<div class="row">
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+    <div class="page-header">
+    <h1>Enumerate an iterator</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/utils-data-enum.R'><code>R/utils-data-enum.R</code></a></small>
+    <div class="hidden name"><code>enumerate.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Enumerate an iterator</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>enumerate</span>(<span class='kw'>x</span>, <span class='kw'>...</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>x</th>
+      <td><p>the generator to enumerate.</p></td>
+    </tr>
+    <tr>
+      <th>...</th>
+      <td><p>passed to specific methods.</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/index.html b/static/docs/reference/index.html
new file mode 100644
index 000000000..de6c63a9c
--- /dev/null
+++ b/static/docs/reference/index.html
@@ -0,0 +1,3157 @@
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+        <a class="navbar-link" href="../index.html">torch</a>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.3</span>
+      </span>
+    </div>
+
+    <div id="navbar" class="navbar-collapse collapse">
+      <ul class="nav navbar-nav">
+        <li>
+  <a href="../index.html">
+    <span class="fas fa fas fa-home fa-lg"></span>
+     
+  </a>
+</li>
+<li class="dropdown">
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
+    Getting started
+     
+    <span class="caret"></span>
+  </a>
+  <ul class="dropdown-menu" role="menu">
+    <li class="dropdown-header">Beginers guide</li>
+    <li>
+      <a href="../articles/getting-started/warmup.html">Warm-up</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/tensors.html">Tensors</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/tensors-and-autograd.html">Tensors and autograd</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/optim.html">optim: optimizers in torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/custom-nn.html">Custom nn modules</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/control-flow-and-weight-sharing.html">Control flow &amp; Weight sharing</a>
+    </li>
+    <li class="dropdown-header">Torch Mechanics</li>
+    <li>
+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/neural-networks.html">Neural networks</a>
+    </li>
+  </ul>
+</li>
+<li class="dropdown">
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
+    Articles
+     
+    <span class="caret"></span>
+  </a>
+  <ul class="dropdown-menu" role="menu">
+    <li class="dropdown-header">Tensors</li>
+    <li>
+      <a href="../articles/tensor-creation.html">Creating tensors</a>
+    </li>
+    <li>
+      <a href="../articles/indexing.html">Indexing</a>
+    </li>
+    <li>
+      <a href="../articles/tensor/index.html">Tensor class</a>
+    </li>
+    <li class="dropdown-header">Datasets</li>
+    <li>
+      <a href="../articles/loading-data.html">Loading Data</a>
+    </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-header">Examples</li>
+<li>
+  <a href="../reference/index.html">Reference</a>
+</li>
+<li>
+  <a href="../news/index.html">Changelog</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="contents col-md-9">
+    <div class="page-header">
+      <h1>Reference</h1>
+    </div>
+
+    <table class="ref-index">
+
+    <colgroup>
+      
+      <col class="alias" />
+      <col class="title" />
+    </colgroup>
+
+    <tbody>
+      <tr>
+        <th colspan="2">
+          <h2 id="section-tensor-creation-utilities" class="hasAnchor"><a href="#section-tensor-creation-utilities" class="anchor"></a>Tensor creation utilities</h2>
+          <p class="section-desc"></p>
+        </th>
+      </tr>
+      
+      
+    </tbody><tbody>
+      
+      
+      <tr>
+        
+        <td>
+          <p><code><a href="torch_empty.html">torch_empty()</a></code> </p>
+        </td>
+        <td><p>Empty</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_arange.html">torch_arange()</a></code> </p>
+        </td>
+        <td><p>Arange</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_eye.html">torch_eye()</a></code> </p>
+        </td>
+        <td><p>Eye</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_full.html">torch_full()</a></code> </p>
+        </td>
+        <td><p>Full</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_linspace.html">torch_linspace()</a></code> </p>
+        </td>
+        <td><p>Linspace</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_logspace.html">torch_logspace()</a></code> </p>
+        </td>
+        <td><p>Logspace</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_ones.html">torch_ones()</a></code> </p>
+        </td>
+        <td><p>Ones</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_rand.html">torch_rand()</a></code> </p>
+        </td>
+        <td><p>Rand</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_randint.html">torch_randint()</a></code> </p>
+        </td>
+        <td><p>Randint</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_randn.html">torch_randn()</a></code> </p>
+        </td>
+        <td><p>Randn</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_randperm.html">torch_randperm()</a></code> </p>
+        </td>
+        <td><p>Randperm</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_zeros.html">torch_zeros()</a></code> </p>
+        </td>
+        <td><p>Zeros</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_empty_like.html">torch_empty_like()</a></code> </p>
+        </td>
+        <td><p>Empty_like</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_full_like.html">torch_full_like()</a></code> </p>
+        </td>
+        <td><p>Full_like</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_ones_like.html">torch_ones_like()</a></code> </p>
+        </td>
+        <td><p>Ones_like</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_rand_like.html">torch_rand_like()</a></code> </p>
+        </td>
+        <td><p>Rand_like</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_randint_like.html">torch_randint_like()</a></code> </p>
+        </td>
+        <td><p>Randint_like</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_randn_like.html">torch_randn_like()</a></code> </p>
+        </td>
+        <td><p>Randn_like</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_zeros_like.html">torch_zeros_like()</a></code> </p>
+        </td>
+        <td><p>Zeros_like</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="as_array.html">as_array()</a></code> </p>
+        </td>
+        <td><p>Converts to array</p></td>
+      </tr>
+    </tbody><tbody>
+      <tr>
+        <th colspan="2">
+          <h2 id="section-tensor-attributes" class="hasAnchor"><a href="#section-tensor-attributes" class="anchor"></a>Tensor attributes</h2>
+          <p class="section-desc"></p>
+        </th>
+      </tr>
+      
+      
+    </tbody><tbody>
+      
+      
+      <tr>
+        
+        <td>
+          <p><code><a href="default_dtype.html">torch_set_default_dtype()</a></code> <code><a href="default_dtype.html">torch_get_default_dtype()</a></code> </p>
+        </td>
+        <td><p>Gets and sets the default floating point dtype.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="is_torch_device.html">is_torch_device()</a></code> </p>
+        </td>
+        <td><p>Checks if object is a device</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="is_torch_dtype.html">is_torch_dtype()</a></code> </p>
+        </td>
+        <td><p>Check if object is a torch data type</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_dtype.html">torch_float32()</a></code> <code><a href="torch_dtype.html">torch_float()</a></code> <code><a href="torch_dtype.html">torch_float64()</a></code> <code><a href="torch_dtype.html">torch_double()</a></code> <code><a href="torch_dtype.html">torch_float16()</a></code> <code><a href="torch_dtype.html">torch_half()</a></code> <code><a href="torch_dtype.html">torch_uint8()</a></code> <code><a href="torch_dtype.html">torch_int8()</a></code> <code><a href="torch_dtype.html">torch_int16()</a></code> <code><a href="torch_dtype.html">torch_short()</a></code> <code><a href="torch_dtype.html">torch_int32()</a></code> <code><a href="torch_dtype.html">torch_int()</a></code> <code><a href="torch_dtype.html">torch_int64()</a></code> <code><a href="torch_dtype.html">torch_long()</a></code> <code><a href="torch_dtype.html">torch_bool()</a></code> <code><a href="torch_dtype.html">torch_quint8()</a></code> <code><a href="torch_dtype.html">torch_qint8()</a></code> <code><a href="torch_dtype.html">torch_qint32()</a></code> </p>
+        </td>
+        <td><p>Torch data types</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_finfo.html">torch_finfo()</a></code> </p>
+        </td>
+        <td><p>Floating point type info</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_iinfo.html">torch_iinfo()</a></code> </p>
+        </td>
+        <td><p>Integer type info</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_qscheme.html">torch_per_channel_affine()</a></code> <code><a href="torch_qscheme.html">torch_per_tensor_affine()</a></code> <code><a href="torch_qscheme.html">torch_per_channel_symmetric()</a></code> <code><a href="torch_qscheme.html">torch_per_tensor_symmetric()</a></code> </p>
+        </td>
+        <td><p>Creates the corresponding Scheme object</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_reduction.html">torch_reduction_sum()</a></code> <code><a href="torch_reduction.html">torch_reduction_mean()</a></code> <code><a href="torch_reduction.html">torch_reduction_none()</a></code> </p>
+        </td>
+        <td><p>Creates the reduction objet</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="is_torch_layout.html">is_torch_layout()</a></code> </p>
+        </td>
+        <td><p>Check if an object is a torch layout.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="is_torch_memory_format.html">is_torch_memory_format()</a></code> </p>
+        </td>
+        <td><p>Check if an object is a memory format</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="is_torch_qscheme.html">is_torch_qscheme()</a></code> </p>
+        </td>
+        <td><p>Checks if an object is a QScheme</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="is_undefined_tensor.html">is_undefined_tensor()</a></code> </p>
+        </td>
+        <td><p>Checks if a tensor is undefined</p></td>
+      </tr>
+    </tbody><tbody>
+      <tr>
+        <th colspan="2">
+          <h2 id="section-serialization" class="hasAnchor"><a href="#section-serialization" class="anchor"></a>Serialization</h2>
+          <p class="section-desc"></p>
+        </th>
+      </tr>
+      
+      
+    </tbody><tbody>
+      
+      
+      <tr>
+        
+        <td>
+          <p><code><a href="load_state_dict.html">load_state_dict()</a></code> </p>
+        </td>
+        <td><p>Load a state dict file</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_load.html">torch_load()</a></code> </p>
+        </td>
+        <td><p>Loads a saved object</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_save.html">torch_save()</a></code> </p>
+        </td>
+        <td><p>Saves an object to a disk file.</p></td>
+      </tr>
+    </tbody><tbody>
+      <tr>
+        <th colspan="2">
+          <h2 id="section-mathematical-operations-on-tensors" class="hasAnchor"><a href="#section-mathematical-operations-on-tensors" class="anchor"></a>Mathematical operations on tensors</h2>
+          <p class="section-desc"></p>
+        </th>
+      </tr>
+      
+      
+    </tbody><tbody>
+      
+      
+      <tr>
+        
+        <td>
+          <p><code><a href="torch_abs.html">torch_abs()</a></code> </p>
+        </td>
+        <td><p>Abs</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_acos.html">torch_acos()</a></code> </p>
+        </td>
+        <td><p>Acos</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_adaptive_avg_pool1d.html">torch_adaptive_avg_pool1d()</a></code> </p>
+        </td>
+        <td><p>Adaptive_avg_pool1d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_add.html">torch_add()</a></code> </p>
+        </td>
+        <td><p>Add</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_addbmm.html">torch_addbmm()</a></code> </p>
+        </td>
+        <td><p>Addbmm</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_addcdiv.html">torch_addcdiv()</a></code> </p>
+        </td>
+        <td><p>Addcdiv</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_addcmul.html">torch_addcmul()</a></code> </p>
+        </td>
+        <td><p>Addcmul</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_addmm.html">torch_addmm()</a></code> </p>
+        </td>
+        <td><p>Addmm</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_addmv.html">torch_addmv()</a></code> </p>
+        </td>
+        <td><p>Addmv</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_addr.html">torch_addr()</a></code> </p>
+        </td>
+        <td><p>Addr</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_allclose.html">torch_allclose()</a></code> </p>
+        </td>
+        <td><p>Allclose</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_angle.html">torch_angle()</a></code> </p>
+        </td>
+        <td><p>Angle</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_argmax.html">torch_argmax()</a></code> </p>
+        </td>
+        <td><p>Argmax</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_argmin.html">torch_argmin()</a></code> </p>
+        </td>
+        <td><p>Argmin</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_argsort.html">torch_argsort()</a></code> </p>
+        </td>
+        <td><p>Argsort</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_as_strided.html">torch_as_strided()</a></code> </p>
+        </td>
+        <td><p>As_strided</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_asin.html">torch_asin()</a></code> </p>
+        </td>
+        <td><p>Asin</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_atan.html">torch_atan()</a></code> </p>
+        </td>
+        <td><p>Atan</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_atan2.html">torch_atan2()</a></code> </p>
+        </td>
+        <td><p>Atan2</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_avg_pool1d.html">torch_avg_pool1d()</a></code> </p>
+        </td>
+        <td><p>Avg_pool1d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_baddbmm.html">torch_baddbmm()</a></code> </p>
+        </td>
+        <td><p>Baddbmm</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_bartlett_window.html">torch_bartlett_window()</a></code> </p>
+        </td>
+        <td><p>Bartlett_window</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_bernoulli.html">torch_bernoulli()</a></code> </p>
+        </td>
+        <td><p>Bernoulli</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_bincount.html">torch_bincount()</a></code> </p>
+        </td>
+        <td><p>Bincount</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_bitwise_and.html">torch_bitwise_and()</a></code> </p>
+        </td>
+        <td><p>Bitwise_and</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_bitwise_not.html">torch_bitwise_not()</a></code> </p>
+        </td>
+        <td><p>Bitwise_not</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_bitwise_or.html">torch_bitwise_or()</a></code> </p>
+        </td>
+        <td><p>Bitwise_or</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_bitwise_xor.html">torch_bitwise_xor()</a></code> </p>
+        </td>
+        <td><p>Bitwise_xor</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_blackman_window.html">torch_blackman_window()</a></code> </p>
+        </td>
+        <td><p>Blackman_window</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_bmm.html">torch_bmm()</a></code> </p>
+        </td>
+        <td><p>Bmm</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_broadcast_tensors.html">torch_broadcast_tensors()</a></code> </p>
+        </td>
+        <td><p>Broadcast_tensors</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_can_cast.html">torch_can_cast()</a></code> </p>
+        </td>
+        <td><p>Can_cast</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_cartesian_prod.html">torch_cartesian_prod()</a></code> </p>
+        </td>
+        <td><p>Cartesian_prod</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_cat.html">torch_cat()</a></code> </p>
+        </td>
+        <td><p>Cat</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_cdist.html">torch_cdist()</a></code> </p>
+        </td>
+        <td><p>Cdist</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_ceil.html">torch_ceil()</a></code> </p>
+        </td>
+        <td><p>Ceil</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_celu.html">torch_celu()</a></code> </p>
+        </td>
+        <td><p>Celu</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_celu_.html">torch_celu_()</a></code> </p>
+        </td>
+        <td><p>Celu_</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_chain_matmul.html">torch_chain_matmul()</a></code> </p>
+        </td>
+        <td><p>Chain_matmul</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_cholesky.html">torch_cholesky()</a></code> </p>
+        </td>
+        <td><p>Cholesky</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_cholesky_inverse.html">torch_cholesky_inverse()</a></code> </p>
+        </td>
+        <td><p>Cholesky_inverse</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_cholesky_solve.html">torch_cholesky_solve()</a></code> </p>
+        </td>
+        <td><p>Cholesky_solve</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_chunk.html">torch_chunk()</a></code> </p>
+        </td>
+        <td><p>Chunk</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_clamp.html">torch_clamp()</a></code> </p>
+        </td>
+        <td><p>Clamp</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_combinations.html">torch_combinations()</a></code> </p>
+        </td>
+        <td><p>Combinations</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_conj.html">torch_conj()</a></code> </p>
+        </td>
+        <td><p>Conj</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_conv1d.html">torch_conv1d()</a></code> </p>
+        </td>
+        <td><p>Conv1d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_conv2d.html">torch_conv2d()</a></code> </p>
+        </td>
+        <td><p>Conv2d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_conv3d.html">torch_conv3d()</a></code> </p>
+        </td>
+        <td><p>Conv3d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_conv_tbc.html">torch_conv_tbc()</a></code> </p>
+        </td>
+        <td><p>Conv_tbc</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_conv_transpose1d.html">torch_conv_transpose1d()</a></code> </p>
+        </td>
+        <td><p>Conv_transpose1d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_conv_transpose2d.html">torch_conv_transpose2d()</a></code> </p>
+        </td>
+        <td><p>Conv_transpose2d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_conv_transpose3d.html">torch_conv_transpose3d()</a></code> </p>
+        </td>
+        <td><p>Conv_transpose3d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_cos.html">torch_cos()</a></code> </p>
+        </td>
+        <td><p>Cos</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_cosh.html">torch_cosh()</a></code> </p>
+        </td>
+        <td><p>Cosh</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_cosine_similarity.html">torch_cosine_similarity()</a></code> </p>
+        </td>
+        <td><p>Cosine_similarity</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_cross.html">torch_cross()</a></code> </p>
+        </td>
+        <td><p>Cross</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_cummax.html">torch_cummax()</a></code> </p>
+        </td>
+        <td><p>Cummax</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_cummin.html">torch_cummin()</a></code> </p>
+        </td>
+        <td><p>Cummin</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_cumprod.html">torch_cumprod()</a></code> </p>
+        </td>
+        <td><p>Cumprod</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_cumsum.html">torch_cumsum()</a></code> </p>
+        </td>
+        <td><p>Cumsum</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_det.html">torch_det()</a></code> </p>
+        </td>
+        <td><p>Det</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_device.html">torch_device()</a></code> </p>
+        </td>
+        <td><p>Create a Device object</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_diag.html">torch_diag()</a></code> </p>
+        </td>
+        <td><p>Diag</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_diag_embed.html">torch_diag_embed()</a></code> </p>
+        </td>
+        <td><p>Diag_embed</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_diagflat.html">torch_diagflat()</a></code> </p>
+        </td>
+        <td><p>Diagflat</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_diagonal.html">torch_diagonal()</a></code> </p>
+        </td>
+        <td><p>Diagonal</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_digamma.html">torch_digamma()</a></code> </p>
+        </td>
+        <td><p>Digamma</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_dist.html">torch_dist()</a></code> </p>
+        </td>
+        <td><p>Dist</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_div.html">torch_div()</a></code> </p>
+        </td>
+        <td><p>Div</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_dot.html">torch_dot()</a></code> </p>
+        </td>
+        <td><p>Dot</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_eig.html">torch_eig()</a></code> </p>
+        </td>
+        <td><p>Eig</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_einsum.html">torch_einsum()</a></code> </p>
+        </td>
+        <td><p>Einsum</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_empty_strided.html">torch_empty_strided()</a></code> </p>
+        </td>
+        <td><p>Empty_strided</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_eq.html">torch_eq()</a></code> </p>
+        </td>
+        <td><p>Eq</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_equal.html">torch_equal()</a></code> </p>
+        </td>
+        <td><p>Equal</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_erf.html">torch_erf()</a></code> </p>
+        </td>
+        <td><p>Erf</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_erfc.html">torch_erfc()</a></code> </p>
+        </td>
+        <td><p>Erfc</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_erfinv.html">torch_erfinv()</a></code> </p>
+        </td>
+        <td><p>Erfinv</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_exp.html">torch_exp()</a></code> </p>
+        </td>
+        <td><p>Exp</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_expm1.html">torch_expm1()</a></code> </p>
+        </td>
+        <td><p>Expm1</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_fft.html">torch_fft()</a></code> </p>
+        </td>
+        <td><p>Fft</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_flatten.html">torch_flatten()</a></code> </p>
+        </td>
+        <td><p>Flatten</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_flip.html">torch_flip()</a></code> </p>
+        </td>
+        <td><p>Flip</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_floor.html">torch_floor()</a></code> </p>
+        </td>
+        <td><p>Floor</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_floor_divide.html">torch_floor_divide()</a></code> </p>
+        </td>
+        <td><p>Floor_divide</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_fmod.html">torch_fmod()</a></code> </p>
+        </td>
+        <td><p>Fmod</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_frac.html">torch_frac()</a></code> </p>
+        </td>
+        <td><p>Frac</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_gather.html">torch_gather()</a></code> </p>
+        </td>
+        <td><p>Gather</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_ge.html">torch_ge()</a></code> </p>
+        </td>
+        <td><p>Ge</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_generator.html">torch_generator()</a></code> </p>
+        </td>
+        <td><p>Create a Generator object</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_geqrf.html">torch_geqrf()</a></code> </p>
+        </td>
+        <td><p>Geqrf</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_ger.html">torch_ger()</a></code> </p>
+        </td>
+        <td><p>Ger</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_gt.html">torch_gt()</a></code> </p>
+        </td>
+        <td><p>Gt</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_hamming_window.html">torch_hamming_window()</a></code> </p>
+        </td>
+        <td><p>Hamming_window</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_hann_window.html">torch_hann_window()</a></code> </p>
+        </td>
+        <td><p>Hann_window</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_histc.html">torch_histc()</a></code> </p>
+        </td>
+        <td><p>Histc</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_ifft.html">torch_ifft()</a></code> </p>
+        </td>
+        <td><p>Ifft</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_imag.html">torch_imag()</a></code> </p>
+        </td>
+        <td><p>Imag</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_index_select.html">torch_index_select()</a></code> </p>
+        </td>
+        <td><p>Index_select</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_inverse.html">torch_inverse()</a></code> </p>
+        </td>
+        <td><p>Inverse</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_irfft.html">torch_irfft()</a></code> </p>
+        </td>
+        <td><p>Irfft</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_is_complex.html">torch_is_complex()</a></code> </p>
+        </td>
+        <td><p>Is_complex</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_is_floating_point.html">torch_is_floating_point()</a></code> </p>
+        </td>
+        <td><p>Is_floating_point</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_is_installed.html">torch_is_installed()</a></code> </p>
+        </td>
+        <td><p>Verifies if torch is installed</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_isfinite.html">torch_isfinite()</a></code> </p>
+        </td>
+        <td><p>Isfinite</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_isinf.html">torch_isinf()</a></code> </p>
+        </td>
+        <td><p>Isinf</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_isnan.html">torch_isnan()</a></code> </p>
+        </td>
+        <td><p>Isnan</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_kthvalue.html">torch_kthvalue()</a></code> </p>
+        </td>
+        <td><p>Kthvalue</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_layout.html">torch_strided()</a></code> <code><a href="torch_layout.html">torch_sparse_coo()</a></code> </p>
+        </td>
+        <td><p>Creates the corresponding layout</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_le.html">torch_le()</a></code> </p>
+        </td>
+        <td><p>Le</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_lerp.html">torch_lerp()</a></code> </p>
+        </td>
+        <td><p>Lerp</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_lgamma.html">torch_lgamma()</a></code> </p>
+        </td>
+        <td><p>Lgamma</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_log.html">torch_log()</a></code> </p>
+        </td>
+        <td><p>Log</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_log10.html">torch_log10()</a></code> </p>
+        </td>
+        <td><p>Log10</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_log1p.html">torch_log1p()</a></code> </p>
+        </td>
+        <td><p>Log1p</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_log2.html">torch_log2()</a></code> </p>
+        </td>
+        <td><p>Log2</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_logdet.html">torch_logdet()</a></code> </p>
+        </td>
+        <td><p>Logdet</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_logical_and.html">torch_logical_and()</a></code> </p>
+        </td>
+        <td><p>Logical_and</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_logical_not.html">torch_logical_not</a></code> </p>
+        </td>
+        <td><p>Logical_not</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_logical_or.html">torch_logical_or()</a></code> </p>
+        </td>
+        <td><p>Logical_or</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_logical_xor.html">torch_logical_xor()</a></code> </p>
+        </td>
+        <td><p>Logical_xor</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_logsumexp.html">torch_logsumexp()</a></code> </p>
+        </td>
+        <td><p>Logsumexp</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_lstsq.html">torch_lstsq()</a></code> </p>
+        </td>
+        <td><p>Lstsq</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_lt.html">torch_lt()</a></code> </p>
+        </td>
+        <td><p>Lt</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_lu.html">torch_lu()</a></code> </p>
+        </td>
+        <td><p>LU</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_lu_solve.html">torch_lu_solve()</a></code> </p>
+        </td>
+        <td><p>Lu_solve</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_manual_seed.html">torch_manual_seed()</a></code> </p>
+        </td>
+        <td><p>Sets the seed for generating random numbers.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_masked_select.html">torch_masked_select()</a></code> </p>
+        </td>
+        <td><p>Masked_select</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_matmul.html">torch_matmul()</a></code> </p>
+        </td>
+        <td><p>Matmul</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_matrix_power.html">torch_matrix_power()</a></code> </p>
+        </td>
+        <td><p>Matrix_power</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_matrix_rank.html">torch_matrix_rank()</a></code> </p>
+        </td>
+        <td><p>Matrix_rank</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_max.html">torch_max</a></code> </p>
+        </td>
+        <td><p>Max</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_mean.html">torch_mean()</a></code> </p>
+        </td>
+        <td><p>Mean</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_median.html">torch_median()</a></code> </p>
+        </td>
+        <td><p>Median</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_memory_format.html">torch_contiguous_format()</a></code> <code><a href="torch_memory_format.html">torch_preserve_format()</a></code> <code><a href="torch_memory_format.html">torch_channels_last_format()</a></code> </p>
+        </td>
+        <td><p>Memory format</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_meshgrid.html">torch_meshgrid()</a></code> </p>
+        </td>
+        <td><p>Meshgrid</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_min.html">torch_min</a></code> </p>
+        </td>
+        <td><p>Min</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_mm.html">torch_mm()</a></code> </p>
+        </td>
+        <td><p>Mm</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_mode.html">torch_mode()</a></code> </p>
+        </td>
+        <td><p>Mode</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_mul.html">torch_mul()</a></code> </p>
+        </td>
+        <td><p>Mul</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_multinomial.html">torch_multinomial()</a></code> </p>
+        </td>
+        <td><p>Multinomial</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_mv.html">torch_mv()</a></code> </p>
+        </td>
+        <td><p>Mv</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_mvlgamma.html">torch_mvlgamma()</a></code> </p>
+        </td>
+        <td><p>Mvlgamma</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_narrow.html">torch_narrow()</a></code> </p>
+        </td>
+        <td><p>Narrow</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_ne.html">torch_ne()</a></code> </p>
+        </td>
+        <td><p>Ne</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_neg.html">torch_neg()</a></code> </p>
+        </td>
+        <td><p>Neg</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_nonzero.html">torch_nonzero()</a></code> </p>
+        </td>
+        <td><p>Nonzero</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_norm.html">torch_norm()</a></code> </p>
+        </td>
+        <td><p>Norm</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_normal.html">torch_normal()</a></code> </p>
+        </td>
+        <td><p>Normal</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_orgqr.html">torch_orgqr()</a></code> </p>
+        </td>
+        <td><p>Orgqr</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_ormqr.html">torch_ormqr()</a></code> </p>
+        </td>
+        <td><p>Ormqr</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_pdist.html">torch_pdist()</a></code> </p>
+        </td>
+        <td><p>Pdist</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_pinverse.html">torch_pinverse()</a></code> </p>
+        </td>
+        <td><p>Pinverse</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_pixel_shuffle.html">torch_pixel_shuffle()</a></code> </p>
+        </td>
+        <td><p>Pixel_shuffle</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_poisson.html">torch_poisson()</a></code> </p>
+        </td>
+        <td><p>Poisson</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_polygamma.html">torch_polygamma()</a></code> </p>
+        </td>
+        <td><p>Polygamma</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_pow.html">torch_pow()</a></code> </p>
+        </td>
+        <td><p>Pow</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_prod.html">torch_prod()</a></code> </p>
+        </td>
+        <td><p>Prod</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_promote_types.html">torch_promote_types()</a></code> </p>
+        </td>
+        <td><p>Promote_types</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_qr.html">torch_qr()</a></code> </p>
+        </td>
+        <td><p>Qr</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_quantize_per_channel.html">torch_quantize_per_channel()</a></code> </p>
+        </td>
+        <td><p>Quantize_per_channel</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_quantize_per_tensor.html">torch_quantize_per_tensor()</a></code> </p>
+        </td>
+        <td><p>Quantize_per_tensor</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_range.html">torch_range()</a></code> </p>
+        </td>
+        <td><p>Range</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_real.html">torch_real()</a></code> </p>
+        </td>
+        <td><p>Real</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_reciprocal.html">torch_reciprocal()</a></code> </p>
+        </td>
+        <td><p>Reciprocal</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_relu.html">torch_relu()</a></code> </p>
+        </td>
+        <td><p>Relu</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_relu_.html">torch_relu_()</a></code> </p>
+        </td>
+        <td><p>Relu_</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_remainder.html">torch_remainder()</a></code> </p>
+        </td>
+        <td><p>Remainder</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_renorm.html">torch_renorm()</a></code> </p>
+        </td>
+        <td><p>Renorm</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_repeat_interleave.html">torch_repeat_interleave()</a></code> </p>
+        </td>
+        <td><p>Repeat_interleave</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_reshape.html">torch_reshape()</a></code> </p>
+        </td>
+        <td><p>Reshape</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_result_type.html">torch_result_type()</a></code> </p>
+        </td>
+        <td><p>Result_type</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_rfft.html">torch_rfft()</a></code> </p>
+        </td>
+        <td><p>Rfft</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_roll.html">torch_roll()</a></code> </p>
+        </td>
+        <td><p>Roll</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_rot90.html">torch_rot90()</a></code> </p>
+        </td>
+        <td><p>Rot90</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_round.html">torch_round()</a></code> </p>
+        </td>
+        <td><p>Round</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_rrelu_.html">torch_rrelu_()</a></code> </p>
+        </td>
+        <td><p>Rrelu_</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_rsqrt.html">torch_rsqrt()</a></code> </p>
+        </td>
+        <td><p>Rsqrt</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_selu.html">torch_selu()</a></code> </p>
+        </td>
+        <td><p>Selu</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_selu_.html">torch_selu_()</a></code> </p>
+        </td>
+        <td><p>Selu_</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_sigmoid.html">torch_sigmoid()</a></code> </p>
+        </td>
+        <td><p>Sigmoid</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_sign.html">torch_sign()</a></code> </p>
+        </td>
+        <td><p>Sign</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_sin.html">torch_sin()</a></code> </p>
+        </td>
+        <td><p>Sin</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_sinh.html">torch_sinh()</a></code> </p>
+        </td>
+        <td><p>Sinh</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_slogdet.html">torch_slogdet()</a></code> </p>
+        </td>
+        <td><p>Slogdet</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_solve.html">torch_solve()</a></code> </p>
+        </td>
+        <td><p>Solve</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_sort.html">torch_sort()</a></code> </p>
+        </td>
+        <td><p>Sort</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_sparse_coo_tensor.html">torch_sparse_coo_tensor()</a></code> </p>
+        </td>
+        <td><p>Sparse_coo_tensor</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_split.html">torch_split()</a></code> </p>
+        </td>
+        <td><p>Split</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_sqrt.html">torch_sqrt()</a></code> </p>
+        </td>
+        <td><p>Sqrt</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_square.html">torch_square()</a></code> </p>
+        </td>
+        <td><p>Square</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_squeeze.html">torch_squeeze()</a></code> </p>
+        </td>
+        <td><p>Squeeze</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_stack.html">torch_stack()</a></code> </p>
+        </td>
+        <td><p>Stack</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_std.html">torch_std()</a></code> </p>
+        </td>
+        <td><p>Std</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_std_mean.html">torch_std_mean()</a></code> </p>
+        </td>
+        <td><p>Std_mean</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_stft.html">torch_stft()</a></code> </p>
+        </td>
+        <td><p>Stft</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_sum.html">torch_sum()</a></code> </p>
+        </td>
+        <td><p>Sum</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_svd.html">torch_svd()</a></code> </p>
+        </td>
+        <td><p>Svd</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_symeig.html">torch_symeig()</a></code> </p>
+        </td>
+        <td><p>Symeig</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_t.html">torch_t()</a></code> </p>
+        </td>
+        <td><p>T</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_take.html">torch_take()</a></code> </p>
+        </td>
+        <td><p>Take</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_tan.html">torch_tan()</a></code> </p>
+        </td>
+        <td><p>Tan</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_tanh.html">torch_tanh()</a></code> </p>
+        </td>
+        <td><p>Tanh</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_tensor.html">torch_tensor()</a></code> </p>
+        </td>
+        <td><p>Converts R objects to a torch tensor</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_tensordot.html">torch_tensordot()</a></code> </p>
+        </td>
+        <td><p>Tensordot</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_threshold_.html">torch_threshold_()</a></code> </p>
+        </td>
+        <td><p>Threshold_</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_topk.html">torch_topk()</a></code> </p>
+        </td>
+        <td><p>Topk</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_trace.html">torch_trace()</a></code> </p>
+        </td>
+        <td><p>Trace</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_transpose.html">torch_transpose()</a></code> </p>
+        </td>
+        <td><p>Transpose</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_trapz.html">torch_trapz()</a></code> </p>
+        </td>
+        <td><p>Trapz</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_triangular_solve.html">torch_triangular_solve()</a></code> </p>
+        </td>
+        <td><p>Triangular_solve</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_tril.html">torch_tril()</a></code> </p>
+        </td>
+        <td><p>Tril</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_tril_indices.html">torch_tril_indices()</a></code> </p>
+        </td>
+        <td><p>Tril_indices</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_triu.html">torch_triu()</a></code> </p>
+        </td>
+        <td><p>Triu</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_triu_indices.html">torch_triu_indices()</a></code> </p>
+        </td>
+        <td><p>Triu_indices</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_true_divide.html">torch_true_divide()</a></code> </p>
+        </td>
+        <td><p>TRUE_divide</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_trunc.html">torch_trunc()</a></code> </p>
+        </td>
+        <td><p>Trunc</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_unbind.html">torch_unbind()</a></code> </p>
+        </td>
+        <td><p>Unbind</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_unique_consecutive.html">torch_unique_consecutive()</a></code> </p>
+        </td>
+        <td><p>Unique_consecutive</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_unsqueeze.html">torch_unsqueeze()</a></code> </p>
+        </td>
+        <td><p>Unsqueeze</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_var.html">torch_var()</a></code> </p>
+        </td>
+        <td><p>Var</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_var_mean.html">torch_var_mean()</a></code> </p>
+        </td>
+        <td><p>Var_mean</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="torch_where.html">torch_where()</a></code> </p>
+        </td>
+        <td><p>Where</p></td>
+      </tr>
+    </tbody><tbody>
+      <tr>
+        <th colspan="2">
+          <h2 id="section-neural-network-modules" class="hasAnchor"><a href="#section-neural-network-modules" class="anchor"></a>Neural network modules</h2>
+          <p class="section-desc"></p>
+        </th>
+      </tr>
+      
+      
+    </tbody><tbody>
+      
+      
+      <tr>
+        
+        <td>
+          <p><code><a href="nn_adaptive_avg_pool1d.html">nn_adaptive_avg_pool1d()</a></code> </p>
+        </td>
+        <td><p>Applies a 1D adaptive average pooling over an input signal composed of several input planes.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_adaptive_avg_pool2d.html">nn_adaptive_avg_pool2d()</a></code> </p>
+        </td>
+        <td><p>Applies a 2D adaptive average pooling over an input signal composed of several input planes.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_adaptive_avg_pool3d.html">nn_adaptive_avg_pool3d()</a></code> </p>
+        </td>
+        <td><p>Applies a 3D adaptive average pooling over an input signal composed of several input planes.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_adaptive_log_softmax_with_loss.html">nn_adaptive_log_softmax_with_loss()</a></code> </p>
+        </td>
+        <td><p>AdaptiveLogSoftmaxWithLoss module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_adaptive_max_pool1d.html">nn_adaptive_max_pool1d()</a></code> </p>
+        </td>
+        <td><p>Applies a 1D adaptive max pooling over an input signal composed of several input planes.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_adaptive_max_pool2d.html">nn_adaptive_max_pool2d()</a></code> </p>
+        </td>
+        <td><p>Applies a 2D adaptive max pooling over an input signal composed of several input planes.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_adaptive_max_pool3d.html">nn_adaptive_max_pool3d()</a></code> </p>
+        </td>
+        <td><p>Applies a 3D adaptive max pooling over an input signal composed of several input planes.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_avg_pool1d.html">nn_avg_pool1d()</a></code> </p>
+        </td>
+        <td><p>Applies a 1D average pooling over an input signal composed of several
+input planes.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_avg_pool2d.html">nn_avg_pool2d()</a></code> </p>
+        </td>
+        <td><p>Applies a 2D average pooling over an input signal composed of several input
+planes.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_avg_pool3d.html">nn_avg_pool3d()</a></code> </p>
+        </td>
+        <td><p>Applies a 3D average pooling over an input signal composed of several input
+planes.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_batch_norm1d.html">nn_batch_norm1d()</a></code> </p>
+        </td>
+        <td><p>BatchNorm1D module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_batch_norm2d.html">nn_batch_norm2d()</a></code> </p>
+        </td>
+        <td><p>BatchNorm2D</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_bce_loss.html">nn_bce_loss()</a></code> </p>
+        </td>
+        <td><p>Binary cross entropy loss</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_bilinear.html">nn_bilinear()</a></code> </p>
+        </td>
+        <td><p>Bilinear module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_celu.html">nn_celu()</a></code> </p>
+        </td>
+        <td><p>CELU module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_conv1d.html">nn_conv1d()</a></code> </p>
+        </td>
+        <td><p>Conv1D module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_conv2d.html">nn_conv2d()</a></code> </p>
+        </td>
+        <td><p>Conv2D module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_conv3d.html">nn_conv3d()</a></code> </p>
+        </td>
+        <td><p>Conv3D module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_conv_transpose1d.html">nn_conv_transpose1d()</a></code> </p>
+        </td>
+        <td><p>ConvTranspose1D</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_conv_transpose2d.html">nn_conv_transpose2d()</a></code> </p>
+        </td>
+        <td><p>ConvTranpose2D module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_conv_transpose3d.html">nn_conv_transpose3d()</a></code> </p>
+        </td>
+        <td><p>ConvTranpose3D module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_cross_entropy_loss.html">nn_cross_entropy_loss()</a></code> </p>
+        </td>
+        <td><p>CrossEntropyLoss module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_dropout.html">nn_dropout()</a></code> </p>
+        </td>
+        <td><p>Dropout module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_dropout2d.html">nn_dropout2d()</a></code> </p>
+        </td>
+        <td><p>Dropout2D module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_dropout3d.html">nn_dropout3d()</a></code> </p>
+        </td>
+        <td><p>Dropout3D module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_elu.html">nn_elu()</a></code> </p>
+        </td>
+        <td><p>ELU module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_embedding.html">nn_embedding()</a></code> </p>
+        </td>
+        <td><p>Embedding module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_fractional_max_pool2d.html">nn_fractional_max_pool2d()</a></code> </p>
+        </td>
+        <td><p>Applies a 2D fractional max pooling over an input signal composed of several input planes.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_fractional_max_pool3d.html">nn_fractional_max_pool3d()</a></code> </p>
+        </td>
+        <td><p>Applies a 3D fractional max pooling over an input signal composed of several input planes.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_gelu.html">nn_gelu()</a></code> </p>
+        </td>
+        <td><p>GELU module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_glu.html">nn_glu()</a></code> </p>
+        </td>
+        <td><p>GLU module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_hardshrink.html">nn_hardshrink()</a></code> </p>
+        </td>
+        <td><p>Hardshwink module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_hardsigmoid.html">nn_hardsigmoid()</a></code> </p>
+        </td>
+        <td><p>Hardsigmoid module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_hardswish.html">nn_hardswish()</a></code> </p>
+        </td>
+        <td><p>Hardswish module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_hardtanh.html">nn_hardtanh()</a></code> </p>
+        </td>
+        <td><p>Hardtanh module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_identity.html">nn_identity()</a></code> </p>
+        </td>
+        <td><p>Identity module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_init_calculate_gain.html">nn_init_calculate_gain()</a></code> </p>
+        </td>
+        <td><p>Calculate gain</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_init_constant_.html">nn_init_constant_()</a></code> </p>
+        </td>
+        <td><p>Constant initialization</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_init_dirac_.html">nn_init_dirac_()</a></code> </p>
+        </td>
+        <td><p>Dirac initialization</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_init_eye_.html">nn_init_eye_()</a></code> </p>
+        </td>
+        <td><p>Eye initialization</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_init_kaiming_normal_.html">nn_init_kaiming_normal_()</a></code> </p>
+        </td>
+        <td><p>Kaiming normal initialization</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_init_kaiming_uniform_.html">nn_init_kaiming_uniform_()</a></code> </p>
+        </td>
+        <td><p>Kaiming uniform initialization</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_init_normal_.html">nn_init_normal_()</a></code> </p>
+        </td>
+        <td><p>Normal initialization</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_init_ones_.html">nn_init_ones_()</a></code> </p>
+        </td>
+        <td><p>Ones initialization</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_init_orthogonal_.html">nn_init_orthogonal_()</a></code> </p>
+        </td>
+        <td><p>Orthogonal initialization</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_init_sparse_.html">nn_init_sparse_()</a></code> </p>
+        </td>
+        <td><p>Sparse initialization</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_init_trunc_normal_.html">nn_init_trunc_normal_()</a></code> </p>
+        </td>
+        <td><p>Truncated normal initialization</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_init_uniform_.html">nn_init_uniform_()</a></code> </p>
+        </td>
+        <td><p>Uniform initialization</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_init_xavier_normal_.html">nn_init_xavier_normal_()</a></code> </p>
+        </td>
+        <td><p>Xavier normal initialization</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_init_xavier_uniform_.html">nn_init_xavier_uniform_()</a></code> </p>
+        </td>
+        <td><p>Xavier uniform initialization</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_init_zeros_.html">nn_init_zeros_()</a></code> </p>
+        </td>
+        <td><p>Zeros initialization</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_leaky_relu.html">nn_leaky_relu()</a></code> </p>
+        </td>
+        <td><p>LeakyReLU module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_linear.html">nn_linear()</a></code> </p>
+        </td>
+        <td><p>Linear module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_log_sigmoid.html">nn_log_sigmoid()</a></code> </p>
+        </td>
+        <td><p>LogSigmoid module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_log_softmax.html">nn_log_softmax()</a></code> </p>
+        </td>
+        <td><p>LogSoftmax module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_lp_pool1d.html">nn_lp_pool1d()</a></code> </p>
+        </td>
+        <td><p>Applies a 1D power-average pooling over an input signal composed of several input
+planes.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_lp_pool2d.html">nn_lp_pool2d()</a></code> </p>
+        </td>
+        <td><p>Applies a 2D power-average pooling over an input signal composed of several input
+planes.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_max_pool1d.html">nn_max_pool1d()</a></code> </p>
+        </td>
+        <td><p>MaxPool1D module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_max_pool2d.html">nn_max_pool2d()</a></code> </p>
+        </td>
+        <td><p>MaxPool2D module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_max_pool3d.html">nn_max_pool3d()</a></code> </p>
+        </td>
+        <td><p>Applies a 3D max pooling over an input signal composed of several input
+planes.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_max_unpool1d.html">nn_max_unpool1d()</a></code> </p>
+        </td>
+        <td><p>Computes a partial inverse of <code>MaxPool1d</code>.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_max_unpool2d.html">nn_max_unpool2d()</a></code> </p>
+        </td>
+        <td><p>Computes a partial inverse of <code>MaxPool2d</code>.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_max_unpool3d.html">nn_max_unpool3d()</a></code> </p>
+        </td>
+        <td><p>Computes a partial inverse of <code>MaxPool3d</code>.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_module.html">nn_module()</a></code> </p>
+        </td>
+        <td><p>Base class for all neural network modules.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_module_list.html">nn_module_list()</a></code> </p>
+        </td>
+        <td><p>Holds submodules in a list.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_multihead_attention.html">nn_multihead_attention()</a></code> </p>
+        </td>
+        <td><p>MultiHead attention</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_prelu.html">nn_prelu()</a></code> </p>
+        </td>
+        <td><p>PReLU module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_relu.html">nn_relu()</a></code> </p>
+        </td>
+        <td><p>ReLU module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_relu6.html">nn_relu6()</a></code> </p>
+        </td>
+        <td><p>ReLu6 module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_rnn.html">nn_rnn()</a></code> </p>
+        </td>
+        <td><p>RNN module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_rrelu.html">nn_rrelu()</a></code> </p>
+        </td>
+        <td><p>RReLU module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_selu.html">nn_selu()</a></code> </p>
+        </td>
+        <td><p>SELU module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_sequential.html">nn_sequential()</a></code> </p>
+        </td>
+        <td><p>A sequential container</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_sigmoid.html">nn_sigmoid()</a></code> </p>
+        </td>
+        <td><p>Sigmoid module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_softmax.html">nn_softmax()</a></code> </p>
+        </td>
+        <td><p>Softmax module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_softmax2d.html">nn_softmax2d()</a></code> </p>
+        </td>
+        <td><p>Softmax2d module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_softmin.html">nn_softmin()</a></code> </p>
+        </td>
+        <td><p>Softmin</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_softplus.html">nn_softplus()</a></code> </p>
+        </td>
+        <td><p>Softplus module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_softshrink.html">nn_softshrink()</a></code> </p>
+        </td>
+        <td><p>Softshrink module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_softsign.html">nn_softsign()</a></code> </p>
+        </td>
+        <td><p>Softsign module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_tanh.html">nn_tanh()</a></code> </p>
+        </td>
+        <td><p>Tanh module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_tanhshrink.html">nn_tanhshrink()</a></code> </p>
+        </td>
+        <td><p>Tanhshrink module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_threshold.html">nn_threshold()</a></code> </p>
+        </td>
+        <td><p>Threshoold module</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_utils_rnn_pack_padded_sequence.html">nn_utils_rnn_pack_padded_sequence()</a></code> </p>
+        </td>
+        <td><p>Packs a Tensor containing padded sequences of variable length.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_utils_rnn_pack_sequence.html">nn_utils_rnn_pack_sequence()</a></code> </p>
+        </td>
+        <td><p>Packs a list of variable length Tensors</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_utils_rnn_pad_packed_sequence.html">nn_utils_rnn_pad_packed_sequence()</a></code> </p>
+        </td>
+        <td><p>Pads a packed batch of variable length sequences.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nn_utils_rnn_pad_sequence.html">nn_utils_rnn_pad_sequence()</a></code> </p>
+        </td>
+        <td><p>Pad a list of variable length Tensors with <code>padding_value</code></p></td>
+      </tr>
+    </tbody><tbody>
+      <tr>
+        <th colspan="2">
+          <h2 id="section-neural-networks-functional-module" class="hasAnchor"><a href="#section-neural-networks-functional-module" class="anchor"></a>Neural networks functional module</h2>
+          <p class="section-desc"></p>
+        </th>
+      </tr>
+      
+      
+    </tbody><tbody>
+      
+      
+      <tr>
+        
+        <td>
+          <p><code><a href="nnf_adaptive_avg_pool1d.html">nnf_adaptive_avg_pool1d()</a></code> </p>
+        </td>
+        <td><p>Adaptive_avg_pool1d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_adaptive_avg_pool2d.html">nnf_adaptive_avg_pool2d()</a></code> </p>
+        </td>
+        <td><p>Adaptive_avg_pool2d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_adaptive_avg_pool3d.html">nnf_adaptive_avg_pool3d()</a></code> </p>
+        </td>
+        <td><p>Adaptive_avg_pool3d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_adaptive_max_pool1d.html">nnf_adaptive_max_pool1d()</a></code> </p>
+        </td>
+        <td><p>Adaptive_max_pool1d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_adaptive_max_pool2d.html">nnf_adaptive_max_pool2d()</a></code> </p>
+        </td>
+        <td><p>Adaptive_max_pool2d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_adaptive_max_pool3d.html">nnf_adaptive_max_pool3d()</a></code> </p>
+        </td>
+        <td><p>Adaptive_max_pool3d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_affine_grid.html">nnf_affine_grid()</a></code> </p>
+        </td>
+        <td><p>Affine_grid</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_alpha_dropout.html">nnf_alpha_dropout()</a></code> </p>
+        </td>
+        <td><p>Alpha_dropout</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_avg_pool1d.html">nnf_avg_pool1d()</a></code> </p>
+        </td>
+        <td><p>Avg_pool1d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_avg_pool2d.html">nnf_avg_pool2d()</a></code> </p>
+        </td>
+        <td><p>Avg_pool2d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_avg_pool3d.html">nnf_avg_pool3d()</a></code> </p>
+        </td>
+        <td><p>Avg_pool3d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_batch_norm.html">nnf_batch_norm()</a></code> </p>
+        </td>
+        <td><p>Batch_norm</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_bilinear.html">nnf_bilinear()</a></code> </p>
+        </td>
+        <td><p>Bilinear</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_binary_cross_entropy.html">nnf_binary_cross_entropy()</a></code> </p>
+        </td>
+        <td><p>Binary_cross_entropy</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_binary_cross_entropy_with_logits.html">nnf_binary_cross_entropy_with_logits()</a></code> </p>
+        </td>
+        <td><p>Binary_cross_entropy_with_logits</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_celu.html">nnf_celu()</a></code> <code><a href="nnf_celu.html">nnf_celu_()</a></code> </p>
+        </td>
+        <td><p>Celu</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_conv1d.html">nnf_conv1d()</a></code> </p>
+        </td>
+        <td><p>Conv1d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_conv2d.html">nnf_conv2d()</a></code> </p>
+        </td>
+        <td><p>Conv2d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_conv3d.html">nnf_conv3d()</a></code> </p>
+        </td>
+        <td><p>Conv3d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_conv_tbc.html">nnf_conv_tbc()</a></code> </p>
+        </td>
+        <td><p>Conv_tbc</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_conv_transpose1d.html">nnf_conv_transpose1d()</a></code> </p>
+        </td>
+        <td><p>Conv_transpose1d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_conv_transpose2d.html">nnf_conv_transpose2d()</a></code> </p>
+        </td>
+        <td><p>Conv_transpose2d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_conv_transpose3d.html">nnf_conv_transpose3d()</a></code> </p>
+        </td>
+        <td><p>Conv_transpose3d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_cosine_embedding_loss.html">nnf_cosine_embedding_loss()</a></code> </p>
+        </td>
+        <td><p>Cosine_embedding_loss</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_cosine_similarity.html">nnf_cosine_similarity()</a></code> </p>
+        </td>
+        <td><p>Cosine_similarity</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_cross_entropy.html">nnf_cross_entropy()</a></code> </p>
+        </td>
+        <td><p>Cross_entropy</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_ctc_loss.html">nnf_ctc_loss()</a></code> </p>
+        </td>
+        <td><p>Ctc_loss</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_dropout.html">nnf_dropout()</a></code> </p>
+        </td>
+        <td><p>Dropout</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_dropout2d.html">nnf_dropout2d()</a></code> </p>
+        </td>
+        <td><p>Dropout2d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_dropout3d.html">nnf_dropout3d()</a></code> </p>
+        </td>
+        <td><p>Dropout3d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_elu.html">nnf_elu()</a></code> <code><a href="nnf_elu.html">nnf_elu_()</a></code> </p>
+        </td>
+        <td><p>Elu</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_embedding.html">nnf_embedding()</a></code> </p>
+        </td>
+        <td><p>Embedding</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_embedding_bag.html">nnf_embedding_bag()</a></code> </p>
+        </td>
+        <td><p>Embedding_bag</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_fold.html">nnf_fold()</a></code> </p>
+        </td>
+        <td><p>Fold</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_fractional_max_pool2d.html">nnf_fractional_max_pool2d()</a></code> </p>
+        </td>
+        <td><p>Fractional_max_pool2d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_fractional_max_pool3d.html">nnf_fractional_max_pool3d()</a></code> </p>
+        </td>
+        <td><p>Fractional_max_pool3d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_gelu.html">nnf_gelu()</a></code> </p>
+        </td>
+        <td><p>Gelu</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_glu.html">nnf_glu()</a></code> </p>
+        </td>
+        <td><p>Glu</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_grid_sample.html">nnf_grid_sample()</a></code> </p>
+        </td>
+        <td><p>Grid_sample</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_group_norm.html">nnf_group_norm()</a></code> </p>
+        </td>
+        <td><p>Group_norm</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_gumbel_softmax.html">nnf_gumbel_softmax()</a></code> </p>
+        </td>
+        <td><p>Gumbel_softmax</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_hardshrink.html">nnf_hardshrink()</a></code> </p>
+        </td>
+        <td><p>Hardshrink</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_hardsigmoid.html">nnf_hardsigmoid()</a></code> </p>
+        </td>
+        <td><p>Hardsigmoid</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_hardswish.html">nnf_hardswish()</a></code> </p>
+        </td>
+        <td><p>Hardswish</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_hardtanh.html">nnf_hardtanh()</a></code> <code><a href="nnf_hardtanh.html">nnf_hardtanh_()</a></code> </p>
+        </td>
+        <td><p>Hardtanh</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_hinge_embedding_loss.html">nnf_hinge_embedding_loss()</a></code> </p>
+        </td>
+        <td><p>Hinge_embedding_loss</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_instance_norm.html">nnf_instance_norm()</a></code> </p>
+        </td>
+        <td><p>Instance_norm</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_interpolate.html">nnf_interpolate()</a></code> </p>
+        </td>
+        <td><p>Interpolate</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_kl_div.html">nnf_kl_div()</a></code> </p>
+        </td>
+        <td><p>Kl_div</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_l1_loss.html">nnf_l1_loss()</a></code> </p>
+        </td>
+        <td><p>L1_loss</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_layer_norm.html">nnf_layer_norm()</a></code> </p>
+        </td>
+        <td><p>Layer_norm</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_leaky_relu.html">nnf_leaky_relu()</a></code> </p>
+        </td>
+        <td><p>Leaky_relu</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_linear.html">nnf_linear()</a></code> </p>
+        </td>
+        <td><p>Linear</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_local_response_norm.html">nnf_local_response_norm()</a></code> </p>
+        </td>
+        <td><p>Local_response_norm</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_log_softmax.html">nnf_log_softmax()</a></code> </p>
+        </td>
+        <td><p>Log_softmax</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_logsigmoid.html">nnf_logsigmoid()</a></code> </p>
+        </td>
+        <td><p>Logsigmoid</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_lp_pool1d.html">nnf_lp_pool1d()</a></code> </p>
+        </td>
+        <td><p>Lp_pool1d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_lp_pool2d.html">nnf_lp_pool2d()</a></code> </p>
+        </td>
+        <td><p>Lp_pool2d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_margin_ranking_loss.html">nnf_margin_ranking_loss()</a></code> </p>
+        </td>
+        <td><p>Margin_ranking_loss</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_max_pool1d.html">nnf_max_pool1d()</a></code> </p>
+        </td>
+        <td><p>Max_pool1d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_max_pool2d.html">nnf_max_pool2d()</a></code> </p>
+        </td>
+        <td><p>Max_pool2d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_max_pool3d.html">nnf_max_pool3d()</a></code> </p>
+        </td>
+        <td><p>Max_pool3d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_max_unpool1d.html">nnf_max_unpool1d()</a></code> </p>
+        </td>
+        <td><p>Max_unpool1d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_max_unpool2d.html">nnf_max_unpool2d()</a></code> </p>
+        </td>
+        <td><p>Max_unpool2d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_max_unpool3d.html">nnf_max_unpool3d()</a></code> </p>
+        </td>
+        <td><p>Max_unpool3d</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_mse_loss.html">nnf_mse_loss()</a></code> </p>
+        </td>
+        <td><p>Mse_loss</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_multi_head_attention_forward.html">nnf_multi_head_attention_forward()</a></code> </p>
+        </td>
+        <td><p>Multi head attention forward</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_multi_margin_loss.html">nnf_multi_margin_loss()</a></code> </p>
+        </td>
+        <td><p>Multi_margin_loss</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_multilabel_margin_loss.html">nnf_multilabel_margin_loss()</a></code> </p>
+        </td>
+        <td><p>Multilabel_margin_loss</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_multilabel_soft_margin_loss.html">nnf_multilabel_soft_margin_loss()</a></code> </p>
+        </td>
+        <td><p>Multilabel_soft_margin_loss</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_nll_loss.html">nnf_nll_loss()</a></code> </p>
+        </td>
+        <td><p>Nll_loss</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_normalize.html">nnf_normalize()</a></code> </p>
+        </td>
+        <td><p>Normalize</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_one_hot.html">nnf_one_hot()</a></code> </p>
+        </td>
+        <td><p>One_hot</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_pad.html">nnf_pad()</a></code> </p>
+        </td>
+        <td><p>Pad</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_pairwise_distance.html">nnf_pairwise_distance()</a></code> </p>
+        </td>
+        <td><p>Pairwise_distance</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_pdist.html">nnf_pdist()</a></code> </p>
+        </td>
+        <td><p>Pdist</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_pixel_shuffle.html">nnf_pixel_shuffle()</a></code> </p>
+        </td>
+        <td><p>Pixel_shuffle</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_poisson_nll_loss.html">nnf_poisson_nll_loss()</a></code> </p>
+        </td>
+        <td><p>Poisson_nll_loss</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_prelu.html">nnf_prelu()</a></code> </p>
+        </td>
+        <td><p>Prelu</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_relu.html">nnf_relu()</a></code> <code><a href="nnf_relu.html">nnf_relu_()</a></code> </p>
+        </td>
+        <td><p>Relu</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_relu6.html">nnf_relu6()</a></code> </p>
+        </td>
+        <td><p>Relu6</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_rrelu.html">nnf_rrelu()</a></code> <code><a href="nnf_rrelu.html">nnf_rrelu_()</a></code> </p>
+        </td>
+        <td><p>Rrelu</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_selu.html">nnf_selu()</a></code> <code><a href="nnf_selu.html">nnf_selu_()</a></code> </p>
+        </td>
+        <td><p>Selu</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_sigmoid.html">nnf_sigmoid()</a></code> </p>
+        </td>
+        <td><p>Sigmoid</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_smooth_l1_loss.html">nnf_smooth_l1_loss()</a></code> </p>
+        </td>
+        <td><p>Smooth_l1_loss</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_soft_margin_loss.html">nnf_soft_margin_loss()</a></code> </p>
+        </td>
+        <td><p>Soft_margin_loss</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_softmax.html">nnf_softmax()</a></code> </p>
+        </td>
+        <td><p>Softmax</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_softmin.html">nnf_softmin()</a></code> </p>
+        </td>
+        <td><p>Softmin</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_softplus.html">nnf_softplus()</a></code> </p>
+        </td>
+        <td><p>Softplus</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_softshrink.html">nnf_softshrink()</a></code> </p>
+        </td>
+        <td><p>Softshrink</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_softsign.html">nnf_softsign()</a></code> </p>
+        </td>
+        <td><p>Softsign</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_tanhshrink.html">nnf_tanhshrink()</a></code> </p>
+        </td>
+        <td><p>Tanhshrink</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_threshold.html">nnf_threshold()</a></code> <code><a href="nnf_threshold.html">nnf_threshold_()</a></code> </p>
+        </td>
+        <td><p>Threshold</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_triplet_margin_loss.html">nnf_triplet_margin_loss()</a></code> </p>
+        </td>
+        <td><p>Triplet_margin_loss</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="nnf_unfold.html">nnf_unfold()</a></code> </p>
+        </td>
+        <td><p>Unfold</p></td>
+      </tr>
+    </tbody><tbody>
+      <tr>
+        <th colspan="2">
+          <h2 id="section-optimizers" class="hasAnchor"><a href="#section-optimizers" class="anchor"></a>Optimizers</h2>
+          <p class="section-desc"></p>
+        </th>
+      </tr>
+      
+      
+    </tbody><tbody>
+      
+      
+      <tr>
+        
+        <td>
+          <p><code><a href="optim_adam.html">optim_adam()</a></code> </p>
+        </td>
+        <td><p>Implements Adam algorithm.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="optim_required.html">optim_required()</a></code> </p>
+        </td>
+        <td><p>Dummy value indicating a required value.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="optim_sgd.html">optim_sgd()</a></code> </p>
+        </td>
+        <td><p>SGD optimizer</p></td>
+      </tr>
+    </tbody><tbody>
+      <tr>
+        <th colspan="2">
+          <h2 id="section-datasets" class="hasAnchor"><a href="#section-datasets" class="anchor"></a>Datasets</h2>
+          <p class="section-desc"></p>
+        </th>
+      </tr>
+      
+      
+    </tbody><tbody>
+      
+      
+      <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="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="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="tensor_dataset.html">tensor_dataset()</a></code> </p>
+        </td>
+        <td><p>Dataset wrapping tensors.</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>
+    </tbody><tbody>
+      <tr>
+        <th colspan="2">
+          <h2 id="section-autograd" class="hasAnchor"><a href="#section-autograd" class="anchor"></a>Autograd</h2>
+          <p class="section-desc"></p>
+        </th>
+      </tr>
+      
+      
+    </tbody><tbody>
+      
+      
+      <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="with_no_grad.html">with_no_grad()</a></code> </p>
+        </td>
+        <td><p>Temporarily modify gradient recording.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="with_enable_grad.html">with_enable_grad()</a></code> </p>
+        </td>
+        <td><p>Enable grad</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="AutogradContext.html">AutogradContext</a></code> </p>
+        </td>
+        <td><p>Class representing the context.</p></td>
+      </tr>
+    </tbody><tbody>
+      <tr>
+        <th colspan="2">
+          <h2 id="section-cuda-utilities" class="hasAnchor"><a href="#section-cuda-utilities" class="anchor"></a>Cuda utilities</h2>
+          <p class="section-desc"></p>
+        </th>
+      </tr>
+      
+      
+    </tbody><tbody>
+      
+      
+      <tr>
+        
+        <td>
+          <p><code><a href="cuda_current_device.html">cuda_current_device()</a></code> </p>
+        </td>
+        <td><p>Returns the index of a currently selected device.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="cuda_device_count.html">cuda_device_count()</a></code> </p>
+        </td>
+        <td><p>Returns the number of GPUs available.</p></td>
+      </tr><tr>
+        
+        <td>
+          <p><code><a href="cuda_is_available.html">cuda_is_available()</a></code> </p>
+        </td>
+        <td><p>Returns a bool indicating if CUDA is currently available.</p></td>
+      </tr>
+    </tbody><tbody>
+      <tr>
+        <th colspan="2">
+          <h2 id="section-installation" class="hasAnchor"><a href="#section-installation" class="anchor"></a>Installation</h2>
+          <p class="section-desc"></p>
+        </th>
+      </tr>
+      
+      
+    </tbody><tbody>
+      
+      
+      <tr>
+        
+        <td>
+          <p><code><a href="install_torch.html">install_torch()</a></code> </p>
+        </td>
+        <td><p>Install Torch</p></td>
+      </tr>
+    </tbody>
+    </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> 1.6.1.</p>
+</div>
+
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diff --git a/static/docs/reference/install_torch.html b/static/docs/reference/install_torch.html
new file mode 100644
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+        <a class="navbar-link" href="../index.html">torch</a>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.3</span>
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+     
+  </a>
+</li>
+<li class="dropdown">
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
+    Getting started
+     
+    <span class="caret"></span>
+  </a>
+  <ul class="dropdown-menu" role="menu">
+    <li class="dropdown-header">Beginers guide</li>
+    <li>
+      <a href="../articles/getting-started/warmup.html">Warm-up</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/tensors.html">Tensors</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/tensors-and-autograd.html">Tensors and autograd</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/optim.html">optim: optimizers in torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/custom-nn.html">Custom nn modules</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/control-flow-and-weight-sharing.html">Control flow &amp; Weight sharing</a>
+    </li>
+    <li class="dropdown-header">Torch Mechanics</li>
+    <li>
+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/neural-networks.html">Neural networks</a>
+    </li>
+  </ul>
+</li>
+<li class="dropdown">
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
+    Articles
+     
+    <span class="caret"></span>
+  </a>
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+    <li class="dropdown-header">Tensors</li>
+    <li>
+      <a href="../articles/tensor-creation.html">Creating tensors</a>
+    </li>
+    <li>
+      <a href="../articles/indexing.html">Indexing</a>
+    </li>
+    <li>
+      <a href="../articles/tensor/index.html">Tensor class</a>
+    </li>
+    <li class="dropdown-header">Datasets</li>
+    <li>
+      <a href="../articles/loading-data.html">Loading Data</a>
+    </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-header">Examples</li>
+<li>
+  <a href="../reference/index.html">Reference</a>
+</li>
+<li>
+  <a href="../news/index.html">Changelog</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>Install Torch</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/install.R'><code>R/install.R</code></a></small>
+    <div class="hidden name"><code>install_torch.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Installs Torch and its dependencies.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>install_torch</span>(
+  version = <span class='st'>"1.5.0"</span>,
+  type = <span class='fu'>install_type</span>(version = <span class='kw'>version</span>),
+  reinstall = <span class='fl'>FALSE</span>,
+  path = <span class='fu'>install_path</span>(),
+  <span class='kw'>...</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>version</th>
+      <td><p>The Torch version to install.</p></td>
+    </tr>
+    <tr>
+      <th>type</th>
+      <td><p>The installation type for Torch. Valid values are <code>"cpu"</code> or the 'CUDA' version.</p></td>
+    </tr>
+    <tr>
+      <th>reinstall</th>
+      <td><p>Re-install Torch even if its already installed?</p></td>
+    </tr>
+    <tr>
+      <th>path</th>
+      <td><p>Optional path to install or check for an already existing installation.</p></td>
+    </tr>
+    <tr>
+      <th>...</th>
+      <td><p>other optional arguments (like <code>load</code> for manual installation.)</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>When using <code>path</code> to install in a specific location, make sure the <code>TORCH_HOME</code> environment
+variable is set to this same path to reuse this installation. The <code>TORCH_INSTALL</code> environment
+variable can be set to <code>0</code> to prevent auto-installing torch and <code>TORCH_LOAD</code> set to <code>0</code>
+to avoid loading dependencies automatically. These environment variables are meant for advanced use
+cases and troubleshootinng only.</p>
+
+  </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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+  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+</div>
+
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+   </div>
+
+  
+
+
+  </body>
+</html>
+
+
diff --git a/static/docs/reference/is_dataloader.html b/static/docs/reference/is_dataloader.html
new file mode 100644
index 000000000..5688453ef
--- /dev/null
+++ b/static/docs/reference/is_dataloader.html
@@ -0,0 +1,237 @@
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+     
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+  <ul class="dropdown-menu" role="menu">
+    <li class="dropdown-header">Beginers guide</li>
+    <li>
+      <a href="../articles/getting-started/warmup.html">Warm-up</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/tensors.html">Tensors</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/tensors-and-autograd.html">Tensors and autograd</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/optim.html">optim: optimizers in torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/custom-nn.html">Custom nn modules</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/control-flow-and-weight-sharing.html">Control flow &amp; Weight sharing</a>
+    </li>
+    <li class="dropdown-header">Torch Mechanics</li>
+    <li>
+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/neural-networks.html">Neural networks</a>
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+    <h1>Checks if the object is 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>is_dataloader.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Checks if the object is a dataloader</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>is_dataloader</span>(<span class='kw'>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 check</p></td>
+    </tr>
+    </table>
+
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+    <h1>Checks if object is a device</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/device.R'><code>R/device.R</code></a></small>
+    <div class="hidden name"><code>is_torch_device.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Checks if object is a device</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>is_torch_device</span>(<span class='kw'>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 check</p></td>
+    </tr>
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+
+
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diff --git a/static/docs/reference/is_torch_dtype.html b/static/docs/reference/is_torch_dtype.html
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+    <h1>Check if object is a torch data type</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/dtype.R'><code>R/dtype.R</code></a></small>
+    <div class="hidden name"><code>is_torch_dtype.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Check if object is a torch data type</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>is_torch_dtype</span>(<span class='kw'>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 check.</p></td>
+    </tr>
+    </table>
+
+
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+    <h1>Check if an object is a torch layout.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/layout.R'><code>R/layout.R</code></a></small>
+    <div class="hidden name"><code>is_torch_layout.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Check if an object is a torch layout.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>is_torch_layout</span>(<span class='kw'>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 check</p></td>
+    </tr>
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+
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diff --git a/static/docs/reference/is_torch_memory_format.html b/static/docs/reference/is_torch_memory_format.html
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+    <h1>Check if an object is a memory format</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/memory_format.R'><code>R/memory_format.R</code></a></small>
+    <div class="hidden name"><code>is_torch_memory_format.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Check if an object is a memory format</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>is_torch_memory_format</span>(<span class='kw'>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 check</p></td>
+    </tr>
+    </table>
+
+
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+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/is_torch_qscheme.html b/static/docs/reference/is_torch_qscheme.html
new file mode 100644
index 000000000..aee7821a6
--- /dev/null
+++ b/static/docs/reference/is_torch_qscheme.html
@@ -0,0 +1,237 @@
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+    <h1>Checks if an object is a QScheme</h1>
+    <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>is_torch_qscheme.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Checks if an object is a QScheme</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>is_torch_qscheme</span>(<span class='kw'>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 check</p></td>
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+    <h1>Checks if a tensor is undefined</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/tensor.R'><code>R/tensor.R</code></a></small>
+    <div class="hidden name"><code>is_undefined_tensor.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Checks if a tensor is undefined</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>is_undefined_tensor</span>(<span class='kw'>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>tensor to check</p></td>
+    </tr>
+    </table>
+
+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Load a state dict file</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/save.R'><code>R/save.R</code></a></small>
+    <div class="hidden name"><code>load_state_dict.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>This function should only be used to load models saved in python.
+For it to work correctly you need to use <code>torch.save</code> with the flag:
+<code>_use_new_zipfile_serialization=True</code> and also remove all <code>nn.Parameter</code>
+classes from the tensors in the dict.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>load_state_dict</span>(<span class='kw'>path</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>path</th>
+      <td><p>to the state dict file</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+
+    <p>a named list of tensors.</p>
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>The above might change with development of <a href='https://github.com/pytorch/pytorch/issues/37213'>this</a>
+in pytorch's C++ api.</p>
+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Applies a 1D adaptive average pooling over an input signal composed of several input planes.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_adaptive_avg_pool1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>The output size is H, for any input size.
+The number of output features is equal to the number of input planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_adaptive_avg_pool1d</span>(<span class='kw'>output_size</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>output_size</th>
+      <td><p>the target output size H</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'># target output size of 5</span>
+<span class='kw'>m</span> <span class='op'>=</span> <span class='fu'>nn_adaptive_avg_pool1d</span>(<span class='fl'>5</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>8</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nn_adaptive_avg_pool2d.html b/static/docs/reference/nn_adaptive_avg_pool2d.html
new 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>Applies a 2D adaptive average pooling over an input signal composed of several input planes.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_adaptive_avg_pool2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>The output is of size H x W, for any input size.
+The number of output features is equal to the number of input planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_adaptive_avg_pool2d</span>(<span class='kw'>output_size</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>output_size</th>
+      <td><p>the target output size of the image of the form H x W.
+Can be a tuple (H, W) or a single H for a square image H x H.
+H and W can be either a <code>int</code>, or <code>NULL</code> which means the size will
+be the same as that of the input.</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'># target output size of 5x7</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_avg_pool2d</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>,<span class='fl'>7</span>))
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>8</span>, <span class='fl'>9</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+<span class='co'># target output size of 7x7 (square)</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_avg_pool2d</span>(<span class='fl'>7</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>10</span>, <span class='fl'>9</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </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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+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Applies a 3D adaptive average pooling over an input signal composed of several input planes.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_adaptive_avg_pool3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>The output is of size D x H x W, for any input size.
+The number of output features is equal to the number of input planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_adaptive_avg_pool3d</span>(<span class='kw'>output_size</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>output_size</th>
+      <td><p>the target output size of the form D x H x W.
+Can be a tuple (D, H, W) or a single number D for a cube D x D x D.
+D, H and W can be either a <code>int</code>, or <code>None</code> which means the size will
+be the same as that of the input.</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'># target output size of 5x7x9</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_avg_pool3d</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>,<span class='fl'>7</span>,<span class='fl'>9</span>))
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>8</span>, <span class='fl'>9</span>, <span class='fl'>10</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+<span class='co'># target output size of 7x7x7 (cube)</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_avg_pool3d</span>(<span class='fl'>7</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>10</span>, <span class='fl'>9</span>, <span class='fl'>8</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nn_adaptive_log_softmax_with_loss.html b/static/docs/reference/nn_adaptive_log_softmax_with_loss.html
new file mode 100644
index 000000000..d2f39a7ed
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+Efficient softmax approximation for GPUs by Edouard Grave, Armand Joulin, Moustapha Cissé, David Grangier, and Hervé Jégou" />
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+    <div class="page-header">
+    <h1>AdaptiveLogSoftmaxWithLoss module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn_adaptive.R'><code>R/nn_adaptive.R</code></a></small>
+    <div class="hidden name"><code>nn_adaptive_log_softmax_with_loss.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Efficient softmax approximation as described in
+<a href='https://arxiv.org/abs/1609.04309'>Efficient softmax approximation for GPUs by Edouard Grave, Armand Joulin, Moustapha Cissé, David Grangier, and Hervé Jégou</a></p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_adaptive_log_softmax_with_loss</span>(
+  <span class='kw'>in_features</span>,
+  <span class='kw'>n_classes</span>,
+  <span class='kw'>cutoffs</span>,
+  div_value = <span class='fl'>4</span>,
+  head_bias = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>in_features</th>
+      <td><p>(int): Number of features in the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>n_classes</th>
+      <td><p>(int): Number of classes in the dataset</p></td>
+    </tr>
+    <tr>
+      <th>cutoffs</th>
+      <td><p>(Sequence): Cutoffs used to assign targets to their buckets</p></td>
+    </tr>
+    <tr>
+      <th>div_value</th>
+      <td><p>(float, optional): value used as an exponent to compute sizes
+of the clusters. Default: 4.0</p></td>
+    </tr>
+    <tr>
+      <th>head_bias</th>
+      <td><p>(bool, optional): If <code>True</code>, adds a bias term to the 'head' of the
+adaptive softmax. Default: <code>False</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+
+    <p><code>NamedTuple</code> with <code>output</code> and <code>loss</code> fields:</p><ul>
+<li><p><strong>output</strong> is a Tensor of size <code>N</code> containing computed target
+log probabilities for each example</p></li>
+<li><p><strong>loss</strong> is a Scalar representing the computed negative
+log likelihood loss</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>Adaptive softmax is an approximate strategy for training models with large
+output spaces. It is most effective when the label distribution is highly
+imbalanced, for example in natural language modelling, where the word
+frequency distribution approximately follows the Zipf's law.</p>
+<p>Adaptive softmax partitions the labels into several clusters, according to
+their frequency. These clusters may contain different number of targets
+each.</p>
+<p>Additionally, clusters containing less frequent labels assign lower
+dimensional embeddings to those labels, which speeds up the computation.
+For each minibatch, only clusters for which at least one target is
+present are evaluated.</p>
+<p>The idea is that the clusters which are accessed frequently
+(like the first one, containing most frequent labels), should also be cheap
+to compute -- that is, contain a small number of assigned labels.
+We highly recommend taking a look at the original paper for more details.</p><ul>
+<li><p><code>cutoffs</code> should be an ordered Sequence of integers sorted
+in the increasing order.
+It controls number of clusters and the partitioning of targets into
+clusters. For example setting <code>cutoffs = c(10, 100, 1000)</code>
+means that first <code>10</code> targets will be assigned
+to the 'head' of the adaptive softmax, targets <code>11, 12, ..., 100</code> will be
+assigned to the first cluster, and targets <code>101, 102, ..., 1000</code> will be
+assigned to the second cluster, while targets
+<code>1001, 1002, ..., n_classes - 1</code> will be assigned
+to the last, third cluster.</p></li>
+<li><p><code>div_value</code> is used to compute the size of each additional cluster,
+which is given as
+\(\left\lfloor\frac{\mbox{in\_features}}{\mbox{div\_value}^{idx}}\right\rfloor\),
+where \(idx\) is the cluster index (with clusters
+for less frequent words having larger indices,
+and indices starting from \(1\)).</p></li>
+<li><p><code>head_bias</code> if set to True, adds a bias term to the 'head' of the
+adaptive softmax. See paper for details. Set to False in the official
+implementation.</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>This module returns a <code>NamedTuple</code> with <code>output</code>
+and <code>loss</code> fields. See further documentation for details.</p>
+<p>To compute log-probabilities for all classes, the <code>log_prob</code>
+method can be used.</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p>Labels passed as inputs to this module should be sorted according to
+their frequency. This means that the most frequent label should be
+represented by the index <code>0</code>, and the least frequent
+label should be represented by the index <code>n_classes - 1</code>.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>input: \((N, \mbox{in\_features})\)</p></li>
+<li><p>target: \((N)\) where each value satisfies \(0 &lt;= \mbox{target[i]} &lt;= \mbox{n\_classes}\)</p></li>
+<li><p>output1: \((N)\)</p></li>
+<li><p>output2: <code>Scalar</code></p></li>
+</ul>
+
+
+  </div>
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Applies a 1D adaptive max pooling over an input signal composed of several input planes.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_adaptive_max_pool1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>The output size is H, for any input size.
+The number of output features is equal to the number of input planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_adaptive_max_pool1d</span>(<span class='kw'>output_size</span>, return_indices = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>output_size</th>
+      <td><p>the target output size H</p></td>
+    </tr>
+    <tr>
+      <th>return_indices</th>
+      <td><p>if <code>TRUE</code>, will return the indices along with the outputs.
+Useful to pass to <code><a href='nn_max_unpool1d.html'>nn_max_unpool1d()</a></code>. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'># target output size of 5</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_max_pool1d</span>(<span class='fl'>5</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>8</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nn_adaptive_max_pool2d.html b/static/docs/reference/nn_adaptive_max_pool2d.html
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index 000000000..1cbc81eef
--- /dev/null
+++ b/static/docs/reference/nn_adaptive_max_pool2d.html
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+<div class="row">
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+    <div class="page-header">
+    <h1>Applies a 2D adaptive max pooling over an input signal composed of several input planes.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_adaptive_max_pool2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>The output is of size H x W, for any input size.
+The number of output features is equal to the number of input planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_adaptive_max_pool2d</span>(<span class='kw'>output_size</span>, return_indices = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>output_size</th>
+      <td><p>the target output size of the image of the form H x W.
+Can be a tuple <code>(H, W)</code> or a single H for a square image H x H.
+H and W can be either a <code>int</code>, or <code>None</code> which means the size will
+be the same as that of the input.</p></td>
+    </tr>
+    <tr>
+      <th>return_indices</th>
+      <td><p>if <code>TRUE</code>, will return the indices along with the outputs.
+Useful to pass to <code><a href='nn_max_unpool2d.html'>nn_max_unpool2d()</a></code>. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'># target output size of 5x7</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_max_pool2d</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>,<span class='fl'>7</span>))
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>8</span>, <span class='fl'>9</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+<span class='co'># target output size of 7x7 (square)</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_max_pool2d</span>(<span class='fl'>7</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>10</span>, <span class='fl'>9</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
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+The number of output features is equal to the number of input planes." />
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+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Applies a 3D adaptive max pooling over an input signal composed of several input planes.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_adaptive_max_pool3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>The output is of size D x H x W, for any input size.
+The number of output features is equal to the number of input planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_adaptive_max_pool3d</span>(<span class='kw'>output_size</span>, return_indices = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>output_size</th>
+      <td><p>the target output size of the image of the form D x H x W.
+Can be a tuple (D, H, W) or a single D for a cube D x D x D.
+D, H and W can be either a <code>int</code>, or <code>None</code> which means the size will
+be the same as that of the input.</p></td>
+    </tr>
+    <tr>
+      <th>return_indices</th>
+      <td><p>if <code>TRUE</code>, will return the indices along with the outputs.
+Useful to pass to <code><a href='nn_max_unpool3d.html'>nn_max_unpool3d()</a></code>. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'># target output size of 5x7x9</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_max_pool3d</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>,<span class='fl'>7</span>,<span class='fl'>9</span>))
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>8</span>, <span class='fl'>9</span>, <span class='fl'>10</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+<span class='co'># target output size of 7x7x7 (cube)</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_adaptive_max_pool3d</span>(<span class='fl'>7</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>64</span>, <span class='fl'>10</span>, <span class='fl'>9</span>, <span class='fl'>8</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Applies a 1D average pooling over an input signal composed of several
+input planes.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_avg_pool1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>In the simplest case, the output value of the layer with input size \((N, C, L)\),
+output \((N, C, L_{out})\) and <code>kernel_size</code> \(k\)
+can be precisely described as:</p>
+<p>$$
+  \mbox{out}(N_i, C_j, l) = \frac{1}{k} \sum_{m=0}^{k-1}
+\mbox{input}(N_i, C_j, \mbox{stride} \times l + m)
+$$</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_avg_pool1d</span>(
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='kw'>NULL</span>,
+  padding = <span class='fl'>0</span>,
+  ceil_mode = <span class='fl'>FALSE</span>,
+  count_include_pad = <span class='fl'>TRUE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>the size of the window</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit zero padding to be added on both sides</p></td>
+    </tr>
+    <tr>
+      <th>ceil_mode</th>
+      <td><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
+    </tr>
+    <tr>
+      <th>count_include_pad</th>
+      <td><p>when TRUE, will include the zero-padding in the averaging calculation</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on both sides
+for <code>padding</code> number of points.</p>
+<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code> can each be
+an <code>int</code> or a one-element tuple.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C, L_{in})\)</p></li>
+<li><p>Output: \((N, C, L_{out})\), where</p></li>
+</ul>
+
+<p>$$
+  L_{out} = \left\lfloor \frac{L_{in} +
+      2 \times \mbox{padding} - \mbox{kernel\_size}}{\mbox{stride}} + 1\right\rfloor
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+  
+<span class='co'># pool with window of size=3, stride=2</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_avg_pool1d</span>(<span class='fl'>3</span>, stride=<span class='fl'>2</span>)
+<span class='fu'>m</span>(<span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>8</span>))
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;   0.3975 -0.0079 -0.0240
+#&gt; [ CPUFloatType{1,1,3} ]</div></pre>
+  </div>
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+$$
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Applies a 2D average pooling over an input signal composed of several input
+planes.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_avg_pool2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>In the simplest case, the output value of the layer with input size \((N, C, H, W)\),
+output \((N, C, H_{out}, W_{out})\) and <code>kernel_size</code> \((kH, kW)\)
+can be precisely described as:</p>
+<p>$$
+  out(N_i, C_j, h, w)  = \frac{1}{kH * kW} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1}
+input(N_i, C_j, stride[0] \times h + m, stride[1] \times w + n)
+$$</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_avg_pool2d</span>(
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='kw'>NULL</span>,
+  padding = <span class='fl'>0</span>,
+  ceil_mode = <span class='fl'>FALSE</span>,
+  count_include_pad = <span class='fl'>TRUE</span>,
+  divisor_override = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>the size of the window</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit zero padding to be added on both sides</p></td>
+    </tr>
+    <tr>
+      <th>ceil_mode</th>
+      <td><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
+    </tr>
+    <tr>
+      <th>count_include_pad</th>
+      <td><p>when TRUE, will include the zero-padding in the averaging calculation</p></td>
+    </tr>
+    <tr>
+      <th>divisor_override</th>
+      <td><p>if specified, it will be used as divisor, otherwise <code>kernel_size</code> will be used</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on both sides
+for <code>padding</code> number of points.</p>
+<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code> can either be:</p><ul>
+<li><p>a single <code>int</code> -- in which case the same value is used for the height and width dimension</p></li>
+<li><p>a <code>tuple</code> of two ints -- in which case, the first <code>int</code> is used for the height dimension,
+and the second <code>int</code> for the width dimension</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C, H_{in}, W_{in})\)</p></li>
+<li><p>Output: \((N, C, H_{out}, W_{out})\), where</p></li>
+</ul>
+
+<p>$$
+  H_{out} = \left\lfloor\frac{H_{in}  + 2 \times \mbox{padding}[0] -
+      \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor
+$$
+$$
+  W_{out} = \left\lfloor\frac{W_{in}  + 2 \times \mbox{padding}[1] -
+      \mbox{kernel\_size}[1]}{\mbox{stride}[1]} + 1\right\rfloor
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+  
+<span class='co'># pool of square window of size=3, stride=2</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_avg_pool2d</span>(<span class='fl'>3</span>, stride=<span class='fl'>2</span>)
+<span class='co'># pool of non-square window</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_avg_pool2d</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>), stride=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>))
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>32</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/nn_avg_pool3d.html b/static/docs/reference/nn_avg_pool3d.html
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+<meta property="og:title" content="Applies a 3D average pooling over an input signal composed of several input
+planes. — nn_avg_pool3d" />
+<meta property="og:description" content="In the simplest case, the output value of the layer with input size \((N, C, D, H, W)\),
+output \((N, C, D_{out}, H_{out}, W_{out})\) and kernel_size \((kD, kH, kW)\)
+can be precisely described as:
+$$
+\begin{array}{ll}
+\mbox{out}(N_i, C_j, d, h, w) = &amp;amp; \sum_{k=0}^{kD-1} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1} \\
+&amp;amp; \frac{\mbox{input}(N_i, C_j, \mbox{stride}[0] \times d + k, \mbox{stride}[1] \times h + m, \mbox{stride}[2] \times w + n)}{kD \times kH \times kW}
+\end{array}
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+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Applies a 3D average pooling over an input signal composed of several input
+planes.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_avg_pool3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>In the simplest case, the output value of the layer with input size \((N, C, D, H, W)\),
+output \((N, C, D_{out}, H_{out}, W_{out})\) and <code>kernel_size</code> \((kD, kH, kW)\)
+can be precisely described as:</p>
+<p>$$
+\begin{array}{ll}
+\mbox{out}(N_i, C_j, d, h, w) = &amp; \sum_{k=0}^{kD-1} \sum_{m=0}^{kH-1} \sum_{n=0}^{kW-1} \\
+&amp; \frac{\mbox{input}(N_i, C_j, \mbox{stride}[0] \times d + k, \mbox{stride}[1] \times h + m, \mbox{stride}[2] \times w + n)}{kD \times kH \times kW}
+\end{array}
+$$</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_avg_pool3d</span>(
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='kw'>NULL</span>,
+  padding = <span class='fl'>0</span>,
+  ceil_mode = <span class='fl'>FALSE</span>,
+  count_include_pad = <span class='fl'>TRUE</span>,
+  divisor_override = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>the size of the window</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit zero padding to be added on all three sides</p></td>
+    </tr>
+    <tr>
+      <th>ceil_mode</th>
+      <td><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
+    </tr>
+    <tr>
+      <th>count_include_pad</th>
+      <td><p>when TRUE, will include the zero-padding in the averaging calculation</p></td>
+    </tr>
+    <tr>
+      <th>divisor_override</th>
+      <td><p>if specified, it will be used as divisor, otherwise <code>kernel_size</code> will be used</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on all three sides
+for <code>padding</code> number of points.</p>
+<p>The parameters <code>kernel_size</code>, <code>stride</code> can either be:</p><ul>
+<li><p>a single <code>int</code> -- in which case the same value is used for the depth, height and width dimension</p></li>
+<li><p>a <code>tuple</code> of three ints -- in which case, the first <code>int</code> is used for the depth dimension,
+the second <code>int</code> for the height dimension and the third <code>int</code> for the width dimension</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C, D_{in}, H_{in}, W_{in})\)</p></li>
+<li><p>Output: \((N, C, D_{out}, H_{out}, W_{out})\), where</p></li>
+</ul>
+
+<p>$$
+  D_{out} = \left\lfloor\frac{D_{in} + 2 \times \mbox{padding}[0] -
+      \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor
+$$
+$$
+  H_{out} = \left\lfloor\frac{H_{in} + 2 \times \mbox{padding}[1] -
+      \mbox{kernel\_size}[1]}{\mbox{stride}[1]} + 1\right\rfloor
+$$
+$$
+  W_{out} = \left\lfloor\frac{W_{in} + 2 \times \mbox{padding}[2] -
+      \mbox{kernel\_size}[2]}{\mbox{stride}[2]} + 1\right\rfloor
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+  
+<span class='co'># pool of square window of size=3, stride=2</span>
+<span class='kw'>m</span> <span class='op'>=</span> <span class='fu'>nn_avg_pool3d</span>(<span class='fl'>3</span>, stride=<span class='fl'>2</span>)
+<span class='co'># pool of non-square window</span>
+<span class='kw'>m</span> <span class='op'>=</span> <span class='fu'>nn_avg_pool3d</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>, <span class='fl'>2</span>), stride=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>2</span>))
+<span class='kw'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>,<span class='fl'>44</span>, <span class='fl'>31</span>)
+<span class='kw'>output</span> <span class='op'>=</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>BatchNorm1D module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-batchnorm.R'><code>R/nn-batchnorm.R</code></a></small>
+    <div class="hidden name"><code>nn_batch_norm1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies Batch Normalization over a 2D or 3D input (a mini-batch of 1D
+inputs with optional additional channel dimension) as described in the paper
+<a href='https://arxiv.org/abs/1502.03167'>Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift</a></p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_batch_norm1d</span>(
+  <span class='kw'>num_features</span>,
+  eps = <span class='fl'>1e-05</span>,
+  momentum = <span class='fl'>0.1</span>,
+  affine = <span class='fl'>TRUE</span>,
+  track_running_stats = <span class='fl'>TRUE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>num_features</th>
+      <td><p>\(C\) from an expected input of size
+\((N, C, L)\) or \(L\) from input of size \((N, L)\)</p></td>
+    </tr>
+    <tr>
+      <th>eps</th>
+      <td><p>a value added to the denominator for numerical stability.
+Default: 1e-5</p></td>
+    </tr>
+    <tr>
+      <th>momentum</th>
+      <td><p>the value used for the running_mean and running_var
+computation. Can be set to <code>NULL</code> for cumulative moving average
+(i.e. simple average). Default: 0.1</p></td>
+    </tr>
+    <tr>
+      <th>affine</th>
+      <td><p>a boolean value that when set to <code>TRUE</code>, this module has
+learnable affine parameters. Default: <code>TRUE</code></p></td>
+    </tr>
+    <tr>
+      <th>track_running_stats</th>
+      <td><p>a boolean value that when set to <code>TRUE</code>, this
+module tracks the running mean and variance, and when set to <code>FALSE</code>,
+this module does not track such statistics and always uses batch
+statistics in both training and eval modes. Default: <code>TRUE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+y = \frac{x - \mathrm{E}[x]}{\sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta
+$$</p>
+<p>The mean and standard-deviation are calculated per-dimension over
+the mini-batches and \(\gamma\) and \(\beta\) are learnable parameter vectors
+of size <code>C</code> (where <code>C</code> is the input size). By default, the elements of \(\gamma\)
+are set to 1 and the elements of \(\beta\) are set to 0.</p>
+<p>Also by default, during training this layer keeps running estimates of its
+computed mean and variance, which are then used for normalization during
+evaluation. The running estimates are kept with a default :attr:<code>momentum</code>
+of 0.1.
+If <code>track_running_stats</code> is set to <code>FALSE</code>, this layer then does not
+keep running estimates, and batch statistics are instead used during
+evaluation time as well.</p>
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+
+
+<p>This <code>momentum</code> argument is different from one used in optimizer
+classes and the conventional notion of momentum. Mathematically, the
+update rule for running statistics here is
+\(\hat{x}_{\mbox{new}} = (1 - \mbox{momentum}) \times \hat{x} + \mbox{momentum} \times x_t\),
+where \(\hat{x}\) is the estimated statistic and \(x_t\) is the
+new observed value.</p>
+<p>Because the Batch Normalization is done over the <code>C</code> dimension, computing statistics
+on <code>(N, L)</code> slices, it's common terminology to call this Temporal Batch Normalization.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C)\) or \((N, C, L)\)</p></li>
+<li><p>Output: \((N, C)\) or \((N, C, L)\) (same shape as input)</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'># With Learnable Parameters</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_batch_norm1d</span>(<span class='fl'>100</span>)
+<span class='co'># Without Learnable Parameters</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_batch_norm1d</span>(<span class='fl'>100</span>, affine = <span class='fl'>FALSE</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>100</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>) 
+
+}
+</div></pre>
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+      <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>
+     
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+</li>
+      </ul>
+      
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+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>BatchNorm2D</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-batchnorm.R'><code>R/nn-batchnorm.R</code></a></small>
+    <div class="hidden name"><code>nn_batch_norm2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies Batch Normalization over a 4D input (a mini-batch of 2D inputs
+additional channel dimension) as described in the paper
+<a href='https://arxiv.org/abs/1502.03167'>Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift</a>.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_batch_norm2d</span>(
+  <span class='kw'>num_features</span>,
+  eps = <span class='fl'>1e-05</span>,
+  momentum = <span class='fl'>0.1</span>,
+  affine = <span class='fl'>TRUE</span>,
+  track_running_stats = <span class='fl'>TRUE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>num_features</th>
+      <td><p>\(C\) from an expected input of size
+\((N, C, H, W)\)</p></td>
+    </tr>
+    <tr>
+      <th>eps</th>
+      <td><p>a value added to the denominator for numerical stability.
+Default: 1e-5</p></td>
+    </tr>
+    <tr>
+      <th>momentum</th>
+      <td><p>the value used for the running_mean and running_var
+computation. Can be set to <code>None</code> for cumulative moving average
+(i.e. simple average). Default: 0.1</p></td>
+    </tr>
+    <tr>
+      <th>affine</th>
+      <td><p>a boolean value that when set to <code>TRUE</code>, this module has
+learnable affine parameters. Default: <code>TRUE</code></p></td>
+    </tr>
+    <tr>
+      <th>track_running_stats</th>
+      <td><p>a boolean value that when set to <code>TRUE</code>, this
+module tracks the running mean and variance, and when set to <code>FALSE</code>,
+this module does not track such statistics and uses batch statistics instead
+in both training and eval modes if the running mean and variance are <code>None</code>.
+Default: <code>TRUE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+  y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta
+$$</p>
+<p>The mean and standard-deviation are calculated per-dimension over
+the mini-batches and \(\gamma\) and \(\beta\) are learnable parameter vectors
+of size <code>C</code> (where <code>C</code> is the input size). By default, the elements of \(\gamma\) are set
+to 1 and the elements of \(\beta\) are set to 0. The standard-deviation is calculated
+via the biased estimator, equivalent to <code><a href='torch_var.html'>torch_var(input, unbiased=FALSE)</a></code>.
+Also by default, during training this layer keeps running estimates of its
+computed mean and variance, which are then used for normalization during
+evaluation. The running estimates are kept with a default <code>momentum</code>
+of 0.1.</p>
+<p>If <code>track_running_stats</code> is set to <code>FALSE</code>, this layer then does not
+keep running estimates, and batch statistics are instead used during
+evaluation time as well.</p>
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>This <code>momentum</code> argument is different from one used in optimizer
+classes and the conventional notion of momentum. Mathematically, the
+update rule for running statistics here is
+\(\hat{x}_{\mbox{new}} = (1 - \mbox{momentum}) \times \hat{x} + \mbox{momentum} \times x_t\),
+where \(\hat{x}\) is the estimated statistic and \(x_t\) is the
+new observed value.
+Because the Batch Normalization is done over the <code>C</code> dimension, computing statistics
+on <code>(N, H, W)</code> slices, it's common terminology to call this Spatial Batch Normalization.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C, H, W)\)</p></li>
+<li><p>Output: \((N, C, H, W)\) (same shape as input)</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'># With Learnable Parameters</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_batch_norm2d</span>(<span class='fl'>100</span>)
+<span class='co'># Without Learnable Parameters</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_batch_norm2d</span>(<span class='fl'>100</span>, affine=<span class='fl'>FALSE</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>100</span>, <span class='fl'>35</span>, <span class='fl'>45</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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+<div class="row">
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+    <div class="page-header">
+    <h1>Binary cross entropy loss</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-loss.R'><code>R/nn-loss.R</code></a></small>
+    <div class="hidden name"><code>nn_bce_loss.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Creates a criterion that measures the Binary Cross Entropy
+between the target and the output:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_bce_loss</span>(weight = <span class='kw'>NULL</span>, reduction = <span class='st'>"mean"</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>weight</th>
+      <td><p>(Tensor, optional): a manual rescaling weight given to the loss
+of each batch element. If given, has to be a Tensor of size <code>nbatch</code>.</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional): Specifies the reduction to apply to the output:
+<code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
+<code>'mean'</code>: the sum of the output will be divided by the number of
+elements in the output, <code>'sum'</code>: the output will be summed. Note: <code>size_average</code>
+and <code>reduce</code> are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>The unreduced (i.e. with <code>reduction</code> set to <code>'none'</code>) loss can be described as:
+$$
+  \ell(x, y) = L = \{l_1,\dots,l_N\}^\top, \quad
+l_n = - w_n \left[ y_n \cdot \log x_n + (1 - y_n) \cdot \log (1 - x_n) \right]
+$$
+where \(N\) is the batch size. If <code>reduction</code> is not <code>'none'</code>
+(default <code>'mean'</code>), then</p>
+<p>$$
+  \ell(x, y) = \left\{ \begin{array}{ll}
+\mbox{mean}(L), &amp; \mbox{if reduction} = \mbox{'mean';}\\
+\mbox{sum}(L),  &amp; \mbox{if reduction} = \mbox{'sum'.}
+\end{array}
+\right.
+$$</p>
+<p>This is used for measuring the error of a reconstruction in for example
+an auto-encoder. Note that the targets \(y\) should be numbers
+between 0 and 1.</p>
+<p>Notice that if \(x_n\) is either 0 or 1, one of the log terms would be
+mathematically undefined in the above loss equation. PyTorch chooses to set
+\(\log (0) = -\infty\), since \(\lim_{x\to 0} \log (x) = -\infty\).</p>
+<p>However, an infinite term in the loss equation is not desirable for several reasons.
+For one, if either \(y_n = 0\) or \((1 - y_n) = 0\), then we would be
+multiplying 0 with infinity. Secondly, if we have an infinite loss value, then
+we would also have an infinite term in our gradient, since
+\(\lim_{x\to 0} \frac{d}{dx} \log (x) = \infty\).</p>
+<p>This would make BCELoss's backward method nonlinear with respect to \(x_n\),
+and using it for things like linear regression would not be straight-forward.
+Our solution is that BCELoss clamps its log function outputs to be greater than
+or equal to -100. This way, we can always have a finite loss value and a linear
+backward method.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where \(*\) means, any number of additional
+dimensions</p></li>
+<li><p>Target: \((N, *)\), same shape as the input</p></li>
+<li><p>Output: scalar. If <code>reduction</code> is <code>'none'</code>, then \((N, *)\), same
+shape as input.</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_sigmoid.html'>nn_sigmoid</a></span>()
+<span class='kw'>loss</span> <span class='op'>&lt;-</span> <span class='fu'>nn_bce_loss</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>3</span>, requires_grad=<span class='fl'>TRUE</span>)
+<span class='kw'>target</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_rand.html'>torch_rand</a></span>(<span class='fl'>3</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>loss</span>(<span class='fu'>m</span>(<span class='kw'>input</span>), <span class='kw'>target</span>)
+<span class='kw'>output</span><span class='op'>$</span><span class='fu'>backward</span>()
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/nn_bilinear.html b/static/docs/reference/nn_bilinear.html
new file mode 100644
index 000000000..b2507d126
--- /dev/null
+++ b/static/docs/reference/nn_bilinear.html
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+    <h1>Bilinear module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-linear.R'><code>R/nn-linear.R</code></a></small>
+    <div class="hidden name"><code>nn_bilinear.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a bilinear transformation to the incoming data
+\(y = x_1^T A x_2 + b\)</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_bilinear</span>(<span class='kw'>in1_features</span>, <span class='kw'>in2_features</span>, <span class='kw'>out_features</span>, bias = <span class='fl'>TRUE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>in1_features</th>
+      <td><p>size of each first input sample</p></td>
+    </tr>
+    <tr>
+      <th>in2_features</th>
+      <td><p>size of each second input sample</p></td>
+    </tr>
+    <tr>
+      <th>out_features</th>
+      <td><p>size of each output sample</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>If set to <code>FALSE</code>, the layer will not learn an additive bias.
+Default: <code>TRUE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input1: \((N, *, H_{in1})\) \(H_{in1}=\mbox{in1\_features}\) and
+\(*\) means any number of additional dimensions. All but the last
+dimension of the inputs should be the same.</p></li>
+<li><p>Input2: \((N, *, H_{in2})\) where \(H_{in2}=\mbox{in2\_features}\).</p></li>
+<li><p>Output: \((N, *, H_{out})\) where \(H_{out}=\mbox{out\_features}\)
+and all but the last dimension are the same shape as the input.</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
+
+    
+
+<ul>
+<li><p>weight: the learnable weights of the module of shape
+\((\mbox{out\_features}, \mbox{in1\_features}, \mbox{in2\_features})\).
+The values are initialized from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\), where
+\(k = \frac{1}{\mbox{in1\_features}}\)</p></li>
+<li><p>bias: the learnable bias of the module of shape \((\mbox{out\_features})\).
+If <code>bias</code> is <code>TRUE</code>, the values are initialized from
+\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\), where
+\(k = \frac{1}{\mbox{in1\_features}}\)</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_bilinear</span>(<span class='fl'>20</span>, <span class='fl'>30</span>, <span class='fl'>50</span>)
+<span class='kw'>input1</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>128</span>, <span class='fl'>20</span>)
+<span class='kw'>input2</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>128</span>, <span class='fl'>30</span>)
+<span class='kw'>output</span> <span class='op'>=</span> <span class='fu'>m</span>(<span class='kw'>input1</span>, <span class='kw'>input2</span>)
+<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='kw'>output</span><span class='op'>$</span><span class='fu'>size</span>()) 
+
+}
+</div><div class='output co'>#&gt; [1] 128  50</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>CELU module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_celu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the element-wise function:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_celu</span>(alpha = <span class='fl'>1</span>, inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>alpha</th>
+      <td><p>the \(\alpha\) value for the CELU formulation. Default: 1.0</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+  \mbox{CELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x/\alpha) - 1))
+$$</p>
+<p>More details can be found in the paper
+<a href='https://arxiv.org/abs/1704.07483'>Continuously Differentiable Exponential Linear Units</a>.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_celu</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
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+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+\((N, C_{\mbox{in}}, L)\) and output \((N, C_{\mbox{out}}, L_{\mbox{out}})\) can be
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+    <div class="page-header">
+    <h1>Conv1D module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-conv.R'><code>R/nn-conv.R</code></a></small>
+    <div class="hidden name"><code>nn_conv1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 1D convolution over an input signal composed of several input
+planes.
+In the simplest case, the output value of the layer with input size
+\((N, C_{\mbox{in}}, L)\) and output \((N, C_{\mbox{out}}, L_{\mbox{out}})\) can be
+precisely described as:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_conv1d</span>(
+  <span class='kw'>in_channels</span>,
+  <span class='kw'>out_channels</span>,
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='fl'>1</span>,
+  padding = <span class='fl'>0</span>,
+  dilation = <span class='fl'>1</span>,
+  groups = <span class='fl'>1</span>,
+  bias = <span class='fl'>TRUE</span>,
+  padding_mode = <span class='st'>"zeros"</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>in_channels</th>
+      <td><p>(int): Number of channels in the input image</p></td>
+    </tr>
+    <tr>
+      <th>out_channels</th>
+      <td><p>(int): Number of channels produced by the convolution</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>(int or tuple): Size of the convolving kernel</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>(int or tuple, optional): Zero-padding added to both sides of
+the input. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>(int or tuple, optional): Spacing between kernel
+elements. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>(int, optional): Number of blocked connections from input
+channels to output channels. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>(bool, optional): If <code>TRUE</code>, adds a learnable bias to the
+output. Default: <code>TRUE</code></p></td>
+    </tr>
+    <tr>
+      <th>padding_mode</th>
+      <td><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>,
+<code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+\mbox{out}(N_i, C_{\mbox{out}_j}) = \mbox{bias}(C_{\mbox{out}_j}) +
+  \sum_{k = 0}^{C_{in} - 1} \mbox{weight}(C_{\mbox{out}_j}, k)
+\star \mbox{input}(N_i, k)
+$$</p>
+<p>where \(\star\) is the valid
+<a href='https://en.wikipedia.org/wiki/Cross-correlation'>cross-correlation</a> operator,
+\(N\) is a batch size, \(C\) denotes a number of channels,
+\(L\) is a length of signal sequence.</p><ul>
+<li><p><code>stride</code> controls the stride for the cross-correlation, a single
+number or a one-element tuple.</p></li>
+<li><p><code>padding</code> controls the amount of implicit zero-paddings on both sides
+for <code>padding</code> number of points.</p></li>
+<li><p><code>dilation</code> controls the spacing between the kernel points; also
+known as the à trous algorithm. It is harder to describe, but this
+<a href='https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md'>link</a>
+has a nice visualization of what <code>dilation</code> does.</p></li>
+<li><p><code>groups</code> controls the connections between inputs and outputs.
+<code>in_channels</code> and <code>out_channels</code> must both be divisible by
+<code>groups</code>. For example,</p><ul>
+<li><p>At groups=1, all inputs are convolved to all outputs.</p></li>
+<li><p>At groups=2, the operation becomes equivalent to having two conv
+layers side by side, each seeing half the input channels,
+and producing half the output channels, and both subsequently
+concatenated.</p></li>
+<li><p>At groups= <code>in_channels</code>, each input channel is convolved with
+its own set of filters,
+of size \(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\).</p></li>
+</ul></li>
+</ul>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+
+
+<p>Depending of the size of your kernel, several (of the last)
+columns of the input might be lost, because it is a valid
+<code>cross-correlation</code><em>, and not a full <code>cross-correlation</code></em>.
+It is up to the user to add proper padding.</p>
+<p>When <code>groups == in_channels</code> and <code>out_channels == K * in_channels</code>,
+where <code>K</code> is a positive integer, this operation is also termed in
+literature as depthwise convolution.
+In other words, for an input of size \((N, C_{in}, L_{in})\),
+a depthwise convolution with a depthwise multiplier <code>K</code>, can be constructed by arguments
+\((C_{\mbox{in}}=C_{in}, C_{\mbox{out}}=C_{in} \times K, ..., \mbox{groups}=C_{in})\).</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C_{in}, L_{in})\)</p></li>
+<li><p>Output: \((N, C_{out}, L_{out})\) where</p></li>
+</ul>
+
+<p>$$
+  L_{out} = \left\lfloor\frac{L_{in} + 2 \times \mbox{padding} - \mbox{dilation}
+    \times (\mbox{kernel\_size} - 1) - 1}{\mbox{stride}} + 1\right\rfloor
+$$</p>
+    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
+
+    
+
+<ul>
+<li><p>weight (Tensor): the learnable weights of the module of shape
+\((\mbox{out\_channels}, \frac{\mbox{in\_channels}}{\mbox{groups}}, \mbox{kernel\_size})\).
+The values of these weights are sampled from
+\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
+\(k = \frac{groups}{C_{\mbox{in}} * \mbox{kernel\_size}}\)</p></li>
+<li><p>bias (Tensor): the learnable bias of the module of shape
+(out_channels). If <code>bias</code> is <code>TRUE</code>, then the values of these weights are
+sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
+\(k = \frac{groups}{C_{\mbox{in}} * \mbox{kernel\_size}}\)</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv1d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>3</span>, stride=<span class='fl'>2</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
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+    <div class="page-header">
+    <h1>Conv2D module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-conv.R'><code>R/nn-conv.R</code></a></small>
+    <div class="hidden name"><code>nn_conv2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 2D convolution over an input signal composed of several input
+planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_conv2d</span>(
+  <span class='kw'>in_channels</span>,
+  <span class='kw'>out_channels</span>,
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='fl'>1</span>,
+  padding = <span class='fl'>0</span>,
+  dilation = <span class='fl'>1</span>,
+  groups = <span class='fl'>1</span>,
+  bias = <span class='fl'>TRUE</span>,
+  padding_mode = <span class='st'>"zeros"</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>in_channels</th>
+      <td><p>(int): Number of channels in the input image</p></td>
+    </tr>
+    <tr>
+      <th>out_channels</th>
+      <td><p>(int): Number of channels produced by the convolution</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>(int or tuple): Size of the convolving kernel</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>(int or tuple, optional): Zero-padding added to both sides of
+the input. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>(int, optional): Number of blocked connections from input
+channels to output channels. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>(bool, optional): If <code>TRUE</code>, adds a learnable bias to the
+output. Default: <code>TRUE</code></p></td>
+    </tr>
+    <tr>
+      <th>padding_mode</th>
+      <td><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>,
+<code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>In the simplest case, the output value of the layer with input size
+\((N, C_{\mbox{in}}, H, W)\) and output \((N, C_{\mbox{out}}, H_{\mbox{out}}, W_{\mbox{out}})\)
+can be precisely described as:</p>
+<p>$$
+\mbox{out}(N_i, C_{\mbox{out}_j}) = \mbox{bias}(C_{\mbox{out}_j}) +
+  \sum_{k = 0}^{C_{\mbox{in}} - 1} \mbox{weight}(C_{\mbox{out}_j}, k) \star \mbox{input}(N_i, k)
+$$</p>
+<p>where \(\star\) is the valid 2D cross-correlation operator,
+\(N\) is a batch size, \(C\) denotes a number of channels,
+\(H\) is a height of input planes in pixels, and \(W\) is
+width in pixels.</p><ul>
+<li><p><code>stride</code> controls the stride for the cross-correlation, a single
+number or a tuple.</p></li>
+<li><p><code>padding</code> controls the amount of implicit zero-paddings on both
+sides for <code>padding</code> number of points for each dimension.</p></li>
+<li><p><code>dilation</code> controls the spacing between the kernel points; also
+known as the à trous algorithm. It is harder to describe, but this <code>link</code>_
+has a nice visualization of what <code>dilation</code> does.</p></li>
+<li><p><code>groups</code> controls the connections between inputs and outputs.
+<code>in_channels</code> and <code>out_channels</code> must both be divisible by
+<code>groups</code>. For example,</p><ul>
+<li><p>At groups=1, all inputs are convolved to all outputs.</p></li>
+<li><p>At groups=2, the operation becomes equivalent to having two conv
+layers side by side, each seeing half the input channels,
+and producing half the output channels, and both subsequently
+concatenated.</p></li>
+<li><p>At groups= <code>in_channels</code>, each input channel is convolved with
+its own set of filters, of size:
+\(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\).</p></li>
+</ul></li>
+</ul>
+
+<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>dilation</code> can either be:</p><ul>
+<li><p>a single <code>int</code> -- in which case the same value is used for the height and
+width dimension</p></li>
+<li><p>a <code>tuple</code> of two ints -- in which case, the first <code>int</code> is used for the height dimension,
+and the second <code>int</code> for the width dimension</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+
+
+<p>Depending of the size of your kernel, several (of the last)
+columns of the input might be lost, because it is a valid cross-correlation,
+and not a full cross-correlation.
+It is up to the user to add proper padding.</p>
+<p>When <code>groups == in_channels</code> and <code>out_channels == K * in_channels</code>,
+where <code>K</code> is a positive integer, this operation is also termed in
+literature as depthwise convolution.
+In other words, for an input of size :math:<code>(N, C_{in}, H_{in}, W_{in})</code>,
+a depthwise convolution with a depthwise multiplier <code>K</code>, can be constructed by arguments
+\((in\_channels=C_{in}, out\_channels=C_{in} \times K, ..., groups=C_{in})\).</p>
+<p>In some circumstances when using the CUDA backend with CuDNN, this operator
+may select a nondeterministic algorithm to increase performance. If this is
+undesirable, you can try to make the operation deterministic (potentially at
+a performance cost) by setting <code>backends_cudnn_deterministic = TRUE</code>.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C_{in}, H_{in}, W_{in})\)</p></li>
+<li><p>Output: \((N, C_{out}, H_{out}, W_{out})\) where
+$$
+  H_{out} = \left\lfloor\frac{H_{in}  + 2 \times \mbox{padding}[0] - \mbox{dilation}[0]
+    \times (\mbox{kernel\_size}[0] - 1) - 1}{\mbox{stride}[0]} + 1\right\rfloor
+$$
+$$
+  W_{out} = \left\lfloor\frac{W_{in}  + 2 \times \mbox{padding}[1] - \mbox{dilation}[1]
+    \times (\mbox{kernel\_size}[1] - 1) - 1}{\mbox{stride}[1]} + 1\right\rfloor
+$$</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
+
+    
+
+<ul>
+<li><p>weight (Tensor): the learnable weights of the module of shape
+\((\mbox{out\_channels}, \frac{\mbox{in\_channels}}{\mbox{groups}}\),
+\(\mbox{kernel\_size[0]}, \mbox{kernel\_size[1]})\).
+The values of these weights are sampled from
+\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
+\(k = \frac{groups}{C_{\mbox{in}} * \prod_{i=0}^{1}\mbox{kernel\_size}[i]}\)</p></li>
+<li><p>bias (Tensor): the learnable bias of the module of shape
+(out_channels). If <code>bias</code> is <code>TRUE</code>,
+then the values of these weights are
+sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
+\(k = \frac{groups}{C_{\mbox{in}} * \prod_{i=0}^{1}\mbox{kernel\_size}[i]}\)</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='co'># With square kernels and equal stride</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv2d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>3</span>, stride = <span class='fl'>2</span>)
+<span class='co'># non-square kernels and unequal stride and with padding</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv2d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>), stride=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>), padding=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>2</span>))
+<span class='co'># non-square kernels and unequal stride and with padding and dilation</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv2d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>), stride=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>), padding=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>2</span>), dilation=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>1</span>))
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>100</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)  
+
+}
+</div></pre>
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+<meta property="og:description" content="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:" />
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+<div class="row">
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+    <div class="page-header">
+    <h1>Conv3D module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-conv.R'><code>R/nn-conv.R</code></a></small>
+    <div class="hidden name"><code>nn_conv3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 3D convolution over an input signal composed of several input
+planes.
+In the simplest case, the output value of the layer with input size \((N, C_{in}, D, H, W)\)
+and output \((N, C_{out}, D_{out}, H_{out}, W_{out})\) can be precisely described as:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_conv3d</span>(
+  <span class='kw'>in_channels</span>,
+  <span class='kw'>out_channels</span>,
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='fl'>1</span>,
+  padding = <span class='fl'>0</span>,
+  dilation = <span class='fl'>1</span>,
+  groups = <span class='fl'>1</span>,
+  bias = <span class='fl'>TRUE</span>,
+  padding_mode = <span class='st'>"zeros"</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>in_channels</th>
+      <td><p>(int): Number of channels in the input image</p></td>
+    </tr>
+    <tr>
+      <th>out_channels</th>
+      <td><p>(int): Number of channels produced by the convolution</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>(int or tuple): Size of the convolving kernel</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>(int or tuple, optional): Zero-padding added to all three sides of the input. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>(int, optional): Number of blocked connections from input channels to output channels. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>(bool, optional): If <code>TRUE</code>, adds a learnable bias to the output. Default: <code>TRUE</code></p></td>
+    </tr>
+    <tr>
+      <th>padding_mode</th>
+      <td><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>, <code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+  out(N_i, C_{out_j}) = bias(C_{out_j}) +
+  \sum_{k = 0}^{C_{in} - 1} weight(C_{out_j}, k) \star input(N_i, k)
+$$</p>
+<p>where \(\star\) is the valid 3D <code>cross-correlation</code> operator</p><ul>
+<li><p><code>stride</code> controls the stride for the cross-correlation.</p></li>
+<li><p><code>padding</code> controls the amount of implicit zero-paddings on both
+sides for <code>padding</code> number of points for each dimension.</p></li>
+<li><p><code>dilation</code> controls the spacing between the kernel points; also known as the à trous algorithm.
+It is harder to describe, but this <code>link</code>_ has a nice visualization of what <code>dilation</code> does.</p></li>
+<li><p><code>groups</code> controls the connections between inputs and outputs.
+<code>in_channels</code> and <code>out_channels</code> must both be divisible by
+<code>groups</code>. For example,</p></li>
+<li><p>At groups=1, all inputs are convolved to all outputs.</p></li>
+<li><p>At groups=2, the operation becomes equivalent to having two conv
+layers side by side, each seeing half the input channels,
+and producing half the output channels, and both subsequently
+concatenated.</p></li>
+<li><p>At groups= <code>in_channels</code>, each input channel is convolved with
+its own set of filters, of size
+\(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\).</p></li>
+</ul>
+
+<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>dilation</code> can either be:</p><ul>
+<li><p>a single <code>int</code> -- in which case the same value is used for the depth, height and width dimension</p></li>
+<li><p>a <code>tuple</code> of three ints -- in which case, the first <code>int</code> is used for the depth dimension,
+the second <code>int</code> for the height dimension and the third <code>int</code> for the width dimension</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>Depending of the size of your kernel, several (of the last)
+columns of the input might be lost, because it is a valid <code>cross-correlation</code><em>,
+and not a full <code>cross-correlation</code></em>.
+It is up to the user to add proper padding.</p>
+<p>When <code>groups == in_channels</code> and <code>out_channels == K * in_channels</code>,
+where <code>K</code> is a positive integer, this operation is also termed in
+literature as depthwise convolution.
+In other words, for an input of size \((N, C_{in}, D_{in}, H_{in}, W_{in})\),
+a depthwise convolution with a depthwise multiplier <code>K</code>, can be constructed by arguments
+\((in\_channels=C_{in}, out\_channels=C_{in} \times K, ..., groups=C_{in})\).</p>
+<p>In some circumstances when using the CUDA backend with CuDNN, this operator
+may select a nondeterministic algorithm to increase performance. If this is
+undesirable, you can try to make the operation deterministic (potentially at
+a performance cost) by setting <code>torch.backends.cudnn.deterministic = TRUE</code>.
+Please see the notes on :doc:<code>/notes/randomness</code> for background.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C_{in}, D_{in}, H_{in}, W_{in})\)</p></li>
+<li><p>Output: \((N, C_{out}, D_{out}, H_{out}, W_{out})\) where
+$$
+  D_{out} = \left\lfloor\frac{D_{in} + 2 \times \mbox{padding}[0] - \mbox{dilation}[0]
+    \times (\mbox{kernel\_size}[0] - 1) - 1}{\mbox{stride}[0]} + 1\right\rfloor
+ $$
+$$
+  H_{out} = \left\lfloor\frac{H_{in} + 2 \times \mbox{padding}[1] - \mbox{dilation}[1]
+    \times (\mbox{kernel\_size}[1] - 1) - 1}{\mbox{stride}[1]} + 1\right\rfloor
+ $$
+$$
+  W_{out} = \left\lfloor\frac{W_{in} + 2 \times \mbox{padding}[2] - \mbox{dilation}[2]
+    \times (\mbox{kernel\_size}[2] - 1) - 1}{\mbox{stride}[2]} + 1\right\rfloor
+ $$</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
+
+    
+
+<ul>
+<li><p>weight (Tensor): the learnable weights of the module of shape
+\((\mbox{out\_channels}, \frac{\mbox{in\_channels}}{\mbox{groups}},\)
+\(\mbox{kernel\_size[0]}, \mbox{kernel\_size[1]}, \mbox{kernel\_size[2]})\).
+The values of these weights are sampled from
+\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
+\(k = \frac{groups}{C_{\mbox{in}} * \prod_{i=0}^{2}\mbox{kernel\_size}[i]}\)</p></li>
+<li><p>bias (Tensor):   the learnable bias of the module of shape (out_channels). If <code>bias</code> is <code>True</code>,
+then the values of these weights are
+sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
+\(k = \frac{groups}{C_{\mbox{in}} * \prod_{i=0}^{2}\mbox{kernel\_size}[i]}\)</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'># With square kernels and equal stride</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv3d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>3</span>, stride=<span class='fl'>2</span>)
+<span class='co'># non-square kernels and unequal stride and with padding</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv3d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>, <span class='fl'>2</span>), stride=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>1</span>), padding=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>2</span>, <span class='fl'>0</span>))
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>10</span>, <span class='fl'>50</span>, <span class='fl'>100</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+    <div class="page-header">
+    <h1>ConvTranspose1D</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-conv.R'><code>R/nn-conv.R</code></a></small>
+    <div class="hidden name"><code>nn_conv_transpose1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 1D transposed convolution operator over an input image
+composed of several input planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_conv_transpose1d</span>(
+  <span class='kw'>in_channels</span>,
+  <span class='kw'>out_channels</span>,
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='fl'>1</span>,
+  padding = <span class='fl'>0</span>,
+  output_padding = <span class='fl'>0</span>,
+  groups = <span class='fl'>1</span>,
+  bias = <span class='fl'>TRUE</span>,
+  dilation = <span class='fl'>1</span>,
+  padding_mode = <span class='st'>"zeros"</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>in_channels</th>
+      <td><p>(int): Number of channels in the input image</p></td>
+    </tr>
+    <tr>
+      <th>out_channels</th>
+      <td><p>(int): Number of channels produced by the convolution</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>(int or tuple): Size of the convolving kernel</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>(int or tuple, optional): <code>dilation * (kernel_size - 1) - padding</code> zero-padding
+will be added to both sides of the input. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>output_padding</th>
+      <td><p>(int or tuple, optional): Additional size added to one side
+of the output shape. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>(int, optional): Number of blocked connections from input channels to output channels. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>(bool, optional): If <code>True</code>, adds a learnable bias to the output. Default: <code>TRUE</code></p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding_mode</th>
+      <td><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>,
+<code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>This module can be seen as the gradient of Conv1d with respect to its input.
+It is also known as a fractionally-strided convolution or
+a deconvolution (although it is not an actual deconvolution operation).</p><ul>
+<li><p><code>stride</code> controls the stride for the cross-correlation.</p></li>
+<li><p><code>padding</code> controls the amount of implicit zero-paddings on both
+sides for <code>dilation * (kernel_size - 1) - padding</code> number of points. See note
+below for details.</p></li>
+<li><p><code>output_padding</code> controls the additional size added to one side
+of the output shape. See note below for details.</p></li>
+<li><p><code>dilation</code> controls the spacing between the kernel points; also known as the
+à trous algorithm. It is harder to describe, but this <a href='https://github.com/vdumoulin/conv_arithmetic'>link</a>
+has a nice visualization of what <code>dilation</code> does.</p></li>
+<li><p><code>groups</code> controls the connections between inputs and outputs.
+<code>in_channels</code> and <code>out_channels</code> must both be divisible by
+<code>groups</code>. For example,</p><ul>
+<li><p>At groups=1, all inputs are convolved to all outputs.</p></li>
+<li><p>At groups=2, the operation becomes equivalent to having two conv
+layers side by side, each seeing half the input channels,
+and producing half the output channels, and both subsequently
+concatenated.</p></li>
+<li><p>At groups= <code>in_channels</code>, each input channel is convolved with
+its own set of filters (of size
+\(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\)).</p></li>
+</ul></li>
+</ul>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>Depending of the size of your kernel, several (of the last)
+columns of the input might be lost, because it is a valid <code>cross-correlation</code><em>,
+and not a full <code>cross-correlation</code></em>.
+It is up to the user to add proper padding.</p>
+<p>The <code>padding</code> argument effectively adds <code>dilation * (kernel_size - 1) - padding</code>
+amount of zero padding to both sizes of the input. This is set so that
+when a <code>~torch.nn.Conv1d</code> and a <code>~torch.nn.ConvTranspose1d</code>
+are initialized with same parameters, they are inverses of each other in
+regard to the input and output shapes. However, when <code>stride &gt; 1</code>,
+<code>~torch.nn.Conv1d</code> maps multiple input shapes to the same output
+shape. <code>output_padding</code> is provided to resolve this ambiguity by
+effectively increasing the calculated output shape on one side. Note
+that <code>output_padding</code> is only used to find output shape, but does
+not actually add zero-padding to output.</p>
+<p>In some circumstances when using the CUDA backend with CuDNN, this operator
+may select a nondeterministic algorithm to increase performance. If this is
+undesirable, you can try to make the operation deterministic (potentially at
+a performance cost) by setting <code>torch.backends.cudnn.deterministic = TRUE</code>.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C_{in}, L_{in})\)</p></li>
+<li><p>Output: \((N, C_{out}, L_{out})\) where
+$$
+  L_{out} = (L_{in} - 1) \times \mbox{stride} - 2 \times \mbox{padding} + \mbox{dilation}
+\times (\mbox{kernel\_size} - 1) + \mbox{output\_padding} + 1
+$$</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
+
+    
+
+<ul>
+<li><p>weight (Tensor): the learnable weights of the module of shape
+\((\mbox{in\_channels}, \frac{\mbox{out\_channels}}{\mbox{groups}},\)
+\(\mbox{kernel\_size})\).
+The values of these weights are sampled from
+\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
+\(k = \frac{groups}{C_{\mbox{out}} * \mbox{kernel\_size}}\)</p></li>
+<li><p>bias (Tensor):   the learnable bias of the module of shape (out_channels).
+If <code>bias</code> is <code>TRUE</code>, then the values of these weights are
+sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
+\(k = \frac{groups}{C_{\mbox{out}} * \mbox{kernel\_size}}\)</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv_transpose1d</span>(<span class='fl'>32</span>, <span class='fl'>16</span>, <span class='fl'>2</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>10</span>, <span class='fl'>32</span>, <span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>ConvTranpose2D module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-conv.R'><code>R/nn-conv.R</code></a></small>
+    <div class="hidden name"><code>nn_conv_transpose2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 2D transposed convolution operator over an input image
+composed of several input planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_conv_transpose2d</span>(
+  <span class='kw'>in_channels</span>,
+  <span class='kw'>out_channels</span>,
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='fl'>1</span>,
+  padding = <span class='fl'>0</span>,
+  output_padding = <span class='fl'>0</span>,
+  groups = <span class='fl'>1</span>,
+  bias = <span class='fl'>TRUE</span>,
+  dilation = <span class='fl'>1</span>,
+  padding_mode = <span class='st'>"zeros"</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>in_channels</th>
+      <td><p>(int): Number of channels in the input image</p></td>
+    </tr>
+    <tr>
+      <th>out_channels</th>
+      <td><p>(int): Number of channels produced by the convolution</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>(int or tuple): Size of the convolving kernel</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>(int or tuple, optional): <code>dilation * (kernel_size - 1) - padding</code> zero-padding
+will be added to both sides of each dimension in the input. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>output_padding</th>
+      <td><p>(int or tuple, optional): Additional size added to one side
+of each dimension in the output shape. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>(int, optional): Number of blocked connections from input channels to output channels. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>(bool, optional): If <code>True</code>, adds a learnable bias to the output. Default: <code>True</code></p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding_mode</th>
+      <td><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>,
+<code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>This module can be seen as the gradient of Conv2d with respect to its input.
+It is also known as a fractionally-strided convolution or
+a deconvolution (although it is not an actual deconvolution operation).</p><ul>
+<li><p><code>stride</code> controls the stride for the cross-correlation.</p></li>
+<li><p><code>padding</code> controls the amount of implicit zero-paddings on both
+sides for <code>dilation * (kernel_size - 1) - padding</code> number of points. See note
+below for details.</p></li>
+<li><p><code>output_padding</code> controls the additional size added to one side
+of the output shape. See note below for details.</p></li>
+<li><p><code>dilation</code> controls the spacing between the kernel points; also known as the à trous algorithm.
+It is harder to describe, but this <code>link</code>_ has a nice visualization of what <code>dilation</code> does.</p></li>
+<li><p><code>groups</code> controls the connections between inputs and outputs.
+<code>in_channels</code> and <code>out_channels</code> must both be divisible by
+<code>groups</code>. For example,</p><ul>
+<li><p>At groups=1, all inputs are convolved to all outputs.</p></li>
+<li><p>At groups=2, the operation becomes equivalent to having two conv
+layers side by side, each seeing half the input channels,
+and producing half the output channels, and both subsequently
+concatenated.</p></li>
+<li><p>At groups= <code>in_channels</code>, each input channel is convolved with
+its own set of filters (of size
+\(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\)).</p></li>
+</ul></li>
+</ul>
+
+<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>output_padding</code>
+can either be:</p><ul>
+<li><p>a single <code>int</code> -- in which case the same value is used for the height and width dimensions</p></li>
+<li><p>a <code>tuple</code> of two ints -- in which case, the first <code>int</code> is used for the height dimension,
+and the second <code>int</code> for the width dimension</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>Depending of the size of your kernel, several (of the last)
+columns of the input might be lost, because it is a valid <code>cross-correlation</code>_,
+and not a full <code>cross-correlation</code>. It is up to the user to add proper padding.</p>
+<p>The <code>padding</code> argument effectively adds <code>dilation * (kernel_size - 1) - padding</code>
+amount of zero padding to both sizes of the input. This is set so that
+when a <a href='nn_conv2d.html'>nn_conv2d</a> and a nn_conv_transpose2d are initialized with same
+parameters, they are inverses of each other in
+regard to the input and output shapes. However, when <code>stride &gt; 1</code>,
+<a href='nn_conv2d.html'>nn_conv2d</a> maps multiple input shapes to the same output
+shape. <code>output_padding</code> is provided to resolve this ambiguity by
+effectively increasing the calculated output shape on one side. Note
+that <code>output_padding</code> is only used to find output shape, but does
+not actually add zero-padding to output.</p>
+<p>In some circumstances when using the CUDA backend with CuDNN, this operator
+may select a nondeterministic algorithm to increase performance. If this is
+undesirable, you can try to make the operation deterministic (potentially at
+a performance cost) by setting <code>torch.backends.cudnn.deterministic = TRUE</code>.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C_{in}, H_{in}, W_{in})\)</p></li>
+<li><p>Output: \((N, C_{out}, H_{out}, W_{out})\) where
+$$
+  H_{out} = (H_{in} - 1) \times \mbox{stride}[0] - 2 \times \mbox{padding}[0] + \mbox{dilation}[0]
+\times (\mbox{kernel\_size}[0] - 1) + \mbox{output\_padding}[0] + 1
+$$
+$$
+  W_{out} = (W_{in} - 1) \times \mbox{stride}[1] - 2 \times \mbox{padding}[1] + \mbox{dilation}[1]
+\times (\mbox{kernel\_size}[1] - 1) + \mbox{output\_padding}[1] + 1
+$$</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
+
+    
+
+<ul>
+<li><p>weight (Tensor): the learnable weights of the module of shape
+\((\mbox{in\_channels}, \frac{\mbox{out\_channels}}{\mbox{groups}},\)
+\(\mbox{kernel\_size[0]}, \mbox{kernel\_size[1]})\).
+The values of these weights are sampled from
+\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
+\(k = \frac{groups}{C_{\mbox{out}} * \prod_{i=0}^{1}\mbox{kernel\_size}[i]}\)</p></li>
+<li><p>bias (Tensor):   the learnable bias of the module of shape (out_channels)
+If <code>bias</code> is <code>True</code>, then the values of these weights are
+sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
+\(k = \frac{groups}{C_{\mbox{out}} * \prod_{i=0}^{1}\mbox{kernel\_size}[i]}\)</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'># With square kernels and equal stride</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv_transpose2d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>3</span>, stride=<span class='fl'>2</span>)
+<span class='co'># non-square kernels and unequal stride and with padding</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv_transpose2d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>), stride=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>), padding=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>2</span>))
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>100</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+<span class='co'># exact output size can be also specified as an argument</span>
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>, <span class='fl'>16</span>, <span class='fl'>12</span>, <span class='fl'>12</span>)
+<span class='kw'>downsample</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_conv2d.html'>nn_conv2d</a></span>(<span class='fl'>16</span>, <span class='fl'>16</span>, <span class='fl'>3</span>, stride=<span class='fl'>2</span>, padding=<span class='fl'>1</span>)
+<span class='kw'>upsample</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv_transpose2d</span>(<span class='fl'>16</span>, <span class='fl'>16</span>, <span class='fl'>3</span>, stride=<span class='fl'>2</span>, padding=<span class='fl'>1</span>)
+<span class='kw'>h</span> <span class='op'>&lt;-</span> <span class='fu'>downsample</span>(<span class='kw'>input</span>)
+<span class='kw'>h</span><span class='op'>$</span><span class='fu'>size</span>()
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>upsample</span>(<span class='kw'>h</span>, output_size=<span class='kw'>input</span><span class='op'>$</span><span class='fu'>size</span>())
+<span class='kw'>output</span><span class='op'>$</span><span class='fu'>size</span>()
+
+}
+</div><div class='output co'>#&gt; [1]  1 16 12 12</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>ConvTranpose3D module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-conv.R'><code>R/nn-conv.R</code></a></small>
+    <div class="hidden name"><code>nn_conv_transpose3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 3D transposed convolution operator over an input image composed of several input
+planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_conv_transpose3d</span>(
+  <span class='kw'>in_channels</span>,
+  <span class='kw'>out_channels</span>,
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='fl'>1</span>,
+  padding = <span class='fl'>0</span>,
+  output_padding = <span class='fl'>0</span>,
+  groups = <span class='fl'>1</span>,
+  bias = <span class='fl'>TRUE</span>,
+  dilation = <span class='fl'>1</span>,
+  padding_mode = <span class='st'>"zeros"</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>in_channels</th>
+      <td><p>(int): Number of channels in the input image</p></td>
+    </tr>
+    <tr>
+      <th>out_channels</th>
+      <td><p>(int): Number of channels produced by the convolution</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>(int or tuple): Size of the convolving kernel</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>(int or tuple, optional): Stride of the convolution. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>(int or tuple, optional): <code>dilation * (kernel_size - 1) - padding</code> zero-padding
+will be added to both sides of each dimension in the input. Default: 0
+output_padding (int or tuple, optional): Additional size added to one side
+of each dimension in the output shape. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>output_padding</th>
+      <td><p>(int or tuple, optional): Additional size added to one side
+of each dimension in the output shape. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>(int, optional): Number of blocked connections from input channels to output channels. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>(bool, optional): If <code>True</code>, adds a learnable bias to the output. Default: <code>True</code></p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>(int or tuple, optional): Spacing between kernel elements. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding_mode</th>
+      <td><p>(string, optional): <code>'zeros'</code>, <code>'reflect'</code>, <code>'replicate'</code> or <code>'circular'</code>. Default: <code>'zeros'</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>The transposed convolution operator multiplies each input value element-wise by a learnable kernel,
+and sums over the outputs from all input feature planes.</p>
+<p>This module can be seen as the gradient of Conv3d with respect to its input.
+It is also known as a fractionally-strided convolution or
+a deconvolution (although it is not an actual deconvolution operation).</p><ul>
+<li><p><code>stride</code> controls the stride for the cross-correlation.</p></li>
+<li><p><code>padding</code> controls the amount of implicit zero-paddings on both
+sides for <code>dilation * (kernel_size - 1) - padding</code> number of points. See note
+below for details.</p></li>
+<li><p><code>output_padding</code> controls the additional size added to one side
+of the output shape. See note below for details.</p></li>
+<li><p><code>dilation</code> controls the spacing between the kernel points; also known as the à trous algorithm.
+It is harder to describe, but this <code>link</code>_ has a nice visualization of what <code>dilation</code> does.</p></li>
+<li><p><code>groups</code> controls the connections between inputs and outputs.
+<code>in_channels</code> and <code>out_channels</code> must both be divisible by
+<code>groups</code>. For example,</p><ul>
+<li><p>At groups=1, all inputs are convolved to all outputs.</p></li>
+<li><p>At groups=2, the operation becomes equivalent to having two conv
+layers side by side, each seeing half the input channels,
+and producing half the output channels, and both subsequently
+concatenated.</p></li>
+<li><p>At groups= <code>in_channels</code>, each input channel is convolved with
+its own set of filters (of size
+\(\left\lfloor\frac{out\_channels}{in\_channels}\right\rfloor\)).</p></li>
+</ul></li>
+</ul>
+
+<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>output_padding</code>
+can either be:</p><ul>
+<li><p>a single <code>int</code> -- in which case the same value is used for the depth, height and width dimensions</p></li>
+<li><p>a <code>tuple</code> of three ints -- in which case, the first <code>int</code> is used for the depth dimension,
+the second <code>int</code> for the height dimension and the third <code>int</code> for the width dimension</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>Depending of the size of your kernel, several (of the last)
+columns of the input might be lost, because it is a valid <code>cross-correlation</code><em>,
+and not a full <code>cross-correlation</code></em>.
+It is up to the user to add proper padding.</p>
+<p>The <code>padding</code> argument effectively adds <code>dilation * (kernel_size - 1) - padding</code>
+amount of zero padding to both sizes of the input. This is set so that
+when a <code>~torch.nn.Conv3d</code> and a <code>~torch.nn.ConvTranspose3d</code>
+are initialized with same parameters, they are inverses of each other in
+regard to the input and output shapes. However, when <code>stride &gt; 1</code>,
+<code>~torch.nn.Conv3d</code> maps multiple input shapes to the same output
+shape. <code>output_padding</code> is provided to resolve this ambiguity by
+effectively increasing the calculated output shape on one side. Note
+that <code>output_padding</code> is only used to find output shape, but does
+not actually add zero-padding to output.</p>
+<p>In some circumstances when using the CUDA backend with CuDNN, this operator
+may select a nondeterministic algorithm to increase performance. If this is
+undesirable, you can try to make the operation deterministic (potentially at
+a performance cost) by setting <code>torch.backends.cudnn.deterministic = TRUE</code>.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C_{in}, D_{in}, H_{in}, W_{in})\)</p></li>
+<li><p>Output: \((N, C_{out}, D_{out}, H_{out}, W_{out})\) where
+$$
+  D_{out} = (D_{in} - 1) \times \mbox{stride}[0] - 2 \times \mbox{padding}[0] + \mbox{dilation}[0]
+\times (\mbox{kernel\_size}[0] - 1) + \mbox{output\_padding}[0] + 1
+$$
+$$
+  H_{out} = (H_{in} - 1) \times \mbox{stride}[1] - 2 \times \mbox{padding}[1] + \mbox{dilation}[1]
+\times (\mbox{kernel\_size}[1] - 1) + \mbox{output\_padding}[1] + 1
+$$
+$$
+  W_{out} = (W_{in} - 1) \times \mbox{stride}[2] - 2 \times \mbox{padding}[2] + \mbox{dilation}[2]
+\times (\mbox{kernel\_size}[2] - 1) + \mbox{output\_padding}[2] + 1
+$$</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
+
+    
+
+<ul>
+<li><p>weight (Tensor): the learnable weights of the module of shape
+\((\mbox{in\_channels}, \frac{\mbox{out\_channels}}{\mbox{groups}},\)
+\(\mbox{kernel\_size[0]}, \mbox{kernel\_size[1]}, \mbox{kernel\_size[2]})\).
+The values of these weights are sampled from
+\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
+\(k = \frac{groups}{C_{\mbox{out}} * \prod_{i=0}^{2}\mbox{kernel\_size}[i]}\)</p></li>
+<li><p>bias (Tensor):   the learnable bias of the module of shape (out_channels)
+If <code>bias</code> is <code>True</code>, then the values of these weights are
+sampled from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
+\(k = \frac{groups}{C_{\mbox{out}} * \prod_{i=0}^{2}\mbox{kernel\_size}[i]}\)</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='co'># With square kernels and equal stride</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv_transpose3d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>3</span>, stride=<span class='fl'>2</span>)
+<span class='co'># non-square kernels and unequal stride and with padding</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_conv_transpose3d</span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>, <span class='fl'>2</span>), stride=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>1</span>), padding=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>4</span>, <span class='fl'>2</span>))
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>10</span>, <span class='fl'>50</span>, <span class='fl'>100</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+}
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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diff --git a/static/docs/reference/nn_cross_entropy_loss.html b/static/docs/reference/nn_cross_entropy_loss.html
new file mode 100644
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--- /dev/null
+++ b/static/docs/reference/nn_cross_entropy_loss.html
@@ -0,0 +1,310 @@
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+    <h1>CrossEntropyLoss module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-loss.R'><code>R/nn-loss.R</code></a></small>
+    <div class="hidden name"><code>nn_cross_entropy_loss.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>This criterion combines <code><a href='nn_log_softmax.html'>nn_log_softmax()</a></code> and <code>nn_nll_loss()</code> in one single class.
+It is useful when training a classification problem with <code>C</code> classes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_cross_entropy_loss</span>(weight = <span class='kw'>NULL</span>, ignore_index = <span class='op'>-</span><span class='fl'>100</span>, reduction = <span class='st'>"mean"</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>weight</th>
+      <td><p>(Tensor, optional): a manual rescaling weight given to each class.
+If given, has to be a Tensor of size <code>C</code></p></td>
+    </tr>
+    <tr>
+      <th>ignore_index</th>
+      <td><p>(int, optional): Specifies a target value that is ignored
+and does not contribute to the input gradient. When <code>size_average</code> is
+<code>TRUE</code>, the loss is averaged over non-ignored targets.</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional): Specifies the reduction to apply to the output:
+<code>'none'</code> | <code>'mean'</code> | <code>'sum'</code>. <code>'none'</code>: no reduction will be applied,
+<code>'mean'</code>: the sum of the output will be divided by the number of
+elements in the output, <code>'sum'</code>: the output will be summed. Note: <code>size_average</code>
+and <code>reduce</code> are in the process of being deprecated, and in the meantime,
+specifying either of those two args will override <code>reduction</code>. Default: <code>'mean'</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>If provided, the optional argument <code>weight</code> should be a 1D <code>Tensor</code>
+assigning weight to each of the classes.</p>
+<p>This is particularly useful when you have an unbalanced training set.
+The <code>input</code> is expected to contain raw, unnormalized scores for each class.
+<code>input</code> has to be a Tensor of size either \((minibatch, C)\) or
+\((minibatch, C, d_1, d_2, ..., d_K)\)
+with \(K \geq 1\) for the <code>K</code>-dimensional case (described later).</p>
+<p>This criterion expects a class index in the range \([0, C-1]\) as the
+<code>target</code> for each value of a 1D tensor of size <code>minibatch</code>; if <code>ignore_index</code>
+is specified, this criterion also accepts this class index (this index may not
+necessarily be in the class range).</p>
+<p>The loss can be described as:
+$$
+  \mbox{loss}(x, class) = -\log\left(\frac{\exp(x[class])}{\sum_j \exp(x[j])}\right)
+= -x[class] + \log\left(\sum_j \exp(x[j])\right)
+$$
+or in the case of the <code>weight</code> argument being specified:
+$$
+  \mbox{loss}(x, class) = weight[class] \left(-x[class] + \log\left(\sum_j \exp(x[j])\right)\right)
+$$</p>
+<p>The losses are averaged across observations for each minibatch.
+Can also be used for higher dimension inputs, such as 2D images, by providing
+an input of size \((minibatch, C, d_1, d_2, ..., d_K)\) with \(K \geq 1\),
+where \(K\) is the number of dimensions, and a target of appropriate shape
+(see below).</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C)\) where <code>C = number of classes</code>, or
+\((N, C, d_1, d_2, ..., d_K)\) with \(K \geq 1\)
+in the case of <code>K</code>-dimensional loss.</p></li>
+<li><p>Target: \((N)\) where each value is \(0 \leq \mbox{targets}[i] \leq C-1\), or
+\((N, d_1, d_2, ..., d_K)\) with \(K \geq 1\) in the case of
+K-dimensional loss.</p></li>
+<li><p>Output: scalar.
+If <code>reduction</code> is <code>'none'</code>, then the same size as the target:
+\((N)\), or
+\((N, d_1, d_2, ..., d_K)\) with \(K \geq 1\) in the case
+of K-dimensional loss.</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>loss</span> <span class='op'>&lt;-</span> <span class='fu'>nn_cross_entropy_loss</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>, requires_grad=<span class='fl'>TRUE</span>)
+<span class='kw'>target</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randint.html'>torch_randint</a></span>(low = <span class='fl'>1</span>, high = <span class='fl'>5</span>, size = <span class='fl'>3</span>, dtype = <span class='fu'><a href='torch_dtype.html'>torch_long</a></span>())
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>loss</span>(<span class='kw'>input</span>, <span class='kw'>target</span>)
+<span class='kw'>output</span><span class='op'>$</span><span class='fu'>backward</span>()
+
+}
+</div></pre>
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diff --git a/static/docs/reference/nn_dropout.html b/static/docs/reference/nn_dropout.html
new file mode 100644
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+<meta property="og:description" content="During training, randomly zeroes some of the elements of the input
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+    <h1>Dropout module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-dropout.R'><code>R/nn-dropout.R</code></a></small>
+    <div class="hidden name"><code>nn_dropout.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>During training, randomly zeroes some of the elements of the input
+tensor with probability <code>p</code> using samples from a Bernoulli
+distribution. Each channel will be zeroed out independently on every forward
+call.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_dropout</span>(p = <span class='fl'>0.5</span>, inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>p</th>
+      <td><p>probability of an element to be zeroed. Default: 0.5</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>If set to <code>TRUE</code>, will do this operation in-place. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>This has proven to be an effective technique for regularization and
+preventing the co-adaptation of neurons as described in the paper
+<a href='https://arxiv.org/abs/1207.0580'>Improving neural networks by preventing co-adaptation of feature detectors</a>.</p>
+<p>Furthermore, the outputs are scaled by a factor of :math:<code>\frac{1}{1-p}</code> during
+training. This means that during evaluation the module simply computes an
+identity function.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((*)\). Input can be of any shape</p></li>
+<li><p>Output: \((*)\). Output is of the same shape as input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_dropout</span>(p = <span class='fl'>0.2</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Dropout2D module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-dropout.R'><code>R/nn-dropout.R</code></a></small>
+    <div class="hidden name"><code>nn_dropout2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Randomly zero out entire channels (a channel is a 2D feature map,
+e.g., the \(j\)-th channel of the \(i\)-th sample in the
+batched input is a 2D tensor \(\mbox{input}[i, j]\)).</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_dropout2d</span>(p = <span class='fl'>0.5</span>, inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>p</th>
+      <td><p>(float, optional): probability of an element to be zero-ed.</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>(bool, optional): If set to <code>TRUE</code>, will do this operation
+in-place</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>Each channel will be zeroed out independently on every forward call with
+probability <code>p</code> using samples from a Bernoulli distribution.
+Usually the input comes from <a href='nn_conv2d.html'>nn_conv2d</a> modules.</p>
+<p>As described in the paper
+<a href='https://arxiv.org/abs/1411.4280'>Efficient Object Localization Using Convolutional Networks</a> ,
+if adjacent pixels within feature maps are strongly correlated
+(as is normally the case in early convolution layers) then i.i.d. dropout
+will not regularize the activations and will otherwise just result
+in an effective learning rate decrease.
+In this case, nn_dropout2d will help promote independence between
+feature maps and should be used instead.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C, H, W)\)</p></li>
+<li><p>Output: \((N, C, H, W)\) (same shape as input)</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_dropout2d</span>(p = <span class='fl'>0.2</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>32</span>, <span class='fl'>32</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+    <div class="page-header">
+    <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>(p = <span class='fl'>0.5</span>, inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>p</th>
+      <td><p>(float, optional): probability of an element to be zeroed.</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>(bool, optional): If set to <code>TRUE</code>, will do this operation
+in-place</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>Each channel will be zeroed out independently on every forward call with
+probability <code>p</code> using samples from a Bernoulli distribution.
+Usually the input comes from <a href='nn_conv2d.html'>nn_conv2d</a> modules.</p>
+<p>As described in the paper
+<a href='https://arxiv.org/abs/1411.4280'>Efficient Object Localization Using Convolutional Networks</a> ,
+if adjacent pixels within feature maps are strongly correlated
+(as is normally the case in early convolution layers) then i.i.d. dropout
+will not regularize the activations and will otherwise just result
+in an effective learning rate decrease.</p>
+<p>In this case, nn_dropout3d will help promote independence between
+feature maps and should be used instead.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C, D, H, W)\)</p></li>
+<li><p>Output: \((N, C, D, H, W)\) (same shape as input)</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_dropout3d</span>(p = <span class='fl'>0.2</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>4</span>, <span class='fl'>32</span>, <span class='fl'>32</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+<div class="row">
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+    <div class="page-header">
+    <h1>ELU module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_elu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the element-wise function:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_elu</span>(alpha = <span class='fl'>1</span>, inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>alpha</th>
+      <td><p>the \(\alpha\) value for the ELU formulation. Default: 1.0</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+  \mbox{ELU}(x) = \max(0,x) + \min(0, \alpha * (\exp(x) - 1))
+$$</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_elu</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span>  <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/nn_embedding.html b/static/docs/reference/nn_embedding.html
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+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Embedding module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-sparse.R'><code>R/nn-sparse.R</code></a></small>
+    <div class="hidden name"><code>nn_embedding.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>A simple lookup table that stores embeddings of a fixed dictionary and size.
+This module is often used to store word embeddings and retrieve them using indices.
+The input to the module is a list of indices, and the output is the corresponding
+word embeddings.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_embedding</span>(
+  <span class='kw'>num_embeddings</span>,
+  <span class='kw'>embedding_dim</span>,
+  padding_idx = <span class='kw'>NULL</span>,
+  max_norm = <span class='kw'>NULL</span>,
+  norm_type = <span class='fl'>2</span>,
+  scale_grad_by_freq = <span class='fl'>FALSE</span>,
+  sparse = <span class='fl'>FALSE</span>,
+  .weight = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>num_embeddings</th>
+      <td><p>(int): size of the dictionary of embeddings</p></td>
+    </tr>
+    <tr>
+      <th>embedding_dim</th>
+      <td><p>(int): the size of each embedding vector</p></td>
+    </tr>
+    <tr>
+      <th>padding_idx</th>
+      <td><p>(int, optional): If given, pads the output with the embedding vector at <code>padding_idx</code>
+(initialized to zeros) whenever it encounters the index.</p></td>
+    </tr>
+    <tr>
+      <th>max_norm</th>
+      <td><p>(float, optional): If given, each embedding vector with norm larger than <code>max_norm</code>
+is renormalized to have norm <code>max_norm</code>.</p></td>
+    </tr>
+    <tr>
+      <th>norm_type</th>
+      <td><p>(float, optional): The p of the p-norm to compute for the <code>max_norm</code> option. Default <code>2</code>.</p></td>
+    </tr>
+    <tr>
+      <th>scale_grad_by_freq</th>
+      <td><p>(boolean, optional): If given, this will scale gradients by the inverse of frequency of
+the words in the mini-batch. Default <code>False</code>.</p></td>
+    </tr>
+    <tr>
+      <th>sparse</th>
+      <td><p>(bool, optional): If <code>True</code>, gradient w.r.t. <code>weight</code> matrix will be a sparse tensor.</p></td>
+    </tr>
+    <tr>
+      <th>.weight</th>
+      <td><p>(Tensor) embeddings weights (in case you want to set it manually)</p>
+<p>See Notes for more details regarding sparse gradients.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>Keep in mind that only a limited number of optimizers support
+sparse gradients: currently it's <code>optim.SGD</code> (<code>CUDA</code> and <code>CPU</code>),
+<code>optim.SparseAdam</code> (<code>CUDA</code> and <code>CPU</code>) and <code>optim.Adagrad</code> (<code>CPU</code>)</p>
+<p>With <code>padding_idx</code> set, the embedding vector at
+<code>padding_idx</code> is initialized to all zeros. However, note that this
+vector can be modified afterwards, e.g., using a customized
+initialization method, and thus changing the vector used to pad the
+output. The gradient for this vector from nn_embedding
+is always zero.</p>
+    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
+
+    
+
+<ul>
+<li><p>weight (Tensor): the learnable weights of the module of shape (num_embeddings, embedding_dim)
+initialized from \(\mathcal{N}(0, 1)\)</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((*)\), LongTensor of arbitrary shape containing the indices to extract</p></li>
+<li><p>Output: \((*, H)\), where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is the input shape and \(H=\mbox{embedding\_dim}\)</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'># an Embedding module containing 10 tensors of size 3</span>
+<span class='kw'>embedding</span> <span class='op'>&lt;-</span> <span class='fu'>nn_embedding</span>(<span class='fl'>10</span>, <span class='fl'>3</span>)
+<span class='co'># a batch of 2 samples of 4 indices each</span>
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>4</span>,<span class='fl'>5</span>),<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>,<span class='fl'>3</span>,<span class='fl'>2</span>,<span class='fl'>9</span>)), dtype = <span class='fu'><a href='torch_dtype.html'>torch_long</a></span>())
+<span class='fu'>embedding</span>(<span class='kw'>input</span>)
+<span class='co'># example with padding_idx</span>
+<span class='kw'>embedding</span> <span class='op'>&lt;-</span> <span class='fu'>nn_embedding</span>(<span class='fl'>10</span>, <span class='fl'>3</span>, padding_idx=<span class='fl'>1</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>3</span>,<span class='fl'>1</span>,<span class='fl'>6</span>), nrow = <span class='fl'>1</span>), dtype = <span class='fu'><a href='torch_dtype.html'>torch_long</a></span>())
+<span class='fu'>embedding</span>(<span class='kw'>input</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;   0.0000  0.0000  0.0000
+#&gt;  -1.2943 -1.0279  0.6483
+#&gt;   0.0000  0.0000  0.0000
+#&gt;   0.4053  0.7866 -0.3922
+#&gt; [ CPUFloatType{1,4,3} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/nn_fractional_max_pool2d.html b/static/docs/reference/nn_fractional_max_pool2d.html
new file mode 100644
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+    <div class="page-header">
+    <h1>Applies a 2D fractional max pooling over an input signal composed of several input planes.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_fractional_max_pool2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Fractional MaxPooling is described in detail in the paper
+<a href='https://arxiv.org/abs/1412.6071'>Fractional MaxPooling</a> by Ben Graham</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_fractional_max_pool2d</span>(
+  <span class='kw'>kernel_size</span>,
+  output_size = <span class='kw'>NULL</span>,
+  output_ratio = <span class='kw'>NULL</span>,
+  return_indices = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>the size of the window to take a max over.
+Can be a single number k (for a square kernel of k x k) or a tuple <code>(kh, kw)</code></p></td>
+    </tr>
+    <tr>
+      <th>output_size</th>
+      <td><p>the target output size of the image of the form <code>oH x oW</code>.
+Can be a tuple <code>(oH, oW)</code> or a single number oH for a square image <code>oH x oH</code></p></td>
+    </tr>
+    <tr>
+      <th>output_ratio</th>
+      <td><p>If one wants to have an output size as a ratio of the input size, this option can be given.
+This has to be a number or tuple in the range (0, 1)</p></td>
+    </tr>
+    <tr>
+      <th>return_indices</th>
+      <td><p>if <code>TRUE</code>, will return the indices along with the outputs.
+Useful to pass to <code><a href='nn_max_unpool2d.html'>nn_max_unpool2d()</a></code>. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>The max-pooling operation is applied in \(kH \times kW\) regions by a stochastic
+step size determined by the target output size.
+The number of output features is equal to the number of input planes.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'># pool of square window of size=3, and target output size 13x12</span>
+<span class='kw'>m</span> <span class='op'>=</span> <span class='fu'>nn_fractional_max_pool2d</span>(<span class='fl'>3</span>, output_size=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>13</span>, <span class='fl'>12</span>))
+<span class='co'># pool of square window and target output size being half of input image size</span>
+<span class='kw'>m</span> <span class='op'>=</span> <span class='fu'>nn_fractional_max_pool2d</span>(<span class='fl'>3</span>, output_ratio=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0.5</span>, <span class='fl'>0.5</span>))
+<span class='kw'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>32</span>)
+<span class='kw'>output</span> <span class='op'>=</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Applies a 3D fractional max pooling over an input signal composed of several input planes.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_fractional_max_pool3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Fractional MaxPooling is described in detail in the paper
+<a href='https://arxiv.org/abs/1412.6071'>Fractional MaxPooling</a> by Ben Graham</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_fractional_max_pool3d</span>(
+  <span class='kw'>kernel_size</span>,
+  output_size = <span class='kw'>NULL</span>,
+  output_ratio = <span class='kw'>NULL</span>,
+  return_indices = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>the size of the window to take a max over.
+Can be a single number k (for a square kernel of k x k x k) or a tuple <code>(kt x kh x kw)</code></p></td>
+    </tr>
+    <tr>
+      <th>output_size</th>
+      <td><p>the target output size of the image of the form <code>oT x oH x oW</code>.
+Can be a tuple <code>(oT, oH, oW)</code> or a single number oH for a square image <code>oH x oH x oH</code></p></td>
+    </tr>
+    <tr>
+      <th>output_ratio</th>
+      <td><p>If one wants to have an output size as a ratio of the input size, this option can be given.
+This has to be a number or tuple in the range (0, 1)</p></td>
+    </tr>
+    <tr>
+      <th>return_indices</th>
+      <td><p>if <code>TRUE</code>, will return the indices along with the outputs.
+Useful to pass to <code><a href='nn_max_unpool3d.html'>nn_max_unpool3d()</a></code>. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>The max-pooling operation is applied in \(kTxkHxkW\) regions by a stochastic
+step size determined by the target output size.
+The number of output features is equal to the number of input planes.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'># pool of cubic window of size=3, and target output size 13x12x11</span>
+<span class='kw'>m</span> <span class='op'>=</span> <span class='fu'>nn_fractional_max_pool3d</span>(<span class='fl'>3</span>, output_size=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>13</span>, <span class='fl'>12</span>, <span class='fl'>11</span>))
+<span class='co'># pool of cubic window and target output size being half of input size</span>
+<span class='kw'>m</span> <span class='op'>=</span> <span class='fu'>nn_fractional_max_pool3d</span>(<span class='fl'>3</span>, output_ratio=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0.5</span>, <span class='fl'>0.5</span>, <span class='fl'>0.5</span>))
+<span class='kw'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>32</span>, <span class='fl'>16</span>)
+<span class='kw'>output</span> <span class='op'>=</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
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+<meta property="og:title" content="GELU module — nn_gelu" />
+<meta property="og:description" content="Applies the Gaussian Error Linear Units function:
+$$\mbox{GELU}(x) = x * \Phi(x)$$" />
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+    <div class="page-header">
+    <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><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>=</span> <span class='fu'>nn_gelu</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+of the input matrices and \(b\) is the second half." />
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+    <div class="page-header">
+    <h1>GLU module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_glu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the gated linear unit function
+\({GLU}(a, b)= a \otimes \sigma(b)\) where \(a\) is the first half
+of the input matrices and \(b\) is the second half.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_glu</span>(dim = <span class='op'>-</span><span class='fl'>1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>dim</th>
+      <td><p>(int): the dimension on which to split the input. Default: -1</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((\ast_1, N, \ast_2)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((\ast_1, M, \ast_2)\) where \(M=N/2\)</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_glu</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>4</span>, <span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nn_hardshrink.html b/static/docs/reference/nn_hardshrink.html
new file mode 100644
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--- /dev/null
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+<meta property="og:description" content="Applies the hard shrinkage function element-wise:" />
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+<div class="row">
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+    <div class="page-header">
+    <h1>Hardshwink module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_hardshrink.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the hard shrinkage function element-wise:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_hardshrink</span>(lambd = <span class='fl'>0.5</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>lambd</th>
+      <td><p>the \(\lambda\) value for the Hardshrink formulation. Default: 0.5</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+  \mbox{HardShrink}(x) =
+  \left\{ \begin{array}{ll}
+x, &amp; \mbox{ if } x &gt; \lambda \\
+x, &amp; \mbox{ if } x &lt; -\lambda \\
+0, &amp; \mbox{ otherwise }
+\end{array}
+\right.
+$$</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_hardshrink</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
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+    <div class="page-header">
+    <h1>Hardsigmoid module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_hardsigmoid.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the element-wise function:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_hardsigmoid</span>()</pre>
+
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+\mbox{Hardsigmoid}(x) = \left\{ \begin{array}{ll}
+  0 &amp; \mbox{if~} x \le -3, \\
+  1 &amp; \mbox{if~} x \ge +3, \\
+  x / 6 + 1 / 2 &amp; \mbox{otherwise}
+\end{array}
+\right.
+$$</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_hardsigmoid</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
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diff --git a/static/docs/reference/nn_hardswish.html b/static/docs/reference/nn_hardswish.html
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+<div class="row">
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+    <div class="page-header">
+    <h1>Hardswish module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_hardswish.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the hardswish function, element-wise, as described in the paper:
+<a href='https://arxiv.org/abs/1905.02244'>Searching for MobileNetV3</a></p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_hardswish</span>()</pre>
+
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$ \mbox{Hardswish}(x) = \left\{
+  \begin{array}{ll}
+  0 &amp; \mbox{if } x \le -3, \\
+  x &amp; \mbox{if } x \ge +3, \\
+  x \cdot (x + 3)/6 &amp; \mbox{otherwise}
+  \end{array}
+  \right. $$</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_hardswish</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+}
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+HardTanh is defined as:" />
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+
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+    <div class="page-header">
+    <h1>Hardtanh module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_hardtanh.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the HardTanh function element-wise
+HardTanh is defined as:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_hardtanh</span>(min_val = <span class='op'>-</span><span class='fl'>1</span>, max_val = <span class='fl'>1</span>, inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>min_val</th>
+      <td><p>minimum value of the linear region range. Default: -1</p></td>
+    </tr>
+    <tr>
+      <th>max_val</th>
+      <td><p>maximum value of the linear region range. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+\mbox{HardTanh}(x) = \left\{ \begin{array}{ll}
+  1 &amp; \mbox{ if } x &gt; 1 \\
+  -1 &amp; \mbox{ if } x &lt; -1 \\
+  x &amp; \mbox{ otherwise } \\
+\end{array}
+\right.
+$$</p>
+<p>The range of the linear region :math:<code>[-1, 1]</code> can be adjusted using
+<code>min_val</code> and <code>max_val</code>.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_hardtanh</span>(<span class='op'>-</span><span class='fl'>2</span>, <span class='fl'>2</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nn_identity.html b/static/docs/reference/nn_identity.html
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--- /dev/null
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@@ -0,0 +1,246 @@
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+<div class="row">
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+    <div class="page-header">
+    <h1>Identity module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-linear.R'><code>R/nn-linear.R</code></a></small>
+    <div class="hidden name"><code>nn_identity.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>A placeholder identity operator that is argument-insensitive.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_identity</span>(<span class='kw'>...</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>...</th>
+      <td><p>any arguments (unused)</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_identity</span>(<span class='fl'>54</span>, unused_argument1 = <span class='fl'>0.1</span>, unused_argument2 = <span class='fl'>FALSE</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>128</span>, <span class='fl'>20</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='kw'>output</span><span class='op'>$</span><span class='fu'>size</span>())
+
+}
+</div><div class='output co'>#&gt; [1] 128  20</div></pre>
+  </div>
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diff --git a/static/docs/reference/nn_init_calculate_gain.html b/static/docs/reference/nn_init_calculate_gain.html
new file mode 100644
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--- /dev/null
+++ b/static/docs/reference/nn_init_calculate_gain.html
@@ -0,0 +1,241 @@
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+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Calculate gain</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_calculate_gain.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Return the recommended gain value for the given nonlinearity function.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_init_calculate_gain</span>(<span class='kw'>nonlinearity</span>, param = <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>nonlinearity</th>
+      <td><p>the non-linear function</p></td>
+    </tr>
+    <tr>
+      <th>param</th>
+      <td><p>optional parameter for the non-linear function</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nn_init_constant_.html b/static/docs/reference/nn_init_constant_.html
new file mode 100644
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Constant 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_constant_.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Fills the input Tensor with the value <code>val</code>.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_init_constant_</span>(<span class='kw'>tensor</span>, <span class='kw'>val</span>)</pre>
+
+    <h2 class="hasAnchor" 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>val</th>
+      <td><p>the value to fill the tensor with</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
+<span class='fu'>nn_init_constant_</span>(<span class='kw'>w</span>, <span class='fl'>0.3</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.3000  0.3000  0.3000  0.3000  0.3000
+#&gt;  0.3000  0.3000  0.3000  0.3000  0.3000
+#&gt;  0.3000  0.3000  0.3000  0.3000  0.3000
+#&gt; [ CPUFloatType{3,5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
+  </div>
+</div>
+
+
+      <footer>
+      <div class="copyright">
+  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+</div>
+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Dirac initialization</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
+    <div class="hidden name"><code>nn_init_dirac_.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Fills the 3, 4, 5-dimensional input <code>Tensor</code> with the Dirac
+delta function. Preserves the identity of the inputs in <code>Convolutional</code>
+layers, where as many input channels are preserved as possible. In case
+of groups&gt;1, each group of channels preserves identity.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_init_dirac_</span>(<span class='kw'>tensor</span>, groups = <span class='fl'>1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensor</th>
+      <td><p>a 3, 4, 5-dimensional <code>torch.Tensor</code></p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>(optional) number of groups in the conv layer (default: 1)</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>16</span>, <span class='fl'>5</span>, <span class='fl'>5</span>)
+<span class='fu'>nn_init_dirac_</span>(<span class='kw'>w</span>)
+}
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nn_init_eye_.html b/static/docs/reference/nn_init_eye_.html
new file mode 100644
index 000000000..2491551f6
--- /dev/null
+++ b/static/docs/reference/nn_init_eye_.html
@@ -0,0 +1,252 @@
+<!-- Generated by pkgdown: do not edit by hand -->
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+<meta property="og:description" content="Fills the 2-dimensional input Tensor with the identity matrix.
+Preserves the identity of the inputs in Linear layers, where as
+many inputs are preserved as possible." />
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+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Eye 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_eye_.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Fills the 2-dimensional input <code>Tensor</code> with the identity matrix.
+Preserves the identity of the inputs in <code>Linear</code> layers, where as
+many inputs are preserved as possible.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_init_eye_</span>(<span class='kw'>tensor</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensor</th>
+      <td><p>a 2-dimensional torch tensor.</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
+<span class='fu'>nn_init_eye_</span>(<span class='kw'>w</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1  0  0  0  0
+#&gt;  0  1  0  0  0
+#&gt;  0  0  1  0  0
+#&gt; [ CPUFloatType{3,5} ]</div></pre>
+  </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/static/docs/reference/nn_init_kaiming_normal_.html b/static/docs/reference/nn_init_kaiming_normal_.html
new file mode 100644
index 000000000..89b43854d
--- /dev/null
+++ b/static/docs/reference/nn_init_kaiming_normal_.html
@@ -0,0 +1,273 @@
+<!-- Generated by pkgdown: do not edit by hand -->
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+<html lang="en">
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Kaiming normal initialization</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
+    <div class="hidden name"><code>nn_init_kaiming_normal_.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Fills the input <code>Tensor</code> with values according to the method
+described in <code>Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification</code> - He, K. et al. (2015), using a
+normal distribution.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_init_kaiming_normal_</span>(
+  <span class='kw'>tensor</span>,
+  a = <span class='fl'>0</span>,
+  mode = <span class='st'>"fan_in"</span>,
+  nonlinearity = <span class='st'>"leaky_relu"</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensor</th>
+      <td><p>an n-dimensional <code>torch.Tensor</code></p></td>
+    </tr>
+    <tr>
+      <th>a</th>
+      <td><p>the negative slope of the rectifier used after this layer (only used
+with <code>'leaky_relu'</code>)</p></td>
+    </tr>
+    <tr>
+      <th>mode</th>
+      <td><p>either 'fan_in' (default) or 'fan_out'. Choosing 'fan_in' preserves
+the magnitude of the variance of the weights in the forward pass. Choosing
+'fan_out' preserves the magnitudes in the backwards pass.</p></td>
+    </tr>
+    <tr>
+      <th>nonlinearity</th>
+      <td><p>the non-linear function. recommended to use only with 'relu'
+or 'leaky_relu' (default).</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
+<span class='fu'>nn_init_kaiming_normal_</span>(<span class='kw'>w</span>, mode = <span class='st'>"fan_in"</span>, nonlinearity = <span class='st'>"leaky_relu"</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0.5594  0.2408  0.3946  0.5860 -0.4834
+#&gt; -0.0442  0.7170 -0.3028  0.4015 -0.8906
+#&gt; -0.5157 -0.1763  0.9366  0.4640 -0.5356
+#&gt; [ CPUFloatType{3,5} ]</div></pre>
+  </div>
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diff --git a/static/docs/reference/nn_init_kaiming_uniform_.html b/static/docs/reference/nn_init_kaiming_uniform_.html
new file mode 100644
index 000000000..6c7275339
--- /dev/null
+++ b/static/docs/reference/nn_init_kaiming_uniform_.html
@@ -0,0 +1,273 @@
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+<meta property="og:description" content="Fills the input Tensor with values according to the method
+described in Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification - He, K. et al. (2015), using a
+uniform distribution." />
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+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Kaiming uniform initialization</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
+    <div class="hidden name"><code>nn_init_kaiming_uniform_.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Fills the input <code>Tensor</code> with values according to the method
+described in <code>Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification</code> - He, K. et al. (2015), using a
+uniform distribution.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_init_kaiming_uniform_</span>(
+  <span class='kw'>tensor</span>,
+  a = <span class='fl'>0</span>,
+  mode = <span class='st'>"fan_in"</span>,
+  nonlinearity = <span class='st'>"leaky_relu"</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensor</th>
+      <td><p>an n-dimensional <code>torch.Tensor</code></p></td>
+    </tr>
+    <tr>
+      <th>a</th>
+      <td><p>the negative slope of the rectifier used after this layer (only used
+with <code>'leaky_relu'</code>)</p></td>
+    </tr>
+    <tr>
+      <th>mode</th>
+      <td><p>either 'fan_in' (default) or 'fan_out'. Choosing 'fan_in' preserves
+the magnitude of the variance of the weights in the forward pass. Choosing
+'fan_out' preserves the magnitudes in the backwards pass.</p></td>
+    </tr>
+    <tr>
+      <th>nonlinearity</th>
+      <td><p>the non-linear function. recommended to use only with 'relu'
+or 'leaky_relu' (default).</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
+<span class='fu'>nn_init_kaiming_uniform_</span>(<span class='kw'>w</span>, mode = <span class='st'>"fan_in"</span>, nonlinearity = <span class='st'>"leaky_relu"</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0.7460  0.2070 -0.1066 -0.4344 -0.4666
+#&gt; -0.5351 -0.4524  0.0950 -1.0077 -0.2169
+#&gt; -0.9525  0.8753  0.0070 -0.4553 -0.3445
+#&gt; [ CPUFloatType{3,5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nn_init_normal_.html b/static/docs/reference/nn_init_normal_.html
new file mode 100644
index 000000000..1611f7674
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+++ b/static/docs/reference/nn_init_normal_.html
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+<div class="row">
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+    <div class="page-header">
+    <h1>Normal initialization</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
+    <div class="hidden name"><code>nn_init_normal_.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Fills the input Tensor with values drawn from the normal distribution</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_init_normal_</span>(<span class='kw'>tensor</span>, mean = <span class='fl'>0</span>, std = <span class='fl'>1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensor</th>
+      <td><p>an n-dimensional Tensor</p></td>
+    </tr>
+    <tr>
+      <th>mean</th>
+      <td><p>the mean of the normal distribution</p></td>
+    </tr>
+    <tr>
+      <th>std</th>
+      <td><p>the standard deviation of the normal distribution</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
+<span class='fu'>nn_init_normal_</span>(<span class='kw'>w</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -1.0569 -1.0900  1.2740 -1.7728  0.0593
+#&gt; -1.7131 -0.1353  0.8191  0.1481 -0.9940
+#&gt; -0.7544 -1.0298  0.4237  1.4650  0.0575
+#&gt; [ CPUFloatType{3,5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nn_init_ones_.html b/static/docs/reference/nn_init_ones_.html
new file mode 100644
index 000000000..ffc2743e5
--- /dev/null
+++ b/static/docs/reference/nn_init_ones_.html
@@ -0,0 +1,248 @@
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Ones 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_ones_.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Fills the input Tensor with the scalar value <code>1</code></p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_init_ones_</span>(<span class='kw'>tensor</span>)</pre>
+
+    <h2 class="hasAnchor" 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>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
+<span class='fu'>nn_init_ones_</span>(<span class='kw'>w</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1  1  1  1  1
+#&gt;  1  1  1  1  1
+#&gt;  1  1  1  1  1
+#&gt; [ CPUFloatType{3,5} ]</div></pre>
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diff --git a/static/docs/reference/nn_init_orthogonal_.html b/static/docs/reference/nn_init_orthogonal_.html
new file mode 100644
index 000000000..8c116004b
--- /dev/null
+++ b/static/docs/reference/nn_init_orthogonal_.html
@@ -0,0 +1,258 @@
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+<meta property="og:description" content="Fills the input Tensor with a (semi) orthogonal matrix, as
+described in Exact solutions to the nonlinear dynamics of learning in deep linear neural networks - Saxe, A. et al. (2013). The input tensor must have
+at least 2 dimensions, and for tensors with more than 2 dimensions the
+trailing dimensions are flattened." />
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+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Orthogonal initialization</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
+    <div class="hidden name"><code>nn_init_orthogonal_.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Fills the input <code>Tensor</code> with a (semi) orthogonal matrix, as
+described in <code>Exact solutions to the nonlinear dynamics of learning in deep linear neural networks</code> - Saxe, A. et al. (2013). The input tensor must have
+at least 2 dimensions, and for tensors with more than 2 dimensions the
+trailing dimensions are flattened.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_init_orthogonal_</span>(<span class='kw'>tensor</span>, gain = <span class='fl'>1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensor</th>
+      <td><p>an n-dimensional <code>Tensor</code></p></td>
+    </tr>
+    <tr>
+      <th>gain</th>
+      <td><p>optional scaling factor</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>,<span class='fl'>5</span>)
+<span class='fu'>nn_init_orthogonal_</span>(<span class='kw'>w</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0.0261 -0.1169  0.8583  0.1248 -0.4831
+#&gt; -0.1584 -0.1637  0.4579 -0.4078  0.7564
+#&gt;  0.4959 -0.5308  0.0252  0.6151  0.3053
+#&gt; [ CPUFloatType{3,5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nn_init_sparse_.html b/static/docs/reference/nn_init_sparse_.html
new file mode 100644
index 000000000..9a302fcc3
--- /dev/null
+++ b/static/docs/reference/nn_init_sparse_.html
@@ -0,0 +1,258 @@
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+<meta property="og:title" content="Sparse initialization — nn_init_sparse_" />
+<meta property="og:description" content="Fills the 2D input Tensor as a sparse matrix, where the
+non-zero elements will be drawn from the normal distribution
+as described in Deep learning via Hessian-free optimization - Martens, J. (2010)." />
+
+
+
+
+<!-- mathjax -->
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+      
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+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Sparse initialization</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
+    <div class="hidden name"><code>nn_init_sparse_.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Fills the 2D input <code>Tensor</code> as a sparse matrix, where the
+non-zero elements will be drawn from the normal distribution
+as described in <code>Deep learning via Hessian-free optimization</code> - Martens, J. (2010).</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_init_sparse_</span>(<span class='kw'>tensor</span>, <span class='kw'>sparsity</span>, std = <span class='fl'>0.01</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensor</th>
+      <td><p>an n-dimensional <code>Tensor</code></p></td>
+    </tr>
+    <tr>
+      <th>sparsity</th>
+      <td><p>The fraction of elements in each column to be set to zero</p></td>
+    </tr>
+    <tr>
+      <th>std</th>
+      <td><p>the standard deviation of the normal distribution used to generate
+the non-zero values</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
+<span class='fu'>nn_init_sparse_</span>(<span class='kw'>w</span>, sparsity = <span class='fl'>0.1</span>)
+}
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nn_init_trunc_normal_.html b/static/docs/reference/nn_init_trunc_normal_.html
new file mode 100644
index 000000000..87dbff64d
--- /dev/null
+++ b/static/docs/reference/nn_init_trunc_normal_.html
@@ -0,0 +1,266 @@
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+<meta property="og:description" content="Fills the input Tensor with values drawn from a truncated
+normal distribution." />
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Truncated normal initialization</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
+    <div class="hidden name"><code>nn_init_trunc_normal_.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Fills the input Tensor with values drawn from a truncated
+normal distribution.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_init_trunc_normal_</span>(<span class='kw'>tensor</span>, mean = <span class='fl'>0</span>, std = <span class='fl'>1</span>, a = <span class='op'>-</span><span class='fl'>2</span>, b = <span class='op'>-</span><span class='fl'>2</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensor</th>
+      <td><p>an n-dimensional Tensor</p></td>
+    </tr>
+    <tr>
+      <th>mean</th>
+      <td><p>the mean of the normal distribution</p></td>
+    </tr>
+    <tr>
+      <th>std</th>
+      <td><p>the standard deviation of the normal distribution</p></td>
+    </tr>
+    <tr>
+      <th>a</th>
+      <td><p>the minimum cutoff value</p></td>
+    </tr>
+    <tr>
+      <th>b</th>
+      <td><p>the maximum cutoff value</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
+<span class='fu'>nn_init_trunc_normal_</span>(<span class='kw'>w</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -2 -2 -2 -2 -2
+#&gt; -2 -2 -2 -2 -2
+#&gt; -2 -2 -2 -2 -2
+#&gt; [ CPUFloatType{3,5} ]</div></pre>
+  </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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+
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+</div>
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+
diff --git a/static/docs/reference/nn_init_uniform_.html b/static/docs/reference/nn_init_uniform_.html
new file mode 100644
index 000000000..98b02d9aa
--- /dev/null
+++ b/static/docs/reference/nn_init_uniform_.html
@@ -0,0 +1,256 @@
+<!-- Generated by pkgdown: do not edit by hand -->
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+<meta property="og:description" content="Fills the input Tensor with values drawn from the uniform distribution" />
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+    <li class="dropdown-header">Beginers guide</li>
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+    </li>
+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
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+     
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+      
+    </div><!--/.nav-collapse -->
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+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>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_uniform_.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Fills the input Tensor with values drawn from the uniform distribution</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_init_uniform_</span>(<span class='kw'>tensor</span>, a = <span class='fl'>0</span>, b = <span class='fl'>1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensor</th>
+      <td><p>an n-dimensional Tensor</p></td>
+    </tr>
+    <tr>
+      <th>a</th>
+      <td><p>the lower bound of the uniform distribution</p></td>
+    </tr>
+    <tr>
+      <th>b</th>
+      <td><p>the upper bound of the uniform distribution</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
+<span class='fu'>nn_init_uniform_</span>(<span class='kw'>w</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.8556  0.9331  0.3515  0.8071  0.4948
+#&gt;  0.6075  0.9042  0.7181  0.7329  0.7563
+#&gt;  0.2584  0.5293  0.9757  0.3030  0.3341
+#&gt; [ CPUFloatType{3,5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nn_init_xavier_normal_.html b/static/docs/reference/nn_init_xavier_normal_.html
new file mode 100644
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--- /dev/null
+++ b/static/docs/reference/nn_init_xavier_normal_.html
@@ -0,0 +1,256 @@
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+<meta property="og:description" content="Fills the input Tensor with values according to the method
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Xavier normal initialization</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
+    <div class="hidden name"><code>nn_init_xavier_normal_.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Fills the input <code>Tensor</code> with values according to the method
+described in <code>Understanding the difficulty of training deep feedforward neural networks</code> - Glorot, X. &amp; Bengio, Y. (2010), using a normal
+distribution.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_init_xavier_normal_</span>(<span class='kw'>tensor</span>, gain = <span class='fl'>1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensor</th>
+      <td><p>an n-dimensional <code>Tensor</code></p></td>
+    </tr>
+    <tr>
+      <th>gain</th>
+      <td><p>an optional scaling factor</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
+<span class='fu'>nn_init_xavier_normal_</span>(<span class='kw'>w</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.2535 -0.2197  0.5425 -3.0052 -4.2446
+#&gt; -0.3570 -1.6970 -2.0154 -0.5348  2.7582
+#&gt;  0.8714 -0.8924  0.7675  3.2553 -1.4333
+#&gt; [ CPUFloatType{3,5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nn_init_xavier_uniform_.html b/static/docs/reference/nn_init_xavier_uniform_.html
new file mode 100644
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--- /dev/null
+++ b/static/docs/reference/nn_init_xavier_uniform_.html
@@ -0,0 +1,256 @@
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+<meta property="og:description" content="Fills the input Tensor with values according to the method
+described in Understanding the difficulty of training deep feedforward neural networks - Glorot, X. &amp;amp; Bengio, Y. (2010), using a uniform
+distribution." />
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+
+
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+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Xavier 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_xavier_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>Understanding the difficulty of training deep feedforward neural networks</code> - Glorot, X. &amp; Bengio, Y. (2010), using a uniform
+distribution.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_init_xavier_uniform_</span>(<span class='kw'>tensor</span>, gain = <span class='fl'>1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensor</th>
+      <td><p>an n-dimensional <code>Tensor</code></p></td>
+    </tr>
+    <tr>
+      <th>gain</th>
+      <td><p>an optional scaling factor</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
+<span class='fu'>nn_init_xavier_uniform_</span>(<span class='kw'>w</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.3397  1.1040 -3.0453 -1.7935  0.9545
+#&gt; -0.0194 -2.4483  2.9345  2.2750 -2.4048
+#&gt; -0.4406 -2.2409  0.4155 -0.1573  1.9776
+#&gt; [ CPUFloatType{3,5} ]</div></pre>
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+    <div class="page-header">
+    <h1>Zeros initialization</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-init.R'><code>R/nn-init.R</code></a></small>
+    <div class="hidden name"><code>nn_init_zeros_.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Fills the input Tensor with the scalar value <code>0</code></p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_init_zeros_</span>(<span class='kw'>tensor</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensor</th>
+      <td><p>an n-dimensional tensor</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>w</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>)
+<span class='fu'>nn_init_zeros_</span>(<span class='kw'>w</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0  0  0  0  0
+#&gt;  0  0  0  0  0
+#&gt;  0  0  0  0  0
+#&gt; [ CPUFloatType{3,5} ]</div></pre>
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+    <div class="page-header">
+    <h1>LeakyReLU 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_leaky_relu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the element-wise function:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_leaky_relu</span>(negative_slope = <span class='fl'>0.01</span>, inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>negative_slope</th>
+      <td><p>Controls the angle of the negative slope. Default: 1e-2</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+  \mbox{LeakyReLU}(x) = \max(0, x) + \mbox{negative\_slope} * \min(0, x)
+$$
+or</p>
+<p>$$
+  \mbox{LeakyRELU}(x) =
+  \left\{ \begin{array}{ll}
+x, &amp; \mbox{ if } x \geq 0 \\
+\mbox{negative\_slope} \times x, &amp; \mbox{ otherwise }
+\end{array}
+\right.
+$$</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_leaky_relu</span>(<span class='fl'>0.1</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <h1>Linear module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-linear.R'><code>R/nn-linear.R</code></a></small>
+    <div class="hidden name"><code>nn_linear.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a linear transformation to the incoming data: <code>y = xA^T + b</code></p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_linear</span>(<span class='kw'>in_features</span>, <span class='kw'>out_features</span>, bias = <span class='fl'>TRUE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>in_features</th>
+      <td><p>size of each input sample</p></td>
+    </tr>
+    <tr>
+      <th>out_features</th>
+      <td><p>size of each output sample</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>If set to <code>FALSE</code>, the layer will not learn an additive bias.
+Default: <code>TRUE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: <code>(N, *, H_in)</code> where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means any number of
+additional dimensions and <code>H_in = in_features</code>.</p></li>
+<li><p>Output: <code>(N, *, H_out)</code> where all but the last dimension
+are the same shape as the input and :math:<code>H_out = out_features</code>.</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
+
+    
+
+<ul>
+<li><p>weight: the learnable weights of the module of shape
+<code>(out_features, in_features)</code>. The values are
+initialized from \(U(-\sqrt{k}, \sqrt{k})\)s, where
+\(k = \frac{1}{\mbox{in\_features}}\)</p></li>
+<li><p>bias: the learnable bias of the module of shape \((\mbox{out\_features})\).
+If <code>bias</code> is <code>TRUE</code>, the values are initialized from
+\(\mathcal{U}(-\sqrt{k}, \sqrt{k})\) where
+\(k = \frac{1}{\mbox{in\_features}}\)</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_linear</span>(<span class='fl'>20</span>, <span class='fl'>30</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>128</span>, <span class='fl'>20</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='kw'>output</span><span class='op'>$</span><span class='fu'>size</span>())
+
+}
+</div><div class='output co'>#&gt; [1] 128  30</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+<meta property="og:title" content="LogSigmoid module — nn_log_sigmoid" />
+<meta property="og:description" content="Applies the element-wise function:
+$$
+  \mbox{LogSigmoid}(x) = \log\left(\frac{ 1 }{ 1 + \exp(-x)}\right)
+ $$" />
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+
+
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+</div><!--/.navbar -->
+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>LogSigmoid module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_log_sigmoid.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the element-wise function:
+$$
+  \mbox{LogSigmoid}(x) = \log\left(\frac{ 1 }{ 1 + \exp(-x)}\right)
+ $$</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_log_sigmoid</span>()</pre>
+
+
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_log_sigmoid</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+<meta property="og:description" content="Applies the \(\log(\mbox{Softmax}(x))\) function to an n-dimensional
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>LogSoftmax module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_log_softmax.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the \(\log(\mbox{Softmax}(x))\) function to an n-dimensional
+input Tensor. The LogSoftmax formulation can be simplified as:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_log_softmax</span>(<span class='kw'>dim</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>dim</th>
+      <td><p>(int): A dimension along which LogSoftmax will be computed.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+
+    <p>a Tensor of the same dimension and shape as the input with
+values in the range [-inf, 0)</p>
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+  \mbox{LogSoftmax}(x_{i}) = \log\left(\frac{\exp(x_i) }{ \sum_j \exp(x_j)} \right)
+$$</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((*)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((*)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_log_softmax</span>(<span class='fl'>1</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/nn_lp_pool1d.html b/static/docs/reference/nn_lp_pool1d.html
new file mode 100644
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--- /dev/null
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+    <div class="page-header">
+    <h1>Applies a 1D power-average pooling over an input signal composed of several input
+planes.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_lp_pool1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>On each window, the function computed is:</p>
+<p>$$
+  f(X) = \sqrt[p]{\sum_{x \in X} x^{p}}
+$$</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_lp_pool1d</span>(<span class='kw'>norm_type</span>, <span class='kw'>kernel_size</span>, stride = <span class='kw'>NULL</span>, ceil_mode = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>norm_type</th>
+      <td><p>if inf than one gets max pooling if 0 you get sum pooling (
+proportional to the avg pooling)</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>a single int, the size of the window</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>a single int, the stride of the window. Default value is <code>kernel_size</code></p></td>
+    </tr>
+    <tr>
+      <th>ceil_mode</th>
+      <td><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    
+<ul>
+<li><p>At p = \(\infty\), one gets Max Pooling</p></li>
+<li><p>At p = 1, one gets Sum Pooling (which is proportional to Average Pooling)</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>If the sum to the power of <code>p</code> is zero, the gradient of this function is
+not defined. This implementation will set the gradient to zero in this case.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C, L_{in})\)</p></li>
+<li><p>Output: \((N, C, L_{out})\), where</p></li>
+</ul>
+
+<p>$$
+  L_{out} = \left\lfloor\frac{L_{in} - \mbox{kernel\_size}}{\mbox{stride}} + 1\right\rfloor
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'># power-2 pool of window of length 3, with stride 2.</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_lp_pool1d</span>(<span class='fl'>2</span>, <span class='fl'>3</span>, stride=<span class='fl'>2</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Applies a 2D power-average pooling over an input signal composed of several input
+planes.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_lp_pool2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>On each window, the function computed is:</p>
+<p>$$
+  f(X) = \sqrt[p]{\sum_{x \in X} x^{p}}
+$$</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_lp_pool2d</span>(<span class='kw'>norm_type</span>, <span class='kw'>kernel_size</span>, stride = <span class='kw'>NULL</span>, ceil_mode = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>norm_type</th>
+      <td><p>if inf than one gets max pooling if 0 you get sum pooling (
+proportional to the avg pooling)</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>the size of the window</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
+    </tr>
+    <tr>
+      <th>ceil_mode</th>
+      <td><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    
+<ul>
+<li><p>At p = \(\infty\), one gets Max Pooling</p></li>
+<li><p>At p = 1, one gets Sum Pooling (which is proportional to average pooling)</p></li>
+</ul>
+
+<p>The parameters <code>kernel_size</code>, <code>stride</code> can either be:</p><ul>
+<li><p>a single <code>int</code> -- in which case the same value is used for the height and width dimension</p></li>
+<li><p>a <code>tuple</code> of two ints -- in which case, the first <code>int</code> is used for the height dimension,
+and the second <code>int</code> for the width dimension</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>If the sum to the power of <code>p</code> is zero, the gradient of this function is
+not defined. This implementation will set the gradient to zero in this case.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C, H_{in}, W_{in})\)</p></li>
+<li><p>Output: \((N, C, H_{out}, W_{out})\), where</p></li>
+</ul>
+
+<p>$$
+  H_{out} = \left\lfloor\frac{H_{in} - \mbox{kernel\_size}[0]}{\mbox{stride}[0]} + 1\right\rfloor
+$$
+$$
+  W_{out} = \left\lfloor\frac{W_{in} - \mbox{kernel\_size}[1]}{\mbox{stride}[1]} + 1\right\rfloor
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+  
+<span class='co'># power-2 pool of square window of size=3, stride=2</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_lp_pool2d</span>(<span class='fl'>2</span>, <span class='fl'>3</span>, stride=<span class='fl'>2</span>)
+<span class='co'># pool of non-square window of power 1.2</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_lp_pool2d</span>(<span class='fl'>1.2</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>), stride=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>))
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>32</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
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+
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+    <div class="page-header">
+    <h1>MaxPool1D module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_max_pool1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 1D max pooling over an input signal composed of several input
+planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_max_pool1d</span>(
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='kw'>NULL</span>,
+  padding = <span class='fl'>0</span>,
+  dilation = <span class='fl'>1</span>,
+  return_indices = <span class='fl'>FALSE</span>,
+  ceil_mode = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>the size of the window to take a max over</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit zero padding to be added on both sides</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>a parameter that controls the stride of elements in the window</p></td>
+    </tr>
+    <tr>
+      <th>return_indices</th>
+      <td><p>if <code>TRUE</code>, will return the max indices along with the outputs.
+Useful for  <code><a href='nn_max_unpool1d.html'>nn_max_unpool1d()</a></code> later.</p></td>
+    </tr>
+    <tr>
+      <th>ceil_mode</th>
+      <td><p>when <code>TRUE</code>, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>In the simplest case, the output value of the layer with input size \((N, C, L)\)
+and output \((N, C, L_{out})\) can be precisely described as:</p>
+<p>$$
+  out(N_i, C_j, k) = \max_{m=0, \ldots, \mbox{kernel\_size} - 1}
+input(N_i, C_j, stride \times k + m)
+$$</p>
+<p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on both sides
+for <code>padding</code> number of points. <code>dilation</code> controls the spacing between the kernel points.
+It is harder to describe, but this <a href='https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md'>link</a>
+has a nice visualization of what <code>dilation</code> does.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C, L_{in})\)</p></li>
+<li><p>Output: \((N, C, L_{out})\), where</p></li>
+</ul>
+
+<p>$$
+  L_{out} = \left\lfloor \frac{L_{in} + 2 \times \mbox{padding} - \mbox{dilation}
+    \times (\mbox{kernel\_size} - 1) - 1}{\mbox{stride}} + 1\right\rfloor
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'># pool of size=3, stride=2</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_max_pool1d</span>(<span class='fl'>3</span>, stride=<span class='fl'>2</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
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+<div class="row">
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+    <div class="page-header">
+    <h1>MaxPool2D module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_max_pool2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 2D max pooling over an input signal composed of several input
+planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_max_pool2d</span>(
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='kw'>NULL</span>,
+  padding = <span class='fl'>0</span>,
+  dilation = <span class='fl'>1</span>,
+  return_indices = <span class='fl'>FALSE</span>,
+  ceil_mode = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>the size of the window to take a max over</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit zero padding to be added on both sides</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>a parameter that controls the stride of elements in the window</p></td>
+    </tr>
+    <tr>
+      <th>return_indices</th>
+      <td><p>if <code>TRUE</code>, will return the max indices along with the outputs.
+Useful for <code><a href='nn_max_unpool2d.html'>nn_max_unpool2d()</a></code> later.</p></td>
+    </tr>
+    <tr>
+      <th>ceil_mode</th>
+      <td><p>when <code>TRUE</code>, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>In the simplest case, the output value of the layer with input size \((N, C, H, W)\),
+output \((N, C, H_{out}, W_{out})\) and <code>kernel_size</code> \((kH, kW)\)
+can be precisely described as:</p>
+<p>$$
+\begin{array}{ll}
+out(N_i, C_j, h, w) ={} &amp; \max_{m=0, \ldots, kH-1} \max_{n=0, \ldots, kW-1} \\
+&amp; \mbox{input}(N_i, C_j, \mbox{stride[0]} \times h + m,
+               \mbox{stride[1]} \times w + n)
+\end{array}
+$$</p>
+<p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on both sides
+for <code>padding</code> number of points. <code>dilation</code> controls the spacing between the kernel points.
+It is harder to describe, but this <code>link</code> has a nice visualization of what <code>dilation</code> does.</p>
+<p>The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>dilation</code> can either be:</p><ul>
+<li><p>a single <code>int</code> -- in which case the same value is used for the height and width dimension</p></li>
+<li><p>a <code>tuple</code> of two ints -- in which case, the first <code>int</code> is used for the height dimension,
+and the second <code>int</code> for the width dimension</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C, H_{in}, W_{in})\)</p></li>
+<li><p>Output: \((N, C, H_{out}, W_{out})\), where</p></li>
+</ul>
+
+<p>$$
+  H_{out} = \left\lfloor\frac{H_{in} + 2 * \mbox{padding[0]} - \mbox{dilation[0]}
+    \times (\mbox{kernel\_size[0]} - 1) - 1}{\mbox{stride[0]}} + 1\right\rfloor
+$$</p>
+<p>$$
+  W_{out} = \left\lfloor\frac{W_{in} + 2 * \mbox{padding[1]} - \mbox{dilation[1]}
+    \times (\mbox{kernel\_size[1]} - 1) - 1}{\mbox{stride[1]}} + 1\right\rfloor
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'># pool of square window of size=3, stride=2</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_max_pool2d</span>(<span class='fl'>3</span>, stride=<span class='fl'>2</span>)
+<span class='co'># pool of non-square window</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_max_pool2d</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>), stride=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>))
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>32</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
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+    <div class="page-header">
+    <h1>Applies a 3D max pooling over an input signal composed of several input
+planes.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_max_pool3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>In the simplest case, the output value of the layer with input size \((N, C, D, H, W)\),
+output \((N, C, D_{out}, H_{out}, W_{out})\) and <code>kernel_size</code> \((kD, kH, kW)\)
+can be precisely described as:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_max_pool3d</span>(
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='kw'>NULL</span>,
+  padding = <span class='fl'>0</span>,
+  dilation = <span class='fl'>1</span>,
+  return_indices = <span class='fl'>FALSE</span>,
+  ceil_mode = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>the size of the window to take a max over</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the window. Default value is <code>kernel_size</code></p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit zero padding to be added on all three sides</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>a parameter that controls the stride of elements in the window</p></td>
+    </tr>
+    <tr>
+      <th>return_indices</th>
+      <td><p>if <code>TRUE</code>, will return the max indices along with the outputs.
+Useful for <code>torch_nn.MaxUnpool3d</code> later</p></td>
+    </tr>
+    <tr>
+      <th>ceil_mode</th>
+      <td><p>when TRUE, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+\begin{array}{ll}
+\mbox{out}(N_i, C_j, d, h, w) = &amp; \max_{k=0, \ldots, kD-1} \max_{m=0, \ldots, kH-1} \max_{n=0, \ldots, kW-1} \\
+ &amp; \mbox{input}(N_i, C_j, \mbox{stride[0]} \times d + k, \mbox{stride[1]} \times h + m, \mbox{stride[2]} \times w + n)
+\end{array}
+$$</p>
+<p>If <code>padding</code> is non-zero, then the input is implicitly zero-padded on both sides
+for <code>padding</code> number of points. <code>dilation</code> controls the spacing between the kernel points.
+It is harder to describe, but this <code>link</code>_ has a nice visualization of what <code>dilation</code> does.
+The parameters <code>kernel_size</code>, <code>stride</code>, <code>padding</code>, <code>dilation</code> can either be:</p><ul>
+<li><p>a single <code>int</code> -- in which case the same value is used for the depth, height and width dimension</p></li>
+<li><p>a <code>tuple</code> of three ints -- in which case, the first <code>int</code> is used for the depth dimension,
+the second <code>int</code> for the height dimension and the third <code>int</code> for the width dimension</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C, D_{in}, H_{in}, W_{in})\)</p></li>
+<li><p>Output: \((N, C, D_{out}, H_{out}, W_{out})\), where
+$$
+  D_{out} = \left\lfloor\frac{D_{in} + 2 \times \mbox{padding}[0] - \mbox{dilation}[0] \times
+    (\mbox{kernel\_size}[0] - 1) - 1}{\mbox{stride}[0]} + 1\right\rfloor
+$$</p></li>
+</ul>
+
+<p>$$
+  H_{out} = \left\lfloor\frac{H_{in} + 2 \times \mbox{padding}[1] - \mbox{dilation}[1] \times
+    (\mbox{kernel\_size}[1] - 1) - 1}{\mbox{stride}[1]} + 1\right\rfloor
+$$</p>
+<p>$$
+  W_{out} = \left\lfloor\frac{W_{in} + 2 \times \mbox{padding}[2] - \mbox{dilation}[2] \times
+    (\mbox{kernel\_size}[2] - 1) - 1}{\mbox{stride}[2]} + 1\right\rfloor
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'># pool of square window of size=3, stride=2</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_max_pool3d</span>(<span class='fl'>3</span>, stride=<span class='fl'>2</span>)
+<span class='co'># pool of non-square window</span>
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_max_pool3d</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>, <span class='fl'>2</span>), stride=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>2</span>))
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>,<span class='fl'>44</span>, <span class='fl'>31</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
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+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Computes a partial inverse of <code>MaxPool1d</code>.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_max_unpool1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p><code>MaxPool1d</code> is not fully invertible, since the non-maximal values are lost.
+<code>MaxUnpool1d</code> takes in as input the output of <code>MaxPool1d</code>
+including the indices of the maximal values and computes a partial inverse
+in which all non-maximal values are set to zero.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_max_unpool1d</span>(<span class='kw'>kernel_size</span>, stride = <span class='kw'>NULL</span>, padding = <span class='fl'>0</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>(int or tuple): Size of the max pooling window.</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>(int or tuple): Stride of the max pooling window.
+It is set to <code>kernel_size</code> by default.</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>(int or tuple): Padding that was added to the input</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p><code>MaxPool1d</code> can map several input sizes to the same output
+sizes. Hence, the inversion process can get ambiguous.
+To accommodate this, you can provide the needed output size
+as an additional argument <code>output_size</code> in the forward call.
+See the Inputs and Example below.</p>
+    <h2 class="hasAnchor" id="inputs"><a class="anchor" href="#inputs"></a>Inputs</h2>
+
+    
+
+<ul>
+<li><p><code>input</code>: the input Tensor to invert</p></li>
+<li><p><code>indices</code>: the indices given out by <code><a href='nn_max_pool1d.html'>nn_max_pool1d()</a></code></p></li>
+<li><p><code>output_size</code> (optional): the targeted output size</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C, H_{in})\)</p></li>
+<li><p>Output: \((N, C, H_{out})\), where
+$$
+  H_{out} = (H_{in} - 1) \times \mbox{stride}[0] - 2 \times \mbox{padding}[0] + \mbox{kernel\_size}[0]
+$$
+or as given by <code>output_size</code> in the call operator</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>pool</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_max_pool1d.html'>nn_max_pool1d</a></span>(<span class='fl'>2</span>, stride=<span class='fl'>2</span>, return_indices=<span class='fl'>TRUE</span>)
+<span class='kw'>unpool</span> <span class='op'>&lt;-</span> <span class='fu'>nn_max_unpool1d</span>(<span class='fl'>2</span>, stride=<span class='fl'>2</span>)
+
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/array.html'>array</a></span>(<span class='fl'>1</span><span class='op'>:</span><span class='fl'>8</span><span class='op'>/</span><span class='fl'>1</span>, dim = <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>8</span>)))
+<span class='kw'>out</span> <span class='op'>&lt;-</span> <span class='fu'>pool</span>(<span class='kw'>input</span>)
+<span class='fu'>unpool</span>(<span class='kw'>out</span>[[<span class='fl'>1</span>]], <span class='kw'>out</span>[[<span class='fl'>2</span>]])
+
+<span class='co'># Example showcasing the use of output_size</span>
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/array.html'>array</a></span>(<span class='fl'>1</span><span class='op'>:</span><span class='fl'>8</span><span class='op'>/</span><span class='fl'>1</span>, dim = <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>8</span>)))
+<span class='kw'>out</span> <span class='op'>&lt;-</span> <span class='fu'>pool</span>(<span class='kw'>input</span>)
+<span class='fu'>unpool</span>(<span class='kw'>out</span>[[<span class='fl'>1</span>]], <span class='kw'>out</span>[[<span class='fl'>2</span>]], output_size=<span class='kw'>input</span><span class='op'>$</span><span class='fu'>size</span>())
+<span class='fu'>unpool</span>(<span class='kw'>out</span>[[<span class='fl'>1</span>]], <span class='kw'>out</span>[[<span class='fl'>2</span>]])
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,1,.,.) = 
+#&gt;   0
+#&gt;   2
+#&gt;   0
+#&gt;   4
+#&gt;   0
+#&gt;   6
+#&gt;   0
+#&gt;   8
+#&gt; [ CPUFloatType{1,1,8,1} ]</div></pre>
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+<meta property="og:description" content="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." />
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+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Computes a partial inverse of <code>MaxPool2d</code>.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_max_unpool2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p><code>MaxPool2d</code> is not fully invertible, since the non-maximal values are lost.
+<code>MaxUnpool2d</code> takes in as input the output of <code>MaxPool2d</code>
+including the indices of the maximal values and computes a partial inverse
+in which all non-maximal values are set to zero.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_max_unpool2d</span>(<span class='kw'>kernel_size</span>, stride = <span class='kw'>NULL</span>, padding = <span class='fl'>0</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>(int or tuple): Size of the max pooling window.</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>(int or tuple): Stride of the max pooling window.
+It is set to <code>kernel_size</code> by default.</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>(int or tuple): Padding that was added to the input</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p><code>MaxPool2d</code> can map several input sizes to the same output
+sizes. Hence, the inversion process can get ambiguous.
+To accommodate this, you can provide the needed output size
+as an additional argument <code>output_size</code> in the forward call.
+See the Inputs and Example below.</p>
+    <h2 class="hasAnchor" id="inputs"><a class="anchor" href="#inputs"></a>Inputs</h2>
+
+    
+
+<ul>
+<li><p><code>input</code>: the input Tensor to invert</p></li>
+<li><p><code>indices</code>: the indices given out by <code><a href='nn_max_pool2d.html'>nn_max_pool2d()</a></code></p></li>
+<li><p><code>output_size</code> (optional): the targeted output size</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C, H_{in}, W_{in})\)</p></li>
+<li><p>Output: \((N, C, H_{out}, W_{out})\), where
+$$
+  H_{out} = (H_{in} - 1) \times \mbox{stride[0]} - 2 \times \mbox{padding[0]} + \mbox{kernel\_size[0]}
+$$
+$$
+  W_{out} = (W_{in} - 1) \times \mbox{stride[1]} - 2 \times \mbox{padding[1]} + \mbox{kernel\_size[1]}
+$$
+or as given by <code>output_size</code> in the call operator</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>pool</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_max_pool2d.html'>nn_max_pool2d</a></span>(<span class='fl'>2</span>, stride=<span class='fl'>2</span>, return_indices=<span class='fl'>TRUE</span>)
+<span class='kw'>unpool</span> <span class='op'>&lt;-</span> <span class='fu'>nn_max_unpool2d</span>(<span class='fl'>2</span>, stride=<span class='fl'>2</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>4</span>,<span class='fl'>4</span>)
+<span class='kw'>out</span> <span class='op'>&lt;-</span> <span class='fu'>pool</span>(<span class='kw'>input</span>)
+<span class='fu'>unpool</span>(<span class='kw'>out</span>[[<span class='fl'>1</span>]], <span class='kw'>out</span>[[<span class='fl'>2</span>]])
+
+<span class='co'># specify a different output size than input size</span>
+<span class='fu'>unpool</span>(<span class='kw'>out</span>[[<span class='fl'>1</span>]], <span class='kw'>out</span>[[<span class='fl'>2</span>]], output_size=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>5</span>, <span class='fl'>5</span>))
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,1,.,.) = 
+#&gt;   0.0000  0.0000  0.0000  1.6626  1.3884
+#&gt;   0.0000  0.0000  0.0000  0.4660  0.0000
+#&gt;   0.0000  1.6033  0.0000  0.0000  0.0000
+#&gt;   0.0000  0.0000  0.0000  0.0000  0.0000
+#&gt;   0.0000  0.0000  0.0000  0.0000  0.0000
+#&gt; [ CPUFloatType{1,1,5,5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Computes a partial inverse of <code>MaxPool3d</code>.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-pooling.R'><code>R/nn-pooling.R</code></a></small>
+    <div class="hidden name"><code>nn_max_unpool3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p><code>MaxPool3d</code> is not fully invertible, since the non-maximal values are lost.
+<code>MaxUnpool3d</code> takes in as input the output of <code>MaxPool3d</code>
+including the indices of the maximal values and computes a partial inverse
+in which all non-maximal values are set to zero.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_max_unpool3d</span>(<span class='kw'>kernel_size</span>, stride = <span class='kw'>NULL</span>, padding = <span class='fl'>0</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>(int or tuple): Size of the max pooling window.</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>(int or tuple): Stride of the max pooling window.
+It is set to <code>kernel_size</code> by default.</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>(int or tuple): Padding that was added to the input</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p><code>MaxPool3d</code> can map several input sizes to the same output
+sizes. Hence, the inversion process can get ambiguous.
+To accommodate this, you can provide the needed output size
+as an additional argument <code>output_size</code> in the forward call.
+See the Inputs section below.</p>
+    <h2 class="hasAnchor" id="inputs"><a class="anchor" href="#inputs"></a>Inputs</h2>
+
+    
+
+<ul>
+<li><p><code>input</code>: the input Tensor to invert</p></li>
+<li><p><code>indices</code>: the indices given out by <code><a href='nn_max_pool3d.html'>nn_max_pool3d()</a></code></p></li>
+<li><p><code>output_size</code> (optional): the targeted output size</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C, D_{in}, H_{in}, W_{in})\)</p></li>
+<li><p>Output: \((N, C, D_{out}, H_{out}, W_{out})\), where</p></li>
+</ul>
+
+<p>$$
+  D_{out} = (D_{in} - 1) \times \mbox{stride[0]} - 2 \times \mbox{padding[0]} + \mbox{kernel\_size[0]}
+$$
+$$
+  H_{out} = (H_{in} - 1) \times \mbox{stride[1]} - 2 \times \mbox{padding[1]} + \mbox{kernel\_size[1]}
+$$
+$$
+  W_{out} = (W_{in} - 1) \times \mbox{stride[2]} - 2 \times \mbox{padding[2]} + \mbox{kernel\_size[2]}
+$$</p>
+<p>or as given by <code>output_size</code> in the call operator</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+  
+<span class='co'># pool of square window of size=3, stride=2</span>
+<span class='kw'>pool</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_max_pool3d.html'>nn_max_pool3d</a></span>(<span class='fl'>3</span>, stride=<span class='fl'>2</span>, return_indices=<span class='fl'>TRUE</span>)
+<span class='kw'>unpool</span> <span class='op'>&lt;-</span> <span class='fu'>nn_max_unpool3d</span>(<span class='fl'>3</span>, stride=<span class='fl'>2</span>)
+<span class='kw'>out</span> <span class='op'>&lt;-</span> <span class='fu'>pool</span>(<span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>51</span>, <span class='fl'>33</span>, <span class='fl'>15</span>))
+<span class='kw'>unpooled_output</span> <span class='op'>&lt;-</span> <span class='fu'>unpool</span>(<span class='kw'>out</span>[[<span class='fl'>1</span>]], <span class='kw'>out</span>[[<span class='fl'>2</span>]])
+<span class='kw'>unpooled_output</span><span class='op'>$</span><span class='fu'>size</span>()
+
+}
+</div><div class='output co'>#&gt; [1] 20 16 51 33 15</div></pre>
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Base class for all neural network modules.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn.R'><code>R/nn.R</code></a></small>
+    <div class="hidden name"><code>nn_module.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Your models should also subclass this class.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_module</span>(
+  classname = <span class='kw'>NULL</span>,
+  inherit = <span class='kw'>nn_Module</span>,
+  <span class='kw'>...</span>,
+  parent_env = <span class='fu'><a href='https://rdrr.io/r/base/sys.parent.html'>parent.frame</a></span>()
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>classname</th>
+      <td><p>an optional name for the module</p></td>
+    </tr>
+    <tr>
+      <th>inherit</th>
+      <td><p>an optional module to inherit from</p></td>
+    </tr>
+    <tr>
+      <th>...</th>
+      <td><p>methods implementation</p></td>
+    </tr>
+    <tr>
+      <th>parent_env</th>
+      <td><p>passed to <code><a href='https://rdrr.io/pkg/R6/man/R6Class.html'>R6::R6Class()</a></code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>Modules can also contain other Modules, allowing to nest them in a tree
+structure. You can assign the submodules as regular attributes.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>model</span> <span class='op'>&lt;-</span> <span class='fu'>nn_module</span>(
+ initialize = <span class='fu'>function</span>() {
+   <span class='kw'>self</span><span class='op'>$</span><span class='kw'>conv1</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_conv2d.html'>nn_conv2d</a></span>(<span class='fl'>1</span>, <span class='fl'>20</span>, <span class='fl'>5</span>)
+   <span class='kw'>self</span><span class='op'>$</span><span class='kw'>conv2</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_conv2d.html'>nn_conv2d</a></span>(<span class='fl'>20</span>, <span class='fl'>20</span>, <span class='fl'>5</span>)
+ },
+ forward = <span class='fu'>function</span>(<span class='kw'>input</span>) {
+   <span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='kw'>self</span><span class='op'>$</span><span class='fu'>conv1</span>(<span class='kw'>input</span>)
+   <span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nnf_relu.html'>nnf_relu</a></span>(<span class='kw'>input</span>)
+   <span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='kw'>self</span><span class='op'>$</span><span class='fu'>conv2</span>(<span class='kw'>input</span>)
+   <span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nnf_relu.html'>nnf_relu</a></span>(<span class='kw'>input</span>)
+   <span class='kw'>input</span>
+ }
+)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <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>
+
+<div class="pkgdown">
+  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+</div>
+
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+
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+
diff --git a/static/docs/reference/nn_module_list.html b/static/docs/reference/nn_module_list.html
new file mode 100644
index 000000000..852cb4bad
--- /dev/null
+++ b/static/docs/reference/nn_module_list.html
@@ -0,0 +1,257 @@
+<!-- Generated by pkgdown: do not edit by hand -->
+<!DOCTYPE html>
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+<meta property="og:description" content="nn_module_list can be indexed like a regular R list, but
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+nn_module methods." />
+
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+    <li class="dropdown-header">Beginers guide</li>
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+      <a href="../articles/getting-started/tensors.html">Tensors</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/tensors-and-autograd.html">Tensors and autograd</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
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+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
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+    </li>
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+    </li>
+    <li>
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+    </li>
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+</li>
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+</li>
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+    <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>Holds submodules in a list.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn.R'><code>R/nn.R</code></a></small>
+    <div class="hidden name"><code>nn_module_list.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>nn_module_list can be indexed like a regular R list, but
+modules it contains are properly registered, and will be visible by all
+<code>nn_module</code> methods.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_module_list</span>(modules = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>())</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>modules</th>
+      <td><p>a list of modules to add</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>my_module</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_module.html'>nn_module</a></span>(
+ initialize = <span class='fu'>function</span>() {
+   <span class='kw'>self</span><span class='op'>$</span><span class='kw'>linears</span> <span class='op'>&lt;-</span> <span class='fu'>nn_module_list</span>(<span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span>(<span class='fl'>1</span><span class='op'>:</span><span class='fl'>10</span>, <span class='fu'>function</span>(<span class='kw'>x</span>) <span class='fu'><a href='nn_linear.html'>nn_linear</a></span>(<span class='fl'>10</span>, <span class='fl'>10</span>)))
+ },
+ forward = <span class='fu'>function</span>(<span class='kw'>x</span>) {
+  <span class='co'>for</span> (<span class='kw'>i</span> <span class='co'>in</span> <span class='fl'>1</span><span class='op'>:</span><span class='fu'><a href='https://rdrr.io/r/base/length.html'>length</a></span>(<span class='kw'>self</span><span class='op'>$</span><span class='kw'>linears</span>))
+    <span class='kw'>x</span> <span class='op'>&lt;-</span> <span class='kw'>self</span><span class='op'>$</span><span class='kw'>linears</span>[[<span class='kw'>i</span>]](<span class='kw'>x</span>)
+  <span class='kw'>x</span>
+ }
+)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
+  </div>
+</div>
+
+
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diff --git a/static/docs/reference/nn_multihead_attention.html b/static/docs/reference/nn_multihead_attention.html
new file mode 100644
index 000000000..277b3d21d
--- /dev/null
+++ b/static/docs/reference/nn_multihead_attention.html
@@ -0,0 +1,330 @@
+<!-- Generated by pkgdown: do not edit by hand -->
+<!DOCTYPE html>
+<html lang="en">
+  <head>
+  <meta charset="utf-8">
+<meta http-equiv="X-UA-Compatible" content="IE=edge">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+
+<title>MultiHead attention — nn_multihead_attention • torch</title>
+
+
+<!-- jquery -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script>
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+<meta property="og:description" content="Allows the model to jointly attend to information
+from different representation subspaces.
+See reference: Attention Is All You Need" />
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+    </li>
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+      <a href="../articles/getting-started/tensors-and-autograd.html">Tensors and autograd</a>
+    </li>
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+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
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+    </li>
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+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
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+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
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+</div><!--/.navbar -->
+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <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='kw'>embed_dim</span>,
+  <span class='kw'>num_heads</span>,
+  dropout = <span class='fl'>0</span>,
+  bias = <span class='fl'>TRUE</span>,
+  add_bias_kv = <span class='fl'>FALSE</span>,
+  add_zero_attn = <span class='fl'>FALSE</span>,
+  kdim = <span class='kw'>NULL</span>,
+  vdim = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>embed_dim</th>
+      <td><p>total dimension of the model.</p></td>
+    </tr>
+    <tr>
+      <th>num_heads</th>
+      <td><p>parallel attention heads.</p></td>
+    </tr>
+    <tr>
+      <th>dropout</th>
+      <td><p>a Dropout layer on attn_output_weights. Default: 0.0.</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>add bias as module parameter. Default: True.</p></td>
+    </tr>
+    <tr>
+      <th>add_bias_kv</th>
+      <td><p>add bias to the key and value sequences at dim=0.</p></td>
+    </tr>
+    <tr>
+      <th>add_zero_attn</th>
+      <td><p>add a new batch of zeros to the key and
+value sequences at dim=1.</p></td>
+    </tr>
+    <tr>
+      <th>kdim</th>
+      <td><p>total number of features in key. Default: <code>NULL</code></p></td>
+    </tr>
+    <tr>
+      <th>vdim</th>
+      <td><p>total number of features in value. Default: <code>NULL</code>.
+Note: if kdim and vdim are <code>NULL</code>, they will be set to embed_dim such that
+query, key, and value have the same number of features.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+  \mbox{MultiHead}(Q, K, V) = \mbox{Concat}(head_1,\dots,head_h)W^O
+\mbox{where} head_i = \mbox{Attention}(QW_i^Q, KW_i^K, VW_i^V)
+$$</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+
+<p>Inputs:</p><ul>
+<li><p>query: \((L, N, E)\) where L is the target sequence length, N is the batch size, E is
+the embedding dimension.</p></li>
+<li><p>key: \((S, N, E)\), where S is the source sequence length, N is the batch size, E is
+the embedding dimension.</p></li>
+<li><p>value: \((S, N, E)\) where S is the source sequence length, N is the batch size, E is
+the embedding dimension.</p></li>
+<li><p>key_padding_mask: \((N, S)\) where N is the batch size, S is the source sequence length.
+If a ByteTensor is provided, the non-zero positions will be ignored while the position
+with the zero positions will be unchanged. If a BoolTensor is provided, the positions with the
+value of <code>True</code> will be ignored while the position with the value of <code>False</code> will be unchanged.</p></li>
+<li><p>attn_mask: 2D mask \((L, S)\) where L is the target sequence length, S is the source sequence length.
+3D mask \((N*num_heads, L, S)\) where N is the batch size, L is the target sequence length,
+S is the source sequence length. attn_mask ensure that position i is allowed to attend the unmasked
+positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend
+while the zero positions will be unchanged. If a BoolTensor is provided, positions with <code>True</code>
+is not allowed to attend while <code>False</code> values will be unchanged. If a FloatTensor
+is provided, it will be added to the attention weight.</p></li>
+</ul>
+
+<p>Outputs:</p><ul>
+<li><p>attn_output: \((L, N, E)\) where L is the target sequence length, N is the batch size,
+E is the embedding dimension.</p></li>
+<li><p>attn_output_weights: \((N, L, S)\) where N is the batch size,
+L is the target sequence length, S is the source sequence length.</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='kw'>multihead_attn</span> <span class='op'>=</span> <span class='fu'>nn_multihead_attention</span>(<span class='kw'>embed_dim</span>, <span class='kw'>num_heads</span>)
+<span class='kw'>out</span> <span class='op'>&lt;-</span> <span class='fu'>multihead_attn</span>(<span class='kw'>query</span>, <span class='kw'>key</span>, <span class='kw'>value</span>)
+<span class='kw'>attn_output</span> <span class='op'>&lt;-</span> <span class='kw'>out</span>[[<span class='fl'>1</span>]]
+<span class='kw'>attn_output_weights</span> <span class='op'>&lt;-</span> <span class='kw'>out</span>[[<span class='fl'>2</span>]]
+}
+
+}
+</div></pre>
+  </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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+  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
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diff --git a/static/docs/reference/nn_prelu.html b/static/docs/reference/nn_prelu.html
new file mode 100644
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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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+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>PReLU module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_prelu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the element-wise function:
+$$
+  \mbox{PReLU}(x) = \max(0,x) + a * \min(0,x)
+$$
+or
+$$
+  \mbox{PReLU}(x) =
+  \left\{ \begin{array}{ll}
+x, &amp; \mbox{ if } x \geq 0 \\
+ax, &amp; \mbox{ otherwise }
+\end{array}
+\right.
+$$</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_prelu</span>(num_parameters = <span class='fl'>1</span>, init = <span class='fl'>0.25</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>num_parameters</th>
+      <td><p>(int): number of \(a\) to learn.
+Although it takes an int as input, there is only two values are legitimate:
+1, or the number of channels at input. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>init</th>
+      <td><p>(float): the initial value of \(a\). Default: 0.25</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>Here \(a\) is a learnable parameter. When called without arguments, <code>nn.prelu()</code> uses a single
+parameter \(a\) across all input channels. If called with <code>nn_prelu(nChannels)</code>,
+a separate \(a\) is used for each input channel.</p>
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>weight decay should not be used when learning \(a\) for good performance.</p>
+<p>Channel dim is the 2nd dim of input. When input has dims &lt; 2, then there is
+no channel dim and the number of channels = 1.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
+
+    
+
+<ul>
+<li><p>weight (Tensor): the learnable weights of shape (<code>num_parameters</code>).</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_prelu</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
+  </div>
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diff --git a/static/docs/reference/nn_relu.html b/static/docs/reference/nn_relu.html
new file mode 100644
index 000000000..5fa42bd92
--- /dev/null
+++ b/static/docs/reference/nn_relu.html
@@ -0,0 +1,260 @@
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+$$\mbox{ReLU}(x) = (x)^+ = \max(0, x)$$" />
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+    <h1>ReLU module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_relu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the rectified linear unit function element-wise
+$$\mbox{ReLU}(x) = (x)^+ = \max(0, x)$$</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_relu</span>(inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>inplace</th>
+      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_relu</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.1347
+#&gt;  0.1303
+#&gt; [ CPUFloatType{2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nn_relu6.html b/static/docs/reference/nn_relu6.html
new file mode 100644
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>ReLu6 module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_relu6.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the element-wise function:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_relu6</span>(inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>inplace</th>
+      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+  \mbox{ReLU6}(x) = \min(\max(0,x), 6)
+$$</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_relu6</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
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diff --git a/static/docs/reference/nn_rnn.html b/static/docs/reference/nn_rnn.html
new file mode 100644
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+++ b/static/docs/reference/nn_rnn.html
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+    <div class="page-header">
+    <h1>RNN module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-rnn.R'><code>R/nn-rnn.R</code></a></small>
+    <div class="hidden name"><code>nn_rnn.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a multi-layer Elman RNN with \(\tanh\) or \(\mbox{ReLU}\) non-linearity
+to an input sequence.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_rnn</span>(
+  <span class='kw'>input_size</span>,
+  <span class='kw'>hidden_size</span>,
+  num_layers = <span class='fl'>1</span>,
+  nonlinearity = <span class='kw'>NULL</span>,
+  bias = <span class='fl'>TRUE</span>,
+  batch_first = <span class='fl'>FALSE</span>,
+  dropout = <span class='fl'>0</span>,
+  bidirectional = <span class='fl'>FALSE</span>,
+  <span class='kw'>...</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input_size</th>
+      <td><p>The number of expected features in the input <code>x</code></p></td>
+    </tr>
+    <tr>
+      <th>hidden_size</th>
+      <td><p>The number of features in the hidden state <code>h</code></p></td>
+    </tr>
+    <tr>
+      <th>num_layers</th>
+      <td><p>Number of recurrent layers. E.g., setting <code>num_layers=2</code>
+would mean stacking two RNNs together to form a <code>stacked RNN</code>,
+with the second RNN taking in outputs of the first RNN and
+computing the final results. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>nonlinearity</th>
+      <td><p>The non-linearity to use. Can be either <code>'tanh'</code> or
+<code>'relu'</code>. Default: <code>'tanh'</code></p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>If <code>FALSE</code>, then the layer does not use bias weights <code>b_ih</code> and
+<code>b_hh</code>. Default: <code>TRUE</code></p></td>
+    </tr>
+    <tr>
+      <th>batch_first</th>
+      <td><p>If <code>TRUE</code>, then the input and output tensors are provided
+as <code>(batch, seq, feature)</code>. Default: <code>FALSE</code></p></td>
+    </tr>
+    <tr>
+      <th>dropout</th>
+      <td><p>If non-zero, introduces a <code>Dropout</code> layer on the outputs of each
+RNN layer except the last layer, with dropout probability equal to
+<code>dropout</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>bidirectional</th>
+      <td><p>If <code>TRUE</code>, becomes a bidirectional RNN. Default: <code>FALSE</code></p></td>
+    </tr>
+    <tr>
+      <th>...</th>
+      <td><p>other arguments that can be passed to the super class.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>For each element in the input sequence, each layer computes the following
+function:</p>
+<p>$$
+h_t = \tanh(W_{ih} x_t + b_{ih} + W_{hh} h_{(t-1)} + b_{hh})
+$$</p>
+<p>where \(h_t\) is the hidden state at time <code>t</code>, \(x_t\) is
+the input at time <code>t</code>, and \(h_{(t-1)}\) is the hidden state of the
+previous layer at time <code>t-1</code> or the initial hidden state at time <code>0</code>.
+If <code>nonlinearity</code> is <code>'relu'</code>, then \(\mbox{ReLU}\) is used instead of
+\(\tanh\).</p>
+    <h2 class="hasAnchor" id="inputs"><a class="anchor" href="#inputs"></a>Inputs</h2>
+
+    
+
+<ul>
+<li><p><strong>input</strong> of shape <code>(seq_len, batch, input_size)</code>: tensor containing the features
+of the input sequence. The input can also be a packed variable length
+sequence.</p></li>
+<li><p><strong>h_0</strong> of shape <code>(num_layers * num_directions, batch, hidden_size)</code>: tensor
+containing the initial hidden state for each element in the batch.
+Defaults to zero if not provided. If the RNN is bidirectional,
+num_directions should be 2, else it should be 1.</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="outputs"><a class="anchor" href="#outputs"></a>Outputs</h2>
+
+    
+
+<ul>
+<li><p><strong>output</strong> of shape <code>(seq_len, batch, num_directions * hidden_size)</code>: tensor
+containing the output features (<code>h_t</code>) from the last layer of the RNN,
+for each <code>t</code>.  If a :class:<code>nn_packed_sequence</code> has
+been given as the input, the output will also be a packed sequence.
+For the unpacked case, the directions can be separated
+using <code>output$view(seq_len, batch, num_directions, hidden_size)</code>,
+with forward and backward being direction <code>0</code> and <code>1</code> respectively.
+Similarly, the directions can be separated in the packed case.</p></li>
+<li><p><strong>h_n</strong> of shape <code>(num_layers * num_directions, batch, hidden_size)</code>: tensor
+containing the hidden state for <code>t = seq_len</code>.
+Like <em>output</em>, the layers can be separated using
+<code>h_n$view(num_layers, num_directions, batch, hidden_size)</code>.</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input1: \((L, N, H_{in})\) tensor containing input features where
+\(H_{in}=\mbox{input\_size}\) and <code>L</code> represents a sequence length.</p></li>
+<li><p>Input2: \((S, N, H_{out})\) tensor
+containing the initial hidden state for each element in the batch.
+\(H_{out}=\mbox{hidden\_size}\)
+Defaults to zero if not provided. where \(S=\mbox{num\_layers} * \mbox{num\_directions}\)
+If the RNN is bidirectional, num_directions should be 2, else it should be 1.</p></li>
+<li><p>Output1: \((L, N, H_{all})\) where \(H_{all}=\mbox{num\_directions} * \mbox{hidden\_size}\)</p></li>
+<li><p>Output2: \((S, N, H_{out})\) tensor containing the next hidden state
+for each element in the batch</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="attributes"><a class="anchor" href="#attributes"></a>Attributes</h2>
+
+    
+
+<ul>
+<li><p><code>weight_ih_l[k]</code>: the learnable input-hidden weights of the k-th layer,
+of shape <code>(hidden_size, input_size)</code> for <code>k = 0</code>. Otherwise, the shape is
+<code>(hidden_size, num_directions * hidden_size)</code></p></li>
+<li><p><code>weight_hh_l[k]</code>: the learnable hidden-hidden weights of the k-th layer,
+of shape <code>(hidden_size, hidden_size)</code></p></li>
+<li><p><code>bias_ih_l[k]</code>: the learnable input-hidden bias of the k-th layer,
+of shape <code>(hidden_size)</code></p></li>
+<li><p><code>bias_hh_l[k]</code>: the learnable hidden-hidden bias of the k-th layer,
+of shape <code>(hidden_size)</code></p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+
+
+<p>All the weights and biases are initialized from \(\mathcal{U}(-\sqrt{k}, \sqrt{k})\)
+where \(k = \frac{1}{\mbox{hidden\_size}}\)</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>rnn</span> <span class='op'>&lt;-</span> <span class='fu'>nn_rnn</span>(<span class='fl'>10</span>, <span class='fl'>20</span>, <span class='fl'>2</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>5</span>, <span class='fl'>3</span>, <span class='fl'>10</span>)
+<span class='kw'>h0</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>20</span>)
+<span class='fu'>rnn</span>(<span class='kw'>input</span>, <span class='kw'>h0</span>)
+
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;  Columns 1 to 9  0.0589  0.6076  0.5733  0.4325 -0.7804 -0.5884 -0.7990 -0.5201 -0.5453
+#&gt;  -0.3423 -0.1028  0.8203 -0.4395 -0.8638 -0.2453 -0.7419 -0.5734 -0.9229
+#&gt;  -0.0001  0.2485 -0.1473  0.5797 -0.1626 -0.1213 -0.3884 -0.5131  0.2432
+#&gt; 
+#&gt; Columns 10 to 18  0.2049  0.6141  0.0159 -0.5202 -0.0390  0.8628  0.0559 -0.6653  0.5929
+#&gt;   0.1484 -0.3955  0.4318 -0.6923  0.6768  0.9427 -0.5935 -0.1124  0.8008
+#&gt;  -0.1115  0.3922 -0.0407  0.7891 -0.1390  0.3705 -0.2486  0.8745 -0.3595
+#&gt; 
+#&gt; Columns 19 to 20  0.0777 -0.9009
+#&gt;  -0.0200 -0.2583
+#&gt;  -0.1381 -0.4595
+#&gt; 
+#&gt; (2,.,.) = 
+#&gt;  Columns 1 to 9 -0.5747 -0.2374  0.3985  0.6116 -0.7816  0.0973 -0.2544 -0.2252 -0.2584
+#&gt;  -0.2011 -0.3030  0.4559  0.2132 -0.6755  0.1609 -0.8239 -0.2612 -0.6095
+#&gt;  -0.5134  0.3075 -0.2427  0.1875 -0.3368  0.1697 -0.3024 -0.5115  0.0946
+#&gt; 
+#&gt; Columns 10 to 18  0.5029 -0.4663 -0.6517 -0.3916 -0.3694 -0.3079  0.0697  0.4641  0.4321
+#&gt;   0.8177  0.0184 -0.5879 -0.3717 -0.0954  0.1382 -0.1993  0.5621 -0.1939
+#&gt;   0.3419 -0.0541  0.0665  0.0764  0.6821  0.0040  0.0504  0.6075  0.0703
+#&gt; 
+#&gt; Columns 19 to 20 -0.1393 -0.0169
+#&gt;  -0.2283  0.0531
+#&gt;  -0.0904  0.2892
+#&gt; 
+#&gt; (3,.,.) = 
+#&gt;  Columns 1 to 9  0.4901  0.5564  0.4157 -0.1239 -0.3616 -0.3069 -0.7718  0.2739  0.1213
+#&gt;   0.4148  0.1776  0.2237  0.0323 -0.5166 -0.3952 -0.6464  0.1560  0.1433
+#&gt;   0.3964  0.0038  0.3253  0.0715  0.1306 -0.0327 -0.2969  0.1245 -0.0192
+#&gt; 
+#&gt; Columns 10 to 18  0.7138 -0.0042  0.1386 -0.0459 -0.4418  0.2562 -0.1983  0.0256  0.4770
+#&gt;   0.7310  0.4867 -0.2056 -0.4410 -0.1978  0.4279 -0.1295  0.2951  0.4745
+#&gt;   0.6958  0.5029  0.0787 -0.2960  0.3043  0.3281 -0.3449  0.3081  0.4197
+#&gt; 
+#&gt; Columns 19 to 20  0.1745 -0.1350
+#&gt;   0.0668 -0.4015
+#&gt;   0.5455  0.3373
+#&gt; 
+#&gt; (4,.,.) = 
+#&gt;  Columns 1 to 9 -0.1242 -0.1825  0.0703  0.3368 -0.1879 -0.6188 -0.3606  0.2400 -0.4979
+#&gt;  -0.2285 -0.1238  0.2816  0.7158 -0.3795 -0.1800 -0.0903  0.0951 -0.4055
+#&gt;  -0.4161  0.1562  0.1320  0.5917 -0.2003  0.5784 -0.1302 -0.1113 -0.7354
+#&gt; 
+#&gt; Columns 10 to 18  0.6470  0.0346 -0.6063 -0.0664  0.3145  0.3909 -0.3163  0.0040  0.6242
+#&gt;   0.3401 -0.2107 -0.5455 -0.4844 -0.0769  0.0620 -0.1112  0.5133  0.6606
+#&gt;   0.4108 -0.3172 -0.3713 -0.5535  0.3277  0.0108 -0.0800  0.5410  0.1190
+#&gt; 
+#&gt; Columns 19 to 20 -0.1511 -0.0390
+#&gt;   0.2056  0.1060
+#&gt;   0.1649  0.4299
+#&gt; 
+#&gt; (5,.,.) = 
+#&gt;  Columns 1 to 9 -0.0957  0.3769  0.1599  0.2209 -0.5452 -0.2272 -0.5200 -0.0675 -0.3636
+#&gt;   0.3314  0.3243  0.5617  0.3386 -0.1621  0.4163 -0.6332 -0.0253 -0.0027
+#&gt;   0.1690  0.3272  0.5195 -0.1727 -0.1203 -0.0701 -0.5596 -0.2452  0.1866
+#&gt; 
+#&gt; Columns 10 to 18  0.7646  0.0595 -0.1395 -0.1890  0.1511  0.3273 -0.2851  0.1429  0.4489
+#&gt;   0.7122  0.0548 -0.1359 -0.2292 -0.1363 -0.1930 -0.3282  0.5265  0.3602
+#&gt;   0.7579  0.1487  0.1720 -0.0712 -0.0081  0.3472 -0.1236  0.3269  0.3627
+#&gt; 
+#&gt; Columns 19 to 20 -0.3324  0.0199
+#&gt;   0.4319  0.0719
+#&gt;   0.0541 -0.1996
+#&gt; [ CPUFloatType{5,3,20} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;  Columns 1 to 9 -0.0859  0.2441 -0.3785  0.4773 -0.6194  0.7348 -0.2389  0.3932 -0.5751
+#&gt;  -0.3461 -0.4299 -0.5382 -0.0556  0.2770 -0.4997  0.3906 -0.4267 -0.2962
+#&gt;   0.0901 -0.0487 -0.2221  0.7402 -0.3091  0.1068  0.2309  0.0844 -0.8371
+#&gt; 
+#&gt; Columns 10 to 18 -0.7465 -0.0237 -0.5216  0.2091  0.7810  0.0142 -0.0300  0.0860 -0.4565
+#&gt;   0.4484 -0.2549  0.6987 -0.4158 -0.6085 -0.0974  0.0892 -0.3520  0.0667
+#&gt;  -0.1097 -0.0399  0.2584 -0.1982  0.0520 -0.0103 -0.0936  0.3060  0.0546
+#&gt; 
+#&gt; Columns 19 to 20  0.1902  0.0056
+#&gt;   0.3507  0.1104
+#&gt;  -0.0194  0.3957
+#&gt; 
+#&gt; (2,.,.) = 
+#&gt;  Columns 1 to 9 -0.0957  0.3769  0.1599  0.2209 -0.5452 -0.2272 -0.5200 -0.0675 -0.3636
+#&gt;   0.3314  0.3243  0.5617  0.3386 -0.1621  0.4163 -0.6332 -0.0253 -0.0027
+#&gt;   0.1690  0.3272  0.5195 -0.1727 -0.1203 -0.0701 -0.5596 -0.2452  0.1866
+#&gt; 
+#&gt; Columns 10 to 18  0.7646  0.0595 -0.1395 -0.1890  0.1511  0.3273 -0.2851  0.1429  0.4489
+#&gt;   0.7122  0.0548 -0.1359 -0.2292 -0.1363 -0.1930 -0.3282  0.5265  0.3602
+#&gt;   0.7579  0.1487  0.1720 -0.0712 -0.0081  0.3472 -0.1236  0.3269  0.3627
+#&gt; 
+#&gt; Columns 19 to 20 -0.3324  0.0199
+#&gt;   0.4319  0.0719
+#&gt;   0.0541 -0.1996
+#&gt; [ CPUFloatType{2,3,20} ]
+#&gt; </div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+
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+<div class="row">
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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>(lower = <span class='fl'>1</span><span class='op'>/</span><span class='fl'>8</span>, upper = <span class='fl'>1</span><span class='op'>/</span><span class='fl'>3</span>, inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>lower</th>
+      <td><p>lower bound of the uniform distribution. Default: \(\frac{1}{8}\)</p></td>
+    </tr>
+    <tr>
+      <th>upper</th>
+      <td><p>upper bound of the uniform distribution. Default: \(\frac{1}{3}\)</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p><code>Empirical Evaluation of Rectified Activations in Convolutional Network</code>.</p>
+<p>The function is defined as:</p>
+<p>$$
+\mbox{RReLU}(x) =
+\left\{ \begin{array}{ll}
+x &amp; \mbox{if } x \geq 0 \\
+ax &amp; \mbox{ otherwise }
+\end{array}
+\right.
+$$</p>
+<p>where \(a\) is randomly sampled from uniform distribution
+\(\mathcal{U}(\mbox{lower}, \mbox{upper})\).
+See: https://arxiv.org/pdf/1505.00853.pdf</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_rrelu</span>(<span class='fl'>0.1</span>, <span class='fl'>0.3</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.5388
+#&gt;  1.3588
+#&gt; [ CPUFloatType{2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+<div class="row">
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+    <div class="page-header">
+    <h1>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>(inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>inplace</th>
+      <td><p>(bool, optional): can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+  \mbox{SELU}(x) = \mbox{scale} * (\max(0,x) + \min(0, \alpha * (\exp(x) - 1)))
+$$</p>
+<p>with \(\alpha = 1.6732632423543772848170429916717\) and
+\(\mbox{scale} = 1.0507009873554804934193349852946\).</p>
+<p>More details can be found in the paper
+<a href='https://arxiv.org/abs/1706.02515'>Self-Normalizing Neural Networks</a>.</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_selu</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <h1>A sequential container</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn.R'><code>R/nn.R</code></a></small>
+    <div class="hidden name"><code>nn_sequential.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>A sequential container.
+Modules will be added to it in the order they are passed in the constructor.
+See examples.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_sequential</span>(<span class='kw'>...</span>, name = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>...</th>
+      <td><p>sequence of modules to be added</p></td>
+    </tr>
+    <tr>
+      <th>name</th>
+      <td><p>optional name for the generated module.</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>model</span> <span class='op'>&lt;-</span> <span class='fu'>nn_sequential</span>(
+  <span class='fu'><a href='nn_conv2d.html'>nn_conv2d</a></span>(<span class='fl'>1</span>, <span class='fl'>20</span>, <span class='fl'>5</span>),
+  <span class='fu'><a href='nn_relu.html'>nn_relu</a></span>(),
+  <span class='fu'><a href='nn_conv2d.html'>nn_conv2d</a></span>(<span class='fl'>20</span>, <span class='fl'>64</span>, <span class='fl'>5</span>),
+  <span class='fu'><a href='nn_relu.html'>nn_relu</a></span>()
+)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>32</span>, <span class='fl'>1</span>, <span class='fl'>28</span>, <span class='fl'>28</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>model</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <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="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Sigmoid module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_sigmoid.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the element-wise function:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_sigmoid</span>()</pre>
+
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+  \mbox{Sigmoid}(x) = \sigma(x) = \frac{1}{1 + \exp(-x)}
+$$</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_sigmoid</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</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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+Softmax is defined as:" />
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+    <div class="page-header">
+    <h1>Softmax module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_softmax.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the Softmax function to an n-dimensional input Tensor
+rescaling them so that the elements of the n-dimensional output Tensor
+lie in the range <code>[0,1]</code> and sum to 1.
+Softmax is defined as:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_softmax</span>(<span class='kw'>dim</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>dim</th>
+      <td><p>(int): A dimension along which Softmax will be computed (so every slice
+along dim will sum to 1).</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+
+    <p>:
+a Tensor of the same dimension and shape as the input with
+values in the range <code>[0, 1]</code></p>
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+  \mbox{Softmax}(x_{i}) = \frac{\exp(x_i)}{\sum_j \exp(x_j)}
+$$</p>
+<p>When the input Tensor is a sparse tensor then the unspecifed
+values are treated as <code>-Inf</code>.</p>
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>This module doesn't work directly with NLLLoss,
+which expects the Log to be computed between the Softmax and itself.
+Use <code>LogSoftmax</code> instead (it's faster and has better numerical properties).</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((*)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((*)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_softmax</span>(<span class='fl'>1</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Softmax2d module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_softmax2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies SoftMax over features to each spatial location.
+When given an image of <code>Channels x Height x Width</code>, it will
+apply <code>Softmax</code> to each location \((Channels, h_i, w_j)\)</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_softmax2d</span>()</pre>
+
+
+    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+
+    <p>a Tensor of the same dimension and shape as the input with
+values in the range <code>[0, 1]</code></p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, C, H, W)\)</p></li>
+<li><p>Output: \((N, C, H, W)\) (same shape as input)</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_softmax2d</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>12</span>, <span class='fl'>13</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+<meta property="og:description" content="Applies the Softmin function to an n-dimensional input Tensor
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+Softmin is defined as:" />
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Softmin</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_softmin.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the Softmin function to an n-dimensional input Tensor
+rescaling them so that the elements of the n-dimensional output Tensor
+lie in the range <code>[0, 1]</code> and sum to 1.
+Softmin is defined as:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_softmin</span>(<span class='kw'>dim</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>dim</th>
+      <td><p>(int): A dimension along which Softmin will be computed (so every slice
+along dim will sum to 1).</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+
+    <p>a Tensor of the same dimension and shape as the input, with
+values in the range <code>[0, 1]</code>.</p>
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+  \mbox{Softmin}(x_{i}) = \frac{\exp(-x_i)}{\sum_j \exp(-x_j)}
+$$</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((*)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((*)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_softmin</span>(dim = <span class='fl'>1</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+<meta property="og:description" content="Applies the element-wise function:
+$$
+  \mbox{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x))
+$$" />
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+    <div class="page-header">
+    <h1>Softplus module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_softplus.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the element-wise function:
+$$
+  \mbox{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x))
+$$</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_softplus</span>(beta = <span class='fl'>1</span>, threshold = <span class='fl'>20</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>beta</th>
+      <td><p>the \(\beta\) value for the Softplus formulation. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>threshold</th>
+      <td><p>values above this revert to a linear function. Default: 20</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>SoftPlus is a smooth approximation to the ReLU function and can be used
+to constrain the output of a machine to always be positive.
+For numerical stability the implementation reverts to the linear function
+when \(input \times \beta &gt; threshold\).</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_softplus</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
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+<div class="row">
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+    <div class="page-header">
+    <h1>Softshrink module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_softshrink.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the soft shrinkage function elementwise:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_softshrink</span>(lambd = <span class='fl'>0.5</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>lambd</th>
+      <td><p>the \(\lambda\) (must be no less than zero) value for the Softshrink formulation. Default: 0.5</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+  \mbox{SoftShrinkage}(x) =
+  \left\{ \begin{array}{ll}
+x - \lambda, &amp; \mbox{ if } x &gt; \lambda \\
+x + \lambda, &amp; \mbox{ if } x &lt; -\lambda \\
+0, &amp; \mbox{ otherwise }
+\end{array}
+\right.
+$$</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_softshrink</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
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+  \mbox{SoftSign}(x) = \frac{x}{ 1 + |x|}
+$$" />
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Softsign module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_softsign.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the element-wise function:
+$$
+  \mbox{SoftSign}(x) = \frac{x}{ 1 + |x|}
+$$</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_softsign</span>()</pre>
+
+
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_softsign</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Tanh module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_tanh.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the element-wise function:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_tanh</span>()</pre>
+
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+  \mbox{Tanh}(x) = \tanh(x) = \frac{\exp(x) - \exp(-x)} {\exp(x) + \exp(-x)}
+$$</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_tanh</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/nn_tanhshrink.html b/static/docs/reference/nn_tanhshrink.html
new file mode 100644
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+
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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><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_tanhshrink</span>()
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+<div class="row">
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+    <div class="page-header">
+    <h1>Threshoold module</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-activation.R'><code>R/nn-activation.R</code></a></small>
+    <div class="hidden name"><code>nn_threshold.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Thresholds each element of the input Tensor.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_threshold</span>(<span class='kw'>threshold</span>, <span class='kw'>value</span>, inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>threshold</th>
+      <td><p>The value to threshold at</p></td>
+    </tr>
+    <tr>
+      <th>value</th>
+      <td><p>The value to replace with</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>can optionally do the operation in-place. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>Threshold is defined as:
+$$
+  y = 
+  \left\{ \begin{array}{ll}
+  x, &amp;\mbox{ if } x &gt; \mbox{threshold} \\
+  \mbox{value}, &amp;\mbox{ otherwise }
+  \end{array}
+  \right.
+$$</p>
+    <h2 class="hasAnchor" id="shape"><a class="anchor" href="#shape"></a>Shape</h2>
+
+    
+
+<ul>
+<li><p>Input: \((N, *)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> means, any number of additional
+dimensions</p></li>
+<li><p>Output: \((N, *)\), same shape as the input</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>m</span> <span class='op'>&lt;-</span> <span class='fu'>nn_threshold</span>(<span class='fl'>0.1</span>, <span class='fl'>20</span>)
+<span class='kw'>input</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>)
+<span class='kw'>output</span> <span class='op'>&lt;-</span> <span class='fu'>m</span>(<span class='kw'>input</span>)
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/nn_utils_rnn_pack_padded_sequence.html b/static/docs/reference/nn_utils_rnn_pack_padded_sequence.html
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+  <a href="../news/index.html">Changelog</a>
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+    <div class="page-header">
+    <h1>Packs a Tensor containing padded sequences of variable length.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-utils-rnn.R'><code>R/nn-utils-rnn.R</code></a></small>
+    <div class="hidden name"><code>nn_utils_rnn_pack_padded_sequence.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p><code>input</code> can be of size <code>T x B x *</code> where <code>T</code> is the length of the
+longest sequence (equal to <code>lengths[1]</code>), <code>B</code> is the batch size, and
+<code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is any number of dimensions (including 0). If <code>batch_first</code> is
+<code>TRUE</code>, <code>B x T x *</code> <code>input</code> is expected.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_utils_rnn_pack_padded_sequence</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>lengths</span>,
+  batch_first = <span class='fl'>FALSE</span>,
+  enforce_sorted = <span class='fl'>TRUE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(Tensor): padded batch of variable length sequences.</p></td>
+    </tr>
+    <tr>
+      <th>lengths</th>
+      <td><p>(Tensor): list of sequences lengths of each batch element.</p></td>
+    </tr>
+    <tr>
+      <th>batch_first</th>
+      <td><p>(bool, optional): if <code>TRUE</code>, the input is expected in <code>B x T x *</code>
+format.</p></td>
+    </tr>
+    <tr>
+      <th>enforce_sorted</th>
+      <td><p>(bool, optional): if <code>TRUE</code>, the input is expected to
+contain sequences sorted by length in a decreasing order. If
+<code>FALSE</code>, the input will get sorted unconditionally. Default: <code>TRUE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+
+    <p>a <code>PackedSequence</code> object</p>
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>For unsorted sequences, use <code>enforce_sorted = FALSE</code>. If <code>enforce_sorted</code> is
+<code>TRUE</code>, the sequences should be sorted by length in a decreasing order, i.e.
+<code>input[,1]</code> should be the longest sequence, and <code>input[,B]</code> the shortest
+one. <code>enforce_sorted = TRUE</code> is only necessary for ONNX export.</p>
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>This function accepts any input that has at least two dimensions. You
+can apply it to pack the labels, and use the output of the RNN with
+them to compute the loss directly. A Tensor can be retrieved from
+a <code>PackedSequence</code> object by accessing its <code>.data</code> attribute.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nn_utils_rnn_pack_sequence.html b/static/docs/reference/nn_utils_rnn_pack_sequence.html
new file mode 100644
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--- /dev/null
+++ b/static/docs/reference/nn_utils_rnn_pack_sequence.html
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Packs a list of variable length Tensors</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-utils-rnn.R'><code>R/nn-utils-rnn.R</code></a></small>
+    <div class="hidden name"><code>nn_utils_rnn_pack_sequence.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p><code>sequences</code> should be a list of Tensors of size <code>L x *</code>, where <code>L</code> is
+the length of a sequence and <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is any number of trailing dimensions,
+including zero.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_utils_rnn_pack_sequence</span>(<span class='kw'>sequences</span>, enforce_sorted = <span class='fl'>TRUE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>sequences</th>
+      <td><p><code>(list[Tensor])</code>: A list of sequences of decreasing length.</p></td>
+    </tr>
+    <tr>
+      <th>enforce_sorted</th>
+      <td><p>(bool, optional): if <code>TRUE</code>, checks that the input
+contains sequences sorted by length in a decreasing order. If
+<code>FALSE</code>, this condition is not checked. Default: <code>TRUE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+
+    <p>a <code>PackedSequence</code> object</p>
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>For unsorted sequences, use <code>enforce_sorted = FALSE</code>. If <code>enforce_sorted</code>
+is <code>TRUE</code>, the sequences should be sorted in the order of decreasing length.
+<code>enforce_sorted = TRUE</code> is only necessary for ONNX export.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>3</span>), dtype = <span class='fu'><a href='torch_dtype.html'>torch_long</a></span>())
+<span class='kw'>y</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>), dtype = <span class='fu'><a href='torch_dtype.html'>torch_long</a></span>())
+<span class='kw'>z</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>6</span>), dtype = <span class='fu'><a href='torch_dtype.html'>torch_long</a></span>())
+
+<span class='kw'>p</span> <span class='op'>&lt;-</span> <span class='fu'>nn_utils_rnn_pack_sequence</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>x</span>, <span class='kw'>y</span>, <span class='kw'>z</span>))
+
+}
+</div></pre>
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+      
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+</div><!--/.navbar -->
+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Pads a packed batch of variable length sequences.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-utils-rnn.R'><code>R/nn-utils-rnn.R</code></a></small>
+    <div class="hidden name"><code>nn_utils_rnn_pad_packed_sequence.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>It is an inverse operation to <code><a href='nn_utils_rnn_pack_padded_sequence.html'>nn_utils_rnn_pack_padded_sequence()</a></code>.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nn_utils_rnn_pad_packed_sequence</span>(
+  <span class='kw'>sequence</span>,
+  batch_first = <span class='fl'>FALSE</span>,
+  padding_value = <span class='fl'>0</span>,
+  total_length = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>sequence</th>
+      <td><p>(PackedSequence): batch to pad</p></td>
+    </tr>
+    <tr>
+      <th>batch_first</th>
+      <td><p>(bool, optional): if <code>True</code>, the output will be in ``B x T x *`
+format.</p></td>
+    </tr>
+    <tr>
+      <th>padding_value</th>
+      <td><p>(float, optional): values for padded elements.</p></td>
+    </tr>
+    <tr>
+      <th>total_length</th>
+      <td><p>(int, optional): if not <code>NULL</code>, the output will be padded to
+have length <code>total_length</code>. This method will throw <code>ValueError</code>
+if <code>total_length</code> is less than the max sequence length in
+<code>sequence</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+
+    <p>Tuple of Tensor containing the padded sequence, and a Tensor
+containing the list of lengths of each sequence in the batch.
+Batch elements will be re-ordered as they were ordered originally when
+the batch was passed to <code><a href='nn_utils_rnn_pack_padded_sequence.html'>nn_utils_rnn_pack_padded_sequence()</a></code> or
+<code><a href='nn_utils_rnn_pack_sequence.html'>nn_utils_rnn_pack_sequence()</a></code>.</p>
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>The returned Tensor's data will be of size <code>T x B x *</code>, where <code>T</code> is the length
+of the longest sequence and <code>B</code> is the batch size. If <code>batch_first</code> is <code>TRUE</code>,
+the data will be transposed into <code>B x T x *</code> format.</p>
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p><code>total_length</code> is useful to implement the
+<code>pack sequence -&gt; recurrent network -&gt; unpack sequence</code> pattern in a
+<code>nn_module</code> wrapped in <code>~torch.nn.DataParallel</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>seq</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>0</span>), <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>,<span class='fl'>0</span>,<span class='fl'>0</span>), <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>,<span class='fl'>5</span>,<span class='fl'>6</span>)))        
+<span class='kw'>lens</span> <span class='op'>&lt;-</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>,<span class='fl'>1</span>,<span class='fl'>3</span>)
+<span class='kw'>packed</span> <span class='op'>&lt;-</span> <span class='fu'><a href='nn_utils_rnn_pack_padded_sequence.html'>nn_utils_rnn_pack_padded_sequence</a></span>(<span class='kw'>seq</span>, <span class='kw'>lens</span>, batch_first = <span class='fl'>TRUE</span>,
+                                            enforce_sorted = <span class='fl'>FALSE</span>)
+<span class='kw'>packed</span>
+<span class='fu'>nn_utils_rnn_pad_packed_sequence</span>(<span class='kw'>packed</span>, batch_first=<span class='fl'>TRUE</span>)
+
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt;  1  2  0
+#&gt;  3  0  0
+#&gt;  4  5  6
+#&gt; [ CPUFloatType{3,3} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt;  2
+#&gt;  1
+#&gt;  3
+#&gt; [ CPULongType{3} ]
+#&gt; </div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/nn_utils_rnn_pad_sequence.html b/static/docs/reference/nn_utils_rnn_pad_sequence.html
new file mode 100644
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--- /dev/null
+++ b/static/docs/reference/nn_utils_rnn_pad_sequence.html
@@ -0,0 +1,276 @@
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+sequences with size L x * and if batch_first is False, and T x B x *
+otherwise." />
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Pad a list of variable length Tensors with <code>padding_value</code></h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nn-utils-rnn.R'><code>R/nn-utils-rnn.R</code></a></small>
+    <div class="hidden name"><code>nn_utils_rnn_pad_sequence.Rd</code></div>
+    </div>
+
+    <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='kw'>sequences</span>, batch_first = <span class='fl'>FALSE</span>, padding_value = <span class='fl'>0</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>sequences</th>
+      <td><p><code>(list[Tensor])</code>: list of variable length sequences.</p></td>
+    </tr>
+    <tr>
+      <th>batch_first</th>
+      <td><p>(bool, optional): output will be in <code>B x T x *</code> if <code>TRUE</code>,
+or in <code>T x B x *</code> otherwise</p></td>
+    </tr>
+    <tr>
+      <th>padding_value</th>
+      <td><p>(float, optional): value for padded elements. Default: 0.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+
+    <p>Tensor of size <code>T x B x *</code> if <code>batch_first</code> is <code>FALSE</code>.
+Tensor of size <code>B x T x *</code> otherwise</p>
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p><code>B</code> is batch size. It is equal to the number of elements in <code>sequences</code>.
+<code>T</code> is length of the longest sequence.
+<code>L</code> is length of the sequence.
+<code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is any number of trailing dimensions, including none.</p>
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>This function returns a Tensor of size <code>T x B x *</code> or <code>B x T x *</code>
+where <code>T</code> is the length of the longest sequence. This function assumes
+trailing dimensions and type of all the Tensors in sequences are same.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>a</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_ones.html'>torch_ones</a></span>(<span class='fl'>25</span>, <span class='fl'>300</span>)
+<span class='kw'>b</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_ones.html'>torch_ones</a></span>(<span class='fl'>22</span>, <span class='fl'>300</span>)
+<span class='kw'>c</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_ones.html'>torch_ones</a></span>(<span class='fl'>15</span>, <span class='fl'>300</span>)
+<span class='fu'>nn_utils_rnn_pad_sequence</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>a</span>, <span class='kw'>b</span>, <span class='kw'>c</span>))<span class='op'>$</span><span class='fu'>size</span>()
+
+}
+</div><div class='output co'>#&gt; [1]  25   3 300</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/nnf_adaptive_avg_pool1d.html b/static/docs/reference/nnf_adaptive_avg_pool1d.html
new file mode 100644
index 000000000..ec2ccdf8c
--- /dev/null
+++ b/static/docs/reference/nnf_adaptive_avg_pool1d.html
@@ -0,0 +1,243 @@
+<!-- Generated by pkgdown: do not edit by hand -->
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+    <div class="page-header">
+    <h1>Adaptive_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_adaptive_avg_pool1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 1D adaptive average pooling over an input signal composed of
+several input planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_adaptive_avg_pool1d</span>(<span class='kw'>input</span>, <span class='kw'>output_size</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape (minibatch , in_channels , iW)</p></td>
+    </tr>
+    <tr>
+      <th>output_size</th>
+      <td><p>the target output size (single integer)</p></td>
+    </tr>
+    </table>
+
+
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+    <div class="page-header">
+    <h1>Adaptive_avg_pool2d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
+    <div class="hidden name"><code>nnf_adaptive_avg_pool2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 2D adaptive average pooling over an input signal composed of
+several input planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_adaptive_avg_pool2d</span>(<span class='kw'>input</span>, <span class='kw'>output_size</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor (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>
+    </table>
+
+
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index 000000000..e919dbe4e
--- /dev/null
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+    <h1>Adaptive_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_adaptive_avg_pool3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 3D adaptive average pooling over an input signal composed of
+several input planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_adaptive_avg_pool3d</span>(<span class='kw'>input</span>, <span class='kw'>output_size</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor (minibatch, in_channels , iT * iH , iW)</p></td>
+    </tr>
+    <tr>
+      <th>output_size</th>
+      <td><p>the target output size (single integer or triple-integer tuple)</p></td>
+    </tr>
+    </table>
+
+
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+
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+    <div class="page-header">
+    <h1>Adaptive_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_adaptive_max_pool1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 1D adaptive max pooling over an input signal composed of
+several input planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_adaptive_max_pool1d</span>(<span class='kw'>input</span>, <span class='kw'>output_size</span>, return_indices = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape (minibatch , in_channels , iW)</p></td>
+    </tr>
+    <tr>
+      <th>output_size</th>
+      <td><p>the target output size (single integer)</p></td>
+    </tr>
+    <tr>
+      <th>return_indices</th>
+      <td><p>whether to return pooling indices. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+
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+    <div class="page-header">
+    <h1>Adaptive_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_adaptive_max_pool2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 2D adaptive max pooling over an input signal composed of
+several input planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_adaptive_max_pool2d</span>(<span class='kw'>input</span>, <span class='kw'>output_size</span>, return_indices = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor (minibatch, in_channels , iH , iW)</p></td>
+    </tr>
+    <tr>
+      <th>output_size</th>
+      <td><p>the target output size (single integer or double-integer tuple)</p></td>
+    </tr>
+    <tr>
+      <th>return_indices</th>
+      <td><p>whether to return pooling indices. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+
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+    <div class="page-header">
+    <h1>Adaptive_max_pool3d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
+    <div class="hidden name"><code>nnf_adaptive_max_pool3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 3D adaptive max pooling over an input signal composed of
+several input planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_adaptive_max_pool3d</span>(<span class='kw'>input</span>, <span class='kw'>output_size</span>, return_indices = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor (minibatch, in_channels , iT * iH , iW)</p></td>
+    </tr>
+    <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>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/reference/nnf_affine_grid.html b/static/docs/reference/nnf_affine_grid.html
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+
+      
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Affine_grid</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-vision.R'><code>R/nnf-vision.R</code></a></small>
+    <div class="hidden name"><code>nnf_affine_grid.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Generates a 2D or 3D flow field (sampling grid), given a batch of
+affine matrices <code>theta</code>.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_affine_grid</span>(<span class='kw'>theta</span>, <span class='kw'>size</span>, align_corners = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>theta</th>
+      <td><p>(Tensor) input batch of affine matrices with shape
+(\(N \times 2 \times 3\)) for 2D or  (\(N \times 3 \times 4\)) for 3D</p></td>
+    </tr>
+    <tr>
+      <th>size</th>
+      <td><p>(torch.Size) the target output image size. (\(N \times C \times H \times W\)
+for 2D or \(N \times C \times D \times H \times W\) for 3D)
+Example: torch.Size((32, 3, 24, 24))</p></td>
+    </tr>
+    <tr>
+      <th>align_corners</th>
+      <td><p>(bool, optional) if <code>True</code>, consider <code>-1</code> and <code>1</code>
+to refer to the centers of the corner pixels rather than the image corners.
+Refer to <code><a href='nnf_grid_sample.html'>nnf_grid_sample()</a></code> for a more complete description. A grid generated by
+<code>nnf_affine_grid()</code> should be passed to <code><a href='nnf_grid_sample.html'>nnf_grid_sample()</a></code>  with the same setting for
+this option. Default: <code>False</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+
+
+<p>This function is often used in conjunction with <code><a href='nnf_grid_sample.html'>nnf_grid_sample()</a></code>
+to build <code>Spatial Transformer Networks</code>_ .</p>
+
+  </div>
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+    <div class="page-header">
+    <h1>Alpha_dropout</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-dropout.R'><code>R/nnf-dropout.R</code></a></small>
+    <div class="hidden name"><code>nnf_alpha_dropout.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies alpha dropout to the input.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_alpha_dropout</span>(<span class='kw'>input</span>, p = <span class='fl'>0.5</span>, training = <span class='fl'>FALSE</span>, inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>p</th>
+      <td><p>probability of an element to be zeroed. Default: 0.5</p></td>
+    </tr>
+    <tr>
+      <th>training</th>
+      <td><p>apply dropout if is <code>TRUE</code>. Default: <code>TRUE</code></p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>If set to <code>TRUE</code>, will do this operation in-place.
+Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+
+  </div>
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diff --git a/static/docs/reference/nnf_avg_pool1d.html b/static/docs/reference/nnf_avg_pool1d.html
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+    <div class="page-header">
+    <h1>Avg_pool1d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
+    <div class="hidden name"><code>nnf_avg_pool1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 1D average pooling over an input signal composed of several
+input planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_avg_pool1d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='kw'>NULL</span>,
+  padding = <span class='fl'>0</span>,
+  ceil_mode = <span class='fl'>FALSE</span>,
+  count_include_pad = <span class='fl'>TRUE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape (minibatch , in_channels , iW)</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>the size of the window. Can be a single number or a
+tuple <code>(kW,)</code>.</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the window. Can be a single number or a tuple
+<code>(sW,)</code>. Default: <code>kernel_size</code></p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit zero paddings on both sides of the input. Can be a
+single number or a tuple <code>(padW,)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>ceil_mode</th>
+      <td><p>when True, will use <code>ceil</code> instead of <code>floor</code> to compute the
+output shape. Default: <code>FALSE</code></p></td>
+    </tr>
+    <tr>
+      <th>count_include_pad</th>
+      <td><p>when True, will include the zero-padding in the
+averaging calculation. Default: <code>TRUE</code></p></td>
+    </tr>
+    </table>
+
+
+  </div>
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+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/nnf_avg_pool2d.html b/static/docs/reference/nnf_avg_pool2d.html
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+<meta property="og:description" content="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." />
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+
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+
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+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Avg_pool2d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
+    <div class="hidden name"><code>nnf_avg_pool2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies 2D average-pooling operation in \(kH * kW\) regions by step size
+\(sH * sW\) steps. The number of output features is equal to the number of
+input planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_avg_pool2d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='kw'>NULL</span>,
+  padding = <span class='fl'>0</span>,
+  ceil_mode = <span class='fl'>FALSE</span>,
+  count_include_pad = <span class='fl'>TRUE</span>,
+  divisor_override = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor (minibatch, in_channels , iH , iW)</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>size of the pooling region. Can be a single number or a
+tuple <code>(kH, kW)</code></p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>stride of the pooling operation. Can be a single number or a
+tuple <code>(sH, sW)</code>. Default: <code>kernel_size</code></p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit zero paddings on both sides of the input. Can be a
+single number or a tuple <code>(padH, padW)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>ceil_mode</th>
+      <td><p>when True, will use <code>ceil</code> instead of <code>floor</code> in the formula
+to compute the output shape. Default: <code>FALSE</code></p></td>
+    </tr>
+    <tr>
+      <th>count_include_pad</th>
+      <td><p>when True, will include the zero-padding in the
+averaging calculation. Default: <code>TRUE</code></p></td>
+    </tr>
+    <tr>
+      <th>divisor_override</th>
+      <td><p>if specified, it will be used as divisor, otherwise
+size of the pooling region will be used. Default: <code>NULL</code></p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nnf_avg_pool3d.html b/static/docs/reference/nnf_avg_pool3d.html
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Avg_pool3d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
+    <div class="hidden name"><code>nnf_avg_pool3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies 3D average-pooling operation in \(kT * kH * kW\) regions by step
+size \(sT * sH * sW\) steps. The number of output features is equal to
+\(\lfloor \frac{ \mbox{input planes} }{sT} \rfloor\).</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_avg_pool3d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='kw'>NULL</span>,
+  padding = <span class='fl'>0</span>,
+  ceil_mode = <span class='fl'>FALSE</span>,
+  count_include_pad = <span class='fl'>TRUE</span>,
+  divisor_override = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor (minibatch, in_channels , iT * iH , iW)</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>size of the pooling region. Can be a single number or a
+tuple <code>(kT, kH, kW)</code></p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>stride of the pooling operation. Can be a single number or a
+tuple <code>(sT, sH, sW)</code>. Default: <code>kernel_size</code></p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit zero paddings on both sides of the input. Can be a
+single number or a tuple <code>(padT, padH, padW)</code>, Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>ceil_mode</th>
+      <td><p>when True, will use <code>ceil</code> instead of <code>floor</code> in the formula
+to compute the output shape</p></td>
+    </tr>
+    <tr>
+      <th>count_include_pad</th>
+      <td><p>when True, will include the zero-padding in the
+averaging calculation</p></td>
+    </tr>
+    <tr>
+      <th>divisor_override</th>
+      <td><p>NA if specified, it will be used as divisor, otherwise
+size of the pooling region will be used. Default: <code>NULL</code></p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nnf_batch_norm.html b/static/docs/reference/nnf_batch_norm.html
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+    <div class="page-header">
+    <h1>Batch_norm</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-batchnorm.R'><code>R/nnf-batchnorm.R</code></a></small>
+    <div class="hidden name"><code>nnf_batch_norm.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies Batch Normalization for each channel across a batch of data.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_batch_norm</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>running_mean</span>,
+  <span class='kw'>running_var</span>,
+  weight = <span class='kw'>NULL</span>,
+  bias = <span class='kw'>NULL</span>,
+  training = <span class='fl'>FALSE</span>,
+  momentum = <span class='fl'>0.1</span>,
+  eps = <span class='fl'>1e-05</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor</p></td>
+    </tr>
+    <tr>
+      <th>running_mean</th>
+      <td><p>the running_mean tensor</p></td>
+    </tr>
+    <tr>
+      <th>running_var</th>
+      <td><p>the running_var tensor</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>the weight tensor</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>the bias tensor</p></td>
+    </tr>
+    <tr>
+      <th>training</th>
+      <td><p>bool wether it's training. Default: FALSE</p></td>
+    </tr>
+    <tr>
+      <th>momentum</th>
+      <td><p>the value used for the <code>running_mean</code> and <code>running_var</code> computation.
+Can be set to None for cumulative moving average (i.e. simple average). Default: 0.1</p></td>
+    </tr>
+    <tr>
+      <th>eps</th>
+      <td><p>a value added to the denominator for numerical stability. Default: 1e-5</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nnf_bilinear.html b/static/docs/reference/nnf_bilinear.html
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+
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+    <div class="page-header">
+    <h1>Bilinear</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-linear.R'><code>R/nnf-linear.R</code></a></small>
+    <div class="hidden name"><code>nnf_bilinear.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a bilinear transformation to the incoming data:
+\(y = x_1 A x_2 + b\)</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_bilinear</span>(<span class='kw'>input1</span>, <span class='kw'>input2</span>, <span class='kw'>weight</span>, bias = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input1</th>
+      <td><p>\((N, *, H_{in1})\) where \(H_{in1}=\mbox{in1\_features}\)
+and \(*\) means any number of additional dimensions.
+All but the last dimension of the inputs should be the same.</p></td>
+    </tr>
+    <tr>
+      <th>input2</th>
+      <td><p>\((N, *, H_{in2})\) where \(H_{in2}=\mbox{in2\_features}\)</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>\((\mbox{out\_features}, \mbox{in1\_features},
+\mbox{in2\_features})\)</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>\((\mbox{out\_features})\)</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
+
+    <p>output \((N, *, H_{out})\) where \(H_{out}=\mbox{out\_features}\)
+and all but the last dimension are the same shape as the input.</p>
+
+  </div>
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+    <div class="page-header">
+    <h1>Binary_cross_entropy</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
+    <div class="hidden name"><code>nnf_binary_cross_entropy.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Function that measures the Binary Cross Entropy
+between the target and the output.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_binary_cross_entropy</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>target</span>,
+  weight = <span class='kw'>NULL</span>,
+  reduction = <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"mean"</span>, <span class='st'>"sum"</span>, <span class='st'>"none"</span>)
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
+    </tr>
+    <tr>
+      <th>target</th>
+      <td><p>tensor (N,*) , same shape as the input</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>(tensor) weight for each value.</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'</p></td>
+    </tr>
+    </table>
+
+
+  </div>
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+
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+    <div class="page-header">
+    <h1>Binary_cross_entropy_with_logits</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
+    <div class="hidden name"><code>nnf_binary_cross_entropy_with_logits.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Function that measures Binary Cross Entropy between target and output
+logits.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_binary_cross_entropy_with_logits</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>target</span>,
+  weight = <span class='kw'>NULL</span>,
+  reduction = <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"mean"</span>, <span class='st'>"sum"</span>, <span class='st'>"none"</span>),
+  pos_weight = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>Tensor of arbitrary shape</p></td>
+    </tr>
+    <tr>
+      <th>target</th>
+      <td><p>Tensor of the same shape as input</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>(Tensor, optional) a manual rescaling weight if provided it's
+repeated to match input tensor shape.</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'</p></td>
+    </tr>
+    <tr>
+      <th>pos_weight</th>
+      <td><p>(Tensor, optional) a weight of positive examples.
+Must be a vector with length equal to the number of classes.</p></td>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/reference/nnf_celu.html b/static/docs/reference/nnf_celu.html
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+<meta property="og:description" content="Applies element-wise, \(CELU(x) = max(0,x) + min(0, \alpha * (exp(x \alpha) - 1))\)." />
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+<div class="row">
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+    <div class="page-header">
+    <h1>Celu</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
+    <div class="hidden name"><code>nnf_celu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies element-wise, \(CELU(x) = max(0,x) + min(0, \alpha * (exp(x \alpha) - 1))\).</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_celu</span>(<span class='kw'>input</span>, alpha = <span class='fl'>1</span>, inplace = <span class='fl'>FALSE</span>)
+
+<span class='fu'>nnf_celu_</span>(<span class='kw'>input</span>, alpha = <span class='fl'>1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
+    </tr>
+    <tr>
+      <th>alpha</th>
+      <td><p>the alpha value for the CELU formulation. Default: 1.0</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
+    </tr>
+    </table>
+
+
+  </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="row">
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+    <div class="page-header">
+    <h1>Conv1d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-conv.R'><code>R/nnf-conv.R</code></a></small>
+    <div class="hidden name"><code>nnf_conv1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 1D convolution over an input signal composed of several input
+planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_conv1d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>weight</span>,
+  bias = <span class='kw'>NULL</span>,
+  stride = <span class='fl'>1</span>,
+  padding = <span class='fl'>0</span>,
+  dilation = <span class='fl'>1</span>,
+  groups = <span class='fl'>1</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape (minibatch, in_channels , iW)</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>filters of shape (out_channels, in_channels/groups , kW)</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>optional bias of shape (out_channels). Default: <code>NULL</code></p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the convolving kernel. Can be a single number or
+a one-element tuple <code>(sW,)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit paddings on both sides of the input. Can be a
+single number or a one-element tuple <code>(padW,)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>the spacing between kernel elements. Can be a single number or
+a one-element tuple <code>(dW,)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>split input into groups, <code>in_channels</code> should be divisible by
+the number of groups. Default: 1</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Conv2d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-conv.R'><code>R/nnf-conv.R</code></a></small>
+    <div class="hidden name"><code>nnf_conv2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 2D convolution over an input image composed of several input
+planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_conv2d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>weight</span>,
+  bias = <span class='kw'>NULL</span>,
+  stride = <span class='fl'>1</span>,
+  padding = <span class='fl'>0</span>,
+  dilation = <span class='fl'>1</span>,
+  groups = <span class='fl'>1</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape (minibatch, in_channels, iH , iW)</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>filters of shape (out_channels , in_channels/groups, kH , kW)</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>optional bias tensor of shape (out_channels). Default: <code>NULL</code></p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the convolving kernel. Can be a single number or a
+tuple <code>(sH, sW)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit paddings on both sides of the input. Can be a
+single number or a tuple <code>(padH, padW)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>the spacing between kernel elements. Can be a single number or
+a tuple <code>(dH, dW)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>split input into groups, <code>in_channels</code> should be divisible by the
+number of groups. Default: 1</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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diff --git a/static/docs/reference/nnf_conv3d.html b/static/docs/reference/nnf_conv3d.html
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+    <div class="page-header">
+    <h1>Conv3d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-conv.R'><code>R/nnf-conv.R</code></a></small>
+    <div class="hidden name"><code>nnf_conv3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 3D convolution over an input image composed of several input
+planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_conv3d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>weight</span>,
+  bias = <span class='kw'>NULL</span>,
+  stride = <span class='fl'>1</span>,
+  padding = <span class='fl'>0</span>,
+  dilation = <span class='fl'>1</span>,
+  groups = <span class='fl'>1</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape (minibatch, in_channels , iT , iH , iW)</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>filters of shape (out_channels , in_channels/groups, kT , kH , kW)</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>optional bias tensor of shape (out_channels). Default: <code>NULL</code></p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the convolving kernel. Can be a single number or a
+tuple <code>(sT, sH, sW)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit paddings on both sides of the input. Can be a
+single number or a tuple <code>(padT, padH, padW)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>the spacing between kernel elements. Can be a single number or
+a tuple <code>(dT, dH, dW)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>split input into groups, <code>in_channels</code> should be divisible by
+the number of groups. Default: 1</p></td>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/reference/nnf_conv_tbc.html b/static/docs/reference/nnf_conv_tbc.html
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Conv_tbc</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-conv.R'><code>R/nnf-conv.R</code></a></small>
+    <div class="hidden name"><code>nnf_conv_tbc.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 1-dimensional sequence convolution over an input sequence.
+Input and output dimensions are (Time, Batch, Channels) - hence TBC.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_conv_tbc</span>(<span class='kw'>input</span>, <span class='kw'>weight</span>, <span class='kw'>bias</span>, pad = <span class='fl'>0</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape \((\mbox{sequence length} \times 
+batch \times \mbox{in\_channels})\)</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>filter of shape (\(\mbox{kernel width} \times \mbox{in\_channels} 
+\times \mbox{out\_channels}\))</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>bias of shape (\(\mbox{out\_channels}\))</p></td>
+    </tr>
+    <tr>
+      <th>pad</th>
+      <td><p>number of timesteps to pad. Default: 0</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/nnf_conv_transpose1d.html b/static/docs/reference/nnf_conv_transpose1d.html
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+    <div class="page-header">
+    <h1>Conv_transpose1d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-conv.R'><code>R/nnf-conv.R</code></a></small>
+    <div class="hidden name"><code>nnf_conv_transpose1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 1D transposed convolution operator over an input signal
+composed of several input planes, sometimes also called "deconvolution".</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_conv_transpose1d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>weight</span>,
+  bias = <span class='kw'>NULL</span>,
+  stride = <span class='fl'>1</span>,
+  padding = <span class='fl'>0</span>,
+  output_padding = <span class='fl'>0</span>,
+  groups = <span class='fl'>1</span>,
+  dilation = <span class='fl'>1</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape (minibatch, in_channels , iW)</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>filters of shape (out_channels, in_channels/groups , kW)</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>optional bias of shape (out_channels). Default: <code>NULL</code></p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the convolving kernel. Can be a single number or
+a one-element tuple <code>(sW,)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit paddings on both sides of the input. Can be a
+single number or a one-element tuple <code>(padW,)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>output_padding</th>
+      <td><p>padding applied to the output</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>split input into groups, <code>in_channels</code> should be divisible by
+the number of groups. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>the spacing between kernel elements. Can be a single number or
+a one-element tuple <code>(dW,)</code>. Default: 1</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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+      <div class="copyright">
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diff --git a/static/docs/reference/nnf_conv_transpose2d.html b/static/docs/reference/nnf_conv_transpose2d.html
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+<meta property="og:description" content="Applies a 2D transposed convolution operator over an input image
+composed of several input planes, sometimes also called &quot;deconvolution&quot;." />
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+
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Conv_transpose2d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-conv.R'><code>R/nnf-conv.R</code></a></small>
+    <div class="hidden name"><code>nnf_conv_transpose2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 2D transposed convolution operator over an input image
+composed of several input planes, sometimes also called "deconvolution".</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_conv_transpose2d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>weight</span>,
+  bias = <span class='kw'>NULL</span>,
+  stride = <span class='fl'>1</span>,
+  padding = <span class='fl'>0</span>,
+  output_padding = <span class='fl'>0</span>,
+  groups = <span class='fl'>1</span>,
+  dilation = <span class='fl'>1</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape (minibatch, in_channels, iH , iW)</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>filters of shape (out_channels , in_channels/groups, kH , kW)</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>optional bias tensor of shape (out_channels). Default: <code>NULL</code></p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the convolving kernel. Can be a single number or a
+tuple <code>(sH, sW)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit paddings on both sides of the input. Can be a
+single number or a tuple <code>(padH, padW)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>output_padding</th>
+      <td><p>padding applied to the output</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>split input into groups, <code>in_channels</code> should be divisible by the
+number of groups. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>the spacing between kernel elements. Can be a single number or
+a tuple <code>(dH, dW)</code>. Default: 1</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/nnf_conv_transpose3d.html b/static/docs/reference/nnf_conv_transpose3d.html
new file mode 100644
index 000000000..c18b0b124
--- /dev/null
+++ b/static/docs/reference/nnf_conv_transpose3d.html
@@ -0,0 +1,280 @@
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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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_transpose3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 3D transposed convolution operator over an input image
+composed of several input planes, sometimes also called "deconvolution"</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_conv_transpose3d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>weight</span>,
+  bias = <span class='kw'>NULL</span>,
+  stride = <span class='fl'>1</span>,
+  padding = <span class='fl'>0</span>,
+  output_padding = <span class='fl'>0</span>,
+  groups = <span class='fl'>1</span>,
+  dilation = <span class='fl'>1</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape (minibatch, in_channels , iT , iH , iW)</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>filters of shape (out_channels , in_channels/groups, kT , kH , kW)</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>optional bias tensor of shape (out_channels). Default: <code>NULL</code></p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the convolving kernel. Can be a single number or a
+tuple <code>(sT, sH, sW)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit paddings on both sides of the input. Can be a
+single number or a tuple <code>(padT, padH, padW)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>output_padding</th>
+      <td><p>padding applied to the output</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>split input into groups, <code>in_channels</code> should be divisible by
+the number of groups. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>the spacing between kernel elements. Can be a single number or
+a tuple <code>(dT, dH, dW)</code>. Default: 1</p></td>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/reference/nnf_cosine_embedding_loss.html b/static/docs/reference/nnf_cosine_embedding_loss.html
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+<meta property="og:description" content="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." />
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+<div class="row">
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+    <div class="page-header">
+    <h1>Cosine_embedding_loss</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
+    <div class="hidden name"><code>nnf_cosine_embedding_loss.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Creates a criterion that measures the loss given input tensors x_1, x_2 and a
+Tensor label y with values 1 or -1. This is used for measuring whether two inputs
+are similar or dissimilar, using the cosine distance, and is typically used
+for learning nonlinear embeddings or semi-supervised learning.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_cosine_embedding_loss</span>(
+  <span class='kw'>input1</span>,
+  <span class='kw'>input2</span>,
+  <span class='kw'>target</span>,
+  margin = <span class='fl'>0</span>,
+  reduction = <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"mean"</span>, <span class='st'>"sum"</span>, <span class='st'>"none"</span>)
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input1</th>
+      <td><p>the input x_1 tensor</p></td>
+    </tr>
+    <tr>
+      <th>input2</th>
+      <td><p>the input x_2 tensor</p></td>
+    </tr>
+    <tr>
+      <th>target</th>
+      <td><p>the target tensor</p></td>
+    </tr>
+    <tr>
+      <th>margin</th>
+      <td><p>Should be a number from -1 to 1 , 0 to 0.5 is suggested. If margin
+is missing, the default value is 0.</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nnf_cosine_similarity.html b/static/docs/reference/nnf_cosine_similarity.html
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--- /dev/null
+++ b/static/docs/reference/nnf_cosine_similarity.html
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+
+      
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Cosine_similarity</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-distance.R'><code>R/nnf-distance.R</code></a></small>
+    <div class="hidden name"><code>nnf_cosine_similarity.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Returns cosine similarity between x1 and x2, computed along dim.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_cosine_similarity</span>(<span class='kw'>x1</span>, <span class='kw'>x2</span>, dim = <span class='fl'>1</span>, eps = <span class='fl'>1e-08</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>x1</th>
+      <td><p>(Tensor) First input.</p></td>
+    </tr>
+    <tr>
+      <th>x2</th>
+      <td><p>(Tensor) Second input (of size matching x1).</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int, optional) Dimension of vectors. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>eps</th>
+      <td><p>(float, optional) Small value to avoid division by zero.
+Default: 1e-8</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$
+    \mbox{similarity} = \frac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert _2 \cdot \Vert x_2 \Vert _2, \epsilon)}
+$$</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+<div class="row">
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+    <div class="page-header">
+    <h1>Cross_entropy</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
+    <div class="hidden name"><code>nnf_cross_entropy.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>This criterion combines <code>log_softmax</code> and <code>nll_loss</code> in a single
+function.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_cross_entropy</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>target</span>,
+  weight = <span class='kw'>NULL</span>,
+  ignore_index = <span class='op'>-</span><span class='fl'>100</span>,
+  reduction = <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"mean"</span>, <span class='st'>"sum"</span>, <span class='st'>"none"</span>)
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(Tensor) \((N, C)\) where <code>C = number of classes</code> or \((N, C, H, W)\)
+in case of 2D Loss, or \((N, C, d_1, d_2, ..., d_K)\) where \(K \geq 1\)
+in the case of K-dimensional loss.</p></td>
+    </tr>
+    <tr>
+      <th>target</th>
+      <td><p>(Tensor) \((N)\) where each value is \(0 \leq \mbox{targets}[i] \leq C-1\),
+or \((N, d_1, d_2, ..., d_K)\) where \(K \geq 1\) for K-dimensional loss.</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>(Tensor, optional) a manual rescaling weight given to each class. If
+given, has to be a Tensor of size <code>C</code></p></td>
+    </tr>
+    <tr>
+      <th>ignore_index</th>
+      <td><p>(int, optional) Specifies a target value that is ignored
+and does not contribute to the input gradient.</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Ctc_loss</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
+    <div class="hidden name"><code>nnf_ctc_loss.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>The Connectionist Temporal Classification loss.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_ctc_loss</span>(
+  <span class='kw'>log_probs</span>,
+  <span class='kw'>targets</span>,
+  <span class='kw'>input_lengths</span>,
+  <span class='kw'>target_lengths</span>,
+  blank = <span class='fl'>0</span>,
+  reduction = <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"mean"</span>, <span class='st'>"sum"</span>, <span class='st'>"none"</span>),
+  zero_infinity = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>log_probs</th>
+      <td><p>\((T, N, C)\) where C = number of characters in alphabet including blank,
+T = input length, and N = batch size. The logarithmized probabilities of
+the outputs (e.g. obtained with <a href='nnf_log_softmax.html'>nnf_log_softmax</a>).</p></td>
+    </tr>
+    <tr>
+      <th>targets</th>
+      <td><p>\((N, S)\) or <code>(sum(target_lengths))</code>. Targets cannot be blank.
+In the second form, the targets are assumed to be concatenated.</p></td>
+    </tr>
+    <tr>
+      <th>input_lengths</th>
+      <td><p>\((N)\). Lengths of the inputs (must each be \(\leq T\))</p></td>
+    </tr>
+    <tr>
+      <th>target_lengths</th>
+      <td><p>\((N)\). Lengths of the targets</p></td>
+    </tr>
+    <tr>
+      <th>blank</th>
+      <td><p>(int, optional) Blank label. Default \(0\).</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'</p></td>
+    </tr>
+    <tr>
+      <th>zero_infinity</th>
+      <td><p>(bool, optional) Whether to zero infinite losses and the
+associated gradients. Default: <code>FALSE</code> Infinite losses mainly occur when the
+inputs are too short to be aligned to the targets.</p></td>
+    </tr>
+    </table>
+
+
+  </div>
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new file mode 100644
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+<meta property="og:description" content="During training, randomly zeroes some of the elements of the input
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Dropout</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-dropout.R'><code>R/nnf-dropout.R</code></a></small>
+    <div class="hidden name"><code>nnf_dropout.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>During training, randomly zeroes some of the elements of the input
+tensor with probability <code>p</code> using samples from a Bernoulli
+distribution.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_dropout</span>(<span class='kw'>input</span>, p = <span class='fl'>0.5</span>, training = <span class='fl'>TRUE</span>, inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>p</th>
+      <td><p>probability of an element to be zeroed. Default: 0.5</p></td>
+    </tr>
+    <tr>
+      <th>training</th>
+      <td><p>apply dropout if is <code>TRUE</code>. Default: <code>TRUE</code></p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>If set to <code>TRUE</code>, will do this operation in-place.
+Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/nnf_dropout2d.html b/static/docs/reference/nnf_dropout2d.html
new file mode 100644
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+<meta property="og:description" content="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).
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+<div class="row">
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+    <div class="page-header">
+    <h1>Dropout2d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-dropout.R'><code>R/nnf-dropout.R</code></a></small>
+    <div class="hidden name"><code>nnf_dropout2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Randomly zero out entire channels (a channel is a 2D feature map,
+e.g., the \(j\)-th channel of the \(i\)-th sample in the
+batched input is a 2D tensor \(input[i, j]\)) of the input tensor).
+Each channel will be zeroed out independently on every forward call with
+probability <code>p</code> using samples from a Bernoulli distribution.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_dropout2d</span>(<span class='kw'>input</span>, p = <span class='fl'>0.5</span>, training = <span class='fl'>TRUE</span>, inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>p</th>
+      <td><p>probability of a channel to be zeroed. Default: 0.5</p></td>
+    </tr>
+    <tr>
+      <th>training</th>
+      <td><p>apply dropout if is <code>TRUE</code>. Default: <code>TRUE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>If set to <code>TRUE</code>, will do this operation in-place.
+Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+
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+e.g., the \(j\)-th channel of the \(i\)-th sample in the
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+
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+
+<div class="row">
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+    <div class="page-header">
+    <h1>Dropout3d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-dropout.R'><code>R/nnf-dropout.R</code></a></small>
+    <div class="hidden name"><code>nnf_dropout3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Randomly zero out entire channels (a channel is a 3D feature map,
+e.g., the \(j\)-th channel of the \(i\)-th sample in the
+batched input is a 3D tensor \(input[i, j]\)) of the input tensor).
+Each channel will be zeroed out independently on every forward call with
+probability <code>p</code> using samples from a Bernoulli distribution.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_dropout3d</span>(<span class='kw'>input</span>, p = <span class='fl'>0.5</span>, training = <span class='fl'>TRUE</span>, inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>p</th>
+      <td><p>probability of a channel to be zeroed. Default: 0.5</p></td>
+    </tr>
+    <tr>
+      <th>training</th>
+      <td><p>apply dropout if is <code>TRUE</code>. Default: <code>TRUE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>If set to <code>TRUE</code>, will do this operation in-place.
+Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+
+  </div>
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+      <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-header">Examples</li>
+<li>
+  <a href="../reference/index.html">Reference</a>
+</li>
+<li>
+  <a href="../news/index.html">Changelog</a>
+</li>
+      </ul>
+      <ul class="nav navbar-nav navbar-right">
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+  <a href="https://github.com/mlverse/torch/">
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+     
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+      
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+</div><!--/.navbar -->
+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Elu</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
+    <div class="hidden name"><code>nnf_elu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies element-wise,
+$$ELU(x) = max(0,x) + min(0, \alpha * (exp(x) - 1))$$.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_elu</span>(<span class='kw'>input</span>, alpha = <span class='fl'>1</span>, inplace = <span class='fl'>FALSE</span>)
+
+<span class='fu'>nnf_elu_</span>(<span class='kw'>input</span>, alpha = <span class='fl'>1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
+    </tr>
+    <tr>
+      <th>alpha</th>
+      <td><p>the alpha value for the ELU formulation. Default: 1.0</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>)
+<span class='kw'>y</span> <span class='op'>&lt;-</span> <span class='fu'>nnf_elu</span>(<span class='kw'>x</span>, alpha = <span class='fl'>1</span>)
+<span class='fu'>nnf_elu_</span>(<span class='kw'>x</span>, alpha = <span class='fl'>1</span>)
+<span class='fu'><a href='torch_equal.html'>torch_equal</a></span>(<span class='kw'>x</span>, <span class='kw'>y</span>)
+
+}
+</div><div class='output co'>#&gt; [1] TRUE</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
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diff --git a/static/docs/reference/nnf_embedding.html b/static/docs/reference/nnf_embedding.html
new file mode 100644
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+++ b/static/docs/reference/nnf_embedding.html
@@ -0,0 +1,282 @@
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Embedding</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-embedding.R'><code>R/nnf-embedding.R</code></a></small>
+    <div class="hidden name"><code>nnf_embedding.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>A simple lookup table that looks up embeddings in a fixed dictionary and size.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_embedding</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>weight</span>,
+  padding_idx = <span class='kw'>NULL</span>,
+  max_norm = <span class='kw'>NULL</span>,
+  norm_type = <span class='fl'>2</span>,
+  scale_grad_by_freq = <span class='fl'>FALSE</span>,
+  sparse = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(LongTensor) Tensor containing indices into the embedding matrix</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>(Tensor) The embedding matrix with number of rows equal to the
+maximum possible index + 1, and number of columns equal to the embedding size</p></td>
+    </tr>
+    <tr>
+      <th>padding_idx</th>
+      <td><p>(int, optional) If given, pads the output with the embedding
+vector at <code>padding_idx</code> (initialized to zeros) whenever it encounters the index.</p></td>
+    </tr>
+    <tr>
+      <th>max_norm</th>
+      <td><p>(float, optional) If given, each embedding vector with norm larger
+than <code>max_norm</code> is renormalized to have norm <code>max_norm</code>. Note: this will modify
+<code>weight</code> in-place.</p></td>
+    </tr>
+    <tr>
+      <th>norm_type</th>
+      <td><p>(float, optional) The p of the p-norm to compute for the <code>max_norm</code>
+option. Default <code>2</code>.</p></td>
+    </tr>
+    <tr>
+      <th>scale_grad_by_freq</th>
+      <td><p>(boolean, optional) If given, this will scale gradients
+by the inverse of frequency of the words in the mini-batch. Default <code>FALSE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>sparse</th>
+      <td><p>(bool, optional) If <code>TRUE</code>, gradient w.r.t. <code>weight</code> will be a
+sparse tensor. See Notes under <code>nn_embedding</code> for more details regarding
+sparse gradients.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>This module is often used to retrieve word embeddings using indices.
+The input to the module is a list of indices, and the embedding matrix,
+and the output is the corresponding word embeddings.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/nnf_embedding_bag.html b/static/docs/reference/nnf_embedding_bag.html
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Embedding_bag</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-embedding.R'><code>R/nnf-embedding.R</code></a></small>
+    <div class="hidden name"><code>nnf_embedding_bag.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Computes sums, means or maxes of <code>bags</code> of embeddings, without instantiating the
+intermediate embeddings.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_embedding_bag</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>weight</span>,
+  offsets = <span class='kw'>NULL</span>,
+  max_norm = <span class='kw'>NULL</span>,
+  norm_type = <span class='fl'>2</span>,
+  scale_grad_by_freq = <span class='fl'>FALSE</span>,
+  mode = <span class='st'>"mean"</span>,
+  sparse = <span class='fl'>FALSE</span>,
+  per_sample_weights = <span class='kw'>NULL</span>,
+  include_last_offset = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(LongTensor) Tensor containing bags of indices into the embedding matrix</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>(Tensor) The embedding matrix with number of rows equal to the
+maximum possible index + 1, and number of columns equal to the embedding size</p></td>
+    </tr>
+    <tr>
+      <th>offsets</th>
+      <td><p>(LongTensor, optional) Only used when <code>input</code> is 1D. <code>offsets</code>
+determines the starting index position of each bag (sequence) in <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>max_norm</th>
+      <td><p>(float, optional) If given, each embedding vector with norm
+larger than <code>max_norm</code> is renormalized to have norm <code>max_norm</code>.
+Note: this will modify <code>weight</code> in-place.</p></td>
+    </tr>
+    <tr>
+      <th>norm_type</th>
+      <td><p>(float, optional) The <code>p</code> in the <code>p</code>-norm to compute for the
+<code>max_norm</code> option. Default <code>2</code>.</p></td>
+    </tr>
+    <tr>
+      <th>scale_grad_by_freq</th>
+      <td><p>(boolean, optional) if given, this will scale gradients
+by the inverse of frequency of the words in the mini-batch. Default <code>FALSE</code>.                                            Note: this option is not supported when <code>mode="max"</code>.</p></td>
+    </tr>
+    <tr>
+      <th>mode</th>
+      <td><p>(string, optional) <code>"sum"</code>, <code>"mean"</code> or <code>"max"</code>. Specifies
+the way to reduce the bag. Default: 'mean'</p></td>
+    </tr>
+    <tr>
+      <th>sparse</th>
+      <td><p>(bool, optional) if <code>TRUE</code>, gradient w.r.t. <code>weight</code> will be a
+sparse tensor. See Notes under <code>nn_embedding</code> for more details regarding
+sparse gradients. Note: this option is not supported when <code>mode="max"</code>.</p></td>
+    </tr>
+    <tr>
+      <th>per_sample_weights</th>
+      <td><p>(Tensor, optional) a tensor of float / double weights,
+or NULL to indicate all weights should be taken to be 1. If specified,
+<code>per_sample_weights</code> must have exactly the same shape as input and is treated
+as having the same <code>offsets</code>, if those are not <code>NULL</code>.</p></td>
+    </tr>
+    <tr>
+      <th>include_last_offset</th>
+      <td><p>(bool, optional) if <code>TRUE</code>, the size of offsets is
+equal to the number of bags + 1.</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
+  </div>
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+
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+</div>
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+</div>
+
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diff --git a/static/docs/reference/nnf_fold.html b/static/docs/reference/nnf_fold.html
new file mode 100644
index 000000000..6ac948878
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+tensor." />
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Fold</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-fold.R'><code>R/nnf-fold.R</code></a></small>
+    <div class="hidden name"><code>nnf_fold.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Combines an array of sliding local blocks into a large containing
+tensor.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_fold</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>output_size</span>,
+  <span class='kw'>kernel_size</span>,
+  dilation = <span class='fl'>1</span>,
+  padding = <span class='fl'>0</span>,
+  stride = <span class='fl'>1</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>output_size</th>
+      <td><p>the shape of the spatial dimensions of the output (i.e.,
+<code>output$sizes()[-c(1,2)]</code>)</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>the size of the sliding blocks</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>a parameter that controls the stride of elements within the
+neighborhood. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit zero padding to be added on both sides of input.
+Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the sliding blocks in the input spatial dimensions.
+Default: 1</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+
+<p>Currently, only 4-D output tensors (batched image-like tensors) are
+supported.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/nnf_fractional_max_pool2d.html b/static/docs/reference/nnf_fractional_max_pool2d.html
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index 000000000..a7afbdf5b
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+    <div class="page-header">
+    <h1>Fractional_max_pool2d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
+    <div class="hidden name"><code>nnf_fractional_max_pool2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies 2D fractional max pooling over an input signal composed of several input planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_fractional_max_pool2d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>kernel_size</span>,
+  output_size = <span class='kw'>NULL</span>,
+  output_ratio = <span class='kw'>NULL</span>,
+  return_indices = <span class='fl'>FALSE</span>,
+  random_samples = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>the size of the window to take a max over. Can be a
+single number \(k\) (for a square kernel of \(k * k\)) or
+a tuple <code>(kH, kW)</code></p></td>
+    </tr>
+    <tr>
+      <th>output_size</th>
+      <td><p>the target output size of the image of the form \(oH * oW\).
+Can be a tuple <code>(oH, oW)</code> or a single number \(oH\) for a square image \(oH * oH\)</p></td>
+    </tr>
+    <tr>
+      <th>output_ratio</th>
+      <td><p>If one wants to have an output size as a ratio of the input size,
+this option can be given. This has to be a number or tuple in the range (0, 1)</p></td>
+    </tr>
+    <tr>
+      <th>return_indices</th>
+      <td><p>if <code>True</code>, will return the indices along with the outputs.</p></td>
+    </tr>
+    <tr>
+      <th>random_samples</th>
+      <td><p>optional random samples.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>Fractional MaxPooling is described in detail in the paper <code>Fractional MaxPooling</code>_ by Ben Graham</p>
+<p>The max-pooling operation is applied in \(kH * kW\) regions by a stochastic
+step size determined by the target output size.
+The number of output features is equal to the number of input planes.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <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='kw'>input</span>,
+  <span class='kw'>kernel_size</span>,
+  output_size = <span class='kw'>NULL</span>,
+  output_ratio = <span class='kw'>NULL</span>,
+  return_indices = <span class='fl'>FALSE</span>,
+  random_samples = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>the size of the window to take a max over. Can be a single number \(k\)
+(for a square kernel of \(k * k * k\)) or a tuple <code>(kT, kH, kW)</code></p></td>
+    </tr>
+    <tr>
+      <th>output_size</th>
+      <td><p>the target output size of the form \(oT * oH * oW\).
+Can be a tuple <code>(oT, oH, oW)</code> or a single number \(oH\) for a cubic output
+\(oH * oH * oH\)</p></td>
+    </tr>
+    <tr>
+      <th>output_ratio</th>
+      <td><p>If one wants to have an output size as a ratio of the
+input size, this option can be given. This has to be a number or tuple in the
+range (0, 1)</p></td>
+    </tr>
+    <tr>
+      <th>return_indices</th>
+      <td><p>if <code>True</code>, will return the indices along with the outputs.</p></td>
+    </tr>
+    <tr>
+      <th>random_samples</th>
+      <td><p>undocumented argument.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>Fractional MaxPooling is described in detail in the paper <code>Fractional MaxPooling</code>_ by Ben Graham</p>
+<p>The max-pooling operation is applied in \(kT * kH * kW\) regions by a stochastic
+step size determined by the target output size.
+The number of output features is equal to the number of input planes.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <h1>Gelu</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_gelu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Gelu</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_gelu</span>(<span class='kw'>input</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="gelu-input-gt-tensor-"><a class="anchor" href="#gelu-input-gt-tensor-"></a>gelu(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>Applies element-wise the function
+\(GELU(x) = x * \Phi(x)\)</p>
+<p>where \(\Phi(x)\) is the Cumulative Distribution Function for
+Gaussian Distribution.</p>
+<p>See <a href='https://arxiv.org/abs/1606.08415'>Gaussian Error Linear Units (GELUs)</a>.</p>
+
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+    <h1>Glu</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_glu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>The gated linear unit. Computes:</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_glu</span>(<span class='kw'>input</span>, dim = <span class='op'>-</span><span class='fl'>1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(Tensor) input tensor</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) dimension on which to split the input. Default: -1</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$GLU(a, b) = a \otimes \sigma(b)$$</p>
+<p>where <code>input</code> is split in half along <code>dim</code> to form <code>a</code> and <code>b</code>, \(\sigma\)
+is the sigmoid function and \(\otimes\) is the element-wise product
+between matrices.</p>
+<p>See <a href='https://arxiv.org/abs/1612.08083'>Language Modeling with Gated Convolutional Networks</a>.</p>
+
+  </div>
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diff --git a/static/docs/reference/nnf_grid_sample.html b/static/docs/reference/nnf_grid_sample.html
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+    </li>
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Grid_sample</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-vision.R'><code>R/nnf-vision.R</code></a></small>
+    <div class="hidden name"><code>nnf_grid_sample.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Given an <code>input</code> and a flow-field <code>grid</code>, computes the
+<code>output</code> using <code>input</code> values and pixel locations from <code>grid</code>.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_grid_sample</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>grid</span>,
+  mode = <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"bilinear"</span>, <span class='st'>"nearest"</span>),
+  padding_mode = <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"zeros"</span>, <span class='st'>"border"</span>, <span class='st'>"reflection"</span>),
+  align_corners = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(Tensor) input of shape \((N, C, H_{\mbox{in}}, W_{\mbox{in}})\) (4-D case)                    or \((N, C, D_{\mbox{in}}, H_{\mbox{in}}, W_{\mbox{in}})\) (5-D case)</p></td>
+    </tr>
+    <tr>
+      <th>grid</th>
+      <td><p>(Tensor) flow-field of shape \((N, H_{\mbox{out}}, W_{\mbox{out}}, 2)\) (4-D case)                   or \((N, D_{\mbox{out}}, H_{\mbox{out}}, W_{\mbox{out}}, 3)\) (5-D case)</p></td>
+    </tr>
+    <tr>
+      <th>mode</th>
+      <td><p>(str) interpolation mode to calculate output values <code>'bilinear'</code> | <code>'nearest'</code>.
+Default: <code>'bilinear'</code></p></td>
+    </tr>
+    <tr>
+      <th>padding_mode</th>
+      <td><p>(str) padding mode for outside grid values <code>'zeros'</code> | <code>'border'</code>
+| <code>'reflection'</code>. Default: <code>'zeros'</code></p></td>
+    </tr>
+    <tr>
+      <th>align_corners</th>
+      <td><p>(bool, optional) Geometrically, we consider the pixels of the
+input  as squares rather than points. If set to <code>True</code>, the extrema (<code>-1</code> and
+<code>1</code>) are considered as referring to the center points of the input's corner pixels.
+If set to <code>False</code>, they are instead considered as referring to the corner
+points of the input's corner pixels, making the sampling more resolution
+agnostic. This option parallels the <code>align_corners</code> option in  <code><a href='nnf_interpolate.html'>nnf_interpolate()</a></code>, and
+so whichever option is used here should also be used there to resize the input
+image before grid sampling. Default: <code>False</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>Currently, only spatial (4-D) and volumetric (5-D) <code>input</code> are
+supported.</p>
+<p>In the spatial (4-D) case, for <code>input</code> with shape
+\((N, C, H_{\mbox{in}}, W_{\mbox{in}})\) and <code>grid</code> with shape
+\((N, H_{\mbox{out}}, W_{\mbox{out}}, 2)\), the output will have shape
+\((N, C, H_{\mbox{out}}, W_{\mbox{out}})\).</p>
+<p>For each output location <code>output[n, :, h, w]</code>, the size-2 vector
+<code>grid[n, h, w]</code> specifies <code>input</code> pixel locations <code>x</code> and <code>y</code>,
+which are used to interpolate the output value <code>output[n, :, h, w]</code>.
+In the case of 5D inputs, <code>grid[n, d, h, w]</code> specifies the
+<code>x</code>, <code>y</code>, <code>z</code> pixel locations for interpolating
+<code>output[n, :, d, h, w]</code>. <code>mode</code> argument specifies <code>nearest</code> or
+<code>bilinear</code> interpolation method to sample the input pixels.</p>
+<p><code>grid</code> specifies the sampling pixel locations normalized by the
+<code>input</code> spatial dimensions. Therefore, it should have most values in
+the range of <code>[-1, 1]</code>. For example, values <code>x = -1, y = -1</code> is the
+left-top pixel of <code>input</code>, and values  <code>x = 1, y = 1</code> is the
+right-bottom pixel of <code>input</code>.</p>
+<p>If <code>grid</code> has values outside the range of <code>[-1, 1]</code>, the corresponding
+outputs are handled as defined by <code>padding_mode</code>. Options are</p><ul>
+<li><p><code>padding_mode="zeros"</code>: use <code>0</code> for out-of-bound grid locations,</p></li>
+<li><p><code>padding_mode="border"</code>: use border values for out-of-bound grid locations,</p></li>
+<li><p><code>padding_mode="reflection"</code>: use values at locations reflected by
+the border for out-of-bound grid locations. For location far away
+from the border, it will keep being reflected until becoming in bound,
+e.g., (normalized) pixel location <code>x = -3.5</code> reflects by border <code>-1</code>
+and becomes <code>x' = 1.5</code>, then reflects by border <code>1</code> and becomes
+<code>x'' = -0.5</code>.</p></li>
+</ul>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+
+
+<p>This function is often used in conjunction with <code><a href='nnf_affine_grid.html'>nnf_affine_grid()</a></code>
+to build <code>Spatial Transformer Networks</code>_ .</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/nnf_group_norm.html b/static/docs/reference/nnf_group_norm.html
new file mode 100644
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+    <li class="dropdown-header">Torch Mechanics</li>
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+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
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+    <div class="page-header">
+    <h1>Group_norm</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-normalization.R'><code>R/nnf-normalization.R</code></a></small>
+    <div class="hidden name"><code>nnf_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='kw'>input</span>, <span class='kw'>num_groups</span>, weight = <span class='kw'>NULL</span>, bias = <span class='kw'>NULL</span>, eps = <span class='fl'>1e-05</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>num_groups</th>
+      <td><p>number of groups to separate the channels into</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>the weight tensor</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>the bias tensor</p></td>
+    </tr>
+    <tr>
+      <th>eps</th>
+      <td><p>a value added to the denominator for numerical stability. Default: 1e-5</p></td>
+    </tr>
+    </table>
+
+
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+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nnf_gumbel_softmax.html b/static/docs/reference/nnf_gumbel_softmax.html
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--- /dev/null
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+    <h1>Gumbel_softmax</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
+    <div class="hidden name"><code>nnf_gumbel_softmax.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Samples from the Gumbel-Softmax distribution and
+optionally discretizes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_gumbel_softmax</span>(<span class='kw'>logits</span>, tau = <span class='fl'>1</span>, hard = <span class='fl'>FALSE</span>, dim = <span class='op'>-</span><span class='fl'>1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>logits</th>
+      <td><p><code>[..., num_features]</code> unnormalized log probabilities</p></td>
+    </tr>
+    <tr>
+      <th>tau</th>
+      <td><p>non-negative scalar temperature</p></td>
+    </tr>
+    <tr>
+      <th>hard</th>
+      <td><p>if <code>True</code>, the returned samples will be discretized as one-hot vectors,        but will be differentiated as if it is the soft sample in autograd</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) A dimension along which softmax will be computed. Default: -1.</p></td>
+    </tr>
+    </table>
+
+
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+    <div class="page-header">
+    <h1>Hardshrink</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_hardshrink.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the hard shrinkage function element-wise</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_hardshrink</span>(<span class='kw'>input</span>, lambd = <span class='fl'>0.5</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
+    </tr>
+    <tr>
+      <th>lambd</th>
+      <td><p>the lambda value for the Hardshrink formulation. Default: 0.5</p></td>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/reference/nnf_hardsigmoid.html b/static/docs/reference/nnf_hardsigmoid.html
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+    <h1>Hardsigmoid</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>
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+    </div>
+
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+    <p>Applies the element-wise function \(\mbox{Hardsigmoid}(x) = \frac{ReLU6(x + 3)}{6}\)</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_hardsigmoid</span>(<span class='kw'>input</span>, inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>NA If set to <code>True</code>, will do this operation in-place. Default: <code>False</code></p></td>
+    </tr>
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+
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+    <h1>Hardswish</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
+    <div class="hidden name"><code>nnf_hardswish.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the hardswish function, element-wise, as described in the paper:
+Searching for MobileNetV3.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_hardswish</span>(<span class='kw'>input</span>, inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>$$ \mbox{Hardswish}(x) = \left\{
+  \begin{array}{ll}
+  0 &amp; \mbox{if } x \le -3, \\
+  x &amp; \mbox{if } x \ge +3, \\
+  x \cdot (x + 3)/6 &amp; \mbox{otherwise}
+  \end{array}
+  \right. $$</p>
+
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Hardtanh</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
+    <div class="hidden name"><code>nnf_hardtanh.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the HardTanh function element-wise.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_hardtanh</span>(<span class='kw'>input</span>, min_val = <span class='op'>-</span><span class='fl'>1</span>, max_val = <span class='fl'>1</span>, inplace = <span class='fl'>FALSE</span>)
+
+<span class='fu'>nnf_hardtanh_</span>(<span class='kw'>input</span>, min_val = <span class='op'>-</span><span class='fl'>1</span>, max_val = <span class='fl'>1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
+    </tr>
+    <tr>
+      <th>min_val</th>
+      <td><p>minimum value of the linear region range. Default: -1</p></td>
+    </tr>
+    <tr>
+      <th>max_val</th>
+      <td><p>maximum value of the linear region range. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nnf_hinge_embedding_loss.html b/static/docs/reference/nnf_hinge_embedding_loss.html
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+<meta property="og:description" content="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.
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+embeddings or semi-supervised learning." />
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Hinge_embedding_loss</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
+    <div class="hidden name"><code>nnf_hinge_embedding_loss.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Measures the loss given an input tensor xx and a labels tensor yy (containing 1 or -1).
+This is usually used for measuring whether two inputs are similar or dissimilar, e.g.
+using the L1 pairwise distance as xx , and is typically used for learning nonlinear
+embeddings or semi-supervised learning.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_hinge_embedding_loss</span>(<span class='kw'>input</span>, <span class='kw'>target</span>, margin = <span class='fl'>1</span>, reduction = <span class='st'>"mean"</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" 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>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>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/nnf_instance_norm.html b/static/docs/reference/nnf_instance_norm.html
new file mode 100644
index 000000000..3b30820f5
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+++ b/static/docs/reference/nnf_instance_norm.html
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+<!-- Generated by pkgdown: do not edit by hand -->
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+
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+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Instance_norm</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-instancenorm.R'><code>R/nnf-instancenorm.R</code></a></small>
+    <div class="hidden name"><code>nnf_instance_norm.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies Instance Normalization for each channel in each data sample in a
+batch.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_instance_norm</span>(
+  <span class='kw'>input</span>,
+  running_mean = <span class='kw'>NULL</span>,
+  running_var = <span class='kw'>NULL</span>,
+  weight = <span class='kw'>NULL</span>,
+  bias = <span class='kw'>NULL</span>,
+  use_input_stats = <span class='fl'>TRUE</span>,
+  momentum = <span class='fl'>0.1</span>,
+  eps = <span class='fl'>1e-05</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>running_mean</th>
+      <td><p>the running_mean tensor</p></td>
+    </tr>
+    <tr>
+      <th>running_var</th>
+      <td><p>the running var tensor</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>the weight tensor</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>the bias tensor</p></td>
+    </tr>
+    <tr>
+      <th>use_input_stats</th>
+      <td><p>whether to use input stats</p></td>
+    </tr>
+    <tr>
+      <th>momentum</th>
+      <td><p>a double for the momentum</p></td>
+    </tr>
+    <tr>
+      <th>eps</th>
+      <td><p>an eps double for numerical stability</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+
+      
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Interpolate</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-upsampling.R'><code>R/nnf-upsampling.R</code></a></small>
+    <div class="hidden name"><code>nnf_interpolate.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Down/up samples the input to either the given <code>size</code> or the given
+<code>scale_factor</code></p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_interpolate</span>(
+  <span class='kw'>input</span>,
+  size = <span class='kw'>NULL</span>,
+  scale_factor = <span class='kw'>NULL</span>,
+  mode = <span class='st'>"nearest"</span>,
+  align_corners = <span class='fl'>FALSE</span>,
+  recompute_scale_factor = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(Tensor) the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>size</th>
+      <td><p>(int or <code>Tuple[int]</code> or <code>Tuple[int, int]</code> or <code>Tuple[int, int, int]</code>)
+output spatial size.</p></td>
+    </tr>
+    <tr>
+      <th>scale_factor</th>
+      <td><p>(float or <code>Tuple[float]</code>) multiplier for spatial size.
+Has to match input size if it is a tuple.</p></td>
+    </tr>
+    <tr>
+      <th>mode</th>
+      <td><p>(str) algorithm used for upsampling: 'nearest' | 'linear' | 'bilinear'
+| 'bicubic' | 'trilinear' | 'area' Default: 'nearest'</p></td>
+    </tr>
+    <tr>
+      <th>align_corners</th>
+      <td><p>(bool, optional) Geometrically, we consider the pixels
+of the input and output as squares rather than points. If set to TRUE,
+the input and output tensors are aligned by the center points of their corner
+pixels, preserving the values at the corner pixels. If set to False, the
+input and output tensors are aligned by the corner points of their corner pixels,
+and the interpolation uses edge value padding for out-of-boundary values,
+making this operation <em>independent</em> of input size when <code>scale_factor</code> is kept
+the same. This only has an effect when <code>mode</code>  is <code>'linear'</code>, <code>'bilinear'</code>,
+<code>'bicubic'</code> or <code>'trilinear'</code>.  Default: <code>False</code></p></td>
+    </tr>
+    <tr>
+      <th>recompute_scale_factor</th>
+      <td><p>(bool, optional) recompute the scale_factor
+for use in the interpolation calculation.  When <code>scale_factor</code> is passed
+as a parameter, it is used to compute the <code>output_size</code>.  If <code>recompute_scale_factor</code>
+is ```True`` or not specified, a new <code>scale_factor</code> will be computed based on
+the output and input sizes for use in the interpolation computation (i.e. the
+computation will be identical to if the computed `output_size` were passed-in
+explicitly).  Otherwise, the passed-in `scale_factor` will be used in the
+interpolation computation.  Note that when `scale_factor` is floating-point,
+the recomputed scale_factor may differ from the one passed in due to rounding
+and precision issues.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>The algorithm used for interpolation is determined by <code>mode</code>.</p>
+<p>Currently temporal, spatial and volumetric sampling are supported, i.e.
+expected inputs are 3-D, 4-D or 5-D in shape.</p>
+<p>The input dimensions are interpreted in the form:
+<code>mini-batch x channels x [optional depth] x [optional height] x width</code>.</p>
+<p>The modes available for resizing are: <code>nearest</code>, <code>linear</code> (3D-only),
+<code>bilinear</code>, <code>bicubic</code> (4D-only), <code>trilinear</code> (5D-only), <code>area</code></p>
+
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+    <h1>Kl_div</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
+    <div class="hidden name"><code>nnf_kl_div.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>The Kullback-Leibler divergence Loss.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_kl_div</span>(<span class='kw'>input</span>, <span class='kw'>target</span>, reduction = <span class='st'>"mean"</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
+    </tr>
+    <tr>
+      <th>target</th>
+      <td><p>tensor (N,*) , same shape as the input</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'</p></td>
+    </tr>
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>L1_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_l1_loss.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Function that takes the mean element-wise absolute value difference.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_l1_loss</span>(<span class='kw'>input</span>, <span class='kw'>target</span>, reduction = <span class='st'>"mean"</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
+    </tr>
+    <tr>
+      <th>target</th>
+      <td><p>tensor (N,*) , same shape as the input</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'</p></td>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/reference/nnf_layer_norm.html b/static/docs/reference/nnf_layer_norm.html
new file mode 100644
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+
+      
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+<div class="row">
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+    <div class="page-header">
+    <h1>Layer_norm</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-normalization.R'><code>R/nnf-normalization.R</code></a></small>
+    <div class="hidden name"><code>nnf_layer_norm.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies Layer Normalization for last certain number of dimensions.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_layer_norm</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>normalized_shape</span>,
+  weight = <span class='kw'>NULL</span>,
+  bias = <span class='kw'>NULL</span>,
+  eps = <span class='fl'>1e-05</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="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>normalized_shape</th>
+      <td><p>input shape from an expected input of size. If a single
+integer is used, it is treated as a singleton list, and this module will normalize
+over the last dimension which is expected to be of that specific size.</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>the weight tensor</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>the bias tensor</p></td>
+    </tr>
+    <tr>
+      <th>eps</th>
+      <td><p>a value added to the denominator for numerical stability. Default: 1e-5</p></td>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/reference/nnf_leaky_relu.html b/static/docs/reference/nnf_leaky_relu.html
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+<meta property="og:description" content="Applies element-wise,
+\(LeakyReLU(x) = max(0, x) + negative_slope * min(0, x)\)" />
+
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+
+      
+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Leaky_relu</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_leaky_relu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies element-wise,
+\(LeakyReLU(x) = max(0, x) + negative_slope * min(0, x)\)</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_leaky_relu</span>(<span class='kw'>input</span>, negative_slope = <span class='fl'>0.01</span>, inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
+    </tr>
+    <tr>
+      <th>negative_slope</th>
+      <td><p>Controls the angle of the negative slope. Default: 1e-2</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
+    </tr>
+    </table>
+
+
+  </div>
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diff --git a/static/docs/reference/nnf_linear.html b/static/docs/reference/nnf_linear.html
new file mode 100644
index 000000000..45d222cf2
--- /dev/null
+++ b/static/docs/reference/nnf_linear.html
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+    <h1>Linear</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-linear.R'><code>R/nnf-linear.R</code></a></small>
+    <div class="hidden name"><code>nnf_linear.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a linear transformation to the incoming data: \(y = xA^T + b\).</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_linear</span>(<span class='kw'>input</span>, <span class='kw'>weight</span>, bias = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
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+    <h1>Local_response_norm</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-normalization.R'><code>R/nnf-normalization.R</code></a></small>
+    <div class="hidden name"><code>nnf_local_response_norm.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies local response normalization over an input signal composed of
+several input planes, where channels occupy the second dimension.
+Applies normalization across channels.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_local_response_norm</span>(<span class='kw'>input</span>, <span class='kw'>size</span>, alpha = <span class='fl'>1e-04</span>, beta = <span class='fl'>0.75</span>, k = <span class='fl'>1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="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>size</th>
+      <td><p>amount of neighbouring channels used for normalization</p></td>
+    </tr>
+    <tr>
+      <th>alpha</th>
+      <td><p>multiplicative factor. Default: 0.0001</p></td>
+    </tr>
+    <tr>
+      <th>beta</th>
+      <td><p>exponent. Default: 0.75</p></td>
+    </tr>
+    <tr>
+      <th>k</th>
+      <td><p>additive factor. Default: 1</p></td>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/reference/nnf_log_softmax.html b/static/docs/reference/nnf_log_softmax.html
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+<meta property="og:description" content="Applies a softmax followed by a logarithm." />
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+    <div class="page-header">
+    <h1>Log_softmax</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
+    <div class="hidden name"><code>nnf_log_softmax.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a softmax followed by a logarithm.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_log_softmax</span>(<span class='kw'>input</span>, dim = <span class='kw'>NULL</span>, dtype = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <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>
+
+  </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>Logsigmoid</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_logsigmoid.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies element-wise \(LogSigmoid(x_i) = log(\frac{1}{1 + exp(-x_i)})\)</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_logsigmoid</span>(<span class='kw'>input</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/reference/nnf_lp_pool1d.html b/static/docs/reference/nnf_lp_pool1d.html
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+<meta property="og:description" content="Applies a 1D power-average pooling over an input signal composed of
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+zero, the gradient is set to zero as well." />
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+    <div class="page-header">
+    <h1>Lp_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_lp_pool1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 1D power-average pooling over an input signal composed of
+several input planes. If the sum of all inputs to the power of <code>p</code> is
+zero, the gradient is set to zero as well.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_lp_pool1d</span>(<span class='kw'>input</span>, <span class='kw'>norm_type</span>, <span class='kw'>kernel_size</span>, stride = <span class='kw'>NULL</span>, ceil_mode = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="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>norm_type</th>
+      <td><p>if inf than one gets max pooling if 0 you get sum pooling (
+proportional to the avg pooling)</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>a single int, the size of the window</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>a single int, the stride of the window. Default value is kernel_size</p></td>
+    </tr>
+    <tr>
+      <th>ceil_mode</th>
+      <td><p>when True, will use ceil instead of floor to compute the output shape</p></td>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/reference/nnf_lp_pool2d.html b/static/docs/reference/nnf_lp_pool2d.html
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--- /dev/null
+++ b/static/docs/reference/nnf_lp_pool2d.html
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+    <div class="page-header">
+    <h1>Lp_pool2d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
+    <div class="hidden name"><code>nnf_lp_pool2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 2D power-average pooling over an input signal composed of
+several input planes. If the sum of all inputs to the power of <code>p</code> is
+zero, the gradient is set to zero as well.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_lp_pool2d</span>(<span class='kw'>input</span>, <span class='kw'>norm_type</span>, <span class='kw'>kernel_size</span>, stride = <span class='kw'>NULL</span>, ceil_mode = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="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>norm_type</th>
+      <td><p>if inf than one gets max pooling if 0 you get sum pooling (
+proportional to the avg pooling)</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>a single int, the size of the window</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>a single int, the stride of the window. Default value is kernel_size</p></td>
+    </tr>
+    <tr>
+      <th>ceil_mode</th>
+      <td><p>when True, will use ceil instead of floor to compute the output shape</p></td>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/reference/nnf_margin_ranking_loss.html b/static/docs/reference/nnf_margin_ranking_loss.html
new file mode 100644
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+<meta property="og:description" content="Creates a criterion that measures the loss given inputs x1 , x2 , two 1D
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Margin_ranking_loss</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
+    <div class="hidden name"><code>nnf_margin_ranking_loss.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Creates a criterion that measures the loss given inputs x1 , x2 , two 1D
+mini-batch Tensors, and a label 1D mini-batch tensor y (containing 1 or -1).</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_margin_ranking_loss</span>(<span class='kw'>input1</span>, <span class='kw'>input2</span>, <span class='kw'>target</span>, margin = <span class='fl'>0</span>, reduction = <span class='st'>"mean"</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input1</th>
+      <td><p>the first tensor</p></td>
+    </tr>
+    <tr>
+      <th>input2</th>
+      <td><p>the second input tensor</p></td>
+    </tr>
+    <tr>
+      <th>target</th>
+      <td><p>the target tensor</p></td>
+    </tr>
+    <tr>
+      <th>margin</th>
+      <td><p>Has a default value of 00 .</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'</p></td>
+    </tr>
+    </table>
+
+
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+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nnf_max_pool1d.html b/static/docs/reference/nnf_max_pool1d.html
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+++ b/static/docs/reference/nnf_max_pool1d.html
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+    <h1>Max_pool1d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
+    <div class="hidden name"><code>nnf_max_pool1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 1D max pooling over an input signal composed of several input
+planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_max_pool1d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='kw'>NULL</span>,
+  padding = <span class='fl'>0</span>,
+  dilation = <span class='fl'>1</span>,
+  ceil_mode = <span class='fl'>FALSE</span>,
+  return_indices = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape (minibatch , in_channels , iW)</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>the size of the window. Can be a single number or a
+tuple <code>(kW,)</code>.</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the window. Can be a single number or a tuple
+<code>(sW,)</code>. Default: <code>kernel_size</code></p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit zero paddings on both sides of the input. Can be a
+single number or a tuple <code>(padW,)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>controls the spacing between the kernel points; also known as
+the à trous algorithm.</p></td>
+    </tr>
+    <tr>
+      <th>ceil_mode</th>
+      <td><p>when True, will use <code>ceil</code> instead of <code>floor</code> to compute the
+output shape. Default: <code>FALSE</code></p></td>
+    </tr>
+    <tr>
+      <th>return_indices</th>
+      <td><p>whether to return the indices where the max occurs.</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/nnf_max_pool2d.html b/static/docs/reference/nnf_max_pool2d.html
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+
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+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Max_pool2d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
+    <div class="hidden name"><code>nnf_max_pool2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 2D max pooling over an input signal composed of several input
+planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_max_pool2d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='kw'>kernel_size</span>,
+  padding = <span class='fl'>0</span>,
+  dilation = <span class='fl'>1</span>,
+  ceil_mode = <span class='fl'>FALSE</span>,
+  return_indices = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor (minibatch, in_channels , iH , iW)</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>size of the pooling region. Can be a single number or a
+tuple <code>(kH, kW)</code></p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>stride of the pooling operation. Can be a single number or a
+tuple <code>(sH, sW)</code>. Default: <code>kernel_size</code></p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit zero paddings on both sides of the input. Can be a
+single number or a tuple <code>(padH, padW)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>controls the spacing between the kernel points; also known as
+the à trous algorithm.</p></td>
+    </tr>
+    <tr>
+      <th>ceil_mode</th>
+      <td><p>when True, will use <code>ceil</code> instead of <code>floor</code> in the formula
+to compute the output shape. Default: <code>FALSE</code></p></td>
+    </tr>
+    <tr>
+      <th>return_indices</th>
+      <td><p>whether to return the indices where the max occurs.</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/nnf_max_pool3d.html b/static/docs/reference/nnf_max_pool3d.html
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index 000000000..840ffcf2e
--- /dev/null
+++ b/static/docs/reference/nnf_max_pool3d.html
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+    <h1>Max_pool3d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
+    <div class="hidden name"><code>nnf_max_pool3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a 3D max pooling over an input signal composed of several input
+planes.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_max_pool3d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='kw'>NULL</span>,
+  padding = <span class='fl'>0</span>,
+  dilation = <span class='fl'>1</span>,
+  ceil_mode = <span class='fl'>FALSE</span>,
+  return_indices = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor (minibatch, in_channels , iT * iH , iW)</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>size of the pooling region. Can be a single number or a
+tuple <code>(kT, kH, kW)</code></p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>stride of the pooling operation. Can be a single number or a
+tuple <code>(sT, sH, sW)</code>. Default: <code>kernel_size</code></p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit zero paddings on both sides of the input. Can be a
+single number or a tuple <code>(padT, padH, padW)</code>, Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>controls the spacing between the kernel points; also known as
+the à trous algorithm.</p></td>
+    </tr>
+    <tr>
+      <th>ceil_mode</th>
+      <td><p>when True, will use <code>ceil</code> instead of <code>floor</code> in the formula
+to compute the output shape</p></td>
+    </tr>
+    <tr>
+      <th>return_indices</th>
+      <td><p>whether to return the indices where the max occurs.</p></td>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/reference/nnf_max_unpool1d.html b/static/docs/reference/nnf_max_unpool1d.html
new file mode 100644
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--- /dev/null
+++ b/static/docs/reference/nnf_max_unpool1d.html
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+    <div class="page-header">
+    <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='kw'>input</span>,
+  <span class='kw'>indices</span>,
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='kw'>NULL</span>,
+  padding = <span class='fl'>0</span>,
+  output_size = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>the input Tensor to invert</p></td>
+    </tr>
+    <tr>
+      <th>indices</th>
+      <td><p>the indices given out by max pool</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>Size of the max pooling window.</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>Stride of the max pooling window. It is set to kernel_size by default.</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>Padding that was added to the input</p></td>
+    </tr>
+    <tr>
+      <th>output_size</th>
+      <td><p>the targeted output size</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/nnf_max_unpool2d.html b/static/docs/reference/nnf_max_unpool2d.html
new file mode 100644
index 000000000..eaeb2fe6c
--- /dev/null
+++ b/static/docs/reference/nnf_max_unpool2d.html
@@ -0,0 +1,264 @@
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+    <div class="page-header">
+    <h1>Max_unpool2d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
+    <div class="hidden name"><code>nnf_max_unpool2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Computes a partial inverse of <code>MaxPool2d</code>.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_max_unpool2d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>indices</span>,
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='kw'>NULL</span>,
+  padding = <span class='fl'>0</span>,
+  output_size = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>the input Tensor to invert</p></td>
+    </tr>
+    <tr>
+      <th>indices</th>
+      <td><p>the indices given out by max pool</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>Size of the max pooling window.</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>Stride of the max pooling window. It is set to kernel_size by default.</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>Padding that was added to the input</p></td>
+    </tr>
+    <tr>
+      <th>output_size</th>
+      <td><p>the targeted output size</p></td>
+    </tr>
+    </table>
+
+
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Max_unpool3d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-pooling.R'><code>R/nnf-pooling.R</code></a></small>
+    <div class="hidden name"><code>nnf_max_unpool3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Computes a partial inverse of <code>MaxPool3d</code>.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_max_unpool3d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>indices</span>,
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='kw'>NULL</span>,
+  padding = <span class='fl'>0</span>,
+  output_size = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>the input Tensor to invert</p></td>
+    </tr>
+    <tr>
+      <th>indices</th>
+      <td><p>the indices given out by max pool</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>Size of the max pooling window.</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>Stride of the max pooling window. It is set to kernel_size by default.</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>Padding that was added to the input</p></td>
+    </tr>
+    <tr>
+      <th>output_size</th>
+      <td><p>the targeted output size</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/nnf_mse_loss.html b/static/docs/reference/nnf_mse_loss.html
new file mode 100644
index 000000000..39a803971
--- /dev/null
+++ b/static/docs/reference/nnf_mse_loss.html
@@ -0,0 +1,248 @@
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+<div class="row">
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+    <div class="page-header">
+    <h1>Mse_loss</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
+    <div class="hidden name"><code>nnf_mse_loss.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Measures the element-wise mean squared error.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_mse_loss</span>(<span class='kw'>input</span>, <span class='kw'>target</span>, reduction = <span class='st'>"mean"</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
+    </tr>
+    <tr>
+      <th>target</th>
+      <td><p>tensor (N,*) , same shape as the input</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'</p></td>
+    </tr>
+    </table>
+
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Multi head attention forward</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
+    <div class="hidden name"><code>nnf_multi_head_attention_forward.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Allows the model to jointly attend to information from different representation
+subspaces. See reference: Attention Is All You Need</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_multi_head_attention_forward</span>(
+  <span class='kw'>query</span>,
+  <span class='kw'>key</span>,
+  <span class='kw'>value</span>,
+  <span class='kw'>embed_dim_to_check</span>,
+  <span class='kw'>num_heads</span>,
+  <span class='kw'>in_proj_weight</span>,
+  <span class='kw'>in_proj_bias</span>,
+  <span class='kw'>bias_k</span>,
+  <span class='kw'>bias_v</span>,
+  <span class='kw'>add_zero_attn</span>,
+  <span class='kw'>dropout_p</span>,
+  <span class='kw'>out_proj_weight</span>,
+  <span class='kw'>out_proj_bias</span>,
+  training = <span class='fl'>TRUE</span>,
+  key_padding_mask = <span class='kw'>NULL</span>,
+  need_weights = <span class='fl'>TRUE</span>,
+  attn_mask = <span class='kw'>NULL</span>,
+  use_separate_proj_weight = <span class='fl'>FALSE</span>,
+  q_proj_weight = <span class='kw'>NULL</span>,
+  k_proj_weight = <span class='kw'>NULL</span>,
+  v_proj_weight = <span class='kw'>NULL</span>,
+  static_k = <span class='kw'>NULL</span>,
+  static_v = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>query</th>
+      <td><p>\((L, N, E)\) where L is the target sequence length, N is the batch size, E is
+the embedding dimension.</p></td>
+    </tr>
+    <tr>
+      <th>key</th>
+      <td><p>\((S, N, E)\), where S is the source sequence length, N is the batch size, E is
+the embedding dimension.</p></td>
+    </tr>
+    <tr>
+      <th>value</th>
+      <td><p>\((S, N, E)\) where S is the source sequence length, N is the batch size, E is
+the embedding dimension.</p></td>
+    </tr>
+    <tr>
+      <th>embed_dim_to_check</th>
+      <td><p>total dimension of the model.</p></td>
+    </tr>
+    <tr>
+      <th>num_heads</th>
+      <td><p>parallel attention heads.</p></td>
+    </tr>
+    <tr>
+      <th>in_proj_weight</th>
+      <td><p>input projection weight and bias.</p></td>
+    </tr>
+    <tr>
+      <th>in_proj_bias</th>
+      <td><p>currently undocumented.</p></td>
+    </tr>
+    <tr>
+      <th>bias_k</th>
+      <td><p>bias of the key and value sequences to be added at dim=0.</p></td>
+    </tr>
+    <tr>
+      <th>bias_v</th>
+      <td><p>currently undocumented.</p></td>
+    </tr>
+    <tr>
+      <th>add_zero_attn</th>
+      <td><p>add a new batch of zeros to the key and
+value sequences at dim=1.</p></td>
+    </tr>
+    <tr>
+      <th>dropout_p</th>
+      <td><p>probability of an element to be zeroed.</p></td>
+    </tr>
+    <tr>
+      <th>out_proj_weight</th>
+      <td><p>the output projection weight and bias.</p></td>
+    </tr>
+    <tr>
+      <th>out_proj_bias</th>
+      <td><p>currently undocumented.</p></td>
+    </tr>
+    <tr>
+      <th>training</th>
+      <td><p>apply dropout if is <code>TRUE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>key_padding_mask</th>
+      <td><p>\((N, S)\) where N is the batch size, S is the source sequence length.
+If a ByteTensor is provided, the non-zero positions will be ignored while the position
+with the zero positions will be unchanged. If a BoolTensor is provided, the positions with the
+value of <code>True</code> will be ignored while the position with the value of <code>False</code> will be unchanged.</p></td>
+    </tr>
+    <tr>
+      <th>need_weights</th>
+      <td><p>output attn_output_weights.</p></td>
+    </tr>
+    <tr>
+      <th>attn_mask</th>
+      <td><p>2D mask \((L, S)\) where L is the target sequence length, S is the source sequence length.
+3D mask \((N*num_heads, L, S)\) where N is the batch size, L is the target sequence length,
+S is the source sequence length. attn_mask ensure that position i is allowed to attend the unmasked
+positions. If a ByteTensor is provided, the non-zero positions are not allowed to attend
+while the zero positions will be unchanged. If a BoolTensor is provided, positions with <code>True</code>
+is not allowed to attend while <code>False</code> values will be unchanged. If a FloatTensor
+is provided, it will be added to the attention weight.</p></td>
+    </tr>
+    <tr>
+      <th>use_separate_proj_weight</th>
+      <td><p>the function accept the proj. weights for
+query, key, and value in different forms. If false, in_proj_weight will be used,
+which is a combination of q_proj_weight, k_proj_weight, v_proj_weight.</p></td>
+    </tr>
+    <tr>
+      <th>q_proj_weight</th>
+      <td><p>input projection weight and bias.</p></td>
+    </tr>
+    <tr>
+      <th>k_proj_weight</th>
+      <td><p>currently undocumented.</p></td>
+    </tr>
+    <tr>
+      <th>v_proj_weight</th>
+      <td><p>currently undocumented.</p></td>
+    </tr>
+    <tr>
+      <th>static_k</th>
+      <td><p>static key and value used for attention operators.</p></td>
+    </tr>
+    <tr>
+      <th>static_v</th>
+      <td><p>currently undocumented.</p></td>
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+    <div class="page-header">
+    <h1>Multi_margin_loss</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
+    <div class="hidden name"><code>nnf_multi_margin_loss.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Creates a criterion that optimizes a multi-class classification hinge loss
+(margin-based loss) between input x (a 2D mini-batch Tensor) and output y
+(which is a 1D tensor of target class indices, <code>0 &lt;= y &lt;= x$size(2) - 1</code> ).</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_multi_margin_loss</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>target</span>,
+  p = <span class='fl'>1</span>,
+  margin = <span class='fl'>1</span>,
+  weight = <span class='kw'>NULL</span>,
+  reduction = <span class='st'>"mean"</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
+    </tr>
+    <tr>
+      <th>target</th>
+      <td><p>tensor (N,*) , same shape as the input</p></td>
+    </tr>
+    <tr>
+      <th>p</th>
+      <td><p>Has a default value of 1. 1 and 2 are the only supported values.</p></td>
+    </tr>
+    <tr>
+      <th>margin</th>
+      <td><p>Has a default value of 1.</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>a manual rescaling weight given to each class. If given, it has to
+be a Tensor of size C. Otherwise, it is treated as if having all ones.</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'</p></td>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/reference/nnf_multilabel_margin_loss.html b/static/docs/reference/nnf_multilabel_margin_loss.html
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+<meta property="og:description" content="Creates a criterion that optimizes a multi-class multi-classification hinge loss
+(margin-based loss) between input x (a 2D mini-batch Tensor) and output y (which
+is a 2D Tensor of target class indices)." />
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Multilabel_margin_loss</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
+    <div class="hidden name"><code>nnf_multilabel_margin_loss.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Creates a criterion that optimizes a multi-class multi-classification hinge loss
+(margin-based loss) between input x (a 2D mini-batch Tensor) and output y (which
+is a 2D Tensor of target class indices).</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_multilabel_margin_loss</span>(<span class='kw'>input</span>, <span class='kw'>target</span>, reduction = <span class='st'>"mean"</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
+    </tr>
+    <tr>
+      <th>target</th>
+      <td><p>tensor (N,*) , same shape as the input</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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+    <h1>Multilabel_soft_margin_loss</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
+    <div class="hidden name"><code>nnf_multilabel_soft_margin_loss.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Creates a criterion that optimizes a multi-label one-versus-all loss based on
+max-entropy, between input x and target y of size (N, C).</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_multilabel_soft_margin_loss</span>(<span class='kw'>input</span>, <span class='kw'>target</span>, <span class='kw'>weight</span>, reduction = <span class='st'>"mean"</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
+    </tr>
+    <tr>
+      <th>target</th>
+      <td><p>tensor (N,*) , same shape as the input</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>weight tensor to apply on the loss.</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'</p></td>
+    </tr>
+    </table>
+
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+    <div class="page-header">
+    <h1>Nll_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_nll_loss.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>The negative log likelihood loss.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_nll_loss</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>target</span>,
+  weight = <span class='kw'>NULL</span>,
+  ignore_index = <span class='op'>-</span><span class='fl'>100</span>,
+  reduction = <span class='st'>"mean"</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>\((N, C)\) where <code>C = number of classes</code> or \((N, C, H, W)\) in
+case of 2D Loss, or \((N, C, d_1, d_2, ..., d_K)\) where \(K \geq 1\) in
+the case of K-dimensional loss.</p></td>
+    </tr>
+    <tr>
+      <th>target</th>
+      <td><p>\((N)\) where each value is \(0 \leq \mbox{targets}[i] \leq C-1\),
+or \((N, d_1, d_2, ..., d_K)\) where \(K \geq 1\) for K-dimensional loss.</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>(Tensor, optional) a manual rescaling weight given to each class.
+If given, has to be a Tensor of size <code>C</code></p></td>
+    </tr>
+    <tr>
+      <th>ignore_index</th>
+      <td><p>(int, optional) Specifies a target value that is ignored and
+does not contribute to the input gradient.</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <h1>Normalize</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-normalization.R'><code>R/nnf-normalization.R</code></a></small>
+    <div class="hidden name"><code>nnf_normalize.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Performs \(L_p\) normalization of inputs over specified dimension.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_normalize</span>(<span class='kw'>input</span>, p = <span class='fl'>2</span>, dim = <span class='fl'>1</span>, eps = <span class='fl'>1e-12</span>, out = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of any shape</p></td>
+    </tr>
+    <tr>
+      <th>p</th>
+      <td><p>(float) the exponent value in the norm formulation. Default: 2</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the dimension to reduce. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>eps</th>
+      <td><p>(float) small value to avoid division by zero. Default: 1e-12</p></td>
+    </tr>
+    <tr>
+      <th>out</th>
+      <td><p>(Tensor, optional) the output tensor. If <code>out</code> is used, this                            operation won't be differentiable.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <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>
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diff --git a/static/docs/reference/nnf_one_hot.html b/static/docs/reference/nnf_one_hot.html
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+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>One_hot</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-embedding.R'><code>R/nnf-embedding.R</code></a></small>
+    <div class="hidden name"><code>nnf_one_hot.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Takes LongTensor with index values of shape <code>(*)</code> and returns a tensor
+of shape <code>(*, num_classes)</code> that have zeros everywhere except where the
+index of last dimension matches the corresponding value of the input tensor,
+in which case it will be 1.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_one_hot</span>(<span class='kw'>tensor</span>, num_classes = <span class='op'>-</span><span class='fl'>1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensor</th>
+      <td><p>(LongTensor) class values of any shape.</p></td>
+    </tr>
+    <tr>
+      <th>num_classes</th>
+      <td><p>(int) Total number of classes. If set to -1, the number
+of classes will be inferred as one greater than the largest class value in
+the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>One-hot on Wikipedia: https://en.wikipedia.org/wiki/One-hot</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+
diff --git a/static/docs/reference/nnf_pad.html b/static/docs/reference/nnf_pad.html
new file mode 100644
index 000000000..aeb442200
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+++ b/static/docs/reference/nnf_pad.html
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+    <h1>Pad</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-padding.R'><code>R/nnf-padding.R</code></a></small>
+    <div class="hidden name"><code>nnf_pad.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Pads tensor.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_pad</span>(<span class='kw'>input</span>, <span class='kw'>pad</span>, mode = <span class='st'>"constant"</span>, value = <span class='fl'>0</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(Tensor) N-dimensional tensor</p></td>
+    </tr>
+    <tr>
+      <th>pad</th>
+      <td><p>(tuple) m-elements tuple, where \(\frac{m}{2} \leq\) input dimensions
+and \(m\) is even.</p></td>
+    </tr>
+    <tr>
+      <th>mode</th>
+      <td><p>'constant', 'reflect', 'replicate' or 'circular'. Default: 'constant'</p></td>
+    </tr>
+    <tr>
+      <th>value</th>
+      <td><p>fill value for 'constant' padding. Default: 0.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="padding-size"><a class="anchor" href="#padding-size"></a>Padding size</h2>
+
+    
+
+
+<p>The padding size by which to pad some dimensions of <code>input</code>
+are described starting from the last dimension and moving forward.
+\(\left\lfloor\frac{\mbox{len(pad)}}{2}\right\rfloor\) dimensions
+of <code>input</code> will be padded.
+For example, to pad only the last dimension of the input tensor, then
+<code>pad</code> has the form
+\((\mbox{padding\_left}, \mbox{padding\_right})\);
+to pad the last 2 dimensions of the input tensor, then use
+\((\mbox{padding\_left}, \mbox{padding\_right},\)
+\(\mbox{padding\_top}, \mbox{padding\_bottom})\);
+to pad the last 3 dimensions, use
+\((\mbox{padding\_left}, \mbox{padding\_right},\)
+\(\mbox{padding\_top}, \mbox{padding\_bottom}\)
+\(\mbox{padding\_front}, \mbox{padding\_back})\).</p>
+    <h2 class="hasAnchor" id="padding-mode"><a class="anchor" href="#padding-mode"></a>Padding mode</h2>
+
+    
+
+
+<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/static/docs/reference/nnf_pairwise_distance.html b/static/docs/reference/nnf_pairwise_distance.html
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+    <div class="page-header">
+    <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='kw'>x1</span>, <span class='kw'>x2</span>, p = <span class='fl'>2</span>, eps = <span class='fl'>1e-06</span>, keepdim = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>x1</th>
+      <td><p>(Tensor) First input.</p></td>
+    </tr>
+    <tr>
+      <th>x2</th>
+      <td><p>(Tensor) Second input (of size matching x1).</p></td>
+    </tr>
+    <tr>
+      <th>p</th>
+      <td><p>the norm degree. Default: 2</p></td>
+    </tr>
+    <tr>
+      <th>eps</th>
+      <td><p>(float, optional) Small value to avoid division by zero.
+Default: 1e-8</p></td>
+    </tr>
+    <tr>
+      <th>keepdim</th>
+      <td><p>Determines whether or not to keep the vector dimension. Default: False</p></td>
+    </tr>
+    </table>
+
+
+  </div>
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diff --git a/static/docs/reference/nnf_pdist.html b/static/docs/reference/nnf_pdist.html
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+<div class="row">
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+    <div class="page-header">
+    <h1>Pdist</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_pdist.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Computes the p-norm distance between every pair of row vectors in the input.
+This is identical to the upper triangular portion, excluding the diagonal, of
+<code>torch_norm(input[:, None] - input, dim=2, p=p)</code>. This function will be faster
+if the rows are contiguous.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_pdist</span>(<span class='kw'>input</span>, p = <span class='fl'>2</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape \(N \times M\).</p></td>
+    </tr>
+    <tr>
+      <th>p</th>
+      <td><p>p value for the p-norm distance to calculate between each vector pair
+\(\in [0, \infty]\).</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>If input has shape \(N \times M\) then the output will have shape
+\(\frac{1}{2} N (N - 1)\).</p>
+
+  </div>
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diff --git a/static/docs/reference/nnf_pixel_shuffle.html b/static/docs/reference/nnf_pixel_shuffle.html
new file mode 100644
index 000000000..2024598b7
--- /dev/null
+++ b/static/docs/reference/nnf_pixel_shuffle.html
@@ -0,0 +1,243 @@
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+<meta property="og:description" content="Rearranges elements in a tensor of shape \((*, C \times r^2, H, W)\) to a
+tensor of shape \((*, C, H \times r, W \times r)\)." />
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Pixel_shuffle</h1>
+    <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>
+    <div class="hidden name"><code>nnf_pixel_shuffle.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <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>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_pixel_shuffle</span>(<span class='kw'>input</span>, <span class='kw'>upscale_factor</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>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>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nnf_poisson_nll_loss.html b/static/docs/reference/nnf_poisson_nll_loss.html
new file mode 100644
index 000000000..4212a07df
--- /dev/null
+++ b/static/docs/reference/nnf_poisson_nll_loss.html
@@ -0,0 +1,271 @@
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+    <h1>Poisson_nll_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_poisson_nll_loss.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Poisson negative log likelihood loss.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_poisson_nll_loss</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>target</span>,
+  log_input = <span class='fl'>TRUE</span>,
+  full = <span class='fl'>FALSE</span>,
+  eps = <span class='fl'>1e-08</span>,
+  reduction = <span class='st'>"mean"</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
+    </tr>
+    <tr>
+      <th>target</th>
+      <td><p>tensor (N,*) , same shape as the input</p></td>
+    </tr>
+    <tr>
+      <th>log_input</th>
+      <td><p>if <code>TRUE</code> the loss is computed as \(\exp(\mbox{input}) - \mbox{target} * \mbox{input}\),
+if <code>FALSE</code> then loss is \(\mbox{input} - \mbox{target} * \log(\mbox{input}+\mbox{eps})\).
+Default: <code>TRUE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>full</th>
+      <td><p>whether to compute full loss, i. e. to add the Stirling approximation
+term. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>eps</th>
+      <td><p>(float, optional) Small value to avoid evaluation of \(\log(0)\) when
+<code>log_input</code>=<code>FALSE</code>. Default: 1e-8</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'</p></td>
+    </tr>
+    </table>
+
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+    <div class="page-header">
+    <h1>Prelu</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
+    <div class="hidden name"><code>nnf_prelu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies element-wise the function
+\(PReLU(x) = max(0,x) + weight * min(0,x)\)
+where weight is a learnable parameter.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_prelu</span>(<span class='kw'>input</span>, <span class='kw'>weight</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>(Tensor) the learnable weights</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+    <div class="page-header">
+    <h1>Relu</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_relu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the rectified linear unit function element-wise.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_relu</span>(<span class='kw'>input</span>, inplace = <span class='fl'>FALSE</span>)
+
+<span class='fu'>nnf_relu_</span>(<span class='kw'>input</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
+    </tr>
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+
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+
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Relu6</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_relu6.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the element-wise function \(ReLU6(x) = min(max(0,x), 6)\).</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_relu6</span>(<span class='kw'>input</span>, inplace = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
+    </tr>
+    </table>
+
+
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+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
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+
+      
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+<div class="row">
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+    <div class="page-header">
+    <h1>Rrelu</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_rrelu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Randomized leaky ReLU.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_rrelu</span>(<span class='kw'>input</span>, lower = <span class='fl'>1</span><span class='op'>/</span><span class='fl'>8</span>, upper = <span class='fl'>1</span><span class='op'>/</span><span class='fl'>3</span>, training = <span class='fl'>FALSE</span>, inplace = <span class='fl'>FALSE</span>)
+
+<span class='fu'>nnf_rrelu_</span>(<span class='kw'>input</span>, lower = <span class='fl'>1</span><span class='op'>/</span><span class='fl'>8</span>, upper = <span class='fl'>1</span><span class='op'>/</span><span class='fl'>3</span>, training = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="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>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>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/reference/nnf_selu.html b/static/docs/reference/nnf_selu.html
new file mode 100644
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+<meta property="og:description" content="Applies element-wise,
+$$SELU(x) = scale * (max(0,x) + min(0, \alpha * (exp(x) - 1)))$$,
+with \(\alpha=1.6732632423543772848170429916717\) and
+\(scale=1.0507009873554804934193349852946\)." />
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+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Selu</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_selu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies element-wise,
+$$SELU(x) = scale * (max(0,x) + min(0, \alpha * (exp(x) - 1)))$$,
+with \(\alpha=1.6732632423543772848170429916717\) and
+\(scale=1.0507009873554804934193349852946\).</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_selu</span>(<span class='kw'>input</span>, inplace = <span class='fl'>FALSE</span>)
+
+<span class='fu'>nnf_selu_</span>(<span class='kw'>input</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>)
+<span class='kw'>y</span> <span class='op'>&lt;-</span> <span class='fu'>nnf_selu</span>(<span class='kw'>x</span>)
+<span class='fu'>nnf_selu_</span>(<span class='kw'>x</span>)
+<span class='fu'><a href='torch_equal.html'>torch_equal</a></span>(<span class='kw'>x</span>, <span class='kw'>y</span>)
+
+}
+</div><div class='output co'>#&gt; [1] TRUE</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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diff --git a/static/docs/reference/nnf_sigmoid.html b/static/docs/reference/nnf_sigmoid.html
new file mode 100644
index 000000000..b1110569e
--- /dev/null
+++ b/static/docs/reference/nnf_sigmoid.html
@@ -0,0 +1,238 @@
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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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_sigmoid.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies element-wise \(Sigmoid(x_i) = \frac{1}{1 + exp(-x_i)}\)</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_sigmoid</span>(<span class='kw'>input</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
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diff --git a/static/docs/reference/nnf_smooth_l1_loss.html b/static/docs/reference/nnf_smooth_l1_loss.html
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+    <h1>Smooth_l1_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_smooth_l1_loss.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Function that uses a squared term if the absolute
+element-wise error falls below 1 and an L1 term otherwise.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_smooth_l1_loss</span>(<span class='kw'>input</span>, <span class='kw'>target</span>, reduction = <span class='st'>"mean"</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
+    </tr>
+    <tr>
+      <th>target</th>
+      <td><p>tensor (N,*) , same shape as the input</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'</p></td>
+    </tr>
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+
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diff --git a/static/docs/reference/nnf_soft_margin_loss.html b/static/docs/reference/nnf_soft_margin_loss.html
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Soft_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_soft_margin_loss.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Creates a criterion that optimizes a two-class classification logistic loss
+between input tensor x and target tensor y (containing 1 or -1).</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_soft_margin_loss</span>(<span class='kw'>input</span>, <span class='kw'>target</span>, reduction = <span class='st'>"mean"</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>tensor (N,*) where ** means, any number of additional dimensions</p></td>
+    </tr>
+    <tr>
+      <th>target</th>
+      <td><p>tensor (N,*) , same shape as the input</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'</p></td>
+    </tr>
+    </table>
+
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+    <h1>Softmax</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
+    <div class="hidden name"><code>nnf_softmax.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies a softmax function.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_softmax</span>(<span class='kw'>input</span>, <span class='kw'>dim</span>, dtype = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(Tensor) input</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) A dimension along which softmax will be computed.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.      If specified, the input tensor is casted to <code>dtype</code> before the operation      is performed. This is useful for preventing data type overflows.
+Default: NULL.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>Softmax is defined as:</p>
+<p>$$Softmax(x_{i}) = exp(x_i)/\sum_j exp(x_j)$$</p>
+<p>It is applied to all slices along dim, and will re-scale them so that the elements
+lie in the range <code>[0, 1]</code> and sum to 1.</p>
+
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+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-activation.R'><code>R/nnf-activation.R</code></a></small>
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+
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+    <p>Applies a softmin function.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_softmin</span>(<span class='kw'>input</span>, <span class='kw'>dim</span>, dtype = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(Tensor) input</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) A dimension along which softmin will be computed
+(so every slice        along dim will sum to 1).</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.      If specified, the input tensor is casted to <code>dtype</code> before the operation      is performed.
+This is useful for preventing data type overflows. Default: NULL.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>Note that</p>
+<p>$$Softmin(x) = Softmax(-x)$$.</p>
+<p>See <a href='nnf_softmax.html'>nnf_softmax</a> definition for mathematical formula.</p>
+
+  </div>
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+<meta property="og:description" content="Applies element-wise, the function \(Softplus(x) = 1/\beta * log(1 + exp(\beta * x))\)." />
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+    <h1>Softplus</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_softplus.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies element-wise, the function \(Softplus(x) = 1/\beta * log(1 + exp(\beta * x))\).</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_softplus</span>(<span class='kw'>input</span>, beta = <span class='fl'>1</span>, threshold = <span class='fl'>20</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
+    </tr>
+    <tr>
+      <th>beta</th>
+      <td><p>the beta value for the Softplus formulation. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>threshold</th>
+      <td><p>values above this revert to a linear function. Default: 20</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>For numerical stability the implementation reverts to the linear function
+when \(input * \beta &gt; threshold\).</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Softshrink</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_softshrink.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies the soft shrinkage function elementwise</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_softshrink</span>(<span class='kw'>input</span>, lambd = <span class='fl'>0.5</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
+    </tr>
+    <tr>
+      <th>lambd</th>
+      <td><p>the lambda (must be no less than zero) value for the Softshrink
+formulation. Default: 0.5</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nnf_softsign.html b/static/docs/reference/nnf_softsign.html
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+    <h1>Softsign</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_softsign.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies element-wise, the function \(SoftSign(x) = x/(1 + |x|\)</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_softsign</span>(<span class='kw'>input</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
+    </tr>
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+
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diff --git a/static/docs/reference/nnf_tanhshrink.html b/static/docs/reference/nnf_tanhshrink.html
new file mode 100644
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+    <h1>Tanhshrink</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_tanhshrink.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Applies element-wise, \(Tanhshrink(x) = x - Tanh(x)\)</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_tanhshrink</span>(<span class='kw'>input</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
+    </tr>
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+
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diff --git a/static/docs/reference/nnf_threshold.html b/static/docs/reference/nnf_threshold.html
new file mode 100644
index 000000000..37401c556
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+    <h1>Threshold</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_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'>nnf_threshold</span>(<span class='kw'>input</span>, <span class='kw'>threshold</span>, <span class='kw'>value</span>, inplace = <span class='fl'>FALSE</span>)
+
+<span class='fu'>nnf_threshold_</span>(<span class='kw'>input</span>, <span class='kw'>threshold</span>, <span class='kw'>value</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(N,*) tensor, where * means, any number of additional
+dimensions</p></td>
+    </tr>
+    <tr>
+      <th>threshold</th>
+      <td><p>The value to threshold at</p></td>
+    </tr>
+    <tr>
+      <th>value</th>
+      <td><p>The value to replace with</p></td>
+    </tr>
+    <tr>
+      <th>inplace</th>
+      <td><p>can optionally do the operation in-place. Default: FALSE</p></td>
+    </tr>
+    </table>
+
+
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diff --git a/static/docs/reference/nnf_triplet_margin_loss.html b/static/docs/reference/nnf_triplet_margin_loss.html
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+<meta property="og:description" content="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)." />
+
+
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+
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+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Triplet_margin_loss</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-loss.R'><code>R/nnf-loss.R</code></a></small>
+    <div class="hidden name"><code>nnf_triplet_margin_loss.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Creates a criterion that measures the triplet loss given an input tensors x1 ,
+x2 , x3 and a margin with a value greater than 0 . This is used for measuring
+a relative similarity between samples. A triplet is composed by a, p and n (i.e.,
+anchor, positive examples and negative examples respectively). The shapes of all
+input tensors should be (N, D).</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_triplet_margin_loss</span>(
+  <span class='kw'>anchor</span>,
+  <span class='kw'>positive</span>,
+  <span class='kw'>negative</span>,
+  margin = <span class='fl'>1</span>,
+  p = <span class='fl'>2</span>,
+  eps = <span class='fl'>1e-06</span>,
+  swap = <span class='fl'>FALSE</span>,
+  reduction = <span class='st'>"mean"</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>anchor</th>
+      <td><p>the anchor input tensor</p></td>
+    </tr>
+    <tr>
+      <th>positive</th>
+      <td><p>the positive input tensor</p></td>
+    </tr>
+    <tr>
+      <th>negative</th>
+      <td><p>the negative input tensor</p></td>
+    </tr>
+    <tr>
+      <th>margin</th>
+      <td><p>Default: 1.</p></td>
+    </tr>
+    <tr>
+      <th>p</th>
+      <td><p>The norm degree for pairwise distance. Default: 2.</p></td>
+    </tr>
+    <tr>
+      <th>eps</th>
+      <td><p>(float, optional) Small value to avoid division by zero.</p></td>
+    </tr>
+    <tr>
+      <th>swap</th>
+      <td><p>The distance swap is described in detail in the paper Learning shallow
+convolutional feature descriptors with triplet losses by V. Balntas, E. Riba et al.
+Default: <code>FALSE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>reduction</th>
+      <td><p>(string, optional) – Specifies the reduction to apply to the
+output: 'none' | 'mean' | 'sum'. 'none': no reduction will be applied, 'mean':
+the sum of the output will be divided by the number of elements in the output,
+'sum': the output will be summed. Default: 'mean'</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/nnf_unfold.html b/static/docs/reference/nnf_unfold.html
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Unfold</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/nnf-fold.R'><code>R/nnf-fold.R</code></a></small>
+    <div class="hidden name"><code>nnf_unfold.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Extracts sliding local blocks from an batched input tensor.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>nnf_unfold</span>(<span class='kw'>input</span>, <span class='kw'>kernel_size</span>, dilation = <span class='fl'>1</span>, padding = <span class='fl'>0</span>, stride = <span class='fl'>1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>the size of the sliding blocks</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>a parameter that controls the stride of elements within the
+neighborhood. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit zero padding to be added on both sides of input.
+Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the sliding blocks in the input spatial dimensions.
+Default: 1</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+
+<p>Currently, only 4-D input tensors (batched image-like tensors) are
+supported.</p>
+
+
+<p>More than one element of the unfolded tensor may refer to a single
+memory location. As a result, in-place operations (especially ones that
+are vectorized) may result in incorrect behavior. If you need to write
+to the tensor, please clone it first.</p>
+
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diff --git a/static/docs/reference/optim_adam.html b/static/docs/reference/optim_adam.html
new file mode 100644
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+    <div class="page-header">
+    <h1>Implements Adam algorithm.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/optim-adam.R'><code>R/optim-adam.R</code></a></small>
+    <div class="hidden name"><code>optim_adam.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>It has been proposed in <a href='https://arxiv.org/abs/1412.6980'>Adam: A Method for Stochastic Optimization</a>.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>optim_adam</span>(
+  <span class='kw'>params</span>,
+  lr = <span class='fl'>0.001</span>,
+  betas = <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0.9</span>, <span class='fl'>0.999</span>),
+  eps = <span class='fl'>1e-08</span>,
+  weight_decay = <span class='fl'>0</span>,
+  amsgrad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>params</th>
+      <td><p>(iterable): iterable of parameters to optimize or dicts defining
+parameter groups</p></td>
+    </tr>
+    <tr>
+      <th>lr</th>
+      <td><p>(float, optional): learning rate (default: 1e-3)</p></td>
+    </tr>
+    <tr>
+      <th>betas</th>
+      <td><p>(<code>Tuple[float, float]</code>, optional): coefficients used for computing
+running averages of gradient and its square (default: (0.9, 0.999))</p></td>
+    </tr>
+    <tr>
+      <th>eps</th>
+      <td><p>(float, optional): term added to the denominator to improve
+numerical stability (default: 1e-8)</p></td>
+    </tr>
+    <tr>
+      <th>weight_decay</th>
+      <td><p>(float, optional): weight decay (L2 penalty) (default: 0)</p></td>
+    </tr>
+    <tr>
+      <th>amsgrad</th>
+      <td><p>(boolean, optional): whether to use the AMSGrad variant of this
+algorithm from the paper <a href='https://openreview.net/forum?id=ryQu7f-RZ'>On the Convergence of Adam and Beyond</a>
+(default: FALSE)</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='kw'>optimizer</span> <span class='op'>&lt;-</span> <span class='fu'>optim_adam</span>(<span class='kw'>model</span><span class='op'>$</span><span class='fu'>parameters</span>(), lr=<span class='fl'>0.1</span>)
+<span class='kw'>optimizer</span><span class='op'>$</span><span class='fu'>zero_grad</span>()
+<span class='fu'>loss_fn</span>(<span class='fu'>model</span>(<span class='kw'>input</span>), <span class='kw'>target</span>)<span class='op'>$</span><span class='fu'>backward</span>()
+<span class='kw'>optimizer</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/stats/step.html'>step</a></span>()
+}
+
+}
+</div></pre>
+  </div>
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diff --git a/static/docs/reference/optim_required.html b/static/docs/reference/optim_required.html
new file mode 100644
index 000000000..8b916e3de
--- /dev/null
+++ b/static/docs/reference/optim_required.html
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+    <h1>Dummy value indicating a required value.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/optim.R'><code>R/optim.R</code></a></small>
+    <div class="hidden name"><code>optim_required.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>export</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>optim_required</span>()</pre>
+
+
+
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+<meta property="og:description" content="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." />
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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='kw'>params</span>,
+  lr = <span class='fu'><a href='optim_required.html'>optim_required</a></span>(),
+  momentum = <span class='fl'>0</span>,
+  dampening = <span class='fl'>0</span>,
+  weight_decay = <span class='fl'>0</span>,
+  nesterov = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>params</th>
+      <td><p>(iterable): iterable of parameters to optimize or dicts defining
+parameter groups</p></td>
+    </tr>
+    <tr>
+      <th>lr</th>
+      <td><p>(float): learning rate</p></td>
+    </tr>
+    <tr>
+      <th>momentum</th>
+      <td><p>(float, optional): momentum factor (default: 0)</p></td>
+    </tr>
+    <tr>
+      <th>dampening</th>
+      <td><p>(float, optional): dampening for momentum (default: 0)</p></td>
+    </tr>
+    <tr>
+      <th>weight_decay</th>
+      <td><p>(float, optional): weight decay (L2 penalty) (default: 0)</p></td>
+    </tr>
+    <tr>
+      <th>nesterov</th>
+      <td><p>(bool, optional): enables Nesterov momentum (default: FALSE)</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+
+
+<p>The implementation of SGD with Momentum-Nesterov subtly differs from
+Sutskever et. al. and implementations in some other frameworks.</p>
+<p>Considering the specific case of Momentum, the update can be written as
+$$
+  \begin{array}{ll}
+v_{t+1} &amp; = \mu * v_{t} + g_{t+1}, \\
+p_{t+1} &amp; = p_{t} - \mbox{lr} * v_{t+1},
+\end{array}
+$$</p>
+<p>where \(p\), \(g\), \(v\) and \(\mu\) denote the
+parameters, gradient, velocity, and momentum respectively.</p>
+<p>This is in contrast to Sutskever et. al. and
+other frameworks which employ an update of the form</p>
+<p>$$
+  \begin{array}{ll}
+v_{t+1} &amp; = \mu * v_{t} + \mbox{lr} * g_{t+1}, \\
+p_{t+1} &amp; = p_{t} - v_{t+1}.
+\end{array}
+$$
+The Nesterov version is analogously modified.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='kw'>optimizer</span> <span class='op'>&lt;-</span> <span class='fu'>optim_sgd</span>(<span class='kw'>model</span><span class='op'>$</span><span class='fu'>parameters</span>(), lr=<span class='fl'>0.1</span>, momentum=<span class='fl'>0.9</span>)
+<span class='kw'>optimizer</span><span class='op'>$</span><span class='fu'>zero_grad</span>()
+<span class='fu'>loss_fn</span>(<span class='fu'>model</span>(<span class='kw'>input</span>), <span class='kw'>target</span>)<span class='op'>$</span><span class='fu'>backward</span>()
+<span class='kw'>optimizer</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/stats/step.html'>step</a></span>()
+}
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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diff --git a/static/docs/reference/pipe.html b/static/docs/reference/pipe.html
new file mode 100644
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+    <h1>Dataset wrapping tensors.</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>tensor_dataset.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Each sample will be retrieved by indexing tensors along the first dimension.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>tensor_dataset</span>(<span class='kw'>...</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>...</th>
+      <td><p>tensors that have the same size of the first dimension.</p></td>
+    </tr>
+    </table>
+
+
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+    </li>
+    <li>
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+    </li>
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+    <div class="page-header">
+    <h1>Abs</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_abs.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Abs</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_abs</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="abs-input-gt-tensor-"><a class="anchor" href="#abs-input-gt-tensor-"></a>abs(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the element-wise absolute value of the given <code>input</code> tensor.</p>
+<p>$$
+    \mbox{out}_{i} = |\mbox{input}_{i}|
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_abs</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>1</span>, <span class='op'>-</span><span class='fl'>2</span>, <span class='fl'>3</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1
+#&gt;  2
+#&gt;  3
+#&gt; [ CPUFloatType{3} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+      <a href="../articles/using-autograd.html">Using autograd</a>
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+
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Acos</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_acos.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Acos</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_acos</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="acos-input-gt-tensor-"><a class="anchor" href="#acos-input-gt-tensor-"></a>acos(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the arccosine  of the elements of <code>input</code>.</p>
+<p>$$
+    \mbox{out}_{i} = \cos^{-1}(\mbox{input}_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_acos</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.7871
+#&gt;  1.0800
+#&gt;  1.4084
+#&gt;  1.7142
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Adaptive_avg_pool1d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_adaptive_avg_pool1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Adaptive_avg_pool1d</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_adaptive_avg_pool1d</span>(<span class='kw'>self</span>, <span class='kw'>output_size</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>output_size</th>
+      <td><p>the target output size (single integer)</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="adaptive-avg-pool-d-input-output-size-gt-tensor-"><a class="anchor" href="#adaptive-avg-pool-d-input-output-size-gt-tensor-"></a>adaptive_avg_pool1d(input, output_size) -&gt; Tensor </h2>
+
+    
+
+
+<p>Applies a 1D adaptive average pooling over an input signal composed of
+several input planes.</p>
+<p>See <code><a href='nn_adaptive_avg_pool1d.html'>nn_adaptive_avg_pool1d()</a></code> for details and output shape.</p>
+
+  </div>
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+
+      
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Add</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_add.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Add</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_add</span>(<span class='kw'>self</span>, <span class='kw'>other</span>, alpha = <span class='fl'>1L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor/Number) the second input tensor/number.</p></td>
+    </tr>
+    <tr>
+      <th>alpha</th>
+      <td><p>(Number) the scalar multiplier for <code>other</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="add-input-other-out-null-"><a class="anchor" href="#add-input-other-out-null-"></a>add(input, other, out=NULL) </h2>
+
+    
+
+
+<p>Adds the scalar <code>other</code> to each element of the input <code>input</code>
+and returns a new resulting tensor.</p>
+<p>$$
+    \mbox{out} = \mbox{input} + \mbox{other}
+$$
+If <code>input</code> is of type FloatTensor or DoubleTensor, <code>other</code> must be
+a real number, otherwise it should be an integer.</p>
+    <h2 class="hasAnchor" id="add-input-other-alpha-out-null-"><a class="anchor" href="#add-input-other-alpha-out-null-"></a>add(input, other, *, alpha=1, out=NULL) </h2>
+
+    
+
+
+<p>Each element of the tensor <code>other</code> is multiplied by the scalar
+<code>alpha</code> and added to each element of the tensor <code>input</code>.
+The resulting tensor is returned.</p>
+<p>The shapes of <code>input</code> and <code>other</code> must be
+broadcastable .</p>
+<p>$$
+    \mbox{out} = \mbox{input} + \mbox{alpha} \times \mbox{other}
+$$
+If <code>other</code> is of type FloatTensor or DoubleTensor, <code>alpha</code> must be
+a real number, otherwise it should be an integer.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_add</span>(<span class='kw'>a</span>, <span class='fl'>20</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>1</span>))
+<span class='kw'>b</span>
+<span class='fu'>torch_add</span>(<span class='kw'>a</span>, <span class='kw'>b</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0.1740 -0.4454  0.7719  0.2179
+#&gt;  0.1897 -0.0817  1.1355  0.5816
+#&gt; -0.2508 -0.5222  0.6951  0.1411
+#&gt; -1.4621 -1.7335 -0.5163 -1.0702
+#&gt; [ CPUFloatType{4,4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Addbmm</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_addbmm.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Addbmm</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_addbmm</span>(<span class='kw'>self</span>, <span class='kw'>batch1</span>, <span class='kw'>batch2</span>, beta = <span class='fl'>1L</span>, alpha = <span class='fl'>1L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) matrix to be added</p></td>
+    </tr>
+    <tr>
+      <th>batch1</th>
+      <td><p>(Tensor) the first batch of matrices to be multiplied</p></td>
+    </tr>
+    <tr>
+      <th>batch2</th>
+      <td><p>(Tensor) the second batch of matrices to be multiplied</p></td>
+    </tr>
+    <tr>
+      <th>beta</th>
+      <td><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></td>
+    </tr>
+    <tr>
+      <th>alpha</th>
+      <td><p>(Number, optional) multiplier for <code>batch1 @ batch2</code> (\(\alpha\))</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="addbmm-input-batch-batch-beta-alpha-out-null-gt-tensor-"><a class="anchor" href="#addbmm-input-batch-batch-beta-alpha-out-null-gt-tensor-"></a>addbmm(input, batch1, batch2, *, beta=1, alpha=1, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Performs a batch matrix-matrix product of matrices stored
+in <code>batch1</code> and <code>batch2</code>,
+with a reduced add step (all matrix multiplications get accumulated
+along the first dimension).
+<code>input</code> is added to the final result.</p>
+<p><code>batch1</code> and <code>batch2</code> must be 3-D tensors each containing the
+same number of matrices.</p>
+<p>If <code>batch1</code> is a \((b \times n \times m)\) tensor, <code>batch2</code> is a
+\((b \times m \times p)\) tensor, <code>input</code> must be
+broadcastable  with a \((n \times p)\) tensor
+and <code>out</code> will be a \((n \times p)\) tensor.</p>
+<p>$$
+    out = \beta\ \mbox{input} + \alpha\ (\sum_{i=0}^{b-1} \mbox{batch1}_i \mathbin{@} \mbox{batch2}_i)
+$$
+For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, arguments <code>beta</code> and <code>alpha</code>
+must be real numbers, otherwise they should be integers.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>M</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>))
+<span class='kw'>batch1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>4</span>))
+<span class='kw'>batch2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>4</span>, <span class='fl'>5</span>))
+<span class='fu'>torch_addbmm</span>(<span class='kw'>M</span>, <span class='kw'>batch1</span>, <span class='kw'>batch2</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  4.3623 -13.1864  1.5644  6.3273 -2.3118
+#&gt;  1.5691 -5.3031 -0.9555  2.9742  2.8950
+#&gt; -7.7087 -2.5553 -4.0583 -2.0273  4.9884
+#&gt; [ CPUFloatType{3,5} ]</div></pre>
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+    <div class="page-header">
+    <h1>Addcdiv</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_addcdiv.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Addcdiv</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_addcdiv</span>(<span class='kw'>self</span>, <span class='kw'>tensor1</span>, <span class='kw'>tensor2</span>, value = <span class='fl'>1L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the tensor to be added</p></td>
+    </tr>
+    <tr>
+      <th>tensor1</th>
+      <td><p>(Tensor) the numerator tensor</p></td>
+    </tr>
+    <tr>
+      <th>tensor2</th>
+      <td><p>(Tensor) the denominator tensor</p></td>
+    </tr>
+    <tr>
+      <th>value</th>
+      <td><p>(Number, optional) multiplier for \(\mbox{tensor1} / \mbox{tensor2}\)</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="addcdiv-input-tensor-tensor-value-out-null-gt-tensor-"><a class="anchor" href="#addcdiv-input-tensor-tensor-value-out-null-gt-tensor-"></a>addcdiv(input, tensor1, tensor2, *, value=1, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Performs the element-wise division of <code>tensor1</code> by <code>tensor2</code>,
+multiply the result by the scalar <code>value</code> and add it to <code>input</code>.</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p>Integer division with addcdiv is deprecated, and in a future release
+addcdiv will perform a true division of <code>tensor1</code> and <code>tensor2</code>.
+The current addcdiv behavior can be replicated using <code><a href='torch_floor_divide.html'>torch_floor_divide()</a></code>
+for integral inputs
+(<code>input</code> + <code>value</code> * <code>tensor1</code> // <code>tensor2</code>)
+and <code><a href='torch_div.html'>torch_div()</a></code> for float inputs
+(<code>input</code> + <code>value</code> * <code>tensor1</code> / <code>tensor2</code>).
+The new addcdiv behavior can be implemented with <code><a href='torch_true_divide.html'>torch_true_divide()</a></code>
+(<code>input</code> + <code>value</code> * torch.true_divide(<code>tensor1</code>,
+<code>tensor2</code>).</p>
+<p>$$
+    \mbox{out}_i = \mbox{input}_i + \mbox{value} \times \frac{\mbox{tensor1}_i}{\mbox{tensor2}_i}
+$$</p>
+<p>The shapes of <code>input</code>, <code>tensor1</code>, and <code>tensor2</code> must be
+broadcastable .</p>
+<p>For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, <code>value</code> must be
+a real number, otherwise an integer.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>t</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
+<span class='kw'>t1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>1</span>))
+<span class='kw'>t2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
+<span class='fu'>torch_addcdiv</span>(<span class='kw'>t</span>, <span class='kw'>t1</span>, <span class='kw'>t2</span>, <span class='fl'>0.1</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.2466  0.6010  1.8962
+#&gt;  0.0637  0.7913  2.4221
+#&gt;  0.8083  0.0164  0.2814
+#&gt; [ CPUFloatType{3,3} ]</div></pre>
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Addcmul</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_addcmul.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Addcmul</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_addcmul</span>(<span class='kw'>self</span>, <span class='kw'>tensor1</span>, <span class='kw'>tensor2</span>, value = <span class='fl'>1L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the tensor to be added</p></td>
+    </tr>
+    <tr>
+      <th>tensor1</th>
+      <td><p>(Tensor) the tensor to be multiplied</p></td>
+    </tr>
+    <tr>
+      <th>tensor2</th>
+      <td><p>(Tensor) the tensor to be multiplied</p></td>
+    </tr>
+    <tr>
+      <th>value</th>
+      <td><p>(Number, optional) multiplier for \(tensor1 .* tensor2\)</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="addcmul-input-tensor-tensor-value-out-null-gt-tensor-"><a class="anchor" href="#addcmul-input-tensor-tensor-value-out-null-gt-tensor-"></a>addcmul(input, tensor1, tensor2, *, value=1, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Performs the element-wise multiplication of <code>tensor1</code>
+by <code>tensor2</code>, multiply the result by the scalar <code>value</code>
+and add it to <code>input</code>.</p>
+<p>$$
+    \mbox{out}_i = \mbox{input}_i + \mbox{value} \times \mbox{tensor1}_i \times \mbox{tensor2}_i
+$$
+The shapes of <code>tensor</code>, <code>tensor1</code>, and <code>tensor2</code> must be
+broadcastable .</p>
+<p>For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, <code>value</code> must be
+a real number, otherwise an integer.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>t</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
+<span class='kw'>t1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>1</span>))
+<span class='kw'>t2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
+<span class='fu'>torch_addcmul</span>(<span class='kw'>t</span>, <span class='kw'>t1</span>, <span class='kw'>t2</span>, <span class='fl'>0.1</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0.9987 -0.1486  1.1388
+#&gt; -1.0322 -0.0828  0.9679
+#&gt; -0.9683 -0.2083  1.2937
+#&gt; [ CPUFloatType{3,3} ]</div></pre>
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+      <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-header">Examples</li>
+<li>
+  <a href="../reference/index.html">Reference</a>
+</li>
+<li>
+  <a href="../news/index.html">Changelog</a>
+</li>
+      </ul>
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+        <li>
+  <a href="https://github.com/mlverse/torch/">
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+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Addmm</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_addmm.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Addmm</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_addmm</span>(<span class='kw'>self</span>, <span class='kw'>mat1</span>, <span class='kw'>mat2</span>, beta = <span class='fl'>1L</span>, alpha = <span class='fl'>1L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) matrix to be added</p></td>
+    </tr>
+    <tr>
+      <th>mat1</th>
+      <td><p>(Tensor) the first matrix to be multiplied</p></td>
+    </tr>
+    <tr>
+      <th>mat2</th>
+      <td><p>(Tensor) the second matrix to be multiplied</p></td>
+    </tr>
+    <tr>
+      <th>beta</th>
+      <td><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></td>
+    </tr>
+    <tr>
+      <th>alpha</th>
+      <td><p>(Number, optional) multiplier for \(mat1 @ mat2\) (\(\alpha\))</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="addmm-input-mat-mat-beta-alpha-out-null-gt-tensor-"><a class="anchor" href="#addmm-input-mat-mat-beta-alpha-out-null-gt-tensor-"></a>addmm(input, mat1, mat2, *, beta=1, alpha=1, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Performs a matrix multiplication of the matrices <code>mat1</code> and <code>mat2</code>.
+The matrix <code>input</code> is added to the final result.</p>
+<p>If <code>mat1</code> is a \((n \times m)\) tensor, <code>mat2</code> is a
+\((m \times p)\) tensor, then <code>input</code> must be
+broadcastable  with a \((n \times p)\) tensor
+and <code>out</code> will be a \((n \times p)\) tensor.</p>
+<p><code>alpha</code> and <code>beta</code> are scaling factors on matrix-vector product between
+<code>mat1</code> and <code>mat2</code> and the added matrix <code>input</code> respectively.</p>
+<p>$$
+    \mbox{out} = \beta\ \mbox{input} + \alpha\ (\mbox{mat1}_i \mathbin{@} \mbox{mat2}_i)
+$$
+For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, arguments <code>beta</code> and
+<code>alpha</code> must be real numbers, otherwise they should be integers.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>M</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+<span class='kw'>mat1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+<span class='kw'>mat2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='fu'>torch_addmm</span>(<span class='kw'>M</span>, <span class='kw'>mat1</span>, <span class='kw'>mat2</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.0521  1.5207  0.4070
+#&gt;  1.0992 -2.9412  0.1886
+#&gt; [ CPUFloatType{2,3} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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diff --git a/static/docs/reference/torch_addmv.html b/static/docs/reference/torch_addmv.html
new file mode 100644
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+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Addmv</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_addmv.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Addmv</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_addmv</span>(<span class='kw'>self</span>, <span class='kw'>mat</span>, <span class='kw'>vec</span>, beta = <span class='fl'>1L</span>, alpha = <span class='fl'>1L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) vector to be added</p></td>
+    </tr>
+    <tr>
+      <th>mat</th>
+      <td><p>(Tensor) matrix to be multiplied</p></td>
+    </tr>
+    <tr>
+      <th>vec</th>
+      <td><p>(Tensor) vector to be multiplied</p></td>
+    </tr>
+    <tr>
+      <th>beta</th>
+      <td><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></td>
+    </tr>
+    <tr>
+      <th>alpha</th>
+      <td><p>(Number, optional) multiplier for \(mat @ vec\) (\(\alpha\))</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="addmv-input-mat-vec-beta-alpha-out-null-gt-tensor-"><a class="anchor" href="#addmv-input-mat-vec-beta-alpha-out-null-gt-tensor-"></a>addmv(input, mat, vec, *, beta=1, alpha=1, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Performs a matrix-vector product of the matrix <code>mat</code> and
+the vector <code>vec</code>.
+The vector <code>input</code> is added to the final result.</p>
+<p>If <code>mat</code> is a \((n \times m)\) tensor, <code>vec</code> is a 1-D tensor of
+size <code>m</code>, then <code>input</code> must be
+broadcastable  with a 1-D tensor of size <code>n</code> and
+<code>out</code> will be 1-D tensor of size <code>n</code>.</p>
+<p><code>alpha</code> and <code>beta</code> are scaling factors on matrix-vector product between
+<code>mat</code> and <code>vec</code> and the added tensor <code>input</code> respectively.</p>
+<p>$$
+    \mbox{out} = \beta\ \mbox{input} + \alpha\ (\mbox{mat} \mathbin{@} \mbox{vec})
+$$
+For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, arguments <code>beta</code> and
+<code>alpha</code> must be real numbers, otherwise they should be integers</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>M</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>))
+<span class='kw'>mat</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+<span class='kw'>vec</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>))
+<span class='fu'>torch_addmv</span>(<span class='kw'>M</span>, <span class='kw'>mat</span>, <span class='kw'>vec</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  2.8130
+#&gt;  1.3307
+#&gt; [ CPUFloatType{2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_addr.html b/static/docs/reference/torch_addr.html
new file mode 100644
index 000000000..bc7ddf9d3
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@@ -0,0 +1,286 @@
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+</li>
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+
+      
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_addr.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Addr</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_addr</span>(<span class='kw'>self</span>, <span class='kw'>vec1</span>, <span class='kw'>vec2</span>, beta = <span class='fl'>1L</span>, alpha = <span class='fl'>1L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) matrix to be added</p></td>
+    </tr>
+    <tr>
+      <th>vec1</th>
+      <td><p>(Tensor) the first vector of the outer product</p></td>
+    </tr>
+    <tr>
+      <th>vec2</th>
+      <td><p>(Tensor) the second vector of the outer product</p></td>
+    </tr>
+    <tr>
+      <th>beta</th>
+      <td><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></td>
+    </tr>
+    <tr>
+      <th>alpha</th>
+      <td><p>(Number, optional) multiplier for \(\mbox{vec1} \otimes \mbox{vec2}\) (\(\alpha\))</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="addr-input-vec-vec-beta-alpha-out-null-gt-tensor-"><a class="anchor" href="#addr-input-vec-vec-beta-alpha-out-null-gt-tensor-"></a>addr(input, vec1, vec2, *, beta=1, alpha=1, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Performs the outer-product of vectors <code>vec1</code> and <code>vec2</code>
+and adds it to the matrix <code>input</code>.</p>
+<p>Optional values <code>beta</code> and <code>alpha</code> are scaling factors on the
+outer product between <code>vec1</code> and <code>vec2</code> and the added matrix
+<code>input</code> respectively.</p>
+<p>$$
+    \mbox{out} = \beta\ \mbox{input} + \alpha\ (\mbox{vec1} \otimes \mbox{vec2})
+$$
+If <code>vec1</code> is a vector of size <code>n</code> and <code>vec2</code> is a vector
+of size <code>m</code>, then <code>input</code> must be
+broadcastable  with a matrix of size
+\((n \times m)\) and <code>out</code> will be a matrix of size
+\((n \times m)\).</p>
+<p>For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, arguments <code>beta</code> and
+<code>alpha</code> must be real numbers, otherwise they should be integers</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>vec1</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>4.</span>)
+<span class='kw'>vec2</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>3.</span>)
+<span class='kw'>M</span> <span class='op'>=</span> <span class='fu'><a href='torch_zeros.html'>torch_zeros</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>))
+<span class='fu'>torch_addr</span>(<span class='kw'>M</span>, <span class='kw'>vec1</span>, <span class='kw'>vec2</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1  2
+#&gt;  2  4
+#&gt;  3  6
+#&gt; [ CPUFloatType{3,2} ]</div></pre>
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diff --git a/static/docs/reference/torch_allclose.html b/static/docs/reference/torch_allclose.html
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Allclose</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_allclose.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Allclose</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_allclose</span>(<span class='kw'>self</span>, <span class='kw'>other</span>, rtol = <span class='fl'>1e-05</span>, atol = <span class='fl'>0</span>, equal_nan = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) first tensor to compare</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor) second tensor to compare</p></td>
+    </tr>
+    <tr>
+      <th>rtol</th>
+      <td><p>(float, optional) relative tolerance. Default: 1e-05</p></td>
+    </tr>
+    <tr>
+      <th>atol</th>
+      <td><p>(float, optional) absolute tolerance. Default: 1e-08</p></td>
+    </tr>
+    <tr>
+      <th>equal_nan</th>
+      <td><p>(bool, optional) if <code>TRUE</code>, then two <code>NaN</code> s will be compared as equal. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="allclose-input-other-rtol-e-atol-e-equal-nan-false-gt-bool-"><a class="anchor" href="#allclose-input-other-rtol-e-atol-e-equal-nan-false-gt-bool-"></a>allclose(input, other, rtol=1e-05, atol=1e-08, equal_nan=False) -&gt; bool </h2>
+
+    
+
+
+<p>This function checks if all <code>input</code> and <code>other</code> satisfy the condition:</p>
+<p>$$
+    \vert \mbox{input} - \mbox{other} \vert \leq \mbox{atol} + \mbox{rtol} \times \vert \mbox{other} \vert
+$$
+elementwise, for all elements of <code>input</code> and <code>other</code>. The behaviour of this function is analogous to
+<code>numpy.allclose &lt;https://docs.scipy.org/doc/numpy/reference/generated/numpy.allclose.html&gt;</code>_</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_allclose</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10000.</span>, <span class='fl'>1e-07</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10000.1</span>, <span class='fl'>1e-08</span>)))
+<span class='fu'>torch_allclose</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10000.</span>, <span class='fl'>1e-08</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10000.1</span>, <span class='fl'>1e-09</span>)))
+<span class='fu'>torch_allclose</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1.0</span>, <span class='fl'>NaN</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1.0</span>, <span class='fl'>NaN</span>)))
+<span class='fu'>torch_allclose</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1.0</span>, <span class='fl'>NaN</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1.0</span>, <span class='fl'>NaN</span>)), equal_nan=<span class='fl'>TRUE</span>)
+}
+</div><div class='output co'>#&gt; [1] TRUE</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_angle.html b/static/docs/reference/torch_angle.html
new file mode 100644
index 000000000..501c1b0c7
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Angle</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_angle.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Angle</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_angle</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="angle-input-gt-tensor-"><a class="anchor" href="#angle-input-gt-tensor-"></a>angle(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the element-wise angle (in radians) of the given <code>input</code> tensor.</p>
+<p>$$
+    \mbox{out}_{i} = angle(\mbox{input}_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='fu'>torch_angle</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>1</span> <span class='op'>+</span> <span class='fl'>1i</span>, <span class='op'>-</span><span class='fl'>2</span> <span class='op'>+</span> <span class='fl'>2i</span>, <span class='fl'>3</span> <span class='op'>-</span> <span class='fl'>3i</span>)))<span class='op'>*</span><span class='fl'>180</span><span class='op'>/</span><span class='fl'>3.14159</span>
+}
+
+}
+</div></pre>
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diff --git a/static/docs/reference/torch_arange.html b/static/docs/reference/torch_arange.html
new file mode 100644
index 000000000..89ee0b115
--- /dev/null
+++ b/static/docs/reference/torch_arange.html
@@ -0,0 +1,295 @@
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+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
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+    <div class="page-header">
+    <h1>Arange</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_arange.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Arange</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_arange</span>(
+  <span class='kw'>start</span>,
+  <span class='kw'>end</span>,
+  step = <span class='fl'>1</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>start</th>
+      <td><p>(Number) the starting value for the set of points. Default: <code>0</code>.</p></td>
+    </tr>
+    <tr>
+      <th>end</th>
+      <td><p>(Number) the ending value for the set of points</p></td>
+    </tr>
+    <tr>
+      <th>step</th>
+      <td><p>(Number) the gap between each pair of adjacent points. Default: <code>1</code>.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). If <code>dtype</code> is not given, infer the data type from the other input        arguments. If any of <code>start</code>, <code>end</code>, or <code>stop</code> are floating-point, the        <code>dtype</code> is inferred to be the default dtype, see        <code>~torch.get_default_dtype</code>. Otherwise, the <code>dtype</code> is inferred to        be <code>torch.int64</code>.</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="arange-start-end-step-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#arange-start-end-step-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>arange(start=0, end, step=1, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a 1-D tensor of size \(\left\lceil \frac{\mbox{end} - \mbox{start}}{\mbox{step}} \right\rceil\)
+with values from the interval <code>[start, end)</code> taken with common difference
+<code>step</code> beginning from <code>start</code>.</p>
+<p>Note that non-integer <code>step</code> is subject to floating point rounding errors when
+comparing against <code>end</code>; to avoid inconsistency, we advise adding a small epsilon to <code>end</code>
+in such cases.</p>
+<p>$$
+    \mbox{out}_{{i+1}} = \mbox{out}_{i} + \mbox{step}
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_arange</span>(start = <span class='fl'>0</span>, end = <span class='fl'>5</span>)
+<span class='fu'>torch_arange</span>(<span class='fl'>1</span>, <span class='fl'>4</span>)
+<span class='fu'>torch_arange</span>(<span class='fl'>1</span>, <span class='fl'>2.5</span>, <span class='fl'>0.5</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.0000
+#&gt;  1.5000
+#&gt;  2.0000
+#&gt; [ CPUFloatType{3} ]</div></pre>
+  </div>
+  <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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+
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+</div>
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
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+    <div class="page-header">
+    <h1>Argmax</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_argmax.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Argmax</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_argmax</span>(<span class='kw'>self</span>, dim = <span class='kw'>NULL</span>, keepdim = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the dimension to reduce. If <code>NULL</code>, the argmax of the flattened input is returned.</p></td>
+    </tr>
+    <tr>
+      <th>keepdim</th>
+      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not. Ignored if <code>dim=NULL</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="argmax-input-gt-longtensor-"><a class="anchor" href="#argmax-input-gt-longtensor-"></a>argmax(input) -&gt; LongTensor </h2>
+
+    
+
+
+<p>Returns the indices of the maximum value of all elements in the <code>input</code> tensor.</p>
+<p>This is the second value returned by <code>torch_max</code>. See its
+documentation for the exact semantics of this method.</p>
+    <h2 class="hasAnchor" id="argmax-input-dim-keepdim-false-gt-longtensor-"><a class="anchor" href="#argmax-input-dim-keepdim-false-gt-longtensor-"></a>argmax(input, dim, keepdim=False) -&gt; LongTensor </h2>
+
+    
+
+
+<p>Returns the indices of the maximum values of a tensor across a dimension.</p>
+<p>This is the second value returned by <code>torch_max</code>. See its
+documentation for the exact semantics of this method.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_argmax</span>(<span class='kw'>a</span>)
+}
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_argmax</span>(<span class='kw'>a</span>, dim=<span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  2
+#&gt;  0
+#&gt;  1
+#&gt;  1
+#&gt; [ CPULongType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+
+      
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Argmin</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_argmin.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Argmin</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_argmin</span>(<span class='kw'>self</span>, dim = <span class='kw'>NULL</span>, keepdim = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the dimension to reduce. If <code>NULL</code>, the argmin of the flattened input is returned.</p></td>
+    </tr>
+    <tr>
+      <th>keepdim</th>
+      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not. Ignored if <code>dim=NULL</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="argmin-input-gt-longtensor-"><a class="anchor" href="#argmin-input-gt-longtensor-"></a>argmin(input) -&gt; LongTensor </h2>
+
+    
+
+
+<p>Returns the indices of the minimum value of all elements in the <code>input</code> tensor.</p>
+<p>This is the second value returned by <code>torch_min</code>. See its
+documentation for the exact semantics of this method.</p>
+    <h2 class="hasAnchor" id="argmin-input-dim-keepdim-false-out-null-gt-longtensor-"><a class="anchor" href="#argmin-input-dim-keepdim-false-out-null-gt-longtensor-"></a>argmin(input, dim, keepdim=False, out=NULL) -&gt; LongTensor </h2>
+
+    
+
+
+<p>Returns the indices of the minimum values of a tensor across a dimension.</p>
+<p>This is the second value returned by <code>torch_min</code>. See its
+documentation for the exact semantics of this method.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_argmin</span>(<span class='kw'>a</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_argmin</span>(<span class='kw'>a</span>, dim=<span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0
+#&gt;  1
+#&gt;  1
+#&gt;  1
+#&gt; [ CPULongType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Argsort</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_argsort.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Argsort</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_argsort</span>(<span class='kw'>self</span>, dim = <span class='op'>-</span><span class='fl'>1L</span>, descending = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int, optional) the dimension to sort along</p></td>
+    </tr>
+    <tr>
+      <th>descending</th>
+      <td><p>(bool, optional) controls the sorting order (ascending or descending)</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="argsort-input-dim-descending-false-gt-longtensor-"><a class="anchor" href="#argsort-input-dim-descending-false-gt-longtensor-"></a>argsort(input, dim=-1, descending=False) -&gt; LongTensor </h2>
+
+    
+
+
+<p>Returns the indices that sort a tensor along a given dimension in ascending
+order by value.</p>
+<p>This is the second value returned by <code>torch_sort</code>.  See its documentation
+for the exact semantics of this method.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_argsort</span>(<span class='kw'>a</span>, dim=<span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  3  0  0  2
+#&gt;  2  1  1  3
+#&gt;  1  2  3  1
+#&gt;  0  3  2  0
+#&gt; [ CPULongType{4,4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
+  </div>
+</div>
+
+
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+      <div class="copyright">
+  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+</div>
+
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diff --git a/static/docs/reference/torch_as_strided.html b/static/docs/reference/torch_as_strided.html
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+    <h1>As_strided</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_as_strided.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>As_strided</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_as_strided</span>(<span class='kw'>self</span>, <span class='kw'>size</span>, <span class='kw'>stride</span>, storage_offset = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>size</th>
+      <td><p>(tuple or ints) the shape of the output tensor</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>(tuple or ints) the stride of the output tensor</p></td>
+    </tr>
+    <tr>
+      <th>storage_offset</th>
+      <td><p>(int, optional) the offset in the underlying storage of the output tensor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="as-strided-input-size-stride-storage-offset-gt-tensor-"><a class="anchor" href="#as-strided-input-size-stride-storage-offset-gt-tensor-"></a>as_strided(input, size, stride, storage_offset=0) -&gt; Tensor </h2>
+
+    
+
+
+<p>Create a view of an existing <code>torch_Tensor</code> <code>input</code> with specified
+<code>size</code>, <code>stride</code> and <code>storage_offset</code>.</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p>More than one element of a created tensor may refer to a single memory
+location. As a result, in-place operations (especially ones that are
+vectorized) may result in incorrect behavior. If you need to write to
+the tensors, please clone them first.</p><pre>Many PyTorch functions, which return a view of a tensor, are internally
+implemented with this function. Those functions, like
+`torch_Tensor.expand`, are easier to read and are therefore more
+advisable to use.
+</pre>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='kw'>x</span>
+<span class='kw'>t</span> <span class='op'>=</span> <span class='fu'>torch_as_strided</span>(<span class='kw'>x</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>), <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>))
+<span class='kw'>t</span>
+<span class='kw'>t</span> <span class='op'>=</span> <span class='fu'>torch_as_strided</span>(<span class='kw'>x</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>), <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>), <span class='fl'>1</span>)
+<span class='kw'>t</span>
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  2.2728  1.0136
+#&gt; -0.1885  1.2535
+#&gt; [ CPUFloatType{2,2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Asin</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_asin.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Asin</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_asin</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="asin-input-out-null-gt-tensor-"><a class="anchor" href="#asin-input-out-null-gt-tensor-"></a>asin(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the arcsine  of the elements of <code>input</code>.</p>
+<p>$$
+    \mbox{out}_{i} = \sin^{-1}(\mbox{input}_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_asin</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; 0.01 *
+#&gt;     nan
+#&gt;  7.9841
+#&gt;     nan
+#&gt; -30.8534
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+    <div class="page-header">
+    <h1>Atan</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_atan.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Atan</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_atan</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="atan-input-out-null-gt-tensor-"><a class="anchor" href="#atan-input-out-null-gt-tensor-"></a>atan(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the arctangent  of the elements of <code>input</code>.</p>
+<p>$$
+    \mbox{out}_{i} = \tan^{-1}(\mbox{input}_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_atan</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0.5608
+#&gt;  0.9247
+#&gt;  1.1369
+#&gt;  0.8835
+#&gt; [ CPUFloatType{4} ]</div></pre>
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diff --git a/static/docs/reference/torch_atan2.html b/static/docs/reference/torch_atan2.html
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+    <div class="page-header">
+    <h1>Atan2</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_atan2.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Atan2</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_atan2</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the first input tensor</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor) the second input tensor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="atan-input-other-out-null-gt-tensor-"><a class="anchor" href="#atan-input-other-out-null-gt-tensor-"></a>atan2(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Element-wise arctangent of \(\mbox{input}_{i} / \mbox{other}_{i}\)
+with consideration of the quadrant. Returns a new tensor with the signed angles
+in radians between vector \((\mbox{other}_{i}, \mbox{input}_{i})\)
+and vector \((1, 0)\). (Note that \(\mbox{other}_{i}\), the second
+parameter, is the x-coordinate, while \(\mbox{input}_{i}\), the first
+parameter, is the y-coordinate.)</p>
+<p>The shapes of <code>input</code> and <code>other</code> must be
+broadcastable .</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_atan2</span>(<span class='kw'>a</span>, <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0.5145
+#&gt;  0.3180
+#&gt;  1.6062
+#&gt;  2.3995
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
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+    <div class="page-header">
+    <h1>Avg_pool1d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_avg_pool1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Avg_pool1d</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_avg_pool1d</span>(
+  <span class='kw'>self</span>,
+  <span class='kw'>kernel_size</span>,
+  stride = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(),
+  padding = <span class='fl'>0L</span>,
+  ceil_mode = <span class='fl'>FALSE</span>,
+  count_include_pad = <span class='fl'>TRUE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iW)\)</p></td>
+    </tr>
+    <tr>
+      <th>kernel_size</th>
+      <td><p>the size of the window. Can be a single number or a tuple <code>(kW,)</code></p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the window. Can be a single number or a tuple <code>(sW,)</code>. Default: <code>kernel_size</code></p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit zero paddings on both sides of the input. Can be a single number or a tuple <code>(padW,)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>ceil_mode</th>
+      <td><p>when <code>TRUE</code>, will use <code>ceil</code> instead of <code>floor</code> to compute the output shape. Default: <code>FALSE</code></p></td>
+    </tr>
+    <tr>
+      <th>count_include_pad</th>
+      <td><p>when <code>TRUE</code>, will include the zero-padding in the averaging calculation. Default: <code>TRUE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="avg-pool-d-input-kernel-size-stride-null-padding-ceil-mode-false-count-include-pad-true-gt-tensor-"><a class="anchor" href="#avg-pool-d-input-kernel-size-stride-null-padding-ceil-mode-false-count-include-pad-true-gt-tensor-"></a>avg_pool1d(input, kernel_size, stride=NULL, padding=0, ceil_mode=FALSE, count_include_pad=TRUE) -&gt; Tensor </h2>
+
+    
+
+
+<p>Applies a 1D average pooling over an input signal composed of several
+input planes.</p>
+<p>See <code><a href='nn_avg_pool1d.html'>nn_avg_pool1d()</a></code> for details and output shape.</p>
+
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+    <div class="page-header">
+    <h1>Baddbmm</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_baddbmm.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Baddbmm</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_baddbmm</span>(<span class='kw'>self</span>, <span class='kw'>batch1</span>, <span class='kw'>batch2</span>, beta = <span class='fl'>1L</span>, alpha = <span class='fl'>1L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the tensor to be added</p></td>
+    </tr>
+    <tr>
+      <th>batch1</th>
+      <td><p>(Tensor) the first batch of matrices to be multiplied</p></td>
+    </tr>
+    <tr>
+      <th>batch2</th>
+      <td><p>(Tensor) the second batch of matrices to be multiplied</p></td>
+    </tr>
+    <tr>
+      <th>beta</th>
+      <td><p>(Number, optional) multiplier for <code>input</code> (\(\beta\))</p></td>
+    </tr>
+    <tr>
+      <th>alpha</th>
+      <td><p>(Number, optional) multiplier for \(\mbox{batch1} \mathbin{@} \mbox{batch2}\) (\(\alpha\))</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="baddbmm-input-batch-batch-beta-alpha-out-null-gt-tensor-"><a class="anchor" href="#baddbmm-input-batch-batch-beta-alpha-out-null-gt-tensor-"></a>baddbmm(input, batch1, batch2, *, beta=1, alpha=1, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Performs a batch matrix-matrix product of matrices in <code>batch1</code>
+and <code>batch2</code>.
+<code>input</code> is added to the final result.</p>
+<p><code>batch1</code> and <code>batch2</code> must be 3-D tensors each containing the same
+number of matrices.</p>
+<p>If <code>batch1</code> is a \((b \times n \times m)\) tensor, <code>batch2</code> is a
+\((b \times m \times p)\) tensor, then <code>input</code> must be
+broadcastable  with a
+\((b \times n \times p)\) tensor and <code>out</code> will be a
+\((b \times n \times p)\) tensor. Both <code>alpha</code> and <code>beta</code> mean the
+same as the scaling factors used in <code>torch_addbmm</code>.</p>
+<p>$$
+    \mbox{out}_i = \beta\ \mbox{input}_i + \alpha\ (\mbox{batch1}_i \mathbin{@} \mbox{batch2}_i)
+$$
+For inputs of type <code>FloatTensor</code> or <code>DoubleTensor</code>, arguments <code>beta</code> and
+<code>alpha</code> must be real numbers, otherwise they should be integers.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>M</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>5</span>))
+<span class='kw'>batch1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>4</span>))
+<span class='kw'>batch2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>4</span>, <span class='fl'>5</span>))
+<span class='fu'>torch_baddbmm</span>(<span class='kw'>M</span>, <span class='kw'>batch1</span>, <span class='kw'>batch2</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;   3.2950  1.1934  1.0951 -0.1641 -0.1625
+#&gt;   0.0234  0.6164  1.1209 -1.4287 -1.4717
+#&gt;   1.4437  0.0008  0.1610 -0.6639 -3.1524
+#&gt; 
+#&gt; (2,.,.) = 
+#&gt;  -1.1120 -1.6823  4.3323 -4.6368  5.3289
+#&gt;  -0.0067  0.9474 -2.1810 -2.4339 -1.4058
+#&gt;  -0.0811  1.0781  0.5114 -0.6792  5.5863
+#&gt; 
+#&gt; (3,.,.) = 
+#&gt;  -1.0254  1.4248  2.5140  0.1094  0.8005
+#&gt;  -0.0712  3.0454 -0.8717 -1.3384 -1.1721
+#&gt;  -1.0192 -0.3714  0.0774 -0.0318  1.3226
+#&gt; 
+#&gt; (4,.,.) = 
+#&gt;   0.7244  1.0012  0.7026 -0.1657 -0.5903
+#&gt;  -1.1023  0.7397 -2.5617 -2.8854 -0.4641
+#&gt;  -1.8027  1.8865 -5.9998 -2.7661 -4.2857
+#&gt; 
+#&gt; (5,.,.) = 
+#&gt;   0.7827  0.5095 -4.7454 -0.9276  0.9389
+#&gt;   0.2047 -0.1286 -0.0545  0.0190  2.7191
+#&gt;  -0.1130 -1.0268  0.5965  0.4682 -1.9710
+#&gt; 
+#&gt; (6,.,.) = 
+#&gt;   0.3016 -0.8588  0.4652  2.3788 -3.3351
+#&gt;   0.6153  1.9567 -0.4531 -3.5487 -0.0359
+#&gt;  -1.0311  1.7841 -1.4333 -1.4575 -5.2221
+#&gt; 
+#&gt; (7,.,.) = 
+#&gt;   0.6391  1.4053  1.4987 -1.3632  1.9467
+#&gt;  -0.3214  1.5380 -1.9825  5.0689 -1.2355
+#&gt;  -2.0131  0.5735 -0.3492  1.2934 -0.4853
+#&gt; 
+#&gt; (8,.,.) = 
+#&gt;  -3.1747  0.3503 -0.8512 -0.8386  1.8389
+#&gt;  -0.4581 -0.1452 -0.2862 -2.5819  0.0945
+#&gt;   1.9201 -3.9626 -1.0644  1.4395 -0.9784
+#&gt; 
+#&gt; (9,.,.) = 
+#&gt;   2.0265 -1.0222  0.4194  3.9349 -1.2036
+#&gt;   0.2550  3.1147 -1.8770 -1.3894  0.1003
+#&gt;  -0.7578 -3.4052 -1.9434 -5.4013 -1.7570
+#&gt; 
+#&gt; (10,.,.) = 
+#&gt;   3.5818  2.3017 -0.6280 -1.3274 -2.3944
+#&gt;  -1.8303  1.0979 -0.9950 -1.9600 -1.0145
+#&gt;  -0.5720 -0.0300 -1.1251  4.3175 -0.5519
+#&gt; [ CPUFloatType{10,3,5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Bartlett_window</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
+    <div class="hidden name"><code>torch_bartlett_window.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Bartlett_window</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_bartlett_window</span>(
+  <span class='kw'>window_length</span>,
+  periodic = <span class='fl'>TRUE</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>window_length</th>
+      <td><p>(int) the size of returned window</p></td>
+    </tr>
+    <tr>
+      <th>periodic</th>
+      <td><p>(bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). Only floating point types are supported.</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned window tensor. Only          <code>torch_strided</code> (dense layout) is supported.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>If `window_length` \eqn{=1}, the returned window contains a single value 1.
+</pre>
+
+    <h2 class="hasAnchor" id="bartlett-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#bartlett-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>bartlett_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Bartlett window function.</p>
+<p>$$
+    w[n] = 1 - \left| \frac{2n}{N-1} - 1 \right| = \left\{ \begin{array}{ll}
+        \frac{2n}{N - 1} &amp; \mbox{if } 0 \leq n \leq \frac{N - 1}{2} \\
+        2 - \frac{2n}{N - 1} &amp; \mbox{if } \frac{N - 1}{2} &lt; n &lt; N \\
+    \end{array}
+    \right. ,
+$$
+where \(N\) is the full window size.</p>
+<p>The input <code>window_length</code> is a positive integer controlling the
+returned window size. <code>periodic</code> flag determines whether the returned
+window trims off the last duplicate value from the symmetric window and is
+ready to be used as a periodic window with functions like
+<code>torch_stft</code>. Therefore, if <code>periodic</code> is true, the \(N\) in
+above formula is in fact \(\mbox{window\_length} + 1\). Also, we always have
+<code>torch_bartlett_window(L, periodic=TRUE)</code> equal to
+<code>torch_bartlett_window(L + 1, periodic=False)[:-1])</code>.</p>
+
+  </div>
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diff --git a/static/docs/reference/torch_bernoulli.html b/static/docs/reference/torch_bernoulli.html
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+    <h1>Bernoulli</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_bernoulli.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Bernoulli</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_bernoulli</span>(<span class='kw'>self</span>, <span class='kw'>p</span>, generator = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor of probability values for the Bernoulli
+distribution</p></td>
+    </tr>
+    <tr>
+      <th>p</th>
+      <td><p>(Number) a probability value. If <code>p</code> is passed than it's used instead of
+the values in <code>self</code> tensor.</p></td>
+    </tr>
+    <tr>
+      <th>generator</th>
+      <td><p>(<code>torch.Generator</code>, optional) a pseudorandom number generator for sampling</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="bernoulli-input-generator-null-out-null-gt-tensor-"><a class="anchor" href="#bernoulli-input-generator-null-out-null-gt-tensor-"></a>bernoulli(input, *, generator=NULL, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Draws binary random numbers (0 or 1) from a Bernoulli distribution.</p>
+<p>The <code>input</code> tensor should be a tensor containing probabilities
+to be used for drawing the binary random number.
+Hence, all values in <code>input</code> have to be in the range:
+\(0 \leq \mbox{input}_i \leq 1\).</p>
+<p>The \(\mbox{i}^{th}\) element of the output tensor will draw a
+value \(1\) according to the \(\mbox{i}^{th}\) probability value given
+in <code>input</code>.</p>
+<p>$$
+    \mbox{out}_{i} \sim \mathrm{Bernoulli}(p = \mbox{input}_{i})
+$$
+The returned <code>out</code> tensor only has values 0 or 1 and is of the same
+shape as <code>input</code>.</p>
+<p><code>out</code> can have integral <code>dtype</code>, but <code>input</code> must have floating
+point <code>dtype</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))<span class='op'>$</span><span class='fu'>uniform_</span>(<span class='fl'>0</span>, <span class='fl'>1</span>)  <span class='co'># generate a uniform random matrix with range c(0, 1)</span>
+<span class='kw'>a</span>
+<span class='fu'>torch_bernoulli</span>(<span class='kw'>a</span>)
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_ones.html'>torch_ones</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>)) <span class='co'># probability of drawing "1" is 1</span>
+<span class='fu'>torch_bernoulli</span>(<span class='kw'>a</span>)
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_zeros.html'>torch_zeros</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>)) <span class='co'># probability of drawing "1" is 0</span>
+<span class='fu'>torch_bernoulli</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0  0  0
+#&gt;  0  0  0
+#&gt;  0  0  0
+#&gt; [ CPUFloatType{3,3} ]</div></pre>
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+    <div class="page-header">
+    <h1>Bincount</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_bincount.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Bincount</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_bincount</span>(<span class='kw'>self</span>, weights = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(), minlength = <span class='fl'>0L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) 1-d int tensor</p></td>
+    </tr>
+    <tr>
+      <th>weights</th>
+      <td><p>(Tensor) optional, weight for each value in the input tensor.        Should be of same size as input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>minlength</th>
+      <td><p>(int) optional, minimum number of bins. Should be non-negative.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="bincount-input-weights-null-minlength-gt-tensor-"><a class="anchor" href="#bincount-input-weights-null-minlength-gt-tensor-"></a>bincount(input, weights=NULL, minlength=0) -&gt; Tensor </h2>
+
+    
+
+
+<p>Count the frequency of each value in an array of non-negative ints.</p>
+<p>The number of bins (size 1) is one larger than the largest value in
+<code>input</code> unless <code>input</code> is empty, in which case the result is a
+tensor of size 0. If <code>minlength</code> is specified, the number of bins is at least
+<code>minlength</code> and if <code>input</code> is empty, then the result is tensor of size
+<code>minlength</code> filled with zeros. If <code>n</code> is the value at position <code>i</code>,
+<code>out[n] += weights[i]</code> if <code>weights</code> is specified else
+<code>out[n] += 1</code>.</p>
+<p>.. include:: cuda_deterministic.rst</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_randint.html'>torch_randint</a></span>(<span class='fl'>0</span>, <span class='fl'>8</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>5</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>())
+<span class='kw'>weights</span> <span class='op'>=</span> <span class='fu'><a href='torch_linspace.html'>torch_linspace</a></span>(<span class='fl'>0</span>, <span class='fl'>1</span>, steps=<span class='fl'>5</span>)
+<span class='kw'>input</span>
+<span class='kw'>weights</span>
+<span class='fu'>torch_bincount</span>(<span class='kw'>input</span>, <span class='kw'>weights</span>)
+<span class='kw'>input</span><span class='op'>$</span><span class='fu'>bincount</span>(<span class='kw'>weights</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.0000
+#&gt;  0.0000
+#&gt;  0.7500
+#&gt;  1.0000
+#&gt;  0.2500
+#&gt;  0.0000
+#&gt;  0.0000
+#&gt;  0.5000
+#&gt; [ CPUFloatType{8} ]</div></pre>
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+    <div class="page-header">
+    <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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_bitwise_and.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Bitwise_and</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_bitwise_and</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>NA the first input tensor</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>NA the second input tensor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="bitwise-and-input-other-out-null-gt-tensor-"><a class="anchor" href="#bitwise-and-input-other-out-null-gt-tensor-"></a>bitwise_and(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the bitwise AND of <code>input</code> and <code>other</code>. The input tensor must be of
+integral or Boolean types. For bool tensors, it computes the logical AND.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <h1>Bitwise_not</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_bitwise_not.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Bitwise_not</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_bitwise_not</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="bitwise-not-input-out-null-gt-tensor-"><a class="anchor" href="#bitwise-not-input-out-null-gt-tensor-"></a>bitwise_not(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the bitwise NOT of the given input tensor. The input tensor must be of
+integral or Boolean types. For bool tensors, it computes the logical NOT.</p>
+
+  </div>
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+    <h1>Bitwise_or</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_bitwise_or.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Bitwise_or</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_bitwise_or</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>NA the first input tensor</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>NA the second input tensor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="bitwise-or-input-other-out-null-gt-tensor-"><a class="anchor" href="#bitwise-or-input-other-out-null-gt-tensor-"></a>bitwise_or(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<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>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <h1>Bitwise_xor</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_bitwise_xor.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Bitwise_xor</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_bitwise_xor</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>NA the first input tensor</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>NA the second input tensor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="bitwise-xor-input-other-out-null-gt-tensor-"><a class="anchor" href="#bitwise-xor-input-other-out-null-gt-tensor-"></a>bitwise_xor(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the bitwise XOR of <code>input</code> and <code>other</code>. The input tensor must be of
+integral or Boolean types. For bool tensors, it computes the logical XOR.</p>
+
+  </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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+    <h1>Blackman_window</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
+    <div class="hidden name"><code>torch_blackman_window.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Blackman_window</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_blackman_window</span>(
+  <span class='kw'>window_length</span>,
+  periodic = <span class='fl'>TRUE</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>window_length</th>
+      <td><p>(int) the size of returned window</p></td>
+    </tr>
+    <tr>
+      <th>periodic</th>
+      <td><p>(bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). Only floating point types are supported.</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned window tensor. Only          <code>torch_strided</code> (dense layout) is supported.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>If `window_length` \eqn{=1}, the returned window contains a single value 1.
+</pre>
+
+    <h2 class="hasAnchor" id="blackman-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#blackman-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>blackman_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Blackman window function.</p>
+<p>$$
+    w[n] = 0.42 - 0.5 \cos \left( \frac{2 \pi n}{N - 1} \right) + 0.08 \cos \left( \frac{4 \pi n}{N - 1} \right)
+$$
+where \(N\) is the full window size.</p>
+<p>The input <code>window_length</code> is a positive integer controlling the
+returned window size. <code>periodic</code> flag determines whether the returned
+window trims off the last duplicate value from the symmetric window and is
+ready to be used as a periodic window with functions like
+<code>torch_stft</code>. Therefore, if <code>periodic</code> is true, the \(N\) in
+above formula is in fact \(\mbox{window\_length} + 1\). Also, we always have
+<code>torch_blackman_window(L, periodic=TRUE)</code> equal to
+<code>torch_blackman_window(L + 1, periodic=False)[:-1])</code>.</p>
+
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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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_bmm.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Bmm</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_bmm</span>(<span class='kw'>self</span>, <span class='kw'>mat2</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the first batch of matrices to be multiplied</p></td>
+    </tr>
+    <tr>
+      <th>mat2</th>
+      <td><p>(Tensor) the second batch of matrices to be multiplied</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>This function does not broadcast .
+For broadcasting matrix products, see <code><a href='torch_matmul.html'>torch_matmul</a></code>.</p>
+    <h2 class="hasAnchor" id="bmm-input-mat-out-null-gt-tensor-"><a class="anchor" href="#bmm-input-mat-out-null-gt-tensor-"></a>bmm(input, mat2, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Performs a batch matrix-matrix product of matrices stored in <code>input</code>
+and <code>mat2</code>.</p>
+<p><code>input</code> and <code>mat2</code> must be 3-D tensors each containing
+the same number of matrices.</p>
+<p>If <code>input</code> is a \((b \times n \times m)\) tensor, <code>mat2</code> is a
+\((b \times m \times p)\) tensor, <code>out</code> will be a
+\((b \times n \times p)\) tensor.</p>
+<p>$$
+    \mbox{out}_i = \mbox{input}_i \mathbin{@} \mbox{mat2}_i
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>4</span>))
+<span class='kw'>mat2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>4</span>, <span class='fl'>5</span>))
+<span class='kw'>res</span> <span class='op'>=</span> <span class='fu'>torch_bmm</span>(<span class='kw'>input</span>, <span class='kw'>mat2</span>)
+<span class='kw'>res</span>
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;   1.0892  1.3748  0.8459 -2.0545 -1.4386
+#&gt;   0.3660 -0.1655  0.0205 -0.7982 -1.6220
+#&gt;   0.4613  0.7003  0.2010  1.5919  1.4269
+#&gt; 
+#&gt; (2,.,.) = 
+#&gt;  -2.2132 -1.0830  0.8680 -0.3013  0.5952
+#&gt;  -1.7194  1.7257 -1.8858  0.5295  0.0113
+#&gt;  -1.9067 -3.6883  3.4484 -0.6959  1.6596
+#&gt; 
+#&gt; (3,.,.) = 
+#&gt;   0.4246 -0.4291  0.5495  0.0139 -2.8828
+#&gt;   0.8869 -1.7168  0.9370  2.8693 -0.8925
+#&gt;  -0.1330 -0.6530  0.2961 -0.4661  0.2099
+#&gt; 
+#&gt; (4,.,.) = 
+#&gt;   3.2950  1.2318 -0.2358  3.7370  7.2076
+#&gt;   0.5021 -7.1274 -0.6974  2.2166 -1.0051
+#&gt;   0.5575 -0.9316 -0.4947  1.2719 -0.3476
+#&gt; 
+#&gt; (5,.,.) = 
+#&gt;  -0.7364 -1.5675  1.2250  0.6341  2.4889
+#&gt;  -3.3237  2.9684  3.2533  0.5785 -1.9284
+#&gt;  -3.7940  1.4235  3.7042 -0.9522 -1.3960
+#&gt; 
+#&gt; (6,.,.) = 
+#&gt;  -3.5668 -1.3443  5.1221 -1.1451  0.9565
+#&gt;  -1.3876  1.5025 -3.9358  1.3078  0.9739
+#&gt;  -1.9256 -2.0872  7.0016 -1.1434  0.6967
+#&gt; 
+#&gt; (7,.,.) = 
+#&gt;  -0.2430 -1.1612  0.2267 -0.4137  0.3275
+#&gt;  -1.0699  1.5163  1.1168  0.4205  1.8671
+#&gt;  -1.1855  5.5310  1.8558  4.2721  1.0853
+#&gt; 
+#&gt; (8,.,.) = 
+#&gt;  -1.0895 -2.0372 -2.1277  0.7614 -1.5783
+#&gt;  -0.6140  0.0763  1.5264 -0.7559 -0.3574
+#&gt;   0.2487 -1.0749  0.2471  1.2148  0.5383
+#&gt; 
+#&gt; (9,.,.) = 
+#&gt;  -0.7402  0.6009  1.3001  0.8246 -0.4576
+#&gt;  -0.1567 -0.6394  0.0932 -0.1400 -0.7153
+#&gt;  -0.5724 -0.2588 -1.4616 -1.0371 -0.5793
+#&gt; 
+#&gt; (10,.,.) = 
+#&gt;   7.8231  2.6005 -0.8253 -0.6163 -4.4321
+#&gt;   3.5758  0.7528  0.7691  3.3599 -1.3278
+#&gt;  -3.2284 -0.5208 -1.5541 -2.5006  2.0721
+#&gt; [ CPUFloatType{10,3,5} ]</div></pre>
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diff --git a/static/docs/reference/torch_broadcast_tensors.html b/static/docs/reference/torch_broadcast_tensors.html
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+    <div class="page-header">
+    <h1>Broadcast_tensors</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_broadcast_tensors.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Broadcast_tensors</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_broadcast_tensors</span>(<span class='kw'>tensors</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensors</th>
+      <td><p>a list containing any number of tensors of the same type</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="broadcast-tensors-tensors-gt-list-of-tensors-"><a class="anchor" href="#broadcast-tensors-tensors-gt-list-of-tensors-"></a>broadcast_tensors(tensors) -&gt; List of Tensors </h2>
+
+    
+
+
+<p>Broadcasts the given tensors according to broadcasting-semantics.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0</span>, <span class='fl'>3</span>)<span class='op'>$</span><span class='fu'>view</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
+<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0</span>, <span class='fl'>2</span>)<span class='op'>$</span><span class='fu'>view</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>))
+<span class='kw'>out</span> <span class='op'>=</span> <span class='fu'>torch_broadcast_tensors</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>x</span>, <span class='kw'>y</span>))
+<span class='kw'>out</span>[[<span class='fl'>1</span>]]
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0  1  2
+#&gt;  0  1  2
+#&gt; [ CPUFloatType{2,3} ]</div></pre>
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+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_can_cast.html b/static/docs/reference/torch_can_cast.html
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+++ b/static/docs/reference/torch_can_cast.html
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+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
+    Getting started
+     
+    <span class="caret"></span>
+  </a>
+  <ul class="dropdown-menu" role="menu">
+    <li class="dropdown-header">Beginers guide</li>
+    <li>
+      <a href="../articles/getting-started/warmup.html">Warm-up</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/tensors.html">Tensors</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/tensors-and-autograd.html">Tensors and autograd</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
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+    </li>
+    <li class="dropdown-header">Torch Mechanics</li>
+    <li>
+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
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+      <a href="../articles/loading-data.html">Loading Data</a>
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+</li>
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+</li>
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+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Can_cast</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_can_cast.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Can_cast</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_can_cast</span>(<span class='kw'>from</span>, <span class='kw'>to</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>from</th>
+      <td><p>(dtype) The original <code>torch_dtype</code>.</p></td>
+    </tr>
+    <tr>
+      <th>to</th>
+      <td><p>(dtype) The target <code>torch_dtype</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="can-cast-from-to-gt-bool-"><a class="anchor" href="#can-cast-from-to-gt-bool-"></a>can_cast(from, to) -&gt; bool </h2>
+
+    
+
+
+<p>Determines if a type conversion is allowed under PyTorch casting rules
+described in the type promotion documentation .</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_can_cast</span>(<span class='fu'><a href='torch_dtype.html'>torch_double</a></span>(), <span class='fu'><a href='torch_dtype.html'>torch_float</a></span>())
+<span class='fu'>torch_can_cast</span>(<span class='fu'><a href='torch_dtype.html'>torch_float</a></span>(), <span class='fu'><a href='torch_dtype.html'>torch_int</a></span>())
+}
+</div><div class='output co'>#&gt; [1] FALSE</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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diff --git a/static/docs/reference/torch_cartesian_prod.html b/static/docs/reference/torch_cartesian_prod.html
new file mode 100644
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--- /dev/null
+++ b/static/docs/reference/torch_cartesian_prod.html
@@ -0,0 +1,254 @@
+<!-- Generated by pkgdown: do not edit by hand -->
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+    </li>
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+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/neural-networks.html">Neural networks</a>
+    </li>
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+      <a href="../articles/tensor-creation.html">Creating tensors</a>
+    </li>
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+      <a href="../articles/using-autograd.html">Using autograd</a>
+    </li>
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+      
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+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Cartesian_prod</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_cartesian_prod.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Do cartesian product of the given sequence of tensors.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_cartesian_prod</span>(<span class='kw'>tensors</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensors</th>
+      <td><p>a list containing any number of 1 dimensional tensors.</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>)
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>)
+<span class='kw'>tensor_a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='kw'>a</span>)
+<span class='kw'>tensor_b</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='kw'>b</span>)
+<span class='fu'>torch_cartesian_prod</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>tensor_a</span>, <span class='kw'>tensor_b</span>))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1  4
+#&gt;  1  5
+#&gt;  2  4
+#&gt;  2  5
+#&gt;  3  4
+#&gt;  3  5
+#&gt; [ CPUFloatType{6,2} ]</div></pre>
+  </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>
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+</div>
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diff --git a/static/docs/reference/torch_cat.html b/static/docs/reference/torch_cat.html
new file mode 100644
index 000000000..2841ea8b9
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+++ b/static/docs/reference/torch_cat.html
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+    <div class="page-header">
+    <h1>Cat</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_cat.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Cat</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_cat</span>(<span class='kw'>tensors</span>, dim = <span class='fl'>1L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensors</th>
+      <td><p>(sequence of Tensors) any python sequence of tensors of the same type.        Non-empty tensors provided must have the same shape, except in the        cat dimension.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int, optional) the dimension over which the tensors are concatenated</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="cat-tensors-dim-out-null-gt-tensor-"><a class="anchor" href="#cat-tensors-dim-out-null-gt-tensor-"></a>cat(tensors, dim=0, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Concatenates the given sequence of <code>seq</code> tensors in the given dimension.
+All tensors must either have the same shape (except in the concatenating
+dimension) or be empty.</p>
+<p><code>torch_cat</code> can be seen as an inverse operation for <code><a href='torch_split.html'>torch_split()</a></code>
+and <code><a href='torch_chunk.html'>torch_chunk</a></code>.</p>
+<p><code>torch_cat</code> can be best understood via examples.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+<span class='kw'>x</span>
+<span class='fu'>torch_cat</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>x</span>, <span class='kw'>x</span>, <span class='kw'>x</span>), <span class='fl'>1</span>)
+<span class='fu'>torch_cat</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>x</span>, <span class='kw'>x</span>, <span class='kw'>x</span>), <span class='fl'>2</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.4974 -0.0008 -0.8116  0.4974 -0.0008 -0.8116  0.4974 -0.0008 -0.8116
+#&gt;  0.0269 -0.0270  0.1641  0.0269 -0.0270  0.1641  0.0269 -0.0270  0.1641
+#&gt; [ CPUFloatType{2,9} ]</div></pre>
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+    <div class="page-header">
+    <h1>Cdist</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_cdist.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Cdist</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_cdist</span>(<span class='kw'>x1</span>, <span class='kw'>x2</span>, p = <span class='fl'>2L</span>, compute_mode = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>x1</th>
+      <td><p>(Tensor) input tensor of shape \(B \times P \times M\).</p></td>
+    </tr>
+    <tr>
+      <th>x2</th>
+      <td><p>(Tensor) input tensor of shape \(B \times R \times M\).</p></td>
+    </tr>
+    <tr>
+      <th>p</th>
+      <td><p>NA p value for the p-norm distance to calculate between each vector pair        \(\in [0, \infty]\).</p></td>
+    </tr>
+    <tr>
+      <th>compute_mode</th>
+      <td><p>NA 'use_mm_for_euclid_dist_if_necessary' - will use matrix multiplication approach to calculate        euclidean distance (p = 2) if P &gt; 25 or R &gt; 25        'use_mm_for_euclid_dist' - will always use matrix multiplication approach to calculate        euclidean distance (p = 2)        'donot_use_mm_for_euclid_dist' - will never use matrix multiplication approach to calculate        euclidean distance (p = 2)        Default: use_mm_for_euclid_dist_if_necessary.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
+
+    
+
+
+<p>Computes batched the p-norm distance between each pair of the two collections of row vectors.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_ceil.html b/static/docs/reference/torch_ceil.html
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+
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+    <div class="page-header">
+    <h1>Ceil</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_ceil.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Ceil</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_ceil</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="ceil-input-out-null-gt-tensor-"><a class="anchor" href="#ceil-input-out-null-gt-tensor-"></a>ceil(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the ceil of the elements of <code>input</code>,
+the smallest integer greater than or equal to each element.</p>
+<p>$$
+    \mbox{out}_{i} = \left\lceil \mbox{input}_{i} \right\rceil = \left\lfloor \mbox{input}_{i} \right\rfloor + 1
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_ceil</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1
+#&gt; -1
+#&gt; -0
+#&gt;  1
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
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+    <div class="page-header">
+    <h1>Celu</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_celu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Celu</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_celu</span>(<span class='kw'>self</span>, alpha = <span class='fl'>1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>alpha</th>
+      <td><p>the alpha value for the CELU formulation. Default: 1.0</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="celu-input-alpha-gt-tensor-"><a class="anchor" href="#celu-input-alpha-gt-tensor-"></a>celu(input, alpha=1.) -&gt; Tensor </h2>
+
+    
+
+
+<p>See <code><a href='nnf_celu.html'>nnf_celu()</a></code> for more info.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <h1>Celu_</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
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+    <p>Celu_</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_celu_</span>(<span class='kw'>self</span>, alpha = <span class='fl'>1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>alpha</th>
+      <td><p>the alpha value for the CELU formulation. Default: 1.0</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="celu-input-alpha-gt-tensor-"><a class="anchor" href="#celu-input-alpha-gt-tensor-"></a>celu_(input, alpha=1.) -&gt; Tensor </h2>
+
+    
+
+
+<p>In-place version of <code><a href='torch_celu.html'>torch_celu()</a></code>.</p>
+
+  </div>
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+    <div class="page-header">
+    <h1>Chain_matmul</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_chain_matmul.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Chain_matmul</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_chain_matmul</span>(<span class='kw'>matrices</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>matrices</th>
+      <td><p>(Tensors...) a sequence of 2 or more 2-D tensors whose product is to be determined.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
+
+    
+
+
+<p>Returns the matrix product of the \(N\) 2-D tensors. This product is efficiently computed
+using the matrix chain order algorithm which selects the order in which incurs the lowest cost in terms
+of arithmetic operations (<code>[CLRS]</code>_). Note that since this is a function to compute the product, \(N\)
+needs to be greater than or equal to 2; if equal to 2 then a trivial matrix-matrix product is returned.
+If \(N\) is 1, then this is a no-op - the original matrix is returned as is.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>4</span>))
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>))
+<span class='kw'>c</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>, <span class='fl'>6</span>))
+<span class='kw'>d</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>6</span>, <span class='fl'>7</span>))
+<span class='fu'>torch_chain_matmul</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>a</span>, <span class='kw'>b</span>, <span class='kw'>c</span>, <span class='kw'>d</span>))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  -2.9763   5.1541   0.4802   4.2000  -0.3407  -3.9994  -9.5460
+#&gt;   9.4136 -12.4751 -16.3055 -13.4903   0.4411  12.5776  21.3514
+#&gt;   3.9498  -8.2069  -5.7414   1.2695   6.5496  18.3628  12.8464
+#&gt; [ CPUFloatType{3,7} ]</div></pre>
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Cholesky</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_cholesky.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Cholesky</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_cholesky</span>(<span class='kw'>self</span>, upper = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor \(A\) of size \((*, n, n)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is zero or more
+batch dimensions consisting of symmetric positive-definite matrices.</p></td>
+    </tr>
+    <tr>
+      <th>upper</th>
+      <td><p>(bool, optional) flag that indicates whether to return a
+upper or lower triangular matrix. Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="cholesky-input-upper-false-out-null-gt-tensor-"><a class="anchor" href="#cholesky-input-upper-false-out-null-gt-tensor-"></a>cholesky(input, upper=False, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the Cholesky decomposition of a symmetric positive-definite
+matrix \(A\) or for batches of symmetric positive-definite matrices.</p>
+<p>If <code>upper</code> is <code>TRUE</code>, the returned matrix <code>U</code> is upper-triangular, and
+the decomposition has the form:</p>
+<p>$$
+  A = U^TU
+$$
+If <code>upper</code> is <code>FALSE</code>, the returned matrix <code>L</code> is lower-triangular, and
+the decomposition has the form:</p>
+<p>$$
+    A = LL^T
+$$
+If <code>upper</code> is <code>TRUE</code>, and \(A\) is a batch of symmetric positive-definite
+matrices, then the returned tensor will be composed of upper-triangular Cholesky factors
+of each of the individual matrices. Similarly, when <code>upper</code> is <code>FALSE</code>, the returned
+tensor will be composed of lower-triangular Cholesky factors of each of the individual
+matrices.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='kw'>a</span>, <span class='kw'>a</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>()) <span class='co'># make symmetric positive-definite</span>
+<span class='kw'>l</span> <span class='op'>=</span> <span class='fu'>torch_cholesky</span>(<span class='kw'>a</span>)
+<span class='kw'>a</span>
+<span class='kw'>l</span>
+<span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='kw'>l</span>, <span class='kw'>l</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>())
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>, <span class='fl'>2</span>))
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span>(<span class='kw'>a</span>, <span class='kw'>a</span><span class='op'>$</span><span class='fu'>transpose</span>(<span class='op'>-</span><span class='fl'>1</span>, <span class='op'>-</span><span class='fl'>2</span>)) <span class='op'>+</span> <span class='fl'>1e-03</span> <span class='co'># make symmetric positive-definite</span>
+<span class='kw'>l</span> <span class='op'>=</span> <span class='fu'>torch_cholesky</span>(<span class='kw'>a</span>)
+<span class='kw'>z</span> <span class='op'>=</span> <span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span>(<span class='kw'>l</span>, <span class='kw'>l</span><span class='op'>$</span><span class='fu'>transpose</span>(<span class='op'>-</span><span class='fl'>1</span>, <span class='op'>-</span><span class='fl'>2</span>))
+<span class='fu'><a href='torch_max.html'>torch_max</a></span>(<span class='fu'><a href='torch_abs.html'>torch_abs</a></span>(<span class='kw'>z</span> <span class='op'>-</span> <span class='kw'>a</span>)) <span class='co'># Max non-zero</span>
+}
+}
+</div></pre>
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+    <div class="page-header">
+    <h1>Cholesky_inverse</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_cholesky_inverse.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Cholesky_inverse</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_cholesky_inverse</span>(<span class='kw'>self</span>, upper = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input 2-D tensor \(u\), a upper or lower triangular           Cholesky factor</p></td>
+    </tr>
+    <tr>
+      <th>upper</th>
+      <td><p>(bool, optional) whether to return a lower (default) or upper triangular matrix</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="cholesky-inverse-input-upper-false-out-null-gt-tensor-"><a class="anchor" href="#cholesky-inverse-input-upper-false-out-null-gt-tensor-"></a>cholesky_inverse(input, upper=False, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the inverse of a symmetric positive-definite matrix \(A\) using its
+Cholesky factor \(u\): returns matrix <code>inv</code>. The inverse is computed using
+LAPACK routines <code>dpotri</code> and <code>spotri</code> (and the corresponding MAGMA routines).</p>
+<p>If <code>upper</code> is <code>FALSE</code>, \(u\) is lower triangular
+such that the returned tensor is</p>
+<p>$$
+    inv = (uu^{{T}})^{{-1}}
+$$
+If <code>upper</code> is <code>TRUE</code> or not provided, \(u\) is upper
+triangular such that the returned tensor is</p>
+<p>$$
+    inv = (u^T u)^{{-1}}
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='kw'>a</span>, <span class='kw'>a</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>()) <span class='op'>+</span> <span class='fl'>1e-05</span> <span class='op'>*</span> <span class='fu'><a href='torch_eye.html'>torch_eye</a></span>(<span class='fl'>3</span>) <span class='co'># make symmetric positive definite</span>
+<span class='kw'>u</span> <span class='op'>=</span> <span class='fu'><a href='torch_cholesky.html'>torch_cholesky</a></span>(<span class='kw'>a</span>)
+<span class='kw'>a</span>
+<span class='fu'>torch_cholesky_inverse</span>(<span class='kw'>u</span>)
+<span class='kw'>a</span><span class='op'>$</span><span class='fu'>inverse</span>()
+}
+}
+</div></pre>
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+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Cholesky_solve</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_cholesky_solve.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Cholesky_solve</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_cholesky_solve</span>(<span class='kw'>self</span>, <span class='kw'>input2</span>, upper = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) input matrix \(b\) of size \((*, m, k)\),                where \(*\) is zero or more batch dimensions</p></td>
+    </tr>
+    <tr>
+      <th>input2</th>
+      <td><p>(Tensor) input matrix \(u\) of size \((*, m, m)\),                where \(*\) is zero of more batch dimensions composed of                upper or lower triangular Cholesky factor</p></td>
+    </tr>
+    <tr>
+      <th>upper</th>
+      <td><p>(bool, optional) whether to consider the Cholesky factor as a                            lower or upper triangular matrix. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="cholesky-solve-input-input-upper-false-out-null-gt-tensor-"><a class="anchor" href="#cholesky-solve-input-input-upper-false-out-null-gt-tensor-"></a>cholesky_solve(input, input2, upper=False, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Solves a linear system of equations with a positive semidefinite
+matrix to be inverted given its Cholesky factor matrix \(u\).</p>
+<p>If <code>upper</code> is <code>FALSE</code>, \(u\) is and lower triangular and <code>c</code> is
+returned such that:</p>
+<p>$$
+    c = (u u^T)^{{-1}} b
+$$
+If <code>upper</code> is <code>TRUE</code> or not provided, \(u\) is upper triangular
+and <code>c</code> is returned such that:</p>
+<p>$$
+    c = (u^T u)^{{-1}} b
+$$
+<code>torch_cholesky_solve(b, u)</code> can take in 2D inputs <code>b, u</code> or inputs that are
+batches of 2D matrices. If the inputs are batches, then returns
+batched outputs <code>c</code></p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='kw'>a</span>, <span class='kw'>a</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>()) <span class='co'># make symmetric positive definite</span>
+<span class='kw'>u</span> <span class='op'>=</span> <span class='fu'><a href='torch_cholesky.html'>torch_cholesky</a></span>(<span class='kw'>a</span>)
+<span class='kw'>a</span>
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>))
+<span class='kw'>b</span>
+<span class='fu'>torch_cholesky_solve</span>(<span class='kw'>b</span>, <span class='kw'>u</span>)
+<span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='kw'>a</span><span class='op'>$</span><span class='fu'>inverse</span>(), <span class='kw'>b</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -4.4143  1.6973
+#&gt;  3.7547 -2.0436
+#&gt;  1.5229 -2.0137
+#&gt; [ CPUFloatType{3,2} ]</div></pre>
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+    <div class="page-header">
+    <h1>Chunk</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_chunk.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Chunk</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_chunk</span>(<span class='kw'>self</span>, <span class='kw'>chunks</span>, dim = <span class='fl'>1L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the tensor to split</p></td>
+    </tr>
+    <tr>
+      <th>chunks</th>
+      <td><p>(int) number of chunks to return</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) dimension along which to split the tensor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="chunk-input-chunks-dim-gt-list-of-tensors-"><a class="anchor" href="#chunk-input-chunks-dim-gt-list-of-tensors-"></a>chunk(input, chunks, dim=0) -&gt; List of Tensors </h2>
+
+    
+
+
+<p>Splits a tensor into a specific number of chunks. Each chunk is a view of
+the input tensor.</p>
+<p>Last chunk will be smaller if the tensor size along the given dimension
+<code>dim</code> is not divisible by <code>chunks</code>.</p>
+
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+    <div class="page-header">
+    <h1>Clamp</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_clamp.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Clamp</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_clamp</span>(<span class='kw'>self</span>, min = <span class='kw'>NULL</span>, max = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>min</th>
+      <td><p>(Number) lower-bound of the range to be clamped to</p></td>
+    </tr>
+    <tr>
+      <th>max</th>
+      <td><p>(Number) upper-bound of the range to be clamped to</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="clamp-input-min-max-out-null-gt-tensor-"><a class="anchor" href="#clamp-input-min-max-out-null-gt-tensor-"></a>clamp(input, min, max, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Clamp all elements in <code>input</code> into the range <code><a href='https://rdrr.io/r/base/Extract.html'>[</a></code> <code>min</code>, <code>max</code> <code>]</code> and return
+a resulting tensor:</p>
+<p>$$
+    y_i = \left\{ \begin{array}{ll}
+        \mbox{min} &amp; \mbox{if } x_i &lt; \mbox{min} \\
+        x_i &amp; \mbox{if } \mbox{min} \leq x_i \leq \mbox{max} \\
+        \mbox{max} &amp; \mbox{if } x_i &gt; \mbox{max}
+    \end{array}
+    \right.
+$$
+If <code>input</code> is of type <code>FloatTensor</code> or <code>DoubleTensor</code>, args <code>min</code>
+and <code>max</code> must be real numbers, otherwise they should be integers.</p>
+    <h2 class="hasAnchor" id="clamp-input-min-out-null-gt-tensor-"><a class="anchor" href="#clamp-input-min-out-null-gt-tensor-"></a>clamp(input, *, min, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Clamps all elements in <code>input</code> to be larger or equal <code>min</code>.</p>
+<p>If <code>input</code> is of type <code>FloatTensor</code> or <code>DoubleTensor</code>, <code>value</code>
+should be a real number, otherwise it should be an integer.</p>
+    <h2 class="hasAnchor" id="clamp-input-max-out-null-gt-tensor-"><a class="anchor" href="#clamp-input-max-out-null-gt-tensor-"></a>clamp(input, *, max, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Clamps all elements in <code>input</code> to be smaller or equal <code>max</code>.</p>
+<p>If <code>input</code> is of type <code>FloatTensor</code> or <code>DoubleTensor</code>, <code>value</code>
+should be a real number, otherwise it should be an integer.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_clamp</span>(<span class='kw'>a</span>, min=<span class='op'>-</span><span class='fl'>0.5</span>, max=<span class='fl'>0.5</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_clamp</span>(<span class='kw'>a</span>, min=<span class='fl'>0.5</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_clamp</span>(<span class='kw'>a</span>, max=<span class='fl'>0.5</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0.1812
+#&gt; -0.9782
+#&gt;  0.5000
+#&gt;  0.2475
+#&gt; [ CPUFloatType{4} ]</div></pre>
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+    <div class="page-header">
+    <h1>Combinations</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_combinations.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Combinations</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_combinations</span>(<span class='kw'>self</span>, r = <span class='fl'>2L</span>, with_replacement = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) 1D vector.</p></td>
+    </tr>
+    <tr>
+      <th>r</th>
+      <td><p>(int, optional) number of elements to combine</p></td>
+    </tr>
+    <tr>
+      <th>with_replacement</th>
+      <td><p>(boolean, optional) whether to allow duplication in combination</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="combinations-input-r-with-replacement-false-gt-seq-"><a class="anchor" href="#combinations-input-r-with-replacement-false-gt-seq-"></a>combinations(input, r=2, with_replacement=False) -&gt; seq </h2>
+
+    
+
+
+<p>Compute combinations of length \(r\) of the given tensor. The behavior is similar to
+python's <code>itertools.combinations</code> when <code>with_replacement</code> is set to <code>False</code>, and
+<code>itertools.combinations_with_replacement</code> when <code>with_replacement</code> is set to <code>TRUE</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>)
+<span class='kw'>tensor_a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='kw'>a</span>)
+<span class='fu'>torch_combinations</span>(<span class='kw'>tensor_a</span>)
+<span class='fu'>torch_combinations</span>(<span class='kw'>tensor_a</span>, r=<span class='fl'>3</span>)
+<span class='fu'>torch_combinations</span>(<span class='kw'>tensor_a</span>, with_replacement=<span class='fl'>TRUE</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1  1
+#&gt;  1  2
+#&gt;  1  3
+#&gt;  2  2
+#&gt;  2  3
+#&gt;  3  3
+#&gt; [ CPUFloatType{6,2} ]</div></pre>
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+<div class="row">
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+    <div class="page-header">
+    <h1>Conj</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_conj.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Conj</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_conj</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="conj-input-gt-tensor-"><a class="anchor" href="#conj-input-gt-tensor-"></a>conj(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the element-wise conjugate of the given <code>input</code> tensor.</p>
+<p>$$
+    \mbox{out}_{i} = conj(\mbox{input}_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='fu'>torch_conj</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>1</span> <span class='op'>+</span> <span class='fl'>1i</span>, <span class='op'>-</span><span class='fl'>2</span> <span class='op'>+</span> <span class='fl'>2i</span>, <span class='fl'>3</span> <span class='op'>-</span> <span class='fl'>3i</span>)))
+}
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
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diff --git a/static/docs/reference/torch_conv1d.html b/static/docs/reference/torch_conv1d.html
new file mode 100644
index 000000000..297f1ce91
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+++ b/static/docs/reference/torch_conv1d.html
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+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Conv1d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_conv1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Conv1d</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_conv1d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>weight</span>,
+  bias = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(),
+  stride = <span class='fl'>1L</span>,
+  padding = <span class='fl'>0L</span>,
+  dilation = <span class='fl'>1L</span>,
+  groups = <span class='fl'>1L</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iW)\)</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>filters of shape \((\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kW)\)</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>optional bias of shape \((\mbox{out\_channels})\). Default: <code>NULL</code></p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the convolving kernel. Can be a single number or      a one-element tuple <code>(sW,)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit paddings on both sides of the input. Can be a      single number or a one-element tuple <code>(padW,)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>the spacing between kernel elements. Can be a single number or      a one-element tuple <code>(dW,)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by      the number of groups. Default: 1</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-"><a class="anchor" href="#conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-"></a>conv1d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -&gt; Tensor </h2>
+
+    
+
+
+<p>Applies a 1D convolution over an input signal composed of several input
+planes.</p>
+<p>See <code><a href='nn_conv1d.html'>nn_conv1d()</a></code> for details and output shape.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>filters</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>33</span>, <span class='fl'>16</span>, <span class='fl'>3</span>))
+<span class='kw'>inputs</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>))
+<span class='fu'><a href='nnf_conv1d.html'>nnf_conv1d</a></span>(<span class='kw'>inputs</span>, <span class='kw'>filters</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;  Columns 1 to 8  -9.2155 -13.3788  -4.8815  -5.9509   1.0559  11.3390   4.1760  -7.3364
+#&gt;    0.9687   3.5362   6.5343   9.7170   3.5029  -0.5239  11.8401  -3.6896
+#&gt;    9.2053  -0.8704   2.5167   5.3901   5.4175 -10.4469  -1.7450  -8.4401
+#&gt;   17.3697   8.0818  -6.5866  -5.1253  -1.3999 -14.3850  -0.6795   6.7809
+#&gt;    7.6453   6.5674   2.8867   6.4723   7.2736  -9.4473  -1.8036  -1.8636
+#&gt;  -10.4610   0.7640 -11.0805   7.2157  -3.6219 -11.6838  -7.2940   6.4684
+#&gt;   -5.6155  -2.4741   4.2673  -2.6692  -2.5511   5.6617   8.3692  11.8541
+#&gt;    2.9653  10.0184   2.3498  10.2008  -0.0906  -8.3016  -7.6074  -8.9669
+#&gt;   -0.1892  -1.4338  -8.1527   6.1579  14.1115   6.3555  -5.8888   1.3635
+#&gt;   -0.8715   3.2313  -5.0396  -8.8492  -2.6872   2.5312   1.5977   4.7325
+#&gt;   -0.6426   6.7878  10.6713   4.4664  15.5926 -12.9809   8.5686 -14.1252
+#&gt;   -9.4297   2.5895   6.8971  -1.2187  -8.5115   2.7068  -3.6668   3.7638
+#&gt;   -4.8841   1.5752  -8.0881  -0.7977 -13.0366   1.6083   8.0490  -1.1757
+#&gt;    9.2181 -18.6418   5.3897  -7.0765  10.9595  -1.2353  11.2700   5.9045
+#&gt;   -3.9796  18.5287  -6.2100   2.2016  -4.9045  13.3548   4.2783   7.5602
+#&gt;    6.4389  -3.1190   4.0417   3.9676   5.6056  -6.5717  -6.5705   1.6651
+#&gt;    7.8152  -2.1107  -8.4163   3.4218 -11.4875  -1.3201  10.1588   4.6279
+#&gt;    3.1470  -0.6810  -4.7713   2.0391  -4.2066 -20.4188  -4.7113   8.9348
+#&gt;   -3.2366  -6.0332   2.2069   8.2774  12.2769  14.3749 -12.7319  -0.7708
+#&gt;    0.6335  -1.3701  -9.1195  11.4271  -5.1396   5.6112   0.7465   0.8958
+#&gt;   -4.4243   3.2009   3.4101   0.8854  -6.3913  -5.3965  -5.5959   6.9523
+#&gt;   -2.0583   3.5878   2.1682   1.0807   2.3449  -7.7811   5.4776  -2.1308
+#&gt;  -14.8754  -3.5022  -4.9228   2.9605   5.1781  -0.4115   1.6601   6.8168
+#&gt;   -8.0577  -3.7750 -12.4450  -7.1238  -3.8977  14.8252   8.1138   3.9659
+#&gt;   -8.2239   3.0827   1.9788  -3.2824   1.4724  -0.2935   9.8999   4.8447
+#&gt;    5.2614  -0.9639 -11.0854  -1.7399  -8.8694   3.1894  -9.9635  -0.2191
+#&gt;   -6.9460   2.0936  -4.6144   2.4964  -2.3149  -4.9224   5.6279  17.6400
+#&gt;    2.7239  -7.1132  -3.2270  -4.4261  -0.1128   2.0319   5.7296  -8.8630
+#&gt;    1.2327   2.0651   8.5595  -5.4172  13.7335   3.8594  13.4662  10.1180
+#&gt;  -10.8110  -5.3874   1.2913  -6.1393 -10.3391   6.3426  20.4897 -13.8959
+#&gt;   -1.9465  -9.7411  -2.5889  -3.1008  -4.2539   6.0369  -6.8057   4.0780
+#&gt;    2.4637   0.0831  -4.2466  -2.7181   1.9345  -4.0950  -4.5429  -2.3028
+#&gt;  -11.2792  -0.2996   1.2558  -2.7901   3.7011  10.1111   3.7003  -1.7684
+#&gt; 
+#&gt; Columns 9 to 16  -1.2297  14.3283   2.2631   1.3546  -1.5108  -7.3943   8.3483   6.9639
+#&gt;   -0.8142  -4.1600   0.6429   3.5323   1.7201   2.6613  -5.5547  -6.9555
+#&gt;   -0.4468  -9.6767   3.4243  -5.5370   7.9330  -3.7124  -8.0854   2.8903
+#&gt;   -0.8646   3.8281 -11.0271   3.5653   6.2605  -8.7873 -19.2922   1.3006
+#&gt;    1.9641  -0.2966   1.1310  -8.4550  12.6014  -0.7149  -4.5330   3.5843
+#&gt;    1.9168  17.5806  -0.1329   5.5879   8.6691 -11.9588   3.0880  -8.4299
+#&gt;    7.6088  -4.8286  -7.4288  -2.1821 -10.3345   2.2468  -1.3855   6.6098
+#&gt;    3.9023   2.7969  -6.6740  -2.6189   3.4172  -7.3487   4.3757  20.1543
+#&gt;   -9.2841  -5.3292   7.3305  -6.0223   6.0400   4.6032  -4.5086   1.9101
+#&gt;    2.6685 -11.7018 -10.9301   7.0899   5.2995  10.9822  -4.8582 -10.7569
+#&gt;    3.1771  -2.5365  17.9825 -12.2625   9.7687  -3.4043  -8.8135   4.3696
+#&gt;    5.2823   0.7356  -4.1298  -0.8445  -1.4917   6.5445  13.9097  -1.9308
+#&gt;    1.4821   4.8847  -0.5632  -3.9153   0.0182  -6.5668   3.3724  -0.0089
+#&gt;    2.0266  -4.8865  10.2491   1.0754  -5.8866  -3.6270  -8.2653   6.7061
+#&gt;   -2.9120   2.2089  -9.9280  -5.7288  -0.5021   3.7785   2.3965   2.2677
+#&gt;    2.3213  -1.8852   6.9190   1.6314  -2.5556   1.8858 -11.7685  -8.4369
+#&gt;    9.6492  -2.9285  12.2038  -0.3930  -8.1063   2.2913   4.9456  10.8998
+#&gt;   -0.6015  -0.4647  -5.5746  15.3407 -13.0841   6.5545  -4.9857  -2.0244
+#&gt;    4.8784  -8.7558   6.3537   4.8576   4.3489  -6.7662  -8.7465   2.3861
+#&gt;   -6.2488  11.2704  -1.0825   6.4878  -7.8461   5.2779   5.0536   4.8804
+#&gt;    7.4775  -4.1008  -3.6440  -0.9562   1.8611   3.2053   3.7267   6.6024
+#&gt;    1.7077  16.4328   1.1904  11.2724  -1.9391 -12.8586   3.8936   3.2751
+#&gt;  -12.2966  -2.3560  -0.6443   3.9921  -7.5230   0.9491  14.3577  -4.5637
+#&gt;   -0.1001 -11.4522  -0.7943  -1.2022   5.1980  -8.8641 -13.6163 -11.5659
+#&gt;    3.8534   1.4053   4.2852  -9.7152  -4.8377   5.4733  11.9647  -3.4902
+#&gt;    5.6387  12.2535  -4.6256   9.8812 -10.5789   3.1894  12.6462   2.0144
+#&gt;   12.5536  -5.3916  -6.8821  -7.8555   0.9683 -10.4353  -7.7733  -3.1686
+#&gt;    3.2834 -13.1777   0.7912   7.4581   7.8920  15.8003  -5.9017   6.8834
+#&gt;    6.0552   1.3924   6.7256  -5.4646  -5.5976  -8.2639   0.7305   4.5903
+#&gt;   -3.8023  -2.4783   6.5691  -2.8264   5.2719  20.0680   3.1247   8.2646
+#&gt;    5.6692  -8.6198 -15.6985  11.5830 -14.8205  10.7908  14.9619  17.0967
+#&gt;   -8.9506  -2.7822   1.0922 -10.7574  -0.3966 -10.7521  14.5444 -18.7963
+#&gt;   -6.0050   6.5820  -2.7543   4.5226   1.5246   9.3887   2.3188 -16.2012
+#&gt; 
+#&gt; Columns 17 to 24  -4.8695   3.7966   2.5417   6.4239   1.1281 -12.0619   0.3447  -1.8495
+#&gt;   -1.4736 -17.1540   3.4796   0.8374   9.6773  -2.4723   3.6881   1.1677
+#&gt;  -14.2464   0.4473  -4.9651   0.1220  -3.6831   6.5208   2.0460  -4.2853
+#&gt;   -1.5036   4.6976  -3.8827 -16.1917  -1.0532  -6.9277  -9.8995   6.4730
+#&gt;    0.7103  -1.6257   3.9315 -10.7942   4.8641  -4.6957   3.4183   2.0056
+#&gt;   -5.2118   3.0820  14.3102 -12.4407  -4.1198  -3.3126 -12.5885  -0.2005
+#&gt;   -2.3870   7.6276   5.7137   5.6988  -1.1870  -2.0965   1.6126   2.9134
+#&gt;   14.2791   9.6267   9.3154   0.1505  13.1650  -2.0966   7.8122  -0.5654
+#&gt;  -12.7522  -0.3538   7.9037  10.9583  -2.5624  12.9610   0.0900  -2.0126
+#&gt;    4.2564  -0.3686 -12.5285   3.6130   0.7694  -9.3640 -11.3119   4.9255
+#&gt;   -6.6944 -12.5297   7.5991  -9.9898  -5.4475  -1.1485   2.1881  -6.5117
+#&gt;    6.7062  -5.6601  -0.6736   4.1640  -6.3789  -4.6947   3.3107   0.1270
+#&gt;    1.4707   1.9069   7.0601 -11.8777   0.2879  -8.3140  10.3291   5.2538
+#&gt;    4.4303  -6.6440 -10.1144  -3.8942 -15.6688  10.0768  -4.0495   3.0229
+#&gt;    0.7410   3.9392  -5.8770  -9.6422  -4.4338   7.3714   1.1163   1.7708
+#&gt;    9.1297   2.9569 -12.4715   3.4630   2.7644  -0.6531 -16.8944   1.5479
+#&gt;   -1.2994   8.4404   0.0242   6.1673  -5.8023   1.5684  14.3514  -0.2133
+#&gt;   -3.3769   6.3468  -2.9930  -3.8928 -14.3513  -1.1740 -16.5160   3.8613
+#&gt;   -2.0130   9.6563   3.1024  12.3306   0.5746   5.1057   0.9603   0.5509
+#&gt;    3.8197  15.7484   0.7000  -5.7188  -3.2259  11.2313  -2.1890   2.5566
+#&gt;   -6.0698   5.0667  -0.8852   2.6291   4.4607  -2.9877  -0.4767  -2.3615
+#&gt;    3.3213   2.0417  -7.6848   2.5958   3.6713   3.4101   2.9736   6.2485
+#&gt;    6.5813  -5.7292  -4.1808  -4.0592  -2.1550  -1.4666 -10.1642  -4.0215
+#&gt;   -2.6416   1.1199  -1.7281  -1.9130 -22.5373 -15.1305  -2.5205   8.0506
+#&gt;   -5.9875  -3.0936   2.9764   6.2160  -1.3205  11.5670   1.5781  -3.7485
+#&gt;    7.4648  16.2491  -5.4137   6.2873  13.0638  15.2053   0.0131   0.5840
+#&gt;    6.0694   2.7495  15.8616  -9.4167  -2.1686   0.9645   7.1023   3.1712
+#&gt;   -4.9406  -1.4991  -8.2263 -11.4416  -7.9785  -4.3864  -0.9767   6.5426
+#&gt;   -0.4732  -1.7360  -5.7882   1.7442  -9.5797  -1.2339   2.6533   1.0732
+#&gt;    2.7094  -7.0719   0.8703  13.5209  14.0761 -16.0680   4.5148   7.2220
+#&gt;    5.4432  14.7888  -6.7092  17.5242 -11.2132  -9.3064  -1.8499  -3.9303
+#&gt;   17.9627 -11.4976   9.9461  -0.3440  -4.5030  -1.2401  -0.3820  -8.0093
+#&gt;  -14.7131 -13.0150  -0.9647  -3.1112 -12.8724  -8.7595 -10.1707  -8.1402
+#&gt; 
+#&gt; Columns 25 to 32   1.8547  10.9740  -4.0183  17.7692   9.3797  -5.4057  -1.2404   3.7908
+#&gt;    0.2848   5.8314   3.8718   8.4746   9.3938  -0.3301  -3.7456  -1.8790
+#&gt;   -7.1713   0.7990  -0.0932   4.3337   6.5727   1.5852  -3.6544  -5.7062
+#&gt;   -1.1724 -10.0680 -15.0025  -6.9689  -9.6004   7.3883   1.7708   7.3982
+#&gt;   -0.5131   8.0941  -0.3890   1.9520  -1.9299   3.8167   3.7566   1.6492
+#&gt;    7.9042  -2.2868   8.7217   5.5398  -6.5718   2.3075 -18.9907   1.2899
+#&gt;    2.5883  -1.2177 -12.2389   3.3108  -1.5127  -6.1679  -3.8565  13.5627
+#&gt;   -5.1450   5.9179  -3.6925 -10.1028  -7.9639   3.4012   4.1468   2.0707
+#&gt;   -9.5785  -2.3109   1.5583  -3.7295   7.2034   4.7588 -15.4971 -11.6062
+#&gt;    3.6796  13.0790   2.7777  -2.5593   4.2404   6.4327   2.8832  -7.3154
+#&gt;   -8.5260   4.1016 -11.3898   3.9401   1.4674 -13.3377   0.7232   0.7624
+#&gt;    6.6091   0.1427  -1.6529  -1.2078  -0.8534  -1.6778   7.6013  -0.3548
+#&gt;    5.6827  -5.8490   3.1977   8.0379  -7.3799   4.6825  -1.2672   7.2958
+#&gt;   -1.9738   3.6717 -10.3418 -12.5794   9.9656  -5.8658   7.0017  -3.5312
+#&gt;    2.2719  -1.7249  -9.5627   7.5573 -12.4113  10.4928   7.8016   0.9843
+#&gt;    8.1371   6.7174   2.2147   0.4939   8.8809  -7.9415   5.5854  15.3170
+#&gt;  -11.3932  -1.0371   4.5466  -5.9271  -3.3451   4.6784  -2.8040   8.8742
+#&gt;    2.4757  -1.5845  -1.1163  -0.1127   3.7453  -1.4919   3.8370  16.2731
+#&gt;    5.6458   4.9561   7.9401  -3.1958   3.8495  -2.7730 -12.7283  -2.1418
+#&gt;    3.3611   7.1985   7.2372 -11.4225  -0.6149  -0.4715  -1.4611   4.9257
+#&gt;   -9.7007  -0.9226  -4.5642  -5.6105  -7.3980   3.1887  -2.8203   1.5539
+#&gt;    2.0339  -7.3502  -4.2530   0.4539  -2.2738  -7.3877   3.7860   1.1196
+#&gt;    1.9136  -2.9839  -6.5101   9.0191   0.3577  -2.4797  -0.3782   5.8724
+#&gt;    5.7521   3.2082  16.4502   0.2664  -4.4580   8.6922  -6.5666 -15.9535
+#&gt;  -18.4346 -10.0809   3.6438   2.8954 -10.2978   6.2441   9.7118  -1.3140
+#&gt;   -0.3073  -8.0157  -2.1891  -3.7042  -4.4942  -1.6702   3.9804   1.5787
+#&gt;   -1.2848   0.6467  -5.3219  -3.3679  -3.5367   4.1371 -11.7713  20.9084
+#&gt;    4.5120   9.6014  -1.7913  -0.7869  14.9937  13.7954   0.0611 -12.0633
+#&gt;   -3.4014  -2.2428  -3.0984  -1.5958  -3.6751  -7.7030  14.9411  10.2175
+#&gt;   -1.6834   8.8222  -4.5406  12.8437   7.8638   0.8852   4.3863 -19.6971
+#&gt;    0.6685  -5.6872 -17.9757  -7.1754  -1.7314   0.7158   9.9081   8.7799
+#&gt;    1.5819  -4.4604  15.8837  -4.2586  -3.4666   4.1039   0.2826  -8.2974
+#&gt;    1.1024  10.2194   4.3061   9.3984  10.0735   2.7659  -1.0396 -15.0943
+#&gt; 
+#&gt; Columns 33 to 40   3.5032   1.6151  13.6459  -6.1995  -9.8488  12.3514   2.2729   2.7821
+#&gt;  -10.2329   4.8449   1.8473 -11.9907  -1.5774   4.7261  -4.6764   3.4796
+#&gt;    3.8205  -5.8217   0.2988  -1.5748   1.5653 -11.6889   1.7686   1.9090
+#&gt;   13.4749  -8.6482  13.3443   4.5522  -1.9011   0.3399  -5.3454  -9.5580
+#&gt;    7.1295  -9.1016  -0.1034   2.5529   4.4182   7.2676   8.8859   0.2998
+#&gt;   13.1355  -0.0824  -3.8959   2.2275   1.8356  -5.7940   2.1526  -2.6223
+#&gt;   -4.7088  -2.6130   3.9118  -0.8039  -0.8296   7.5224   0.0166   7.7747
+#&gt;   -0.1367  10.0605  -3.1626  -4.9235   9.0574   8.3608  -0.6431   1.6203
+#&gt;    3.9886  -7.4451   1.0348  -6.2363   2.4075 -16.8698   7.9134  13.1190
+#&gt;   -5.1753 -10.0095   4.1602  -4.6037   7.9717   2.1947  -3.1838   1.4414
+#&gt;   -5.7036  17.6836   0.8175 -12.0724  10.1267 -12.3621  -6.8936  12.6102
+#&gt;    0.2967  -3.6497  -0.3328  -0.5213   2.7169   4.6599  -0.7938  -6.6801
+#&gt;   -4.6639  10.4488   1.7619   8.8252   4.8165   2.1116 -14.7942   0.6958
+#&gt;   10.5806  -5.0217  10.8438  15.9300   3.0902  -4.6161   7.6645  -3.7093
+#&gt;   -1.9758   8.7825   5.5702  -1.1931  20.7325  -1.6902  -6.5710   8.5370
+#&gt;   -3.4315  -3.9690  -2.7181   3.9475  -5.2950  -4.0683   5.1271  -8.8019
+#&gt;   -2.4889   7.4373  -1.2045   4.6231   6.4070   1.5636   1.8816   2.0895
+#&gt;   -2.4631   8.3441   2.7115  12.9789  11.4943  -4.0801  -2.7654 -14.1363
+#&gt;    2.3585  -3.1852  -4.5072   3.1720   2.2918  -3.6508  -0.0585   4.2550
+#&gt;   -7.1227  14.0587  -9.2695   2.0586   8.1077  -8.0613  -0.0276  -2.7233
+#&gt;   11.5503  -1.0402  -0.7068   7.1030   9.6921   4.0622   1.5635  -0.8581
+#&gt;  -12.0199   3.8998  -3.4075  -9.5343  -9.1043  -7.0554  -2.9911  -6.6626
+#&gt;   -5.6089   1.6227   6.6670   2.8772   2.9687 -11.4097  11.1582   2.4165
+#&gt;    6.3019   3.1575   7.2511   8.8503  20.9788 -13.1751  -7.8800   5.9546
+#&gt;    6.2591  -1.8061   0.9736  -4.3092  10.0755   3.7018   1.4896   5.1624
+#&gt;    2.0282   5.3408  -1.1668   2.4538   0.4726   9.4383   3.5097  -6.0822
+#&gt;   -9.5976  -1.6313  10.0339   0.4870   8.2388   2.8234  -7.8079  11.4701
+#&gt;   -1.7225   9.5804   6.9717  17.5916  14.5329   2.5304 -11.2523   1.8387
+#&gt;   -2.7241   0.4796   2.8995  -2.0427   5.6780   2.6961   1.2184   1.7330
+#&gt;  -14.0950  14.8797   6.8516  -4.5487  -0.0471  19.4064  -9.8698   2.2338
+#&gt;   -3.9601   2.5363   8.8149   5.9725   3.4330  12.5849   5.8831  -0.3196
+#&gt;    6.8494  -7.2550   1.9249  -1.6378  -4.1263  -2.4659  -3.9709  -8.6884
+#&gt;    3.5879  -0.8758   7.2625  -4.9020  -4.2377  -0.3593  -5.0603   1.3574
+#&gt; 
+#&gt; Columns 41 to 48   4.7544  -3.8532   1.3537  18.0648  10.9368  -3.5256   3.2463 -11.0885
+#&gt;  -11.3760  -8.4545  -6.2563   5.3757   8.7233  -3.5149   6.3589  -8.7134
+#&gt;   -6.3385   3.0080  -2.7377 -11.4562  -1.4043 -11.9691   4.4455 -10.3454
+#&gt;   -1.7988  12.7498   3.4409 -11.4334  -3.6944   3.1703 -18.5209  13.9670
+#&gt;   -7.0067   0.2700   0.6592  -2.8205  -2.1231   2.6654   4.0859  -0.1331
+#&gt;   -0.1821  10.3581  -1.0482  18.0136   5.8157   0.6943   5.0057  -9.2490
+#&gt;   16.3705  -3.4258   0.1996   2.7516  -2.5854   3.3346  -5.4813  10.1746
+#&gt;   -4.4923  10.9602  -2.0492  -6.0832   3.6700  -0.7903  10.9270   6.1423
+#&gt;   -3.9708   2.4995  -6.0148   3.8555   8.7296   7.2791  -1.8554  -6.3309
+#&gt;    1.3187  -1.9468  -4.6737   7.0641  -0.5110   3.2483   5.6464  11.6303
+#&gt;  -12.5985  -1.3315  12.5610  -5.4499  -1.1348  -1.6720  -4.7975  -6.8854
+#&gt;    2.6793   0.8099  -0.8208   2.0605   0.2827  -2.0339   6.4241   0.4315
+#&gt;    7.9504   9.4412  11.9954  -4.2733  -6.7321 -10.7687   5.7605   1.0645
+#&gt;   10.1457  -0.1423  -4.4749   3.1614   0.5063  13.8041   0.4637  -2.7746
+#&gt;   -8.8189  13.9607   6.1141  -4.7353  -4.7814 -11.4741  15.6631   3.3705
+#&gt;  -12.9460   3.0432  22.2455   5.7695  -7.4932   4.4910  -7.6372   1.0687
+#&gt;    7.6966   7.9218   6.8505  -3.2164  -2.5481  -1.6493   0.3228  11.9375
+#&gt;   -5.3288   6.7775  21.9513  -2.1556 -16.4669   3.2823 -15.1787  -1.6592
+#&gt;   -5.5416   1.3512  11.4356  13.0597   8.0178  -9.0771   7.9728 -10.5826
+#&gt;    4.3661   8.3695   7.9370   4.1265  -1.1032   3.1672   8.7553   7.8063
+#&gt;    0.4222   2.5522 -11.8432   1.0525   6.8551  -9.1025   0.2412   4.1610
+#&gt;   -1.5518  12.8515  13.8811  -0.2850   6.1632   3.1550  -3.3247  -6.2828
+#&gt;   -0.3344  -8.8364   6.2271   3.0440 -13.1455   8.6795  -3.1012  -0.8562
+#&gt;    1.7786   9.6441  -1.1621  -0.8870   1.6428  -6.4055   8.3717  14.0249
+#&gt;   -4.4673   1.8278   0.4781   0.6198  -1.5879  -7.5952  -3.1377  -3.3075
+#&gt;    4.9676   6.9847  -2.2823   8.5192   4.7167   2.9328  -1.9337   2.2208
+#&gt;    5.1345   4.7293   6.2842   6.9622  -3.0074   2.0482  -8.6324  11.0786
+#&gt;    9.9139  -5.6082   1.8029  -2.0476  -3.7845  -0.8135   2.5508   7.4146
+#&gt;   -1.6769   8.3854  17.1547   3.6791   1.2201 -11.0141   1.3322  -1.4765
+#&gt;   12.4462  -1.2692  -3.4418  -0.4186  10.4377   5.5881   5.5438   4.7403
+#&gt;   16.0646   6.3947  -3.8445  -5.2718   2.5817   4.1743  -0.7499   2.3548
+#&gt;    9.2805   0.4435 -12.8332   2.4571 -14.3137  -8.1492  19.0161  -4.3424
+#&gt;    1.7508  -5.0976  -9.9643  10.7083   4.8640   0.9441  -0.6708 -10.0837
+#&gt; 
+#&gt; (2,.,.) = 
+#&gt;  Columns 1 to 6  1.1040e+01  2.2389e+00  4.4628e+00  5.7466e+00 -2.6627e-01 -8.1387e+00
+#&gt;   4.2474e+00  6.3469e+00  1.3473e+01  6.8586e+00 -3.1554e+00 -1.1583e+01
+#&gt;  -1.1430e+01 -3.9404e+00  6.4832e+00  8.8420e-01 -3.3189e+00 -7.1515e-01
+#&gt;  -5.3851e+00 -1.9954e+01 -3.6777e+00 -3.9789e+00  6.8651e+00 -1.2576e+01
+#&gt;  -1.2494e-01  4.7613e+00  8.2518e+00  5.1161e+00  2.6879e+00 -1.1478e+00
+#&gt;   8.1778e+00  1.4736e+01  8.8339e+00 -5.1749e+00  1.2643e+01 -1.4105e+01
+#&gt;   8.0566e+00 -1.2687e+01 -9.9194e-01  4.1174e+00 -2.6348e+00 -4.0748e+00
+#&gt;  -8.4652e+00  3.9182e-01  8.5976e+00 -1.7796e+00  1.9045e+00  2.7233e+00
+#&gt;  -4.8983e+00 -5.3415e+00  1.3234e+01 -2.1544e+00  5.6411e+00  1.3019e+01
+#&gt;   9.0287e+00  1.1770e+01  1.4281e-01  8.8434e+00 -7.3315e+00 -1.2302e+01
+#&gt;  -4.0297e-01 -5.8028e+00 -2.7611e+00 -2.3790e+00 -9.7937e+00  1.5359e+01
+#&gt;   6.2538e+00  4.3550e+00 -8.2056e+00  2.8763e+00 -1.0728e+00 -1.0987e+00
+#&gt;   2.8934e+00 -4.6745e+00 -9.3801e+00 -3.2556e+00 -1.2905e+00  1.5976e+00
+#&gt;   6.5937e+00 -1.0203e+01 -4.2327e+00  3.3749e+00  5.8875e+00  9.6595e+00
+#&gt;   1.0829e+01 -1.6179e+01  1.3427e+01  4.3272e+00 -4.3343e+00  1.1981e+00
+#&gt;  -1.6241e+00 -5.2897e+00  4.4671e-01  3.6279e+00 -1.6293e+01 -9.8559e+00
+#&gt;  -9.2592e-01  1.3693e+01  1.5238e+00  1.2777e+01 -1.5975e+00  2.5551e-01
+#&gt;   2.8383e+00 -9.2113e+00  2.6174e+00 -8.6557e+00 -1.3164e+01  3.6949e+00
+#&gt;  -1.8526e+00  6.1922e+00  3.9519e+00 -1.0871e+00 -1.0004e+01  2.8450e+00
+#&gt;   3.3199e+00  5.9430e+00 -1.6988e+00  3.6933e+00 -1.6695e+00  9.7463e-01
+#&gt;   6.8643e-01 -1.8230e-01  2.7946e+00 -9.0175e-01  7.5062e+00  2.9221e+00
+#&gt;  -1.0841e+01 -7.1443e+00  3.9800e-01 -4.6693e+00 -1.6905e-01 -9.1411e-01
+#&gt;   8.9760e+00  6.5289e+00  2.0646e+00  2.0664e+00 -7.4163e+00  3.1503e+00
+#&gt;   3.2287e-02  9.4439e+00 -1.1551e+01 -5.0678e+00  1.9310e+00  3.4262e+00
+#&gt;  -1.2702e+00  3.0408e+00  2.1001e+00 -3.5340e+00 -3.2536e+00  6.8281e+00
+#&gt;  -7.6196e+00  5.7684e+00  3.2679e-01  5.5921e+00  9.2952e+00 -4.7166e+00
+#&gt;   1.3529e+00  3.3966e-01 -1.8293e+00  1.5309e+01 -1.0006e+00 -7.7701e+00
+#&gt;   1.0581e+01  7.6868e+00 -1.2268e+01  8.1392e+00  4.6540e+00  9.6224e+00
+#&gt;  -5.1705e+00 -1.1192e+01  9.5458e+00 -8.6510e-04 -1.1793e+01  8.4559e+00
+#&gt;   1.8310e+01  1.4111e+01  1.5774e+00 -2.1493e+00 -5.2412e-01  9.6496e+00
+#&gt;  -2.8237e+00  2.5916e+00  2.1321e+00 -5.9730e+00 -3.1469e+00  2.4400e+00
+#&gt;  -1.5843e+01  1.3855e+01 -1.3872e+01 -1.8868e+00  1.0080e+01 -8.2378e+00
+#&gt;   8.8551e+00  4.9966e+00 -6.3457e+00 -1.0323e+01  5.0205e+00 -9.3470e-01
+#&gt; 
+#&gt; Columns 7 to 12  6.8024e+00  6.8142e+00  4.5797e+00  6.9012e+00  1.0459e+01  1.0657e+00
+#&gt;  -6.9185e-01 -6.4943e+00  5.4717e+00 -3.6463e+00  4.3075e+00  7.7041e+00
+#&gt;  -4.0044e+00  4.6633e+00  5.0276e+00 -1.4530e+00  6.8511e+00  1.2912e+00
+#&gt;  -1.3504e+01 -1.1171e+01 -5.0406e+00 -7.0066e+00 -1.7130e+01 -2.3064e-01
+#&gt;  -2.8190e+00  4.1526e+00  7.7969e+00 -6.6416e+00  3.5648e-01 -4.1884e+00
+#&gt;   2.6731e+00 -9.3361e-01 -1.9250e-01  7.6096e-01 -4.5198e+00 -4.9756e+00
+#&gt;  -7.7441e+00  3.3261e+00 -1.1134e+01  1.9768e+00  3.2081e+00 -9.4453e+00
+#&gt;  -1.4551e+00  1.6707e+01  1.1992e+01 -1.2095e+01 -8.7680e+00 -5.7492e+00
+#&gt;  -2.0924e+01  9.0712e+00  4.3001e+00  2.3030e+00  1.5413e+00  1.7113e+00
+#&gt;  -2.4857e+00 -8.2347e+00 -1.2573e+01 -1.0862e+01 -4.1379e+00 -6.6089e+00
+#&gt;  -1.5728e+01  2.3377e+00 -3.2312e+00 -8.4338e+00  1.4856e+01 -1.8265e+01
+#&gt;   5.0071e+00 -6.7687e+00 -3.5333e+00 -6.7396e+00  2.0115e+00 -5.8410e+00
+#&gt;   1.7353e+01 -9.7737e+00  5.5499e+00  1.0203e+01 -3.5550e+00 -8.5098e+00
+#&gt;   1.4913e+00  7.1976e+00  5.9653e+00 -9.6813e+00  5.5417e+00  2.8466e+00
+#&gt;   3.6951e+00  1.7859e-02 -5.5736e+00  2.3252e+01 -6.3235e+00 -1.6749e+01
+#&gt;   1.2651e+00  4.7339e-01  1.5961e+00  9.3233e+00  1.4838e+00 -4.3081e+00
+#&gt;   1.2277e+00  1.7227e+01 -6.4262e-01 -2.7803e+00  2.6236e+00 -1.0040e+00
+#&gt;   1.9608e+00  3.1339e+00 -4.8171e+00  5.3754e+00 -4.6124e+00 -6.1681e+00
+#&gt;  -3.4816e-01  2.3533e+00  1.2979e+01 -3.9980e+00  1.0758e+01  1.9073e+00
+#&gt;   1.0177e+01  1.0798e+01 -8.6815e+00  7.7241e+00 -1.2984e+01 -5.2720e+00
+#&gt;  -2.7988e+00  7.2102e+00 -4.4924e+00 -6.3182e+00 -1.2623e-01  1.3144e+00
+#&gt;  -3.5231e+00  4.8029e+00  8.3415e-01  1.5933e+00 -4.7331e+00  6.3149e-01
+#&gt;   5.2782e+00 -4.3263e+00 -1.2520e+01  4.0108e+00  3.0807e+00 -8.3008e+00
+#&gt;   3.2957e+00 -9.9090e+00 -7.1363e+00  8.8062e-02 -8.3463e+00 -7.5363e+00
+#&gt;  -5.6222e-03 -2.9559e+00 -7.3561e+00 -9.5151e-01  1.2968e+01 -1.3217e-01
+#&gt;   6.8911e+00  9.1908e+00 -3.3467e+00  1.7311e+01 -3.0840e+00  1.2651e+01
+#&gt;  -7.3550e+00  6.4477e+00 -4.7388e+00 -1.8323e+00  7.4196e+00 -1.2512e+01
+#&gt;   8.4276e+00 -7.1850e+00 -1.0022e+00 -5.9997e+00  6.7695e+00  5.1475e+00
+#&gt;   7.7367e-03  7.5006e+00  5.3158e+00  5.5656e+00  5.8394e+00 -4.7957e+00
+#&gt;   8.5397e+00  8.4526e+00  2.3535e+00  7.2155e+00  7.2376e+00  3.4551e-01
+#&gt;  -3.7541e+00  1.3644e+01 -1.1201e+01 -9.4936e+00 -1.4025e+01  4.1256e+00
+#&gt;   1.5837e+01 -1.2085e+01  1.8337e+01 -7.5301e-03  7.2932e-01 -3.3146e+00
+#&gt;  -8.1402e+00 -1.4226e+01 -1.6781e+01 -2.6737e+00 -5.7033e-01  4.2115e+00
+#&gt; 
+#&gt; Columns 13 to 18 -7.6116e+00 -2.2572e+01 -7.3696e+00  1.2489e+01 -3.6996e+00 -8.4814e-01
+#&gt;  -6.5330e+00 -1.4115e+01 -1.6207e+01  4.8898e+00 -3.3812e+00 -2.8668e+00
+#&gt;   1.0369e+01  8.3727e+00  2.5078e+00 -4.5453e+00 -9.8145e+00  5.5830e+00
+#&gt;   4.2195e+00  3.3645e+00  4.0064e+00 -8.8580e+00  4.7633e+00  2.1263e+01
+#&gt;   1.0394e+00 -4.8507e+00 -1.1569e+00  3.6901e+00 -4.9992e-01 -2.2131e+00
+#&gt;  -1.2750e+01 -3.0266e+01 -1.2321e+01 -1.3099e+01  6.0695e+00  9.5454e+00
+#&gt;  -8.8311e-02 -6.3177e+00  3.9969e+00  3.2729e+00  1.2270e+01  1.4457e+01
+#&gt;   5.8119e+00 -5.0032e+00 -4.5845e+00  5.5784e+00  7.0958e+00 -4.4555e+00
+#&gt;   1.0096e+01  8.4955e+00 -6.9884e+00 -1.1447e+01 -6.7327e+00 -5.6462e+00
+#&gt;  -5.5768e+00 -2.3684e+00  1.2694e+00  4.3510e+00  7.8375e-01  5.9263e+00
+#&gt;   8.8960e+00 -3.4571e+00 -1.0240e+01  6.3798e+00 -7.5809e+00 -1.7999e+00
+#&gt;  -8.4793e+00  9.5572e-01 -1.8657e+00  8.0276e+00 -1.9531e+00  1.1821e+00
+#&gt;  -6.2658e+00 -2.4873e+00  6.7244e+00 -2.7642e+00  4.8987e+00  1.1314e+01
+#&gt;   5.8645e+00  8.9250e+00 -2.0626e-01  2.6696e+00  5.9454e+00 -4.5350e+00
+#&gt;   3.3258e+00 -2.1198e+00  1.3239e+01  6.2403e+00 -1.0381e+01  9.9492e+00
+#&gt;  -7.3888e+00  1.7891e+00 -6.5434e-01 -2.0986e+00  5.7303e+00  3.8639e+00
+#&gt;   4.8493e-01 -1.2327e+01 -1.0240e+01  6.6167e-02  8.3635e+00  5.0293e+00
+#&gt;  -5.1651e-01 -4.9654e+00  5.2045e+00 -1.3461e+01  7.8285e+00  1.7121e+01
+#&gt;  -1.0272e+01  9.2964e+00 -5.5075e+00 -2.4449e+00  2.7975e+00  2.6272e+00
+#&gt;  -4.2984e-01 -9.9611e+00  6.5585e+00  3.3719e+00  2.8657e+00 -1.7146e+00
+#&gt;  -3.3866e+00  5.4789e-03  6.0831e+00 -6.4697e+00  4.7145e+00 -2.1051e+00
+#&gt;   1.2717e+01  7.5293e-01 -1.4031e+00 -4.8171e+00 -1.0169e+01  3.8149e+00
+#&gt;   9.2181e+00 -1.5667e+00  7.8714e+00 -3.2686e+00 -1.9517e+00 -2.9886e+00
+#&gt;  -7.0515e+00  4.5613e+00 -5.9774e+00  1.5230e+00  4.9189e+00  1.2735e+01
+#&gt;  -1.5374e-01  1.9978e+00  7.7595e+00 -1.8426e+00 -4.5077e+00 -4.7388e+00
+#&gt;  -2.7195e+00 -4.0337e+00  5.6005e+00 -1.1810e+00  1.2080e+00 -6.5539e+00
+#&gt;  -8.3179e+00 -1.5172e+01 -6.6159e+00  4.3008e+00  1.7152e+01  1.1670e+01
+#&gt;   9.5280e+00  1.1548e+01 -7.2594e+00 -3.2168e+00  3.3255e+00 -2.1210e+00
+#&gt;  -1.5480e+00  3.5520e+00  8.4671e+00  2.8069e+00 -4.7788e+00  6.6099e+00
+#&gt;  -3.6551e+00 -6.4310e+00 -7.5838e+00  3.0752e+00  1.7736e+00 -1.5723e+01
+#&gt;   2.2205e+00  1.1095e+01  8.1703e+00  1.4510e+00  1.1793e+01  1.3099e+01
+#&gt;  -7.7663e+00  6.1615e+00 -8.8113e+00 -1.3853e+00  5.6379e+00 -3.9343e-01
+#&gt;  -5.7712e+00 -1.0588e+01 -1.5045e+01 -6.2267e-01 -5.5895e+00  2.6833e+00
+#&gt; 
+#&gt; Columns 19 to 24 -5.2464e+00 -7.0874e+00 -4.7609e+00 -3.7282e+00  1.0706e+01  1.5297e+00
+#&gt;  -6.9263e+00  1.1783e+01 -1.1422e+01 -4.0469e+00  6.4235e+00  2.0727e-01
+#&gt;  -4.9231e+00 -1.5128e+01  6.5018e+00  2.1611e+00  2.1524e+00 -1.9323e-01
+#&gt;   4.3420e+00 -1.3789e+01 -6.0091e+00  2.4006e-01 -9.3553e+00 -9.8307e+00
+#&gt;   3.4428e+00  7.1870e+00 -1.1922e+00  1.1430e+00  5.4847e-01  5.8617e+00
+#&gt;   5.1685e+00  6.3463e+00  4.5243e+00 -2.6326e+00 -3.9794e+00 -3.6855e+00
+#&gt;  -2.6699e+00  5.1123e-01  4.6196e+00 -4.7047e+00  4.5384e+00 -1.8685e+01
+#&gt;   5.0849e-01  1.6162e+01  4.2410e+00 -4.2715e+00  3.1371e+00 -4.5112e+00
+#&gt;   1.6873e+01 -1.2267e+01 -4.0977e-01  8.0116e+00 -7.4305e+00  5.1819e+00
+#&gt;   2.2561e+00  9.6738e-01 -2.3555e-01 -1.0170e+01 -4.5446e+00  5.6410e+00
+#&gt;   6.0245e+00  6.3343e+00 -6.5556e+00 -2.2797e+00  2.3705e+00 -3.5569e+00
+#&gt;   1.8994e+00  8.7867e+00  3.5606e+00 -8.5007e-01  2.7260e+00  1.3389e+00
+#&gt;  -1.1418e+01 -9.5140e-01  1.0049e+01 -6.1606e+00  9.7031e+00 -1.6093e+01
+#&gt;   9.8920e+00  1.4330e+01 -6.2582e+00  7.7795e-01 -5.7016e+00  8.8303e+00
+#&gt;   2.4353e+00 -4.5353e+00  7.7747e+00 -8.8086e+00  3.7741e+00 -1.5807e+01
+#&gt;  -8.1247e+00 -8.7499e+00 -2.1982e+00  1.5405e+00 -1.4039e+00  2.3166e+00
+#&gt;   5.2253e+00 -3.7718e+00  8.5709e+00 -5.2631e+00 -7.7831e-02 -6.2829e+00
+#&gt;  -5.4685e+00 -4.3472e+00  3.7960e+00 -2.9034e+00  1.7978e+01 -1.5647e+01
+#&gt;  -3.9083e+00 -4.4565e+00  2.4797e+00 -3.7251e+00  4.1443e+00 -5.5959e-01
+#&gt;   6.9433e+00  1.5254e+00  1.3598e+01  3.9247e-01  6.8856e+00 -1.0771e+01
+#&gt;   8.9114e+00  6.2955e+00  5.4177e+00  9.8818e+00 -5.2334e+00  2.4367e-01
+#&gt;   1.5399e+00  2.3911e-01  2.7749e+00 -4.2119e+00 -5.4261e+00 -1.6565e+00
+#&gt;   2.0196e+00 -5.7851e+00 -1.3748e+00  5.4834e+00 -1.1966e+00 -6.5270e+00
+#&gt;   7.0821e+00 -3.0755e+00  1.2174e+01 -9.4236e-01 -5.6663e+00 -2.5944e+00
+#&gt;   3.3522e+00 -5.0099e+00 -2.1456e+00  1.4415e+00 -6.1322e+00  1.2063e+01
+#&gt;  -4.2653e+00 -6.8232e+00 -1.5760e+00  1.0697e+00 -5.0486e+00  7.7071e+00
+#&gt;  -2.7145e-01  4.4730e+00 -6.8754e+00 -9.2159e-01 -4.1075e+00 -8.8420e+00
+#&gt;   2.1405e+00  4.0768e+00  2.8086e+00 -7.6287e+00  8.4893e+00 -1.3900e+00
+#&gt;   8.6783e+00 -2.5042e+00  1.1771e+00  1.9851e+00 -1.3014e+01 -2.3765e+00
+#&gt;   1.0708e+00  8.6690e+00  2.8070e+00 -3.4836e+00 -2.7329e+00  1.0127e+01
+#&gt;   1.8867e+00 -2.3190e+00  1.6788e+00  3.5833e+00 -7.0983e+00  1.2037e+00
+#&gt;  -8.4881e+00 -4.3573e+00  1.2775e+01  1.5797e+00 -6.7303e+00  2.0272e+01
+#&gt;   8.9483e+00 -8.3223e-01 -6.2088e+00 -2.7979e+00  1.4796e+00  8.8738e+00
+#&gt; 
+#&gt; Columns 25 to 30  4.7605e+00 -1.4757e+00  1.5414e+01 -3.8196e+00 -1.6951e+01 -1.2996e+01
+#&gt;  -1.9973e+00 -4.6919e-01  8.8073e+00 -2.2466e+00  4.9008e+00  1.9037e+00
+#&gt;  -5.7509e-01  2.7547e+00 -1.0311e+01 -2.8828e+00  6.6137e+00  8.7601e+00
+#&gt;   6.2616e+00 -5.0881e-01  1.0574e+00  9.8757e+00  1.5003e+01  6.7349e+00
+#&gt;   4.1598e+00 -6.4312e+00 -1.6850e+01 -1.8725e+00 -8.4253e+00 -5.4590e+00
+#&gt;  -7.0131e+00  9.9630e+00 -4.2980e+00 -2.8688e+00 -4.8960e+00 -1.5651e+01
+#&gt;   1.0108e+01  6.2334e-01  5.0382e+00 -7.0535e+00 -7.1729e+00 -1.6165e+00
+#&gt;   6.7001e+00 -1.1708e+01 -1.5331e+01 -4.2425e+00 -1.5112e+00  6.2536e+00
+#&gt;  -2.0866e+00  6.0834e+00 -9.9227e-01 -1.0545e+01  1.6857e+00 -6.7036e+00
+#&gt;   2.2843e+00 -3.1801e+00  5.8044e+00  5.1553e+00  4.9275e+00 -1.0224e+01
+#&gt;  -4.4992e-01  8.7138e+00  2.1283e-01  1.3802e+00  1.5857e+00  1.1157e+01
+#&gt;  -1.2385e+00 -2.4477e+00  7.9737e+00 -4.4014e+00 -4.9261e+00 -3.1951e+00
+#&gt;  -2.3980e+00  1.3084e+00 -7.7907e+00  7.2450e-01 -1.1586e+01  1.0383e+01
+#&gt;  -1.8079e+01 -9.0459e+00  1.4397e+01 -6.2526e+00  6.3617e+00 -7.8238e+00
+#&gt;  -3.8272e+00 -1.9251e+00  1.0159e+01 -1.6060e+01 -2.2783e+00 -2.2136e-01
+#&gt;   9.4213e-01 -2.9319e+00  2.3522e+00 -1.1946e+01 -5.9928e+00 -8.4409e-01
+#&gt;  -4.5177e+00 -9.0236e+00  3.4303e+00  1.8347e+00 -1.2845e+01  3.2762e+00
+#&gt;  -5.9002e+00 -1.3134e+01  1.5702e+00 -1.1459e+01 -2.4262e+00  1.1036e+01
+#&gt;   7.1187e+00 -4.8343e+00 -1.0063e+01 -4.6802e+00 -7.7001e+00  1.7641e+00
+#&gt;  -6.5346e+00  1.9943e+00  5.7655e+00 -7.6248e+00 -5.8852e+00 -3.1929e+00
+#&gt;   3.2667e+00  3.4035e-01 -1.3065e+01  1.6010e+01  7.5781e+00 -9.4680e+00
+#&gt;  -2.7541e+00  9.0126e-03  3.2973e+00 -8.4961e+00 -3.8408e+00  2.5491e+00
+#&gt;  -8.2020e+00  1.2686e+01  1.0638e+01  1.8329e+01 -2.1860e+00  6.4469e+00
+#&gt;  -2.0596e+01  9.4246e+00  1.9946e+00  9.0939e+00  3.2005e+00  3.8513e+00
+#&gt;  -4.8925e+00  7.0237e-01  5.3233e+00  3.8712e+00  3.7284e+00 -9.4912e+00
+#&gt;   4.7726e+00 -6.0445e-01  6.0763e-02 -1.1063e+00  4.0299e-01 -5.1322e+00
+#&gt;  -1.4589e+01  3.5644e+00 -1.2131e+01 -2.8696e+00 -2.1631e+00  6.4978e-01
+#&gt;  -2.0117e+01 -4.2851e+00 -2.0913e+00  8.8746e+00 -6.0943e+00  6.6141e+00
+#&gt;  -3.0863e+00 -4.9088e+00 -9.5859e-01 -8.5561e+00 -9.7085e+00 -6.5426e+00
+#&gt;  -9.3064e+00  3.8217e+00  4.1449e+00 -1.3908e+00 -1.4476e+01 -1.2525e+01
+#&gt;   2.5078e+00 -2.1096e+00 -2.8901e+00 -5.3604e+00 -3.8419e+00 -1.0655e+00
+#&gt;  -1.2512e+01 -4.7244e+00  1.2641e+00  1.1913e+01  8.4535e-01  9.1668e+00
+#&gt;  -8.5001e+00  1.2013e+01  1.9289e+01  5.0782e+00  7.5338e+00 -9.1519e+00
+#&gt; 
+#&gt; Columns 31 to 36 -1.5825e+01 -6.0525e+00 -3.6059e-01 -3.5320e+00 -1.0963e+01  9.0893e+00
+#&gt;  -7.6471e+00  3.8245e+00  7.4362e+00  9.0340e+00 -3.2226e+00  8.3234e+00
+#&gt;   1.0664e+01  3.1036e+00  7.8870e+00  5.2952e+00 -5.6097e+00  1.1988e+01
+#&gt;   7.5233e+00 -3.8525e-01 -1.0644e+00  2.8296e+00 -8.0137e+00 -1.5562e+01
+#&gt;  -3.9163e+00  2.3552e-01  2.7444e+00  1.1502e+01  2.2503e+00 -1.2105e+00
+#&gt;  -7.3549e+00 -1.0166e+01  1.9247e+00 -1.8355e-01 -8.8643e+00 -2.6318e+00
+#&gt;  -4.0218e+00 -3.7994e+00 -3.6637e+00 -9.0932e+00  8.2074e+00 -1.3125e+00
+#&gt;  -8.5332e+00 -1.0384e+01 -2.4280e+00  4.7237e+00 -2.0530e+00  6.6218e+00
+#&gt;   1.9824e+01 -3.3879e+00 -4.7558e+00  6.3653e+00 -7.8356e+00  2.3336e+00
+#&gt;  -2.3862e+00  4.6184e+00 -5.4476e+00 -1.4052e+00 -3.0102e+00  3.4202e-01
+#&gt;  -4.5635e+00 -9.0183e+00 -9.8438e-01  7.6168e+00 -5.9883e+00  9.1950e+00
+#&gt;  -1.1475e+01  7.0631e+00 -6.4333e+00 -1.1149e-01  6.0743e+00 -2.4352e+00
+#&gt;  -3.1736e+00  2.1220e+00 -6.6557e+00 -3.4379e+00 -2.7073e+00  1.0520e+00
+#&gt;   1.3549e+00 -7.3758e+00  7.0423e+00 -5.1716e+00 -6.6967e+00  1.9914e+00
+#&gt;  -5.8276e-01  1.3280e+00 -4.0232e+00  6.2002e+00 -1.1908e+01  1.5772e+01
+#&gt;  -6.6587e+00 -5.5256e+00  3.1339e+00 -5.5551e-01 -5.4036e+00 -9.0692e+00
+#&gt;  -7.6355e+00 -1.5741e+01  3.9675e-01  3.0156e+00 -2.0665e+00  3.9257e+00
+#&gt;   2.0522e+00 -2.8533e+00  8.6485e+00 -7.8702e+00  4.1215e+00 -3.2170e-01
+#&gt;  -8.2318e-01  3.2963e+00 -1.1312e+01 -8.2619e-01 -4.2630e+00  6.1051e-01
+#&gt;  -1.3481e+01 -1.0202e+01  3.7420e+00 -9.4366e+00 -8.2541e+00  4.4941e+00
+#&gt;   4.6272e+00  1.1581e+01 -4.7529e+00 -4.3113e+00  8.0156e+00  3.4779e+00
+#&gt;   1.7016e+00 -1.1712e+01  7.2312e-01 -7.1206e+00 -9.3145e+00  9.2114e+00
+#&gt;   5.9762e+00 -5.2956e+00 -3.9463e+00 -1.1333e+01 -1.3987e+00 -4.9694e+00
+#&gt;   7.4142e+00 -8.1344e+00 -1.3305e+01 -1.7310e+00 -7.5089e+00 -4.8229e+00
+#&gt;   7.3922e+00  8.3139e+00 -5.2840e+00 -1.4562e+00 -3.4649e+00  1.2898e+01
+#&gt;  -9.4360e+00 -1.3312e+00  1.1261e+01 -5.3233e+00 -3.2232e+00  4.1879e+00
+#&gt;   5.3539e+00  3.0263e+00 -3.2155e+00  3.4591e+00 -3.5687e+00 -6.5521e+00
+#&gt;  -3.5393e+00 -1.3799e+01 -3.3037e+00  1.9697e+00  2.8058e-01 -4.6900e+00
+#&gt;   4.9514e+00  4.1531e-01 -8.0968e+00 -3.9034e+00 -4.8970e+00  1.0481e+01
+#&gt;  -6.1879e+00 -9.2681e+00 -6.2762e+00 -2.2763e+00  4.9857e-02  2.0032e+00
+#&gt;  -3.8395e+00 -1.0061e+01 -7.0415e+00 -1.5563e+01  7.8947e+00 -1.0889e+01
+#&gt;   4.6596e+00 -3.8205e+00 -1.4254e+00  9.8757e+00 -8.8425e+00  1.1036e+00
+#&gt;  -5.7265e+00 -1.0946e+01 -3.0999e+00  5.1787e-02 -4.1085e+00  1.6805e+00
+#&gt; 
+#&gt; Columns 37 to 42 -1.1352e+01  1.1675e+00  4.1326e+00  2.0810e+01 -3.1170e+00 -5.3886e+00
+#&gt;  -6.3405e+00  9.9968e+00  4.5593e+00  4.3253e+00  4.3078e+00 -5.0758e+00
+#&gt;  -1.8509e+00 -2.5069e-01 -1.1716e+01 -6.5717e+00  3.8582e+00  4.9188e+00
+#&gt;   5.6229e+00 -2.9105e+00 -2.3605e+01  8.7952e+00 -3.9078e+00  4.0413e+00
+#&gt;   6.1460e+00  7.2539e+00 -4.2070e+00 -3.4539e+00 -3.6857e-01 -4.5547e+00
+#&gt;  -6.1082e+00  3.7526e+00  8.7095e+00  1.6753e+01 -5.0704e+00 -3.2953e+00
+#&gt;  -5.7529e+00 -3.3428e+00  1.3160e+00  1.1378e+01 -8.3256e+00  9.5483e-02
+#&gt;  -1.5073e+00  6.0489e+00  2.0360e+00  1.3820e+00 -2.4588e+00 -1.2680e+00
+#&gt;  -4.2967e+00 -9.2732e-01 -3.1906e+00 -7.1769e+00  4.9066e+00 -2.2283e+00
+#&gt;  -1.2227e+01  8.5371e+00 -2.6248e+00  2.4014e+00 -5.6499e+00  1.0123e+01
+#&gt;  -8.5863e+00  9.5664e+00 -5.9350e+00 -1.6468e+01  1.0391e+01 -1.0179e+01
+#&gt;   3.1844e+00  1.2364e-01  1.3085e+01 -5.8785e-01  2.3136e+00 -9.7390e+00
+#&gt;   4.6407e+00  4.9859e+00 -1.2739e+00  2.1925e-01  2.5094e-01  3.3886e-01
+#&gt;   1.1058e+01 -1.0747e+01  8.3456e+00 -2.2283e+00  4.7523e+00 -7.7536e+00
+#&gt;  -1.3527e+01  1.4885e+01 -5.2081e+00  9.1698e+00 -7.9931e+00 -8.4733e-01
+#&gt;  -1.9517e+00 -3.4732e+00  5.4558e+00 -2.4234e+00  3.1985e+00 -6.9968e-01
+#&gt;   8.7000e+00 -3.4409e+00 -1.1334e+00  1.4169e+01 -1.9912e+00 -2.1136e+00
+#&gt;  -7.1622e+00  1.2549e+00  4.2199e-01  3.1096e+00  2.5984e+00  4.3691e+00
+#&gt;   3.0234e+00 -2.0207e+00  1.4672e+01 -1.1174e+01  5.1304e+00  7.1506e+00
+#&gt;  -1.1437e+01  3.4149e+00  7.5767e+00  5.9337e+00 -1.2918e+01  3.3527e+00
+#&gt;   1.2484e+00  4.7930e-01 -7.1958e+00  2.7164e+00  2.0207e+00  7.7346e+00
+#&gt;   1.3582e+00  2.8437e+00 -7.3608e-01  2.2923e+00 -7.7159e+00 -3.2784e+00
+#&gt;  -8.8959e+00 -3.6378e+00 -2.6879e+00  5.3815e-01  8.6462e+00  1.2901e+00
+#&gt;  -1.0886e+01 -9.1296e-01 -1.3696e+00  4.0285e+00  1.1338e+01 -3.0686e+00
+#&gt;  -7.4435e+00 -1.6110e+00 -6.7849e+00 -3.5437e+00  6.4861e+00  3.2415e+00
+#&gt;   2.0525e+00 -1.4578e+00 -2.7559e+00  5.7021e+00 -9.3438e+00  9.2374e+00
+#&gt;  -2.7167e+00  1.1972e+01  1.5159e+00  8.5275e+00  5.1403e+00 -4.5702e+00
+#&gt;   8.4622e+00 -1.0705e+00 -2.7975e+00  1.3748e-01  6.0727e+00  9.2312e+00
+#&gt;  -1.2966e+00  1.4843e+00  1.3079e+01 -4.8325e+00 -2.5283e+00 -1.0658e+01
+#&gt;   9.5890e-01  8.5580e+00  8.0375e-01  6.0536e+00 -2.3652e+00  1.9715e+00
+#&gt;  -8.4933e+00 -6.1661e+00 -2.8968e+00  8.8597e+00 -7.1364e+00  2.2233e+00
+#&gt;   6.3513e+00 -1.3840e+01  9.8739e+00 -1.0290e+01  1.4279e+01 -6.2812e+00
+#&gt;  -1.7568e+01  5.2109e+00 -1.6603e+00  5.6085e+00 -3.3452e+00 -2.7954e+00
+#&gt; 
+#&gt; Columns 43 to 48 -1.4316e+01 -1.7188e+00  3.2444e-01  8.9797e+00 -3.2084e+00  7.8822e+00
+#&gt;   3.1460e+00 -6.4460e+00  5.2454e+00 -1.9715e+00  1.6432e+00  1.1744e+01
+#&gt;   8.6926e+00 -2.2753e+00 -4.4450e-01 -3.1107e+00  2.9401e+00  3.6147e+00
+#&gt;   6.4619e+00  3.0811e+00 -9.3294e+00  3.1618e-02 -5.4628e-01 -1.5545e+01
+#&gt;   3.2211e+00  2.7079e+00 -1.7153e+00  6.0905e-01  9.2878e+00  7.0190e+00
+#&gt;  -1.1072e+01 -1.9385e+00  1.8828e+00  1.5127e+00  2.6904e+00 -1.7966e+00
+#&gt;  -9.0222e+00  9.6423e-01  5.8570e+00  2.7499e+00 -3.3381e+00 -9.7036e+00
+#&gt;  -1.7245e+00 -4.4181e-01 -3.4086e-01  6.2400e+00  1.5419e-01  1.2574e+01
+#&gt;   9.0976e+00 -3.2707e+00 -1.0903e+00 -3.3040e+00  9.1338e+00 -1.2215e+01
+#&gt;  -3.1234e+00 -2.8812e-01 -2.1735e+00 -1.0646e+00 -1.2786e+01 -1.5235e+01
+#&gt;   6.6138e+00 -4.3299e+00  8.6403e+00  9.3438e-01 -1.1749e+01  1.8446e+00
+#&gt;   6.2344e-01  9.8338e-01  8.2563e+00 -8.2448e+00 -4.5447e+00  6.5609e+00
+#&gt;  -1.4375e+00  5.1289e+00  5.4863e+00  4.6983e+00 -5.3126e+00  6.0145e+00
+#&gt;   5.5840e+00 -1.5232e-01  1.0648e+01 -8.1426e+00  9.6469e+00  7.1755e+00
+#&gt;  -3.7829e+00  3.3464e+00  1.6952e+01  4.4535e+00 -2.9354e+00  1.5281e+01
+#&gt;  -1.0402e+01 -3.4845e+00  1.1573e+01  2.1074e+00 -8.8245e+00  1.1603e+01
+#&gt;  -1.1021e-01 -6.7476e+00  5.0775e+00  1.6184e+01 -3.9077e+00  8.0282e+00
+#&gt;  -6.1079e+00 -4.1911e+00  1.3648e+01  6.0187e+00 -7.1858e+00  9.6539e+00
+#&gt;   1.7198e+00 -1.1251e+01  3.8847e+00  1.0030e+01 -7.0925e+00 -4.5950e+00
+#&gt;  -8.4614e+00 -9.8298e-01  4.4841e+00  2.8005e+00 -7.3674e+00  1.0545e+01
+#&gt;   5.7187e+00  2.9945e+00 -5.2711e+00 -3.7479e+00  1.5054e+01 -8.7987e+00
+#&gt;  -5.5156e+00  1.2471e+00 -4.1211e+00  3.1218e+00 -6.8213e+00  3.6497e+00
+#&gt;  -6.2814e+00  6.7489e-01  4.7816e-01  5.8086e+00 -7.7224e+00 -1.1090e+01
+#&gt;  -2.7705e+00 -5.7763e+00  4.7694e+00 -1.5708e+00 -9.1193e+00 -1.5565e+01
+#&gt;   9.1999e+00 -3.0123e+00 -2.4817e-01  6.1060e+00  4.1478e+00  1.1212e+00
+#&gt;  -2.9603e+00 -2.1796e+00 -2.0766e-01  7.8280e+00  3.1220e+00  1.4492e+01
+#&gt;   3.6359e+00 -2.0703e+00  4.0872e+00  4.5950e+00  4.9332e+00 -5.6241e+00
+#&gt;   5.1562e+00 -8.1316e+00  4.7583e+00  1.3220e+01 -5.4718e+00 -3.3530e+00
+#&gt;   4.5500e-01  2.9386e-01  7.0324e+00  1.1036e+01  7.7246e+00 -4.6796e+00
+#&gt;  -8.1972e+00  4.4841e+00  4.2891e+00  5.8185e+00 -2.5228e+00  5.5887e-01
+#&gt;  -2.3574e+00  1.5458e+00  3.8627e+00  1.2930e+00 -2.1494e+00 -1.3772e+01
+#&gt;   3.3417e+00 -4.2553e+00 -1.0949e+00  5.9260e+00 -1.1277e+01  4.3272e+00
+#&gt;  -3.4267e+00  9.3572e-01 -1.9961e+00 -4.4621e+00 -5.6221e+00 -1.7291e+01
+#&gt; 
+#&gt; (3,.,.) = 
+#&gt;  Columns 1 to 8   4.9562  -3.7582   5.3943  -3.9742  -5.9963 -12.7356 -12.3488  -0.0281
+#&gt;   11.2481   1.8273   4.5276   3.2045  -7.3007   4.8452  -2.3510   2.6769
+#&gt;   -4.4985  -1.0768   8.5073 -11.6589   4.7996  16.7321  -3.0965  -0.3856
+#&gt;    0.6945  -3.6676   6.7533  -4.5750   4.0977  13.4447 -10.3970  12.6614
+#&gt;   -5.4403  -5.4518   1.6155  -8.4963  -5.9472  14.6116   2.3946  -1.3451
+#&gt;   -5.3157  -2.3617   2.7643  -2.6659   2.6909  -6.9647 -15.1995   5.5772
+#&gt;    3.6503   5.7380  -0.8403   0.6548  -1.7302 -10.1615   5.2492   8.6817
+#&gt;    7.2741   2.6035  -3.6202  -9.2834  -5.8462   8.7866  -2.8505  -1.4817
+#&gt;  -19.9435  -4.4367  -1.1491  11.0581  -7.7709  -0.3405   0.1401   9.1089
+#&gt;    3.6538  -6.3743  -1.6704  -4.4341  11.5087   7.0363   1.0560  -3.5037
+#&gt;   15.8845  -1.4452   2.1056  -6.1563 -17.7140   8.9579 -12.1511   0.6144
+#&gt;    2.2474  -4.6580   0.3898  -1.0848  -1.7472 -14.7547  13.3952  -8.3100
+#&gt;   11.9129   0.8427   9.8857   5.7461   3.1181  -4.4163  -0.3108  -5.2917
+#&gt;   -6.2343   1.1551   4.5635  11.1697   2.7615   2.6141  -7.0863   4.4991
+#&gt;   15.9765   1.4206   5.0062  -2.9111   1.0533   6.9639 -11.3675  11.6931
+#&gt;    1.9163   4.4746  -2.2195  -6.6301   4.6677  12.5423   4.5925  -1.8943
+#&gt;    6.9265   8.7154   4.7434  -9.8159  -4.0058   1.6135   3.6586 -20.6469
+#&gt;    8.3534   3.6571  -8.5923  -1.9266  18.2730   3.6534  -1.9278   3.3406
+#&gt;   -0.0040  10.9221   4.4209  -6.5203   6.8133  10.0404   0.2141  -2.6464
+#&gt;    7.6266  -0.4131 -13.4900  -2.6130   5.9332  -1.1798 -11.2315   5.4657
+#&gt;   -0.3001   3.4722  -2.9582 -10.0573   4.1344  -1.6925   0.8711  14.5282
+#&gt;    1.7112   4.8549  14.1942   2.7680   0.3115  -1.2966   5.9259   0.5383
+#&gt;    0.7748   2.5614  -1.0904  -2.4082   9.1032 -11.0332  -3.9074   1.4275
+#&gt;    2.6380  -1.4095   8.9158  10.1162  18.3666   3.2091  -6.9841  -6.9002
+#&gt;   12.0612   3.2341  -4.0510 -10.9244  -1.5035   4.1921  -1.7979   1.8640
+#&gt;   -3.7546   6.1095  -7.6240  -7.1370   1.2163  -7.0363  -6.3017   0.9980
+#&gt;   -1.8646   7.0013  12.8986   3.9962   4.5674  16.9755  -0.0370  -3.8269
+#&gt;    2.7722  -7.3149   5.8819   4.5695  14.4691   5.7079 -11.5897 -10.8310
+#&gt;    8.9845  13.7087  -0.9190   2.1477   2.1493   9.3396   7.7154  12.4460
+#&gt;    4.9266  -4.6779  -1.5574  12.9128 -12.8062 -12.4111  -8.2159 -14.7549
+#&gt;   -4.2986  -0.1995 -10.0720  -9.3908   4.2700  -8.7040   6.4533 -11.1542
+#&gt;   -0.6362  -0.8948 -10.9689   8.4188   0.0568  -4.8182   3.3533  -5.8279
+#&gt;    2.6255  -7.6898  -7.6644   5.9319  -8.0142 -11.2382 -11.3271  -0.3779
+#&gt; 
+#&gt; Columns 9 to 16  -9.7360  -6.4222  -5.7229   7.4474  -6.9485 -18.3427  -2.7103   3.7782
+#&gt;    1.2750  10.4092   0.1234   9.9826  -7.2135  -5.7552  -1.9998  -1.9475
+#&gt;    1.5161  -2.5462  -1.0372   2.6637   5.9223   6.5293 -17.3755  10.4248
+#&gt;  -11.9943   1.9396 -10.8378 -27.0799  24.8994   3.4319   0.5319 -13.5652
+#&gt;   -2.1803  -3.0364  -3.0484 -11.1355   1.9524  -6.2876  -0.3480   6.6803
+#&gt;  -12.3258 -14.2578 -11.3267  -2.5336 -18.2894  -4.2594  -1.9660  12.5184
+#&gt;   -6.8200  -8.1209  -9.5000   1.3338   1.5568  -5.8006  14.6046  -7.5704
+#&gt;    4.2130   3.7024  -1.5168   2.1240 -12.3614  -7.4998  -5.9788  -5.3655
+#&gt;   -1.4886 -14.2426   8.8877   3.6402 -13.4771   7.7371  -8.9763  -0.4561
+#&gt;  -13.1842  -2.8242  -4.3459  -3.8975  -7.4657   8.8410   4.0576   3.8440
+#&gt;    7.6390   3.3561   0.9564  10.3876  -6.1527  -1.8119   1.3460   5.9928
+#&gt;    8.8839  -0.3631  -3.6710   0.2649   4.8643  -5.2106  14.0710  -2.3089
+#&gt;   -4.5459   2.2723  -1.4164  -5.1818  11.6005   6.0302  -0.6840  -1.0466
+#&gt;   -2.4372  -3.9876  -7.3022  16.0805  28.7031  -6.6200   1.3126  15.1169
+#&gt;   -8.7492 -17.0470  -3.5868   9.9832 -13.2698  14.2807   7.7293  -0.8965
+#&gt;   -8.0431   6.6999  -8.5087 -17.4936  15.0480  -1.8982  -5.8062   0.4042
+#&gt;   -1.0442   1.5605  -3.1818 -10.4155   1.5044   4.2108 -10.3067   2.6241
+#&gt;  -14.2217   8.4671  -2.2625 -13.4366   4.9955  11.3371   3.7654  -5.4279
+#&gt;    5.1664   0.4539  11.9847  -5.2858   7.8836  -3.6860  -7.9037   4.0934
+#&gt;    4.9691   2.4076   9.0446   0.2401 -17.3001  13.3294 -13.2066   4.6766
+#&gt;   10.6484  -4.1710  -4.8643   8.5792   0.4483  -4.1667   2.6186   2.1822
+#&gt;   -4.8166   4.1797  -2.3908  14.4425  -8.0795   2.8944   2.9143   0.6577
+#&gt;   -5.5388  -2.0295  -0.4797   5.0858 -11.7329   4.8734   8.9627   2.9415
+#&gt;   -9.7544  -5.7999  -0.3570  -1.3464   4.3133  23.6557  -4.5242   7.1379
+#&gt;    3.3175  -1.3137  -7.1024   9.0726  -9.1605   4.0476  -3.9804   4.8644
+#&gt;    1.4142  -4.5650  -6.4153   3.6048 -13.3332  -6.1364 -11.7781   1.3500
+#&gt;   -1.1517  -4.6129 -19.7663  -6.7512  17.9404  -8.0486   4.8501   1.3591
+#&gt;   -5.8542   0.8734   3.2384  -1.5739  21.9474  -3.7593  -0.9735  12.2523
+#&gt;   -2.3665  -7.0931   0.6589  16.3679  -4.9314  10.7504   9.8569   0.4346
+#&gt;   -5.0901  -1.9335  -2.1915   7.1066  -6.8045  -7.4043   3.2150   4.0988
+#&gt;   -8.1054  -8.8176 -11.3086  -7.5997   1.9523   2.5381   7.7193  -4.2398
+#&gt;    3.0093   5.9653   4.1393   0.3365  -1.2716  11.9673 -15.8381  -0.9102
+#&gt;   -1.5938  -3.4779  10.4909  12.1245 -15.0920   4.7935   3.1971   2.9640
+#&gt; 
+#&gt; Columns 17 to 24  -7.8446  10.8300   3.1495  -6.4143  -4.9418   1.5859   5.7119   4.1122
+#&gt;   -8.2818  -2.5108   0.3972   5.5391   2.6315   5.6176   4.3199  -1.2731
+#&gt;   -3.7133  -7.0713  -9.7593   3.5059   4.4236   4.6572  -5.9136   6.1746
+#&gt;  -11.5718   3.8795  -2.3085  -3.9470   1.1945  -1.6105  -1.4233   4.4411
+#&gt;    0.0810   6.6555   3.7000  14.6576  11.7133  10.7315   2.1382  -2.0174
+#&gt;   11.3570   3.8331   2.8234  12.0460   5.1824  -8.6710  -6.2794   0.8210
+#&gt;    5.8555   3.8700   1.9546  -6.2020  -2.1532  -0.5159   3.9648   7.4020
+#&gt;    5.3760  -0.2685   7.1646  -0.2744   3.5396   5.4370  -1.4105 -15.6596
+#&gt;   11.7633 -10.5656   6.0806   0.3545   2.7078   0.2011   2.5850  -2.2982
+#&gt;   -1.9521  11.4095   9.7509  -2.7458  -1.9989   1.4426   3.1751   7.4327
+#&gt;   -0.7683 -11.2969   0.6807   1.4418   1.1541   1.8743  -6.6355  -2.5485
+#&gt;    2.3949  11.4733  -4.1730   1.7488  -2.7655   1.3965   2.2042  -7.7285
+#&gt;    2.0386  -3.8169  -5.1341   2.4189  -0.9858  -1.9761  -2.5394  -2.1106
+#&gt;    2.8564  13.8817   3.5312   7.7483   2.4548   3.4022  -6.1050  14.8377
+#&gt;    5.4632  -4.0690   8.5569  -0.5126  13.3378   2.3015   1.6362   4.0142
+#&gt;   -8.9410   1.1707   1.4379  -3.3880   1.1356  -3.8432  -0.2028  14.7388
+#&gt;    9.5698   0.8845   3.6077   1.9991   5.9551   4.0725 -12.3633  -3.3834
+#&gt;    5.4765 -10.8793  -1.3330  -0.4239  -0.2444  -4.3707   4.3048  -2.0963
+#&gt;    7.7052   0.6048  -3.1704   1.7206  -6.3772  -2.8244  -1.9311   7.0662
+#&gt;   13.3648  -1.6975  12.1434   5.2608   3.4101  -5.8776  -9.4342 -11.3617
+#&gt;    4.4433   5.9981  -0.4610   3.9429   2.5325  -6.9121  -0.0131  -6.9599
+#&gt;    4.6063  -0.7668   2.4319 -10.1652  -6.0697  -2.6817  -2.0906  -2.6583
+#&gt;    2.4389   2.1851  -2.5737   4.2586  -0.7670 -10.1724   6.2627   7.3546
+#&gt;    9.2102  12.2677   0.6505   0.8508   8.8404  -2.4235  -0.3184  13.9138
+#&gt;    0.4299  -7.7316  -4.0262  -1.0616  -1.8941  -4.5296  -2.2042   3.9778
+#&gt;   -2.5706  -1.2521   5.6562   1.2311   0.4175 -11.0818  -4.9537  -2.0501
+#&gt;   10.8075   0.0243  -1.6829  -1.8148   3.6318  -7.6898   3.4834  13.7268
+#&gt;   -1.2697   5.0217  -2.6425  11.8827   8.9970   5.9287 -12.2312   7.6060
+#&gt;   11.1753   0.4443  -2.5237  -0.2271  -1.2767   1.0772  11.0985   8.0461
+#&gt;   -5.9804   7.4466  12.3405  -5.5906  -5.0035  11.6740  -8.4227 -11.0415
+#&gt;    8.5981  13.2756   0.7086  -7.9883  -5.1583   0.4455   6.2542  -0.3895
+#&gt;  -10.7068  -0.0316  -7.3415  13.2332  -1.9071   6.7170  -0.1882  -2.2628
+#&gt;   -7.5274  -4.2322   4.3580  -0.3955  -1.2535  -0.3756   4.1293  -3.7303
+#&gt; 
+#&gt; Columns 25 to 32 -11.3413  -0.6342   1.2782  -1.0040   0.3941  -1.2505  -7.8863   2.5857
+#&gt;   -2.3209   5.3974  -0.6238  -1.9349  -5.1256  -0.9068  -1.6616  -2.2188
+#&gt;   -5.5235  -1.3256   7.7680   0.0419   4.6588   8.5270 -14.0638  -5.7350
+#&gt;   23.6620   5.5299   3.8752  -8.5570   1.7381  12.4320   1.1287  -2.2732
+#&gt;    6.0077  11.7650   5.9815  -1.2362  -2.6430  -1.9138  -9.4792  -9.2415
+#&gt;   13.9983   9.2234   2.0461  -5.2399   6.2495  -1.0772  -8.9246   2.9772
+#&gt;    5.9896   4.1438   1.1027  -1.9300   7.9763 -11.2774  -5.4039  -8.4377
+#&gt;   -3.3392   4.2929  -1.5951  -0.7069  -5.5264  -0.2146   2.6092  -3.2463
+#&gt;   -2.7116   2.4720  11.3886   5.4341   2.0100  13.9830   1.6636   1.8237
+#&gt;    8.7153   7.0944   4.8265   2.3740  -2.2886  -3.3963  -8.7984  -2.1433
+#&gt;  -11.5131  12.9237  -5.3272   5.0744  -6.4152  13.7479  -0.6910   2.8149
+#&gt;    3.7939  -4.5852  -9.2190  -6.7536   7.5973  -8.4979   5.2731   3.2761
+#&gt;   -0.0529  -8.7274  -6.3776  -6.3154  10.8041  -0.3989  -2.7530   4.2337
+#&gt;    6.1232 -10.6163  -8.4237  14.1920   1.7618   3.2803   6.9025  -3.0799
+#&gt;   17.9500  -4.1805 -11.3844  -3.2068   5.9128  -1.0761 -21.9036   3.2809
+#&gt;   -3.7840  -8.8256   1.5954   7.9607  -4.1433  -1.0089  -1.6928   9.5089
+#&gt;    3.4930  10.6973  -6.1665  -6.6312  -0.4147   6.9757  -9.2026 -10.0955
+#&gt;    6.7164  -0.0767  -2.7187 -11.2172   2.1860  -1.5859  12.1095   3.5310
+#&gt;   -3.8810 -12.2251  14.5198   6.4680  -4.0062   2.9629   5.1757  14.1900
+#&gt;    4.1051  -0.6954  -8.3595   2.2953   6.4439  15.2789   1.7176   3.2482
+#&gt;   -1.8213  -0.6504   6.6178 -10.4879  11.9210   7.6479  -2.6390   4.8771
+#&gt;   -8.8219  -0.2972  -9.5819   9.2868   6.3488   2.5390  11.7205   2.8740
+#&gt;   -0.9322  -2.2826  -1.1460  -6.5040  -0.7949  -4.9513  -2.1422  -0.7358
+#&gt;   13.7824   4.6837   9.9722  -6.0029   4.5716  12.4649  -9.9900   6.4003
+#&gt;   -0.3243   8.9251  -2.4305  -4.2840  -4.8234   5.5312 -10.5267   6.2358
+#&gt;    0.0412  -7.5629  -0.1690   0.6404  -5.2020  -3.4128  -8.0235  -0.3811
+#&gt;    9.9647   9.9041   0.7346  -8.5584  -0.1955   5.9069  -8.0969  -5.4198
+#&gt;    5.9976  -8.5542   5.4198  -3.4565 -11.3585   6.2732  -7.2283  -6.8408
+#&gt;   -6.9500   4.6178  -6.5540   5.1711   6.6195   2.7765   4.6212   7.9218
+#&gt;  -11.7170  -2.9426  -8.0669   6.0971  -8.5434   3.0232  -5.7801  -4.7574
+#&gt;    8.4081   0.8452   8.0060  -4.1084  -0.0990  -9.2372   9.4923  -5.6565
+#&gt;  -12.8302  -4.7184  -8.1430  -1.4103  -4.2225   4.9293   6.2137   5.6808
+#&gt;    0.6241   2.8501  -1.8625   2.9744  -4.9602   2.5389   8.5130   4.8167
+#&gt; 
+#&gt; Columns 33 to 40  -3.6500  -2.8977  -9.5778   2.7114  -1.0903  -1.8082  -1.5907  -2.7820
+#&gt;   -2.2093   6.8268  -4.4927  -3.6955  -5.9770  -0.7346   4.8322   7.7719
+#&gt;    6.6788  10.7728   3.5679   4.8409  -6.9984  -7.0787   9.2602  -4.7396
+#&gt;   -2.6668   6.8936   3.9008   0.6742   9.4432  10.4602  -8.3675  -7.7000
+#&gt;  -12.6144  -4.0425   4.8042  -1.1339  -1.6229  -3.6535   5.7386  -2.7556
+#&gt;   -6.4437  -3.8952   2.4651  -1.3158   5.6072  -1.4178   4.2024  -5.7168
+#&gt;   -1.0680  -1.5215  -5.3585   4.8564  -1.5816   6.3936  -4.4383  -9.2617
+#&gt;  -16.5886 -10.7780  -3.7502  -4.6051   3.0135   2.6677   6.4653   0.4712
+#&gt;   -2.2415   7.8049  -2.4149  11.1596  -0.9475  -4.2648   7.2630  -3.0681
+#&gt;  -12.9061   3.0613   6.8384   0.5073   5.1894   0.5770   0.2758   4.9556
+#&gt;    7.6316   3.3277   0.5884   6.6436  -7.7094  -0.2284   5.5000 -12.2994
+#&gt;   -7.0150  -4.5404   3.0001   1.8709  -6.1011   3.0254  -6.1995   6.7020
+#&gt;    6.1677  -0.5276  -6.2246  -6.4439  -6.6510  -1.6874   1.4696 -12.6055
+#&gt;   -1.5107   4.3904  -8.7457   4.4525   5.1152 -10.7965 -15.5326  -0.2713
+#&gt;   -4.2965  11.2479  -1.4450   1.0726 -11.2088   2.0681   0.3535  -0.9314
+#&gt;    7.5914   0.7966   1.4415  -1.1603   2.0057   3.8741  -6.7373   3.2602
+#&gt;    0.3362  -3.4889  -8.5000  -1.5220  -2.7686  -1.4101   0.6254  -5.9871
+#&gt;    2.6305   5.9587  -6.0280   4.6396 -11.8046   2.4536  -3.5823 -10.0967
+#&gt;   -0.1964  -8.6662   2.6549   2.3293   0.8565   0.5143  -4.3031  -3.5940
+#&gt;   -7.2017 -10.6218 -12.5678   0.4210   0.4910  -3.6761   8.4244   1.6943
+#&gt;   -8.5529  -2.2835  -4.7047  -2.7886   3.0916  -6.8411  -0.9513   7.0117
+#&gt;    9.7563   2.3959   8.8850   4.0535  -2.0100   4.7188  -4.1533  -8.6275
+#&gt;    4.3480   5.3681  -3.4141   3.3045  -3.1704  -3.2342   5.7581   1.7282
+#&gt;   -6.3818 -10.8806  -5.7432  -4.5982   5.5797  -3.4485  -1.1335  -4.5567
+#&gt;    3.6543   0.1287  -2.1096   7.8689  -2.1157  -5.4079   1.6017  11.3682
+#&gt;   -2.5885   3.2719   2.9070  -5.1318  12.5928   1.2289  -5.1828   6.7898
+#&gt;  -15.2362  -3.3284  11.5042  -0.6523  -1.5897  -0.7097 -11.7615   1.6562
+#&gt;   -1.8654   1.9799  -0.0391  -4.9505  -1.6498  -8.5602   1.9597  -3.4851
+#&gt;    2.5662  -1.3787  -2.7014   7.4054  -2.2759  -8.8455  -8.5638  -6.0573
+#&gt;    7.1246   4.2721  -7.5306 -14.3989  -3.6718   1.1478  12.7831   0.1247
+#&gt;   -7.4369  -7.6452   1.9472   3.2242   3.6734  11.6259 -12.0304   2.0364
+#&gt;    1.5119   3.8946 -12.3758  -7.1929   8.3145 -15.0007   7.2717   2.4664
+#&gt;   -4.5497   2.5463  -2.5948   0.9746  -1.7025  -2.7597   1.9230   1.6655
+#&gt; 
+#&gt; Columns 41 to 48   6.1813   6.2912   7.3241   0.7908   2.1313  11.1295   3.3205  10.2126
+#&gt;   -3.8660   6.1652   5.3523  -1.6550  -7.9229   5.9736 -10.1264  -0.1634
+#&gt;    0.8225  -3.2113  -3.1650  -2.8780 -17.5243  -1.3155   0.5709  -0.4941
+#&gt;    5.9318  -7.8656 -10.8085  13.1046  -5.3957 -13.6984   2.6905   3.0666
+#&gt;    0.4034  -4.6984  -8.2092  -1.9694 -13.3521  -7.1029  -5.8867  -3.3301
+#&gt;   -3.1147   4.8214   2.9073  -2.1222   7.6735   4.6024   9.5707  -5.2442
+#&gt;   15.9312  -0.7414   0.4791  -0.9335   6.7204   2.6602  -2.1277  -4.0374
+#&gt;    2.9042  -2.8153  14.4209   1.4202 -12.9603  13.3820   3.0815   1.4037
+#&gt;  -14.4248   9.5510  -4.2185  -2.1044  -5.7456 -10.7641   8.7872   0.1149
+#&gt;    1.9290  -4.3031  -5.0268   2.0541   9.3620  -6.5746  -6.5838   6.1757
+#&gt;    1.8804   5.5209  -1.6502  -5.0906 -13.2797  19.7620  -3.4012  -4.2540
+#&gt;    1.3196   8.8311  -5.2938   0.7205  11.5397   4.6186   0.1667   4.4330
+#&gt;    8.2320   3.5439  -0.0781  13.5372  -1.3183  -2.8995   7.4505  -0.4583
+#&gt;  -12.9408 -11.3869   1.4935 -12.7002  -5.3621  -8.7744   0.8293  -0.3609
+#&gt;    2.6342  -0.4168  -4.7770   1.8892  -3.1467  -6.2088  -7.4270   0.3072
+#&gt;   -2.0058 -10.8628   3.7055  -5.4891  -0.8779  -0.8320   0.4250   1.8867
+#&gt;    8.6515  -4.1764  12.1561   3.2225  -3.6669   0.8715   1.6936  -4.9513
+#&gt;   -0.8066  -6.8987   2.7274  -2.5657  -2.2977  -2.5447  -5.2651  -1.3667
+#&gt;    0.0641  -1.1050   9.1838  -0.7779  -6.5939  10.3293  -0.6831  -3.4317
+#&gt;   -0.9526  -3.3516  14.0134  -1.8953  10.8101   5.9014  -1.4601  11.3590
+#&gt;   -8.8058   9.7172  -1.8348   2.3232   7.8532   2.6836  -6.7784  -4.1494
+#&gt;   -5.1763 -11.3810   4.2428   2.8889  -9.5280  10.5218  10.3084   2.3278
+#&gt;    3.5902   8.9550   3.7468   0.4195  11.1313  14.4605   7.6010   6.7326
+#&gt;   -1.8484   4.9172  -1.4582   6.6719   2.1260  -9.1362   0.8069   4.6898
+#&gt;   -3.9948   8.9041  -2.0977   0.1985   8.5756  -4.3336  -2.5226  -6.8194
+#&gt;   -6.7353  -5.9661   9.1023  -8.8217   9.8306   6.4111  -5.0250  -0.2583
+#&gt;   -0.5733   2.9590   1.0549  13.1248  -2.2757  -5.6781   4.8615 -10.4787
+#&gt;   -2.8304  -6.6133   1.1348   1.9259  -7.5608  -0.0975  -1.9719  -6.2450
+#&gt;  -14.2544  -2.1351  -3.8724  -1.3873  -5.2126  -4.2660 -13.4798  -2.1145
+#&gt;   -2.4491  11.6818   7.1726   8.7630   6.2928   4.9393   6.2456   3.5497
+#&gt;    4.1296   7.1968   4.9017   0.2784   1.5593  13.0424   1.4521  11.3329
+#&gt;   -2.2085   8.1701  -2.5337  -3.7367   9.3092  -9.0775   8.4497   7.4483
+#&gt;   -2.8945  17.4843 -11.3043  -2.8576  11.5501   8.4810   2.3641   5.4486
+#&gt; 
+#&gt; (4,.,.) = 
+#&gt;  Columns 1 to 8  -8.9373   1.6399  -9.3011   1.4044   3.6362  -3.6884   4.1129  -0.9177
+#&gt;   -1.4104  -3.4010  11.6886  -2.0425   5.0381  10.3211   2.3125   3.7280
+#&gt;   -0.9357  -2.5987  -0.8097  -7.3440  -2.9790   0.2844   1.1797  -2.0219
+#&gt;    0.7954  26.6022  -0.1761 -10.4019  -3.9853  -4.7054  -2.7099 -11.5866
+#&gt;   -4.8281   3.9997   7.6639   2.1329   1.7832  -3.4229  -4.4235  -8.0600
+#&gt;   -4.2906   0.4296  14.0283  11.0699  -2.6126   0.9977 -10.1659 -20.9791
+#&gt;   -0.2850  -4.6091   3.8426  -3.2056   1.4130   0.4385  -2.6187  -4.8739
+#&gt;   -5.6108   1.3174   7.9251  11.1445   2.7707  -7.1793  -7.8399  -3.0120
+#&gt;    5.1576 -13.3771   0.9829  -5.2116  -4.3821   8.2142  -3.4522 -15.3296
+#&gt;   -4.0186  12.0413  -2.8010   5.3009  -1.6269  -7.3934   0.1458   3.5200
+#&gt;    0.1809  -1.2963  14.7856   4.9618  -1.2995   1.4195  -6.5237  -2.4909
+#&gt;   16.6593  -5.1476   4.8693   7.8189  -4.1561  -3.6614   1.0279   9.9730
+#&gt;   -1.9016   5.1086   4.6128  -1.8447   0.4608  10.1100  -4.6261   4.2095
+#&gt;    4.9385  -8.9146  -0.5647  -3.1531   6.9549 -17.0622  -2.5873  -1.0333
+#&gt;   -5.8255   4.3723  14.0465   9.6723  -9.6586   6.4133  -1.3056 -12.5534
+#&gt;   -5.5134   2.5385   3.7693  -1.7086  -4.3682  -3.3993  -7.5111   9.0573
+#&gt;   -4.5008  -2.2101   3.2887   9.2156   8.1258  14.3153  -6.4282  -0.2755
+#&gt;   10.0607  -4.0007   4.2165   8.6627  -5.6962   6.5382  -0.7017  -3.3520
+#&gt;   10.9792 -12.7363 -11.1796   6.1025   8.0711   2.8154  -0.6251   8.5030
+#&gt;  -15.3810  -0.3471   6.0013   8.3160  -2.4042   3.0223 -16.8555  -6.2519
+#&gt;   12.9764  -4.6472   1.4249   8.6086   3.9251   5.5342   1.4575  -9.6606
+#&gt;   -7.7859  -0.0378  -8.7754   0.2117 -11.5043  -5.5715   3.8264   0.7867
+#&gt;   -5.8436   5.1906  -2.9890  12.7835  -2.3970   3.9694  -2.1704  -7.6511
+#&gt;  -11.8085  -0.1376  -7.4560   4.4743   3.5176  -7.5724 -16.7047 -10.5319
+#&gt;   15.3922  -4.8711 -13.4026  11.7450   2.0306   9.9591  15.9956  -2.2847
+#&gt;   -1.6474   2.2722   0.9578  -0.9675  -0.1793   8.5340   6.2574   1.9746
+#&gt;   -0.1732   0.8481   5.0246  10.0355   2.5423   4.7380  -4.1744  -5.7309
+#&gt;    5.0698  -3.8620  -1.4083  10.5425  10.8409   3.9629   7.7026  12.7015
+#&gt;    2.9825  -5.9775  -5.2800   7.1343   4.5012   4.4923   2.3546  -3.2652
+#&gt;   -4.3361  -7.6220  10.1317   2.4227   3.2601   6.8644   4.4243   5.2520
+#&gt;   17.7917  -0.3951  -4.4227  -0.2346  -1.7326  -9.4963  -5.2619  -0.7878
+#&gt;    5.3519   0.6308  -1.0294  -0.9731   6.0362   0.5765  -8.1187  -3.5761
+#&gt;    0.3248  -3.5005   3.3166   1.9265   1.7660  -1.5276   6.8794  -1.3940
+#&gt; 
+#&gt; Columns 9 to 16  -1.9001   0.7127  -3.7042  -3.1003  -2.5198  -2.0575  10.7891 -10.1290
+#&gt;    4.4664  -1.3785   5.6772  -7.5936  -2.2864  -4.5115   5.9416  -2.9930
+#&gt;    1.5377   4.4286   3.8994   7.8767  -6.2099  -9.3024   9.8539   0.7968
+#&gt;    1.3931   1.4117  -5.5809 -17.4775  -3.3827  -6.8981  -7.4701  -0.2287
+#&gt;   -3.5261   7.2352   9.7388  10.1538   9.3579   4.1696   3.4854  -4.4308
+#&gt;    3.2155   2.2456  -5.0688  16.8926   1.8043  11.3323   2.9362 -17.2064
+#&gt;   -2.5780   2.1673   8.9328   1.9443   2.6978  -3.6639   4.2491  -7.3115
+#&gt;   -0.0379  -0.3899 -11.1863  -7.5292   2.2368   5.0501  10.9519   4.0228
+#&gt;   -2.3787  -6.3793   3.8723  19.7264  -2.1229  -8.1372   4.8397  13.4221
+#&gt;   -0.1609   3.9356   7.6169  -0.8369  10.8069   4.9614  -0.4863  -5.5323
+#&gt;   -4.1389   1.4834   7.0239  -4.2083  -5.1313 -15.0423   3.8272  -4.4371
+#&gt;   -4.0908   2.8806   1.0398   1.6341   1.6630   2.1113  -5.2521  -0.4592
+#&gt;   -2.3149  -4.1857  -9.5716   3.2153  -9.7876  -1.8165 -15.7325  -6.8080
+#&gt;    7.6909   9.9126   5.9013   0.0249  14.8729 -14.5049  -1.9218  -2.0016
+#&gt;   11.1113  10.2416  -2.4377  18.4841  -3.1200 -12.1874  -7.3556 -17.1354
+#&gt;    7.0546   0.5588   5.1830 -13.3850 -10.7262  -6.7357  -3.4260   2.6514
+#&gt;   -1.7956   1.6426   1.9519  -1.5552   8.5460   1.7914  -1.6229   0.8920
+#&gt;    6.2778  -9.5054  -7.6767  -8.9659  -8.5650  -6.6644  -4.2292 -12.1641
+#&gt;   -1.5601  -4.5378  -9.5443  -0.4477 -11.3808   2.5598  -7.0608   4.8732
+#&gt;    4.9494  -3.9585 -18.3522   3.4936   5.4241   1.2551   3.3881  -6.7100
+#&gt;   -2.2057   0.6258  -9.7220  -0.1111  -1.0761   5.7311  -0.8607  -5.8128
+#&gt;    4.0180  -1.9138  -2.3049   2.2944  -1.7141  -6.2059   7.9873   0.2988
+#&gt;    8.3576   0.5287   4.6250  12.8253 -11.8698  -3.0675  -2.3879  -3.5915
+#&gt;   -6.9098  -6.2171   2.3000   3.1197   3.8548  11.2952 -14.4839  -5.1035
+#&gt;    3.6927  -0.5734  -9.4985  -1.7064  -4.4952  -2.9143  -6.4201  -2.4317
+#&gt;    5.8637  -5.0499  -5.3575  -5.7396   5.6403   6.2517   1.5342  -3.1273
+#&gt;   -5.5487   2.7170   5.6355  -4.3530  -0.3532   3.4242  -8.7395  -2.6661
+#&gt;    0.6817  -2.5049  -0.9142  -3.5636   9.6664  -0.0384  -3.9433  -1.6649
+#&gt;    5.6631   2.3630  -5.0465   5.2111   2.0411  -5.5932  -6.4517  -6.7330
+#&gt;   -3.7180  -1.7679   6.3940   0.5621   1.1296  -1.2607   7.3263  -7.4035
+#&gt;    1.4558  -3.6146   9.7355  -3.0817  -9.2252  -0.9715   8.1628   1.2281
+#&gt;   -7.5844  -5.2732  -4.6288   1.9936  -0.4796   6.6172  -9.8305  21.6248
+#&gt;   -2.4536  -6.4660   1.2808  -3.9041  -4.9362  -3.5620   3.0095  -9.3904
+#&gt; 
+#&gt; Columns 17 to 24 -16.2372 -19.7274  -0.1741 -11.2008  -3.2621   6.3569  -8.8925  -0.4303
+#&gt;   -5.4173  -7.6782   8.2397   1.4117   3.6337  -3.4932  -0.8219  11.9920
+#&gt;   19.2885   2.5153 -12.7047   1.5867  -8.2949  -3.2381   5.2396   0.1813
+#&gt;   12.2386   6.4969  -4.7977  11.9596  -0.3093  -0.3655  -0.3141   2.8275
+#&gt;    2.3578  -1.3342  -3.8336   2.7758  -1.4061  -8.5146  -0.3398  -8.0748
+#&gt;  -20.0700 -15.1627  -0.5593   6.0172  -8.7065   1.4813   0.0891   1.6563
+#&gt;    0.2059  -4.4305   1.2511   3.0906   0.2580  12.1064 -12.8778   5.0823
+#&gt;   -2.4030 -19.5416   3.9174  16.3369   3.0540  -8.3036   4.4496  12.4252
+#&gt;   -6.7507   0.8657  -8.0464  -0.3716  -0.4432  -3.8865  -0.0692  -9.6786
+#&gt;   -0.0698   4.0154   2.6120  -6.7098   0.9287  -1.1052  -2.9665   4.0454
+#&gt;    4.2824   3.9316   2.0320   2.0653   6.2146 -16.8679   4.5053   4.8263
+#&gt;  -14.8595  13.3765   1.6610   2.0509   7.3690   2.4040   6.5151   1.7843
+#&gt;   10.0346   6.3441  -2.4157  -0.6306   1.8411   5.8174   3.8976   3.5893
+#&gt;   -7.7210  19.3262   7.3632   6.9095  -3.1356  -4.3910  -0.6466 -13.5442
+#&gt;   16.1872  -0.0640  -5.5496   0.1496  -1.1111   0.9940  -6.1868   2.5441
+#&gt;    5.3269   4.6372 -10.4780  -8.9268   3.1330   0.6902  -5.6594  -3.4153
+#&gt;  -10.7116  -2.4769  -6.5876   4.9373  -0.7942  -8.1281   5.3133  -9.1278
+#&gt;    5.4327  10.0923   1.0010  13.1000   1.8219   1.5296  -1.7788   6.2398
+#&gt;    2.8703  -9.3128   8.1365   4.9767   2.0086  -2.9929   4.9331  -7.5836
+#&gt;    4.2043  -3.4412   4.2323  -2.8825  -5.4795  -6.1055   2.8913   7.2720
+#&gt;   -1.9965   2.1791  16.0918  13.4114  -5.0736   6.8383   7.0362   1.3149
+#&gt;   12.9917   1.7107  -9.1155   7.2644   3.6828  -1.5261  -6.1586   9.2209
+#&gt;    5.1585  -6.3146 -10.2284 -17.6754   0.5683  -0.8958  -4.6662   8.7225
+#&gt;   -0.9275   1.6786   6.2533  -7.8757   3.7599  -6.7295  -1.7024   1.6199
+#&gt;   -5.5077   5.7192   0.1597  -1.1456   0.1365  -0.2670   9.3889 -10.7641
+#&gt;   -3.3875 -12.9637   5.6332  -0.7724  -6.7348   6.3785  -6.2865  -7.7037
+#&gt;  -16.6626  10.6243   3.7678   6.9794  10.9552  -4.2435   3.0685   2.4144
+#&gt;   -3.6866   7.1044  -0.2200   0.8470  -3.6753  -7.6094   5.4141 -15.7434
+#&gt;    8.7154   7.6460  11.4999   3.9323   6.8737  -5.2896  -9.2223   1.9661
+#&gt;  -12.7411 -20.4097 -10.3811  -1.7526  -3.3337   3.1244   1.5932  -5.8384
+#&gt;   -6.4045 -21.4485  -3.3501   7.6588  -1.9086   3.0637  -8.2305   4.4404
+#&gt;   -6.2674   3.3319   7.3105 -18.2420   7.0885   1.8895   4.2690   4.4079
+#&gt;  -12.1091  -0.3891   7.2408  -5.9361  -2.7110   3.0618  -4.8158   3.3095
+#&gt; 
+#&gt; Columns 25 to 32   2.3998 -17.3448 -12.2325   0.3315 -15.4217  -1.8867 -15.4240  12.3478
+#&gt;   -3.7151   6.9167   9.9820   1.5672 -11.4572   1.9856   7.3794  -0.1111
+#&gt;    2.3240  -8.5145   0.3523   8.1304  -1.9023   7.6816   0.2831  -4.7317
+#&gt;   23.5435   2.0492 -20.7813  14.2461  11.3960  12.3975 -11.7466   3.0538
+#&gt;   -7.4008  -1.0536  -4.7788  11.5750   4.3103   0.4249   3.9897  -4.0659
+#&gt;   -0.4591  -8.9794   0.9216  -4.6796  -8.7029   9.5869   8.7900  -7.8309
+#&gt;   -2.9604   2.2297   1.1120  -0.7426   7.9197   7.3716 -10.5481   4.1526
+#&gt;   -3.9723   2.9816  -9.3026   5.5909 -10.0158 -15.0935   8.5722   1.3201
+#&gt;    1.1391  -0.4905  11.8503  -1.8477  -2.4021  15.0876  10.7224  -2.2367
+#&gt;    4.5792   0.5044   3.7245   0.0699   7.8139   6.8465  -3.0149   5.3561
+#&gt;   -2.2293  14.3296   1.3361  -7.7686  -0.4618   8.6373  12.8193  -5.5542
+#&gt;    5.4532  -4.3885   9.4846 -17.4665   8.1103  -0.7766  -1.3261   3.4329
+#&gt;   -1.9946  -0.3186   0.0346  -9.3878   3.0484 -10.4834  -2.9084  -7.7686
+#&gt;    2.0548 -10.5485  -3.2662  22.6547  -8.1382  11.3451 -15.2585   6.4609
+#&gt;    0.1131  -9.3124   9.9692   4.3660  -0.1986 -10.1405  -4.0936  -1.1072
+#&gt;   17.0797  -3.4246  -2.5888  -0.7562  -8.3573  -0.1185   1.1050   7.3033
+#&gt;   -6.9426  -6.1876   2.3584   9.0243  -8.2849 -13.8852  -0.2879  -0.9890
+#&gt;   -3.9229   2.9294  14.3624  -6.8364 -14.9054   2.0942  -1.3370  11.7426
+#&gt;   10.4417   2.0153   2.5499   2.6353  -5.2696  -1.1515  12.8907  -6.8000
+#&gt;   -3.3850  -0.4643   8.3822  -4.8786 -18.5677   0.2475  10.1148   5.0903
+#&gt;    6.1073  -5.6765   3.8480   1.6076   0.9365   7.3126  -3.0763  -7.4835
+#&gt;   -6.2270  -7.8434  -1.8985   3.3555  11.7319  -3.2799   2.3244  15.7276
+#&gt;    8.4865   8.8633  21.3272 -16.4600  -3.8408  -8.0375   1.0212  -4.3776
+#&gt;   12.8918   1.8276   4.7197 -12.4718   5.4431  -3.3738   5.3581 -13.0529
+#&gt;   -3.7650  -3.9353  16.2862   4.6456  -7.5221  -0.1671  -6.6627  -1.2377
+#&gt;   -4.4990 -21.3608  -4.0670  15.6238  -7.5423  -9.5070  -1.6854   3.0255
+#&gt;    9.1725   3.4281   5.9490  -2.2386  12.4893  -1.6703  -0.1803  -9.1139
+#&gt;    4.4431   1.9173  -6.4646   9.7265 -12.4915  -3.9208  -2.5061   2.8529
+#&gt;  -10.7019  -8.1675  11.7109  12.2299   3.7126  -1.5526   0.0804   3.4049
+#&gt;   -7.0743   7.0589 -11.8581 -12.1029 -16.7801  -5.9686  -5.1970   0.2153
+#&gt;    1.8325   3.2412   5.7683  -8.6227  -2.8456  -5.8553  -3.2370   6.9899
+#&gt;   12.3013  -5.2388   5.6335  -1.6851 -11.4705  -6.1349   1.0349  -8.6584
+#&gt;    4.2542  14.5048   2.5535 -17.0956  -5.7988  16.1823   3.0141   7.0088
+#&gt; 
+#&gt; Columns 33 to 40   1.7074  -2.7095  -5.2740  -5.3750 -11.1734 -11.5376   1.2613   8.9030
+#&gt;   -9.7125  -3.7753  -7.5031  -0.7469  -0.7143 -11.1453  -3.4587  -9.8992
+#&gt;   -1.4763   0.0384   6.2177   0.9006   3.5500  -0.8569  -2.1706  -1.7766
+#&gt;   -7.8055  -4.5051   2.6745  12.8302   7.7032  11.8959   1.4649 -17.2213
+#&gt;    1.7518  -9.0725  -6.0132  -3.7355  -0.6736  -1.7817   2.1679  -9.3729
+#&gt;   -7.0022 -11.5829 -17.9102 -14.6837  -6.6562  -7.1852   6.0695   5.5540
+#&gt;  -11.7927  -5.6766  -3.0036  -2.3266   1.5770   2.8812   8.7181   0.4599
+#&gt;   -1.0159  -5.8263 -16.4976 -11.9847  12.6645   3.8689  10.7117  -1.5844
+#&gt;    3.7081   0.7444  -8.1803  -6.8067  -7.9231  -3.9081  -5.9678   9.1194
+#&gt;   -8.0077  -4.4849   5.6012   5.4904   0.2560  -4.3410  -1.3505  -9.2316
+#&gt;   -4.5165   0.3389  -5.6415  -6.9742   1.8363   2.0317   0.4151  -3.6613
+#&gt;   -3.5573  -3.8663  -2.2184  -0.8321   3.6882   0.6856   2.9591   3.8303
+#&gt;    8.2161   4.0893   4.6047  -1.8199   4.3622  -5.2914  17.8723  -0.6648
+#&gt;   -1.0932   6.2708   3.1357   9.0462 -10.0658  18.2034 -30.1197   8.5641
+#&gt;  -11.7677   5.8088  -2.8664  -1.4192  13.0200 -14.9370   0.6709  -2.9731
+#&gt;   -3.5297  -1.1649   0.3058   5.2004 -14.7660  14.8186  -7.3994   8.6478
+#&gt;   -2.5049  -1.9505  -0.6840   4.6141 -11.7132  10.3747 -10.6767  -0.7528
+#&gt;  -15.6232  12.4212   5.1873   0.5407   7.8264  10.1926  -2.0064   4.7172
+#&gt;    2.5496  -2.0950  -4.1168   0.9357 -10.7598   6.7054   3.1847  -4.4976
+#&gt;   -1.6174  10.3704 -11.4205  -9.5627  -2.5331   0.6784 -10.3460  10.6275
+#&gt;   -4.5203   0.6928   1.9589  -0.1332  10.7521  -0.8171   3.8033  -2.3770
+#&gt;   -1.5401   5.7727   1.8338  -4.8751  10.4040  -0.1402   0.5706   9.6135
+#&gt;   -5.1437  -0.0033   7.3305  -7.7063  -3.8892 -11.6323  -1.3622  -1.1627
+#&gt;   13.3520  -9.0883  11.4072  -7.6520  -3.3230  -3.5779   4.2729 -15.3720
+#&gt;   -5.4414  10.3479   9.3171   4.2304   2.9741   0.8669 -11.3900  -2.6916
+#&gt;    3.4813   3.9532   0.0644   5.0330  -1.2378  -1.8766  -6.9203  11.8834
+#&gt;  -11.3476 -20.5107  -1.1963  -1.3385  -1.6884  16.3563   1.6985  -9.4230
+#&gt;    9.1603   7.9446   7.2743  15.5263 -12.4083   6.3507 -16.7579  -1.2418
+#&gt;   -5.2214   5.9875   9.8173  -3.4455   9.0035   0.2276  -4.4667  -2.9136
+#&gt;    0.8931  11.0414 -11.4794   1.4996 -14.8614 -12.1374   0.5295  25.4513
+#&gt;   -9.3430  -4.1520  -2.2349   2.5586   4.5160   5.2115   6.6060   6.2346
+#&gt;   14.7500  -6.3226  -1.7460  -1.9870 -12.6927  -0.8219   5.2718  -0.2050
+#&gt;   -1.9830  -0.0687  -4.7570  -4.9114  -1.7514 -18.3255  -2.6171   1.5965
+#&gt; 
+#&gt; Columns 41 to 48  13.1469  16.3241  -9.5096  -7.9503  -5.8903   1.4845   3.5997  -3.2972
+#&gt;   -0.2769  -4.5006   5.6813   2.3416  -2.4813   0.8077   1.2412   3.2028
+#&gt;   -3.1104  -0.1819  -2.6743   2.3650   4.9108  -9.3813  -1.2675  -5.7836
+#&gt;  -16.8457  -0.2598   6.2999   1.5642   2.1965 -10.8874   0.8298   0.6867
+#&gt;    1.6406  -2.1335  -5.3743   4.0202   9.0518  -4.3672  -0.4848   7.5687
+#&gt;   13.2089   0.0184 -15.4310  -3.9341  12.7642   0.4858  -0.8550   0.0537
+#&gt;  -11.6616  -1.7800  -5.4005  -0.6940   0.2097   3.4405  -4.8381  -9.8920
+#&gt;   14.0421  -4.1125  -5.8660   3.0663  -1.4779   6.1736   4.8914  -2.6142
+#&gt;    2.3728  -8.8320  -1.4000 -10.7431  13.7504  -0.3255  -9.8459  -8.4252
+#&gt;   -6.7076  -6.9181   5.7444  11.9882   1.8702  -1.4792  -1.4045  13.8206
+#&gt;    1.9241  -3.6843 -10.4069  -4.3291   4.4341   1.6662  -4.2902  -9.6582
+#&gt;    0.4927  -3.8589   0.4731   1.3278   4.1912   7.5662  -1.6354  10.4284
+#&gt;   -0.5050   8.8472   0.8561  -4.2709  -7.3363   7.3410  12.7846  -8.7095
+#&gt;   -1.2324   0.7144  -2.9817 -14.2587   3.4681 -11.2354  -6.6864   9.6205
+#&gt;    7.9781  -1.9659  -7.9456   0.1395  -2.1838  -1.9923   3.2518 -10.3654
+#&gt;   -2.0977   4.2419   0.9674   2.3197 -10.0479  -7.7920  -2.7702  13.2239
+#&gt;   -2.8001  11.2738  -6.1056  -5.1717  -4.9363   8.1373   5.2983  -8.2018
+#&gt;   -5.6889  -7.8704   2.1990   7.1298  -0.7015  -0.9009   9.0010   4.0715
+#&gt;    4.0209  -1.8575  -4.0008  -2.7558  -4.3971   9.8000  -4.1534  -5.9647
+#&gt;   17.2192   6.2090  -2.3315   3.7335   0.1062   8.8375   4.9632   0.1628
+#&gt;   -6.7845  -9.5097   1.2010  -0.8135   5.6752  -1.4077  -1.1803   8.0863
+#&gt;   11.0266   2.1652 -13.4578   5.4363  -3.1816  -7.5622  12.6702  -5.5490
+#&gt;   11.5781   8.2979   4.9476   7.6994  -0.9640  -6.0966  -2.0411  -4.1657
+#&gt;    2.9375  10.2070  18.4995  -6.5145  -4.5667  15.3985   2.2779 -11.5283
+#&gt;   -2.6135  -4.7623  -5.1036  -5.7793   0.9005  -7.5000   0.2239   0.9951
+#&gt;    8.0807   0.8971 -10.2686  -6.8935 -11.4938   3.7583  -4.6565   1.4418
+#&gt;   -5.9638  -3.1656  -2.6535  -2.4869   8.5280  -0.5289  -8.7591   1.3365
+#&gt;  -11.9118  16.4476   5.1364  -9.7505  -8.8722   9.3799  11.1230  -7.8695
+#&gt;    6.6437  -9.3812  -8.0302  -4.6728  -1.1732  -5.6723   0.8056  -0.3872
+#&gt;   -3.6792  11.9203   4.2696  -6.0437 -18.1020   2.5408  22.5066  -5.7841
+#&gt;   -7.2307  -6.7341   3.0000   4.8462  -2.0850   6.5586  -2.3047  -9.5330
+#&gt;    3.5926   8.4891  14.9880  -8.0819  -0.7994   6.8544  -4.8742   4.6444
+#&gt;   -3.4799  -3.4725   4.6508  -2.2688   3.3988   1.6961  -0.7697  -2.0110
+#&gt; 
+#&gt; (5,.,.) = 
+#&gt;  Columns 1 to 8   0.4391  -1.9098   2.1484   2.2646   5.2900   2.4446  -4.2038  11.1062
+#&gt;    4.8973   4.6691  -0.1577   4.2984   6.9359   6.8015  -8.0088   0.2365
+#&gt;    2.3555  -3.1241   5.4234  -3.6021  -3.1128  -3.5330   0.4087 -14.0087
+#&gt;  -21.4126   4.3750  14.8495   0.9699 -23.3634  -9.8421   5.1767  -0.3107
+#&gt;    8.8056  -6.8391   4.3331   3.3613  -3.3439  -6.6336  -4.3775   4.2387
+#&gt;    0.9767  -5.5275  -5.2243   1.3197  -7.8901 -15.0289   0.7283  -4.1920
+#&gt;    9.3987   3.4653  -3.4113  -5.7025  -3.7569  -2.7637  -8.1449   8.4361
+#&gt;    6.8643  -4.8464   8.5880   0.4491   2.4999   4.5187 -10.8066  -0.0955
+#&gt;   -3.5246   1.8129  -7.8024   3.5076   2.1022  -6.6070  -6.1765 -15.9043
+#&gt;   -3.9591   1.8916  -0.3776   1.3119  -4.7591  -1.5195   1.4179   2.9710
+#&gt;   -1.6701 -15.0615   0.8259   6.0667   5.4124  -2.4430 -16.1106  11.8974
+#&gt;    0.9344   9.1141  -8.1943   5.6707   4.5830  -3.3910   8.2350  10.4890
+#&gt;    9.7430   1.3351   3.8455  -4.2307  -2.5922   7.3867   6.6023   2.8679
+#&gt;   -7.8984   0.8581 -12.1087  -6.0889  -1.5231  -6.6723  10.8628  -5.9127
+#&gt;   -5.5918  -0.2745  -1.9311  -0.2384  -8.2658  -3.8147  -0.2191   7.8357
+#&gt;   -6.2771   7.5198   5.3943   6.4536  -2.6440  -4.4889   0.2298   8.3339
+#&gt;    2.2442  15.3852  -3.1567 -12.4926  -6.3730   6.0722  -6.5579   1.1781
+#&gt;  -12.8170  13.8433  11.4968  -7.2771 -13.1532   0.3584   6.8091   7.4388
+#&gt;   -1.5858  -2.2859   8.7732   2.5848   6.5458  -5.5752  -6.7938   4.3016
+#&gt;    9.7460   2.3616   0.1275  -2.4913   7.3516  10.8242   1.6281   7.0327
+#&gt;    4.9148  -0.2744  -0.9559   0.1036   0.1812   3.7332  -5.5575  -3.6512
+#&gt;   -6.9862  -2.7739   6.1212   5.5122  -1.4691   4.1725   3.8487  -5.0333
+#&gt;   -5.1841  -8.2360  -0.5528  -7.2601  -1.2878   4.2392   1.6847   9.3671
+#&gt;  -17.1302  -2.2287  -6.7963 -14.4281  -0.8908  -3.4021  11.1699  -3.4806
+#&gt;   -3.0202   9.0764  -3.9049  -2.3577  -4.3465  -1.2582   5.9626  -4.2595
+#&gt;    1.1443   6.7906   4.8259   0.8769  -2.3707   1.1460   1.2335  -3.5128
+#&gt;   -2.2088   9.7532  -6.1144   4.8872 -23.0924  -0.9535   0.8545  -1.0388
+#&gt;   -1.0195   5.6566   2.3717  -8.6502  -5.5510  13.9087   2.7717   1.1356
+#&gt;   -3.9602  -2.6769   6.9075  -3.2793   1.0274  -6.7747  -0.8613   9.1491
+#&gt;   10.5364  -5.7910 -14.2881  -3.7272  10.0372  20.2330 -12.9053  -2.4491
+#&gt;  -10.0494   6.0045   1.5262  -9.1165 -13.6801  -4.7538   0.3811   7.3859
+#&gt;    1.4289   5.4423  -2.4157 -13.3889   6.3931  -3.5567  12.3840 -13.6936
+#&gt;   -4.7881  -1.5958  -6.8650   3.8896   3.9981   4.8909   5.9710   5.3680
+#&gt; 
+#&gt; Columns 9 to 16   2.2322  -7.0163   7.4025   1.4474 -16.0608  -8.2812   3.9479 -12.4157
+#&gt;    5.5612   4.6730  14.6888  -6.9282  -2.4945  -2.1730   6.0320  -4.8716
+#&gt;   -3.6143   1.5976  -7.2834  -4.2591  18.0512   3.4045   4.9996   9.2229
+#&gt;   -1.9145   4.3505  -4.2095   8.8816   4.4741  13.3800  -8.6061 -15.9516
+#&gt;   -0.1081  -0.1761  -4.2558  -0.4345   5.6086   1.8441  10.3737   5.1652
+#&gt;    2.2998  -1.7684 -10.6446  -2.0748   9.0934   0.4708 -10.4841   1.5032
+#&gt;   -5.1979  -5.4615   9.0303   6.3415  -5.2508   9.9896  -8.8075   5.4614
+#&gt;   -1.3947   7.3096  -4.8045  -4.1015   5.2261  -4.1460   3.2539   5.1348
+#&gt;   -6.8889   0.6101  -5.8317 -11.2515   5.8717   7.2147   1.3801   8.7224
+#&gt;    7.5277   1.7141   5.3771  -3.1537  -4.7789  -2.5609  -0.3410  -5.8091
+#&gt;   -3.9199   1.3565   0.4403 -10.9124  14.8008  -1.0440   2.7889   3.3954
+#&gt;   -1.7609  -6.7693   3.1827   4.2683 -16.9452   1.0002   2.4671  -4.3169
+#&gt;   -4.8054   1.8201 -14.5093   7.1921   0.2319  -6.2055 -11.8342   6.1623
+#&gt;    1.7320  -9.1120  15.5501   5.9678   4.7828   5.1776  -1.9527 -13.2768
+#&gt;  -10.0481   2.2758   3.7188  -0.5877  -6.5601   4.4341   0.3167  -3.7434
+#&gt;    1.2645  -9.2064   0.6251  -2.0461  -6.9692   3.2809   1.6773  -1.2751
+#&gt;   11.8582  -2.5059  -1.4314   4.9035  -1.7204  -1.3300  -9.6967   6.4524
+#&gt;   -0.5472   2.9117 -10.2421   2.0473  -3.7858   9.6259 -16.1022   3.4859
+#&gt;    2.1549   3.3371 -12.6224   0.8640  -4.7667  -2.4520  -4.1159   2.7331
+#&gt;   -2.1234   5.4642 -14.9485  -9.0224   0.1193  -7.7338 -10.7780   6.8374
+#&gt;    1.2525  12.7835  -8.2089   5.7755   6.3247  -5.0586  -1.0041  -6.4176
+#&gt;   -4.7794  -5.4369   2.2887  -8.2611   0.6869   1.6407   1.2151  15.0891
+#&gt;    0.3344  -3.4176  -5.3505   1.7168  -7.6792  -5.8174  -0.8998  -2.9376
+#&gt;    4.0955   1.8786  -7.8103  -0.1581  10.2054  -7.6718  -3.1871  -4.9185
+#&gt;    8.1981   3.2560   4.0140   1.1940   1.7563  -8.6477  -2.5206  -5.7887
+#&gt;    1.5151   1.2714   4.4403   2.6299   2.0904  -6.3844  -1.2666  -8.5626
+#&gt;  -10.9549  -1.0200   7.7347   7.2965   2.4207   2.6545 -10.7580   3.1383
+#&gt;   12.8288  -0.2713  -9.4372   6.5619  -0.2274  -4.2176 -12.9764  -1.5689
+#&gt;   -6.1175   5.0343   7.6258  -9.1574   0.1178   2.2677   2.1093  -6.2908
+#&gt;    4.1674  -5.4791   2.5661 -10.4149  -7.1355  -9.9736   1.3936   4.6197
+#&gt;   -3.8975  -5.1808  -0.8492  -0.0343 -11.0863   5.1938  -1.0113  -2.9068
+#&gt;    7.4677  -8.5787  -5.1975   1.3381   5.0835  -6.4174   6.8993 -17.7437
+#&gt;    7.7881  -0.0287   5.9182  -7.3728  -1.9345  -3.7842   3.4900 -16.8634
+#&gt; 
+#&gt; Columns 17 to 24   1.6771  15.3690   3.7065  14.0798  -3.7558 -14.2482 -15.8616  -4.6108
+#&gt;   -9.0802   3.9591  -0.1773  -0.3126  -0.1790  -2.6130   8.8423  -5.2414
+#&gt;   -6.9821 -10.3903  -5.4097  -7.7257   2.0685   5.1675  -5.2855   2.8596
+#&gt;   -4.9021  -7.2088   7.6342   5.6961  12.7943  17.9261  11.7175  -0.8168
+#&gt;    2.5747  -3.4802   3.6670  -4.6425   1.4458   7.2083  -4.3110   3.6268
+#&gt;    4.0800  -7.1349   4.5110  11.2754   2.0006   1.3698  -5.7636  -2.4166
+#&gt;   -1.7837   1.6697   4.5390   8.0320   6.6240  -1.3921   5.0736   6.5151
+#&gt;  -10.3835  -2.0104  -2.1694   7.6783  10.7268   8.1382  -3.9772 -10.8648
+#&gt;   18.0275 -15.5641 -13.7067   0.6534  -3.9048   2.4265  12.8767  -5.0597
+#&gt;    8.7382   9.9576   8.0037   5.4577  -7.9091   4.1477   2.0845   2.2091
+#&gt;   -8.5455  -3.5102  -9.4138  -1.0351   5.3878   1.6572  -0.7054  -1.4215
+#&gt;    8.5940  -4.8951   7.0747   0.1936  -1.3161  -1.9277  -4.8872   7.2786
+#&gt;   -8.8369   5.5785   6.5429  -4.4698   2.2424   3.5187 -13.6622  13.3914
+#&gt;    3.3157   1.1893   3.4218   0.6910  15.3152 -25.8847  -1.6705  -7.5419
+#&gt;    1.2810   3.5057  -5.8798  11.3180  -0.9019   0.7256  11.0449   1.4503
+#&gt;  -14.3743   4.2423   7.3155  -1.0684  10.6818   4.7645   4.5571 -16.5317
+#&gt;  -11.8851   5.7834  11.3538   0.4630   7.3968   3.2219  -7.4244  -1.9372
+#&gt;   -9.4704  -4.0520   1.9144   3.3256   8.3051  -3.5069   6.0086   1.8476
+#&gt;    5.8126   5.6365  -3.7384 -11.9932   7.9528   3.3870   0.0428  -8.3264
+#&gt;    3.3812  10.5610  -2.1292   1.3869  -9.4373  -3.9937   8.0604  -2.4713
+#&gt;   -3.9936   0.3866  -7.1512  10.2174  -3.1713   8.1932  -2.9202   3.2001
+#&gt;   -3.5016  -6.3447  -3.2122   5.7092  -0.7514   5.7600   0.1821  -3.7173
+#&gt;    9.9094  -4.9634  -3.0044   1.4675  -5.3280   8.1705   8.6492   9.4478
+#&gt;   12.2444   0.0311   5.9437   0.7478  -6.0319  -0.5225  -5.2490  -0.2744
+#&gt;    6.4196   6.0773  -6.8921   6.7982   1.2868  -0.0129   4.0809  -0.3024
+#&gt;   -4.7182  15.5866   0.2722   6.8277   2.2576  -1.9896  -0.1264  -9.8248
+#&gt;  -12.0991   1.7634   4.6613   8.1298   9.6500   8.8975   3.5907   5.0736
+#&gt;    4.8709  13.9266   3.8506  -6.3351   0.5635  -7.9125  -4.9749   9.1720
+#&gt;    2.3501   4.4443  -3.0040   4.8984   0.7580   5.2943   2.1156  -9.3190
+#&gt;   -2.4022  12.4300  -0.4464   3.9948  -6.7848  -3.4612 -16.2256  -6.0657
+#&gt;    0.3598   5.1294   1.4181  11.9905   7.0827   9.3332  -3.5159  -3.1946
+#&gt;    8.4195 -10.1014   4.3162 -10.1488   9.6877  -3.9963 -13.8788  -3.8362
+#&gt;   10.7531   3.6968  -0.5402  12.8164 -12.9496 -12.6744  -4.4541   4.8352
+#&gt; 
+#&gt; Columns 25 to 32   3.0754   0.9656  11.4195  -1.6933  -2.7757  10.5142   1.0324  11.4810
+#&gt;    8.3067 -12.5141  10.5343  -0.3537   7.5259   6.7108  -2.0825  -1.2409
+#&gt;   -0.3672  -8.2046  -1.6047   0.8231   7.5048   3.2305 -11.0317   2.1045
+#&gt;  -24.1941 -14.2835   0.0603   3.4999   0.9970  -1.3493 -21.5908   3.9657
+#&gt;    5.0227  -1.3011  -1.1914  -0.2580  -2.6511  10.8058  -7.1249   3.4567
+#&gt;   -4.3163  -5.5814  -7.0375  -3.9281  -4.2662   8.3106   6.4370  -6.4413
+#&gt;  -22.2715  -1.5557  -7.1393 -13.8717   4.1784  -3.9443  -0.4707   4.6257
+#&gt;   -1.1398   9.7803  -4.3700  11.5757   3.7639   3.8352   7.0444   9.1612
+#&gt;    7.0127  -1.5018  -6.6758  -9.8043  -5.0684  -8.0241   0.1096  -9.0917
+#&gt;   -1.9075  -4.6612  -3.8810   4.1255  -8.3587  -0.0907  -9.8954   0.6749
+#&gt;   -1.0257  -4.4097 -14.4159  -3.3711   4.3964   6.4112  -3.2049   2.1978
+#&gt;    9.8993  -3.4522   3.5321  -9.1970   8.0096   1.7683  12.9511  -6.5953
+#&gt;   -5.5456   0.3709   5.9697   3.9595   7.7234   2.9263  -6.2956   8.9204
+#&gt;    8.2467  -7.8250   9.2086 -14.7752  -1.7631   2.3624   7.4934  -3.4432
+#&gt;   -3.3292  -8.0017 -11.9161   0.0641   5.8049  15.1777  -4.0188   3.4478
+#&gt;   -5.8577  -7.9515  -4.2061   4.6872   5.1803  10.4527  -3.0946  -8.3235
+#&gt;   -0.1268   4.5932   1.4038   6.5119  -8.2524  -2.4885   1.8776   3.0738
+#&gt;  -11.7010   4.4528  -1.9193   5.6535   7.9257   1.6852   5.6245  -9.1236
+#&gt;   -2.1876   5.5739   2.5077  -1.3249  -0.3368 -12.2279   5.1158   9.0283
+#&gt;    7.9109   9.3029  -0.0488   7.3976 -11.4432   0.1573   8.6820  -6.8024
+#&gt;    7.6055   6.7798   0.9633   0.9630  -8.1765  -2.1018  -4.6992   2.1717
+#&gt;   -5.0130  11.0520 -15.4249   0.6652   4.8052  -0.2627   2.0799  -7.4576
+#&gt;   -5.7443   7.9972 -15.6795  -6.0751 -12.8827   3.2815   1.8649   7.1333
+#&gt;    0.3538  -9.3284  -6.3689  -3.5229 -12.5694  -2.9542   8.0311  22.9101
+#&gt;    9.3625   2.6567  -5.3627  -4.1245 -15.9260  -8.2227   3.4513   5.8330
+#&gt;   10.5383   6.4723   5.7762  13.8513 -11.9889  -0.1678  -6.6589 -16.1521
+#&gt;  -17.6372  -6.7296  -8.8622   0.8981  -4.1522   7.4152   5.9546  -1.9361
+#&gt;    5.4169  -4.5138  11.0641   5.9831 -10.9107   6.2039  -0.9973   4.2854
+#&gt;   -0.9271   3.5063  -8.0455  -7.9462   5.0734  -0.4818   2.4464   3.7186
+#&gt;   12.8333  -0.2911   6.5849   6.0337  -2.4569  12.1207  -9.7083   7.0773
+#&gt;  -10.5021   3.0687  -7.7843  -4.2201  -5.2013 -10.5865   5.5016   2.3961
+#&gt;   12.5794 -20.6448  16.9042   5.0654   7.4768  -1.5273   6.9260   6.6722
+#&gt;    6.9695 -17.0422  -1.9053  -7.2440  -2.6287   7.0495   4.2520  -2.3256
+#&gt; 
+#&gt; Columns 33 to 40 -10.0027   3.8379   1.0881  -5.4896  -3.4261  -2.0429   8.5668   0.6859
+#&gt;   -0.2681   1.0662  -3.8098   3.2342   2.6800  -8.1446  -2.7712  -4.1984
+#&gt;    4.7517  -2.0557  -7.7677   5.1592   0.5944   3.9591   4.3850  -1.2466
+#&gt;    8.1936   3.0673  -0.3543 -10.5834   2.9359 -10.8625   0.6194  -2.0070
+#&gt;   -5.1751  -2.5127  -1.9720  -6.5750  -0.1732   4.2047  13.0346  -2.6657
+#&gt;   -1.8758   8.4967  -2.0558  -4.6847  -2.2359  -7.7241  -3.3445  15.3346
+#&gt;    5.9761   1.3729  -8.9394  -4.0960  -4.1077  -6.0165   0.7958  -1.0748
+#&gt;  -13.7243  -7.8930   2.7955   0.2883  -5.9294  -1.7375   5.3112  -3.4478
+#&gt;   -0.9705   9.0413  -2.2212   3.6686  -5.7288  -4.2316   3.9254   0.0704
+#&gt;    2.9807   0.6605  -7.1388  -3.8520   2.1888  -2.2726   9.6096  -3.6688
+#&gt;   -3.6191  -4.2340   2.6247   6.8139  -7.8175  -4.5176  -1.0235  -5.8777
+#&gt;   -4.2742   5.0907   1.7450  -3.6382  -3.0324   2.9528   0.7540  -0.4634
+#&gt;   10.9585   0.0143  -7.0358   6.8255   4.3354   0.3694  -1.9219   3.4484
+#&gt;    0.5214  -0.1948   7.1384  -7.7696   0.6348  11.3390  -1.7319  -4.2369
+#&gt;   -2.6518   6.9048 -11.4995  -5.9130  11.9696   8.3919  -4.3782 -14.1445
+#&gt;   10.4091  -6.7464  -4.8839  -2.3931   5.8914  -1.0612 -13.6791   2.6800
+#&gt;    2.1091   2.4291 -11.2586   5.8175  -6.0045   7.4189  -3.7206  -0.2299
+#&gt;   15.1141  -1.9943  -2.2590  -0.6403  10.9068   2.3399  -9.3898  -1.8449
+#&gt;   -1.6461  -0.8309   0.3367   1.4051  -2.9572  -1.9485  -0.5922   7.7519
+#&gt;    7.2909  -1.9546   0.1197   1.6492   1.1757  -0.4608  -0.5773   2.3481
+#&gt;    8.1006   3.4007   7.7575  -1.3652  -6.8355  -8.0055  13.8854   5.6229
+#&gt;   -5.3073  -2.1287   4.6341  -0.0629  -3.8079   5.9468  -6.5184  -4.4899
+#&gt;    9.1216   4.1665   1.5098   2.4553  10.6162   3.4273  -9.7515  -5.4893
+#&gt;    8.5937   6.0146 -10.0838   7.7629   5.9513  -9.6387  -0.2607   9.8624
+#&gt;   -0.9794   4.6749  -1.4617  -2.0968  -2.9037   2.0378  -0.8534  -2.7718
+#&gt;   -2.9497  -0.1574   1.7163  -5.7772  10.2611   3.0250  -5.9499   5.6355
+#&gt;   13.6754   6.2193 -15.8523   2.5245  -7.9110  -0.2794   4.3846   2.2639
+#&gt;    1.1008  -3.4523  -4.9767   2.7175  21.2093  10.1844 -13.1447   2.7406
+#&gt;    1.5942   0.2254   2.8626  -4.0579  -6.1620   3.5378   9.9390  -9.9881
+#&gt;   -1.0034  -6.4215   1.5955   3.2818   9.3604  -1.8906  -9.9140   1.6970
+#&gt;    4.4405  -1.0841  -1.2114  -6.8427  -1.5594   4.5440  -5.3968  -2.4016
+#&gt;   -4.8038   6.7719  -0.6516   1.4910   2.9071  -1.2205  -5.2272   5.3886
+#&gt;   -3.9530   7.0034   4.7422  -4.3794   0.1748 -10.2672  -3.6337   1.0284
+#&gt; 
+#&gt; Columns 41 to 48   3.9176   9.3826 -18.1409  -1.2317  18.4283  14.2380  10.9816  16.3214
+#&gt;    9.3704   8.3192  -3.7702   9.3107  -6.9517  -1.9656  -2.1970  -5.9058
+#&gt;   -6.1956   8.0480  -0.4568  -7.2946  -7.2395  10.2508  -1.6460 -17.5588
+#&gt;   -5.8348   6.3836 -15.9138  -7.1003   0.7682  -8.1109  -4.9736   4.3648
+#&gt;  -12.1081   7.9106  11.7615  -7.2715   7.0097   4.9865  -1.4555  -2.7906
+#&gt;   -7.2788  -6.8688  -5.7403   0.8716   2.4997   2.0539   6.3284  -3.5148
+#&gt;   -0.6817 -14.6984  -1.2534  14.0554   3.7611   2.5724  -6.4090   0.5131
+#&gt;    3.0084   3.5823   1.3246   0.2096   5.1705  -0.3375  -4.4088   0.3239
+#&gt;    8.4180  -2.8182  13.1569   7.0721  -9.1781   0.7615  12.5517 -14.7409
+#&gt;   -5.2442   2.1872 -10.3611   3.4234  -7.4395  -6.3410  -0.9863   1.5954
+#&gt;    7.6250   7.5944   9.0311   0.7588  -9.2115  10.0633  -7.0872  -6.7356
+#&gt;    5.4534 -16.1096   5.8245   3.7962   5.2196  -3.4442  -2.0334  -0.8086
+#&gt;   -9.7670  -8.5133  -0.4997   1.8853   1.8471  17.3971  -2.1560   6.0896
+#&gt;   -2.7300   0.1150   7.3367   3.4939   2.0422   2.3700  22.4666   2.8843
+#&gt;    1.3771   0.3651 -12.6147  20.4125   1.0130  10.7655  -0.1040  -6.2868
+#&gt;    7.7744   7.6784 -10.9426  -6.5254  -3.0292  12.7293  -1.1339   1.9785
+#&gt;   -7.1104  -0.2583  -0.5166  10.2442   5.2892   5.7679   2.6449   2.1772
+#&gt;   10.5209 -10.9635 -16.7092  15.8249  -2.4129   7.9576   1.9455  -3.7210
+#&gt;    7.9507   3.4032   6.5550  -0.7690  -1.6202   8.4765  -3.6645  -3.3324
+#&gt;    7.6527   5.3433  -1.8237  17.1745  -9.4284   0.2114  11.2357 -10.2487
+#&gt;    1.7741  -1.8712   2.9795  -0.9704  -2.9409  -6.4351   5.4212  -0.7826
+#&gt;    1.7348   5.4718 -10.0168  -1.3006   4.4316   4.7486  -4.0691   0.6938
+#&gt;    7.6392   6.3423  -4.4581  -9.5702  -1.7579  -6.8640  -5.6261  -9.5138
+#&gt;   -4.7566 -14.3245  -9.4253   2.2101  -8.7745  -0.5797   0.4113   7.4549
+#&gt;    0.7994   8.8246  -6.1234   5.6424   2.0019  -4.7824   1.0309  -0.9710
+#&gt;    0.6113   9.3829  -9.3782   4.8075  13.1326  -1.6003  13.6170  15.4264
+#&gt;  -13.4268  -3.9375  -4.0792  -2.3965   2.2949   1.7475  -2.0046   9.6053
+#&gt;   -5.2425   1.8676   1.0152   6.7712   1.6528   6.5646   7.5009   6.0387
+#&gt;    6.4326  -3.4127  -5.5756  13.0351   6.9856   3.1156   8.2280   1.4301
+#&gt;    7.9989  -1.2362   1.1412   7.9949   3.8730   7.1355   7.6854   5.3798
+#&gt;   10.1957 -12.0346  -5.5023  11.2652  13.5204 -10.4236  -2.8808   1.6140
+#&gt;   -3.5676  -4.3917   1.2228  -6.8104   1.3814 -12.4657   8.9958  -3.9858
+#&gt;   10.2427   0.1554   0.9914   7.9511  -2.0589 -10.3418   4.6433   1.2253
+#&gt; 
+#&gt; (6,.,.) = 
+#&gt;  Columns 1 to 8   7.2243   8.3392  -1.3849  -3.9614  -2.4271 -21.3446  -6.4720  -8.3673
+#&gt;    4.5975  -3.0668   0.3427 -11.5763   6.4074   0.9656   7.8276   6.9289
+#&gt;   -1.3308  -6.9574 -10.1657  -4.7257   2.6630  -3.6961   4.3472   6.0477
+#&gt;    5.5895  -2.4206   8.7348   7.3996  -7.2864  17.2307  -4.4007  -8.8312
+#&gt;    8.1620  -1.6408  -7.1717  -5.5062   5.5247   1.3972  -9.0990   1.6231
+#&gt;   11.3486   4.4702   4.3114   8.5954  -2.8606  -2.3586  -3.0956   3.8136
+#&gt;    0.6174 -10.7279  10.3699  -0.2084  -7.2189   6.3811   7.4373  -5.8331
+#&gt;    4.2271   7.9513  -2.5008  -5.1251  18.8871  -6.0456  -9.1673   8.8651
+#&gt;   13.8916   2.3057  -4.5864   4.5505  -2.0082  -2.5087   0.9130  -5.7247
+#&gt;   -0.8457  -5.6887  -6.4370  14.4166  -2.5539   6.7175   3.5127  -4.8348
+#&gt;    4.2450  -0.7323  -4.0768 -10.9424  15.4770 -13.5984  -3.0378   3.8981
+#&gt;   -3.0415   0.1284  -4.0245  14.9781 -12.0422   2.1056   3.5895   1.7076
+#&gt;   -6.8926  -0.9696   8.6010   1.8853  -8.9117   8.2546   4.2719  -6.5448
+#&gt;   -0.2702   9.9633  13.8592  -7.1001  -0.2188 -10.0001 -13.6396   5.4360
+#&gt;    5.0542  -8.5357  -1.4633  13.0656   0.2974  -2.0189  16.9817  -8.3320
+#&gt;   -3.1462   6.0385  -0.4890  -5.6040  -0.0464   2.3662  -5.6113   3.7265
+#&gt;    2.3273  -0.7567   4.0286   0.0487   6.6288   7.2548  -5.5849  -1.8846
+#&gt;    2.4761  -8.6424   9.6763  13.4918  -8.8691  -7.4611   6.6871   4.9654
+#&gt;   -4.5020   8.7862  -9.5270  -3.2915  16.6043  -4.4464  -4.8521   2.0281
+#&gt;    5.3709   9.0742  -0.1652   0.2802   5.4078 -10.7173   8.5004  -0.5599
+#&gt;    1.3871   2.2028  -6.8016   7.5116   0.0626  -3.4792   4.5549   4.6654
+#&gt;    4.8865   7.4633   2.3566  -2.2726   4.8016  -0.7082  -3.7235   6.7077
+#&gt;   10.7146  -6.8378  -2.8024   9.2662   4.8256  -0.8213   4.1914  -3.8058
+#&gt;   -7.5799   4.1754   8.7691  16.6743  -7.9259  -0.3989   8.3513 -11.5092
+#&gt;   -0.1406  -7.8099 -12.4845  11.8588   6.0273  -7.4990   1.4426   0.4183
+#&gt;    2.9798   5.2181  -9.0369  -0.1315   3.9919 -12.7542  -6.8516  -1.9525
+#&gt;    0.9739   0.1558   9.1333   4.2886  -5.1501  17.3377  -2.2212  11.3604
+#&gt;  -11.1918  -2.9488   7.4725  -1.0213   4.8964  -1.2393  -6.3039   0.7197
+#&gt;    8.7943   7.6386  -3.0797  -1.4985  13.1746  -6.3380   5.8917  -0.8362
+#&gt;   -0.2607  -8.8499   9.9483   1.3202   4.7826  -3.1972  -4.9586  -4.9227
+#&gt;    7.2917  -8.7727   5.4313  17.7468   2.7473  -4.0644  -8.2797  -8.8895
+#&gt;   -0.4174   3.1143  -0.2159   5.9918  -2.2373  -0.7149   4.2587  -1.6637
+#&gt;    5.2812  -2.3961   0.0392   6.4498 -13.8805 -15.2444   6.3047  -6.9894
+#&gt; 
+#&gt; Columns 9 to 16   1.3572  -9.2639  -3.6357   7.5815  16.4982  -5.5324   3.9752  -4.3839
+#&gt;    7.2428  -7.1613   0.4689   3.8176  -2.4002   0.4925   1.8304   2.1115
+#&gt;    0.6485  -3.9916   1.7729   0.7433  -0.5789  -1.0123  -3.7413   5.1510
+#&gt;   -3.0748   6.1491   2.2909  -3.0659  12.8628  -2.2961  22.6958  -0.5127
+#&gt;    6.3820  -2.0064   7.3224   7.8251   1.0473  -8.0397 -10.5067   0.9349
+#&gt;  -11.4967   3.2232   8.5371  10.9801   9.2796  -4.1002  -3.6480   1.2015
+#&gt;   -1.4213   2.9341  -0.9396  10.2263   0.6507  -0.9364   5.5842  -1.1230
+#&gt;    4.6090  -6.3211   9.4724  12.5845  -1.7015   4.1189 -12.2793  -7.4774
+#&gt;   -5.4928  -0.5904  -0.2987  -0.3799   9.6282   4.2732 -10.4704  13.7935
+#&gt;    1.5482  -0.3901   3.1564  -7.1711   4.9637  -5.9584   1.0225  -0.7327
+#&gt;    0.7841 -21.3481  19.5244   5.3517  -6.1240   3.0176  -2.3114   1.9763
+#&gt;    8.1032   2.8423   0.5301   2.2284  -9.0271   3.6010  -9.2538  -4.2355
+#&gt;   -1.7511   8.3882   0.8187   4.0027  -5.3239   4.3685   3.4672 -10.8329
+#&gt;    0.5106   3.5901   1.4955   0.8682   0.7976  -5.2305  -5.9664  -6.8258
+#&gt;   -9.1375   0.9898  16.0165  -7.1575   0.5436   1.2575 -14.7935  -1.9664
+#&gt;    5.6937  -3.7379  -6.6352   3.8704   8.8477   6.6741  17.8161  -0.2798
+#&gt;   -2.6651   4.0551   7.2325   4.8498  -4.1348 -10.7248   5.0212  -7.8985
+#&gt;   -1.6667  12.8498   8.3556   4.0415  -9.2053   1.0740  12.3649  -3.1267
+#&gt;    9.4538  -7.2539   0.2545  14.1017  -0.7963  14.3931 -10.0916   2.4066
+#&gt;  -12.3760   6.1386   5.3391  -9.2521  -9.7553   8.1339  -3.7875 -10.5115
+#&gt;   -3.7960   5.5098   4.1127   3.2031  -3.0166  -2.5891 -10.9013   5.1421
+#&gt;    0.8645 -10.0569   1.1566   4.1279   9.3596  -3.6824   4.2446  -8.3089
+#&gt;  -10.0649 -10.2554  -4.7482 -15.6579 -10.9623  -1.0678  -0.2935   6.8447
+#&gt;   -6.6380   4.3850   0.8957 -11.3851   1.7734   9.1456  -6.6717  -4.7344
+#&gt;   -4.5926   2.4124  11.3638 -13.4111  -0.5375  -0.5407   6.2940   4.6291
+#&gt;  -12.7819   0.4595   0.4682   4.7299   6.6059  -2.4985  -4.2601  -3.3678
+#&gt;   11.4470  -5.6345  10.5911   5.3314   7.6042  -2.4948   7.9147  -5.3031
+#&gt;   -3.2883   6.0255  -3.3904  -6.2489   0.7011  -4.9232  -6.4242 -15.4514
+#&gt;   -0.8515  -8.9004   8.9762   7.3365  -1.6781   1.1573  -1.3595   0.8473
+#&gt;  -10.0676  -4.2501  -4.2951   0.9042  11.0537  -6.8604  -2.8014  -8.3547
+#&gt;   -0.3443   1.6298   2.3734   5.8460  -2.7301  -1.4867  -2.6336   2.5605
+#&gt;   -1.5731   5.2042  -6.0489  -3.9748  -3.5646   4.9206   4.5472  -0.7470
+#&gt;   -4.9682  -0.6410  -2.7100 -11.2327   7.9666   0.3811   5.3322   9.1792
+#&gt; 
+#&gt; Columns 17 to 24  -1.9419  -5.7336  11.9942   4.7601  -1.1021   0.1526  -2.7318   0.4802
+#&gt;    9.9439  -9.4554   1.3996   1.3670   7.8745   4.9322  10.9325  -4.1276
+#&gt;    5.9729  -0.2948  -3.4857  -6.6527   4.3021  -4.2746   4.1884   0.5543
+#&gt;   -3.9633   7.9015  -8.6306   5.5853   0.2484   8.5325  -5.4779  17.4002
+#&gt;   -2.2753   4.9165  -1.4233  -4.3736  -0.2018   2.6888   0.5883   1.5900
+#&gt;    2.7971  -0.4647   0.6217 -12.6645   0.1814  -2.6693  -2.5864   1.4766
+#&gt;    0.1495   3.5149   0.8440  -7.1651   2.7019   5.8067   6.0512   3.3628
+#&gt;    7.3606   0.4496   9.6615  -1.6973  -6.6609 -11.4058  -6.6486 -12.5350
+#&gt;   -5.2156   0.2924  -5.0834 -16.2553   0.3100  -2.5564  11.9973   4.2131
+#&gt;   -5.6752 -10.3026   4.2528   2.2497   4.1874  14.9477  -2.5128   1.4847
+#&gt;   13.0678   5.6539  -7.4028   0.5487   4.6282   0.2611   9.8012  -4.0386
+#&gt;    2.3555  -2.8775   2.5253  -0.3183  -9.4194   7.1053  -2.6442  -2.2717
+#&gt;    3.3136   7.7977  -4.0650   3.8109   3.1865 -17.8396 -12.4813  -4.0486
+#&gt;   -0.3119  -4.4636  -3.1033   1.1887 -11.4041   3.6654   6.1167   4.8608
+#&gt;   19.5184  -7.2677   3.1874  -9.7602   3.3361  -7.0507  -3.6390   7.9522
+#&gt;    0.9450   1.6529  -6.6908  18.1123   3.8180   5.7401   4.9581  -0.9846
+#&gt;    1.0836  12.0571  -5.5106   1.3248   1.5558 -10.3360   0.0579  -2.6499
+#&gt;    3.1612   5.1826  -1.1608   6.2212  -3.1358  -8.7089  -7.4634  -0.5527
+#&gt;   -8.9989  -1.6155   7.4303   6.5009  10.9928  -2.6357   0.0780 -16.1420
+#&gt;    2.0072   0.7219   0.9024  -0.4102   0.1720 -11.4900  -3.7621  -2.7232
+#&gt;   -8.0794  -2.6456   1.0228  -7.7458  -3.6464   0.9997  -3.6155  -3.4252
+#&gt;   14.5272  -0.7068   4.7087   4.9182  -4.6983   4.6151   1.5049   1.8046
+#&gt;   -6.9474  -3.9977  -9.7340  -8.4386   5.1468   1.1116   0.7188   3.6614
+#&gt;  -16.7842  -2.0017   0.0629   1.1708   4.9418 -14.1091  -8.3668  -3.3363
+#&gt;    2.5515  -3.3029   1.2168   3.5226   9.0527   6.2613   1.6803   3.9490
+#&gt;    5.5269   3.0326   5.7349   7.2353  -2.8270  -4.0605  -3.7755   0.9408
+#&gt;   -6.9021  16.4827 -12.1811  -1.6658   1.7088  -5.2144  -4.3741  -6.3384
+#&gt;   -2.3419   6.4386  -2.0155  13.3472   2.9459  -7.8724 -11.8473  -4.2770
+#&gt;    0.5906   1.5462   4.4134  -0.2814   2.9043   1.3267   6.0800  -1.0883
+#&gt;    1.9978  -4.2010   2.7048   2.1470  -0.1110  -1.9612 -11.0203 -14.5765
+#&gt;   -8.3450  -2.3093   1.0679  -7.3374  -6.5944   4.6366   3.8195  -4.5595
+#&gt;   -7.4569   0.4207  -2.8997  -6.5984  -4.0737 -18.4086  -7.7615  -3.0192
+#&gt;   -2.1727 -12.4999   7.7563  -0.1128   6.9967  17.2992   7.5060   4.3193
+#&gt; 
+#&gt; Columns 25 to 32   0.5489  -2.7851   1.2910  -2.2830  10.3267  13.5500   1.8178  -9.0620
+#&gt;   -5.9758  -3.8377  -6.0791  -3.0669  -4.7702   5.1763  -6.1482   8.1660
+#&gt;    2.9291  -6.6904  -2.7593  -3.2061  -6.3909   2.5089  -7.2814   2.2478
+#&gt;   -4.3237  -2.3705   2.0448  -8.5145  -6.7395  -4.7917   1.4278  18.8616
+#&gt;    0.9036  -4.9193  -1.8782   1.3285   2.3220   6.1395   1.8885  -2.8320
+#&gt;   -2.3343  -8.1989   2.6961   9.1824   7.3328   4.9421   3.1049   0.7268
+#&gt;   -4.9143   0.4837   5.9807   1.4539  -0.0714  -3.9909   7.5337   6.3653
+#&gt;   -0.3442   6.1906   1.8804  -0.9160  -0.4821  -3.0456  -0.8775   3.1517
+#&gt;   -1.5952  -0.0023  -7.2607   5.9104   0.8051   8.0313 -10.2251   6.7405
+#&gt;   -2.6411  -3.0006   7.9548  -1.5612   3.7520  10.1731   2.7219  -0.3250
+#&gt;   12.8345   4.2254  -5.2382   0.9249 -11.7240  -6.5759 -12.0975   1.4645
+#&gt;   -4.2454  -0.7754  -3.2727   6.7287  -2.9156  -8.4225   7.2463  -9.1077
+#&gt;   -1.6413 -11.3781   7.5926  10.0934   1.9571  -9.0014   1.8181 -13.9184
+#&gt;   -2.2377  14.4022  -0.3523  -0.0334  -1.3645   8.2180   6.2597   6.9555
+#&gt;   -2.1595 -12.7020   5.5225   8.1006  -5.1287  12.3235  -7.3240   2.6665
+#&gt;   -7.5441  -2.7023  -7.9101  -6.7034   1.2142   0.8901   1.9602  -3.6886
+#&gt;   -1.1957  -9.2353   8.6472   2.5590   9.5425  -0.2925  14.7062   1.1890
+#&gt;  -15.5934  -2.7007  -6.9255   1.8178   5.3152  -5.9453  -8.6526   9.2370
+#&gt;   -6.7564   4.7754  -0.7221  -1.1370   2.2326   2.2628  -1.2350  -9.8540
+#&gt;    1.0357  -0.1774   3.2705   1.8051   2.4900   5.3541  -2.1648   2.5351
+#&gt;   -0.0126   5.2413  -4.6247  -3.5731  -4.6246  -2.1948   9.3360  12.5130
+#&gt;    1.9397   4.2349   2.4305  -1.1732  -1.8129  -3.7480  -8.5892   0.7909
+#&gt;   -2.0414  -0.3399   2.2622   5.8912   5.0537   4.1629  -2.7187  -8.9195
+#&gt;    6.0152  -4.7058  18.0248  13.2227  -1.2188   3.7254  -2.6734  -2.7519
+#&gt;    1.6365  -4.4861  -5.3771   0.5365   2.0207   8.5161  -0.0189   4.6858
+#&gt;    9.8376   0.5626   0.3699   2.3950   7.1727   8.5187   7.6857   6.0384
+#&gt;   -8.6334  -9.1464   8.0587  -0.2189   7.0526   4.0767   7.4329   5.8209
+#&gt;   -0.0165  -5.7914   6.4489   7.2829  13.7562  -5.0777   5.1799 -10.5214
+#&gt;   -3.0783   7.1251 -10.0074  -2.4632  -1.2107  14.3788  -4.4379   4.4232
+#&gt;   -1.1358  -1.6174   4.0920  16.6688  12.7050  -5.0852  -0.8548  -8.4431
+#&gt;   -7.3369   3.8981   1.5434   2.7106   8.9853   0.2032   0.9945  11.2380
+#&gt;    5.3462  -8.5643  -4.6187   9.0935  -6.7361  -5.9773   0.6195 -10.5092
+#&gt;    2.4468  -1.5816  -0.6258   3.5935  -4.3062   0.5268 -13.2230   0.6924
+#&gt; 
+#&gt; Columns 33 to 40  -7.6027  -2.2487   2.1900  11.9675  -3.3487  -9.6965  -2.9535  -0.7557
+#&gt;   -3.2157   4.8165   4.5860  -0.2468  -2.9938  -6.0932  -0.5876  13.6491
+#&gt;    4.5272  -2.9818  -2.8203   6.5853  -5.0184   1.7838   0.4984  -1.9432
+#&gt;    1.1660  -3.9935  -1.8290   5.3999   6.6793   2.8560   4.8642   6.5097
+#&gt;   10.5755   3.2100  -3.6359   0.4694  -0.8818 -11.4375  -4.1460 -10.3099
+#&gt;   -5.6870   6.3213   8.4095   4.9459 -11.2726 -14.5157  -7.3464  -8.7629
+#&gt;   -8.5790   5.5107   6.3126  -5.6498   6.9198  -8.4822  -7.7633  -6.6192
+#&gt;    7.5044  13.3505   9.8939  -4.0529  -5.3569 -12.1263  -5.1392 -12.6179
+#&gt;    0.2750  -8.4532  12.4254   1.4368   5.1401  -3.1507  -4.9469  -3.5927
+#&gt;   -8.8588   1.4924  -2.4861 -10.4314   0.3319   5.8148  -4.7300   9.7827
+#&gt;    5.0628   8.7692  -2.4614   2.7285   3.5396   0.4264   5.5486  -0.9388
+#&gt;    7.9110  -0.0486   4.8019  -7.9210   0.9448   3.8109  -2.0076  -0.4768
+#&gt;    4.4348  11.1488  -8.0820   5.5546  -1.5815   2.9275   6.0719   3.2779
+#&gt;   -2.9327  -8.0244  -2.7404   2.2129  -4.0796  -3.0339   3.3700 -13.2439
+#&gt;   11.6239  -6.7035  -7.9736   4.2794   3.0072  -5.7825 -10.7079   2.9405
+#&gt;   -8.3416  -8.4063   0.1600  20.7802  -0.7024  -1.7364   3.3059   7.9594
+#&gt;    3.2505   0.9716  -5.6477   3.5167  -1.7800  -9.7658  -6.5191  -3.1310
+#&gt;   -7.3276  -1.4290   7.9197  -0.0182  -0.8833   1.8832   8.2552   4.7918
+#&gt;    3.2498  -2.9161  12.6821   6.7317  -6.3797   7.1543   7.1523  -0.3207
+#&gt;   -8.8900   7.2855  -0.7633   5.5332   4.4910   0.5948   4.8468  -6.2392
+#&gt;   -0.6436   4.6097   9.0892 -13.1945   4.9483   1.6102  -2.2883  -5.4042
+#&gt;    1.6717  -2.0507  -4.9608  12.4468  -1.3210   4.8892  -0.3961   2.6101
+#&gt;   -0.7270 -17.2108  -5.1492   6.3074  11.6628   1.2956   4.2730  -0.8359
+#&gt;   -5.0182  -5.8871   8.5032  -2.6112  -6.5407  16.0788  -3.7708   8.1343
+#&gt;    3.7324  -8.7810  -2.2099   2.1963  -2.1305  11.4818  -1.9305   0.0411
+#&gt;   -5.2839  -3.4533   1.1692   0.5344  -2.7929  -5.6060  -5.2001  -3.6439
+#&gt;   -2.6511   4.7609  19.7984  -9.0056  -6.3521  -8.7703 -17.5731   3.9446
+#&gt;   -9.1425   2.8850  -4.3467  -1.8818   3.7963   6.1533   3.1709   2.0593
+#&gt;   -4.9947  -8.8462   1.5875   8.7470  -1.5148  -0.5422   3.0933  -0.3313
+#&gt;  -10.4418  15.3400  -3.9069   1.0759   1.3927 -11.4530  -5.4137   1.2727
+#&gt;   -9.3344 -12.7449  22.2623  -4.2399   2.1708  -1.2364  -6.7290  -2.2009
+#&gt;    4.0125   6.1803  -5.5008   4.6972  -5.7827   2.0033   4.9381  -7.6543
+#&gt;  -12.5544  -0.4399   9.3944  -5.7592   5.7587  11.4921   2.9829   7.2321
+#&gt; 
+#&gt; Columns 41 to 48  -5.2301  -8.3919   0.4745  12.1680   8.2690   0.7700  -1.3986 -15.0949
+#&gt;   -3.8473   5.5070   8.4038  13.3590   0.2387  -7.7313  -1.4964 -15.2668
+#&gt;   -0.8133   0.6185   8.4404   1.8471  -7.6965   9.7787  11.1924   0.3181
+#&gt;   13.5075   5.0517 -10.7942 -10.8533   0.4656  -0.7475   7.3318   0.0555
+#&gt;   -9.7146  -4.7262   7.8232   2.9174  -0.7198  -1.2243   5.9117  -0.7655
+#&gt;   -5.3252   9.9646  10.1692  13.3378   3.5901   1.3333  -4.9229  -6.5326
+#&gt;    2.8730  -7.6642 -10.3643  -0.7128  11.7228   2.5994  -8.3758  -8.6394
+#&gt;  -23.6110  -2.7063   6.4992  -3.1165  -1.9089 -10.5941  -7.0350  -4.2015
+#&gt;   -7.3903  -3.7530  -0.4041  14.3706  10.5003   6.9389   4.9652   4.0909
+#&gt;   13.5233  16.0055  10.8386   2.8506  -2.9455  -4.1969  -7.9186   6.8712
+#&gt;  -12.5978   2.9476  19.6807   1.1032  -4.8529   2.4183  -1.7873 -10.5665
+#&gt;    7.4521   5.0772  -0.3968  -5.8041   3.3329 -11.9797  -6.1260   3.4263
+#&gt;    3.5739  -2.4945   1.2146 -18.5102   2.0377   0.5358   3.7605  -8.1046
+#&gt;   -5.1213  -5.6798 -18.7280  18.6834   3.4769  10.0587   9.7840   1.1688
+#&gt;   -5.1982   2.3021  -9.6020   1.8456  14.4900  -4.3919  -5.9182 -10.1780
+#&gt;    1.4465  -3.4884   0.3757  15.2833  -4.7847  -6.8593   0.2916  -1.5028
+#&gt;  -10.1953  -5.9133  -0.5997  -6.4112   6.6287   3.9975  -8.3894  -9.4420
+#&gt;   11.0555  12.4552  -4.2484  -8.0617  -8.0408  -0.1720  -0.0028  -3.1412
+#&gt;   -2.9399   4.6805  12.8330   6.3452  -5.2365  -4.5358  -8.0995   2.9361
+#&gt;  -11.0020   5.1860  -9.4381   1.0695  -4.4030   5.9915 -11.9550   5.6597
+#&gt;   -1.6070   4.5199   6.4365 -12.4429  -1.7472  -3.1261   4.8034   6.6773
+#&gt;   -4.5037  -9.0047  -2.9427   4.7468  -3.2937   2.3801   5.7475   0.3027
+#&gt;   10.0648  -1.4978  -4.0922   2.3945  -2.6983   4.7756  -5.0650  -9.1463
+#&gt;    4.9168   2.3982  -0.2161 -12.9496   2.4841  20.3679  -3.3443   3.2126
+#&gt;   -1.2465   0.5778   5.6310  -9.3660  -2.0670   3.2506  -6.5215  -5.0134
+#&gt;  -13.8826  -3.2358  -5.5231  10.6802   1.2777   2.4405   2.5325  -0.5911
+#&gt;   -3.6412  -2.0257  10.3329  -9.5415  15.9104  -2.6404  -5.0439 -11.1058
+#&gt;    6.9971   2.9543   1.2786  -5.9788  -1.6136   3.2090   0.8348  -4.1627
+#&gt;   -8.3324 -14.0545  -3.3994   3.9126   0.4258   4.7417  -1.5262  -6.5741
+#&gt;   -3.9503  -8.7550  10.9331   7.1293  -0.4960  -5.8905  -4.1936  -7.0265
+#&gt;    1.8810  -5.1962  -6.0232  -0.7553   3.0264  -6.1715 -10.1147  -6.3372
+#&gt;   -4.4186  -1.0833  -7.4865  -0.2799  -8.6644   8.1144   3.3730   0.9093
+#&gt;    5.1813  12.1565   5.8088   7.9778   2.0510  -3.9625  -5.2565  -4.9910
+#&gt; 
+#&gt; (7,.,.) = 
+#&gt;  Columns 1 to 8 -12.5637  -2.5921   7.1782   7.8186  12.1642  -5.9426 -14.8531   1.3177
+#&gt;    2.1248  -5.9844  -4.4564  -4.5092   6.7187   4.8098  -7.4867  -1.2874
+#&gt;    1.5000 -12.1292   8.3217  -5.3953  -9.2065  -5.9762  -2.2805   7.4073
+#&gt;    0.2294 -16.4882   3.5766   2.2640  -5.6411  11.0628  -1.8238   1.7027
+#&gt;    7.3974  -2.1059   0.7667  -1.1699 -13.5659   0.0552  -7.4127  -2.4198
+#&gt;   -5.3633  -3.2434   9.4996   0.8949  -7.2516   3.3304  -2.1083   6.2119
+#&gt;   -6.0371   0.9534   5.7248   0.9747   3.8753  -9.6830   1.2595   4.4478
+#&gt;    4.3532   4.1490   1.4650   5.5049  -6.8201   0.3387   1.8417  -2.3344
+#&gt;    5.4369  -3.8337 -12.7340   3.3148 -13.8978 -11.2822  -6.7416  -6.7818
+#&gt;    6.7436  -0.0121   3.2164  -8.8355   2.4412   8.5234   0.2344 -11.6883
+#&gt;    4.8147 -10.3121  -5.8911   7.6287  -3.2987  -9.7721   2.5174   7.5296
+#&gt;    2.0386   4.1301  -1.2932   6.6238   0.4111  -3.3190   8.2287  -0.0189
+#&gt;    0.6954   1.3338   5.9806   4.5208  14.4822  -2.7630  -2.8924  10.0675
+#&gt;   -8.4399  -1.8747   4.0748  -3.2390  -3.1167  11.0646   0.7673   2.3934
+#&gt;    6.4320  -4.3108   4.2115   5.2095  -3.3331  -4.7085   0.3097  10.0811
+#&gt;    1.0512  -2.9661   2.0181   5.1407   3.9240   1.8381  -7.2879   3.1933
+#&gt;    6.5491  -4.1914  -1.2071   4.5877   7.1293  -2.2568  -3.5221   0.0526
+#&gt;    2.4886   2.6609   0.6132  -5.0607   8.6768   0.5913   3.3273   4.5410
+#&gt;   -4.4869   4.9565  -0.4279   3.9000  -4.1421  -4.8384 -11.0614  -1.9895
+#&gt;    4.3772  -0.2396   3.2004   3.2212   3.7800   5.5176  -7.6825  -2.1287
+#&gt;   -9.9844   3.5585   3.2863  -5.5902  -4.6006   3.6855   8.6799   1.5601
+#&gt;    1.8843   0.4628   1.8192   0.8578   2.7260  -5.9450   3.4451  -7.6032
+#&gt;    6.0221  -3.7656  -3.4224  -1.3820   9.8885   2.1165   5.3021   7.6218
+#&gt;    7.4587  -8.7724   4.6601   3.4141  12.8465   3.5597   5.6033   5.5195
+#&gt;   -7.8602   4.5715   0.3401  -4.5892  -0.1992  -5.6559   6.1851   7.1525
+#&gt;   -2.7145   0.3003   8.5194  -4.0813  -7.0842  -0.4283 -10.4691  -2.9705
+#&gt;    1.7503  -1.6545   7.4607   6.6784  -1.7863  -1.5975   7.0052   3.1186
+#&gt;    7.2097   4.8913   1.6908  -8.0299   7.3147  13.8606  -6.3935  -0.7843
+#&gt;   -6.8344   8.7484   3.1693   3.3031   3.8666  -3.8317   4.2813   1.1445
+#&gt;    5.0369   3.8190 -10.9977   3.7152  14.4262   3.8348 -17.4962  -0.2185
+#&gt;   -2.1229  -1.1563   3.0345   1.8111  -3.2802 -11.0495   3.9942   0.6429
+#&gt;    7.0119   3.9208  -0.6389  -1.0716   5.4505  14.2304  -1.4294   9.7966
+#&gt;   -3.1686  -0.6396  -6.7988   0.9020   4.8284   8.9844   6.6045  -1.4290
+#&gt; 
+#&gt; Columns 9 to 16   1.3615   6.7430  -9.0831   2.4756   3.7019   4.5959  -9.2389   2.4258
+#&gt;   -6.5283   4.2182 -14.7588   6.0469 -13.5433   0.6464 -14.0126  -0.1585
+#&gt;   -0.4165  -2.1957   9.4785  16.3252 -11.8770  -4.1678  -7.0924   3.0644
+#&gt;    5.4771  11.0229  -6.5639 -17.6075  -7.7505  -4.6961   8.8753   6.9847
+#&gt;   -4.9094   3.5675  -1.0285   6.8008  -0.9435  -2.4753  -5.8107 -11.1148
+#&gt;   -1.3169   3.8941   6.4620   0.5569 -11.8652   1.5728 -10.2236  -9.3686
+#&gt;    3.9423  -0.3688   5.2345  -9.8630  -3.5721  10.8983 -11.1943  -4.6832
+#&gt;    0.7959  -1.5588  -8.7820   3.3694  -5.9139   6.2685   1.9696 -11.8555
+#&gt;   -0.0112  -8.3372  13.6339   3.5996  -6.6341  -3.1870  -2.4487 -17.8097
+#&gt;   -3.1110  -0.1339  -5.4582   3.0029   5.9676   1.6402   4.2996  -1.7442
+#&gt;    3.0221  -7.6143   2.1927  12.5149 -17.3180  -4.4666  -6.2207  -5.8665
+#&gt;   -1.7066  -5.6932  -2.6972  -9.1662  10.9101   0.1779   2.6959  -7.8873
+#&gt;   -5.7650   2.6454   2.5285  -7.5337   1.6641   5.1793  -4.0750   9.8508
+#&gt;    4.6218  -4.3656 -16.7795  -0.9792  -1.7471  -0.4700   0.4574   4.0469
+#&gt;   -0.6444  -3.1186   1.3270  -7.1979 -10.8263   2.7043  -9.5467   2.8626
+#&gt;    0.1377  -1.6119  -4.6476  -1.0249   4.4360 -10.2520  -0.8962   7.3968
+#&gt;    5.6244  -4.7285   1.2343  -6.5620   0.1256   9.5666   1.6124  -5.8117
+#&gt;    9.5864 -12.8068   6.7174  -0.9492 -11.1470   2.9382   2.7348   7.2478
+#&gt;   -1.3516  -9.1671  -9.7949   4.4277   4.5211 -10.2511  -1.0271  -3.8912
+#&gt;   -3.4992  -9.1626   2.4570  -2.6582  -6.1909  16.2682  -2.3738   2.5297
+#&gt;   -4.0920  -8.7836  -0.3173   1.5961 -15.0735   2.7017   6.4111  -4.5299
+#&gt;    0.8594  -4.6585   1.7853   3.0959   4.5689   1.6610   6.1223   1.7822
+#&gt;   11.7365  -2.0738   6.6759  -0.0544  -4.5379  -2.9902   6.5058  -0.1362
+#&gt;    1.2749   7.0817   3.4731  -4.2940  -1.4348  -4.4507   6.7489  -5.4719
+#&gt;    4.1983 -13.3311  -3.0335   4.3130  -6.7434  -3.9381   7.5854   2.0121
+#&gt;   -1.6503  -2.0314  -6.4046   6.1112   3.9059   6.7474   3.3489   7.1325
+#&gt;    5.0405   4.1070 -10.8778  -5.6209  -0.6011 -14.6451  -2.2682  -9.9665
+#&gt;   -0.4697  -4.2318 -14.3726  11.9567   7.8060   4.3527  10.3033   2.6359
+#&gt;    4.1752 -10.0265  -8.4880  -1.4998  -6.4839   3.2508   4.9882   0.2567
+#&gt;   -7.8847   0.6969   0.6589   9.8041   0.4428  12.1619  -8.5297  -7.3596
+#&gt;   13.3403   1.4642  -7.9892  -7.5376   4.4967   7.2595   1.9894  -8.7621
+#&gt;   -3.1823   8.1989  -1.4497   0.5435  -1.0498   1.2208   0.1718   4.7092
+#&gt;   -3.5594  -1.3115  -3.3914   2.5498  -1.9613   1.6140  -5.5963  -1.0378
+#&gt; 
+#&gt; Columns 17 to 24  -6.1352   0.7855   5.2496 -10.7202   1.1006   6.4437   3.0041   2.3786
+#&gt;  -11.1167  10.4239   0.2350   8.9435  -3.6779   4.8471   0.7140   3.8333
+#&gt;    9.4847   0.8165  -3.8158   6.3221 -10.7197   0.8852  -5.6699   9.1540
+#&gt;    4.0945  -3.8234  -0.7001  -1.1315   0.4381  -3.8137   4.0863   6.7160
+#&gt;    1.1146  -8.9878   2.3985  -0.9147  -0.8046   2.3567 -10.0703  -2.1854
+#&gt;    1.8469  -2.6686  -8.5182  -5.4251   6.5746  -7.8745   3.0007  -3.6743
+#&gt;   -4.1822  -9.5143  -4.3121   7.2566   2.6041   0.5658  -2.4030   0.4245
+#&gt;  -11.7281  -0.9243   5.2365  -2.4933   0.3209  -1.5823  -7.3054   0.5439
+#&gt;    1.9351   5.7115 -14.8850   5.1515  -9.2994   0.5744 -10.7098   2.5380
+#&gt;    3.1209  -1.7344   6.0623  -0.9722   1.9104  -4.2705  -4.6320   2.4032
+#&gt;   12.0585   4.4151   4.1575  -0.8302  -9.3978   3.5050  -6.6041  20.1393
+#&gt;    5.0416  -3.7321  -0.9123   1.5950   8.2849   1.2795   3.4207  -7.8130
+#&gt;    1.9756  -6.4791   7.8072   2.6787   4.0306  -9.3902   5.1214   1.6635
+#&gt;   -4.7427   9.7381 -14.8424  12.1369  -2.9461  -2.3381 -12.2591  -4.3829
+#&gt;   -6.2556   1.9847  -1.4314   6.6051   2.5084  -5.3805  -4.3812   0.1995
+#&gt;   -0.7258  -1.5201  -5.3685   5.4863   1.4409  -1.3454  -0.1501  -5.4024
+#&gt;   -4.7067  -4.7743   2.3876   2.4734   2.3301  -0.7725  -2.1543  -1.3893
+#&gt;    1.5617   4.9953 -10.5440  10.1063   7.0310  -3.7266  -2.5219  -3.8014
+#&gt;   -3.4102  11.2320  -3.4485   3.0704  -6.8103   5.8674  -6.1492   3.8883
+#&gt;   -8.3267   1.2774  -1.3571   1.9461   1.9410  -9.3266  -4.4550  -0.7253
+#&gt;    8.4757  -2.4299  -3.2148   4.9527   4.3251   7.9487  -3.1187   0.4195
+#&gt;   -1.0336  -5.3404   6.4247  -7.5989   1.9064  -6.2831   1.7634  -6.0175
+#&gt;   10.4051 -10.9130   6.5344  -3.2940   1.8782  -6.1374   9.9976  -2.0857
+#&gt;    1.6580   5.6035   1.0687   8.1476  -8.0305 -10.1498  -7.4877   0.9982
+#&gt;    1.5491   5.0754   0.2085  -4.2722   6.8387  10.7773   5.8851  -0.3984
+#&gt;   -4.5910   4.0407  -3.8389  -8.2975   5.9267   1.9849   8.1880  -9.6735
+#&gt;    4.7822 -11.5824   0.7062   5.2624   8.5684  -6.3034  -4.5508   0.3868
+#&gt;   -2.6230   9.7855   1.0649   7.0056  -4.0832  -7.7116  -5.7604   2.8494
+#&gt;   -5.7510  -1.5148   1.2035   2.5692   2.1899  10.1006 -14.8982  -4.4679
+#&gt;   -9.8924   1.5419  14.2471  -0.8770  -0.2646  -5.9197  -6.6279   6.6351
+#&gt;   -6.1204  -2.9526  -3.7854  -0.1987   0.0084  -0.0103  -6.4451  -8.6840
+#&gt;   -4.4767  -1.5587  -0.5513  -7.6515   9.3017  -6.6286   6.1258  -8.2799
+#&gt;    2.5453  10.2133   1.3841  -2.8850   2.5809   1.0825   1.8629   9.2428
+#&gt; 
+#&gt; Columns 25 to 32   2.0304   6.7152  -4.7547  12.5145  -4.5016  -4.0179  -1.2330   9.1559
+#&gt;   14.2909   1.4769   0.6636   8.6500  -2.1221   1.7183  -5.6604   9.8726
+#&gt;   -2.7097   4.5264   4.3187  -7.1046  -0.9723   5.9788  -5.5353  -1.4749
+#&gt;   -8.6412   7.7463  -5.2986  16.9666  14.3188  21.4422   3.3956   1.1343
+#&gt;   -7.0317   6.6929  -6.0369   0.6207  -3.9455   1.8005  -0.5599   1.8508
+#&gt;    2.0361   2.7921  -4.3200  -2.3661   2.9078   1.1786   5.4417  -8.1194
+#&gt;   -7.8485  -8.0762   3.2615  -5.6470   0.3354   5.4427  -2.8058  -4.3386
+#&gt;    3.7953   3.5336  -4.9533  -5.2637 -10.4107 -12.4107   2.0646   9.5287
+#&gt;   -2.7825  -4.6040   9.5309  -2.1843   5.5711   2.9852  -1.9312  -6.5354
+#&gt;   -3.6551  -9.9503  -5.3903   3.4260   1.4111  -0.6740   2.9563  -2.8675
+#&gt;   -1.4813   3.6377   2.2588   2.0900   5.0809   5.0369   5.1234   9.0396
+#&gt;    5.2403  -6.4151   6.7793  -3.0877 -12.6484  -1.7808   1.7761  -0.5235
+#&gt;    1.3175   5.3689  -0.2347  -7.3404  -6.2206  -0.3964  -2.9847 -12.7738
+#&gt;    0.5948  -2.7842  -9.6191   8.1294   0.9181   9.2779  -5.9096   8.0563
+#&gt;   -0.3378   2.3956   4.6235  -0.7978  -5.5830  -0.4862  -0.2478  -1.7941
+#&gt;    0.0686  10.2917  -0.9817   5.5907   3.5988  -1.3564   2.4055  -0.9436
+#&gt;    2.0567   6.2534  -2.2078  -6.1647   0.5606  -2.7191   3.2266 -11.3656
+#&gt;    4.2932  -0.4873   8.3800  -2.3044   1.8511   8.4027   6.6752 -16.2451
+#&gt;   -2.3357  -2.6602   7.1631   0.3165   3.9131  -7.8905  -3.5363  -4.4079
+#&gt;    5.4912  -7.6143  -2.0331  -2.5894   3.9076 -18.9411   3.6695 -13.5628
+#&gt;    1.0256   0.8843  -1.2594  -5.4993   1.2796   5.1276  -2.8727   1.2982
+#&gt;    0.8742   6.3731   5.7563   0.4166  -2.4657  -6.1710   5.1675   7.3948
+#&gt;    1.8751  -3.1597   6.6459   5.1926   1.8573 -12.9828 -10.6121  -5.7677
+#&gt;   -3.8644 -11.0444  -4.1877  -8.5450   1.3069  -0.6498  -4.9872 -11.6447
+#&gt;   -1.1720   6.4177   5.2251   4.5926   2.8544   5.3088   4.0496   0.8798
+#&gt;    8.5778   2.0765  -3.0492   2.6605   5.9763  -1.0507  12.9023   2.8845
+#&gt;   -2.5537   6.7471  -2.3166   0.1698   7.3152   4.9079  -5.6434 -18.8992
+#&gt;   -2.4438  -2.8801 -10.5118   4.0220  -6.5804   0.2695  -3.0997  -9.9757
+#&gt;   -1.8960   0.7844   5.0251   5.0129   3.5039  -1.9246  -2.0302  11.9824
+#&gt;    3.2717   6.6071  -7.0335  -4.5295 -23.8735  -9.4977   0.9193   2.7063
+#&gt;   -2.8423 -12.5636   8.4244  -2.9791  -3.4124   3.0636   1.3172  -1.1011
+#&gt;    9.9325   0.5829  -6.6425  -2.1746  -5.1121  -9.9372  -3.6588  -1.7340
+#&gt;    8.5402  -6.0357  -0.9178   7.4435   2.9151   8.6923   3.0402   3.1449
+#&gt; 
+#&gt; Columns 33 to 40 -17.0318 -20.0478 -11.5813  -0.8116   0.6021  -9.7061  -5.2541  -7.5142
+#&gt;  -10.3421  -3.6284  -8.4016  -2.5618   5.3978  -9.7860  10.5355  -5.2481
+#&gt;   -4.6916   1.1132   5.8259   4.2710  -0.0016   8.1836   5.5236 -10.1070
+#&gt;    7.3221   6.5633  -3.7985   3.9131  10.9585 -17.2966  -5.5431  11.2191
+#&gt;  -11.6022  -1.6023  -4.5938  -1.9300  -0.4376  -7.2064  -7.8986  -0.3702
+#&gt;  -14.3030   2.5388   1.2509  -2.7564 -10.5961  -3.5264 -12.7500 -22.1331
+#&gt;   12.6453   7.9274  -8.0327  -1.6566   5.7491  -5.4175  -3.5723  -1.2638
+#&gt;   -8.9598  -3.8361   5.2146  -4.4693  -8.4153  -8.1980   4.2895  -4.7867
+#&gt;   -3.0841   1.6341  -0.0632   4.4309   3.3442   1.7426  11.5374 -17.0239
+#&gt;    3.0663  11.0731  -2.8260  -5.2820   5.2010   4.3655   0.5538   1.9090
+#&gt;   -3.2084   5.2607  -0.3004   3.4364   2.2088   1.5744   6.6938  -7.3395
+#&gt;   -6.4610   5.9028  -1.2363  -2.9217   0.4373   1.8325  -3.0021   3.0616
+#&gt;    2.2177  -4.2643   7.7422  -2.1623  -5.7365   8.1062 -10.1076   5.0764
+#&gt;    0.9173   8.6032   1.4039   4.1027   3.0920 -22.4057   7.6579   4.9080
+#&gt;    0.8167 -10.3278   1.2432  -2.0476   5.4842 -10.7095   2.1405  -3.7050
+#&gt;    1.7803 -22.8740  -1.0902   2.1489   9.6227  -4.6850  -5.7256  11.1705
+#&gt;   14.4816  -9.7424   3.4600   3.2909 -16.3311  -5.0685  -5.5932   9.4876
+#&gt;   -5.1677   2.5401   5.2312  -6.5760   3.9057  -9.6523   2.2021   3.4456
+#&gt;   -6.3658  -2.7545  17.0195  -9.7690   1.8630   9.1328   3.1055   3.9391
+#&gt;    3.1821  -9.0758  10.9002  -3.9246 -12.8605   1.5466  11.6374   0.1441
+#&gt;   -3.7770  18.8824   4.3626  -2.8148  -4.7249   4.2569   8.2437  -7.0431
+#&gt;    3.5508  -6.7707   0.8483  13.8608  -2.7573  -3.5297   1.8513   2.4026
+#&gt;    7.9127   9.2728  -9.9353   3.2328   3.6214   7.5038   0.4667   9.2028
+#&gt;    8.3492   6.9488   7.7495  -9.1828  -5.8187  11.7171  -7.3384  -3.0282
+#&gt;   -5.8212   9.4377   3.9020   0.9103  -9.7031   4.4426  17.3763   0.3510
+#&gt;   -3.7819 -11.6569   7.6439   2.5470  -7.9286  -3.5027   0.1709   7.4708
+#&gt;    4.4982  15.0765  -2.6611  -7.6918  -1.7578  -0.8912  -7.4240   2.7192
+#&gt;    5.5335   7.5726   8.4664  -5.9477  -3.5248  11.7820   0.5398  13.5853
+#&gt;   -5.4325  -2.7990   3.2807   8.9660  -0.7183 -13.0419  13.5544   2.4505
+#&gt;    0.3033  -9.9993 -11.8875   8.8428  -9.5778   5.9503   3.4623 -12.5212
+#&gt;   -3.6057   8.8081  -1.8937   2.5456  -5.3832  -5.6471   1.6094   4.5927
+#&gt;  -10.4760   0.6477   3.8404   2.4465  -5.8038   7.6152  -6.2678  -5.6440
+#&gt;   -9.8051  10.2489  -6.0445  -3.1732   1.9759   6.9409   6.3362 -10.0141
+#&gt; 
+#&gt; Columns 41 to 48  -0.5757  -1.3542   7.1357  -2.2870  -2.8012   3.0657   5.0447 -15.8321
+#&gt;    0.1535   0.3076  17.1299   8.4095  -2.6762   5.0797   1.7882  -4.3992
+#&gt;    0.4592  -3.5133  -1.8708   0.8405   1.9575  -8.9528   7.9915  -4.7535
+#&gt;   -3.2901  -2.6491  -4.4144   9.6573 -10.8269  10.2468  -8.4412  -1.5340
+#&gt;    0.9673  10.0159   4.7422   9.9742  -0.5255  -8.9552   2.3655  -1.5989
+#&gt;    1.6231  -6.4483  10.5318  -4.9875   1.7577   9.2817  -2.5071  -4.0078
+#&gt;    1.4573  -3.2758  -1.6678   5.4910  12.7946   5.9670  -6.0930  -4.4879
+#&gt;   -5.1614  -4.0308   0.7992  -6.0837  -0.3915  -9.4512  -1.2862  -0.5319
+#&gt;    0.4310  -0.9557  -6.4016   2.8146   0.5966  -2.8999   1.7562   5.5143
+#&gt;   -5.1265   4.3655   4.9410  14.8872  -2.8013  -9.8312   7.9825  -4.1148
+#&gt;    1.8815   5.4926  -7.3356   2.1816   3.0059   3.2300   6.5676  -2.0128
+#&gt;    1.1780   8.0908 -12.3752   1.3859   6.4601  -2.0588  -7.2472  -5.7050
+#&gt;    5.9115  -0.6530   0.9260  -8.3563  -0.4082   0.7834  -5.9661  -0.5926
+#&gt;   11.0495  11.7859   3.0577  -4.4598  -4.8840  22.0590  12.1955   4.7068
+#&gt;   20.0985  -4.0410   4.3649  10.0583   6.1918  -1.7880  -1.9070  -6.3624
+#&gt;   -5.8225   0.6477   0.3729  -0.1409   1.5204   7.7822   0.8015  -3.2746
+#&gt;   -2.1589   0.5456  11.5298  11.6881  -2.7155  -5.6491   0.4327   7.7103
+#&gt;    1.0508  -0.0593   8.5939 -11.2689  -8.9750   2.3534  -7.5014   0.4162
+#&gt;   -2.2612   3.0069   6.5653  -7.6077  13.5167  -8.2681   7.5790   3.1596
+#&gt;    5.2002  -9.1811  12.2834 -23.1594  -3.5273  -1.1709  12.7870   4.4330
+#&gt;   -0.0276   3.1669  -2.0981  -4.0901   4.8303  -8.5770   1.6487   8.6810
+#&gt;    7.7900  -6.3951 -12.2464   2.8105  11.5417  14.2515  -2.9096   2.5551
+#&gt;    1.0313 -10.4730 -10.2800 -12.7194   5.4654  -2.9958   4.4049 -11.4623
+#&gt;   -4.4022   3.6837   2.7533  -4.6619  -4.6065  -3.2298  21.5926   3.3128
+#&gt;    5.3238   3.2650   1.3261  13.1070  -3.3967  -4.7740  -5.1533   3.8692
+#&gt;    1.0264  -4.9872   7.8718  -5.9630   3.3917  -7.7953  -2.6791   2.2528
+#&gt;   -7.6961   2.1876   3.5483  16.0663 -10.7377  17.4609  -0.0595   0.9353
+#&gt;   -3.0517   3.1029  19.1180  -5.9878 -10.0726   0.6224   3.0131   4.7465
+#&gt;    9.6907   7.8143   1.0314  12.2337  17.0781   7.3258   8.1910   2.9945
+#&gt;   -3.3594  -1.7302   7.7383   5.9785  -6.6533  -7.8406  -8.4122  -0.4406
+#&gt;   -2.0650  -9.0354  -5.8994   0.9212   3.4177 -13.0390 -11.4290 -11.9110
+#&gt;   -2.7836 -11.7873  -3.5815  -5.7332 -11.7829 -13.6638   8.5906  -4.0360
+#&gt;    5.6294  -4.0216   2.0273   4.5414  -5.5946   3.0184   3.4674  -2.7942
+#&gt; 
+#&gt; (8,.,.) = 
+#&gt;  Columns 1 to 6  1.6767e+01  7.3242e+00  2.2688e+00 -1.1885e+01 -4.0734e+00  3.3318e+00
+#&gt;   1.1166e+01  1.7645e+01 -6.4430e+00 -4.1363e+00 -2.6064e+00 -1.7742e+00
+#&gt;  -5.5551e+00  8.0560e-01 -4.0773e-01  6.0480e+00  2.0295e+01 -3.6027e+00
+#&gt;  -1.4078e+00 -2.3412e+01  1.2144e+01  5.2885e+00  7.3681e+00  2.1041e+00
+#&gt;   2.5697e+00 -6.0522e+00  1.5430e+00 -1.5490e+00  4.9428e+00  7.4056e+00
+#&gt;  -1.9167e+00 -6.7456e+00  7.2300e+00  1.8173e+00  1.1700e+01  4.0283e+00
+#&gt;  -4.1001e+00 -1.3626e+01  7.4635e+00 -2.5361e+00 -1.2605e+01  4.6264e+00
+#&gt;  -3.6862e+00 -8.2448e+00 -1.7109e+00 -8.2455e+00 -6.6692e+00 -1.0588e+00
+#&gt;  -1.0147e+01 -5.4059e+00 -9.8364e+00  1.0415e+01  1.1184e+00  1.0907e+01
+#&gt;   6.4281e+00  6.9009e+00  1.4842e+01 -2.3334e+00 -2.5984e+00 -9.1516e+00
+#&gt;   6.5785e+00 -3.3714e+00 -1.7497e+01  4.7739e+00  7.6406e+00  1.7642e+01
+#&gt;  -5.3044e+00  2.2508e+00 -1.0266e+01  4.9423e+00 -1.1276e+01  5.0438e+00
+#&gt;   5.4556e+00  4.8300e+00 -1.0827e+01 -8.4740e+00  2.4237e+00  2.5224e-02
+#&gt;  -1.2140e+01  4.3315e+00  1.0711e+01  1.6573e+01  7.7428e-01 -8.4217e+00
+#&gt;   4.2287e+00 -1.9173e+00 -5.8150e+00 -7.3568e+00  1.4070e+01 -4.5091e+00
+#&gt;   5.3989e+00  6.2967e+00  2.2752e+00 -7.8721e+00  3.6903e+00 -4.5436e+00
+#&gt;  -3.9085e+00 -6.5628e+00  6.9691e+00 -1.7535e+01  2.0640e+00  2.8361e+00
+#&gt;  -1.1818e+00 -1.7437e+00 -7.5470e+00  1.0356e+00  3.6821e+00 -9.2207e+00
+#&gt;   4.5794e+00 -5.4332e-01 -1.0079e+01  1.0920e+00 -7.0514e+00 -6.2585e+00
+#&gt;  -1.0217e+01  7.5117e+00 -1.0014e+01 -1.2400e+01  1.1899e+01 -6.6089e+00
+#&gt;  -2.2282e+01 -5.4358e+00  1.4162e+00  1.1134e+01  3.8861e+00 -6.9433e+00
+#&gt;  -4.4862e+00 -4.4780e+00  3.9681e+00 -8.0532e+00 -1.5911e+00 -2.8791e+00
+#&gt;   3.1209e+00  2.8594e+00  4.0225e+00 -2.8797e+00  1.2730e+01 -3.6122e+00
+#&gt;   8.4232e+00 -6.2612e+00  1.2269e+01  5.9408e+00  1.1985e+01 -5.5113e+00
+#&gt;  -4.0361e+00 -4.6405e+00 -1.3339e+01  6.2889e+00  9.8516e+00 -7.8917e+00
+#&gt;  -6.0944e+00  5.6074e-01  6.4615e+00 -2.0297e+01  4.6597e+00 -1.1273e+01
+#&gt;  -8.1253e+00 -1.5362e+01  1.7911e+01  1.8621e+00  5.5949e-01  2.8031e+00
+#&gt;   2.0541e+01  2.3189e+00  6.7424e+00 -4.7597e+00 -6.9405e+00 -1.1465e+01
+#&gt;  -1.7620e+01  1.1792e+01 -7.7298e+00  5.4394e+00 -2.1601e+00 -4.3825e+00
+#&gt;   1.9074e+01  1.3653e+01 -1.3568e+01 -1.2175e+01 -1.7947e+01  1.0558e+01
+#&gt;  -1.0617e+01 -1.4844e+01 -1.3629e+00 -3.0530e+00 -4.5964e+00 -8.6009e-01
+#&gt;  -6.2399e+00  1.8821e+01 -1.3541e+01  6.2537e+00  3.8780e+00  2.3052e+00
+#&gt;   1.0811e+01  8.7568e+00 -6.4851e+00  1.1809e+01 -4.8284e+00  7.9604e+00
+#&gt; 
+#&gt; Columns 7 to 12  8.9290e+00  1.3536e+01  1.6096e+01 -7.1693e+00  1.2483e+01  1.1455e+01
+#&gt;   5.2199e+00  5.6739e+00  1.1925e+01  6.1331e+00  3.9429e+00 -4.3705e-02
+#&gt;  -5.7627e+00 -6.5053e+00 -3.2291e+00  8.3001e+00 -7.4037e+00 -3.3170e+00
+#&gt;  -8.6931e+00 -1.4302e+01 -5.0089e+00 -8.2270e+00 -6.2948e+00  7.2968e+00
+#&gt;  -3.5095e+00 -5.2325e+00  7.1503e+00  1.1552e+01 -9.3108e-01 -7.8535e-01
+#&gt;   9.4466e+00  4.1475e+00  6.0636e+00  9.1754e+00  6.2806e+00  2.5567e+00
+#&gt;  -1.3670e+00 -1.1705e+01  4.5845e-02 -9.9646e+00  9.0778e+00  1.5874e+01
+#&gt;  -8.5052e-01 -9.3265e+00  7.1684e+00  1.2459e+01 -7.0863e+00 -1.5340e+00
+#&gt;  -1.0967e+00 -6.8202e+00 -4.1483e+00  4.5109e-01  7.2468e+00 -3.0846e+00
+#&gt;   1.8900e+00 -2.9275e+00  1.2501e+01 -2.6666e+00 -1.0941e+01  7.5141e+00
+#&gt;   1.2399e+00 -6.7585e+00 -3.3682e+00  9.8080e+00 -3.6451e+00 -5.7484e-01
+#&gt;   2.3451e+00  3.7670e+00 -4.2033e-01 -5.0846e+00  4.7556e+00 -1.1006e+00
+#&gt;   8.3667e+00  3.6975e+00 -1.0243e+01 -6.9477e+00  1.0840e+00  8.2389e-01
+#&gt;  -1.3075e+01  3.0193e+00  2.7116e+00 -1.9629e+00  5.1973e+00 -4.0322e+00
+#&gt;   6.1567e+00 -2.2938e+00  9.9950e+00 -3.3615e+00  8.2121e+00 -7.6035e-01
+#&gt;  -2.1567e+00  4.1321e+00  2.0549e+00 -5.1987e-01  7.5985e-01 -2.0032e+00
+#&gt;  -2.2345e+00  3.1456e-01  7.0497e+00  3.9596e+00  4.0817e+00 -7.9889e-01
+#&gt;   1.2495e+01 -6.3476e+00 -5.9824e-01 -5.4388e+00 -8.6041e+00 -4.8455e+00
+#&gt;   1.6624e+01 -1.1884e+01 -2.9734e+00  1.5225e+01 -7.3970e+00  5.7698e+00
+#&gt;   9.8040e+00  4.3454e+00  7.0386e+00  5.5264e+00 -8.3666e+00  3.2071e-01
+#&gt;  -2.4268e+00 -1.5725e+01  9.1409e+00 -4.2469e+00 -2.6802e+00  3.1987e+00
+#&gt;  -6.8343e+00  8.6813e+00 -9.9641e+00  1.4666e-01  6.9162e-01 -8.6080e+00
+#&gt;   9.1200e+00  6.2205e+00  4.8410e+00 -2.9351e+00  4.7084e+00 -1.6139e+00
+#&gt;   1.8724e+01  3.2264e+00 -6.2778e+00  6.9400e+00 -2.6209e+00  9.7244e+00
+#&gt;  -2.7610e+00 -5.3795e+00  1.8431e+00 -2.5759e+00  4.9165e+00 -7.0577e+00
+#&gt;  -3.9320e+00 -4.4469e+00  1.7978e+00 -2.6139e+00 -2.1787e+00 -2.3098e+00
+#&gt;  -9.8916e+00 -1.7884e+01  6.5494e+00  1.5247e+00  9.8584e+00  4.3776e+00
+#&gt;   1.6337e+01 -9.0102e-01 -2.9395e-01  1.1285e+00 -1.3418e+01 -2.7005e+00
+#&gt;  -5.5151e+00  4.3336e+00 -3.2838e+00  1.0588e+00  6.9050e+00 -2.5221e+00
+#&gt;   1.2281e+01  2.1200e+01  1.0232e+01 -9.2227e+00  3.8279e+00  3.2877e+00
+#&gt;   6.6581e+00 -8.2416e+00 -1.6229e+00 -5.3015e+00  4.4514e+00  2.3358e+00
+#&gt;  -4.5829e+00  1.2662e+01 -1.1380e+01  2.7443e+00  4.3965e+00 -4.6810e+00
+#&gt;   1.2336e+01  1.2162e+01  8.9652e-01 -1.9086e+00  1.8242e+00 -3.1326e-01
+#&gt; 
+#&gt; Columns 13 to 18 -6.5864e+00 -1.0712e+01  1.9184e+00 -7.9737e+00  8.0341e-01 -6.6428e+00
+#&gt;   3.9817e+00 -4.6648e+00 -2.4271e+00  1.6329e+00  3.4401e+00  5.3616e+00
+#&gt;   2.2465e+00 -5.6628e+00  4.1712e-01  2.4878e+00  8.9113e+00  1.2079e+01
+#&gt;  -1.0932e-01 -1.2413e+00 -1.4202e-02  1.0870e+00 -3.4432e+00  1.7965e+01
+#&gt;  -1.1225e+00 -3.2036e+00  7.4699e+00 -1.8162e+00  3.0432e-01 -2.7530e+00
+#&gt;   6.3094e+00  1.5723e+00 -4.2568e+00 -1.4982e+00  1.3841e+00 -1.3459e+01
+#&gt;  -1.0469e+00 -3.9008e+00 -1.3422e+01  1.2605e+01 -2.4243e-01 -6.4081e+00
+#&gt;   4.0135e+00  5.0065e+00  7.7655e+00 -3.5427e+00 -7.3873e+00  1.6300e+00
+#&gt;   5.7995e+00  1.1139e+01 -6.9868e+00  3.4718e+00  3.2083e+00  8.6517e+00
+#&gt;   3.8380e-01 -4.6602e+00 -5.0067e-01 -4.6188e-01 -1.0016e+01  7.1506e+00
+#&gt;   8.1005e+00 -8.0245e+00  1.1189e-01  2.7982e+00  1.0693e+01  2.4759e+00
+#&gt;  -4.8095e+00  4.7866e+00 -5.4752e+00 -2.1411e+00  4.1932e+00 -1.1467e+01
+#&gt;   4.3658e-01 -5.7922e+00 -3.6600e+00  4.0172e+00 -4.1100e+00 -3.1343e+00
+#&gt;  -6.9230e+00  1.6722e+01  1.2365e+01 -1.2803e+01  4.0601e+00 -9.9373e+00
+#&gt;  -5.9905e+00 -4.2004e+00 -3.8107e+00  1.3444e+01 -6.3351e+00  1.8679e+00
+#&gt;  -7.2217e+00  1.9600e+00  1.8366e+00 -9.0836e-01  6.3407e+00  2.5531e+00
+#&gt;   5.2419e+00 -1.9273e+00  2.6189e+00 -1.6239e+00 -5.9923e+00 -8.3155e+00
+#&gt;  -5.4557e+00  1.4535e+01 -1.5117e+01  7.2507e+00 -4.4490e+00 -1.6060e-01
+#&gt;   1.4703e+00  9.8438e-01  4.8113e+00 -8.3850e+00  8.4060e+00  7.6364e+00
+#&gt;   2.7511e+00  1.2935e+01  1.8571e+00 -2.6687e+00 -1.7088e+01  4.8235e+00
+#&gt;  -3.5270e+00  7.8432e+00 -3.4736e+00  2.9558e+00 -4.3210e+00  8.6346e+00
+#&gt;   7.3180e+00  4.5550e+00  2.7375e+00  8.4454e-01  3.2313e+00 -8.0284e+00
+#&gt;   3.5681e+00  4.2156e+00 -1.3016e+01 -2.6686e+00  7.8514e+00 -1.1472e+01
+#&gt;   4.8989e+00 -3.6281e+00 -3.1882e+00 -9.6628e-01 -1.1654e+01  3.9920e+00
+#&gt;  -6.1137e+00 -1.6611e+00  7.9596e-01 -2.5879e+00  3.8247e+00  5.4991e+00
+#&gt;  -3.2835e+00  2.8457e+00 -4.2449e+00  5.1501e+00 -8.8733e+00 -4.2499e+00
+#&gt;   2.7270e+00  3.2537e+00 -7.4448e+00 -3.9524e+00  6.0253e+00 -2.9487e+00
+#&gt;   8.9148e+00  1.3280e+00 -4.6664e+00 -1.0784e+01  3.8554e+00 -8.9095e-01
+#&gt;  -5.5961e+00  6.6477e+00  8.5598e+00  2.1061e+00  6.4038e-01  5.2681e+00
+#&gt;   1.1707e+01 -1.2155e+01 -5.7153e+00  2.8509e+00 -1.4552e+00 -1.5719e+01
+#&gt;   2.7364e+00  8.6112e+00 -1.3925e+01 -7.8262e-02 -3.3054e+00 -1.2692e+01
+#&gt;   1.5652e+00 -1.6843e+00  1.4623e+01 -5.7382e+00  1.3702e+01 -6.5341e+00
+#&gt;   8.5536e+00 -2.3798e+00 -1.0280e+01  6.6825e-01  7.5417e-01  4.4648e+00
+#&gt; 
+#&gt; Columns 19 to 24 -4.4853e+00 -2.1131e+00  2.3472e+00  1.5614e+00 -9.1763e+00  1.3149e+00
+#&gt;   3.3663e+00 -1.1476e+00 -4.6271e+00 -6.8828e+00 -2.7274e+00  8.1809e+00
+#&gt;  -6.0512e+00 -1.9255e+00 -1.2470e+00  2.3085e+00  5.1944e+00  2.7115e+00
+#&gt;   6.8703e+00 -9.9798e-01  5.7421e+00  1.2826e+01  1.3569e+01 -1.3485e+01
+#&gt;  -7.8852e+00 -5.3608e+00 -1.2194e+01 -2.2309e+00  7.4194e-01  3.8362e+00
+#&gt;  -7.9683e+00 -3.7411e+00  9.5716e-01  7.7865e+00 -1.2211e+00  1.3266e+01
+#&gt;  -8.6393e+00  1.0575e+01  1.8087e+01 -6.3996e+00 -2.0487e+00 -2.4887e+00
+#&gt;  -1.4954e+01 -2.8493e+00 -1.0947e-01  9.6807e+00  9.7672e+00 -4.4043e+00
+#&gt;  -1.3268e+01  2.1509e+00 -1.2294e+00  1.0422e-01  6.8833e+00  1.1067e+01
+#&gt;  -1.1917e+01 -5.0383e+00  6.8034e+00 -1.3399e+01  3.1128e+00 -1.1073e+00
+#&gt;   1.5490e+00  1.5129e+01 -5.0927e+00  4.9878e+00  1.4499e+01 -2.5065e-01
+#&gt;  -2.5972e+00  7.2299e+00  7.2963e+00 -1.6446e+00 -9.7672e+00  2.2963e-01
+#&gt;   3.6946e-01 -1.1741e+00 -5.4392e+00 -3.6667e+00  2.6146e+00 -4.2971e+00
+#&gt;   6.6354e+00  7.4895e+00 -1.1072e+01  6.1214e+00 -2.4847e+01 -1.9569e+00
+#&gt;  -5.5859e+00  3.3497e-01 -5.0915e+00 -7.4545e+00 -1.7294e+00  6.5300e+00
+#&gt;   6.9209e+00 -2.9821e+00 -1.0543e+01  1.0908e+01  7.0799e+00 -7.8464e-01
+#&gt;  -4.7602e+00 -1.8840e+00 -5.3847e+00 -5.1367e-01 -6.6393e+00  1.1736e+00
+#&gt;  -3.5408e-01  6.9321e+00  4.6526e+00 -3.6509e+00 -1.8282e-01 -8.6614e+00
+#&gt;  -1.3014e+01  9.7575e-01 -3.6201e+00  4.9557e+00  3.5110e+00 -1.1991e+00
+#&gt;   4.2591e-01 -7.2595e+00 -8.4041e+00 -6.0745e+00  5.0202e+00  8.3158e+00
+#&gt;  -1.0734e+01  6.3550e+00  1.2172e+01 -6.8847e+00 -7.6938e+00 -1.1426e+01
+#&gt;   1.0593e+00 -4.3771e+00  1.9166e+00  7.3604e+00  6.9675e+00 -2.2241e+00
+#&gt;   9.0315e+00  2.5758e+00 -1.6054e+00  3.1866e+00 -3.8649e+00  2.2475e+00
+#&gt;  -4.5584e-01 -8.9639e+00  2.4443e+00 -1.3255e+01 -7.6367e+00 -1.4364e+00
+#&gt;   6.0904e+00  5.5175e+00  2.9946e+00 -1.7121e+00 -1.1451e+01 -2.2756e+00
+#&gt;   4.5679e+00 -2.0458e+01  8.9792e-01  8.5564e+00 -6.2259e+00 -2.7014e+00
+#&gt;  -4.5265e+00  3.2387e+00 -6.7524e-01 -6.3879e+00  4.6612e+00 -7.4260e+00
+#&gt;   4.2265e+00 -9.4775e+00 -1.7111e+01 -4.3356e+00 -1.8778e+01 -6.7874e+00
+#&gt;  -2.2547e+00  6.5539e+00  2.1854e-01 -1.2272e+01 -5.7822e+00 -6.0883e+00
+#&gt;  -8.5049e+00 -2.1965e+00 -5.3985e+00  9.2189e-02 -3.4820e+00  1.0115e+01
+#&gt;  -8.2111e+00  7.4293e+00  2.7259e+01  7.2645e+00  1.7943e-01 -9.3449e+00
+#&gt;   7.5274e+00 -5.9399e+00 -7.6304e+00  1.6460e+01 -9.6886e+00  8.5740e+00
+#&gt;   1.2634e+01  7.2209e+00  9.3985e+00 -1.0274e+01 -6.0598e+00  9.2750e+00
+#&gt; 
+#&gt; Columns 25 to 30 -1.6500e+00  1.2556e+01  4.7886e+00 -1.2065e+01 -1.1266e+01  1.6267e+01
+#&gt;   5.6686e+00  7.5015e+00  4.9051e+00 -5.6231e+00 -8.5556e+00 -8.8199e+00
+#&gt;   2.3384e+00  1.2451e+00 -3.1599e+00  1.2265e+01 -4.1203e+00 -5.0749e+00
+#&gt;  -1.7114e+00 -7.9214e+00  2.9079e+00  3.2128e+00  2.6000e-01 -1.0404e+01
+#&gt;   6.9109e+00  8.9490e+00 -8.1327e+00  3.3842e+00  9.4082e-01 -1.2268e+01
+#&gt;   1.9725e+01  7.5589e+00 -3.3212e+00 -6.2901e+00  4.8042e+00  9.6396e+00
+#&gt;   1.6098e-01  6.2835e+00 -4.2581e+00 -9.5596e+00  5.3666e-01  4.8876e+00
+#&gt;   1.1118e+00  7.3037e+00 -8.2213e+00  1.0765e+00 -4.7982e+00 -1.8013e+01
+#&gt;   2.7118e+00  1.0105e+01  4.1488e-01  5.4600e+00  2.4152e+00 -6.6327e+00
+#&gt;   8.3795e-01  4.6922e+00 -2.7764e-01 -3.3877e+00  5.3466e+00  7.5152e+00
+#&gt;   8.2482e+00  1.9565e+00 -1.5874e+01  1.2110e+01 -7.5095e+00 -4.2220e+00
+#&gt;   1.7925e+00  4.7533e+00  1.5763e+00 -9.5619e+00  1.4166e+01  8.8671e+00
+#&gt;   1.2803e+00 -7.2786e+00  1.9553e-01  3.9502e+00  8.1943e+00  3.4421e+00
+#&gt;   1.4177e+00 -8.2810e+00  1.8800e+00  4.0896e+00  4.0136e-01  3.8002e+00
+#&gt;   8.3683e+00 -7.2149e+00  2.0337e+00 -7.7635e+00 -1.4227e+00  1.4717e+01
+#&gt;   7.8810e+00 -4.5467e+00  7.2520e+00 -5.8690e+00 -7.0170e+00  1.0698e+01
+#&gt;   6.4822e+00 -3.0455e+00 -5.8516e-01  3.7871e+00 -2.0598e+01  2.5791e+00
+#&gt;   1.1569e+01 -1.0959e+01  4.0629e+00  1.4087e+00  5.1808e+00  1.4298e+01
+#&gt;   9.5239e-01  8.9995e+00 -3.8282e+00 -5.4321e+00 -2.1114e+00 -5.2939e+00
+#&gt;   6.0747e+00 -5.8771e+00  2.1364e+00  3.0143e+00 -1.2609e+01  8.1266e+00
+#&gt;  -6.9970e+00  4.0875e+00 -4.6289e+00  4.5900e+00  5.8148e+00  2.1470e+00
+#&gt;   5.9624e+00 -8.3459e+00 -3.9486e-01  1.1296e+00 -7.7978e+00 -8.5532e+00
+#&gt;   2.5669e-01 -9.6446e+00 -2.1524e+00  8.1044e+00 -6.5395e+00  9.2771e+00
+#&gt;  -1.6091e+00  8.4890e-01  7.0265e+00 -3.2210e+00  3.4743e+00  8.7699e+00
+#&gt;  -4.3547e+00 -1.0256e+01 -5.4367e-01 -1.8768e+00 -9.9176e+00  1.3847e+01
+#&gt;  -3.5261e+00 -7.5636e+00 -2.1715e-01 -7.0807e+00 -1.4738e+01  1.4545e+01
+#&gt;   7.3968e+00  7.2117e+00 -1.4983e+01 -3.1700e-01  4.2418e+00 -3.1306e+00
+#&gt;   1.5941e+00 -2.3137e+00  4.9425e+00  1.1920e+01 -6.7446e+00  1.3191e+00
+#&gt;  -1.9429e+00  8.0296e-01 -7.1434e+00 -6.7915e+00 -5.0054e+00  6.8658e+00
+#&gt;  -8.3416e-01  6.5902e+00  2.2657e+00  4.3535e+00 -6.2116e+00  9.4883e+00
+#&gt;  -4.5389e+00  8.2557e+00 -5.1779e+00 -2.0092e+00  2.3989e+00  1.1704e+01
+#&gt;  -1.7732e+00  7.6986e-01  6.3287e+00  7.3798e+00  2.2958e+00  2.1280e+00
+#&gt;  -2.2058e+00  7.9456e+00  2.4047e+00 -5.4327e+00  4.2458e+00  8.6109e+00
+#&gt; 
+#&gt; Columns 31 to 36  1.6968e+01  1.4056e+00 -1.3579e+01 -9.4824e+00 -3.1901e+00 -1.5056e+01
+#&gt;   3.3381e+00  3.8086e+00 -1.0171e+01 -8.9115e+00  6.1601e+00 -1.3511e+01
+#&gt;  -8.7886e+00  1.1156e+00  2.0683e+00  7.6585e+00  1.0455e+01  9.0514e-02
+#&gt;   2.2720e+00  1.0692e+01  5.2476e+00  1.5036e+01  5.3032e+00  5.4630e+00
+#&gt;   1.5361e+00 -1.8013e+00 -2.5648e+00  3.2145e+00  4.1681e+00  3.9148e+00
+#&gt;   1.1932e+01 -1.6998e+00  6.7076e-01  8.8148e-01 -6.3605e+00  1.7223e-02
+#&gt;  -1.9262e+00 -9.2899e+00 -9.5948e-01  1.8220e+00 -3.3391e+00  5.1185e+00
+#&gt;   6.4869e+00 -4.8870e-01 -1.2153e+00  5.8586e-01  5.8405e+00 -5.5425e+00
+#&gt;  -1.3434e+01 -2.8273e+00 -3.8836e+00  3.6068e+00  4.8271e-01  6.4796e+00
+#&gt;  -2.6460e+00  2.7995e+00 -9.0357e+00  1.2773e+01 -2.1468e-01  2.2131e+00
+#&gt;  -8.1002e-02  6.3334e+00 -3.2733e+00  1.2021e+01  5.1330e+00 -1.6836e-01
+#&gt;   3.5073e+00 -8.2699e+00  1.6375e+00 -3.3127e+00 -4.3346e+00 -1.8677e+00
+#&gt;   7.9125e-01 -9.4631e+00 -2.6451e+00 -9.7767e+00  2.7838e+00 -1.1135e+00
+#&gt;  -8.0633e+00 -3.0523e-01  1.0451e+01  3.4521e+00 -4.9708e+00 -1.2935e+01
+#&gt;  -1.3916e+00 -2.5956e+00 -1.1536e+01 -1.3008e-01  1.2098e+01 -7.4245e+00
+#&gt;  -8.8167e+00 -4.2268e+00 -4.3778e+00 -3.3864e+00  2.3205e+00  2.8307e+00
+#&gt;   1.5622e+01  5.6263e+00 -1.1112e+00 -1.5210e+01 -3.7397e+00  2.1831e+00
+#&gt;  -6.3162e+00 -6.0929e+00  2.0127e+00 -5.9087e+00 -2.6422e+00  1.6015e+00
+#&gt;  -7.7263e+00 -1.6963e+01 -1.6966e+00 -5.4852e+00  5.0603e+00 -1.1660e+01
+#&gt;   8.5882e-01 -1.3065e+00 -3.0651e+00 -1.0387e+01  7.6108e+00  1.0662e-02
+#&gt;   3.0485e+00  1.5695e+00  2.5101e+00  1.7252e+00  3.2778e-01 -2.9300e+00
+#&gt;  -8.3939e+00  5.5164e+00  1.6709e+00  3.5003e-01 -1.2074e+01  2.2491e+00
+#&gt;   8.1846e+00  6.9727e+00  8.1906e+00  4.9120e+00 -1.6916e+00  1.5517e+01
+#&gt;  -5.5969e+00  2.9195e+00 -3.6840e-01  1.7274e+01  4.2599e+00  1.2123e+01
+#&gt;   4.5651e+00  8.8605e+00 -5.9988e+00 -4.6366e+00 -3.1383e+00 -1.1470e+00
+#&gt;   7.4988e+00  4.8204e+00 -4.8057e+00 -1.4195e+01 -1.6288e+00 -7.9563e+00
+#&gt;  -7.8994e-02 -4.8934e+00 -7.4796e+00 -3.6926e+00 -4.7582e+00  4.3932e+00
+#&gt;   5.4703e+00 -2.7433e+00  1.5770e+00 -2.6086e+00  5.7119e+00 -1.0186e+01
+#&gt;  -8.3719e+00 -4.3971e+00 -1.1563e+01 -5.9293e+00 -6.1670e+00 -9.7025e-01
+#&gt;   1.2016e+01  1.7604e+00 -1.4021e+01 -8.7414e+00  3.9784e+00 -7.5222e+00
+#&gt;   4.3311e+00 -1.9517e+00  7.4661e+00  6.4736e+00  7.3379e+00  3.1902e+00
+#&gt;   1.4190e+00 -3.5703e+00  2.5072e+00 -2.0089e+00  7.2083e+00  3.6831e-01
+#&gt;   1.4973e+00  8.0480e+00 -4.3088e+00  5.0519e+00 -1.2431e-02 -3.9246e+00
+#&gt; 
+#&gt; Columns 37 to 42  1.0349e+01  3.5146e+00  4.7248e+00  2.4224e+00  3.1631e+00 -1.8932e+00
+#&gt;   3.7355e+00 -4.9438e+00  5.1820e-01 -1.3734e+01  2.2520e+00  1.2284e+01
+#&gt;  -7.2834e+00 -2.9175e-03 -9.4973e+00  3.5105e+00 -2.9854e+00 -7.4132e+00
+#&gt;   4.6324e+00 -4.4956e+00 -2.1906e+00  6.3653e+00 -1.3877e+01  1.4574e+00
+#&gt;  -6.3226e+00  5.5992e+00 -1.6028e+00  1.2614e+01  2.3026e+00  2.4687e+00
+#&gt;   5.3798e+00 -5.2953e+00  5.1357e+00 -2.8384e+00  8.5415e+00 -4.4999e-01
+#&gt;   1.3274e+01 -5.1165e+00 -9.4891e-01  5.1584e+00 -1.4090e+01 -2.0401e+00
+#&gt;  -1.0071e+01  8.7172e+00  2.4762e-01  4.5268e-01  9.5065e+00  6.5990e+00
+#&gt;   3.8933e+00 -1.4519e+01 -4.2085e+00 -5.6620e+00  2.6159e-02  2.7152e-02
+#&gt;   1.9220e+00  8.1747e+00  5.4029e+00  8.3436e+00  7.5888e+00 -6.9745e+00
+#&gt;  -8.2549e+00  7.4162e-01 -6.1332e+00 -2.8622e+00  3.1681e+00  1.2724e+01
+#&gt;   8.1403e+00  3.7808e+00  2.9839e+00  3.2436e+00 -2.0750e+00 -6.6230e+00
+#&gt;  -3.8046e+00  1.1949e+01  2.0603e+00  1.3661e+01 -4.4081e+00 -8.8752e+00
+#&gt;   4.0630e-02 -9.7636e+00  1.0656e+01 -1.0516e+01  6.4355e+00  2.4332e+01
+#&gt;   4.1552e+00 -4.4734e+00  9.5094e+00 -8.9332e-04  1.4007e+00 -6.9024e+00
+#&gt;  -7.1951e+00 -9.5963e+00  6.6720e+00 -7.8115e+00 -3.9879e+00  1.7070e+01
+#&gt;  -1.1219e+01  3.4550e+00  6.2057e+00  1.7176e+01 -3.6684e+00  9.2366e+00
+#&gt;   1.0725e+01 -4.2982e+00  1.2791e+01  5.9354e+00 -6.8996e+00  1.2754e+00
+#&gt;   3.3122e+00 -5.9823e+00 -1.6210e+01  5.5249e+00  7.5663e+00  1.0667e+01
+#&gt;  -7.7834e-01  2.7156e+00  1.1732e+01 -7.9248e+00  1.3731e+01 -7.1842e-01
+#&gt;   8.4354e+00 -2.6473e+00 -7.0378e+00  9.5294e+00  1.0219e+01  4.3076e+00
+#&gt;  -1.6865e+01 -1.0685e+01  3.3568e+00 -8.7478e+00  5.4412e+00  4.8094e+00
+#&gt;   4.0752e+00  1.9527e+00  2.2571e+00 -3.4768e-01 -2.9087e+00 -8.7077e+00
+#&gt;  -1.1830e+00  1.9568e+01 -5.2073e+00  6.3466e+00  5.4534e+00 -1.6413e+01
+#&gt;   5.9593e+00 -8.8745e+00  2.7595e-01  2.5411e+00  8.2347e+00  4.6738e+00
+#&gt;  -5.1192e+00  5.9188e-01  9.3946e+00 -6.3051e+00  1.1390e+01  6.2909e+00
+#&gt;   2.7971e+00 -7.5224e+00 -3.4047e+00  1.0831e+01 -1.3286e+01  1.6648e+01
+#&gt;   4.9520e-01  2.1406e+01  3.6402e+00  8.1908e+00  2.2226e+00  1.0036e+00
+#&gt;  -5.8694e+00 -1.9102e+01 -1.5645e+00  2.6963e+00  1.2637e+01  1.9963e+01
+#&gt;  -7.9123e+00  1.4164e+01  1.4852e+01 -6.8379e+00  7.8168e+00 -2.5681e+00
+#&gt;   1.5386e+01  6.3521e+00  8.1042e+00  6.7304e+00 -1.3655e+01 -8.0183e+00
+#&gt;  -5.5019e+00  2.0131e+00  5.0412e+00 -7.0756e+00  5.0653e+00 -1.2269e+01
+#&gt;   1.1495e+01  4.0187e+00  4.1938e+00 -1.1167e+01  1.0863e+01 -9.0092e+00
+#&gt; 
+#&gt; Columns 43 to 48  1.9086e+00 -6.0084e+00  1.1663e+01  1.6831e+01  1.2646e+01  1.4408e-01
+#&gt;   7.7910e+00  9.5390e-01  6.2202e-01  8.2527e+00  6.3771e-01 -2.7207e+00
+#&gt;   2.8097e+00 -5.4443e-02  8.8826e+00 -1.0211e+01  5.2685e+00  5.2125e-01
+#&gt;   8.4363e+00  5.9659e+00 -2.7217e+00 -1.3296e+01 -5.1549e+00  1.4063e+01
+#&gt;   1.0081e+01  3.2227e+00  9.6486e+00 -7.0275e+00 -4.2905e+00  8.8995e+00
+#&gt;   8.6326e+00  1.1378e+01 -2.3089e+00  8.4529e+00  1.1244e+00 -9.1948e-01
+#&gt;   3.7497e+00  8.2288e+00  9.7651e+00 -3.2653e+00  1.7484e+00  6.9823e+00
+#&gt;   1.0540e+00 -2.4206e-01 -8.1490e-01 -2.3298e+01 -1.6047e+01  4.2955e+00
+#&gt;   6.5837e+00  1.1039e+01  1.2911e+01  9.7721e+00 -1.1140e-01 -7.1750e+00
+#&gt;  -8.2676e+00 -1.0484e+01  1.3251e+00 -2.4151e+00  3.7316e+00  2.1665e+01
+#&gt;  -2.0550e+00  8.4096e+00  1.5844e+01 -1.5410e+01 -4.6701e+00  1.7298e+00
+#&gt;  -8.3883e+00 -8.4074e-01 -7.2652e+00  3.0749e+00 -5.4976e+00 -7.0820e+00
+#&gt;  -3.3652e+00 -1.8007e+00 -3.4501e-01 -2.0708e-01 -5.1626e+00  2.1304e-01
+#&gt;   1.6354e+01  9.7717e+00 -1.5131e+01  8.6530e+00  1.5403e+01 -6.7722e+00
+#&gt;   1.2625e+01  1.7819e+01  3.2093e+00 -5.8925e+00  6.5707e+00  8.1253e+00
+#&gt;   2.4240e+00  5.0578e+00 -1.0251e+01  6.3943e+00  1.5771e+01 -2.9332e+00
+#&gt;   2.9671e+00  4.1355e+00  5.8757e-01 -1.5622e+01  3.2887e+00  6.6223e+00
+#&gt;  -1.5192e+00  5.2934e+00 -1.2964e+01 -7.3480e+00  2.9503e+00 -6.8738e+00
+#&gt;   4.9112e+00  4.4184e+00  1.4162e+01 -6.7740e+00  2.7057e+00  2.2440e-01
+#&gt;  -1.0507e+00 -2.7629e-01 -1.4210e+01 -5.9002e+00  3.1342e+00 -2.1610e+00
+#&gt;   2.3724e+00 -6.5675e+00 -3.6146e+00 -1.4949e+01 -1.0355e+01 -3.2275e+00
+#&gt;  -3.8806e+00  4.0648e+00 -6.2838e-01  6.4692e-01 -3.7762e+00 -4.5512e+00
+#&gt;   5.0239e-01 -1.0756e+01 -2.6947e-01  1.3397e+00  1.3567e+01  3.3119e+00
+#&gt;  -4.1779e+00 -3.8258e+00  9.0232e+00 -2.8485e+00  9.2501e+00  1.4849e+01
+#&gt;  -2.0827e+00  1.7546e+00 -3.7182e-01  4.5059e+00  1.2492e+00 -8.6025e+00
+#&gt;   8.0779e+00  3.7081e+00 -7.3670e+00  4.0668e+00  6.6366e+00 -3.3706e+00
+#&gt;   1.1349e+01  7.9921e+00  4.4069e+00 -5.2089e+00 -6.7050e+00  1.2475e+01
+#&gt;  -1.4296e+00 -8.9067e+00  7.0266e+00 -2.1112e+00  3.0178e+00  1.9257e+01
+#&gt;   1.1055e+01  1.1767e+01  5.6965e+00 -2.8091e+00  7.3366e+00 -1.0011e+01
+#&gt;  -1.2181e+01 -1.1801e+01  1.3236e+01  1.6488e+01 -6.9282e+00  6.8039e+00
+#&gt;   2.4913e+00 -7.4843e+00  3.3452e+00 -8.6046e+00  3.4812e+00  5.3174e+00
+#&gt;  -3.3165e+00  1.2759e+00 -1.6281e+01  4.1924e+00 -3.1811e+00 -1.9584e+01
+#&gt;  -1.4903e+00 -5.9860e+00  6.4547e+00  2.1014e+01 -4.2636e+00  6.3224e-01
+#&gt; 
+#&gt; (9,.,.) = 
+#&gt;  Columns 1 to 8   4.0794   6.0684  -0.8311   4.8778  -2.0680  -5.0483  -8.1212  -0.4670
+#&gt;   -5.0321  -1.8980  -7.3105   3.9990   2.5946   3.8788  -5.8263  -0.4804
+#&gt;   12.8792 -12.9890   1.0354   0.2266   4.5250  -7.3148   5.9497   3.1359
+#&gt;   -3.6702   1.7862   3.8205  -1.1715   9.8759  -2.4723  -4.8305 -20.5428
+#&gt;   13.3035  -1.2442  -2.8034   4.6820  -5.0879   6.6643   3.9401   2.0031
+#&gt;    3.3032   2.2903  10.9000   9.5233  -8.0382 -17.9027  10.9481   1.0601
+#&gt;    8.1374   2.9132   6.7715   6.9283  -7.8365   0.8160  10.2724 -10.3356
+#&gt;    5.3852  -3.9204  -0.3595  -1.8751  -3.5394  20.6540   9.7091  -1.9489
+#&gt;    0.8717   1.9024  -7.1412   5.2893   2.1951 -23.9145   9.6487   2.1870
+#&gt;   -2.4710  -3.1546  11.8893  -3.1098   5.0098  -6.4714   4.5560  -6.5721
+#&gt;    8.2237  -3.3640  -6.0437  10.2538  -8.5034   9.4943   4.6656   0.5343
+#&gt;    4.6898   4.6695  -6.3377   6.2074  -2.6848   8.3498   1.1032   0.8084
+#&gt;    5.9736  -3.9981  -0.2361  -0.2370 -13.3947   8.2142   0.5673  -6.0443
+#&gt;   -5.6291  18.3545  -9.2522 -10.8182   7.8836 -18.0514 -10.5459   1.1597
+#&gt;    4.2296  -7.4623   6.4381   4.7830  -4.2380  -5.9684  14.1344 -19.2029
+#&gt;   -7.1332  18.8846   5.4187  -9.2719  -5.0517  -2.2815   2.1851   4.0934
+#&gt;   -5.2267  -8.4886  -2.4892   5.5292   4.2910   1.0350  -4.9162  -0.9101
+#&gt;   -8.1712   0.3072   7.1822  -4.1487   4.3976  -9.8743  14.2932 -14.2236
+#&gt;   -3.3688   1.2148   2.7096 -11.7475 -12.5490  -3.2319  -0.9382  15.8213
+#&gt;  -11.5855  -5.4447  11.0557   3.4124   1.1715 -10.6065   6.2076   2.0333
+#&gt;    1.6404  -4.7255   1.6720   5.3021   6.5244 -17.9380   3.5248   4.6255
+#&gt;    4.3144  -9.9478  -5.6320 -11.0713 -11.2514   7.6034   9.0796  -7.7695
+#&gt;  -10.1595   0.9970  14.5487  12.4250  -3.0845  -3.2217   0.8475   4.6780
+#&gt;    5.8065  -2.8322  14.9710  -7.3477   0.8346 -11.5608   1.4898   0.7894
+#&gt;   -7.8699  -5.5137  -6.0862   3.4121  15.1010 -22.8861   3.0404  13.9179
+#&gt;   -6.6843  -4.1122   4.2866  -9.9555   7.2913 -10.2866  -9.7519   2.7445
+#&gt;    1.7172   1.6126  -2.0818   3.9591  -8.4740  -5.7567  -0.8639  -3.9426
+#&gt;   -4.2496   6.4542  -0.2463   1.8102   5.1814  -6.9287  -7.0213   9.4895
+#&gt;    6.3897   0.4685  -0.1945  -5.5021  -6.2104  -7.8718   0.0966   6.0887
+#&gt;   -3.4026  10.1926  -6.2556   8.6156  -7.1564   3.1795   2.7905  -0.4113
+#&gt;    4.7720   1.4452  15.7820  -2.6927   7.7603   8.4853  -2.7105  -5.2556
+#&gt;   -4.3637  11.7902  -7.3200   3.3080  12.9666   8.0143 -15.2166  15.6200
+#&gt;   -2.3635   1.5295   2.4287  10.6374  10.6979  -5.8618   2.1516  -5.7713
+#&gt; 
+#&gt; Columns 9 to 16   6.4279 -16.6403 -18.3629 -17.5751   1.5928 -11.3314   2.3082  -3.3119
+#&gt;   15.1420  -7.8180   5.0243  -2.5117  -0.7384  -3.4678  -2.5084   9.2290
+#&gt;    1.9041   1.7503   2.8069  -6.1292   7.4328   6.1056   9.0484  -3.3383
+#&gt;   16.0871  16.7116   4.0324  -2.9360   9.1257   2.7541   5.0165  -3.1155
+#&gt;   -3.6804  -5.7240  -2.9193  -5.5015   4.6540   2.6300  -0.3867  -5.0567
+#&gt;  -18.2437 -23.9267  -1.5425   4.6601  -2.0346  -0.8490  -5.6068  -3.2220
+#&gt;    7.2447   6.9831   0.4325  -7.8024   1.7635  -8.6065  10.7907   4.7603
+#&gt;   -2.1499 -18.3106  -1.5499   2.3885   4.2410   0.3205  -0.0706 -15.5370
+#&gt;   -7.2642  -3.3160   2.4902   0.7529  -2.6600   1.8324  -4.9594   9.6799
+#&gt;    3.5323   7.4025   8.2952  -5.4834  -2.4099  -5.4144   3.5544  -4.9415
+#&gt;   -6.3884  -3.4431   7.9970   2.7729   3.4578  -0.4114  11.7179   7.9719
+#&gt;  -11.1124  10.7049   2.7164   6.0928 -10.8485  -2.9538  -8.0745   7.4084
+#&gt;   -6.2716   0.8655  -0.5051  -2.3025   2.4459  -2.4969  -2.2801 -12.8532
+#&gt;    1.7287   0.0174  -0.5900   8.8881   4.1497  -3.9259 -13.2147  15.8763
+#&gt;   11.9867  -6.9489  -1.4523  -4.2875  -0.1344  -3.6711   9.0379  -4.5052
+#&gt;    9.7145  -2.6730 -15.3691  -7.4209   1.0313  10.1746   6.8281   8.4267
+#&gt;   -2.2905  -2.6880 -12.2839  -2.8750   7.6246  -3.2068  -3.9528  -8.3951
+#&gt;   -1.7096  -6.3767   1.9717   6.9931  10.4830  -0.2552  -2.3697  -7.0752
+#&gt;   -3.4325 -10.2179  -3.4009  -4.4899  -3.8635   8.8770   2.8008   4.5130
+#&gt;    2.5901 -18.7449  -0.3828   1.1593  -3.4166 -10.1580 -14.9132 -10.8550
+#&gt;   -5.1128  -0.5076  15.3124   0.3820   4.5298   0.6550  -2.3290 -13.3959
+#&gt;   -0.8302   0.2536  -4.9642   4.4154  -5.0953  -4.1481  10.3788   2.9103
+#&gt;    3.0757   5.9847  -0.6975   1.0189  -6.6328  -8.9430  -5.0165   5.8381
+#&gt;  -12.5129   3.4130   7.9631  -1.8649  -8.0697  -7.1588 -13.7465 -16.1740
+#&gt;   -4.3849  -3.6834   1.4595   4.1363   1.2030   4.3309   7.0658   4.2559
+#&gt;    6.0773 -19.6393 -14.0629  -2.3869   4.5976   7.4737   5.9223  -6.7138
+#&gt;   -8.0946   5.0019  -0.4931   0.1409   8.9070  -0.9026  -1.5408   2.8584
+#&gt;   -3.2197  -7.2077   0.3732  -1.0563   8.2695  -7.9381  -2.0098 -11.3553
+#&gt;    3.1388  -3.3002  -3.0741   1.2747   3.5834   5.7041   4.2506  11.6135
+#&gt;   -6.2947 -18.5550  -7.1581  -9.2618  -2.6979 -14.5040   7.1882  -7.9180
+#&gt;    3.8713  -1.1188  -5.3525  -4.0106   3.4619  -9.9489   0.9650   0.0564
+#&gt;   -7.3784   7.5384  -2.5166   9.6918  -0.9690  16.9486 -23.4199   2.4217
+#&gt;   -2.1453   2.9490  10.9724  -0.0240  -6.9801 -10.6743   1.9304   8.1391
+#&gt; 
+#&gt; Columns 17 to 24   8.0659  -9.8109 -17.5197  -1.4956  -2.7980  -1.3839  10.8934  -9.2477
+#&gt;    2.9348   3.9345  -6.3472   6.7811  -6.7502   3.8585   5.7719   7.3738
+#&gt;    3.7759  -1.2448   4.2845  -4.3191   0.2600   4.1004   2.6863   4.5444
+#&gt;   12.4635  13.6476   4.2648  -9.2781  -1.6858  -4.4844   4.1397   5.1457
+#&gt;   -0.3290  -9.6221  -1.0781   1.6044  -0.5301 -11.1221   5.6580  -4.9634
+#&gt;    6.2739  -5.5969  -4.1184  -7.9925   0.6532  -1.1652   3.4576  -5.9513
+#&gt;   10.6276   1.0927  -9.2528   2.5044   9.9256  -5.4391   0.4126  -1.3181
+#&gt;   -0.9920  -2.8386   0.3108  -4.3165   5.5252   7.3908  -0.4162 -14.1043
+#&gt;    2.3134  -7.4856  -0.0544  -5.2347  -0.9355   6.6240  -1.3276  10.7852
+#&gt;    7.8179  -9.1380  -1.4468   6.2373   6.9862   1.4667 -12.8261  -0.8780
+#&gt;    0.6620   5.7587  11.7828  -1.8801  -7.8894  11.5090   4.9039  -5.0160
+#&gt;   -4.0376   1.7842  -7.6461   6.9464   5.3759  -3.3801  -7.9744   1.3897
+#&gt;   -0.2717   4.3383   5.1764 -12.3860  -2.6778  -4.4454   6.4609   3.0602
+#&gt;   -7.5645   9.5570  -7.4381  -4.3807  -1.6359 -14.0753  -0.6845  -5.7670
+#&gt;   12.7688  -8.1250   1.3557  -5.4335  -5.8085   3.7587   3.5603   2.1621
+#&gt;  -11.5333   2.3040  12.5750  -4.4823  -8.4828   2.9692   9.9874  12.1453
+#&gt;    5.8875  -0.1194  -0.8944 -12.4041   1.4352  -8.2784  10.2505  -6.2482
+#&gt;  -14.2126   7.0171  12.9963  -7.4035  -9.4354  -5.3856  -5.4378  14.4636
+#&gt;   -8.6178 -14.3717  10.9290  -1.1101   0.5204  10.4756   1.0523   1.6210
+#&gt;    4.2888  -2.1153  -2.1217  -6.3444  -6.8318  11.7709  -3.7033  10.7405
+#&gt;    8.0226  -4.4725  -2.1581  -2.4154   4.2568  -2.2097 -19.8357   5.1823
+#&gt;    0.3821   3.0292   3.1383   7.3895  -5.0592   9.0426   5.7879 -11.8164
+#&gt;    2.0660  -5.6077   8.9119   0.3280  -2.1825   0.6266  14.0744   2.4195
+#&gt;    1.0469  10.6938   8.7573 -25.5889  -0.2029   9.9365 -13.8058   5.6670
+#&gt;    3.1036  -6.7063   3.8607   2.4476  -3.6309   7.1585   4.8015   1.0726
+#&gt;   -4.9733  -1.1730  -4.1544  -5.9858  -3.5929   4.7898  -5.2797  -4.2409
+#&gt;    7.0606  -1.9421   2.2402  -9.2652   0.2939  -3.5939   5.4470   1.3376
+#&gt;  -12.6290   8.0336  13.5466 -14.6799  -0.1997  -6.8844   6.4218  -7.7101
+#&gt;    4.3238  -7.4270   2.7846  10.7714 -11.9544   8.1263  -7.6938   9.7714
+#&gt;   -9.7766  -5.1577   1.0592  -3.9683  -7.2028   4.2519   9.6452 -16.5199
+#&gt;   -3.3280  -8.5588  -3.2580   1.6575   0.7049   3.9927  -6.2243  -0.4779
+#&gt;  -10.1726  15.6080  -0.0723  -8.2501   4.1061  -1.6644  -0.3377   4.0142
+#&gt;    5.6474   8.4324  -2.6393   4.7854  -1.1411  11.3926  -4.7050   0.1635
+#&gt; 
+#&gt; Columns 25 to 32  11.1852  -7.6783  -4.4760  -6.2867 -14.4768  -5.5915  -2.3897  -1.7277
+#&gt;    2.5738  -1.7489  -0.8135 -12.1182  11.3483   8.8064  -6.4352   5.5023
+#&gt;   -0.9505  -1.6135  -5.0113   9.0222  -3.6225   3.2098   3.7084  -5.9199
+#&gt;    5.4133 -16.0761   7.3412  19.7539   8.0536  -4.3415   2.0151  -6.6417
+#&gt;    0.5821  -9.7743  -8.9166  -2.1462   4.3378   1.4944  13.7540   5.7123
+#&gt;  -11.6263 -14.4128 -12.4400  -6.0476 -11.2459   6.1059   2.8093   9.7881
+#&gt;    6.1340  -1.5156   4.7161 -12.7458   0.4976   8.1295   8.6126   3.0070
+#&gt;   -2.7693  10.9625   2.0651 -19.3172   4.9871   7.9184  13.0141  19.7211
+#&gt;  -11.5707   3.4166 -15.3739  -4.2086   0.3017  -7.8660   1.3321  -8.0142
+#&gt;    5.0140  -4.8983  -7.2128   0.9540   4.8724  -0.1939  -1.9584 -15.3688
+#&gt;   -1.4485   5.1045  -0.0854 -14.6064   9.1149   1.7403   4.2503  -2.0895
+#&gt;   -6.7345   9.3218  -1.2156  -7.1773   1.4140   7.4115   5.7588   8.5236
+#&gt;   -4.1262   0.9882  15.1218  -0.9274  -7.9207   3.1481  -1.6564   4.9407
+#&gt;   -6.0797   2.7882   4.1580  12.1277   6.2366  -2.8015 -11.5175  -3.9804
+#&gt;    0.5365  -2.5556  -2.7976  -9.2249   1.2070  12.6141   5.7339  -0.6995
+#&gt;    1.4168 -14.0447   8.2472   6.8786  -4.0087  -5.6162   0.5010  -4.9696
+#&gt;    3.9318  -7.7873   3.0492  -5.4148  -9.5561   0.0113  14.3477  -3.5788
+#&gt;   -6.2673  -0.4624  16.4651   0.4618   2.4340   7.2395  -5.6198   8.3328
+#&gt;   -7.6682   4.2533   7.8766   0.0981 -10.9411  -9.1038  -4.9945  -0.3450
+#&gt;   -7.6848   0.6633  -4.0730  -7.5607  -8.0175  -0.1730  -4.9055  11.3131
+#&gt;   -5.7167   9.9912  -1.9191   2.7759   4.4893   1.2290   4.7103   8.0413
+#&gt;   -1.6533   8.7521   9.9300   4.1369 -11.7643  -1.3706   4.2014   0.8237
+#&gt;   -2.6175  -8.3520  -3.8934   0.8523 -12.1993  -6.4672  -4.6347  -5.2350
+#&gt;   -6.7551  -3.6316   1.6006   1.2255 -10.7569  -3.4983  -4.6958 -18.2304
+#&gt;   -2.2933  -0.8002   5.0204   9.0503  -7.8023  -1.4203   0.3685  -6.3405
+#&gt;    0.9046   1.7930   4.5703   3.4607  -6.1397  -7.0052  -1.4754  -3.1954
+#&gt;   -2.8187  -9.5606  12.8859  -9.7074   3.5686  12.6171   9.1333  -6.4353
+#&gt;    2.7439  -2.4240   8.5999   3.5995  -3.5231  -9.2750 -11.4203  -4.5213
+#&gt;   -4.9503   6.8071   8.7293   4.0040  -5.3628   4.6692   2.8335  -0.7443
+#&gt;    0.4532   3.8679 -10.4293 -14.7597  -8.6314  -3.1424   3.6973  -1.7702
+#&gt;   -5.7853   3.6905  -8.3404 -10.1098  -4.8931   7.1072   4.4414  -5.0839
+#&gt;   -1.0830   0.6989  -2.6288   9.4472 -13.4725   5.8905  -2.6344   6.1472
+#&gt;    1.6652   5.0443 -13.9105  -2.0468   3.3163  -0.7483 -12.5587  -4.1535
+#&gt; 
+#&gt; Columns 33 to 40   1.2980   5.7922  -2.6835   6.7159   9.6417  -2.6298  -0.8528  -0.4202
+#&gt;    1.8438  -2.5133  -0.9831   2.2937   6.0663   8.0411   4.4082   2.6379
+#&gt;   -8.1019  -1.3295   2.5620  -4.6356  -2.6643   3.4993   0.4289  -7.3983
+#&gt;   -3.0134  -4.4428  -1.4359  -5.3858   8.7989 -13.9669  -7.9427   3.1195
+#&gt;   -4.4803   4.7607  -6.0444  -0.9186  -1.2217   1.9578   2.5686  -0.9102
+#&gt;   -8.7065   4.3657   1.5683   5.5544 -17.4600  12.2941   4.0490  -1.7500
+#&gt;    0.0132   2.6190  -0.4216 -12.0082  -9.7049  -6.1584  -1.5848  -0.1581
+#&gt;    9.8927  20.7229   7.2420   2.7999 -11.8450   1.7785   2.4026  -7.7475
+#&gt;   -7.6714  -5.8588  -2.8094   3.0173 -20.9604  -2.8228   4.7490  -0.5274
+#&gt;   -3.7243  -3.8045  -7.7921 -10.8642   5.1522  -0.0962   2.9651   9.7325
+#&gt;   -6.3715  -0.2433   0.0752  -9.4436  -4.0806   9.0486   9.3798  -6.6853
+#&gt;    8.6038   4.7407   2.6414   5.2493   1.0450  -1.2455  11.3315  -5.4282
+#&gt;   -1.3117  11.8059  14.3912   6.1844   3.2220   5.4814  -6.3132  -1.5302
+#&gt;   -3.6532 -17.5908  -3.5290  -6.2984  -1.7378  -7.9013   6.8136  -2.7963
+#&gt;   -9.3930   3.2737  -2.1988   6.9773   2.0967  -8.0507  -9.9822  -4.4279
+#&gt;    3.3786  -8.5124   6.4465   1.9150  10.1108 -13.1152  -0.7935   9.3200
+#&gt;   10.4707  -8.0685   8.6091  -2.0858   4.2249  -9.6019  -2.6548  -5.5713
+#&gt;    3.5279  -1.3915  16.7588   7.1058   5.7903   2.1495  -3.0769   8.0010
+#&gt;   -7.8178   7.4678  -1.4509   2.5190  -5.2619  -2.2746  16.0377   1.3202
+#&gt;    1.2576   4.2236  11.5194  13.0194  -3.2023   9.1482 -13.7077   4.5081
+#&gt;   -5.5141   7.7185  -0.4163   2.6531  -5.2403  10.6327  -4.4554  -0.7468
+#&gt;    2.4275  -5.7975   1.7842  -2.6893  -2.7460  -4.6191   4.1462  -3.3226
+#&gt;   -8.3906   6.9027  -3.9422   9.1921   0.6376   2.4150 -10.8794   6.3257
+#&gt;    2.2617  -1.5724  -2.1324   1.4894   0.7507   3.9992  10.0400   4.0461
+#&gt;   -3.0378  -7.1001  -6.8504   1.8413   0.2720  -9.0168  -8.3117  -3.9334
+#&gt;   -2.4101 -10.7373   4.4570  -0.2030  -5.8664  -1.8609 -17.1171   4.6217
+#&gt;    9.4701  -5.9071   7.8036  -3.2998  -8.9704   4.3793   0.5720  -6.6505
+#&gt;    0.3020  -6.4357  11.4205 -10.2687   5.2483  -2.8666   7.2009   6.5887
+#&gt;   -7.9362  -6.2417 -10.4147  10.7933  -5.1977 -10.6599  -2.4203   3.5938
+#&gt;   -0.5701   5.9710   9.2179  -2.2129  -3.2973   0.7501   3.1773   6.7176
+#&gt;    8.1722   2.4580   8.0178  -3.3873 -16.7557 -11.7991  -1.3586   4.7263
+#&gt;    8.6734  11.5497   1.4913  17.1166  -5.3510   6.3995  -3.3012  -0.8676
+#&gt;   -1.1865  -5.8330  -5.4534  -3.7517  -4.7330  14.3180   7.2902   7.8606
+#&gt; 
+#&gt; Columns 41 to 48   3.5615  -9.7688  -4.3698   9.4824  -3.1192   4.5284  11.1475   6.8623
+#&gt;    8.3879   2.4612   9.4136  10.2137   1.5750   1.5142  -2.0387  -4.3419
+#&gt;    4.8508   4.3983   0.7569  -4.0457   1.3259   0.2923   1.1547   3.2599
+#&gt;   -1.4479  -3.5318  -3.8466  -4.2446  -7.6283 -11.9392 -19.9979  -4.2009
+#&gt;    9.4132   5.7710   8.7189   9.7178  -1.7340   6.4248  -1.3654  -2.6776
+#&gt;    0.6586  -1.7411   6.1335  -8.5081   9.3015   9.7500  -1.5191  -4.5925
+#&gt;   -5.9769   2.0140  -7.3969  -2.6265   6.8943  -4.0132  -5.8919   6.3048
+#&gt;   -6.8142  -5.2937   3.0421   5.4276  -3.1331   9.1253   3.9011  -1.3756
+#&gt;   -8.5611  -0.3279   6.4001 -12.0204   3.4183   7.4415  -9.0594  11.2156
+#&gt;    8.1157   4.5898  -1.0780   2.5033  -2.1577  -6.4207 -14.1143  -8.2264
+#&gt;    2.4492  -0.1197   4.2152   5.1966 -10.2710   2.1175  -0.4986  -3.3390
+#&gt;   -2.0919   2.5979   8.4456  -1.3720  -3.2895   6.1981   3.1747  -0.2003
+#&gt;   -3.3364   1.5890  -2.6446  -4.0857   8.2205 -18.2212   8.3387  -1.3977
+#&gt;    8.6574   4.8976   4.3133  11.0241  -9.1563  10.2224  -9.8328  -7.7376
+#&gt;    6.9685   6.5083  11.3045 -10.6788   1.1892   1.8784  -3.1766  -4.8822
+#&gt;    8.6182  -6.2387 -18.0660   4.8077   2.5121 -12.4155   0.6158  -1.9639
+#&gt;   -5.5851  11.7859  -0.7654  -3.9968   4.2055  -6.8894   9.0475  -2.4525
+#&gt;   -3.0200  -1.1177   3.6660  -7.0122   2.4538  -0.5365 -10.3393   6.7757
+#&gt;    0.0465  -9.8708  -4.2814   0.0838   4.1645 -10.5060  -0.7702   3.3377
+#&gt;   -0.3392  -3.5162   2.9724 -11.0832   6.6663   0.7159  -1.9853   2.1120
+#&gt;   -4.8323   0.7137  10.9749  -1.8166   4.4660   6.5846 -15.5988  -1.3574
+#&gt;   -4.1564  -3.3339  -5.3380  -8.1182 -10.7575   1.1331   7.1809  -7.1301
+#&gt;    2.8081  -9.6512   2.0413  -3.0798  -5.8924   0.5218  -2.0901   9.6971
+#&gt;   -4.0738   6.1353   3.4338  -5.3469   2.7293 -11.0841   0.2694  -5.0426
+#&gt;   -2.1924  -1.7987  11.2064  -9.8901 -15.7909   6.7003  -0.3568  -1.9345
+#&gt;    0.7802  -2.9752  -9.0987  -4.5661   3.1597   0.8458   0.2071  -4.5137
+#&gt;   -3.6960  -1.9876  -0.2447   3.1732   6.7256  -8.5313  -4.9369 -13.9011
+#&gt;    3.2326  14.2881   3.1216   2.2416   2.1360  -6.8162   0.7646  -7.6339
+#&gt;    4.7298  -4.4067   9.1713   5.2442  -4.9678   4.6076  -9.5215  -4.5575
+#&gt;  -14.0215   3.5428  -1.2730  -1.0921   6.3475   2.0168   9.2348   5.8859
+#&gt;  -18.8806 -10.0248  -4.5760  -8.5119  -1.5314  -2.2611  -1.2832   9.1316
+#&gt;    3.2393  -5.1773  -0.6072   5.6596   3.7721  -0.3780   9.8461   4.7959
+#&gt;    4.3407   7.1963   6.0763  -4.1961  -0.9477  11.8882  -6.1514  -1.7034
+#&gt; 
+#&gt; (10,.,.) = 
+#&gt;  Columns 1 to 6 -1.1510e+00  1.4708e+01  6.1299e+00  1.9380e+00  9.1910e+00 -5.4459e+00
+#&gt;  -2.4515e+00  5.8284e+00 -7.1605e+00  4.7084e+00  3.7136e+00 -9.5652e+00
+#&gt;  -1.7493e+00 -9.4196e+00 -3.2808e+00  1.5286e+00  7.8173e+00  8.9133e+00
+#&gt;  -4.3640e+00  9.2855e-01 -6.1887e+00 -6.1604e-03 -1.9447e+00  1.2969e+01
+#&gt;   9.0246e-02  5.5193e+00 -2.7066e-01 -3.4072e+00 -2.3908e-01 -1.2357e+00
+#&gt;   1.1252e+01  7.1942e+00  1.1412e+01  4.9808e+00 -3.6412e+00  1.6060e+00
+#&gt;  -3.5517e+00 -1.7384e+00  2.6949e+00 -2.0860e+00  1.4382e+00 -6.8337e+00
+#&gt;   3.3280e+00  2.3369e+00 -1.0039e+01 -3.3540e+00  3.2409e+00 -1.2759e+01
+#&gt;   4.4777e+00 -3.3609e+00  4.2431e+00  1.2564e+01 -7.6268e+00  1.8690e+01
+#&gt;   3.0412e+00  1.4121e+00 -6.2280e+00 -6.6548e+00 -1.7658e+00 -5.1015e+00
+#&gt;   1.5029e+00  2.9603e+00 -3.1640e+00  6.9582e-01  8.4434e+00  2.5271e+00
+#&gt;  -4.1522e+00  4.7775e+00 -1.0516e+01 -9.1064e-01 -6.6537e+00 -3.6508e+00
+#&gt;  -8.0951e+00  6.3684e+00 -9.9464e-01 -8.3192e+00  1.2819e+01 -1.2725e+00
+#&gt;   1.7781e+01  1.4571e+01 -5.4962e-01  1.0911e+01 -3.1689e+00  1.7386e+01
+#&gt;  -2.2865e+00  6.4819e+00 -9.7681e+00 -2.3027e-01  6.4727e+00 -5.5001e+00
+#&gt;  -8.3685e+00  1.9537e+00  5.4966e+00 -1.0910e+01 -1.1238e+00  1.0691e+01
+#&gt;   7.1031e+00 -2.3167e+00  1.8460e+01 -3.7902e+00  1.3384e+01 -7.9244e+00
+#&gt;  -2.3056e+00 -3.9997e+00 -2.2651e-01  7.2948e+00 -2.4566e+00  8.5297e+00
+#&gt;   8.0650e-01  3.1507e+00  8.2394e-01  5.3150e+00 -2.3854e+00  4.6375e+00
+#&gt;   8.0931e+00  4.0334e+00  5.6126e+00 -3.0156e+00 -3.7886e+00 -9.7867e+00
+#&gt;  -4.6833e+00  6.6924e-01  5.7609e+00  7.9856e+00 -1.3209e+01 -1.2543e+00
+#&gt;   5.6184e+00 -7.3573e+00 -3.9674e+00 -4.5558e+00  1.1325e+01  1.1856e+01
+#&gt;   2.3098e+00 -7.6610e+00  1.2973e+00 -2.5097e+00 -1.1555e+01 -1.6619e+00
+#&gt;   1.0846e+01  1.2510e+01  5.4761e+00 -3.8703e+00  1.8016e+00 -1.6538e+00
+#&gt;  -1.0300e+00 -5.6389e+00  7.5530e+00  9.5494e+00 -9.2211e+00 -1.8173e+00
+#&gt;   2.5764e+00 -6.1685e-01  1.0452e+01 -3.8054e+00  7.0047e+00  2.2879e-01
+#&gt;   9.5835e+00  3.4535e+00  2.5474e+00 -5.9788e+00  1.5455e+00 -1.1756e+00
+#&gt;   1.0323e+01  8.5548e-02  1.0385e+00 -6.7566e+00  1.1697e+01  4.6183e+00
+#&gt;  -4.9143e+00  9.3366e+00  2.1455e+00  6.2903e+00 -6.1693e+00  1.2083e+01
+#&gt;  -1.3754e+00  3.9394e+00  1.0684e+01 -7.6846e+00  1.5168e+01 -4.2258e+00
+#&gt;   1.2749e+00 -4.5121e+00  2.4132e-01  1.1152e+00 -1.5815e+00  5.2832e-01
+#&gt;  -9.0462e+00  6.7138e+00 -3.1081e+00 -5.1703e+00 -2.0812e+00  6.7342e+00
+#&gt;   4.6960e+00  9.0685e+00 -6.0166e-01  5.2299e+00 -2.3888e+00 -3.4944e+00
+#&gt; 
+#&gt; Columns 7 to 12 -1.0082e+01 -1.0771e+01 -8.9546e+00  1.8042e+00 -2.7159e+00 -1.7762e+00
+#&gt;  -8.4417e+00 -7.9267e+00 -9.4689e+00 -2.6069e+00 -2.0608e+00  1.0649e+00
+#&gt;   1.4768e+00  6.8986e+00 -2.8778e-01 -5.5315e-01  8.9970e-01 -4.4995e+00
+#&gt;   9.3775e+00 -7.7333e+00  1.3670e+01  4.7990e+00 -2.5009e+00 -9.3834e+00
+#&gt;   3.6530e+00 -1.1222e+01 -5.3500e+00 -5.1788e+00  5.5077e+00 -5.6291e-02
+#&gt;  -1.0494e+01 -3.6301e+00 -1.0994e+01 -2.9919e-01  3.4897e+00  5.0841e+00
+#&gt;   6.2714e-01 -4.2035e+00  1.6428e+00  7.9124e-01 -9.2246e+00  3.1176e+00
+#&gt;  -9.9808e+00 -8.9762e+00  5.5213e-01 -3.8203e+00 -4.1071e+00  4.6112e+00
+#&gt;   3.8380e+00  5.4504e+00 -4.7822e+00 -9.8952e+00  4.3101e+00  1.2299e+01
+#&gt;   3.6037e+00  7.2293e+00 -1.0695e+01 -2.0063e+00  6.5496e+00 -2.8960e+00
+#&gt;  -1.3454e+00 -3.6698e-01  9.2473e-01  7.9984e+00  2.8898e+00  9.4804e+00
+#&gt;  -1.0162e+01  4.9699e+00  3.4657e+00 -1.2196e+00  5.4783e-01 -4.5905e+00
+#&gt;  -4.7108e+00 -2.1457e+00  5.7715e+00  9.0599e+00 -1.3146e+01 -1.2753e+01
+#&gt;   1.1410e+01  4.6495e+00  7.7839e+00 -9.2500e-01  1.3030e+01  3.1535e+00
+#&gt;  -7.5110e+00  1.3772e+01 -2.4349e-01  3.3336e+00 -1.5929e+01  6.0472e-01
+#&gt;   8.5152e+00 -7.2888e+00 -2.2775e+00  3.6564e+00  7.3078e+00 -2.1732e+00
+#&gt;  -8.1812e+00 -5.6365e+00  2.7685e+00 -5.5288e+00 -8.5448e+00 -1.0250e+01
+#&gt;   1.2640e+00  1.5171e+00  2.8921e+00  1.3248e+01 -1.1732e+01 -2.6864e+00
+#&gt;   3.8779e+00 -6.2568e+00 -1.8679e+00  3.7103e-01  5.4897e+00  4.7297e+00
+#&gt;  -1.1899e+01  7.1531e+00 -4.0514e+00  4.0651e+00 -6.1371e+00  9.5976e+00
+#&gt;  -2.0377e+00  7.9734e+00  3.2218e+00  2.6228e-01  1.4351e+00  7.6534e+00
+#&gt;  -1.9397e+00  1.1279e+01  4.0064e+00  7.8378e+00 -1.6255e+00 -2.5241e+00
+#&gt;  -8.9815e+00  1.8569e+00 -8.1455e+00  1.0342e+01 -2.6133e+00  1.6513e+00
+#&gt;  -4.7943e+00  4.7982e+00  3.4861e+00  2.0369e+00  2.9492e+00 -1.2867e+01
+#&gt;  -1.0601e+01  1.4021e+01  2.1461e+00 -2.4602e-01 -7.3911e+00  3.3181e+00
+#&gt;  -3.6000e+00  1.0415e+00 -5.2782e-01 -5.1445e+00  2.0022e-01  1.7768e+00
+#&gt;  -4.7952e+00 -6.2317e+00  1.9980e+00  2.2601e+00 -2.6737e+00 -8.5609e+00
+#&gt;   8.5771e+00 -2.4351e+00 -1.1265e+00 -2.6971e+00  5.2554e+00 -1.7823e+01
+#&gt;   6.7204e+00  1.4255e+01  6.1903e+00  5.5310e+00 -7.5880e+00  9.5411e+00
+#&gt;  -7.2577e+00 -6.7162e+00 -9.2917e+00 -2.7819e+00  2.8843e+00 -7.8432e+00
+#&gt;  -3.5886e+00  1.1184e+00  2.5095e+00 -3.2145e+00 -5.5742e-01 -2.6120e+00
+#&gt;  -1.0957e+01  2.7352e+00 -3.9247e+00 -1.8870e+00  9.8008e-01 -1.1580e+00
+#&gt;  -4.1271e+00  4.0719e+00 -1.3393e+01 -4.4279e-01  6.8259e+00  8.2849e+00
+#&gt; 
+#&gt; Columns 13 to 18  1.4518e+00 -1.7907e+00 -7.9384e+00  6.5315e+00  4.2660e+00  1.4122e+01
+#&gt;  -4.7786e+00  9.9377e+00  7.1004e+00  9.3690e+00 -2.6187e+00  1.4162e+01
+#&gt;   9.8217e+00 -1.7407e+00  8.8441e+00  7.0788e+00 -1.1299e+01 -7.9481e+00
+#&gt;   9.8827e+00  5.8448e+00 -9.9542e+00 -7.7639e+00 -4.0326e+00 -3.8740e+00
+#&gt;  -9.0378e-02 -2.6884e+00  4.5369e+00  7.4611e+00 -1.6236e+00  8.2237e-01
+#&gt;   2.6702e+00 -8.4511e+00  7.7322e+00  6.1408e+00 -5.8271e+00  1.1371e+01
+#&gt;   2.4814e+00  1.9165e+00 -1.1149e+00 -7.0363e+00  2.5774e+00  1.0552e+01
+#&gt;  -1.3126e+01  9.4007e-01  1.2221e+01  1.2636e+01 -7.5024e+00 -1.8134e+00
+#&gt;  -3.1079e+00  3.2881e+00  5.4640e+00  8.8803e+00  1.3867e+00 -1.4285e+01
+#&gt;  -4.6069e+00 -7.2583e+00 -8.5716e-01 -6.4116e+00  6.0483e+00  1.4786e+00
+#&gt;  -2.1766e+00  1.1177e+01  7.7207e+00  7.4819e+00 -9.5557e+00 -1.5603e+01
+#&gt;  -3.5557e+00  1.5440e+00  3.4989e-01  1.0391e+00 -1.3528e+00  9.3870e+00
+#&gt;  -3.6253e+00 -1.6029e-02 -8.2342e+00  6.4836e+00  2.6881e+00  2.9792e+00
+#&gt;   5.0866e+00 -1.5473e+01 -9.9891e+00 -7.4947e+00 -1.3624e+00  7.0891e+00
+#&gt;  -8.1447e+00  5.6452e+00  1.6197e+00  1.3334e+01 -1.1119e-01  3.0060e+00
+#&gt;   2.4880e+00 -2.5340e+00 -8.1627e+00  7.4808e-01  1.6019e+00  4.5472e+00
+#&gt;   1.5419e+00  5.1208e+00 -3.9342e+00  5.4785e+00  7.8769e-01  5.2958e+00
+#&gt;  -1.0982e+01 -2.1442e+00  3.8397e+00 -6.2287e+00 -5.1945e+00  4.4689e+00
+#&gt;  -8.0164e+00 -6.6248e+00 -3.7490e+00  1.5171e+01  4.9799e+00 -3.7437e+00
+#&gt;  -1.1349e+01 -2.8744e+00 -2.7050e+00  4.0806e+00  1.3722e+00  4.1536e+00
+#&gt;  -7.2407e-01 -9.3004e+00 -3.5112e+00 -5.6065e+00 -6.6253e+00  5.4250e+00
+#&gt;  -4.0743e+00 -4.7070e+00  1.2838e+01  2.6905e+00 -8.5392e+00 -8.7264e+00
+#&gt;  -1.7624e+00 -5.0954e+00  6.2427e+00 -6.9174e+00  4.3744e+00 -4.5048e+00
+#&gt;   3.0713e+00 -1.1409e+01 -9.2123e+00 -3.3423e+00  1.1981e+01 -1.6001e+01
+#&gt;   3.4765e+00 -2.1769e+00 -2.9399e+00 -4.0751e-01  6.6048e+00 -9.9558e-01
+#&gt;   5.0735e+00 -8.0494e+00 -5.2977e+00 -2.9626e+00 -6.8171e-01  3.6940e+00
+#&gt;  -6.0202e+00 -5.2359e+00 -9.8874e+00  1.0407e+01  7.2523e+00  6.6818e+00
+#&gt;  -4.5084e+00 -8.7041e+00 -1.4744e+01  1.1044e+01  6.7042e+00 -7.7103e+00
+#&gt;  -5.2094e+00 -9.2715e+00 -2.5132e+00 -2.0430e+00  3.9644e+00  1.8019e+00
+#&gt;  -1.0330e+01  1.0737e+01  6.5686e-01  7.8642e+00  9.6778e+00  8.8608e+00
+#&gt;  -2.2792e+00 -2.1896e+00  1.7922e+00 -8.7522e+00 -3.9561e-01  1.1902e+01
+#&gt;   1.1399e+01  4.3614e+00 -3.1763e+00 -3.0516e+00  9.3060e-01 -6.1095e-01
+#&gt;   2.6698e+00  8.5572e+00  9.3059e-01 -8.6888e+00  1.2132e+00  2.0137e+00
+#&gt; 
+#&gt; Columns 19 to 24  3.1242e+00 -1.7359e+00 -3.3670e+00  9.2639e+00  1.1055e+01 -2.3429e+00
+#&gt;  -4.2909e+00  3.3208e+00 -1.5062e+01  4.3947e+00  5.0361e+00 -2.6086e+00
+#&gt;   5.6307e+00 -4.7571e+00 -1.1382e-01 -3.3869e+00 -6.9188e+00  7.4758e+00
+#&gt;   4.4813e+00  5.7593e+00  8.7943e+00 -1.1875e+01 -6.0524e+00  8.9488e+00
+#&gt;   1.0235e+01  8.4402e+00  1.4450e+01  2.7812e+00  7.2263e-02 -3.3179e+00
+#&gt;   1.5148e+01  9.6237e+00  6.9552e+00 -3.8001e-01 -1.0859e+01  6.4538e+00
+#&gt;   5.2313e+00  7.1842e+00 -9.8373e+00 -5.4476e+00  9.7978e+00  2.4108e+00
+#&gt;   1.5244e+01  1.4151e+01  3.2298e+00 -7.6963e+00 -4.4103e+00 -2.9889e+00
+#&gt;   1.0298e+01  6.3490e+00 -4.4758e+00  8.8059e+00 -1.7732e+01 -2.1141e+00
+#&gt;   3.7406e+00 -8.9840e+00  2.5201e+00 -5.0936e+00  6.2817e+00  5.8291e+00
+#&gt;   9.6266e+00  1.9386e+00 -5.5450e+00  7.9442e+00 -6.1569e+00  3.7012e+00
+#&gt;   3.7923e-01  1.5367e+00  4.1575e+00 -6.0499e+00  5.9302e+00  2.2745e+00
+#&gt;  -1.0050e+01 -2.2731e+00  8.6788e+00 -9.9291e+00 -5.6922e-01 -1.3604e+00
+#&gt;  -6.5292e+00  1.3730e+01 -1.0047e+01  9.6298e+00 -9.2012e+00 -4.4376e+00
+#&gt;   4.0541e+00  3.1760e+00  1.2248e+01 -2.8897e+00  3.2389e-01 -1.1149e+01
+#&gt;   2.2017e+00  4.8893e+00  4.7247e+00  6.1307e-01 -1.6959e+00 -1.9882e+00
+#&gt;   1.0252e+01 -2.2491e+00  2.3286e+00 -4.3153e+00  1.2832e+00 -3.1327e+00
+#&gt;  -2.3985e+00 -3.4597e-01  2.4154e+00 -4.7692e+00 -3.3074e+00 -7.4276e-01
+#&gt;  -3.8533e+00  5.3527e+00 -8.8127e+00 -1.4880e+01  4.0541e+00 -3.2262e+00
+#&gt;   1.3978e+00  2.5510e+00  2.0188e+01  9.2224e+00 -1.1162e+01 -6.1515e+00
+#&gt;   4.1505e+00 -6.7055e+00 -4.2155e+00  1.4835e-01  1.5798e+00  9.3494e+00
+#&gt;   1.1808e+01  6.8497e-01  2.3234e+00 -1.0927e+01 -1.1371e+01  6.6349e+00
+#&gt;   4.8906e+00 -5.8453e+00  1.0335e+01  3.2712e-01  6.6653e+00  2.5518e-01
+#&gt;   4.0984e+00 -1.0536e+01  1.5835e+01  2.6016e+00  1.6003e+00  3.5174e+00
+#&gt;  -3.7511e+00 -1.2930e+01 -8.8864e+00  8.9835e+00 -1.7069e+00  2.0311e+00
+#&gt;   8.3025e-01 -3.5143e+00 -1.0223e+00 -5.9961e+00 -4.2259e+00 -4.4451e+00
+#&gt;   1.5221e+01  2.0810e-02 -1.1063e+01 -1.3123e+01 -2.5652e+00  1.4035e+00
+#&gt;  -1.1284e+01  7.8458e+00  5.7174e+00 -6.0471e+00 -6.5509e+00 -1.2211e+01
+#&gt;   3.0079e-02 -2.7623e+00 -5.8705e+00  1.0009e+01 -4.2271e+00 -1.3138e+00
+#&gt;  -6.4721e+00  6.9665e+00 -7.0353e+00  3.5454e+00 -5.5335e-02 -7.7748e+00
+#&gt;   1.4640e+01  1.3125e+01  1.0948e+00 -1.2123e+01  8.9720e+00  4.7290e-01
+#&gt;  -1.4898e+01  4.9410e+00  2.4164e+00  1.2534e+01 -9.3768e-02  1.6201e+00
+#&gt;  -5.7218e+00 -4.0255e+00 -2.5160e+00  1.2270e+01  2.6487e+00  4.5178e+00
+#&gt; 
+#&gt; Columns 25 to 30 -2.2760e+00 -7.3144e+00  4.8292e+00 -4.9657e+00 -7.1232e+00  1.3142e+00
+#&gt;   6.5929e+00  3.1490e+00  6.9204e+00  6.4151e+00 -6.3404e-02 -3.7897e+00
+#&gt;  -4.0338e+00 -4.5316e-01 -7.0003e+00  3.3699e-01  6.8378e-01 -4.5510e+00
+#&gt;   1.6622e+01  5.6350e+00  3.6938e+00 -3.4149e+00  6.7602e+00 -6.1815e+00
+#&gt;  -4.3909e+00  1.5651e+00  2.4429e+00 -3.0016e+00  9.1529e-01 -2.4233e+00
+#&gt;   2.3989e+00  5.4087e+00 -7.5278e+00 -1.5690e-01 -2.3424e+01 -7.2567e+00
+#&gt;   3.1272e-01 -1.2439e+01  6.5578e+00  9.9514e-01  1.0721e+01 -1.0280e+01
+#&gt;   5.9367e+00  4.2118e+00  7.7408e+00  8.6852e+00  1.1312e+01  3.4991e+00
+#&gt;   9.7336e+00 -4.2098e+00 -1.2592e+01  7.2535e-01 -5.9812e+00 -1.1667e+01
+#&gt;  -8.3478e+00 -2.7242e+00  1.3632e+00 -1.0446e+01  1.7912e-01 -3.8909e+00
+#&gt;   6.3733e+00  6.0159e+00  3.1218e+00  2.2932e+00  3.8410e-01 -8.7694e+00
+#&gt;  -8.2073e+00 -1.6457e-01  3.4766e+00 -4.1029e+00  7.8382e+00  4.4347e+00
+#&gt;  -1.7276e+00  4.3395e+00 -5.2719e+00  9.4849e-01  2.7905e+00  1.4499e+00
+#&gt;  -1.5380e+00 -2.5573e+00 -2.8523e+00 -3.6523e+00 -5.3597e+00 -2.7917e+00
+#&gt;   9.0720e-01  1.2394e+00 -3.1198e+00  4.7940e+00  5.8422e+00 -1.1933e+01
+#&gt;  -6.5024e+00 -4.4094e+00  1.1358e+01 -5.3117e+00 -6.8802e+00 -6.3841e-01
+#&gt;   6.5537e+00 -4.6925e+00  2.1657e+00  9.1326e+00 -2.0540e+00 -9.5667e+00
+#&gt;   7.2024e+00  1.1624e+00  6.0365e+00  2.2904e+00  6.0530e+00 -1.9205e+01
+#&gt;   1.4611e+00 -4.2919e+00 -1.4080e-01  2.7746e+00  2.0329e+00  2.0843e+00
+#&gt;   5.4795e+00 -8.7125e-01  2.6906e+00  3.5602e+00 -3.3491e+00  1.1599e+00
+#&gt;  -2.3461e+00  6.5558e+00  8.0490e-01 -1.0127e+00  7.3854e+00  5.4343e+00
+#&gt;  -2.3029e+00  2.5260e+00 -3.6641e+00  2.6716e+00  3.3921e+00  3.6307e+00
+#&gt;  -8.0277e+00 -4.0514e-03 -1.1109e+01 -2.7329e-01 -5.4768e+00 -2.4345e+00
+#&gt;  -4.2815e-01  5.5739e-01 -1.0275e+01 -1.1990e+00 -3.5567e+00  2.1626e+00
+#&gt;  -2.6098e+00  1.0137e+01 -6.7354e+00  6.6041e+00  8.3207e-01  4.4087e+00
+#&gt;   2.3618e+00  1.0020e+00  7.4331e+00  1.3256e+00 -7.2381e+00  6.9058e-01
+#&gt;   4.2137e+00  4.6010e+00 -3.9047e-01  1.5557e+00 -3.4771e+00 -6.7903e+00
+#&gt;   3.8030e+00 -4.4897e+00 -2.7895e-01  8.4346e+00 -2.8834e+00 -7.8370e+00
+#&gt;  -7.1090e+00  5.3218e+00 -8.2733e-01 -5.1612e-01  8.7156e+00 -4.2369e+00
+#&gt;  -8.5179e+00 -1.3605e+00 -1.1377e+00  1.4068e+00 -8.1943e+00 -1.7865e+00
+#&gt;  -5.5196e+00 -7.5730e+00 -5.7586e-01  3.5259e+00  4.4455e+00 -1.3109e+01
+#&gt;  -3.4827e+00  8.6397e+00 -1.4932e+01  4.6374e+00 -9.3930e+00  1.1858e+01
+#&gt;   5.6901e+00  2.3284e+00 -1.0366e+00  6.8662e-01 -5.5608e+00 -2.5195e+00
+#&gt; 
+#&gt; Columns 31 to 36  2.0549e+00 -4.3663e+00  4.6274e+00 -2.7770e+00  4.0951e+00 -3.0836e-01
+#&gt;   1.6512e+01 -5.8965e-01  4.0134e+00  1.1879e+01  1.4031e+01  3.2302e+00
+#&gt;  -2.9421e+00  6.2940e+00  5.0424e+00  3.0158e+00  4.4634e+00 -2.1569e+00
+#&gt;  -1.1351e+01 -3.4089e-01  5.8826e+00  4.3915e+00 -7.7045e+00  7.1392e-02
+#&gt;  -6.5426e+00 -3.9611e+00 -1.5092e-01  4.2079e-01  5.9726e-01  2.1377e+00
+#&gt;  -1.3936e+01  3.3846e+00  5.6410e+00  1.8708e+00 -8.9681e+00 -4.8510e+00
+#&gt;   3.8352e+00  7.1991e-01 -8.4910e-01 -1.2214e+01 -3.6696e+00  9.2911e+00
+#&gt;   1.4515e+01 -2.6906e+00 -7.7254e+00  4.5797e+00  1.1218e+00 -2.0977e+00
+#&gt;  -4.4800e+00  2.6482e+00  6.9065e+00  3.1507e+00  2.0082e+00 -5.3859e+00
+#&gt;  -1.4128e+01  1.3816e-01  2.9477e+00 -1.9106e+00 -3.6697e+00 -2.2761e+00
+#&gt;   5.4178e+00 -5.8320e+00 -5.7231e+00 -1.5806e+00  1.2152e+01 -1.0140e+01
+#&gt;   1.9179e+00 -2.3483e+00  5.8366e-01 -6.8551e+00 -3.2098e+00  8.6950e+00
+#&gt;   3.8399e+00  9.9482e+00  1.5313e+00  6.8653e-02  4.2482e+00  3.6826e+00
+#&gt;  -2.5470e+00 -1.4484e+01 -9.0480e+00 -4.8119e+00 -1.6745e+01 -4.4600e-01
+#&gt;  -5.5114e+00  1.3359e+00 -1.6710e+00 -1.4465e+00  8.3191e+00 -9.3249e+00
+#&gt;   6.0060e-02  9.4988e+00  6.0412e+00  8.8024e-01  6.9324e+00  1.2031e+01
+#&gt;  -5.6851e+00  2.5318e-01 -9.3610e+00  9.1362e-04  9.4072e-01 -4.5883e+00
+#&gt;   1.7162e-01  9.5146e+00  5.0873e+00  7.1316e+00  3.6845e+00  7.4398e+00
+#&gt;   2.5172e+00  7.7000e+00  1.1956e+01  4.7511e+00 -2.8945e+00  1.5428e+01
+#&gt;   5.9333e+00  7.9293e-01 -7.8763e-01  9.9137e+00 -6.2309e+00 -1.2783e+01
+#&gt;  -4.6147e+00 -1.4190e+01  2.4994e+00 -2.6133e+00 -7.3028e+00  5.2317e+00
+#&gt;   1.6406e+00  9.9635e+00 -6.1104e+00 -1.0154e+01  9.4588e+00 -2.3062e+00
+#&gt;  -8.0695e+00  1.3592e+01  1.2976e+00 -1.0431e+01 -1.4089e+00 -6.0822e+00
+#&gt;  -1.1015e+01  1.5045e+01  1.5913e+00  2.8774e+00 -3.9416e+00 -1.8998e+01
+#&gt;  -1.2227e+01 -7.4663e+00 -8.8610e-01 -7.8272e-01  4.3430e+00 -3.1237e+00
+#&gt;  -1.0315e+01 -1.3596e+01 -8.0018e+00 -2.9016e+00  6.0070e-01 -3.3632e+00
+#&gt;  -3.8378e+00  9.5447e+00  5.4928e+00 -1.2036e+00  5.7343e+00  7.3284e+00
+#&gt;  -1.5390e+01 -8.8993e+00  5.3606e-01  6.2705e+00 -1.0778e+01  2.5031e+00
+#&gt;  -4.9752e+00 -3.1720e+00 -2.2769e+00 -7.5106e+00  8.8023e+00  3.0209e+00
+#&gt;   8.2876e+00 -5.6669e+00 -6.2541e+00 -5.9230e+00  7.6724e+00 -6.0533e-01
+#&gt;  -9.2757e-01 -2.7814e+00 -4.3140e+00 -1.3984e+01 -4.1415e+00  1.2586e+00
+#&gt;  -4.3848e-01  1.1996e+01  3.2413e+00  6.3810e+00 -1.3565e+00 -5.3411e+00
+#&gt;  -1.7962e+00 -6.0509e+00  3.4097e+00  2.9417e+00  1.9023e+00 -1.4072e+01
+#&gt; 
+#&gt; Columns 37 to 42 -6.2390e+00  7.4627e+00  1.5723e+00 -2.5190e+00  4.9774e-01  3.5490e+00
+#&gt;  -1.8892e+00  9.1216e+00  7.8881e+00 -6.1767e+00  4.9554e+00  8.8803e-01
+#&gt;  -7.0721e+00  4.0161e+00 -1.0343e+00  1.2696e+01 -6.3369e+00 -2.1948e-01
+#&gt;   1.3684e+01 -9.2964e+00 -8.0246e+00  1.0011e+00 -2.0440e+01 -2.1537e+01
+#&gt;  -6.7623e+00  6.8869e+00 -2.3903e+00  5.0027e+00 -1.3824e+00  7.4134e+00
+#&gt;   1.7012e+00  2.9455e+00  6.5387e+00 -2.4028e+00 -3.4351e+00 -9.1958e+00
+#&gt;   3.7916e+00 -8.9537e+00 -7.2207e+00 -4.9574e+00  8.1806e-01  1.7337e+00
+#&gt;  -3.7733e+00  1.3423e+01  1.3992e+01  1.0672e+00 -1.1168e+00 -5.3264e-01
+#&gt;  -2.9634e+00  4.7297e+00 -5.1181e-01  2.1821e+01  2.2174e+00  2.6987e+00
+#&gt;   8.9396e+00 -7.7852e+00 -8.7303e+00 -2.0843e+00 -5.4726e+00 -3.1127e+00
+#&gt;  -1.3309e+01  1.7285e+01 -3.7094e+00 -1.1378e+00  3.7256e+00 -7.7327e+00
+#&gt;   4.6996e+00 -1.4067e-01 -3.8312e+00 -6.2441e+00  7.1122e-01  2.4115e-01
+#&gt;  -8.7191e+00 -2.7296e+00 -3.7690e+00 -6.6840e+00  2.8123e+00 -9.0216e+00
+#&gt;  -7.8271e+00 -1.4785e+00  1.2006e+00 -3.0301e+00 -3.6380e+00  2.2138e+01
+#&gt;  -1.8020e-02 -7.3946e+00 -1.8668e+01  4.9143e+00  8.0434e+00 -7.5144e+00
+#&gt;   2.9670e-01 -6.9659e+00  6.4073e+00  3.5793e+00 -4.7447e+00 -6.0572e+00
+#&gt;  -7.2437e+00 -8.0995e-01 -1.1089e+01 -2.2674e+00 -4.6293e+00  4.8363e+00
+#&gt;   7.0735e+00 -7.6543e+00  3.0502e-01 -4.8940e+00 -2.1813e+00 -1.7265e+01
+#&gt;  -2.1917e+00  9.3046e-01  4.0582e+00  7.7997e+00  2.3249e+00  2.7131e+00
+#&gt;   6.4485e+00 -1.8380e-01 -2.9445e-01  2.0924e+00  7.4262e+00 -8.2395e+00
+#&gt;  -2.2302e+00  1.7512e+00 -3.4890e+00 -2.6745e+00  3.4653e-01  3.6789e+00
+#&gt;   8.9544e+00 -6.1406e-01  9.0091e+00 -2.5289e+00 -6.6100e+00 -9.2875e+00
+#&gt;  -8.6285e+00 -7.6416e+00 -1.2472e+01 -3.9108e+00  1.2114e+01 -3.0555e+00
+#&gt;  -3.7552e+00 -1.0725e+01 -1.0359e+01  1.1363e+01 -4.7689e+00 -6.3023e+00
+#&gt;  -4.3483e+00 -2.4968e+00 -1.6712e+01 -2.3206e+00  9.0312e+00 -4.3267e+00
+#&gt;   1.7978e+00  1.7474e-01  4.3791e+00 -5.9808e+00  4.6256e+00  7.4821e+00
+#&gt;  -3.0482e+00 -7.1479e+00  1.4285e+00 -4.9415e+00 -5.8376e+00 -5.3080e+00
+#&gt;  -1.7139e+01 -5.7809e+00 -8.0277e+00 -5.4555e+00 -2.2411e+00  1.5101e+00
+#&gt;  -1.6970e+00 -5.6225e+00 -1.0386e+01 -4.1191e+00 -4.6791e-01  1.1010e+00
+#&gt;  -2.1864e+01  1.1266e+00  1.2700e+00 -7.2086e-01  9.0356e+00  1.3241e+01
+#&gt;   1.5384e+00 -1.5492e+01 -2.5981e+00 -2.3097e+00 -7.5876e+00  1.6444e+00
+#&gt;  -9.8640e+00  7.9875e+00  7.5978e+00 -5.3028e+00  7.6840e+00  9.5088e-01
+#&gt;   4.4981e+00  2.9509e+00 -3.7114e+00 -5.6748e+00 -2.7754e+00 -9.2440e+00
+#&gt; 
+#&gt; Columns 43 to 48  7.5252e+00 -4.7240e+00  2.8171e+00  3.8666e+00  1.0257e+01  1.6760e-01
+#&gt;   1.4654e+01 -1.1176e+01  6.4938e+00 -4.7155e+00  1.6589e+01  1.9941e+00
+#&gt;   2.4068e+00  3.9986e+00  2.0345e+00 -5.9613e+00  1.1134e-01  1.2031e+01
+#&gt;  -4.1788e+00 -2.1450e+00 -1.4720e+01  1.2275e+01 -1.3673e+01  8.7555e-01
+#&gt;   6.7612e+00  1.4690e+01 -7.0012e+00 -4.7893e+00  3.9739e+00  7.7816e+00
+#&gt;   5.9188e+00  3.5156e+00 -8.0060e+00 -4.2263e+00  1.0632e+01  4.4382e+00
+#&gt;  -8.1016e-01  3.7534e+00  4.6450e-01 -2.3587e+00 -3.2603e+00  9.0285e+00
+#&gt;   1.1181e+01  6.6565e+00 -1.0311e+00 -1.6246e-01  2.7070e+00  7.7686e-01
+#&gt;   2.6272e+00  2.6926e+00 -2.1784e+00 -3.2185e+00 -2.2183e+00  1.5972e+00
+#&gt;  -5.3338e+00 -1.2750e+01 -4.8989e+00  2.2367e+00 -7.1979e+00 -5.7989e+00
+#&gt;  -8.5229e+00 -2.5259e-01  1.8121e+00 -1.6339e+01  1.4385e+01  3.1480e+00
+#&gt;  -5.5123e+00 -2.9095e+00  9.6932e+00 -7.3890e+00 -2.1089e+00 -5.4664e+00
+#&gt;   5.6571e-01  2.2835e+00  2.0445e+00 -3.1459e+00 -1.8582e+00  1.9847e+00
+#&gt;   1.1227e+01 -3.7863e+00  2.6117e+00 -2.0254e-01  2.9419e+00  3.3584e+00
+#&gt;   1.8669e+01 -2.5709e+00 -7.3639e+00 -1.0516e+01  3.2457e+00  1.2949e+01
+#&gt;  -1.4009e+01 -1.4490e+01  1.3553e+00  2.0864e+00 -3.2283e-01  1.0447e+01
+#&gt;   6.4845e+00  1.0992e+01 -8.9764e+00 -4.2840e+00 -6.8353e+00  2.0669e+01
+#&gt;  -4.7237e+00 -1.5023e+01  5.2116e+00  2.4891e+00 -5.8575e+00  7.5567e+00
+#&gt;  -1.1441e+01 -7.2402e+00  1.1403e+01  3.5053e+00 -9.5773e+00  8.1672e-01
+#&gt;   1.3615e+01 -6.2740e+00 -7.0639e+00  3.1193e+00 -4.7688e+00 -3.8634e+00
+#&gt;   5.3512e+00  2.4257e+00  2.7007e+00 -7.2868e-01 -1.0831e+00 -1.0120e+01
+#&gt;  -1.9115e+00 -3.5195e+00  7.7107e-01 -1.0501e+01 -9.2194e+00 -2.2617e+00
+#&gt;  -1.2850e+01 -8.3718e+00 -2.8517e+00 -3.1152e+00  1.5854e+00 -4.3248e+00
+#&gt;  -7.8824e+00  1.0903e+01  1.5360e-01  4.4607e+00 -6.1502e+00 -5.7983e+00
+#&gt;  -4.9865e+00 -7.0381e+00  3.4789e+00 -6.8242e+00  6.5550e+00  1.0764e+00
+#&gt;   1.3635e+01  1.7434e+00 -1.0626e+01  2.4001e+00  1.0004e+00  4.2354e+00
+#&gt;   5.1739e+00  1.3836e+01 -7.8074e+00 -1.8637e+00  9.1099e+00  3.1453e+00
+#&gt;  -8.1692e+00  6.1423e-01 -4.5621e-01  1.4270e+00 -1.0939e+01  1.0901e+01
+#&gt;   5.8934e+00 -7.4564e+00  4.9315e+00 -8.1992e+00 -4.8292e-01 -2.0732e+00
+#&gt;  -4.3536e+00  1.4776e+00  5.8203e+00 -8.0869e+00  6.1614e+00  9.3853e+00
+#&gt;  -1.3184e+01 -1.1708e+01 -2.4239e+00 -1.0928e+00 -7.8648e+00  7.4512e+00
+#&gt;   3.7807e+00  4.5977e+00  1.4227e+01  4.8748e-01  5.5077e+00 -3.9448e+00
+#&gt;  -1.1252e+01 -1.0866e+01  3.1634e+00  4.5651e+00  1.2367e+01 -1.0874e+01
+#&gt; 
+#&gt; (11,.,.) = 
+#&gt;  Columns 1 to 8  -2.0044   4.3699  -2.4127   4.2359   4.1969  -0.2678   3.8926   7.5892
+#&gt;    6.1867   0.1656  -4.8839  -1.8466  -4.4712  -5.9437 -10.9038   4.0003
+#&gt;    8.4201  -8.1247  -1.8980  -4.0356  -6.8123   4.3460   5.1257  -9.9883
+#&gt;    0.9669   8.8183   8.4483  -4.3425   0.9247   6.5621  12.3047   2.4185
+#&gt;    6.7167  -2.4476  -1.9954  -3.5758  -6.7449  -8.1649  -5.5353  -1.2549
+#&gt;   -2.4296   2.2598  14.8066   4.2901  -8.1575  -0.0060 -10.8620  15.7208
+#&gt;   -3.4516 -10.6639   5.1614  -7.7433   2.2696   2.9458   3.3822   0.3909
+#&gt;    6.6280 -11.6794   0.7623   7.4987   3.8300   1.4233 -13.8746 -11.0928
+#&gt;   11.9090 -16.2859  -2.0074  -5.6302  -6.8406  -7.0040  -0.6425  -3.5537
+#&gt;   -8.4572   1.7583  -2.4892   1.3537   4.7256  -3.4651   3.8739   0.7713
+#&gt;    9.7891 -11.9684  -0.2066  15.0137  -7.3798  -1.7238  11.3389 -16.5909
+#&gt;   -2.2045   8.7826   3.2816   0.7466  -5.2149  -2.3559  -3.3869   1.5777
+#&gt;   -2.5540   7.1872  11.5563  -2.1551  -0.1032  12.4840   6.1519   3.8442
+#&gt;    1.0610  13.9169  -5.9530  -3.8892  -4.4112  -5.2429   9.1333   9.5078
+#&gt;    5.8089  -4.8836  15.1818  -1.9421 -10.6217   6.9401  -3.8909  -1.3724
+#&gt;   -7.8908   5.5054  -8.3785  -8.3396  -3.5264   1.3435  18.2884   6.9249
+#&gt;    6.6423  -7.1030   1.7219   7.1863   7.9274  -0.1598  -4.0641  -9.6481
+#&gt;    5.8048   2.5447  14.5549   6.4658  -1.0197   6.5001   7.7019  -2.9925
+#&gt;   -2.3873 -14.1483  -8.2676   0.0137   7.7786   6.5895   6.4276  -6.6812
+#&gt;   -8.9539 -10.5986   8.0475   3.8106  -2.6754   0.6577  -6.8355   4.2199
+#&gt;    9.1807  -1.5445  11.4027   3.9223   3.0354  -2.9551 -15.0646   0.2942
+#&gt;   -1.2010   3.2711 -18.3424  -2.1797  -5.7391  11.5237  13.7566  -7.5049
+#&gt;   -1.2460  -2.2535   1.8274  10.3330   4.5104  -2.5318   4.1912  -5.8933
+#&gt;   -2.9833  -2.7955  13.6504   4.9941   5.8426   3.9711  12.0044   3.4093
+#&gt;    4.5080   0.0392  -7.4748  10.0702   2.1613   6.0438  -0.6968 -12.2627
+#&gt;   -5.8455  -4.4407   5.9451  -1.1177   7.1119   4.5139  -9.7403   2.9613
+#&gt;    5.2632   2.9664   3.9723   2.8500   3.8921   4.1397   4.4269  -3.3525
+#&gt;   -4.6684   2.3957   6.1306  16.3635  10.9679  -0.5797  -0.5639  -2.7507
+#&gt;    9.3602  -2.3128 -11.8529   3.9773  -1.8450   2.9816  14.0366  -6.2402
+#&gt;   -6.2591  -1.8565  -0.4631  14.7339   2.1109  -2.1565 -12.1677   6.6745
+#&gt;   -1.3769 -11.6554   5.5780   5.5344  12.0577   7.3156  -3.7152  -3.5623
+#&gt;   -7.9856  10.3130  -1.8823   2.1432  -0.4400  -4.6726  -6.2517   1.1903
+#&gt;   -0.9517  -2.9278   1.0489   7.3569   0.5211   0.1772  -0.7285  13.3707
+#&gt; 
+#&gt; Columns 9 to 16   6.3823   3.0219  -2.8446  -4.6026  -3.7400  -4.1116  -7.1448  -7.5928
+#&gt;   -3.3294  -4.4999  -5.0383  -3.9010  -2.0349  -6.5806  -0.7017   0.5485
+#&gt;    0.2619   0.9938  -6.3134  -3.0559   3.3365   1.7051  -0.3802   2.8995
+#&gt;   -8.0614  -0.8129   0.3033  20.1874   1.7606   0.4109   5.9526  10.6269
+#&gt;   -4.0922  -5.5850 -13.6079  -5.6689 -11.0693  -8.8210  -3.5062  -6.0664
+#&gt;    5.8566   2.6684  -5.3008  -9.9224   0.8673 -11.0890   7.2668   5.1669
+#&gt;   -3.4305   4.8934 -13.3550   1.1970  -0.1186   4.9312   0.2421   3.2356
+#&gt;   -0.8893  -1.7562  -4.0922  -1.5024 -24.6047   1.4579  13.1380  -5.0338
+#&gt;    0.1682   2.2131   1.6275  -7.8510   7.6663  -6.3077 -14.3870  22.1853
+#&gt;    5.7356  -5.8148   2.0537  10.2324   2.7517  10.0743  -1.6487  -6.4380
+#&gt;   12.8964  -4.1159  -4.4931   2.6539   3.5004   1.7731  12.6290  -4.6256
+#&gt;    5.3116  -3.7887   4.4995 -14.8593  -4.2049   7.0560  -3.3320  -7.5902
+#&gt;   -4.0621   6.1609  -4.2993  -0.3039  -8.4894  -1.1391  14.2142 -10.8272
+#&gt;   -0.8747  -6.0277   1.8341 -16.5662   9.2631   9.6649   0.5672  -3.7222
+#&gt;    6.7275 -13.1337  -7.5456 -12.3858  -4.7681   9.5557  -5.8113  -4.4115
+#&gt;   -1.1570  -7.7482  -1.9757   2.1171  -0.6067  -3.9330   0.0538   3.5047
+#&gt;    1.0995   1.2456   0.8121  -7.7823  -8.1074  -3.2819   6.8381  -3.0345
+#&gt;    2.5230   1.4343  -1.5845   4.0496  -7.0159  -7.2508  -0.9545   9.0573
+#&gt;   -9.3421  -1.2291   5.5133   4.4957  -4.7450   1.2507  -0.0840   3.7238
+#&gt;    5.9721   0.4973  -0.8971   1.9492 -11.6507  -6.1942  -0.0588   3.0740
+#&gt;    0.6651   8.9529   1.2361  -3.9383  -4.2929   6.1735  -1.6646   4.5894
+#&gt;    9.7561  -8.0153   2.2952   1.5529   0.2461  -0.9306   7.2547  -2.9878
+#&gt;    9.1697   4.8139  13.1205   0.1607  11.1355  -3.9690  -2.9050   9.5772
+#&gt;   13.4256   7.8929   5.7852  -1.8729  -4.2819  12.1169   4.5292  -1.7870
+#&gt;    7.7253  -4.2477  13.1721  -6.5561  10.9189   1.2953   1.1537  -0.6233
+#&gt;   -8.4916  -0.1692   3.4664   7.2991  -1.1835   3.4548  -1.7278   0.9493
+#&gt;   -2.8162  -4.2595  -1.5781  -6.4060   2.9334  -7.0955  12.3795   2.8014
+#&gt;  -10.3906   6.7465   3.1686  -0.3980  -2.3234  -0.3691   9.6803 -14.8288
+#&gt;    4.0592 -10.3295  -3.2436  -3.0166   2.3703  -2.3240  -4.1811   8.5929
+#&gt;   -1.2070  10.0707  -0.4461  -7.8677 -11.1716  -5.1939  14.7600 -11.8722
+#&gt;   -5.8241   4.7446   7.1372  -0.0313  -4.6634   1.8151  -5.5978   7.6861
+#&gt;   -3.1263   5.6811  13.5624 -14.0331   6.8948  -7.2957   8.0425   1.4875
+#&gt;    6.3600   5.2668   0.5438  -0.3248  15.2348   4.9118  -5.3705   0.7440
+#&gt; 
+#&gt; Columns 17 to 24   0.5236   6.8693  -0.4034  -9.7509  -4.5863  18.9446  12.2359  -1.6541
+#&gt;   -2.9956   4.0989 -10.3061   1.1723  -1.5345   7.7239  14.5010  -0.1832
+#&gt;   13.2186   0.7272  -8.6150   3.3184  16.5073   1.1210   1.6489  -2.2074
+#&gt;   -3.4138 -16.0043  17.6044   1.4957 -10.2243  -4.1157  -0.2742   5.9374
+#&gt;   -4.2704   0.2206  -4.0499   8.9308   1.4830  -1.8442  10.6688  -4.1980
+#&gt;   -0.2917 -13.9340   6.4268  12.3239   8.9711   7.4519   7.6545   1.0119
+#&gt;   -2.5605  -1.7786   5.2483 -10.9202  -9.1474  -3.8889  -4.1318  -4.5254
+#&gt;  -10.1291  -4.8287 -14.1620  -6.3614 -11.1534  -8.4878  12.9382  -1.4245
+#&gt;   16.6430  -8.2832  -4.1078  14.2384  13.0596   1.6659  -0.0978   6.2452
+#&gt;   -9.2022   6.4704  15.6910  -2.6631   0.6269   1.7004  -1.7806   0.7099
+#&gt;    3.6032   7.8300 -24.2912  12.1176   9.7138  -0.3804  15.4161 -10.8723
+#&gt;  -10.0752   9.4278   2.1156  -8.8559  -2.5048   6.6876  -4.4726   5.2917
+#&gt;    4.8320  10.4034   0.9745 -12.8283   1.6226  -0.0745  -9.1379  -3.6713
+#&gt;   -5.8681  14.4700  12.2456   1.0197  -7.4787   0.7518   7.1122   3.2161
+#&gt;   13.5318   1.5475  -6.2782 -10.4522   3.0410   8.6182  -2.5363  -7.1108
+#&gt;   -4.6763   0.1332   9.1238  -3.5555  -2.0220  -1.4984   7.1836   8.1871
+#&gt;    1.5535 -12.9286   2.1660  -0.5209  -7.5615  -4.8321  -1.2277  -3.6797
+#&gt;    0.9089 -10.6601  10.4946  -1.6091   6.9751  -8.0629  -4.4556   2.6121
+#&gt;    6.1206   4.4565  -4.0688  -2.7858   5.5975  -1.0423  -3.8477   0.0219
+#&gt;   -2.9188  -6.3728   8.4461  -3.2103  -7.5373   5.3769  -0.4173  -6.6054
+#&gt;    0.5667 -12.7742   2.3528   1.9002   1.5721 -12.6775   0.5521  -3.3713
+#&gt;   -0.7820   4.8531  -1.1454  -4.0201   8.4258   3.1378   5.1369   4.2380
+#&gt;   14.7232  -0.8633  -7.0142   3.2225  -2.5383  -9.4545  -2.2383  -4.1793
+#&gt;    4.2805   1.1091   9.2722   3.3933  -5.3543   0.2697  -3.0848   8.4100
+#&gt;   10.8122  -9.1854 -14.0378   1.1658   8.9828  -7.5437 -14.8283  -1.8276
+#&gt;    0.2647  -4.6193   5.6080  -3.8388  -7.6890   3.8130  -2.9010  -7.1018
+#&gt;   11.1065  -1.7242   2.5281  -2.9828  -4.6451 -11.2156  -1.1177  10.7715
+#&gt;    4.6313   5.9269   6.4877  -5.0038  -7.0321  -7.7048   0.2439  -3.5283
+#&gt;   -3.2314   3.8181  -0.7659   0.2947   3.5189  -0.3271  -1.7176  -9.7372
+#&gt;   -7.1887  16.1351  -6.9254 -10.8912  -5.4141   3.3091  14.0301  -5.9519
+#&gt;   -1.1507  -7.1501   2.9040 -11.2942  -8.9464 -11.5441  -7.8685   8.8295
+#&gt;  -11.5279  10.8920  -2.8373   8.6184 -10.8648   6.1828  -5.2552   8.0270
+#&gt;   -6.4354  -0.0202   2.1212  10.6847   1.9121  10.6659   5.9482   3.1849
+#&gt; 
+#&gt; Columns 25 to 32  -5.0729  -4.2956   2.7086   6.4407  15.8604  -0.3588   3.0230  -8.7129
+#&gt;   -7.9630  -0.9580   5.0442  -2.5717   1.9893   5.3521   6.3859   5.3545
+#&gt;    2.0941   1.9097 -12.0480  -7.8120  -1.1532  11.4927  -8.4720   0.4449
+#&gt;    4.2617  -6.9544  -8.1263  -3.9371  -0.5503  -4.5696  19.1867   7.3988
+#&gt;    5.5247   0.5356   0.7781   1.2835   1.0388  -1.3812   2.8845   9.4449
+#&gt;   -0.6169  10.7332  -4.6661   3.8535  -1.2422  -5.3036   2.2425   9.5974
+#&gt;    4.5023  -8.9653  -4.7451   6.3774  -3.6699  -1.5664   1.6872   4.6048
+#&gt;    3.5291   2.9527   8.3790  -1.0565  -9.1058   1.3912  -4.0224  12.2775
+#&gt;    6.6163  11.7969  -0.0560  -6.3300  -6.5183  -7.4860  -8.6395   9.1613
+#&gt;   -4.0831   0.6745  -8.1844   3.2280  -3.4047  -1.9166   0.7233   4.1844
+#&gt;    6.8935   3.1620  -0.2730  -8.7033  -3.1915   8.9787  -0.6695   3.8883
+#&gt;   -4.3835  -1.0841   5.7311   1.6674   2.7202 -10.2040   6.5731  -3.4295
+#&gt;    6.2861  -4.3858   0.8793   5.7060  -0.1589   6.7821  -7.4484  -0.8987
+#&gt;  -11.4815  -1.8563   8.6766  -5.0828  -0.2160 -10.1367  16.3202 -15.2852
+#&gt;   -0.9740   4.4800   4.1621  11.5320   4.9337   2.3787 -13.8811  13.0770
+#&gt;   -2.3275  -6.4719  -5.6679  -0.2408   3.0308   2.7526   8.1827  -7.3714
+#&gt;   -7.6305  -1.1428   8.5583   6.8418  -1.1626   6.2628   2.1209   7.9571
+#&gt;    1.3825  -4.8033  -1.8967   3.3390  -5.9439   1.8516  11.8852   4.2061
+#&gt;    6.7047  -1.5728   1.9421  -6.5667  -3.1609   2.4729   0.5374   4.6061
+#&gt;    3.9707  13.9025  12.1335   6.5156  -2.7861   0.3097  -8.4294   4.0610
+#&gt;    1.8200  10.6741  -6.9567  -3.4479   6.3126   3.0632  -9.6933   5.1884
+#&gt;   -6.5620   1.9818  -3.6081  -0.1819 -10.5116  -3.7730   3.1047  -5.6763
+#&gt;   11.4688   8.5353   3.8160  13.8754   5.2576  -0.4056  -7.2265  -6.5845
+#&gt;    6.4180  -1.8389  -0.7055   8.5180  -8.2785   7.0835 -19.0100  15.5229
+#&gt;   -6.8713   7.3240  -1.0499  -1.4583  10.3664   5.5127  -1.9181  -5.3814
+#&gt;  -10.7972   5.2820  -1.3866   2.9625   1.5788  10.2621  -6.9272   0.7044
+#&gt;   -9.1831  -3.3115   1.0362  -3.3940  -4.2613   5.9859   9.9480   3.7198
+#&gt;   -8.1137   3.6621   8.2821   1.7979  -1.2668   1.4004   4.5593   1.2181
+#&gt;    1.0208  -0.7449   2.9976   5.4707   2.6559  -3.3447   1.3942  -0.3096
+#&gt;   -4.3125  10.7800   7.2531   9.5339   0.3425  -1.7535  -7.0209 -12.0271
+#&gt;    1.3050   2.2791   6.5436   9.1550 -10.5545  -6.5303   4.5502   9.5093
+#&gt;    8.7469  -4.9613   2.1701   1.9083   9.6481  -6.2135  -2.7381 -14.1108
+#&gt;   -4.4900   5.6005  -2.7029  -1.0956   2.2024  -1.2472   7.6779   1.9417
+#&gt; 
+#&gt; Columns 33 to 40  -0.0019   0.2802  11.2920  -9.1013  -5.6047  -0.0040   5.0107   6.8514
+#&gt;    6.9784  -4.2512  -0.7175  -4.0739   8.1430  -6.1468   2.5069   8.5195
+#&gt;    1.0547  -0.3367  -7.9803  -4.1151  -1.3741  11.1988   1.6343  -2.4667
+#&gt;   -8.8407  -7.3048   0.8817   1.6348  19.7482  -2.2385 -11.9681  -0.4096
+#&gt;   14.3348   8.9444   6.1513  -3.8608  -4.9776   0.7029   4.0575   0.0101
+#&gt;   -1.0902 -10.9092  -6.6897  -5.8111   5.5139   9.2579   5.1977   9.8372
+#&gt;    0.8237  -4.5022  10.2385  12.2472  -1.3605   5.4792  -4.8483  -9.0908
+#&gt;   12.6671   3.5102   6.4837   5.3618   6.4916 -10.6185  -7.3910   6.6741
+#&gt;    1.3334  -0.3487  -7.4179   1.1611  -6.2333   0.7965   3.6717   0.0761
+#&gt;   -6.9966  -4.8677   1.2862  -3.7916   0.1066 -16.2524   6.9257   9.2883
+#&gt;   14.2929 -15.8576   0.3450  -4.3398   3.7223  11.3818   3.6238  -7.7689
+#&gt;    0.7186   3.9697  -1.0347   8.9312  -9.0946  -4.7356   6.0722  -1.6076
+#&gt;   -6.7104  -2.8066  -1.2940  -9.2257   6.4690  11.5947  -4.0898  -7.5930
+#&gt;    9.8058  13.1059  -4.8653  12.2930 -12.9543  -8.7284   2.0773  -2.7334
+#&gt;   10.1541  -2.2709  -0.0826 -13.8402  13.5996  10.3662  -8.8221  -7.8720
+#&gt;    3.1338   3.4794  -8.2790  -1.3139   8.1712   5.2529   0.6040  -7.0523
+#&gt;   -2.3543   9.7280  -3.3076   1.4279   0.8343  -0.5399  -0.3940  -7.1317
+#&gt;    1.3395  -0.0886 -14.3154  -3.6316   4.7655  11.4265  -6.9424  -0.0195
+#&gt;   -7.2402   0.8065  -6.1732   5.3272 -10.5435   0.5907   7.7513   6.7939
+#&gt;    0.6357   3.2251  -4.7789  -8.3201   4.2994  -3.6608 -10.4161  14.7643
+#&gt;    1.0850   5.2056  -1.1094   9.1666  -9.4409  -8.2777   0.9586   8.9043
+#&gt;    9.5923  -4.4406   0.3631  -4.2734   7.5759   5.4691  -4.3900  -6.4848
+#&gt;   -4.8792 -13.1105  -9.9396  -6.0821  -4.2209  12.5849  -2.5196  -7.9711
+#&gt;  -23.4039  -9.2183  -8.1744  -8.4877   4.3403  -1.6354   1.4089   2.3033
+#&gt;    4.3382   4.4030   1.3752  -6.4615  -2.0714   0.8318   4.8986   2.2077
+#&gt;    2.5029  10.4185   1.9057  -7.3488   5.1091  -2.3736  -5.1177   8.5054
+#&gt;  -11.5271   1.1920  -1.4756  -4.4427   8.2370   5.6577   4.2550  -7.8401
+#&gt;    3.8904   4.7149 -14.7242  -5.9000 -12.1250  -3.5753   9.7209   3.8768
+#&gt;   11.7669   3.3826   7.0856   2.0755  -1.0698   3.9380  -8.9544  -1.4195
+#&gt;   18.2892 -11.3806   0.7879  -2.0373  -8.2313  -9.0982  10.7094   0.3240
+#&gt;   -4.2813   1.9703  -0.1238  16.0385  -7.7890 -12.1289 -10.8846   4.2699
+#&gt;   -8.0051  -3.0747   4.6354   1.3537   6.1540  -0.7435  -3.5279  -8.5859
+#&gt;   -1.3912 -15.9602   1.5367  -5.4228  -1.3131  -4.3742   7.9185   9.7122
+#&gt; 
+#&gt; Columns 41 to 48  11.9842  -0.5593  -1.7805  -0.0418   2.9324   4.4272  10.7479  -5.3032
+#&gt;    6.4636  -7.0260  -2.1067  -2.6697  -0.7586  -0.1127  -1.9880   4.2114
+#&gt;   -7.8123   4.6202  -1.8848   2.7081   7.1877 -24.3626   2.2723   5.8324
+#&gt;   -3.6324   2.1598   1.4967   2.0073   2.5986   1.4709   8.2080   8.4126
+#&gt;   -3.0173  -2.4682   1.8973  -0.4220  11.2470  -8.9623  -0.4299   1.1831
+#&gt;  -11.3260   0.8946 -11.9878   8.5653 -13.7869   2.4784  -3.6501   3.3985
+#&gt;    0.7377   3.5998   1.3043  -4.0607   3.9259   5.5577  13.7190  -4.1788
+#&gt;    3.7997  -7.6852  -2.1255   2.1282  16.8586  -0.1670  -0.5216  -5.6847
+#&gt;    0.0417   0.4327   3.9841   3.7029  -3.3788  -1.5266  -3.1260   9.1985
+#&gt;   -7.9677   3.4836   2.0614  -9.5991  -4.0841   5.0656  12.3368  -3.2452
+#&gt;    6.2654  -4.9748   4.1019  -9.4819  30.9802 -20.2084   1.4996  -1.2812
+#&gt;   -2.4100  -1.8367  -0.7233   1.9043  -4.4144   7.0083  -4.5828  -3.8193
+#&gt;   -1.2719   7.6519 -12.3358  -1.2329   2.1559  -0.8138  -0.6092   0.2082
+#&gt;    1.6161   6.0475   0.1815  -5.6616  -2.7878  -1.0785  -2.3120   0.9993
+#&gt;    5.1240  -1.1110  -8.6762   8.2783  -3.1611  -8.0870   3.4626   2.6431
+#&gt;    0.5291   3.3617   0.6551  -8.7125  11.2056   5.7662   5.2713  -2.7741
+#&gt;   11.6409  -2.7260  -4.5710  -3.0022   7.6567  -9.3038  -0.0798   5.8057
+#&gt;   -5.5851   3.2143  -1.6160  -3.7551  -2.0959   1.3541   5.3000  -3.7737
+#&gt;   -3.2166  -2.7181   7.8093  -9.5603   8.7075   7.0108   5.1225 -11.7553
+#&gt;    6.5461  -7.2921  -1.2451   7.5864   6.0977  -1.2823   4.6137  -0.9353
+#&gt;  -13.1320  10.1831   2.5122   6.5948  -8.3315  -9.3659  -1.7092   1.8071
+#&gt;    1.1201  -0.9914  -2.0848   4.5972   2.9202  -1.4931  -2.1520   3.0542
+#&gt;    7.4057   5.9809  -4.3698   1.5064  -2.8317  -2.2117   2.1553  -0.2234
+#&gt;    3.6103   7.1969 -12.8206  -7.6817  -0.5296  -3.9748   5.1967  -1.6486
+#&gt;   -5.1564   6.3437   4.0816  13.9357 -10.6232  -6.3037  -5.1245   4.8427
+#&gt;   -0.6854   1.5132   3.3787   9.7651  -1.6342   3.9888   1.7152  -3.4456
+#&gt;   -2.9518  -1.5337  -3.2749  -7.5476  -2.8363   0.7673  -4.6810   4.3637
+#&gt;    3.4508   5.3307   0.0274 -18.9641  10.7559   0.6545  -2.6214   2.7683
+#&gt;   -0.3699  -2.1346   5.4093  -2.6155  -1.9389  -6.1243   9.4638  -2.6488
+#&gt;    4.5731   9.7016  -3.4944 -14.8443   8.1975   8.4388  -7.5679  -0.7393
+#&gt;    1.9754   2.5775   7.9926   3.1289   0.2847  18.3038  10.5872  -6.6662
+#&gt;   -1.1725   5.3218 -12.1124   0.4054 -11.2314   9.3301  -8.0784   0.4656
+#&gt;    5.2721   1.0929   3.0305   0.9061  -9.7753  12.5669   2.3650   6.1578
+#&gt; 
+#&gt; (12,.,.) = 
+#&gt;  Columns 1 to 8   4.5001  -1.2295  -1.8164   0.6520   3.2763   5.5859   3.9245  -4.6731
+#&gt;   -3.8160   2.2018   5.1452   2.9829   7.7092   2.6458  -3.2690   1.8272
+#&gt;   10.2061   5.9445  -5.4832   5.7572 -13.4972  -9.2555  -4.7143   1.7513
+#&gt;   -7.6837   8.4554  -6.2225  -7.9215 -15.1581   4.2338  -4.3397  -8.5616
+#&gt;   -1.4925   0.9310   0.1745   7.8605 -10.1225  -9.7224 -10.6454  -5.1328
+#&gt;    5.1166  -5.5563  -3.4195  -7.5142  -6.0150   3.4729 -12.7218  -4.4432
+#&gt;   -0.6968   1.9179  -1.0473 -12.1094   1.7510  -3.5513 -10.3687   3.9037
+#&gt;   -8.8777  -8.4637   9.1328   7.5304   2.5435  -0.7738  -4.5382   7.4405
+#&gt;    3.4902  -4.5657  -6.3298  -8.0692   6.9725  -9.0220  -4.4722  -9.1183
+#&gt;    4.3632   7.8274   7.3080   1.6116  -3.2386  -2.5663  -2.4155   2.2608
+#&gt;   -6.8906  -7.7736  -3.5131   5.5850  -7.2101  -7.0828  -3.4648  -1.2636
+#&gt;   -4.9135   7.8486   6.0787   9.6393   6.3337   2.7853  -2.4782  -2.6175
+#&gt;   12.2056  11.0097  -5.6749   6.6335  -0.6023   5.6280  -3.9017  10.6491
+#&gt;   -3.4025 -15.7746 -15.6751 -16.0670   1.3937  -4.1239  -5.8367  -6.8215
+#&gt;   12.1459   3.2775   7.3165  -6.2589  -5.5058  -3.6283  -6.4149  -9.3527
+#&gt;   -1.2078  -0.0522   3.6285   0.1165  -5.0967   3.7390  -1.1201  -9.5000
+#&gt;   -3.5308   2.7058  -3.9890   3.4945  -5.7438  -0.8554  -8.4195   2.9762
+#&gt;    6.6982   3.4786   8.0461  -9.3455  -1.4272   6.8840  -7.2722   1.4466
+#&gt;   -4.4001  -0.1649   2.9268   8.8747  -1.2902   5.5666   2.5303   0.6259
+#&gt;    8.3606  -7.1928  13.2341  -0.3941  18.6431   6.5971  -1.3029   4.8651
+#&gt;    1.9562   5.0652   1.6687   8.4165  -2.4939   3.4391  -3.4970   2.5919
+#&gt;   -6.0691 -18.4595  -1.0337   6.6505  -1.8230  -0.6532  -1.8843  -6.8945
+#&gt;    8.5778   1.2562  13.4635   5.7836   6.2390   6.4734  13.7121  -1.7506
+#&gt;   16.3378  12.4865  -5.7148  -9.5783   1.0756   6.7436  11.4067  -5.2211
+#&gt;    3.4916   0.0112   0.4477   5.7018  -9.1030  -0.5422   2.6076  -5.5469
+#&gt;   -4.8885  -7.4376   2.1646 -15.9880  -2.2590   1.2272   1.8984  -7.8221
+#&gt;   -8.0032  14.9478 -12.9263   1.4746 -10.2535   2.0050  -9.9589   1.8762
+#&gt;    6.5848  19.4712  -4.4186   1.4986  -9.8512   7.1145  -0.2163  10.5178
+#&gt;    0.1889 -11.4429  -1.6276  -2.4710  -0.2683  -0.3900  -5.8692 -18.0839
+#&gt;    9.9172   7.0181   7.4458   3.8973   1.2154  -1.8324  -1.7741  17.6313
+#&gt;   -3.3293  10.3854   7.7157  -2.8775  -0.8746   0.2266  -1.4422  -2.2013
+#&gt;   13.3809  -2.5503   0.5669  -8.6428  12.1443   6.1498   5.0442  -0.9521
+#&gt;    0.7790  -5.2123   4.2130  -6.1849  10.4151   0.1508   3.2000  -3.6462
+#&gt; 
+#&gt; Columns 9 to 16  -4.0860   6.4213  -2.5722  -2.3741   4.1568  -5.8059   1.7523  -1.2796
+#&gt;   -3.1006  -3.4590 -16.6207  -8.0393  -1.1310  -3.5510   4.8503  -2.9037
+#&gt;    3.0810  -3.6914  -6.7095   2.1614  -2.9226 -10.6996  15.6299   6.2988
+#&gt;  -11.1525 -16.5291   9.1431   5.3459   3.2410 -10.4744  -6.6682   1.0263
+#&gt;    1.0383   1.9176  -2.6346  -2.2827  -3.5935  -3.7414   1.7668  10.5311
+#&gt;   -0.7062  -1.7054   0.5477  -2.0877  -7.5597  -2.5377   1.7576  -6.5614
+#&gt;   -8.4782  12.9652  11.9687  -3.9546   9.3618   9.1582 -10.6152  -4.3985
+#&gt;    2.9258   5.9505   1.6781  -2.0285  -6.0790   2.2242   6.5264   6.0665
+#&gt;   12.6039  -2.1076   4.9537 -11.3343   2.3227   1.4610  -2.6695  13.5633
+#&gt;    6.5875 -11.4847   6.2436   2.8999   0.8432  -0.0396   0.9859  -1.0960
+#&gt;   -2.4116   4.0544 -15.8423  -0.0828   0.6131  -5.3123  -0.8512   3.7520
+#&gt;    2.1860  -3.6086  -0.5867  -3.6484   0.5793  10.9506  -6.7126  -4.9702
+#&gt;  -13.6129   5.2558  -3.1411   4.7320   0.3424   7.0105   4.9324  -6.7802
+#&gt;    4.0216   6.1902   5.0901  10.2227  -0.1422 -11.2877  -6.1075   3.0605
+#&gt;    5.2406  -1.4246   3.9030  -2.4046   0.8196  -8.4962   4.4973   6.3254
+#&gt;   -7.6489   6.6798  -7.4386   3.0880  -0.5156  -8.3741   3.2873   6.4475
+#&gt;   -3.4557  14.1308  -2.1692  -4.5955   7.5381   3.6648  -0.5949  12.6182
+#&gt;   -8.7020  -7.9468  -3.3009  -5.2928  -1.0027   7.5290   9.5723  -9.5589
+#&gt;   -1.3027   5.1076   8.0577   0.2842 -11.1052   4.8339   6.8218  -0.0609
+#&gt;    2.5851   0.7047   6.0073   9.2696   5.6514 -10.3622  10.6630   1.7014
+#&gt;    0.3271  -7.3651   4.2371   0.7115  -2.7260   4.1920   1.4280 -10.3795
+#&gt;    3.9296   8.8945  -0.0389  -0.4414  -3.4227  -8.3822  15.2850  20.2510
+#&gt;    5.6511   4.8389   2.8384  -2.2936   2.6809   3.3876  -9.1269  -3.5801
+#&gt;    8.9159 -13.7750  14.9805  15.4946   2.3645   4.7642 -14.0470 -10.4856
+#&gt;    8.8515  -4.1713  -1.1083   5.2987  -3.5451   1.7682   8.2087  -5.3819
+#&gt;    4.3616   9.0161   6.3545   2.6342  -3.4428  -7.2009   6.3231  10.2266
+#&gt;   -1.8823   7.2042   4.8943  -3.7700  -0.2878   4.5386 -11.0202   4.4571
+#&gt;  -10.8001   2.1424  10.2672   8.6709  -4.4365   3.3502  -5.4543  -4.8962
+#&gt;    6.6005  10.0152   1.3169   2.4047  -1.9600 -12.1608  11.7453   8.2295
+#&gt;    0.8924   8.1780  -7.9939  -4.9859   5.4768   8.9285   5.1992  -9.6600
+#&gt;    9.9078   7.4913  11.5323  -4.9031   8.4436  12.0946  -1.5957  -8.7517
+#&gt;    2.2561  -3.1106  -7.4184   4.5571  -2.6696   2.9912  -1.4644 -10.2755
+#&gt;    6.9493 -20.7305  -2.5021   0.0686   7.6799   0.9227  -8.6592 -16.3700
+#&gt; 
+#&gt; Columns 17 to 24  -2.0726  -8.5104   7.9019   0.8095   1.3915  -1.9690   6.3407  -4.2864
+#&gt;    4.3306  -6.2841   5.3894  -0.8770  -8.1014  -1.7010  -6.8155   7.6810
+#&gt;   -6.7169  -8.4859 -10.5882   3.0183   2.8342  -0.5263  -1.2124  -0.5939
+#&gt;   -1.4485  10.2153  -6.5924   0.9652   2.0956   1.7560   1.1663 -10.9764
+#&gt;    1.4974  -3.7323  -9.4498   8.6725  13.9418  -6.7380  -3.5900 -14.4211
+#&gt;   -5.1598  -6.6805   1.9089  11.0177   4.9359  -1.2375 -18.6508  -2.8171
+#&gt;   -4.3521   4.9877   7.8086   2.4810 -13.5333  -5.0737   5.3768   0.7935
+#&gt;    5.4761 -15.1532 -17.5176   7.3348   3.8836   4.4217   1.4999   3.2297
+#&gt;    4.6920 -13.0070   4.5573   4.2250  -7.9661   6.4834  -1.7304  -0.2558
+#&gt;   -6.9633  10.6470   7.2614   2.0687   1.7736  -3.7101  10.6454  -6.3196
+#&gt;    3.5066 -10.6950  -5.6799  -4.6649  -2.4139  -8.7156   0.1989   1.2605
+#&gt;    0.2611   2.4521   4.0678   0.5756   1.4464  -7.7336   2.6385   1.9377
+#&gt;    0.3724  12.6858  -6.6219   1.6642   4.2785  -2.6077  -4.8870  11.6064
+#&gt;   16.4867  -1.3708  10.7355   3.2567  -2.8250  -9.6266 -13.2044  -5.2769
+#&gt;    4.0348 -10.8766   8.0989  11.4609 -13.0163  -0.6370   1.8148  -1.7120
+#&gt;    2.6839   7.6035   2.5099 -13.0648   3.8901   4.8382  -3.4283   2.3372
+#&gt;  -11.1093  -4.6944  -5.4822   9.0872   7.4998  -2.4886  -0.8404   1.1495
+#&gt;   -4.3792   0.9561   7.4340   3.5363  -3.4969   7.9804 -12.3729   6.5105
+#&gt;   21.3925   2.3420  -2.7837  -7.9849  -2.1592   5.0746   0.0229  15.6099
+#&gt;    5.2014  -7.5834   6.1664  -0.7725  -0.5959  10.4268  -6.7089   9.3841
+#&gt;    3.9032   0.0352  -7.8161  11.2603  -4.6685   1.1181  -5.0832   3.3944
+#&gt;    7.3372  -1.8604  -3.0359  -0.2803  -2.4817  -3.0330   4.7001   0.7510
+#&gt;   -8.9387  -7.2561  15.3523  -0.1818  -4.5693  -1.8624   8.3489   3.5271
+#&gt;   -1.2746  25.1526   3.3148  -6.3416   7.5979  -3.0577  -1.0300  -1.1902
+#&gt;   -1.9080  -0.5866   1.9487   4.7672  -0.3371   1.0384  -0.0173   2.7061
+#&gt;   -0.4439  -6.8372   1.0193   3.8842   6.6423   3.5682  -9.6387  -1.2007
+#&gt;   -0.0374  14.5370  -9.4165   8.8781  -1.4394 -11.5466  -5.7869  -0.3195
+#&gt;    5.4837  11.2849   1.7591   1.1738  17.6410   2.1348 -10.2499   1.9873
+#&gt;   16.1238  -0.1729   4.4630   0.1696 -10.7953   1.3255   1.3351   2.5280
+#&gt;   -8.5880  -1.6725   7.6080  -1.2273   1.6391   2.3870   7.2627   4.0279
+#&gt;  -12.7845  -4.4068  10.2311   7.9620  -6.4136  11.8917  12.7687  -2.2985
+#&gt;   -0.3770 -10.9905   0.7378 -15.5345   9.1464   1.6215 -10.1390   9.8305
+#&gt;   -4.4512  -1.1555  16.8918  -3.8699  -9.2140  -3.4994   0.3889  -7.3029
+#&gt; 
+#&gt; Columns 25 to 32  -1.4314   0.2024  -4.2907  -3.4588  -5.4996  -3.6940  -5.0281   1.0755
+#&gt;  -11.5288  -0.3438  -5.6180   5.4355  -1.9867   8.7279  -1.3348  -3.2532
+#&gt;   -1.7205  -2.4896  -6.8395   2.5132   6.8259   7.1714  -8.4811   1.8884
+#&gt;   12.2358  -2.2164   9.4752  -6.3795  10.7066  19.2873  25.2370  -6.6958
+#&gt;  -12.0759 -11.5659  -1.5740   3.2449  -0.4928   2.1976   7.9682  -3.1802
+#&gt;   -5.4648  -1.1428   3.6683   1.4968  11.3532  -2.0583   9.4373  -3.6948
+#&gt;   -0.8467   0.3965  -1.5380  -3.3360  13.1708  -2.0463   2.0306   9.3244
+#&gt;   -6.1372 -11.2179  -2.1520   0.2010  -9.1823  -1.6848   9.5493   2.4082
+#&gt;   -0.1230  -4.3619  -2.8306   2.8916   8.8175   7.8968 -11.3143   7.2510
+#&gt;    3.9155  -5.1768   5.4694   3.1729  -1.6373  -1.1168  -6.9770  -2.4431
+#&gt;   -5.5661  -2.9941   5.8947  11.3340  -0.4344  10.3948   6.8087  14.5704
+#&gt;   -7.4637   5.9919  -1.1063  -2.8024   0.4877   0.9881   0.4987   7.7180
+#&gt;   -0.1479   8.8453  -7.3753 -11.8225   1.0302  -5.8837  -0.5575  -0.0465
+#&gt;   -1.1157  10.4909   9.3996   4.8179   8.5646  -2.8770  -1.1479  -7.2244
+#&gt;   -7.7664   2.7722   0.2519  -5.8544  -0.2825   8.1229   2.5394  -1.9655
+#&gt;    3.7868   6.7755   1.1934  -2.7120  -0.8109  -2.1745   9.7496  -7.8422
+#&gt;   -6.0130  -4.9658   3.0462  -4.8569   3.0011   3.1681  -7.8576   4.7335
+#&gt;   -0.8590  -2.2963   3.9233 -11.4663   2.8552   5.0722  13.3096  -0.2567
+#&gt;    2.4902   6.0696  -4.5408   6.5379  -5.8495  -6.7323  -6.0372   3.8359
+#&gt;   -1.7285   4.3793   8.7735  -2.9332  -6.4038  -0.0454  -4.6984  -8.4368
+#&gt;    4.4295  -4.1835   0.2776   7.2391   8.5665   8.1895  -2.9724   1.7052
+#&gt;    8.5844   0.4590   5.8992  -0.5390  -5.5647  -4.9448  -0.5048  -2.1230
+#&gt;    8.4651   0.7857   8.0964   4.4390  -7.4403  -7.3722  -2.1246  -7.9744
+#&gt;    9.2435   3.5409  -2.5928   6.3410  -3.9584  -1.3803 -16.1972   3.2548
+#&gt;    6.9294  -0.4257   3.1535   1.8400   5.1561   8.7191  -6.5142  -0.4285
+#&gt;    2.4707   2.4562  -0.8055  -6.3825  -8.4170   5.5621   5.7736 -12.0265
+#&gt;   17.0837  -1.2967  -6.9036   3.5851   8.0978   1.0316   1.1240   0.9540
+#&gt;    0.2170  -2.0506  -1.8686  -8.5422  -8.8884  -4.6117  -5.6362  -1.9346
+#&gt;    4.5573  -1.4111   4.1964   4.8692  -5.5536   4.0321  -9.5204  -3.1594
+#&gt;   -6.7277  -0.8789   0.3533 -11.3807  -5.0255  -7.8873 -17.7735   4.3360
+#&gt;    4.5420   0.0373  -0.1702  -0.2021  -1.0652   9.4204  -2.9035  13.7808
+#&gt;   -7.4720   3.0406  -4.3222  -3.9177   1.7697   2.4556  -0.5615  -7.5813
+#&gt;   -1.4074  -0.0846   5.4931   7.8539   1.9790   6.8378  -1.2712   9.0633
+#&gt; 
+#&gt; Columns 33 to 40   6.9240   2.2739   7.0122  -1.2701  -2.0115 -11.0764  -0.4458   1.0459
+#&gt;    7.3224 -17.3510   7.5953  -1.9614   0.2458   4.0317  -6.9640   1.3121
+#&gt;   -1.6173  -3.9226  -3.5357   0.0570   7.2102   5.1422   3.8731   1.4483
+#&gt;    9.0869  -4.2868  -9.8618  -3.8325  -0.4532  17.1419  -8.5024 -10.2601
+#&gt;   -0.2259  -4.7216  -4.4411   1.1250  10.8359   7.9578   2.3752  -4.4357
+#&gt;    5.8127   5.1264  -2.9787  -1.3527  -1.2789  -4.9458  -7.6917  -3.4897
+#&gt;   -1.2848  -5.1894  -6.8718  -5.8907   1.3262   3.1774  -5.5343  -0.2647
+#&gt;   -2.7601  -6.5327   2.7842  -1.9254   3.0175   7.3933  -0.0057   9.5916
+#&gt;    5.9871  -2.5564  -4.1029  -9.8237  -4.6692   8.0873  -0.3651   7.6660
+#&gt;    0.9929  -7.0597   8.7479   1.4223   0.3354   4.1855  -6.3449  -6.7046
+#&gt;   -2.0061 -11.2205  12.1521  -7.9127  -2.7581  -5.9309   0.4322   3.8732
+#&gt;   -4.0634   4.8246  -2.8899  -5.7091   1.0374  -2.4193   3.5607  -0.9288
+#&gt;    5.1217   5.9972  -6.4678  -1.1522  -5.8698  -8.0975   0.9765  -2.1171
+#&gt;   -7.7123  -9.3345   4.5372  -3.0058   6.6523  -2.0967 -14.2951  10.2028
+#&gt;   -0.6436  -8.3312   0.9566  -1.0786  -5.6910   2.0966  -6.9138  12.0494
+#&gt;   -4.8390  -4.3222   4.5728   5.7941  -4.1121  -2.2718  -9.8567  -0.8623
+#&gt;   -3.5303   0.5670  -4.8218   4.8225   1.5968   4.4576  -4.7246  -3.1750
+#&gt;    2.3410  -1.1211  -2.5534  -0.4316  -8.1122  13.7344  -6.8140  -3.7069
+#&gt;  -10.9319  -4.5192  -4.2456   3.3167 -10.8028  -6.1140  -8.8725  11.7455
+#&gt;   -4.2912   1.0876   5.6363  -0.4622  -8.5785   2.3193 -15.1438   9.9583
+#&gt;    2.7829   0.9444  -9.0406  -0.5925  -2.3984   2.4452   3.7937   4.2564
+#&gt;   -1.4780   4.3162   2.4416  -2.1974   1.2660 -11.5144   2.8115   3.2969
+#&gt;   -1.6887   7.3647   3.4006   8.6264  -2.1062 -21.2366   5.4839  -7.3611
+#&gt;   -3.4757  -4.6786   1.9789  -4.2565 -10.2247  -7.5134  -1.3938  -6.8941
+#&gt;    6.3073  -2.8765  -2.7399   1.3770  -4.9203  -3.8809   6.7716   0.3234
+#&gt;    5.7622   8.9038   4.6575  16.3209  -2.9244  -1.3163  -0.1654   8.7446
+#&gt;   -4.1624  -6.9978 -15.7060  -0.7463   1.0695   0.1511  -2.9578 -10.5753
+#&gt;   -7.4290  -1.1818   6.1719  11.9056  -2.4689  -1.7901  -9.7585  -3.2402
+#&gt;   -2.9330 -10.4269  -1.6227  -3.9259  -4.4273  -6.0231   1.4791   2.9367
+#&gt;   10.5194   2.4614  13.6208   1.2071  -6.8071 -14.5845  -7.7553   7.8994
+#&gt;   -2.7698   3.2642 -13.1002   1.3493  -2.2242   0.5566   4.5718  -4.1912
+#&gt;    6.4498  -4.5213   3.0324  -1.5308   1.1251  -1.1830  10.9511 -10.9109
+#&gt;    2.9400  -4.1101   7.9595  -4.0359  -6.0525  -3.9387  -2.4284 -12.6910
+#&gt; 
+#&gt; Columns 41 to 48  -6.0871  -3.5399   0.5556   9.9953   0.1490   3.1870  -6.8017 -13.3374
+#&gt;    1.2691   2.3812  -6.8871   1.8500   5.0405  -0.9936   5.5278   5.6075
+#&gt;   -5.6155  -1.3369  -2.0059   9.6355 -12.1226   1.6743  -0.1978  11.2916
+#&gt;    1.6661   4.4906   7.2712 -12.7531  13.2853   0.7525   3.8068  13.7190
+#&gt;    4.6584  -3.7663  -8.8265   2.4192  -7.8541 -10.5215  -4.8588   1.6292
+#&gt;   -6.0489  -4.9913  13.1335   7.5758  -5.6704  -2.7146   7.0350   3.8747
+#&gt;   -1.6261   4.0118  14.6895   3.5888  -2.2662   3.8648  -1.8610  -6.0246
+#&gt;   14.2621  -8.8456 -21.6802   7.7011  -0.4258  -8.2232   0.4463   1.6795
+#&gt;   -5.0160   6.0531   1.9887   7.9535   3.9957  -0.8742  -5.1014   5.0997
+#&gt;    4.3507   6.6107  -2.5763  -5.0663   5.9065   7.2129  -0.1093   2.1234
+#&gt;    8.6998 -11.3704  -9.0154  16.7581  -7.8162  -9.6847  10.0728   1.3776
+#&gt;   -5.6878  -4.9777   0.8464   5.6317  -4.4443   1.0655  -0.7358  -0.4476
+#&gt;   -3.2676   1.0310   2.7182  -4.5991  -6.6428  11.7684   0.0934   2.0296
+#&gt;    3.3787   5.7968   2.6977  -6.3838   1.3135 -14.7291  -5.4446  -2.5756
+#&gt;   -6.7851  -4.3474   4.3288  -3.5593 -11.8488  12.7276 -11.8330  -1.3451
+#&gt;  -10.9471   2.5203  13.9947  -6.5298   6.3223  -5.1206  -5.3993  -0.0330
+#&gt;    7.5481  -7.9991  -7.1928   0.4923 -10.9895   6.2631   4.2667  -2.1313
+#&gt;  -10.8836  -6.3230   8.0348 -10.5145  -5.3140   1.6496   4.7261   0.3512
+#&gt;   11.6409   8.2070  -3.0861  11.9172  -7.4494   0.6038  -5.9887   4.6739
+#&gt;    2.8818  -3.6726  -2.8435  -3.0786  -3.5361  10.6113  -3.9672 -12.3372
+#&gt;   -5.1373   2.7864   0.3852   2.0220  -4.7495  -2.6269   8.5026   2.4522
+#&gt;   10.0219  -5.2154  -1.9534   1.5399  -0.1130 -11.5162   5.6648   1.9193
+#&gt;   -1.1927  -3.9725   2.2673   7.0946   2.2641   5.1577   6.1204  -6.5758
+#&gt;   -0.5557  19.6535   5.5160  -0.9664  -5.6467  11.3334  -3.9025   2.1416
+#&gt;   -2.0937  -2.5151   8.3120   1.5805  -8.8088   1.0911   6.1394  -5.1742
+#&gt;   -3.1773  -3.2192   3.7206  -6.5699  -1.1953   1.7972  -1.5603  -6.4786
+#&gt;    8.5507  11.2789  15.4065 -11.7882   1.7775   0.7023  10.9679   7.2172
+#&gt;    6.6951   0.6656 -15.2272  -4.2974  -6.1461  -9.1327  -0.0604   8.8080
+#&gt;    3.9147   7.3136  -0.1624  -3.7806 -10.1947  -5.6692  -1.2823 -10.3802
+#&gt;    4.9400  -8.4757 -12.2068   7.9412   8.0861  -6.6263   1.0023  -2.1691
+#&gt;   -3.4307  -2.0088   1.9208   5.2437   4.9451  -4.2088  -8.7323  -1.8118
+#&gt;   -4.3933   5.3883  -6.2951  -0.2386   2.5083  12.3352   5.4033  -1.1363
+#&gt;   -9.1130   6.0769   5.5055   2.3297   3.4682   3.0630   2.3178  -1.0043
+#&gt; 
+#&gt; (13,.,.) = 
+#&gt;  Columns 1 to 8  12.7182   3.8064  -4.6155  -2.5688 -14.8029 -15.1014  14.4892  15.4805
+#&gt;   -2.9289  -1.0909   3.3326   1.2205  -0.1754  -3.5472  -0.4407   3.1290
+#&gt;   -3.2056 -17.0926   3.7162   9.3470   8.8201   1.5397  -7.4968  -8.2629
+#&gt;   -2.9956  13.7263  -3.6488  14.7005  10.6438  -1.2256 -14.0772  -0.5874
+#&gt;   -5.1724  -0.4424  -7.6078  -6.2448  -3.3594   4.1992  -4.1085  -0.3341
+#&gt;  -12.0820  -4.6100  -1.2477  -3.0685   1.4967  -2.3838  10.4906   7.2323
+#&gt;   -3.5632  -6.4135   0.5737  -3.8202  -7.1241  -1.6567   8.9361  -5.5403
+#&gt;    1.4872  -3.9668   4.4526  -1.0609  -7.8944  -0.7267   6.2148  17.0953
+#&gt;   -4.3076  -8.5738 -12.5724   2.4266   4.4529   4.6341 -14.1243   2.0411
+#&gt;   -8.8410   2.2157  -2.9455  -2.8395  -4.8541   8.5633  10.4077 -10.2788
+#&gt;   -4.8465 -24.2523   9.7135   9.3001  -7.8211  13.3860  -2.3863  -3.3853
+#&gt;   -4.7057   5.7289   3.5920 -10.1702   0.9869  11.4714  13.8918  -2.4690
+#&gt;   15.4570  -4.6192   4.4933   2.7143   0.9961   5.4573   6.6889  -3.3549
+#&gt;   -1.1126   4.7426  -5.7291  -8.4460   3.5938 -12.8634 -16.3419   9.6687
+#&gt;   -5.3567  -8.1428  -4.4861  -2.8718 -10.2998  14.9662   7.5159 -16.8273
+#&gt;    0.2225  -6.3716   5.0470   2.5779  -7.1910   0.0347   2.0312   4.2226
+#&gt;   15.0371  -5.1816   0.3470  -2.7460  -5.2628  -6.9144  -5.2145  11.9414
+#&gt;    0.8363   0.7920   6.9030   3.1910   8.0712   7.6977   2.9686  -3.0270
+#&gt;   -1.0038  -8.6622   0.0588   3.2239 -10.7892   3.4944   6.1863   7.9359
+#&gt;   11.5861  -1.0449  -1.9407  -6.4830  -6.3589  -6.8171  11.7829   6.8568
+#&gt;    2.6081   5.8065   2.0014  -1.1764   1.5566  -1.7988   6.6751  -1.2022
+#&gt;   -3.4828  -8.0346   1.7535   5.6982   3.8348  -0.0052   2.4027   2.7767
+#&gt;    1.1414  -1.1804  -3.3855  -0.6290  -5.3685   4.0934   9.0223  -5.5763
+#&gt;   -9.7996 -10.3637   3.0964   2.5960  -3.6950  10.3639  -4.1379  -4.6375
+#&gt;    8.5050   0.8158  -6.0417  -3.8965  -2.2206   3.6987  -0.5295  -1.9969
+#&gt;   15.0140   5.7383   0.3618  -8.7803  -8.2044 -21.6215  11.0205   8.9326
+#&gt;    3.8972  -0.7520  -4.1582   2.4096   0.7532   4.9532  -2.0873   6.8268
+#&gt;   12.8697 -12.6970 -10.1723   3.7770  -0.2269  -6.4761  -2.3072   9.8770
+#&gt;    0.8609  -3.8583  -3.4605  -3.7649 -12.9216   3.7425  -1.5771  -5.6844
+#&gt;   22.6210 -12.0578 -11.3394   0.6404  -6.8014  -2.1969  11.6617   9.4068
+#&gt;    1.5590   1.0693  -3.1193  -8.9169   0.2025  -1.1535  12.7848   5.9852
+#&gt;   10.6550   3.2722   7.2799   1.1171   4.2774  -4.7724  -2.8974   1.9139
+#&gt;  -10.4001   1.4934  -4.0810   1.3808   4.9810  -2.6628   4.0061   0.4019
+#&gt; 
+#&gt; Columns 9 to 16  -3.8668  -2.7478   7.1392  -0.0054 -10.1722   0.1198   2.4732 -10.4295
+#&gt;   -2.5754  -7.8994  -0.1849  -3.9888   4.4277   2.1233  -1.5317  -3.4512
+#&gt;    0.1197  -2.1993  -2.9206   3.3155   1.5636  -2.1639  -5.8681   3.5642
+#&gt;   11.7872  16.3354   1.8357   2.9643  -8.7039 -16.2158   8.4494  18.9837
+#&gt;    0.9327  -6.3295  -8.8967  -0.9992  -1.9962   1.4210  -4.5254   5.3463
+#&gt;    2.7183   2.6481   2.7299  -4.0675  -0.1711   2.8628   0.1096 -13.9721
+#&gt;   -3.0392  11.4258  -5.2078  -6.1831  -8.3396   1.0591   5.2099 -17.4486
+#&gt;    3.5829  -9.0933  -1.7016  -3.1307  -0.3316  -0.5869   1.8062   2.3150
+#&gt;   11.4195   9.0115   0.8662  -4.4907   1.4685   2.5792 -11.6804   1.5862
+#&gt;    4.9865   2.8401 -10.9968   4.7530  12.6697   1.5929  -3.2429  -4.4498
+#&gt;    2.8203  -7.8873   2.5764   4.9312  -2.4513   8.1254  -9.4267  14.4767
+#&gt;  -12.1972   4.5896  -3.4220   0.1365  -1.4710   7.8849  10.8458 -12.1589
+#&gt;   -1.4284   2.1966   3.1238   6.6784 -10.8862  -3.4908  -1.3427  -4.6650
+#&gt;   -7.8388  -6.3305  -7.3320 -13.8610  -4.9487  -6.9116   8.5775  11.5956
+#&gt;    0.0853  -3.0662  -2.6750  -8.6276  -1.1991   7.8136  -2.3070  -9.7200
+#&gt;    5.5640   1.9188   6.5125  10.3249   2.2903 -10.1161  -9.1294  11.1086
+#&gt;   -0.1784  -7.3305  -7.4177  -0.3665  -7.3336   5.7114   0.8584  -0.4436
+#&gt;   -1.0338   1.6243   3.5237   6.4708   2.4569 -15.7223  -2.4562   3.3643
+#&gt;    7.7476  -0.8750  -7.3911  -0.5791   1.9732  -9.2091  -3.6274   1.1787
+#&gt;    6.1295   0.0278   6.0033  -4.9993   9.2848  -0.0427   4.6808   5.4250
+#&gt;   -3.9739   5.5625 -14.3787 -10.4563  -2.6791   5.7062   2.1011 -13.5959
+#&gt;    6.9645  -6.5812   9.3948   5.4502   3.3648  13.1890   5.1092  10.3599
+#&gt;   14.9967   2.4909  -1.4835   2.4225   1.3400   4.2230   1.5507  -4.6680
+#&gt;    7.1955  -4.8003  -1.2917  -5.7070   0.1072  -8.7850  -0.7931   7.4616
+#&gt;    1.6131  -5.1979  -9.2722  -5.3319  -5.0503  12.5062   1.3686  -5.8991
+#&gt;   -6.1673   2.4002   6.9490 -12.5821   2.0743  -1.9734   4.7670 -11.5226
+#&gt;    4.9347   5.6705  -9.3898  -4.2742 -13.3988   4.0754  -5.8353  -2.3527
+#&gt;   -7.3341  -6.5663 -11.6332   2.3066  -2.2382  -7.5558   8.6617  -5.3038
+#&gt;    5.8818  -6.0350  -2.6655  -7.3441  -0.0255  10.9176 -10.1541   8.6460
+#&gt;   -5.3208  -9.5275  -1.1192   8.5280  -2.6452   5.6199  -7.0017 -17.5711
+#&gt;    4.3405  14.7082  -0.3999  -9.3258  -3.6946  -8.6221   6.9970  -2.3698
+#&gt;    1.7119   1.5103  10.6896   4.0890   0.2511 -10.5023   3.5156  -0.9019
+#&gt;   -4.1529   1.8704   6.3679  -3.1304   8.9665   2.7667   1.1333  -0.4228
+#&gt; 
+#&gt; Columns 17 to 24   3.2644  -7.6745  10.2642   6.0378  -0.4222   6.0229  -1.2183  -9.0270
+#&gt;    0.5475   3.1340  -1.7418  11.0928   0.0034   4.6541  -6.4449   6.8976
+#&gt;   -5.2873   8.3848   1.2836  -3.6144  -0.5825   7.4089   5.6819  -6.9342
+#&gt;   -6.3869   6.9111   0.6449  -4.9521  15.6333   1.1160  -3.0849   0.4828
+#&gt;    7.6230  -1.8587   7.6517   7.5453  -5.5194  -6.6080  -2.5460  -4.6176
+#&gt;   10.3962  12.6604  -3.6792  -1.5480   4.6448   9.8750  -3.0308  -5.3889
+#&gt;    7.2503   0.8777  -9.2999  -4.8500  14.7870   9.0033  -5.2733  -0.1219
+#&gt;    4.2802  -6.1003   4.3013   2.7553 -15.3193  -9.5015  -0.1899   8.7518
+#&gt;   -4.2541  10.1026   0.5425  -6.4690  -3.4436  10.0342   1.6691  -7.5241
+#&gt;    6.9315  -2.6069  -5.3847   1.8447   3.9693 -10.8297  -3.8406   2.8337
+#&gt;    1.8448  -1.0881   7.9049   7.4700  -8.6152   9.5878   6.2947  -9.4251
+#&gt;   -1.6250  -5.0960  -5.8780  -6.4262  -0.6168   3.6598  -5.6435   0.1190
+#&gt;    8.8656  -3.5729  -0.7615  -2.3844   0.9248   2.6855   1.6193   5.6035
+#&gt;  -11.0357   8.2507   1.4583  12.7547   8.3913   4.8109  -0.6845 -17.8735
+#&gt;   -5.1945   3.3425  14.4335  -5.6128  -1.2405  16.7262  -8.0684  -3.8181
+#&gt;    1.7073  11.1047   7.8915   2.4152   6.0107   3.5970   4.5400 -14.8674
+#&gt;   -0.2061  -6.4413  10.6123  -5.4404   0.3608 -10.9615  12.0835 -10.8443
+#&gt;    1.0752  16.0308   3.4707 -10.5946   1.7174   9.9684  -1.6525   3.1576
+#&gt;    2.7748   6.7030   3.1177   8.2975  -1.7461 -10.9206   7.0825  -4.9354
+#&gt;    1.3440  -1.1578   4.7978  -5.6843 -10.4707  -6.3842  -5.6464  -2.9741
+#&gt;   -3.5364   5.4568 -13.7798  -1.0582  -4.4093   2.3835   0.6889  -0.1660
+#&gt;   -1.9331   0.0593   9.4289  -1.3197  -3.2413   2.3622  -1.3254  -2.7177
+#&gt;   -4.3343 -19.4646  -0.5988 -13.6729  -8.8478   8.7292   0.9266  -0.4544
+#&gt;    0.7685  12.0428  -7.5370  -4.6764  13.1441  11.0089   2.3001   0.6684
+#&gt;  -12.9065  -0.3823   6.3071   1.0944  -9.8442   0.6113   6.0180  -6.7031
+#&gt;   -9.0167   3.9631   7.3313  -2.6333  -9.3185 -10.9269  -5.3837   2.5250
+#&gt;    4.2730   0.4342  -5.3750  -1.3528  13.0142  -0.3168   0.0088   2.7591
+#&gt;   -7.7178   5.6187   3.7903   2.4726   1.0198 -20.0105  12.6015   6.4381
+#&gt;   -3.6189   9.8294   9.2189  14.6899   0.4935  11.5095  -4.7393 -10.4125
+#&gt;   -4.1638  -2.9080   0.6444   2.5991  -8.8652  -5.8333  12.0345   9.4001
+#&gt;   -9.8705   4.0219  -8.9801 -18.6822   6.6756   0.3598 -11.3057  10.5311
+#&gt;    5.9611  -5.2889  -5.9852   2.2408  -7.3118   5.1187   5.4850   0.3128
+#&gt;    0.6640   2.9601  -4.1693   2.8989   8.1751  13.8882 -11.8104   4.4661
+#&gt; 
+#&gt; Columns 25 to 32   0.1069   4.4300   4.3003   6.5373  -7.5178  -9.7551  -7.0320  -9.3276
+#&gt;  -12.5463  -8.7917  -0.6243   3.1369  -2.3819   1.8997   8.5821   1.8319
+#&gt;    1.0214  -0.2199 -10.3648  -4.5515  -2.5590  -4.8727  -0.3350  -0.8497
+#&gt;   -5.2845  -4.0951   0.4800   3.8095  13.0600  -0.3360  11.9384  19.4068
+#&gt;   -6.6383   1.7713  -5.8471  -3.2602   4.2594  -4.0928   2.9030   8.8970
+#&gt;   -5.2800   4.1837   1.3231  15.0332   0.6706  -8.4351  -4.1162 -10.4221
+#&gt;    0.5118   3.6860   2.4320   8.9058   1.7754   0.0929   1.0673  11.7182
+#&gt;   -1.4659  10.9084  -0.4592   7.5206   8.2967   7.0389   4.2406  11.6816
+#&gt;   -1.0805 -12.3320 -19.3237  -5.8110  -0.9302   1.8380 -19.4156  -7.7184
+#&gt;   -2.6221   1.7152   3.4518   4.9626   2.2381  -2.2154  -3.6776   5.4062
+#&gt;   -8.0877  -4.6032   2.0279  -9.7583   7.7099   3.8745   3.0555   6.6926
+#&gt;    4.7537   6.8879   7.1303  -0.9630   9.1403   2.7630   3.1092   1.5607
+#&gt;    1.4150  18.0743  10.4641  -1.5044  -3.9003 -10.2854   3.7217  -7.7446
+#&gt;    6.4209  -9.0526   6.0607  -6.2215  -6.8936  -5.0646   3.7337  -2.1049
+#&gt;    2.4639  16.8365  -3.5789   3.9893  -8.1617  -2.3536   4.4018   2.1488
+#&gt;    3.4904  -5.3955  17.2709  -0.1046  -0.7013   1.9910  11.2501   2.8145
+#&gt;    5.8840   4.0392   4.6691   8.8491   1.7121  -9.7288   1.5834  -0.9072
+#&gt;    5.7569  -5.7727  12.9906   6.4348  -4.4528   9.0758  -1.0589   4.9945
+#&gt;   -4.2344  -0.3840  -0.8968   0.2907  -1.2674   1.3855  -8.0873  -9.2414
+#&gt;    5.2562   2.3932  12.1631  13.1696  -7.7894  14.9373  -5.3492  -4.2549
+#&gt;  -10.1516   1.2459   0.8897   5.6136   8.0912   5.3998  -3.4557  -4.1128
+#&gt;    9.0228  -0.1128  -3.0950  -2.6131   2.9784   1.2623  -4.3874  -4.5560
+#&gt;    3.7943   6.0817   6.3203  11.0212  -0.4039   7.1245  -0.3493  -9.0718
+#&gt;   -4.3440  15.9696   4.4231   9.1531  -3.5382  -7.9130  -3.9667  -8.0826
+#&gt;    3.7145  -1.6401  -0.5113  -5.6870   2.7285   6.6672   2.8719 -11.5719
+#&gt;   -3.1369  -4.8979  -4.1840   0.1913 -13.0126  -4.8511  -3.0648  -7.7171
+#&gt;    3.0135   1.6006  12.5806  11.2100   7.9489 -15.5392   3.0761   1.9750
+#&gt;    0.2687   1.8509  10.7228  -3.3233   0.6185 -14.0387   1.9764  -4.6311
+#&gt;    5.9052  -0.2443   7.0376   0.0236  -4.4985   7.8975  -6.8967  -6.6564
+#&gt;   -1.9590   6.0037  13.4657  -8.3504   3.7786 -16.6422 -12.9441  -7.0071
+#&gt;    8.0193   3.8135   3.1621  12.9735  10.7427   5.3991  -4.2870  14.5053
+#&gt;    3.5541   2.5437   2.1426   3.5000   0.3929   0.2014  12.6902  -4.4364
+#&gt;   -5.8694  -7.8271  -2.6009   5.2320   5.7658   9.6837  -4.6968   2.5444
+#&gt; 
+#&gt; Columns 33 to 40 -12.4905   0.9962 -12.2520   1.8805   0.7729   2.2591  -2.4218  -1.1181
+#&gt;    4.0905  -4.6726  -4.1660  -6.2877   2.1565  -9.2002  -3.7986  -5.0362
+#&gt;    2.3994   8.7141   3.2630  -0.1725   4.8186   0.7792  -5.8155   6.4687
+#&gt;    1.4119  -7.1807  10.3300 -12.6097  18.3856  12.5793   0.5493   6.3617
+#&gt;   10.8638   7.3653   7.2373  -4.0725   4.0856   8.2117  -9.3570  -7.8061
+#&gt;    7.3977   3.2087  -3.9545  -0.3392   0.3293   0.7637  -0.3166   5.9642
+#&gt;    5.6641  -2.3012   1.2621  -1.1695   2.5621   4.1702   4.9207   3.2028
+#&gt;    7.3067   5.3040   1.6473   1.7503   9.1175   2.8779  -2.2866  -5.0414
+#&gt;    9.1455   1.1883   7.9984  -1.1010   0.7344  -0.3915   6.8009   8.0225
+#&gt;   -7.8686  -5.7029  -1.8316  -0.0498  -4.6592  -1.9285  -3.9908  -2.4433
+#&gt;    2.5765   4.2916  -0.1664  -1.0247   6.6853   3.0413   0.8368   5.5294
+#&gt;    2.1822  -1.1567  -4.4557  11.3355  -9.0053   2.5235  -1.9342  -7.2046
+#&gt;   -0.7974   0.6058  -6.8831   0.0058   2.0477   8.0620  -6.5564   0.8866
+#&gt;   -3.5303  -0.4432   5.9479   5.8558   8.6230  -8.0827   2.2993  -6.1262
+#&gt;    4.9180  -3.5954  -9.6872   6.2345   6.8421   1.4011 -11.3722   1.2919
+#&gt;   -9.6036  -6.8975  -0.2126  -1.7491   3.3766   4.9670   5.3099   1.0471
+#&gt;   -0.6523   8.0283 -13.2220  -1.0271  -1.3558   8.9063 -15.6677  -1.9965
+#&gt;    9.0133  -7.1879   3.1848   3.3971   7.7392   3.0583   4.0695  -1.5547
+#&gt;   -7.8588  -2.7530  -3.3350   4.8636  -6.2975   2.7418   2.4916   3.9969
+#&gt;   -2.1845   0.3225  -7.1511   5.1200   1.0721  -5.5169  -3.6237  -2.7235
+#&gt;    4.9371   5.6697  10.0409  -0.1665   1.5843  -3.9746   5.9828   8.9432
+#&gt;   -8.0029   2.7323  -1.8968  -2.2308  -1.7877  -3.7023   4.5103   1.2744
+#&gt;   -6.0240  -2.3316 -12.1196   6.0434 -12.3856  -0.0222  -0.2043   5.0812
+#&gt;   -0.8801  -0.2558  -8.0960   0.6245   1.0336   1.1230  -1.4520  -2.2997
+#&gt;    0.1147   3.5448  -9.3894   1.3536  -2.8737  -1.1762  -4.0554   5.0370
+#&gt;  -12.9656  -2.1674  -5.5133   0.7504   2.1340  -3.4405  -2.6298   3.6028
+#&gt;    6.0563  -4.7571  -5.6242  -5.9527   7.6094  13.6002 -14.7488   6.5359
+#&gt;  -12.3398   1.0796  -5.4789   5.6667  -0.5361  -2.4495 -18.1071  -4.5523
+#&gt;    2.5113  -0.5029  -0.0765  -4.5280   6.1293  -2.0885  12.7282  -5.5231
+#&gt;  -18.0310   0.9654  -0.9127  -5.1784  -2.2538  -5.6351  -6.1726  -1.2225
+#&gt;    2.4638  -3.7619   1.7139   7.3916   9.0185   7.7355   1.6119  -4.0585
+#&gt;   -1.5231  11.2594 -10.2775  11.4900  -0.3246   6.5134   8.1654  -7.3673
+#&gt;   -1.2108 -10.1927  -2.5560   2.4528  -2.8826  -7.8111   5.8999  -0.9200
+#&gt; 
+#&gt; Columns 41 to 48   0.1988   5.2504  -3.0760  10.2908  -1.3008  -9.9980 -13.6230   5.6009
+#&gt;    1.1537   3.1284  -4.4897   3.9502   5.0897   3.6015   7.8427   1.9832
+#&gt;    5.0285  -1.6221   4.5983 -10.6953  -3.2972   8.4126  -4.6680   2.0519
+#&gt;    4.9383   6.7629   0.1700  -0.7731  10.6456   2.6704  -4.1326  -3.7576
+#&gt;   -6.5911   6.1939   7.0798   1.6225  -1.5633   3.8868   5.1865   9.6929
+#&gt;    3.2894   0.1017  11.9715   0.1416   8.5866  -7.0269   8.0777  -6.1043
+#&gt;   -2.9275 -10.6820   1.3487  13.4299  -3.0858  -4.1574 -12.7612   3.0961
+#&gt;   -3.7740   1.8306  -1.6566  -0.4461  -2.3291   4.8333   4.8621   3.8554
+#&gt;    4.4658   1.1858   8.9472   2.9284  -4.3364  -3.1499  -4.1935   0.7509
+#&gt;   -3.4900  -1.9808  -0.9273   8.6051  -0.6985  -1.2492  -1.7975   3.1656
+#&gt;    9.5229   5.4037   5.7429   2.6790 -12.2003   6.4440  -5.3810   4.4119
+#&gt;    2.7466  -4.2808  -1.5434   7.0435  -1.9691   0.1578  13.3589  -2.7863
+#&gt;   -0.4999  -5.5772  -2.0889  -3.4647  -0.7473   3.2818   4.4717  -5.4836
+#&gt;    3.9357   5.8264   2.2869  -2.3583  -8.4141  -7.6839  -1.1675  -4.9500
+#&gt;   -3.5572   2.3844  12.0013   1.1560 -10.2125   0.8103  -1.5860   4.9254
+#&gt;   -5.5393  15.7903  -2.9637  -7.3790  -2.8508   0.4124  -2.9728  -2.5130
+#&gt;  -15.2763   1.5354  -2.9154   5.6464  -4.8810  12.7598  -8.5844   1.4449
+#&gt;    0.0455  -4.2933   3.1696 -11.3167   5.7426  10.5657   5.2644   9.0828
+#&gt;   -3.8138  -0.8780  -6.8863  -2.3013  -4.3458  -3.6998  -8.0492  -6.8730
+#&gt;   -2.9428   6.1095   3.6243   0.9395   1.2437   1.5961  -2.6437  -7.6080
+#&gt;   -2.8307 -14.9884  -3.5193  -1.7380  13.1939   1.2391  -1.5804   6.7580
+#&gt;    4.3376   1.4819   5.9095 -10.5479  -6.1397  -1.9740  -3.3975 -11.0190
+#&gt;    6.2649   0.7111  13.0831  -2.3153  -0.6452   1.3613  -1.6137  -3.0733
+#&gt;   -2.5901  -5.3288   7.2456  12.8651   1.4870  -3.5211  -5.8653 -25.0905
+#&gt;   -0.2713  -8.3647   0.0763  -5.4667   5.4971  -1.8255 -10.7636   0.0127
+#&gt;   -0.4668   8.2574 -11.0812  -7.1201   1.5301  -6.1162  -2.5051   0.0130
+#&gt;    0.9573  -5.0825  -1.6988   8.7437  -5.5978   2.2083 -11.9892  -2.3081
+#&gt;   -4.2024  -0.2054 -11.7608   0.5975 -10.1964   9.2613   6.0544   5.0097
+#&gt;   -3.8999  -6.2661   8.1442  -2.5761  -2.0750 -13.3615 -13.5262   0.3246
+#&gt;   -4.1377   4.2118 -12.6908   7.3387  -3.0250  -1.6286   4.4283   8.4087
+#&gt;    1.0502  -1.8882   2.5351   5.0288  -0.8796  -3.3671  -5.9324   0.6171
+#&gt;    6.7530   5.1505  -5.3810   3.0831   8.8983  -7.8994  19.5764  -7.6865
+#&gt;   10.5669   6.0169   2.3651   7.6554   3.8816  -5.4518  -0.1022  -2.6415
+#&gt; 
+#&gt; (14,.,.) = 
+#&gt;  Columns 1 to 6 -2.9470e+00 -8.6971e+00 -4.9650e+00  4.1232e+00 -3.1464e+00  5.3055e+00
+#&gt;   1.5427e+00 -1.1095e+01  4.2713e+00  5.7637e+00  3.6362e-01 -9.4469e+00
+#&gt;  -7.6291e-01 -3.9613e+00 -1.6844e+00 -2.7396e+00  1.8311e+00 -4.1143e+00
+#&gt;   8.9186e-03  1.9440e+01 -8.9361e+00 -2.8924e+00 -3.0730e+00  1.9352e+01
+#&gt;  -9.7225e+00 -4.7628e+00 -6.7697e+00  3.6149e+00 -8.2467e+00 -2.5238e+00
+#&gt;  -7.0066e+00 -2.8066e+00 -6.3864e-01  1.5807e+01 -3.9110e+00  3.8801e-01
+#&gt;  -6.5141e+00  1.6159e+00 -2.5237e+00  2.6393e+00  1.0126e+01  2.1174e+00
+#&gt;  -1.9377e+00 -3.7156e+00 -1.0927e+01  2.3730e+00  3.7326e+00 -1.6164e+00
+#&gt;  -1.1518e+01  1.2488e-01 -8.5711e+00  1.2285e+00  1.5453e+00  1.2867e+00
+#&gt;   1.4090e+00  1.2069e+00  4.5120e+00  8.8675e+00 -4.1212e+00  2.0452e+00
+#&gt;   2.1451e+00  7.7938e-01  9.6499e+00  4.1449e+00  1.4574e+00 -8.9375e+00
+#&gt;   1.0404e+01 -3.8630e+00  7.2910e+00 -5.0135e+00  8.1828e+00 -4.1867e-01
+#&gt;  -4.0174e-01  6.0947e+00  3.0210e+00 -1.5495e+01  2.5243e+00 -9.6399e-02
+#&gt;  -1.4876e+00 -3.3305e+00 -8.6169e+00  6.6952e+00 -2.1132e+00  2.6100e+00
+#&gt;  -2.6931e+00  5.3624e+00 -4.1022e+00  5.6431e+00 -2.5500e-01 -9.9579e-01
+#&gt;   6.6913e+00 -1.3960e-01 -3.6253e+00  5.5914e+00 -1.5811e+01 -5.9588e+00
+#&gt;  -8.7709e+00  9.3600e-01 -8.2002e+00  4.3308e+00  6.0203e+00 -5.1867e+00
+#&gt;   2.4328e+00  7.7536e+00  2.4390e+00 -4.8175e+00 -8.0653e+00 -2.9132e+00
+#&gt;  -7.8568e-01 -3.3420e+00  7.9374e-01  1.0131e+00 -5.6944e-01 -1.9089e+01
+#&gt;  -3.7811e+00 -1.9507e+00 -1.2322e+00  4.1061e+00 -8.2291e+00  6.5997e+00
+#&gt;  -7.1566e-01  2.7382e+00  2.6303e+00  3.4786e+00  2.8957e+00  5.6836e+00
+#&gt;   9.6807e+00 -6.2116e+00 -3.0417e+00 -2.4916e+00  3.7252e+00  2.3739e-01
+#&gt;   1.0791e+00  6.4898e+00  1.3307e+01  1.0551e+01 -5.9581e-01  2.3691e+00
+#&gt;  -5.3603e+00  1.0517e+01  1.5184e+00 -9.7263e+00  4.9366e+00  4.8630e+00
+#&gt;  -2.2348e+00  5.4156e+00  7.0300e+00  1.3209e+00 -4.3836e+00  3.8853e+00
+#&gt;  -5.0869e+00 -8.2025e+00 -8.6510e+00  1.0470e+01 -5.3927e+00  2.7191e+00
+#&gt;  -1.1621e+01  4.2484e+00 -4.0592e+00  1.0454e+00  1.1135e+01  2.2191e+00
+#&gt;  -8.1427e-01  3.7419e+00 -6.3552e+00 -9.7194e-01  5.5231e+00 -8.6829e+00
+#&gt;   3.1891e+00 -4.9019e+00  7.8427e+00 -1.7551e+00 -2.0898e+00 -5.0366e+00
+#&gt;  -2.5993e+00 -4.6363e+00  3.5349e+00  2.1700e+00 -3.2316e+00 -3.7960e-01
+#&gt;   8.6088e+00  1.6032e-01  3.2837e+00 -4.5223e+00  1.6399e+01  7.8730e-04
+#&gt;   5.7161e+00 -4.1015e+00  2.2706e+00 -7.0973e+00 -2.1299e+00  6.9960e+00
+#&gt;   7.8803e+00 -2.1816e+00  1.0224e+01  2.5879e+00  1.2398e+00  7.2957e+00
+#&gt; 
+#&gt; Columns 7 to 12 -1.1646e+01  1.0131e+01  2.9991e+00  1.1064e+01 -2.1592e+01 -1.3639e+00
+#&gt;  -7.7052e+00  1.2533e+01 -2.7843e+00  1.1178e+01 -7.1699e-01 -3.4950e+00
+#&gt;   1.7887e+00 -4.3862e-01  4.8924e+00  1.2163e+01  4.5573e+00  3.6067e-01
+#&gt;  -2.0127e+00  9.5348e-01  3.6808e+00 -7.9013e+00  1.7339e+00  1.1636e+00
+#&gt;  -2.2187e-01  3.9322e+00 -8.6138e+00  1.6170e+00  1.3332e+00 -5.1143e+00
+#&gt;   7.7466e+00 -2.5890e-01  6.3755e+00 -1.1040e+01 -1.2306e+01 -6.3907e+00
+#&gt;  -9.5735e+00 -2.1791e-02 -8.1075e+00 -1.0824e+01 -1.0851e+01  1.0848e+01
+#&gt;   7.0934e+00  1.8923e+01 -9.2418e+00 -1.6874e+00 -3.7214e+00 -1.5075e+01
+#&gt;  -2.3427e+00 -8.0402e+00  1.0169e+01  6.0570e-01 -5.0686e+00 -2.0361e+00
+#&gt;  -1.3110e+01 -1.7934e+00 -6.1507e+00 -1.4862e+01  1.0235e+01  4.1763e+00
+#&gt;   5.6747e+00  9.7487e+00 -1.0725e+01  1.5667e+01 -4.6575e+00 -2.7561e+00
+#&gt;   2.7392e+00  3.2462e-01 -6.5484e+00 -1.1757e+01 -1.2866e+00  1.7478e-01
+#&gt;   2.4737e+00  3.8282e+00  1.9346e+00  1.3481e+00 -2.8944e+00 -1.9211e+00
+#&gt;  -2.5990e+00 -1.9938e+01 -1.8334e+01  3.8725e+00  8.6759e+00 -5.0472e+00
+#&gt;  -7.9813e+00 -7.0264e+00 -6.9558e+00  7.8006e+00 -1.2365e+01  3.5747e+00
+#&gt;   5.1817e-01  1.5110e+00  1.7366e+01 -1.8837e+00  9.2961e+00 -5.0517e+00
+#&gt;   3.5365e+00 -2.3325e+00 -2.8275e+00 -3.1046e+00 -5.2681e+00 -8.9198e+00
+#&gt;   6.5027e+00  4.3644e+00  1.2701e+01 -3.7251e+00  2.0427e+00  7.9933e-01
+#&gt;   7.8586e+00 -7.0188e+00  5.9492e+00  2.5074e-01 -3.2924e+00  1.8061e+00
+#&gt;   8.8907e+00 -5.8894e-01  1.5509e+00 -3.8481e+00 -7.0443e+00 -1.4297e+01
+#&gt;   7.1570e-01 -4.8762e+00 -1.1524e+01 -1.3736e+01  2.4542e+00 -1.8681e+00
+#&gt;  -1.7810e+00  2.3527e+00 -6.2339e-01  4.3841e+00  3.7593e-01  2.7804e+00
+#&gt;  -9.2921e-01 -1.0520e+01 -4.9004e+00  4.5912e+00 -9.6276e+00 -5.5966e+00
+#&gt;  -1.2594e+00 -1.3813e+01  1.0828e+01 -1.5079e+01  7.6320e+00  9.8324e-01
+#&gt;   1.4521e+00 -1.4763e+01 -2.9935e+00  7.2191e-01 -9.4360e+00  7.4886e+00
+#&gt;  -2.6939e+00 -6.2316e+00  6.4547e+00 -1.0803e+01  4.7456e-01 -1.2776e+01
+#&gt;  -7.0297e+00 -3.4002e+00 -3.2316e+00 -2.2789e+01  1.9848e-01  9.2831e+00
+#&gt;  -9.8949e+00 -1.2377e+01 -3.3984e+00  6.5185e+00  8.3058e+00 -4.8508e+00
+#&gt;  -9.2559e+00 -9.8128e+00 -1.0571e+01  4.7695e+00 -4.0007e+00  9.6223e+00
+#&gt;  -1.2972e+01  1.3614e+01 -8.2897e+00  1.0589e+01 -6.8666e+00 -5.1787e+00
+#&gt;  -2.1274e+00  2.2466e+00 -4.8599e+00 -1.7663e+01 -1.2687e+01  6.8946e+00
+#&gt;   9.7860e+00  7.6324e+00  8.4055e+00  7.6672e+00  4.1579e-01 -1.3755e+01
+#&gt;  -9.5256e+00  5.1509e+00  5.7313e-01  3.8565e+00 -8.5037e+00  6.5861e+00
+#&gt; 
+#&gt; Columns 13 to 18 -5.8376e+00 -2.9279e+00 -5.7467e+00 -5.5014e+00 -4.6681e+00 -1.1295e+00
+#&gt;  -9.8382e+00  4.1628e+00 -4.8965e+00 -6.0329e+00  1.7361e+00  2.7454e+00
+#&gt;   5.3213e+00 -9.8942e-01  3.3805e+00  4.8124e+00  7.9408e+00 -4.1334e+00
+#&gt;  -8.4554e-01 -5.8992e+00  3.3015e+00  9.2698e+00 -6.1940e+00 -1.8760e+00
+#&gt;  -3.3602e+00 -6.5618e+00 -1.3458e+01 -1.0804e+01 -1.0276e+01 -2.3611e+00
+#&gt;  -3.2807e+00 -4.7024e-01 -4.8106e+00 -7.1463e+00 -1.2021e+01 -2.0456e+00
+#&gt;  -3.3972e-01 -6.5393e+00 -7.5630e-01 -1.8538e+00 -3.5975e+00 -3.9039e+00
+#&gt;  -1.5135e+01  7.3035e-01 -8.3808e+00 -4.8221e+00 -9.3744e+00 -3.2798e-01
+#&gt;   2.4560e-01 -7.5552e-01  1.1924e+00 -3.4284e+00  1.9044e+00 -2.9079e+00
+#&gt;   6.4364e+00 -2.0622e+00 -2.1329e+00  2.0296e+00  5.0129e-01  1.3035e+00
+#&gt;  -7.6127e-01  1.3828e+01 -9.9733e+00  4.2783e+00  2.4848e+00 -9.1430e-01
+#&gt;   6.9285e+00 -1.0299e+01  2.3943e+00 -3.8257e+00  1.1201e+00  1.4560e+00
+#&gt;  -4.5045e-01 -4.0313e+00 -4.1263e+00  5.8071e+00 -2.0225e+00  3.4909e+00
+#&gt;   6.6008e+00  2.6956e+00  1.0403e+01 -1.1447e+01 -5.6352e+00 -5.7858e+00
+#&gt;  -3.8587e+00 -4.4260e+00 -3.5954e+00  1.2241e+00  3.7192e-01  3.6454e+00
+#&gt;  -2.1584e+00 -7.1288e-01 -1.5161e+00 -2.7456e+00  1.8898e+00  4.4897e-01
+#&gt;  -1.1219e+00 -3.7443e+00 -4.8727e+00 -8.3364e+00 -7.5283e+00 -4.1012e+00
+#&gt;  -1.0698e-01 -1.2422e+01 -2.1764e+00 -1.7482e+00  1.9705e+00 -6.7154e+00
+#&gt;   4.0076e+00 -8.2704e-01 -2.5556e-01  2.2082e+00  4.1201e+00  3.2584e+00
+#&gt;  -4.1204e+00  1.1085e+00 -3.8030e+00 -3.9246e-01 -6.4055e-01  7.0182e+00
+#&gt;   4.5501e-01  1.9863e+00  4.2138e+00 -3.2505e+00  2.1790e-01 -6.2447e-01
+#&gt;  -2.7618e+00  8.8997e+00  3.3469e+00 -1.5589e+00 -2.7696e+00 -9.9674e+00
+#&gt;   7.7602e+00  4.1035e+00  6.3377e+00  8.3649e+00  1.8985e+01  1.2069e+01
+#&gt;   8.8029e+00 -3.1696e+00  6.7168e+00  1.4785e+01  8.6215e+00  2.7457e+00
+#&gt;   7.1574e+00 -1.0295e+00  2.9361e-01  3.8908e+00  6.6932e+00  3.7401e+00
+#&gt;  -3.9838e+00  1.7488e+00 -2.1708e+00 -1.1437e+01 -4.0910e+00 -7.1037e+00
+#&gt;  -1.6132e+01 -8.5650e+00 -2.4694e-02 -1.5977e+00 -7.3895e+00  8.8435e-01
+#&gt;   5.3712e+00  1.7691e+00  2.4260e+00 -1.8620e-01  3.9535e+00  1.0418e+00
+#&gt;   9.8902e-01  9.6299e+00 -3.1638e+00 -6.1946e+00 -4.0484e+00 -7.3948e+00
+#&gt;  -7.4685e+00  7.6784e+00 -7.6226e+00 -9.7231e-01  4.6998e+00  1.2277e+00
+#&gt;  -2.1309e+00 -9.5858e+00  3.8287e+00 -4.5513e+00  1.1547e+01 -8.7646e+00
+#&gt;   5.6137e+00 -1.0663e+00 -2.6638e+00  7.5905e+00 -2.1023e+00  4.7568e+00
+#&gt;   2.8300e+00  6.5272e+00 -2.2828e+00  3.1876e+00  6.7941e+00 -1.4367e+00
+#&gt; 
+#&gt; Columns 19 to 24  3.9527e+00  3.9152e+00  8.9049e+00  1.1294e+01 -9.8714e+00 -1.3542e+01
+#&gt;   7.7918e-01 -3.5703e+00  1.1471e+00  1.0779e+01 -4.1714e+00  3.7584e+00
+#&gt;  -1.1080e+00  9.3968e+00 -5.0234e-01 -7.4000e+00 -4.7738e-01  4.4831e+00
+#&gt;  -7.2076e+00 -3.4422e+00 -6.2024e+00  1.1907e+01  1.0278e+01 -1.3093e+01
+#&gt;  -8.0908e+00  4.3010e+00 -3.2117e+00 -8.4518e+00  3.5677e+00 -1.0008e+01
+#&gt;  -1.2001e+01  4.0682e+00 -4.7979e+00  1.7572e+00 -4.5670e+00  3.5043e+00
+#&gt;   5.7016e+00 -8.2803e+00  3.3730e+00  3.0721e+00  5.9141e-01 -4.8445e+00
+#&gt;  -3.6571e+00  3.2611e-01 -9.7575e+00 -3.3793e+00  8.9044e+00  8.9033e+00
+#&gt;  -2.7785e+00  3.7773e-02 -7.7068e+00  4.1643e+00 -1.1703e+01  3.0570e+00
+#&gt;  -3.7972e-01 -6.2760e+00 -2.5783e+00  6.8392e+00  1.0227e+00 -1.8564e+01
+#&gt;  -4.7111e+00  5.1840e+00  1.6555e+00 -1.8181e+01  1.1991e+01  9.1902e+00
+#&gt;  -1.3548e+00 -6.1443e+00  5.9713e+00 -2.2839e+00  3.6700e+00  1.3198e+00
+#&gt;  -6.7875e+00  1.3635e+01  6.8890e+00 -1.0232e+01 -9.0848e+00  5.8770e+00
+#&gt;  -2.5420e+00 -5.9944e+00  6.9125e+00 -5.3103e-01 -1.1211e+01  2.6487e-02
+#&gt;  -1.0139e+01  1.1467e+01 -5.1473e+00  5.1925e+00 -1.4699e+01  2.3066e+00
+#&gt;  -7.9126e+00  2.2568e+00  7.3034e+00 -5.2624e+00 -5.4210e+00  4.4153e+00
+#&gt;   3.0570e-01 -1.5279e+00 -1.7311e+00  7.4561e+00 -8.5627e+00 -1.4265e+00
+#&gt;  -3.0795e+00 -3.2056e+00  2.3707e+00 -3.7382e+00 -5.6777e+00  1.3584e+01
+#&gt;  -6.0551e+00  9.6878e+00 -2.8827e+00 -1.5638e+01 -6.0660e+00  1.1794e+01
+#&gt;   5.2208e-01 -9.5151e-01  4.8781e-01  7.1150e+00 -1.1985e+01  1.1426e+01
+#&gt;   5.2291e+00 -5.9671e+00 -7.7412e+00  7.6153e+00  1.0399e+01 -2.9500e+00
+#&gt;  -6.2040e+00 -2.7724e+00 -2.0706e+00 -9.4660e+00  2.1540e+00  3.0201e+00
+#&gt;   4.2770e+00  8.7830e+00  5.0159e+00 -4.2996e+00  9.8929e+00 -2.7062e+00
+#&gt;   2.9477e-01  1.0222e+01 -4.2887e+00  5.1453e+00 -4.7670e+00 -2.9534e+00
+#&gt;   5.0972e+00  6.2548e+00 -8.7009e+00  9.8776e-01 -2.0427e+00 -6.0557e+00
+#&gt;   3.7945e+00  1.5340e+00 -4.9280e+00  1.0356e+00 -1.1497e+01  1.8980e+00
+#&gt;  -5.5207e+00 -2.3598e+00 -6.4203e+00  5.3196e+00  1.6321e+00 -7.9663e+00
+#&gt;  -3.3121e+00  4.5133e+00  4.7246e+00 -1.1493e+01 -1.7462e+01 -2.9324e+00
+#&gt;  -5.5510e+00 -7.6881e+00  3.9163e+00  2.0472e+00 -4.8133e+00 -8.8644e+00
+#&gt;   3.4029e+00  3.8217e+00  9.1463e+00 -6.9773e-01 -1.1475e+01 -8.3752e+00
+#&gt;   1.2595e+01 -9.3585e+00  2.2784e+00  3.9545e+00  1.0027e+00 -6.0553e+00
+#&gt;  -1.7755e+00  4.8426e+00  3.1131e+00  5.8507e+00  1.4758e+00  8.1132e+00
+#&gt;   8.1158e+00 -2.6878e+00  3.8420e+00  9.7280e+00 -2.0917e+00 -1.7866e+00
+#&gt; 
+#&gt; Columns 25 to 30  2.7514e+00 -2.9044e+00  3.1635e+00  4.8970e+00 -2.7153e+00 -2.1692e+00
+#&gt;   4.0094e+00  5.8292e+00  1.2055e+01  9.6828e+00 -3.7371e+00  3.6239e+00
+#&gt;   1.4078e+00  4.0304e-01  1.2239e+00  8.0703e+00 -9.6928e+00 -6.7939e+00
+#&gt;  -7.4295e+00  7.2254e+00  2.9496e+00  3.6850e+00  1.9936e+01 -7.6404e+00
+#&gt;   7.7423e+00  4.6614e+00  3.0677e+00  1.6276e+00 -8.0058e+00 -1.4904e+01
+#&gt;   2.7324e+00  4.6477e+00  2.9174e+00 -7.6481e+00 -1.5952e+00 -7.8083e+00
+#&gt;  -2.1986e+00  5.1477e+00 -4.2553e-02  1.3089e+00  4.7724e+00 -9.3814e-01
+#&gt;   1.0899e+01 -4.5767e+00  7.8206e+00  4.1006e+00 -9.6803e+00 -9.8559e+00
+#&gt;  -2.5533e+00  5.8232e+00  4.3523e+00  3.5645e+00 -2.8284e-01 -2.3316e+00
+#&gt;  -8.0524e-01  7.9485e+00 -1.6328e+00  6.4514e-01  9.6695e+00  3.4099e+00
+#&gt;   1.4511e+01 -1.5813e+01  1.3674e+01  9.2899e+00 -1.7942e+01  1.4025e+01
+#&gt;  -4.0874e+00  5.8157e+00 -7.6620e-01 -1.0892e+00  6.5480e+00 -3.9128e+00
+#&gt;   3.3873e+00 -1.3534e+01  2.6775e+00  6.6464e+00  5.7631e+00 -6.4381e+00
+#&gt;  -1.1190e+01 -4.2002e-01 -1.3070e+01 -4.3599e+00 -7.0482e+00  2.7458e+00
+#&gt;  -2.0814e+00 -8.8541e+00  1.6759e+01  5.5173e-01 -4.2523e+00  1.2654e+00
+#&gt;  -4.2751e+00  1.3150e+00 -1.3943e-01  3.2242e+00  1.1298e+01  1.2557e+01
+#&gt;   2.9336e+00 -4.9230e+00  6.7140e+00  2.0939e+00 -6.5652e+00 -1.6155e+01
+#&gt;  -8.0575e+00 -1.4924e+00  3.4084e+00 -3.2414e+00  6.2329e+00  5.5945e+00
+#&gt;   3.6416e+00  7.1970e+00  2.9832e+00  4.8062e+00  3.9785e+00  1.2869e+01
+#&gt;  -2.4926e+00 -5.4260e+00  6.8716e+00 -3.1893e+00 -4.6895e+00  1.4379e+00
+#&gt;  -8.6248e-01  4.2699e+00 -1.4311e+01 -6.6221e+00  8.4722e-01 -1.9782e-01
+#&gt;   6.0904e+00 -8.3998e+00  7.4810e-01  8.4135e+00  8.9536e-02  1.5779e+00
+#&gt;   9.0231e-01 -1.2763e+01  2.2154e+00 -6.3118e+00 -5.7779e+00  2.1693e+01
+#&gt;  -7.9907e+00 -1.0028e+01 -2.4887e+00 -2.3234e-01  1.3156e+01  9.6231e+00
+#&gt;   4.0964e+00 -2.1144e+00  1.2411e+00 -2.9359e+00 -2.9314e-02  1.0143e+01
+#&gt;  -3.5980e+00  3.4881e+00 -2.2463e+00 -1.3874e+01 -1.0153e+01 -5.2240e+00
+#&gt;   1.0538e+01 -2.3452e+00  1.2710e+00  7.5885e+00  4.9355e+00 -1.0769e+01
+#&gt;  -2.4105e+00 -3.5217e+00  4.9055e+00  1.9954e+00 -4.5076e+00 -1.8899e-01
+#&gt;   3.2111e+00 -7.7566e+00 -4.4094e+00  9.5524e+00  6.6475e-01  1.4449e+01
+#&gt;   1.2576e+01 -4.6264e+00 -2.9808e+00  4.9721e+00 -7.8618e+00 -2.9451e+00
+#&gt;  -1.2270e+01  7.4681e+00 -8.6775e-01  3.1733e+00 -1.0157e+00 -4.1103e+00
+#&gt;   6.1041e+00 -7.8737e+00  7.6910e-01  2.6178e+00 -2.1165e+00 -2.4539e+00
+#&gt;  -2.9781e+00  5.1067e+00  9.0291e+00  3.7386e+00  4.0225e+00  1.1059e+01
+#&gt; 
+#&gt; Columns 31 to 36 -1.0024e+00 -2.6713e+00  1.2578e+01 -1.7690e+00 -3.4575e+00  3.3586e+00
+#&gt;   6.5265e+00 -6.1522e+00  3.4934e+00 -4.9728e+00  5.8530e+00 -2.0366e+00
+#&gt;   2.4769e+00 -4.4414e+00 -7.5334e-01  1.5265e+01 -1.1129e+01 -2.2469e-01
+#&gt;   8.2506e+00  1.4354e+00  4.4313e-01  5.4406e+00  1.2897e+01  4.4803e+00
+#&gt;  -2.8658e+00 -1.8296e+00 -2.1623e+00 -2.4244e+00 -3.0937e+00 -7.2425e-01
+#&gt;   8.4132e+00  7.2456e-01 -5.0226e-02  5.9712e+00 -3.5478e+00  3.6569e+00
+#&gt;   2.0938e+00  3.0349e+00  1.0649e+01  3.8103e+00 -3.3931e+00  5.1124e+00
+#&gt;  -5.9067e+00  2.8918e-01 -7.7774e+00 -6.2789e+00  2.6692e+00  1.3624e+00
+#&gt;  -7.5225e+00  5.4142e+00 -1.2569e+01  1.1863e+01 -8.2005e-01  9.7496e-01
+#&gt;   4.8118e+00  9.3095e-01  5.2597e+00  5.2339e-01 -2.7255e+00  8.6351e+00
+#&gt;  -2.6015e+00 -9.6194e-01  3.9729e+00 -1.5552e+00  4.7174e+00 -4.7509e+00
+#&gt;  -5.2280e+00  2.3861e+00 -6.1787e+00  2.1090e+00 -8.7541e-01  1.8596e+00
+#&gt;   4.1943e+00 -2.1511e+00  7.2290e+00  1.4783e+00 -6.3846e+00  3.8103e+00
+#&gt;   6.9841e+00  5.9577e+00 -8.0214e+00  3.0639e-01  1.8434e+01 -7.8145e+00
+#&gt;  -2.7008e+00  9.5668e+00  2.2890e+00  2.7696e+00 -1.1703e+01  1.4588e+01
+#&gt;   6.2605e+00 -5.3954e+00  2.2707e+00 -2.5968e+00  4.0614e+00 -6.1080e+00
+#&gt;  -2.1722e+00  2.8522e+00  5.6883e+00  2.5314e+00 -6.3639e+00  4.9474e+00
+#&gt;  -9.4953e-01  1.5049e+00 -3.5119e+00  3.2323e+00  4.7714e+00  2.7848e+00
+#&gt;  -1.2248e+00  5.2855e+00 -1.1353e+01  8.8063e+00 -4.5217e+00 -2.6649e+00
+#&gt;  -2.8687e+00  3.8691e+00 -8.2179e+00 -6.2951e-01 -3.1783e+00  5.1316e+00
+#&gt;   4.0843e+00 -8.5289e+00  1.2830e+00  1.2650e+01 -5.2441e+00  8.3415e-02
+#&gt;  -5.9715e+00 -1.1104e+01  1.9260e+00 -3.7978e+00  3.4070e+00  2.1377e+00
+#&gt;   1.4444e+00  2.2540e+00  3.7112e+00  8.3033e+00 -8.9732e+00  1.7013e+00
+#&gt;  -7.3074e+00  1.8956e+01 -1.8431e+00  1.0475e+01 -8.2182e+00  1.1970e+01
+#&gt;   2.3107e+00  1.4038e+00  8.8203e+00  4.4088e+00 -4.7749e+00  8.1737e-01
+#&gt;   5.5478e+00  4.3362e-01 -3.9417e-01  5.8719e-01 -3.4224e+00  8.3111e-01
+#&gt;   1.9707e+01  1.6573e+00  4.5278e+00  6.3618e+00  6.5622e+00  2.3959e+00
+#&gt;   9.7553e+00  1.3196e+00 -3.7189e+00  1.0158e+00  4.2140e-01 -5.3399e+00
+#&gt;  -1.0143e+01  1.5711e+00  7.4052e+00 -3.8900e+00  1.1622e+00  1.5689e+00
+#&gt;   8.2355e-01 -1.3308e+01  1.7697e+01 -1.3180e+01 -1.5565e+00 -6.2004e+00
+#&gt;   1.5589e+00  3.6957e+00 -3.5663e-01  3.4026e+00  9.4058e-01  8.3490e+00
+#&gt;  -9.1588e+00  3.6904e+00 -1.9694e+00 -5.0691e+00 -1.4893e+00 -2.4870e-01
+#&gt;   1.5074e+00  4.2083e+00  6.5068e+00 -1.4737e+00  7.8155e+00  1.9302e+00
+#&gt; 
+#&gt; Columns 37 to 42 -6.0443e+00 -5.0038e+00  2.4085e+00  6.1652e-01 -2.9455e-01  4.1388e+00
+#&gt;  -2.1589e+00  7.5638e-01 -1.4299e+00  5.4934e+00  1.1379e+01 -1.7734e+01
+#&gt;  -5.5570e+00  1.4518e+00 -5.1437e+00  5.8455e+00 -1.1211e+01 -1.0749e+01
+#&gt;   8.0975e+00  2.3521e+00  3.8920e-01  7.0470e-01  1.1417e+01  5.2706e+00
+#&gt;   2.2747e+00 -2.5634e+00 -2.5810e+00  2.8128e+00 -1.2677e+01 -9.6755e+00
+#&gt;  -6.1550e+00 -4.8737e+00 -1.0272e+01  8.9978e+00  4.5288e+00  2.6653e+00
+#&gt;  -6.3205e+00 -5.1040e+00  3.3478e+00  1.1458e+01  1.1980e+01  1.3942e+01
+#&gt;   2.7540e-01 -1.3097e+01 -1.1894e+01  8.7668e+00 -6.5043e+00  1.0282e+00
+#&gt;  -1.1896e-02 -1.3126e+01 -8.0822e+00 -2.1835e+00  4.4062e+00 -8.4565e+00
+#&gt;  -5.2831e+00 -2.2819e+00  1.3993e+01  6.1044e+00  1.3993e+00  2.7036e+00
+#&gt;   5.0564e+00 -2.4792e+00  2.1099e+00  1.6402e+01 -9.3117e-02 -1.0041e+01
+#&gt;   1.6624e-01 -1.1526e+01  1.0828e+01 -5.7474e+00  4.6739e+00 -3.1839e+00
+#&gt;  -5.9014e+00  2.9813e+00 -1.6901e+01  2.9944e-02 -2.5500e+00  1.0309e+00
+#&gt;   3.1118e+00 -7.3564e-01  5.5841e+00 -1.4666e+01  3.8443e+00  6.1601e+00
+#&gt;  -9.0465e+00 -3.6688e-01 -1.7310e+00 -6.8496e+00  6.2570e+00  5.2079e+00
+#&gt;   2.4793e+00  5.8129e+00 -6.8916e+00 -1.0394e+01  4.2798e+00  4.3335e+00
+#&gt;  -8.6506e+00  1.2264e+00 -4.2477e+00  2.0104e+00  5.3704e-01  5.4668e+00
+#&gt;  -4.2562e+00  1.6511e+00 -7.5951e+00 -1.8699e+01 -4.9848e+00  2.8267e+00
+#&gt;  -4.0240e+00 -6.9409e+00 -3.4937e+00  1.0665e+01 -1.2527e+01 -7.0438e+00
+#&gt;  -2.1840e+00  4.4412e+00 -1.4393e+01 -1.0654e+01 -3.5939e+00  1.2485e+01
+#&gt;   7.2340e+00 -3.2706e+00 -2.2007e+00  6.2254e+00 -6.8434e+00 -1.3621e+00
+#&gt;  -4.5564e+00 -4.0301e+00 -2.7368e+00 -4.7971e+00 -3.8564e+00 -4.0555e-01
+#&gt;   5.1519e-01  1.7264e+00  4.8336e+00 -3.0540e+00  1.3621e+01  2.4650e+01
+#&gt;   3.8365e+00  1.0922e+00 -3.1472e+00  1.0390e+01  9.9348e+00  7.2009e+00
+#&gt;   4.4151e+00  1.0959e+01  1.6859e+01 -2.6747e-01  3.2473e+00 -2.3686e+00
+#&gt;   3.5090e+00  1.3791e+00 -5.6040e+00 -1.0725e+01 -4.0518e+00  7.6260e+00
+#&gt;  -7.0225e+00 -6.5785e+00 -1.0424e+01  9.5121e-01  1.7987e+01  6.9230e-01
+#&gt;   3.2599e+00  4.0881e+00 -1.3803e+01 -1.1379e+00  5.3533e+00 -1.5402e+00
+#&gt;   2.1776e+00 -4.0299e+00 -7.1673e-01 -3.0291e+00 -2.9262e+00  2.9943e-01
+#&gt;  -2.6078e+00 -3.6891e+00 -9.8233e+00  5.5719e+00  1.2748e+01 -3.7681e+00
+#&gt;  -5.5408e+00 -1.4093e+01  4.5600e+00  4.5790e+00  1.8872e+01  1.6317e+01
+#&gt;   1.8432e+00 -5.3269e+00  4.9184e-02 -1.3810e+01 -3.9466e+00  1.8990e+00
+#&gt;   1.1511e+00  1.7908e+00  2.0034e+01  8.6886e+00  1.1405e+01 -4.1812e+00
+#&gt; 
+#&gt; Columns 43 to 48 -5.4550e+00 -8.2143e+00  4.7637e+00 -2.9903e+00 -7.2637e+00 -9.7950e+00
+#&gt;  -5.4919e+00 -9.6887e+00  4.7355e+00 -6.6842e+00 -9.0013e+00 -1.1458e+01
+#&gt;   1.1702e+01  6.5723e+00  4.7653e-01  2.5971e+00 -2.8433e+00 -8.7995e+00
+#&gt;   1.3533e+01  2.4328e+00 -3.6331e+00  1.5955e+00 -2.2165e+00 -1.7262e+01
+#&gt;   3.8044e+00 -2.0567e+00 -3.6479e+00 -4.1294e+00  2.9667e+00 -5.5330e+00
+#&gt;   8.1044e+00 -8.6189e+00 -5.4637e+00 -1.1795e+01 -2.1088e+01 -4.0552e-02
+#&gt;  -2.9690e-01 -6.5130e+00 -9.3919e+00 -1.9817e+00 -6.7502e+00  6.5811e-03
+#&gt;  -1.9122e+00 -1.1089e+01 -7.5356e+00 -4.9767e+00  1.1579e+01  9.7925e+00
+#&gt;  -4.7898e+00  5.8781e+00 -1.0547e+00 -5.0681e-02 -7.6112e+00 -6.2750e+00
+#&gt;   2.2555e+00 -3.7843e+00 -4.7802e+00 -8.5480e-01  1.4987e+01 -7.9747e-01
+#&gt;   1.0052e+01  3.6775e+00 -1.0574e+00  1.0857e+00 -2.2764e+00 -7.6102e-01
+#&gt;  -1.0788e+01 -9.3425e-01  1.2396e+00 -1.4440e+00  3.6199e+00  7.8463e+00
+#&gt;   8.0964e+00  4.8538e+00  2.7574e+00 -5.8478e+00 -6.2870e+00 -2.7797e+00
+#&gt;  -1.7868e+00 -1.0333e+01 -2.0574e+00  4.5673e+00 -2.6122e+00  3.1900e+00
+#&gt;   5.2439e+00  5.9873e-01 -1.0739e+01 -5.0399e+00 -2.8870e+00 -1.0834e+01
+#&gt;  -6.6098e-01 -6.5914e+00  8.4657e-01  8.7373e+00  9.3333e-01 -1.7683e+00
+#&gt;   9.2482e+00  8.5724e+00 -3.8986e+00 -9.4177e+00 -6.1733e+00 -4.7650e+00
+#&gt;   1.3976e+00  5.4746e+00  6.8451e-01  1.0722e+01 -6.5352e+00 -5.5735e+00
+#&gt;  -6.8063e-01 -7.1734e-01 -1.0688e+01  3.1244e+00  9.7000e+00  4.6155e+00
+#&gt;   1.4267e+00  1.2498e+00 -3.2097e-01 -3.9869e-01  6.3889e+00  1.3758e+01
+#&gt;   4.5269e+00 -3.7422e+00 -4.6177e+00 -3.7689e+00  5.7216e+00  5.8025e+00
+#&gt;  -2.6467e+00  2.3303e+00  1.0984e+01  1.1895e+01 -8.3679e-01  7.5611e+00
+#&gt;   1.0457e+00  8.9304e+00 -6.0809e+00  7.7563e+00 -9.1621e+00  1.1348e+00
+#&gt;   2.5585e+00  7.1815e+00 -3.8037e+00 -5.7326e+00 -6.0681e+00 -2.6218e+00
+#&gt;   8.5473e+00  8.0866e+00  1.9267e+00 -5.5515e+00 -1.0811e+00  5.9082e+00
+#&gt;   4.0650e+00 -1.3018e+00 -1.4162e+00 -5.3452e+00  1.6563e+00  3.4752e+00
+#&gt;   3.5318e+00 -2.5060e+00 -1.2967e+01 -1.5486e+01 -1.1734e+01 -7.6932e+00
+#&gt;   1.0912e+01  1.1026e+01  2.3755e+00  4.6339e+00  1.2357e+01 -1.4554e+01
+#&gt;  -5.2169e-01  1.8166e+00  2.7753e+00  6.4007e+00 -4.7057e+00  1.1531e+01
+#&gt;  -1.6006e+00 -7.4824e+00  7.2213e+00 -7.9190e+00  6.7513e+00 -8.9247e+00
+#&gt;  -6.4194e+00  1.5810e+00 -4.7193e+00  6.2955e+00  7.0297e+00  6.8899e+00
+#&gt;   2.6142e+00 -1.6475e+00 -2.5523e+00 -1.0377e+01 -8.8961e+00  6.2604e+00
+#&gt;  -1.8070e+00  6.4933e+00  9.0026e+00 -7.7026e+00  2.2729e+00 -6.4668e+00
+#&gt; 
+#&gt; (15,.,.) = 
+#&gt;  Columns 1 to 8   0.8048  -7.1302  -5.1982   4.7184   2.3049   0.1458  -2.9323   0.9045
+#&gt;   -6.2981   8.2727   2.8437   3.7729  10.3314   2.1071   0.9795   7.1802
+#&gt;    5.0801   1.4905  -5.0455  -4.1448   7.4240  -0.2848  -4.4704   6.6526
+#&gt;   -4.3386  -0.6268 -12.2354  15.0675 -11.1815   7.1535   0.6211  -2.3970
+#&gt;    6.0735   2.6415  -7.8096   0.1928  -6.0289  -3.4852  -1.8490  -6.5341
+#&gt;   -5.5020  -4.1683 -21.8447  -7.1531 -10.1589  -3.6331  -6.6435   8.8253
+#&gt;   -5.4820   1.1505  -1.7518  13.0789   5.8027  -6.2200  -4.9524   6.7853
+#&gt;    3.2055  10.5159  -3.1530  -4.1114  -8.2848   2.9294   9.1991  -6.8600
+#&gt;    0.7853  -0.2040  -2.6513  -8.2570   4.7800  -6.0429  -1.0372   4.5380
+#&gt;  -10.0597  -1.5282   3.6515   4.6160  -6.1629   7.2925   1.3604  -2.4042
+#&gt;    5.5991   8.1511  -5.1106 -12.7090   6.2902 -14.5277   8.0083  -2.9098
+#&gt;   -1.2806  -1.3863   8.2275   2.2868   7.8467   1.3516  -3.8672  -3.2717
+#&gt;    1.9597   3.3709  -8.9261   7.0613  12.2671  -7.5790  -3.4860   9.1355
+#&gt;    0.7325  -5.4724   6.2299  -8.6116 -21.3077   3.4404 -13.1769  -1.2893
+#&gt;   -4.7072   7.5819  -2.1514  10.3851   2.1876  -4.8796  -9.3614   3.8329
+#&gt;   13.7107   5.2684   6.4132  -3.1331  -7.7471   9.2193   3.7722 -10.6658
+#&gt;    6.7938 -13.5632  -3.4073  -6.4364   6.0194  -6.4552  -3.6073  -2.3002
+#&gt;   -3.2604   7.2644  -6.9982   3.0262   0.3497   6.5678  -3.8303  -2.5425
+#&gt;    9.5488  12.8055  10.9672  -4.7713  -4.8149   2.6481  14.4671  -0.8024
+#&gt;   -0.1062   4.0740   0.1192  -2.8975  -3.5445  -4.3332   3.5695  -5.8257
+#&gt;   -5.9688  10.4781  -3.7207   0.8464   3.3031  -2.0499   2.5568   2.4314
+#&gt;    6.8408  -2.4286  -2.0812 -13.5789 -11.9642   1.9043   1.6696  -5.7336
+#&gt;   -0.3294  -4.0051   4.4825   8.7400   4.2389   6.1872  -4.8511   3.3600
+#&gt;   -7.3160  -2.2606   0.7169  -5.0866  -0.0417  -2.8604 -12.7086  11.6121
+#&gt;   -1.7032  -1.4316  11.1245   0.1705   9.2713   1.7239  -2.6730   3.3785
+#&gt;    0.5772  -2.7065  -2.8858  -4.2171 -10.9184   3.6095  -0.5072  -4.4745
+#&gt;   -8.3830  -5.4281  -6.8301  -2.5117  11.5640  -9.2411   5.6765   8.3588
+#&gt;    2.6178  11.9343   9.2576  -3.4401  -3.8225  -3.3271 -12.3327   1.9006
+#&gt;    5.9909   2.2253   5.8577  -8.6208  -2.9306  -6.7949   1.7518  -8.0359
+#&gt;   -3.3776   4.6775   6.3503  -5.2518   6.9190  -4.8524  -8.8138  -1.2051
+#&gt;   -6.7259  -4.4902   7.2270   7.9758   4.6113   2.5120  -4.4357  -6.3322
+#&gt;    7.7574 -13.7695   3.9471  -4.6606   5.5693   7.0922 -10.3325   4.2199
+#&gt;  -10.8634   1.3126   2.9160   2.5213   6.1139  -5.9908  -5.1855   2.8433
+#&gt; 
+#&gt; Columns 9 to 16  -2.6243   0.4328  11.1646   8.5593 -12.1996  -7.4265 -12.9429   7.7398
+#&gt;    3.5453  -0.1579  -4.2605  -9.5878  -3.6614   5.4460  -3.9349   6.1239
+#&gt;   -3.2706  -6.6456 -13.3301   1.4546   6.8946   4.3316   8.8452  -5.5344
+#&gt;    8.2053  -8.7390  13.9397  24.9905   4.2868  -0.4157  15.1577   1.2894
+#&gt;    2.3995   5.4825  -5.9720   1.8767  -4.0682  -6.2251 -14.7602  -7.7949
+#&gt;   -2.3086   8.8263  15.2860   5.3245  -0.6444  -6.3166  -6.5798  -2.2219
+#&gt;   -0.9974   9.5613  14.1790  10.5027  -4.9540 -13.3136   3.9889   0.6169
+#&gt;    4.0747  18.7485  -9.3609 -11.3180  -9.3307   2.9839  -9.8000  -4.7789
+#&gt;   -1.2814  -5.5160   5.1901   6.1370  10.5471  -2.6609   5.4535 -13.1699
+#&gt;    8.7097   4.1678  -5.8543   1.4070   4.3947  -2.2228  -2.4664   8.3158
+#&gt;    4.0793   5.4742 -15.4222   1.8686  12.2919   6.3672  -7.8565   7.6350
+#&gt;    3.6448   6.1630   1.4843  -8.0472  -2.3761  -7.7439   0.7194  10.8694
+#&gt;   -0.4451   7.7842  -7.7088  -6.5198  -5.2164  -7.2539   8.5703   9.0153
+#&gt;   10.1100  -9.1459  23.6299  -3.6850  -6.6768  -9.6936 -10.3292 -13.5748
+#&gt;    9.9733   8.9384  -9.7610   8.3322  -7.7173 -10.8625   0.3825   4.6803
+#&gt;    6.4227  -1.0801  12.4142   1.6283  -1.2001  -4.3134  -6.8145  -0.7343
+#&gt;    4.0345   9.0029   6.5919  -3.8529  -7.8487  -6.0298  -1.6113  -8.0233
+#&gt;    0.3470   8.4495   3.4920  -5.8563  -9.9557 -10.6460   7.1741   9.1540
+#&gt;    0.2047  10.9090  -1.1599  -6.6670  -5.3418   0.5331 -15.7860  -3.4584
+#&gt;    2.2713   7.9691  -2.9790 -11.3593  -3.0517   5.9772  -3.5811   3.4882
+#&gt;   -1.9983   5.4185  -0.5448   1.1243   3.6822  -5.9691   1.3919  -3.8577
+#&gt;   -6.0237   1.4433  -0.1638   0.7065  -6.3657   2.9962   0.7895  -7.5776
+#&gt;   -7.1163   6.1097  -2.6729  16.1532   1.7291   5.4875   0.1021   5.3662
+#&gt;    0.2978   3.3122  -3.9261   2.0635  -0.0566  -5.4560  10.9724   9.1025
+#&gt;    1.3748   4.8877   8.7258   1.8500   6.0718  -2.0596  -7.8329  -2.4864
+#&gt;    2.1345   1.4062   8.9350  -0.1680   0.5001  -3.2611 -15.8572 -11.3055
+#&gt;   10.6560  20.8887  12.0689  -4.7632 -11.5247 -18.9330   7.3607   5.1821
+#&gt;   11.8770   0.2722  -3.6730 -15.7264  -0.4359  -5.6856 -11.3708   0.7707
+#&gt;   -3.0294   9.8799  12.4789   0.2152 -15.0768  -6.8379  -9.5639  -8.6209
+#&gt;   19.6157  -3.2185  -6.2283 -15.6918   4.8533  -2.8065  -9.8387   7.6355
+#&gt;    3.2747  15.9104  18.6214  12.1918  -7.1561  -8.6773   5.4100  -6.6609
+#&gt;    0.7128 -12.3952   4.7757 -10.6673   2.7547  13.9214  -3.6430   9.8891
+#&gt;    6.0475  -2.3505   2.2329   1.5311   8.0533   3.1398  -1.4593  11.9712
+#&gt; 
+#&gt; Columns 17 to 24 -10.7269  -7.0341  -4.5691 -19.3047  11.3635   2.1366 -11.1086   9.7337
+#&gt;   -1.3675  -0.4500 -11.2419 -17.4118   1.6026  -4.6512  -3.6940  12.8833
+#&gt;    1.3415   2.8172   3.8470  -3.8784  -0.9783   9.3358  -1.6777   5.7717
+#&gt;  -15.0082  -2.9175   4.9480  -1.9858  -1.1864  17.0469   8.2187   7.8884
+#&gt;   -2.2238   2.6507   1.9936  -4.5235 -12.0722   8.0846  -5.2894   9.9181
+#&gt;    3.4597  -5.0310 -18.5341  -7.4792  -1.1131   4.3999  10.0050   1.9466
+#&gt;  -10.2704   0.7731   1.3454  -2.3996  20.8438  -5.3910   1.9644   8.1886
+#&gt;    6.5566   1.6228  -7.1471  -9.1481  -2.6575  -5.3536  -5.5653   5.4137
+#&gt;   -1.3179   4.3835  -3.4015  -1.2802  -4.4014  -6.5830  11.9631  -9.0388
+#&gt;   -1.6018  -4.5212   7.1977   7.7312   2.8460   4.3718  -3.1728  -3.9669
+#&gt;    5.3512  -3.0614  -7.0596  -3.8677  -1.1902   2.3101 -14.7512   8.5772
+#&gt;   -4.0062   0.7587  -3.6666  -4.0924   1.1767 -12.3443  -1.2912   0.3650
+#&gt;  -10.4419  -1.7139   2.5798  -6.1205   3.7598   7.3134  -6.3118   9.1148
+#&gt;    4.5062   6.7279  -0.1350   3.8005   7.8222   8.7671   3.8129  -8.1235
+#&gt;  -17.0484   0.8677   5.0648 -11.1856  18.0983  -2.6071   1.6093  18.9739
+#&gt;    4.1095  -4.1844  -1.1886  -8.2871  -4.1897  15.7950   0.3164   7.1537
+#&gt;    6.0536  -3.2139   3.1367  -4.3572  -3.2948  19.5925  -6.7900   7.3236
+#&gt;    9.3605  -2.2390  -7.1153 -11.1644   2.4283  -5.9793  14.6581   4.1310
+#&gt;   11.8628  -5.5734  -1.3126  -0.1468   0.7981   4.7050  -6.4429  -5.4245
+#&gt;    3.4164 -18.6147  -5.6152  -7.5493  -2.3179  -8.2711  -0.4826  -5.3061
+#&gt;    2.7276   0.1413  -2.7000  -2.6496   2.5378  -9.9883   7.9165   2.5381
+#&gt;    6.2961   9.9005  -2.7543   2.1022   2.6281  -0.8926  -2.7783 -13.4023
+#&gt;    0.7441  -3.5912 -10.0708   7.8162   3.0236  -3.2449   1.3602 -10.5133
+#&gt;   -7.4043  -8.2482   1.9733   4.5619  -1.3130  12.2857  -3.8850  -7.3048
+#&gt;    1.8633   0.2468  10.0071  13.2876   2.1962   6.7229   6.5548  -1.1695
+#&gt;    5.3615   1.4604   7.1541   3.6082   6.6734   3.7640   3.4410  -0.6691
+#&gt;  -11.0977  -0.3524  -5.1584  -6.6272   7.4835  18.1811   1.3001  18.6657
+#&gt;    9.3068  -2.9136  17.1541   3.0779  -8.9603  21.3401 -16.4021  -6.1953
+#&gt;   -2.7839   3.9002  -1.7010   4.1217  13.8331  -5.2049   7.4136  -2.0544
+#&gt;    4.3481  -7.7533   4.3164  -6.1902  -6.0672   8.6225 -16.4504   0.4909
+#&gt;    1.8774   3.1294  -6.3901   2.4610  15.0536 -11.9390  11.1065  -4.4631
+#&gt;    2.7281  -3.2621  -4.2025   0.7014 -20.1567   3.1274  -1.7161 -10.5776
+#&gt;    1.0278  -7.0991  -9.2778  -4.2494   1.0259 -11.1924  -0.1329 -13.5445
+#&gt; 
+#&gt; Columns 25 to 32  -1.7462  -1.6269  -0.7915  -0.8197  -3.9971 -10.4806 -10.2680  -2.7534
+#&gt;   -0.2765   6.7450  -2.0712  10.6486 -13.9731  16.2608  -5.7437   3.4435
+#&gt;    7.3143   4.7691   0.7026   2.9372  -7.7866  -4.2075   6.0117  13.2946
+#&gt;   -5.4815  11.9231   6.8967   9.7563  15.3568  -1.2720  16.1653  -1.1744
+#&gt;    3.4551   1.6304   4.1245   0.6843   4.5899  -6.6570  -6.3757   3.0269
+#&gt;    2.4357  -3.9644  -7.9284  -0.0059   2.5904   4.4955   7.8125  -0.6694
+#&gt;   -5.8358  -7.0826  -3.6999  -0.5470  -1.9090   1.5121   7.6959  -6.5233
+#&gt;    6.5241  -0.8224 -16.7985  -0.5287  10.2887  -3.1625 -11.5654   0.9984
+#&gt;   -0.1712   0.7338  -4.5138  -9.9664  -2.2649   0.5123  -5.3444  -5.0459
+#&gt;   -5.5832  -4.8549   5.5569  13.3486  12.9264  -5.5433   1.9425   4.5420
+#&gt;    1.1549  -0.1853  -6.9617   1.7310   1.1553  11.2502  -7.4437  12.6783
+#&gt;    0.9435  -6.3769  12.0910   9.3257  -1.9542  -4.7526  -1.3705  -0.8283
+#&gt;    5.5251   9.6266  -0.4007  -1.7914  -9.0324   1.4803  18.9263   0.3953
+#&gt;   -8.7347  -4.0501   8.7921  11.5538 -12.4976   1.0826 -19.7760 -10.8927
+#&gt;   -2.3679   4.2283   3.5410   7.1489 -13.3737   2.6299  13.3520  -5.4859
+#&gt;    2.0640  -0.0326   2.0723  10.3406  -2.1520   3.9185   1.0878  -4.0766
+#&gt;   14.8852  -2.2712 -14.5972  -7.5811  -3.3774  -6.0105  14.9241  -8.3811
+#&gt;   13.5728  11.6081  -1.0403  11.6453  -8.1370   3.6349  24.3273   3.0830
+#&gt;    3.2080   5.0707  -9.2790  -8.8123   4.9685  -1.6484  -4.7333   5.8462
+#&gt;    2.9742 -10.7546 -12.3970  -0.1404   0.0318  -1.9171   5.8307 -11.9025
+#&gt;    1.2260  -2.8130   0.4420   1.9131   3.8702  -5.9096   1.6282   2.5812
+#&gt;    1.4588   5.4399  -0.8877  -2.4271  -5.0846  -3.9904  -6.7946   1.9089
+#&gt;   -4.8377  -9.2531   5.5385  -5.5002   3.8937   7.2787   7.1057   2.6130
+#&gt;   -8.5997  -3.5724  -3.3184   3.1191  15.3997   9.4976  10.3288  10.3277
+#&gt;    3.6108  -1.1064  -4.6679  -4.5344  -8.6885  -1.0672   8.9834   4.1420
+#&gt;    9.4999   1.1589 -11.8681 -11.2911  -9.8568 -14.2836  -5.4645  -7.7589
+#&gt;   -4.2400  -2.4742  -1.2214   4.4757   1.6532   0.8790  14.6292   0.4674
+#&gt;    8.4445  15.9800   4.0901  -7.3633  -3.6679  -3.6021   9.9330  12.6796
+#&gt;   -7.9000   3.8494   1.4938   0.4920  -3.1299   4.9682  -6.1952   0.9764
+#&gt;    8.7349  -1.2985  -2.5513 -14.5349  -6.8205  -1.1748  -7.4282  -1.3516
+#&gt;   -3.6078  -7.8039   2.0198   1.2103   8.5414  -3.6797  -5.4959  -2.0035
+#&gt;    8.1339  -1.8808  -8.5537   2.8481   5.4491   7.7438   2.7956  -0.6117
+#&gt;   -8.0023  -0.9707   6.7169   0.4876   6.8987   8.7232  -2.7651   8.5866
+#&gt; 
+#&gt; Columns 33 to 40   3.4080  10.7186   5.5730   4.6531   5.7616   3.4705 -11.4040  12.9549
+#&gt;    7.7377   6.0998  -1.5554   4.7076  -8.1016  -9.8182   3.8232  -0.1717
+#&gt;   -5.6604   1.0921 -11.5067  -0.9772   2.9314  -7.4592  -9.5098  -3.6219
+#&gt;   -2.3808  -4.4176  -0.4573   5.1452  -4.5173   2.6218   4.3377  -9.7166
+#&gt;   -3.7069   5.9415  -9.7631  -3.8982  -8.0165  -2.5303  -1.2263   0.7284
+#&gt;   -0.3598  -6.7104  -0.3492  11.4228  11.0449   3.6593   7.6630   4.0337
+#&gt;   -5.1844  -7.9579  -0.3715   3.9422   3.8204   7.4046 -11.1925   0.5115
+#&gt;   -7.0582  -0.8884   6.2280  -2.8426 -14.4377   7.6894   4.5536   2.4210
+#&gt;   -1.4600 -10.2162  -8.5577   1.7303   4.5618 -11.8549   1.7316 -11.9648
+#&gt;   -4.1447  10.3017  13.7237  -4.0415  -4.9265   2.7495   3.8622  -6.7376
+#&gt;  -11.5153   2.8323  -6.5427  12.0739  -0.6204   4.8624  -1.5153   3.3605
+#&gt;   13.2823   5.9974   1.2621  -4.7486   1.5167   5.9744  -0.3291   0.6503
+#&gt;   -1.0654   0.5911  -4.0941   8.9973   1.0366   6.0193   0.5471   0.4281
+#&gt;    0.2312 -10.8132   1.8276  -0.5442  -4.3519   8.0915   4.7438  -0.1606
+#&gt;   -0.0113   2.5344 -11.3341  -2.8751  14.1932  -4.1816   3.7660   1.8073
+#&gt;   16.3061  -0.7111  -8.0503   2.8067   6.5130  -9.8521  -4.1031   1.1970
+#&gt;  -10.5519   0.9086  -8.5823  -0.0814   8.8444  11.8856   1.2298   4.7355
+#&gt;   10.0114  -5.0213  -6.0665  -1.6630   7.1819  -0.7356   4.0499  -2.1666
+#&gt;    0.6757  -0.3162  -3.5780  -4.7094  -2.8122  -1.5863  -1.3931 -10.5494
+#&gt;   -2.0870   0.9603   7.7240  -4.6320   5.4591   0.2743  14.8810  -2.1514
+#&gt;   -1.8110   1.1190  -2.2812   2.8365  -0.6039   1.2504   2.5801   2.9844
+#&gt;    2.4130  -9.4174  -8.4426 -12.0919   7.1439   6.6067  -6.2722   2.3164
+#&gt;   -4.1834  11.1998   3.0447   2.6957  22.4190   7.6576  -5.2527  18.8177
+#&gt;  -20.2723   1.3253  10.8418   9.0811   6.4476   3.7484  16.8607 -14.8302
+#&gt;   -2.7513   6.6503 -12.5883  -1.9143  13.6749  -2.2026  -0.0576   7.5686
+#&gt;    0.3314 -11.3378  -0.9537  -6.7157   3.6694  -7.5760   5.5724   6.0488
+#&gt;   -8.1499  -5.8161  -9.8587  19.9884   2.0863   1.5576  -4.2066  -0.4436
+#&gt;   -7.1017   1.5927   7.4132  -3.1600 -16.2266  17.7469  11.1453  -3.6420
+#&gt;   -3.5196  -6.1302 -17.1096   1.9950  12.6488  -7.2172   1.1653   8.3086
+#&gt;    3.9420   1.1401   4.9253   8.3876  -3.8281   9.5859   8.0676   0.9702
+#&gt;    2.5050  -6.1517   4.7386   5.9677   9.2732   2.9076   0.0195  -0.0915
+#&gt;  -10.9789  -1.3337  12.6203   8.5702  -4.3448  -3.6564   4.6815  10.8041
+#&gt;    3.2875  -0.0805  18.5864  12.3418   1.3776   0.4639  10.9487  -5.0897
+#&gt; 
+#&gt; Columns 41 to 48   4.5416  -4.9024   8.8311  10.3463   0.2651  -8.4339  -6.6832   7.2281
+#&gt;  -10.1922  -8.5999   0.4567   1.2905  11.9904   8.3649  -0.2820  -0.5000
+#&gt;    2.1755  -3.7282   1.3097   2.5777   7.8753  -2.0035  14.1423   6.6787
+#&gt;    1.1655   0.5492   4.4501   0.3171  -5.4586   2.6379   0.0697  -9.4739
+#&gt;   -6.2303 -14.1027   0.0100  -3.9314   5.2509  -8.1181   2.6001   1.2095
+#&gt;   -6.8354   1.3839  10.1818   6.8880 -11.1387 -13.3290   2.5360  -2.4812
+#&gt;    8.3362   9.4515   4.1199  -9.3917 -14.5917   1.4332   0.4153  -3.2342
+#&gt;    0.0806   0.2949  -9.1927 -16.4414   5.4621  -5.5275  -6.3721   2.0919
+#&gt;   -1.5245   1.7350  -2.6612   9.0850  -1.4021  -0.8051   8.9440   5.5216
+#&gt;    2.2669   1.4136   2.1153   1.3907  -1.9028   3.3937   5.5590   1.6864
+#&gt;   -9.5550   9.3133  -0.5917  -6.6588   8.5990  -9.0556  -1.1651   0.3522
+#&gt;   -0.2138   1.6576  -7.4984  -7.4907  -8.1598   2.7208  -5.3047   4.1849
+#&gt;   -8.7061   2.1911  11.3166  -7.0701 -12.3447   0.3809  -8.7935  -1.9093
+#&gt;    5.8310  -3.7924 -14.1226  -0.9867   1.1463 -12.4631  -7.0898 -12.4898
+#&gt;   -4.3149   6.3384  -2.0539  -0.0828 -16.0012  -1.3459  -0.2730 -11.1067
+#&gt;    0.2034  -1.7032  -7.7734   4.6387  -4.2106  -1.3526  -4.4212  -3.4719
+#&gt;  -12.2013  -1.7224   3.9268   3.3508  -8.4421  -6.3359 -12.8927  -2.1470
+#&gt;  -21.1804   6.3041  -4.5008  -0.5402 -20.8187   0.5318   2.9913 -13.6855
+#&gt;   -3.2797   7.9700  -0.3886  -4.6745   2.1036  -4.6107  -4.7608   7.6923
+#&gt;  -15.8307   8.7412  -0.0962   7.8266 -13.6592  -0.8376  -7.8022 -13.3426
+#&gt;    3.4127  -3.5618  -6.9771   2.4669  -2.9717  -8.7935   2.0971  -7.9943
+#&gt;   -5.0785  -3.3369  -9.3461  -5.6587  -0.6787  -3.1871  -9.3324  -3.0281
+#&gt;    2.5578  21.7349  15.3899  10.4132  -2.5514   1.7782  12.5657  -6.7548
+#&gt;    8.6913  11.6463  18.1946   0.7303  -4.4140  -0.7748   4.9841 -15.1897
+#&gt;  -11.2238   6.7093   4.6444  12.6038  -5.5794 -10.4248   4.1176  -1.7744
+#&gt;    1.6525  -3.1180  -0.3574  17.7211  -0.1144 -12.3549  -4.0452  -5.5704
+#&gt;   -4.9651  13.9408   8.6249 -11.5936 -16.6941  -5.3931  -3.6881   6.0155
+#&gt;   -5.4065  -2.5217   1.4385  -0.3922   1.6123  -0.9188  -7.3587  -2.3773
+#&gt;   -8.6857  -5.0989  -6.1459  -7.0026  -8.0787 -14.5593 -10.4636 -12.8228
+#&gt;    2.6677  -4.7651   1.0568   1.3425   2.7920   2.0309  -8.7960   8.8150
+#&gt;    9.0281  12.5455  -2.5788 -12.3628 -11.9505  -4.1404  -0.8740  10.5614
+#&gt;    3.1092  -4.0124   7.5946   5.5313  -0.2117  -1.3845   8.1836   4.1690
+#&gt;    2.6316   0.1588   6.9737  10.9898   3.5931   3.1511   4.9429  -1.8327
+#&gt; 
+#&gt; (16,.,.) = 
+#&gt;  Columns 1 to 8   2.6537  10.0281  -1.3741   0.9900 -12.0867  -4.0296  -7.6636   0.1825
+#&gt;    2.4319  10.9279   6.3170  -3.5904  -3.7944  -9.5351   3.8456   9.2553
+#&gt;   -7.8803  -3.3544  -1.3375  -3.0423  -1.6107   9.5581  -4.5955   2.3276
+#&gt;   -4.4451  -0.2926   3.6284   7.7152  -2.6448  14.0143  24.4411 -11.8132
+#&gt;   -7.9960   8.9860  -4.5813   7.6282  -6.9701   1.1402  -2.3306  -4.5071
+#&gt;   -4.4226  12.0704  11.6664  -6.9571   3.1467  -4.9787   5.0473 -10.9933
+#&gt;   11.2864  -1.5729   1.7127   9.4951  -2.6290   8.0230   0.0378  -8.6601
+#&gt;   -9.4625   3.9488  -2.1094   9.5814 -13.2363  -5.8638  -6.0246  -2.4959
+#&gt;   -0.1048 -13.5206   6.7469  -4.3198   3.5434  -0.1837  -0.8146   6.3350
+#&gt;   -3.9342   7.8975   1.1074  10.9452   2.6244  -1.7462   6.9768  -5.0688
+#&gt;    1.7446  -0.1351  -0.6821   2.8836  -5.9138   7.9013   4.5623  14.5249
+#&gt;    6.4212   0.7026  -1.7461  -4.0589   2.0305  -1.0969  -1.0820  -0.8338
+#&gt;    0.2226  10.2770  -2.7988  -6.5288  -9.7603   3.7378  -2.5321  -3.4819
+#&gt;    3.0488 -11.7935  10.9536  -1.8877  11.9208   2.3212  -2.7537   1.0941
+#&gt;    2.1382   5.3092   7.1833  -6.4094   1.9917   3.8928  -3.1746   0.3565
+#&gt;    8.1730   0.7460   8.6897   3.0966   5.1147  -2.4684  -0.6720  -0.8584
+#&gt;    9.6087  10.6647  -0.1727   1.2635  -4.7348   1.4423 -10.2314   2.2201
+#&gt;    5.6906  -5.4915   6.5053  -7.2581   1.7664  -2.1022   2.1175  -0.0348
+#&gt;    9.1430   1.9296  -7.5327   8.4665  -3.0810   1.2409  -4.6988   4.8817
+#&gt;    1.9465   2.5159   1.7587  -3.4185  -3.5874  -3.4295  -1.3687   2.3190
+#&gt;   -6.6130  -9.0796  -1.8347   2.6115  -1.1207   9.5405  -3.5588  -4.4271
+#&gt;   -4.1785   2.8673   4.9747  -6.1164  12.9784   2.4537  -4.2924   3.0161
+#&gt;    8.0232   4.7341   1.0049  -3.2322   8.3828   4.6395  -7.5344   8.6833
+#&gt;   -4.2088   2.6784   0.1928   0.3867  -0.6369  -3.5143  10.9183   5.2169
+#&gt;    8.9835  -6.3049  -4.8523 -13.6846  18.6929   5.5845  -3.0672   5.3202
+#&gt;   -2.9213  -3.7387   5.5182  -3.6154   1.3478  -1.7320  -1.5216  -6.9537
+#&gt;    9.2671  -3.2407   7.5714  14.1982  -3.3357   1.1475   6.5716 -10.1794
+#&gt;   -4.1577   8.2047   4.1041  12.3912 -11.5370  -5.1664   2.5799   3.8036
+#&gt;    3.8338  -5.2952   6.7403   4.5124  13.3406   8.5143 -11.3196   7.2957
+#&gt;    3.7297  14.7927  -2.2709   1.0681 -16.8867 -19.4837 -11.9069   5.0476
+#&gt;   11.2957  -5.4608   0.8200   7.4871  -1.2263  -0.4746  -6.9441   2.1174
+#&gt;   -9.0418   0.4256   5.4519 -12.3165   6.7599  -6.1433  -4.6055   1.1761
+#&gt;   -1.4819   4.0796   0.9065  -0.9019   5.4088  -7.9440  14.5377   9.0100
+#&gt; 
+#&gt; Columns 9 to 16  -6.8426  -4.7429  -2.6128  -4.6095   2.6961  -0.8184 -12.1658   2.0165
+#&gt;    3.9022   5.0333   6.4491  -2.1842  11.7931  12.3021   3.1147   6.1278
+#&gt;   -6.9729   2.7342   4.6440  12.5455  -3.9127   5.4592   4.1639   1.4389
+#&gt;    8.6323   6.5326   5.2528  -3.1248  -4.5754   1.5873  -5.0231  -3.1225
+#&gt;    2.8939   0.1219   2.8283   3.6899   2.2707   1.9394   0.6548   5.5807
+#&gt;    0.0017  -3.8882  10.1598   7.6993  -0.0444  -7.9066  -4.5745 -12.3957
+#&gt;    0.7785   5.4766   3.5906  -7.7531   1.0911  -5.9815 -12.2547   7.7808
+#&gt;    1.0523  -5.5980   3.0210  -0.2250   1.8311   5.0031  -2.5257   1.5206
+#&gt;   -4.9046  -6.8045   2.1573  10.7057   0.4956   7.5452  -4.8635  -4.9029
+#&gt;    6.7992   6.8604  -8.2919 -13.6156  -2.8000   5.0487  -7.0615  -1.5223
+#&gt;  -12.9161   2.7160   8.2835  13.1736   3.0676  -4.2209   0.4369   8.7412
+#&gt;   -0.3872   1.1803  -6.1056   4.2021   1.5244  -6.7314  -1.8443   9.4874
+#&gt;   -3.6800 -10.8111  -3.7154   7.3191  -1.2210 -15.8530   4.8161  -7.8834
+#&gt;   10.7230   0.1590  -2.8018   5.6006 -12.5767  -6.6004 -16.2261   7.2019
+#&gt;    0.4843  -5.2169  -1.0586   4.1696 -10.5364  -8.4845   2.7401   3.2174
+#&gt;    0.4920  -0.2655  -8.6780  -8.1274  -1.6072   2.6221   0.2542  -3.7309
+#&gt;   -8.9139  -4.1685   0.6980  -4.3601  -0.0753  -2.4713  -6.2920 -11.3251
+#&gt;   -2.9412  -2.9277  -0.9006  -1.0688  -3.6055   7.9323  -7.5708 -12.4720
+#&gt;   -9.2443  -2.1692  -6.7854   1.7093  -8.0463   6.3150   1.6444 -13.0162
+#&gt;   -6.2650  -1.9294  -7.3652  -5.3038  -1.9747  -0.5525  -2.9674  -6.6390
+#&gt;    8.1219  11.4121  -0.0902  -3.8964   6.4237  -0.1665 -11.2574  -0.7778
+#&gt;   -2.7504 -10.8360  -1.8227   4.4445  -2.9894  -0.9454  10.1992   5.0257
+#&gt;   -1.9871  -8.9973  -1.5199  -7.0012  -4.1810  -9.0287   1.5581  -2.0986
+#&gt;   -6.9044  -1.8868  -0.8582   5.7889  -7.4550  -9.6949  -4.8682 -18.4330
+#&gt;   -1.8776  -1.4501  -3.1208  -1.5706  -8.5306   0.0210  -0.4809  -8.2916
+#&gt;   -2.3846   0.7277   2.7183 -12.9379  -4.1543   1.4221   3.2912 -10.6667
+#&gt;   -0.3494   7.2240  10.9552 -11.7152   4.5154  -6.7795 -11.9837  -3.2178
+#&gt;  -13.4474 -15.3883  -3.8726  -5.5541 -11.9312  -2.3773   1.6819  -5.5822
+#&gt;    0.3630   4.3099 -16.3680  -0.3777  -2.2528  -4.0222  -1.3204   0.7607
+#&gt;   -4.4686 -19.8499 -10.0706  -2.9009  12.4972  -4.3342  -9.9224   3.8666
+#&gt;  -10.1040  -6.5496  -5.6094 -16.8005  -4.2389  -6.8117 -18.5713  -1.8870
+#&gt;    1.7998  -1.0995   1.8782  14.4492   0.5544  -1.6617  12.2205 -10.4153
+#&gt;   -6.9645  -1.6298   8.3926   5.3486  -1.4503   2.8527  -1.8521   6.0394
+#&gt; 
+#&gt; Columns 17 to 24  -3.1629  -0.0568   2.9113  14.5280  14.7589   3.1293 -12.1515  -1.2125
+#&gt;   -2.5092  -9.6162   4.5885   2.9099   8.3468  -4.3173  -0.8217  -1.2561
+#&gt;   -6.6908  -9.5136  -6.0419  10.0233  -3.2612  -4.1823  -1.2707   1.4180
+#&gt;  -12.0190   7.8139 -13.8348   0.5338   5.1126   4.1994 -21.2723  -7.0667
+#&gt;   -7.8274  -4.9705  -2.1304   4.1353  -8.3330   1.0937   0.1881  -4.3878
+#&gt;  -12.7670   5.6393   0.2593  22.5342 -10.9020   5.1398 -12.4250  -2.1257
+#&gt;    5.8816  -4.6940  -8.5924  -8.4002  12.8787  -7.0274  -6.9082  -0.0870
+#&gt;    7.1171   3.6475   9.1813  -9.7746 -10.4951   0.8722   1.6396  -7.0983
+#&gt;   -1.2520 -11.8828  -8.1181   8.3765   6.4687  -6.8427   9.5647   6.9806
+#&gt;    6.4404  -2.9231 -11.3833  -3.3352   0.4295   2.9849  -0.1354  -1.9745
+#&gt;   -1.8990  -8.8391  14.1990  -4.9804   4.0018  -5.7063   6.9528 -12.5414
+#&gt;   10.7867  -2.3682  -2.0609 -10.6862   3.9373  -3.7516   7.8622  -7.7914
+#&gt;   -3.6083   9.3223  16.5962   1.2439  -1.8558   3.5668 -11.2451  -6.5508
+#&gt;   -3.8310   5.8341  -5.7072   6.0198   7.0461  -4.2640  -0.8619  12.6715
+#&gt;  -13.2245  -0.7354   3.9689   2.9254   7.2269  -8.9177 -11.8530   4.4396
+#&gt;   -6.9530  -0.6109   2.6992   0.4603   9.3217  -0.9783   0.3191   0.5973
+#&gt;   -2.3645  -0.7434  14.0873   0.1423   2.8636   1.0353  -9.0741   0.3920
+#&gt;   -5.8839   1.9932  12.2294   0.8054   6.2002   2.5218  -4.2763  -7.6965
+#&gt;    0.8513   7.0868   7.3695  -0.3998   7.6253   7.4863   5.9059  -3.1743
+#&gt;    5.8895  11.1340  16.8882   2.2584  -4.4054   2.7326  -6.0199   1.4574
+#&gt;    5.9178   1.1990  -9.1185   6.5885  -5.9489   2.0860   3.2412   2.0335
+#&gt;    2.9457  -4.2214   2.5596   0.2399  -8.8712   3.9793   8.2923  -2.3617
+#&gt;   -1.2055   9.2519   0.7123   9.6024  -3.8783  -0.4447   2.7004   2.2629
+#&gt;   10.3553  16.5592  -0.2477   6.4830   6.7097   1.7149  -4.0558 -19.0939
+#&gt;   -2.5081   3.0439   2.1285   8.5253 -10.4950   0.9928   4.8042   9.3855
+#&gt;  -12.9153   4.9772   3.6428  -0.2269   0.5490   2.9480  -7.1561  11.2938
+#&gt;    3.8155  -3.4244  -3.3108   8.2549   9.9998  -4.2891 -21.9953  -8.6520
+#&gt;  -10.2267  11.0747   8.1871   0.2964   4.0452  13.9275  -8.5610   1.6311
+#&gt;    3.8581  -6.7254   4.5158  11.5645   3.9171  -6.2779   7.3628  -2.6553
+#&gt;   -9.4337  -2.1890  12.6193   0.1468  -3.0054  15.4094   1.0468   9.5217
+#&gt;    2.4823   0.7290 -10.0452  -5.2096   9.9998   3.4954  -4.4458   2.5061
+#&gt;    2.7536  11.1029   3.4073  -1.4913 -11.4818   0.4184  -4.7221  -0.0167
+#&gt;    3.4516   1.3483   3.1828   2.0351   3.8541   7.9194   1.7277  -8.4165
+#&gt; 
+#&gt; Columns 25 to 32   4.0505   8.9516   8.9033  -7.6845  -8.8602   1.5821  -8.4482  -6.3353
+#&gt;    2.2318  11.5904   1.8198   3.8042   2.1653   2.6246  -1.8867  -2.0695
+#&gt;    2.4190   6.7486   1.8535  -1.3479   5.0385   9.5143   5.8353  -9.7707
+#&gt;    0.3160   0.1693   5.0241   2.3182  -5.7190   9.2947  -3.2825   9.5995
+#&gt;    8.3221   0.3005   0.5396  -2.9437 -11.8083   7.2209  -2.4893 -14.6031
+#&gt;    0.1779   4.2406  -7.3055   3.8547  -6.1461  -8.7149 -10.9975 -25.8239
+#&gt;    5.6674  -5.5394   6.8157  -2.5307  -3.3665  -4.5521 -11.3748  10.3743
+#&gt;   10.0272  -0.1362  -2.2936 -12.8996  -9.6990  -3.0641   5.2434  -3.1504
+#&gt;   -4.6833  -3.7969  -2.9460   7.8764   6.6890   1.9937  -0.5641  -7.1264
+#&gt;    2.1160 -10.3251  14.0864  -5.1683   0.7573  -4.8519  -4.4410   3.3829
+#&gt;    4.6316  -0.5813   6.1136  -6.9190  10.1993  -5.3839   3.2224  -3.4258
+#&gt;    5.1394  -6.1845  -3.6422   2.5954  -3.9365  -7.0041   1.5143   0.0143
+#&gt;    7.2168   0.3847  -4.3440  -3.7469   1.3919  -7.6400  -0.1277  -2.9737
+#&gt;  -19.3379   3.5289 -15.4244  12.3707  -1.3818   8.6222 -18.1597  -0.9059
+#&gt;   10.2140   6.9681  -2.4340 -15.3828   8.3304  -4.1101  -3.6007 -12.4290
+#&gt;  -10.1915  15.1152   9.9963  -4.5331  -3.1466   1.2303  -2.1024   0.4509
+#&gt;    1.8998   5.4893   1.7368 -15.3842   0.2894   0.6480  -8.8291 -15.4097
+#&gt;   -7.6948   5.5280  -9.6765  -4.6556  15.5769  -6.7131 -10.0724   2.7616
+#&gt;   -1.0178   4.2935  -0.9750   9.4335  -6.8237  -1.9242   4.2828   6.9149
+#&gt;    0.5209   4.0523  -7.8269  -9.1706  12.9358 -13.0383   0.8386  -5.0926
+#&gt;   -6.7296 -15.0976  -5.1138   2.4820  -1.8629   2.3847   2.7592  -3.1532
+#&gt;   -5.8144   1.5334   9.3806  -8.4229   7.0976  -1.4525  14.7540  -1.5626
+#&gt;   -7.7576  -7.1915   6.7963  -7.5794  13.1287 -11.2520  -5.9283  -4.3028
+#&gt;  -10.0635  -2.0883   8.4976   2.8765   9.4888  -7.2230  -2.2691   6.7774
+#&gt;   -6.2538  -2.5045  -5.7848  -0.1290  11.1655   0.4228   1.2409  -6.2375
+#&gt;    8.9935   3.3222  -6.6574 -15.2159  -4.5156   3.4840  -1.4515 -11.9367
+#&gt;    7.8913  -3.0932  12.0142 -10.1397  -6.4581  -3.4470 -10.4847  -0.8180
+#&gt;   -8.3068   2.8102  -5.8828   8.7412  -5.1551  -0.1998 -16.8988   3.1662
+#&gt;   -6.2529   7.3636   0.1179 -10.5478  11.0667   0.3758   2.9674  -6.9911
+#&gt;   -2.4338   0.0214  -1.6522   0.4852 -12.8848  -3.1333  -9.7729  -2.2721
+#&gt;    0.7034  -8.9684  -5.5336  -6.7640  -2.4479  -1.6852 -10.6143   3.1703
+#&gt;   -0.4357   8.9272  -9.1191   0.3087   0.8216  -0.8100   0.8083 -13.8424
+#&gt;   -6.3769  -3.2665   4.3365  15.7927   6.6798  -6.6067 -13.9550   3.5308
+#&gt; 
+#&gt; Columns 33 to 40  -6.9297 -11.0017  -7.3290  -0.9733  -4.0245   0.7207  -0.8083 -11.6688
+#&gt;    0.9887   6.7787   9.3630  -0.1089   9.4970   1.2405  16.4210   5.2920
+#&gt;   -1.9242   1.4191  -1.4146   0.6657  -1.8452   4.3975   6.8781   4.3683
+#&gt;   -6.2923  -5.6739   2.7374  -5.7019   4.6604   8.9459   1.3710   5.9450
+#&gt;  -11.2656  -1.8411   1.3547   6.1710   0.0621  -7.3169  -2.9158   6.4003
+#&gt;   -0.6419  -4.1902  -7.3557  -7.8505   6.1350   9.9960   5.9794  -7.8682
+#&gt;    0.3170  -0.8213  -2.7116  -1.0315  -3.5961   7.2136  -5.3460  -5.7550
+#&gt;   -9.0557   1.3421   7.3307  10.7694   0.1778 -19.3088  -4.8544  14.3715
+#&gt;  -10.4370   7.7783  -7.5868  -8.5351   2.8893   7.1334  14.0060 -15.1075
+#&gt;   11.9832  -7.7026  -6.9726   1.5130  -6.5501   3.8620  -5.2957  -3.4377
+#&gt;   -2.8358   2.7340  -7.3989  -0.4648  -2.3398   5.1737  13.2490  11.8018
+#&gt;    3.8140   6.2382  -2.4453   5.1233  -0.4008  -5.5641  -6.0084  -3.2443
+#&gt;    7.0816  -1.5572  -3.5374  -2.6719  -1.3513  -0.7589 -18.0983  -1.5163
+#&gt;   -4.1255  -3.3912   7.7073  -9.9341   4.0253 -12.1809  12.0770  -8.5870
+#&gt;   -2.8708   6.5475   1.2193  -6.1879 -14.8457  -1.3914   6.6650 -12.9305
+#&gt;   -0.4384  -6.7738   0.2258  -4.2270  -1.8498   5.0849  -4.2075   0.1013
+#&gt;   -2.4174   6.0093   0.3795   5.1943 -10.8393  -5.2192   1.1280  -1.1510
+#&gt;   -0.2956  -3.8886   5.1311  -9.1582  -2.3592   2.3350  -3.5444  -2.0641
+#&gt;    6.5872  -0.8988  -9.7604  11.0533   3.7212  -6.9216   2.8120  -2.2940
+#&gt;    6.3180   0.1680   5.9209  -2.4832 -12.1692  -3.6259  -7.8507  -6.5393
+#&gt;   -3.1110  12.7384   9.9380  -5.2919   6.5667  -6.4021   5.7608  -1.6890
+#&gt;    1.0232   0.6634   0.9311   4.7265  -3.2909   0.7162  -6.8898  15.5118
+#&gt;    9.0084   7.1687  -3.9944 -12.5334 -15.4884   0.3428  -5.3218 -13.0022
+#&gt;   14.7850  -3.2750 -18.7341  -5.3742  -9.2112   4.6256 -11.5047  -9.0995
+#&gt;    6.2066   9.0095   4.8608  -8.1920  -3.6004  -5.2042  13.9607  -7.7939
+#&gt;   -7.7216  -3.3453   5.6436  -1.4099  -4.4756  -4.7878   6.4228   1.2972
+#&gt;   -1.9253  -5.2992   8.9140   2.5100   3.1209   3.7009  -0.3293   2.2241
+#&gt;    4.0245  -5.1316 -12.5800 -14.4542  -8.1623 -18.2699   0.8162  -7.9554
+#&gt;   -4.4564   6.1181   2.9378  -5.8014  -4.5335  -0.8213   0.8772   1.1595
+#&gt;    3.6789   1.1213  -8.5050 -13.9654  -1.0107 -13.9829   0.1980 -15.7043
+#&gt;   -1.7556   1.8067  -6.1722 -10.4958  -7.0932 -15.9986  -7.6157 -13.4282
+#&gt;   10.2085  -5.0717  -4.6299   6.3475   8.3986   0.7032   2.2475  -8.1552
+#&gt;   12.8033  -4.9932 -17.7588 -13.1457   2.3762   9.0886  12.7045  -8.7254
+#&gt; 
+#&gt; Columns 41 to 48  -7.4805  -1.3145 -13.7431  11.4765  -0.9648 -12.0847   4.7179  -1.4272
+#&gt;   -2.2829   2.5950 -10.7235  -2.1609 -13.8357  -9.4849   4.9866  -4.8983
+#&gt;    2.5329   2.3297  -6.3099  -0.2002   9.5547   0.6236   0.4863  10.6733
+#&gt;    6.6941  -8.6938  -2.0557  -5.7574   1.9355  -6.3002   1.6139   3.4015
+#&gt;   -2.2127   4.6527  -3.2228  -6.1964   3.3754  -3.8984   5.7287  11.5439
+#&gt;    0.4154 -14.2939   3.1326  -4.1760  -2.6193  -3.7922   3.6896   7.2133
+#&gt;    3.8841   5.6008  -0.8244   7.2883  -3.8380  -6.6507  -3.1420   4.8811
+#&gt;   -6.9786  13.0368  -1.1869   6.9741  -2.7768  -2.5288   1.3483  -3.5707
+#&gt;    6.9048  -5.1701   4.1549   5.8017  -8.5916   2.9603  -7.7201   7.7871
+#&gt;  -11.9918   3.8684  -2.6689  -6.1250  -9.2973   1.2899   3.8853  -2.2765
+#&gt;   -7.7719  11.2667 -19.5185   4.6244  -1.3942  -8.3421  -8.6290  -1.2741
+#&gt;    0.4772   4.3330   5.6445  -7.2651  -7.9712  -2.0373  -5.2405  -1.3881
+#&gt;   -2.8972   5.4049  -5.5721  -3.8811  19.0670   3.8932  -2.1869   0.7545
+#&gt;   15.8109  -8.8330   7.4110   1.7833  -0.2962  14.3180   2.0180 -11.7855
+#&gt;    2.8560   6.9780 -17.5480  10.3052  -3.1295  -4.2072   1.4636  -6.0688
+#&gt;    0.8833 -13.2746  -8.3860  -4.9834   3.1834   8.0150   1.5190   0.3426
+#&gt;   -4.9003   1.4852   0.1459  -1.5525   0.3936  -2.1157   2.1987   7.1499
+#&gt;    0.3587  -2.1121  -0.5518  -1.9261  -9.1747  11.4873   6.3786   5.7065
+#&gt;   -6.8265  -2.9418  -4.8807   0.7788   2.3878   3.5582  -4.0625  -3.5805
+#&gt;  -10.6284   3.5065  -4.6024   6.8657 -11.1292  15.1727   3.2767  -8.7483
+#&gt;   -5.0826   2.9713   8.4317  -5.3287  -8.3769  -4.4589  -5.1314   8.1267
+#&gt;    0.7844   0.2151  -0.4487   8.8005   7.6252  -0.6333   5.5800  -3.0729
+#&gt;  -11.8212  -7.3095  -0.3658   0.8800   1.5696  -7.7159  -9.3507  -8.9309
+#&gt;    5.5200  -1.2981   3.8761  -1.1395  14.0902  13.1584   0.2560  -4.1000
+#&gt;   -2.5606  -2.6205   2.2323  -1.8492  -9.6116  -7.1594  -3.9362  -3.7241
+#&gt;   -5.2702 -10.7750  -0.7620   6.2300   0.7716  -0.5113  -4.7611  -2.4473
+#&gt;    0.7879  -8.2632   5.1632  -4.9078   3.8705  -0.7683  -0.0107   7.0924
+#&gt;   -1.8081   0.7194  -2.0832 -16.3230  15.7687   9.7112 -12.0240   2.1642
+#&gt;   -7.5212   1.5560  -3.9287  11.9568  -9.6883   0.9000  13.2467  -7.7876
+#&gt;   -9.0240  15.0953  -5.1962  -7.3965   5.9510   4.0735 -10.1522   0.6561
+#&gt;   -4.0246  -1.4343  12.8208   5.0154 -10.6280  -4.3388  -6.2407   0.0779
+#&gt;    0.7169  -1.2498   4.6823  -0.6321   6.2968   3.7195   3.3005  -6.5767
+#&gt;   -1.1701  -0.4908  -7.7588  -6.1339 -17.1114  -5.4716  -5.5306  -2.6368
+#&gt; 
+#&gt; (17,.,.) = 
+#&gt;  Columns 1 to 8  -2.7704  10.5603   0.7267  -7.7291  -3.7433  -1.6578  -3.3570  -1.1029
+#&gt;    2.5729   2.4487  -2.5715  -7.8141   4.2293 -11.1039   1.5680   8.5610
+#&gt;  -13.6032  -0.1561   2.3610  -0.0955   2.1723   4.9194  -0.1423  -4.7875
+#&gt;  -18.6177 -16.6249   7.0370   0.4203   5.7234   8.8069  -4.4259  -3.4729
+#&gt;   -3.0531  -0.4844  -2.4866  -2.8341  -2.3717   0.2669 -10.3285   1.0651
+#&gt;    6.0293  -2.8649  -5.4014  -7.0805  -7.5987   0.7204  -1.6050 -10.0098
+#&gt;   -3.7357   7.8525   3.0803  -3.4952   0.9365  -1.8273  -1.7159  -4.0662
+#&gt;    7.0489  -2.4002   0.2068  -1.4921   1.5017 -11.9677 -13.1278   9.2036
+#&gt;   -2.5369  -4.1299  -4.7285   6.1634  -4.5615  -4.8606   1.4767   5.1493
+#&gt;  -10.4036 -10.7121   4.2236  -9.0293   6.1111   7.8108  -7.0556  11.0722
+#&gt;    0.9993   4.4852  -8.2668  -3.4930   0.3539  -4.8302  -1.4111   9.8303
+#&gt;    0.9946   3.3462  -5.6483   1.6089  -5.8641   1.4127   2.5571  -4.2720
+#&gt;   -0.2602   3.9641  -1.0204  -2.6129  -3.2721  11.8840  -2.5988  -5.9541
+#&gt;    2.0741   1.7521   1.4423   7.5366   9.0240 -22.3413   1.5828  -1.8651
+#&gt;   -0.4998   3.5266   0.7295  -2.1794   1.4527  -2.7818  -8.7797   1.6922
+#&gt;   -0.0240  -3.9744   2.0120  -8.4228  -0.8473   2.9716  -0.0982  -9.6429
+#&gt;   -6.8080  13.1794   0.6546   1.0582  -5.0821  -9.3842  -4.4272   3.9259
+#&gt;   -2.1990  -5.3386  -2.8094  -4.7910   8.2978  -9.5896   7.6713  -2.5908
+#&gt;   -3.1451   1.2174  -2.7183   0.9241  -3.2326   7.2417  -0.7122  -7.7775
+#&gt;   20.0796  -6.6055   2.9293  -1.0359   2.0235 -13.0951   9.0062   1.1787
+#&gt;   -6.9559   2.6484  -3.8156  -2.2437   1.5578   1.2313   1.7346  -4.0805
+#&gt;    1.6198  -3.9982   5.2072   5.4066   1.4270  -3.5072  -7.0815  -2.8528
+#&gt;   -3.6409  12.0129   5.4795   0.9914   1.0420  -4.8794  12.5829   1.7580
+#&gt;   -5.3814  -1.0697   2.6472  -0.6693   9.2690  18.7628  -1.3712  -0.7372
+#&gt;    0.2149   9.5615  -6.8986  -1.1008  -0.6883   5.1239   1.9762   2.9490
+#&gt;    6.4925  -3.5990   9.6852  -8.9323  -1.3355  -3.7827  -0.8172 -10.7852
+#&gt;   -5.4988   2.4479  -4.5648  -4.2705   7.0228   5.1410 -20.2350   6.6242
+#&gt;  -14.4974   7.3483   5.1486  -2.0932  -1.0024  -1.4598  11.5936   1.4477
+#&gt;    4.3321   7.3866  -2.9594   6.7808   2.5951  -7.6477 -11.4815  -1.3703
+#&gt;   -4.0412  11.8890   1.3561 -10.6676 -13.7145  -2.2311   8.0976  10.0307
+#&gt;  -15.1334   5.8670   5.7254  -1.1431  -0.2199 -11.0346   5.3607   1.5764
+#&gt;    9.1307   0.9232   2.6315   0.2508  -1.3830   4.0534  -3.5289  -7.6598
+#&gt;    5.0985  -5.2785  -1.8614  -4.2947  -5.6576   1.2913   9.4377  10.3827
+#&gt; 
+#&gt; Columns 9 to 16   6.7341   1.9863   8.9667  12.0119   5.6970   0.6868  15.2604  -4.1424
+#&gt;   -4.2998  -8.5678   9.4961   2.9845  -3.5134   1.8394  -0.9021  -2.1620
+#&gt;   -3.2300   5.0553  -2.9733  -1.1748  -0.8557   1.1872  -3.1148   3.1607
+#&gt;  -11.7453  -1.7068  -5.4682 -12.8065  -2.6870   7.2390  -0.2386  -3.2514
+#&gt;    1.0902   0.9017   1.4176   3.7273  -0.6858   1.1306  -0.6308  -6.2185
+#&gt;    5.4508   1.4973   0.2619  -8.4212  13.0744   3.6572  -0.3702   6.1075
+#&gt;    0.2905   4.0318 -10.3974  13.1124   2.7247  -4.9409   5.5627   7.1809
+#&gt;    2.8171   6.4971   1.1240   0.0898   2.5301  -4.2880 -21.7980   2.0108
+#&gt;  -15.7529   4.7049  -3.7587 -10.8786   9.4058   5.3792  -2.8985  -5.8478
+#&gt;    0.7016 -13.5083   2.7154   1.1747  -2.4312  -3.8385   9.1867  11.6649
+#&gt;  -15.5568   4.4197  -7.8619   0.2513 -10.2832  11.4538 -14.1472   5.2680
+#&gt;    8.7746  -9.0890  -3.8961   2.9851   4.9018 -19.8198  10.5791  -2.6983
+#&gt;    9.1059  11.0467  -2.0032  -1.5611  -0.6221  -3.1742  -0.9975   1.7780
+#&gt;   -0.7256   0.6946   3.3463   1.4316   5.4650  -0.5997  -3.0255 -15.4508
+#&gt;   -0.2171   5.6657   9.0344   2.1456  -4.4352  -4.1476   5.2174  -4.4732
+#&gt;    2.8979   7.3370   0.8185   2.5894  -6.4739   3.8603   5.8764   4.0880
+#&gt;    1.1180  17.6122  -5.3422   5.0036   4.3687  -5.0587  -9.5809  10.2824
+#&gt;    3.1349  11.4819  -2.5447  -8.5379  -5.8880  -7.5417 -13.4420   5.0741
+#&gt;    7.3487   4.7572   0.9641   0.0457   8.7562  -0.4766 -11.2908   3.6942
+#&gt;    2.3793   3.5841  -0.3000 -11.0359  -0.0331  -2.5764  -7.8028  12.3705
+#&gt;    1.1415  -4.4475   4.1464  -4.4200   4.0291 -16.3471  -6.8999   3.4234
+#&gt;   -1.4660   1.7638  -7.1693   4.2245  -4.1514   8.3268  -8.7105 -10.3372
+#&gt;   -0.6831  -7.3303  -3.0960  -2.8838  -2.9398   3.8557  12.8259   2.2771
+#&gt;   -1.6265   3.4067  -3.4404 -14.4183   7.1554   9.3766  -4.0037  18.9329
+#&gt;   -0.8861  -2.8863  10.9595  -3.0864   1.1135   0.2013   1.0840   1.5685
+#&gt;   13.0322   4.8567  13.5926   3.8107   8.6678  -0.9883  -7.2145  -1.4742
+#&gt;   -1.9749  -1.3267   1.1079  12.0055  -0.5795   1.0858  -3.1108  -3.2832
+#&gt;   -0.5156  11.9009   5.1915 -12.5264   5.8314   4.6921 -14.1464   8.2093
+#&gt;    0.1146   4.5829   6.4958   5.2190 -10.4601   6.2816  -5.5749 -10.3199
+#&gt;   -2.6049   5.9377  12.4423   2.7328   1.6565   4.3421   2.3708   3.0174
+#&gt;   10.3795  -6.6289  -8.6209   1.4962   0.4001  -4.7952   1.3191  -1.1169
+#&gt;    8.8352  -4.9195   3.9531 -15.5500   7.0633  -5.7947   4.2496  -3.8436
+#&gt;   -7.5820 -15.2724  -0.5123  -9.0253   3.9423   4.5669  13.7463   5.4912
+#&gt; 
+#&gt; Columns 17 to 24   2.8659   7.9970  -2.2369  13.0924   8.6808  -0.2852  -9.3335  14.6500
+#&gt;    7.0788  -7.9463  -3.4163  -1.3608  -4.7428  -0.0517  -4.0457  -4.6531
+#&gt;   -3.1597   1.8588 -12.6884 -11.0860   4.6732   2.3709  -9.3827  -0.1688
+#&gt;   11.8203   2.5997   4.2648   1.1745   5.2093   5.3027   1.5585 -16.1315
+#&gt;  -10.1067   1.5029  -5.0066  -9.1026  -9.8618  -9.7336 -11.3681  -7.6435
+#&gt;    3.4721   1.6037  -5.9775  -4.3065   0.5484   4.4562   1.1557   0.8167
+#&gt;   -0.5257  -0.5474   3.3103  -3.4944   4.4801  -8.4647   0.1863  -5.7290
+#&gt;   -4.6142  -1.2074   1.7776   3.7075   7.3313   2.6778   0.3457  -1.0475
+#&gt;    0.2307   5.1051  -4.3815 -12.3826  -3.9321  13.1424   1.0713 -13.4604
+#&gt;    0.2598   0.6921   4.9805   7.6117  -8.1925  -8.7037  -6.8049  -0.0327
+#&gt;    7.0166  -0.7439   2.9224  -9.7845   6.8724   9.8846  -2.1503  -5.7923
+#&gt;   -2.1224  -1.3258  13.4410   1.4991  -2.4490  -4.1900   4.0207   4.3657
+#&gt;   -3.2526   4.4835   3.9203  -3.7430  11.5783   1.6549   4.3792   6.2467
+#&gt;  -10.0576  -3.4088   4.0432  -5.1732 -17.3550  -7.1546   1.4905   0.1845
+#&gt;   -5.6070   3.0486  -5.6468  -8.1206  -5.4535  -6.7772   0.8812   8.7500
+#&gt;    1.3125  -4.4642  -3.8045   7.3684   1.5014  -5.6692   8.8631   5.4753
+#&gt;    1.1991   8.7489  -6.4687   1.2454   2.9509   0.9164  -8.4742   7.6956
+#&gt;   15.4080  -4.4415  10.2667  -4.4999   5.5235 -13.4351  15.9167   7.7456
+#&gt;    1.6523   4.7800  -0.0088  -3.5715   3.0276  -3.7797  -1.8262   4.8513
+#&gt;    2.5692   0.6369   4.6692  -0.8968  -3.0227   4.5375   7.6932  13.8799
+#&gt;    0.5910  -0.4237   9.7608   4.6418  -0.1699  -7.6320  -2.6444 -10.0515
+#&gt;    1.9812   2.0879  -2.8302  -3.6234   7.9835   9.1231   5.0533   6.6013
+#&gt;   14.6054   4.1328  -6.3627  -7.4908  -1.6861   7.2527   8.0787   7.1441
+#&gt;   -2.2543  -3.0008 -10.7692  -5.7324 -13.2544  -1.8763  -2.6397   8.1258
+#&gt;   12.1308   5.8818   0.9452   7.1992   0.6095  -3.3979  -6.7545   2.6578
+#&gt;    0.9111   8.0921  -0.1917  21.9292   4.9044  -2.3156  -1.1944   3.9495
+#&gt;    9.2807  -2.8576 -10.0671  -3.6933  -3.1867  -0.5104   2.8762 -10.1065
+#&gt;    8.1040   7.1307   5.4396 -10.0216  -8.6300   0.2725 -10.2342   4.1169
+#&gt;    1.8882  -7.2394   1.6500  -6.4690  -6.5163  -8.4244   0.7696   2.0009
+#&gt;   -3.4141   1.8392   4.7830   4.7468   3.8604  12.8414  -2.3081   2.1538
+#&gt;   15.9406   7.2814   5.9098   7.0757   2.5140  -1.6559   7.7894  -2.8529
+#&gt;   -6.7661  -9.5295  -6.3038   9.4841  -4.0221  11.3960  -6.2655   6.3740
+#&gt;   15.0542   2.3747   4.9200   6.3924  -2.9990   8.0179  -2.8307  -2.4978
+#&gt; 
+#&gt; Columns 25 to 32   2.1070   1.1357  -0.6737   1.5143  -8.0808   1.4888   4.5449   1.9640
+#&gt;    4.4534   2.5352  -0.6067   0.8445  -0.0012   1.2110   0.8128   0.5631
+#&gt;   -4.9796   3.3211   6.7698   7.0588   6.1402   3.4425  -6.0022  -3.4105
+#&gt;    1.5599  -5.6939   3.6210  20.0176   0.4168   3.2349   0.4631  10.5732
+#&gt;    9.3376  -4.3873   0.4279  -3.8398  -7.9957  -7.3808  -0.1321   0.8090
+#&gt;    6.2631  -2.7879  11.4126  -0.0802  -5.3166  -7.7050   0.0382  17.2720
+#&gt;   -8.1011   6.2337  -2.9383   4.1201  -4.2928   3.6873  -0.4019   2.7793
+#&gt;    1.7744  -5.0834  -1.6528  -6.6120  -8.9883  -0.6799   0.8348  -0.0160
+#&gt;  -13.1987  -0.0450   0.9701  -1.2428   2.4142  -1.3553 -12.7588   9.6190
+#&gt;    7.0441   6.1168  -3.5449   4.0805  -5.3083   5.9797   8.8975   2.7549
+#&gt;   -3.8628  -1.2028   0.1499   9.4044   5.8880  -0.8799  -1.2509  -5.9398
+#&gt;   -0.9154   2.7582  -0.1849  -8.6003   1.1950   4.5682   7.6503  -1.1930
+#&gt;    1.5326  -8.2111   4.0244  -8.2650   6.7523   5.4653   2.4483  -7.4292
+#&gt;   -4.2883   0.2562  -2.8330  -7.6422 -20.6673  -4.0809   1.7666  -4.3616
+#&gt;   -8.0310   0.3609  -3.1955  -5.4921  -6.0570  18.4650 -14.8922  -2.0463
+#&gt;    0.1960  -3.4853  -5.3803   6.3456   0.3314  -3.9994   2.6963   3.8593
+#&gt;   -1.3784  -1.7650   0.1733  -3.8867  -5.2378  -5.9713   5.2499  -4.7100
+#&gt;  -13.8746   4.6234  -6.7364   8.9808  -0.9970   9.9604   1.1777   2.4911
+#&gt;   -7.9730  -5.1767  -4.4298  -5.4043   6.5203  -6.3957  -7.0252   7.0823
+#&gt;   -7.5692   0.2801  -6.0745  -7.7463 -13.9535   5.2160  -5.9360   0.5628
+#&gt;    1.3956   0.3247   4.9877   0.6447  -8.8323  -2.6314   1.5729  12.2125
+#&gt;  -11.6241  -8.0160  -9.6314  -5.0019  -3.2613   4.8675   3.5103  -3.1493
+#&gt;   -2.5003   5.0246   3.1325  -0.2385   9.1923   2.3005  -0.7771  -2.0385
+#&gt;    4.8006  -2.8842   7.8990   1.0820   7.9377  -3.5297  -0.4941  -3.6435
+#&gt;   -3.0593  -1.8972   0.7686   3.1528   1.8651   3.5969   0.1643   3.4832
+#&gt;   -1.2656  -3.8606  -6.7093  -0.4224 -10.3953  -8.6362  -0.9552   4.5440
+#&gt;    0.2829  -5.4841   2.0222   0.5593  -0.8317 -13.1011  17.1616   8.9623
+#&gt;    9.4220  -2.8666  -5.2726   0.5688   3.8026  -8.5452  -3.6207 -17.8513
+#&gt;  -15.7801  -7.5726  -5.9551  -7.1169 -13.2869   0.5127  -2.9001   0.7436
+#&gt;   17.4660  -1.4932  -7.7020  -5.5903  -1.6124   6.7695  -0.5188  -9.5186
+#&gt;  -13.4290  10.1790  -6.9775   2.2547   5.4104   1.8314   2.4659   7.4010
+#&gt;    9.0881  -1.5618  18.9436  -6.0187   7.7381  -9.9503   2.6436  -4.7875
+#&gt;   -1.2500  11.6404   3.5587  10.2993   6.2472   5.6981  -3.0613   1.0673
+#&gt; 
+#&gt; Columns 33 to 40  -6.0871  -7.8560  -5.1843  10.8660   7.2719  -2.5899  -5.5856   1.1496
+#&gt;    0.0269   4.3973  -2.5121  -1.8696  -6.6044   3.7320   3.2216   7.9404
+#&gt;    2.6763  -5.6407   1.6508  -4.2718  -4.0539  17.6827  -4.3344  -7.8905
+#&gt;    7.9267  15.5898  -0.7649   6.3951  -6.6746   5.9853   5.0476  -9.6068
+#&gt;   -1.3657   3.8838  -2.8952   0.2352  -1.4886   0.8382   1.1905  -0.9388
+#&gt;    1.1199   0.8771  -0.5850  16.6982   9.6759   1.5541  -7.3544  -0.4983
+#&gt;   -5.8925  -3.7602   2.7142   7.4090  -1.1657  -4.8527 -10.1923   9.1371
+#&gt;    4.8444  10.4564 -11.3587  -8.9570  -0.4252   8.3873  -5.0638 -12.8834
+#&gt;    4.1751  -4.8812   3.1374   6.2684   3.2427   4.9984   1.3892  -8.1252
+#&gt;   -3.6175  -2.0191  -2.8556   6.7477  -5.4764   1.6779   6.3614   9.1818
+#&gt;   10.9193  10.6641   0.7074  -2.9042   1.2107   8.3301   5.3420  -7.7657
+#&gt;   -1.8419  -9.6558   9.0585  -6.4754   6.5303 -11.6757  -4.2685   9.5693
+#&gt;   -8.4170  -0.1091   9.5069  -4.8290  -7.7341   3.9342  -4.8279  -8.3420
+#&gt;    2.0843   5.6943   2.3081   2.1958  -1.7479  -8.2845 -17.9574  -1.3221
+#&gt;   -0.1133   0.1275   2.5143   3.3279 -13.4875   3.4412   2.3122 -11.5105
+#&gt;   -0.3208  -0.4993  -2.3072  -3.3562  -9.6989   7.0908  14.1526  -1.9749
+#&gt;    4.1466  -5.4791  -1.0779   2.0537   0.3336   0.2411  -5.6312 -10.7543
+#&gt;   -3.3062  -6.7042   5.1662  10.0127  -9.5516   1.1809  -6.6059 -13.0325
+#&gt;    2.3824  -5.0299  -1.7504 -11.8770   5.7312   2.6594   5.1171  -4.1069
+#&gt;    5.5454   1.3222  -4.5464   6.1858 -11.6493   1.1564   2.2874  -3.6223
+#&gt;    4.7625  -1.0953  -4.0312   1.7370   3.8948  -3.4990 -13.1045  -3.7373
+#&gt;    5.1932  -0.9977  -4.4916  -6.2493   1.4565   8.7194   7.8811  -4.6788
+#&gt;    5.1834  -1.3572  -2.1052   6.3175   1.3448  -7.9595   5.8325   7.4940
+#&gt;   -3.9020  -0.5996  11.2718   6.6009  -4.9164   5.5207  -6.5937  -5.3866
+#&gt;    5.5396  -9.1300  -2.2336   3.5168   2.0850  -6.4280   0.9162  -6.8538
+#&gt;    5.9051   3.5531 -14.0328   7.0267  -3.1132   4.9326   2.0346 -10.9407
+#&gt;  -11.1485   6.5715   6.9071   1.6247   1.6972 -17.4898   3.0868  -1.3412
+#&gt;   -2.8866   6.7616  -4.6754  -1.8187  -7.1617  -3.5344   0.5114  -7.9344
+#&gt;    0.9396 -10.4522   1.2673   2.3761  -4.5045  -5.8762   2.5294  -5.7347
+#&gt;  -11.7426   0.7682  -4.7147   1.3250  -2.2800   8.3357   6.3525  -7.6078
+#&gt;   -4.0112  -7.7059   3.6581   3.6947  10.7464  -6.6490 -12.4564  -3.1245
+#&gt;    0.4887   1.7864   3.1950  -1.9047   1.3532   1.7572  -4.5550  -3.3779
+#&gt;   -3.7773   3.4754   4.8371  10.9474   3.7645  -7.2888   1.0882  11.2958
+#&gt; 
+#&gt; Columns 41 to 48  -6.0387 -11.2056   9.6341   2.8017 -12.2098   1.0781   5.1328  10.4557
+#&gt;    7.2590  -8.2040  -2.9756 -13.0488  -3.0499  -5.4872  -1.5956  10.6881
+#&gt;    1.2324   4.3335  -3.6939  -3.1393  10.2875  -1.5746  -9.9184  -6.8965
+#&gt;   -2.4542 -13.3423   3.5852   6.7524  -1.8796  -4.5611   7.9572  -1.0148
+#&gt;   -1.3396   4.4177  -0.2213   6.2529   3.7517  -0.7370  -3.7494   1.9093
+#&gt;    2.6653   8.3401  -3.1142  -8.5770  -2.8581   5.9668  -4.6497   3.4966
+#&gt;    7.7550  -8.6419   5.5229  -6.6355   3.0054   2.1490   5.7685  10.6066
+#&gt;    7.2441  12.5011  -0.4768  -3.0536  -0.6593   1.0614  -1.2096   7.1685
+#&gt;    5.6518  12.7438   2.9351 -12.3455   2.5277   1.2141  -7.0547  -8.5420
+#&gt;    4.3700  -6.5105  -4.3187   3.5244   9.4576  -1.7260   4.4559   4.3307
+#&gt;    5.7719   1.3263  -4.4880  -9.1984   9.7216   0.2186  -1.0691   5.3772
+#&gt;    1.1191  -1.0495  -0.4789  -0.2497  -2.2224  11.6902  -0.4531  -0.3812
+#&gt;   -6.3349  -0.9310  -4.5166  -0.9552  -2.1166   3.4339   2.0261  -2.2041
+#&gt;   12.0080  -5.1870  13.9815   5.0460  -4.5699  -8.4260  -5.1223  -2.8219
+#&gt;   -3.1362  -1.5496   1.5033 -14.7490  -5.9815   2.0039   0.5765   5.5446
+#&gt;   -5.5697 -14.0296   0.7314  -3.6337   6.9127  -2.6981   5.8657   2.1826
+#&gt;    7.7615   4.0952  -2.6710 -11.4045  -1.7372  -2.0555   9.9285   1.1991
+#&gt;    8.1680  -6.1382   5.9534 -15.0505   4.3782   9.7085   3.4996   4.0257
+#&gt;   -2.0523   0.5244  -3.8419  -0.8932 -11.1999   2.5236   0.9160  -3.5783
+#&gt;    9.2817   3.7083   3.4058  -3.5454   2.9044  -1.7373   5.6485  -4.9491
+#&gt;    2.7605   7.7002  -2.6598  -6.8164   2.5014  -1.5200  -6.9747   0.8509
+#&gt;   -5.1617  11.3389   8.3555  -4.8374  -0.2337   0.2449  -4.0933   3.7114
+#&gt;   -7.2699  -0.3516 -10.9980  -6.3405 -14.9887   2.6767  -5.0563   3.6578
+#&gt;   -1.8291   7.8125 -13.2615   6.5019   5.0765   3.9017   2.8786 -14.1297
+#&gt;   -8.8028  -0.7751   0.2333 -13.4476  -5.6117  11.4009   4.5270  -1.2784
+#&gt;   -6.5362   5.0214  12.6471  -2.5547  -4.7357  -9.1172   0.4524   8.0845
+#&gt;   -0.1702   1.2163  -0.2177 -12.3578  -8.4017  -3.1552  -3.2782  -1.3680
+#&gt;    6.3253  -4.4724   3.3072   5.0575  -4.4235   0.6756   1.1694 -15.7861
+#&gt;   -5.6443   7.3664   4.6533 -11.5215 -10.3234  -0.0212   3.2675   7.5162
+#&gt;   -4.0903  -2.8207  -1.8034  -8.4524   6.7105  -4.1710   5.3431  -0.8284
+#&gt;    1.0200   4.1784   1.0915  -6.5063   0.6506   9.9118  10.2774   0.8494
+#&gt;    2.7498   3.9871 -11.0478  16.3057  -6.8933   4.1712  -5.2481  -2.9344
+#&gt;    9.1198  -6.8364 -11.0031   2.7434  11.8762   8.8112   0.1716   0.3366
+#&gt; 
+#&gt; (18,.,.) = 
+#&gt;  Columns 1 to 8  -3.8668  -5.2559  -1.3421  -3.4314  -5.5902  -1.2348   7.5022   5.1467
+#&gt;    7.3846   6.2321 -11.4451   1.7548 -20.0545   6.2218  -3.4738   5.3867
+#&gt;   -7.4439   8.5497   4.9470   3.4129  -6.3982   6.3543   4.4766  -1.0839
+#&gt;    1.8430   2.1162   6.6763  12.9154  14.1324  -3.0693   2.5126   4.9445
+#&gt;   -0.5132   6.6500   5.4475  -7.0266   4.8493  -3.7801   6.1027  -9.6499
+#&gt;  -19.8086  -7.2166  -5.5340  -1.8946   0.1660  -2.3499  -2.5856  -1.3366
+#&gt;   -0.6751  -0.9848  11.5909   7.2691  -8.7376  -6.1050   1.9485   4.0363
+#&gt;    8.8061   5.0337  -2.1156   0.1317  -2.0671  -4.1119   7.4026  -2.9720
+#&gt;  -11.1357  -4.2862  -1.5577  11.4097 -10.9833   5.1710  -7.6701   3.8730
+#&gt;   -2.0210  -3.4727  -6.0439  -6.3764   4.9350   0.0568  -0.2317  -5.7015
+#&gt;   -8.7964   8.9918   1.7091   1.7820  -1.7458   2.4526  10.1842  -0.5978
+#&gt;    1.5600  -7.0254   6.6511  -0.7881  -2.8247  -1.2636  -6.7303   0.7457
+#&gt;   -9.9962  -3.3429   2.2513  -2.2121  -3.8652  -2.3950   2.6836   1.1698
+#&gt;    5.8844  -0.5286  -6.5471  11.2424   3.0500  -0.0954 -16.5267  -0.3183
+#&gt;   -5.6467   7.9594 -12.9433   3.7634 -18.3734   8.7222   2.4835  -1.1996
+#&gt;   -5.1361   0.8167  -4.7019   3.1790   7.8726  -1.0832  -2.2899  -2.2924
+#&gt;   -4.2281   2.7615  -4.2521 -13.8693  -2.5246 -11.5597   8.7394  -0.5678
+#&gt;   -1.4691  -0.6020 -18.1740  16.6846   0.2870  10.0524  -3.2751  -0.9453
+#&gt;   -1.9186  -1.3545  -4.1410  -4.9974  -6.8474  -1.3620  -6.9289   9.6157
+#&gt;   -1.6624  -1.9641 -24.7416  13.0001  -6.9840   4.9895  -7.7299  -6.8454
+#&gt;    7.8452   0.0362  -4.2189   1.4746  -5.9102   8.0202  -0.0475  -1.3546
+#&gt;   -3.4357  -0.9328   8.5267   1.9319   0.5512 -16.5238  -5.4286  -6.3495
+#&gt;  -17.0909  -5.6792 -10.8661  -6.1793   7.4291  -7.0865   5.0553  -8.5706
+#&gt;  -27.6425  -7.6394 -10.0787   2.3910   9.3487  -0.0833  -4.5750  -1.5715
+#&gt;   -0.3811   7.1165  -9.3529 -10.1394  -2.6536  10.3981   7.1494   6.2481
+#&gt;   10.2249  12.1080  -6.7486  -1.7461  -4.8264  -0.0810  11.8476  -2.3056
+#&gt;  -10.0728   6.0447   4.7626 -13.1478  -1.3342 -20.2861   4.2710   2.8576
+#&gt;   -5.9852  -5.6862  -6.5108  -7.5676  11.4240  -8.8640  -1.1609  -3.7681
+#&gt;   -1.8751   1.1401  -9.4994   5.1197 -14.1007   0.8723  -8.6336   3.2089
+#&gt;   -5.1206 -13.8672  -0.9405 -14.4086  -7.9146  -5.0967   5.1145   1.7116
+#&gt;   -0.0410  -8.1056   8.9243   4.9892  -2.0418   0.7855   1.2673   3.1330
+#&gt;  -12.9567   3.8870  -2.1128  -2.1108   6.1276   7.9823  -2.7277   2.6696
+#&gt;   -9.4991  -8.8730  -2.5337   6.7111   2.3819   9.8990  -4.2177   3.4382
+#&gt; 
+#&gt; Columns 9 to 16   2.6155  -3.1598   0.5561   0.9017  -0.8640 -13.1992 -10.6243   0.8509
+#&gt;   10.1564   4.2755   4.0640  -2.1607   5.6714  -4.9804  -1.0634   0.5981
+#&gt;   -2.5515  -1.1962   0.4510  -7.8476   2.5789  -0.8973   7.3582   4.2570
+#&gt;    7.0324  -5.5265  -3.5624  -5.5224  -4.6802   4.4413  -5.8475 -18.2055
+#&gt;    1.4783   3.6747   5.0357  -3.0983   2.1574 -12.1662   0.8275  -6.6068
+#&gt;   -3.2798  10.5581  -2.9460   4.3101  -4.8227  -1.8283   1.5126   3.0180
+#&gt;    8.6264   3.7093  -6.5137   1.8973  -3.4125   1.3862  -1.5053  -1.1352
+#&gt;    6.5437   7.7399   3.0553  -2.0195   6.2153  -9.7967  -6.6664   7.3130
+#&gt;   -2.9593  -0.3026   3.8410  -4.0726  -4.7563   5.4368   8.2580   3.8967
+#&gt;    2.4642  -4.0941  -6.1839 -10.0695   3.2336   1.1617   3.3472  -2.6711
+#&gt;    6.9811   2.8503  -5.4271  -6.5792   7.9685  -8.0903   5.3719   5.1022
+#&gt;   -5.9984   5.1151   1.4408   6.2052   0.7562   1.3832  -1.8367  -1.3511
+#&gt;    0.7537   2.3841   0.1785   2.3359  -7.0487   6.0956  -9.5434   0.6965
+#&gt;  -10.3330   8.9429   7.5808  10.8931   4.8602 -14.7879   4.3269  -9.7338
+#&gt;    9.5932  -5.9092   3.4381   4.2429 -11.2346   0.6286 -10.5995   8.1673
+#&gt;   -2.3872   2.5932   4.0034  -1.2594  -9.1051  -5.9047  -7.7488  -2.8257
+#&gt;   10.0295   5.3519  -2.4834   2.4865  -6.3106  -6.8691  -0.9673  -1.8831
+#&gt;    0.0335   4.8028  -5.9487   2.8925 -16.2756   5.4352  -4.0029  -3.7733
+#&gt;   -1.5558   5.3827   2.6516 -13.2862  -0.6100   0.2447  -4.9061   6.3422
+#&gt;    3.9959  -1.8448   1.9519  10.5935 -14.5738   1.7463  -4.8509   2.2005
+#&gt;    0.3484  -0.1279   4.0334   1.7024   3.9858  -3.5049   2.5102  -0.7298
+#&gt;    1.4837   9.8924   3.6415   3.7319  -2.4233   1.6162  -5.2266  19.6050
+#&gt;    2.7476  -7.0835 -13.0863   4.3597  -0.2027   8.2926   9.9258   0.5601
+#&gt;    0.0557 -10.4625   0.9327  -7.9863   9.9888   5.2492  -0.1595  -0.1648
+#&gt;   -0.5021  -1.4746  -5.3854  -3.6369 -11.8902  -5.9471   3.5275  -2.5313
+#&gt;    3.1709  -2.3712   2.9537  -1.2457  -8.2462 -14.8765  -9.2953   7.6170
+#&gt;   11.9909  11.4902  -4.1442  -5.9850  -1.6904   3.0835   5.1076  -0.1874
+#&gt;   -7.7562  -0.5789  -3.5707 -10.4800   5.7294  -4.6781  -9.5600 -12.0115
+#&gt;    3.4318   0.3919  11.0936   2.4332  -6.3888  -5.5107   2.1030   9.6941
+#&gt;    5.8946  -0.0350  -0.2174  -5.8357   8.9972  -3.4707  -8.0726  -5.5292
+#&gt;    4.1600  -6.9609  -3.7461 -14.6550  -0.8750   0.0199  -3.6804  -6.1085
+#&gt;  -19.3142   3.0839  -0.7835   8.6145  11.9654   5.3337  13.8425  -3.3817
+#&gt;   -0.5039  -4.3831  -2.1489  -7.5294   2.8402   2.1701   1.9791  -5.4358
+#&gt; 
+#&gt; Columns 17 to 24  -0.7860  -5.2558   8.8485 -20.9941  -9.0655   3.5294 -10.1439 -15.4498
+#&gt;   -9.5280  -5.9107  -4.1893  -8.4292  -4.4653  -0.5671  -8.2579  -5.4353
+#&gt;    0.9009  -5.9605  -5.2960  -0.0580   4.2290   6.6839   4.1097  10.2514
+#&gt;    7.4349  -0.9851   8.0691  10.2494   5.0533  -8.2979   2.1494   8.2970
+#&gt;    2.4778  -6.8777  -4.8544  -1.9120   2.3932  -2.1848  -2.1859   1.3520
+#&gt;    6.7057 -11.6855  -4.0378   3.1691  -2.3374  -4.3761  -7.1103  -8.3558
+#&gt;    1.2603  -5.3465   7.3288   0.0823   3.4294  -5.4725  -1.6565  -1.1264
+#&gt;    4.1951  -0.9868   8.4957  11.0593  -0.4520  -5.0130  -7.8880 -13.4144
+#&gt;    1.7785   2.6223  -8.1713   4.1476   8.1134  12.6581   9.3881   7.1014
+#&gt;  -11.4726  -3.0691   3.9983  -6.6128   2.8902  -4.2356  -8.2007   9.8636
+#&gt;   -5.5642  -5.4402  -7.3251   3.8875   4.0312  -2.7634  -1.9829   9.1520
+#&gt;   -0.9521   6.8743  -6.0237  -2.6349  -3.3604  -9.5427  -6.5401  -3.5823
+#&gt;    5.0215  -0.8272   0.7952   3.4548  -3.2609  -6.3531   1.7328  -9.2392
+#&gt;   10.4847   3.6584   4.0901  -0.0507  -9.9594   5.5010  10.7630   7.5907
+#&gt;   -5.7430  -3.7902  -1.0271   0.4131  -8.0020  -3.5380   2.1057   3.7887
+#&gt;   -3.0929  -0.8988   1.4206  -3.6087   2.6007   2.3756   4.4542   5.3190
+#&gt;    4.8061  -2.6334   6.0383  -3.5185   0.1744  -6.8400  -1.0821 -11.2657
+#&gt;   -3.1119   5.3678   2.1130   0.3014   2.6299  -6.9807  -8.1485   2.6010
+#&gt;   -1.3879  -0.9456  -4.7146  12.1872   0.9405   5.8407  -3.8907   0.7099
+#&gt;   -1.2914   4.6096   4.2702  -5.0991  -2.4684  -3.7089  -1.5956  -3.0278
+#&gt;    0.9443  -3.4175   5.8033   2.0396   2.6150 -13.5490  -5.6807   9.9841
+#&gt;    6.0496   5.3381   2.1143   4.7890   6.5540  11.7075  10.9428   2.0711
+#&gt;   -8.6852  -0.1733   0.9401  -4.7324  -4.7015   2.6617  -5.5006   5.2733
+#&gt;   -7.6679   5.3552  -4.1996   6.3112 -13.5730  -1.9100  -8.2240   6.4587
+#&gt;    1.4878   2.8627  -3.5439  -5.4882   4.8272  -1.4257  -2.8101  15.8103
+#&gt;    6.8095  -4.1825  13.2839  -1.0319   0.8730   6.4668   0.7699  -2.7591
+#&gt;   12.1832  -2.5764   0.0200  10.0529   9.1707 -10.8723  -5.3037   6.6403
+#&gt;   13.7693 -13.7331   1.9829  -0.2371 -11.3306  -1.6697   3.9548  -9.9244
+#&gt;   -8.8938  11.2271  -1.6461  -1.5718   3.9387   6.0064   4.2124  21.8599
+#&gt;    8.8075 -13.4189   1.4054  -8.9115   2.0479 -12.0521   4.1982 -24.0073
+#&gt;   -3.6085   6.9181   7.9440   1.9889   4.7326  -6.7069  -8.5142  -5.6065
+#&gt;    0.1416   6.5440   1.8905   4.0071  -8.3706   7.7118  -0.1617 -10.8803
+#&gt;  -10.0878  -4.8186  -8.6435 -13.7511  -7.2138  -1.0321  -5.4247  -8.6376
+#&gt; 
+#&gt; Columns 25 to 32   5.8348  -7.2655  -1.5712 -10.6256  -7.4372  -1.4391 -11.4552  -7.0477
+#&gt;    0.3226   3.4154  14.7550  -4.5340   4.0858  -3.4755   3.5059  -4.8757
+#&gt;  -11.4289  -0.3527  13.9969   2.6939  -3.1083  -3.0465  -0.0892   7.0621
+#&gt;    5.9597   8.9556   8.5256   2.0979   1.2178   6.0288  -5.4063 -13.5261
+#&gt;   -3.1176   0.5134   8.1586   4.2564   3.3197  -8.7756  -1.9437   8.7018
+#&gt;   -2.9178   2.5326   6.1470  -0.5083  -4.6868  -7.8694  -6.4656   0.8148
+#&gt;    5.0851   4.6391   2.5312  -6.5178  -1.5761   1.3420 -15.3398  -7.1221
+#&gt;    3.2214   2.9499  -5.3047   0.9502  -2.6619  -7.0484   6.5908   4.1343
+#&gt;  -19.2554   0.6993   0.6807  13.9843 -11.8171  -6.7210   0.4342  -8.2857
+#&gt;   -0.2347  -2.5530  -5.5027   6.8211  -3.2994   6.2850  -0.9099  -0.9908
+#&gt;  -10.3087  11.8646   7.4830  -5.8137  -0.1379  -6.7583   8.5932  -4.0547
+#&gt;    4.7290   3.4967  -4.3611  -1.3191   9.5178   3.1170  -4.6229  10.6171
+#&gt;   -2.6505  -1.3561   7.5753 -12.7398  -1.2293   0.8599  -7.6208   9.2033
+#&gt;    4.8080   0.7353  -2.8968  -8.4572   4.6661 -13.1305   1.8542  -9.5437
+#&gt;    0.0877   1.0816   7.2096   3.7373 -13.8221  -5.2305  -8.0411   0.1576
+#&gt;   -0.5292   0.0716  12.2252  -8.5118  -5.0807  -5.6914 -10.2480  -9.0764
+#&gt;    0.1724  -2.2463   3.3778  -3.3526 -14.6650   2.1788  -4.2253  -2.9203
+#&gt;   -9.5554   3.8573  12.3497  -0.4372  -4.4459   4.8477   3.3281 -11.1750
+#&gt;    3.9233 -13.3457  -1.5832  -7.1649  -1.5820 -11.0861   3.7966   0.0886
+#&gt;    3.8153  -7.0028  -6.0608   2.2563 -10.7381   5.2756   1.5506  -0.0917
+#&gt;   -6.7651  -2.4536  -6.6022   6.8422   8.3161   0.5805   2.1232   0.9424
+#&gt;   -0.4326   7.7848  -4.7841   3.1087  -5.1200  -4.0048   0.2341   9.0584
+#&gt;  -11.2660  -2.3502   2.1099  -0.5210  -7.9694  -0.1528   3.6844   0.5896
+#&gt;  -10.2571   7.9452  -2.3368   1.3291  -4.2773   9.0338   1.8009   1.2802
+#&gt;   -7.7180  -3.6896  -9.0833   2.9145  -2.3874  -5.3301   8.4110  -5.9553
+#&gt;    8.5449 -13.3886 -12.8695   0.5592 -11.1546   0.8784  -2.8044 -13.2828
+#&gt;   -4.9942   1.9179   5.1382  -0.4088  -1.0402 -13.1228  -6.7094 -10.3743
+#&gt;    1.4642 -17.0698   3.6303  -5.0720  -6.8388   4.6268  12.0566 -10.4237
+#&gt;   -6.5882   3.0184  -3.5185   0.3690   0.4860 -16.3141  -6.1981  -3.1381
+#&gt;   -4.9649  -4.1968  -2.9713 -10.4720 -15.6504   0.5116   1.8237   1.1983
+#&gt;    2.2559   0.6364  -7.3063  -3.4943  -8.5275  -0.2126   0.0538  -0.7336
+#&gt;    2.4349  -1.4577   7.1449  -1.2379   2.1649   0.5370   1.3001  -8.0382
+#&gt;    2.7743   7.5586  -6.7374   3.7699  -7.0262   9.1465   6.9093  -9.5367
+#&gt; 
+#&gt; Columns 33 to 40   7.5027   6.5333  -0.0116  -5.6049  10.2883   3.3521  12.5631   1.5284
+#&gt;    3.2782  -5.3731  -0.3792  -0.1593  -3.4329   4.7939 -11.5708   0.0229
+#&gt;   -0.3696  -6.6871 -12.1762  15.2650  -5.1596   9.6189   1.4396  -5.4593
+#&gt;   -5.5469  -9.9894  -2.5025  11.4326 -19.5473 -12.5112  -1.3405 -14.6335
+#&gt;    3.3988  -3.4589 -13.5032  -6.4191  10.6713 -10.2992   1.6308  -3.1208
+#&gt;    1.4465  -2.1320  -6.9581   6.5072   3.0123   3.9350  -3.9943   2.0850
+#&gt;    4.2501  -5.4135   0.6682 -17.2987   2.2371  -4.3746  -5.0197   1.8515
+#&gt;   -3.4838   2.9367  -1.8368  -2.3554  13.1617 -18.1611 -11.9097   6.4819
+#&gt;    4.7710  -3.2932  -7.1364   1.5846  -0.7487  19.7362 -11.5068  17.0332
+#&gt;    4.7742   5.7331  -7.4818  -2.4472  -9.6425  -3.7683  11.4337   3.0286
+#&gt;    2.7958  -6.7184 -14.5433   1.4455  20.1422 -12.3778 -10.6355   3.6799
+#&gt;    2.7496   2.2887   3.8542 -11.3247   3.0819  -9.4470  -1.8825   5.2718
+#&gt;   -1.6504   3.3703   0.0674   9.4359   3.2942  -4.4481   6.6880  -6.9526
+#&gt;    9.5634 -12.0699  11.6217   3.5743 -21.1038  19.2175   3.4960  -1.7261
+#&gt;    8.5224 -13.1445 -10.6559  27.6880 -13.1003  -4.8193   3.7349   2.6295
+#&gt;    5.7360  -4.9928  -0.6631  -1.0594 -14.4707  13.4307   3.8149 -11.6830
+#&gt;    4.8149  -2.1025  -2.8097   6.9264   1.9033 -14.3990   2.6695  -4.9869
+#&gt;    4.8526  -9.6141   2.1947  13.7938 -20.2344  10.8255  -9.7898  -8.3773
+#&gt;   -7.9815  15.2409  -3.7938  -5.3458   6.4106  19.3269  -3.7322   5.5608
+#&gt;    9.5753   5.0733  -3.7613  17.2755  -4.4581  -1.9309   2.1579  11.7440
+#&gt;   -0.0213   6.9128   5.5643  -1.6708  -5.6212   3.4767 -15.9278   5.8476
+#&gt;   -2.4029  -7.9804  -5.8736   2.8209   3.1041  -4.5196   1.0162  -6.9719
+#&gt;    2.7896   5.2301   5.4749   6.9678  12.5461  -0.9798  12.0340  -2.3895
+#&gt;    1.9614  12.2263  -2.0617  15.6980  -6.3249  15.8998   7.9298  14.7955
+#&gt;   -1.4827  -4.7149  -2.3174  12.9012 -15.4193   1.5327  -5.1090  -1.9725
+#&gt;   -3.0517   2.2253   0.3231   6.1099  -6.0135  -1.0435   4.3048  -0.0366
+#&gt;   -1.8208  -2.9453  -9.7977   2.7620  10.3637 -10.1950  -0.5822  -7.4750
+#&gt;    3.1300   9.3976   5.5887   0.2071  -5.8449   8.8785  15.9674  -6.2410
+#&gt;   -2.3326  -0.7725  -5.7696   3.1336  -5.6467   4.5828 -10.7735   6.1770
+#&gt;   12.2428   4.0546   7.2391  -9.2279  15.3162   5.3007   8.9519   5.0663
+#&gt;   -2.0020   4.3573   5.1197  -6.6489  -1.9023  -3.3735   0.3393   5.5041
+#&gt;   -2.6711  15.2617   6.2822   1.1592   0.5775  -3.1309   7.5555  -1.9678
+#&gt;   -1.8282   3.8881   2.8608  -9.5459  -5.4770   4.2785   2.9235  10.4418
+#&gt; 
+#&gt; Columns 41 to 48  10.6075   8.1955   6.3594   9.6548  -3.9281  -5.5283  -4.3969  -5.2311
+#&gt;   -0.0549  -6.8490   6.1117  10.3936   0.6194  -1.7077  -2.4671  -8.3272
+#&gt;   -0.7429  -0.7171  -5.5075  -6.5202   4.8573   3.5552 -13.5905   0.2427
+#&gt;   -2.6324   1.8097  -6.6340  15.7721  15.1312  -0.8379  -8.1369  12.4140
+#&gt;   -1.1339   1.6051 -11.0083  -4.0315   6.4343   5.9605 -11.9441  -0.4597
+#&gt;    8.0709  -5.0276   6.3454   1.9127   6.5007   0.9900  -8.9867  -7.4269
+#&gt;    1.6719   4.0843   2.6772  11.4787  -1.9626  -4.5868   6.2614  -4.5262
+#&gt;    5.7108  -1.7419 -15.6123 -16.1967  -6.1014   2.6904   1.5191   5.5925
+#&gt;   -5.1404   5.7229  10.0120  -1.0770  -4.9319  -2.1359   4.9363 -14.8991
+#&gt;   -6.5668  15.5995   1.9847  -2.6831   3.6583   0.6838   2.2724  -0.4155
+#&gt;    0.1617   0.1575  -4.6440   3.1532   1.0411   8.8002  -9.9338  -3.5080
+#&gt;   -9.0213  -0.1910  -1.7430  -2.6874  -3.9154  -1.9654   8.2127 -10.7969
+#&gt;    2.2560   1.8975   0.7868   5.4140  -2.2687  -6.4610  -7.9165  -0.2637
+#&gt;    4.2571  -5.9356   1.2642   2.6764   4.9651   2.8574   3.9194   1.9587
+#&gt;   -3.9367   0.4815   6.5117  14.2914   4.0803  -9.3343  -6.3550  -8.5403
+#&gt;    1.8754   8.8220   4.3447   3.7271   8.3342  -0.7177  -6.6892  -9.8216
+#&gt;    2.9226  -0.6007   2.7407  10.7963  -1.4039  -2.7413   0.1470   0.0305
+#&gt;   -2.0551  -8.6517  -1.0070   0.2237   3.9467  -3.5014  -1.3727   1.9502
+#&gt;    7.4919  14.7980   6.8258 -15.9342  -2.7612  -9.0467  -0.2408  -6.1911
+#&gt;    4.9543   0.7682   8.2976  -7.3017  -1.8134  -4.6807   1.0936   2.2199
+#&gt;    0.2476  -8.2005  -5.5142  -8.9249  -1.5899   1.5298  -0.2571   6.9172
+#&gt;    3.8945  -2.5934  -1.4054   3.2700   1.6870  10.3187  -2.0064   6.1178
+#&gt;   13.7704   1.9819   8.4786  13.2931  -0.3295  -6.7888  -4.4256   3.2921
+#&gt;    3.7184  10.6178   7.6598  -0.3294   0.1958  -7.1434  10.6028  -3.0216
+#&gt;    2.6404  -1.8616  13.4526   8.1869  -2.2343  -6.3750  -4.1366   6.2893
+#&gt;    2.4454  -5.7420   5.2624  -0.4159   2.7483  -5.1336   1.8708   1.7837
+#&gt;    7.8029   6.1585  -6.6585  17.4982  -1.5582   1.0887  -5.1489  -7.9415
+#&gt;    5.6980  -1.8182   0.0233  -0.6164  -0.6737  -2.6375   0.4123  17.0397
+#&gt;    1.3305  -3.6551  12.2228  10.3632   1.2346   3.8860   1.8377  -0.7569
+#&gt;    8.7651  10.0290   5.7246  -1.3062 -12.2864  -5.9924  -4.5115   2.5171
+#&gt;    8.9476   2.4347   4.3081   1.3072 -11.2390  -5.1412   6.4073   2.6543
+#&gt;   -2.9282   0.0720   4.3174  -8.4152  -1.8963  -8.3885   5.4650  -5.5437
+#&gt;   -7.5952   8.7533  17.1642   4.6203  -1.5054   1.8863   1.8967  -3.7182
+#&gt; 
+#&gt; (19,.,.) = 
+#&gt;  Columns 1 to 8  10.8395  10.7757  -3.0870   5.3283  12.8517  -5.9433  -2.2106  14.2127
+#&gt;   -2.8759   1.0786  -4.1254   0.8018  -0.2598  -8.2426   3.3772  -7.8797
+#&gt;    0.0327   5.8181  -4.2337  -7.1393   7.7752   2.3379  -9.4840  -3.3952
+#&gt;   -4.0998   8.0561   2.5215   0.6297   0.6777  10.5896   9.7402 -16.0670
+#&gt;   -1.0089   8.3787  -2.9934  -2.6356  -0.0877   7.0668   4.5577  -0.7420
+#&gt;    9.5502   9.5713   4.4936  -0.6498   6.3097   3.3118   2.3432   8.6385
+#&gt;   -4.0563  -1.2055   3.8899   9.1231   6.7793 -12.0974  -3.1299   9.9029
+#&gt;   -0.7493   0.1963   4.1077  -0.6971   8.1487  19.8076   6.8223  -5.6851
+#&gt;   -6.0644  -7.6939   6.7336 -12.1034  -5.3430  -4.1995  -5.5885   2.5117
+#&gt;   -0.8601   1.1812   5.3597   3.9339  -3.5057  -0.7212   3.2272   3.0324
+#&gt;    1.7426   0.3936   4.8376  -2.4991  -0.5326   2.9792   7.5664   0.8225
+#&gt;   -3.7376   3.5097   2.6515   8.5253   5.7521   8.0427  -4.7524  -1.5751
+#&gt;   -0.2459   5.9787 -11.0733   0.3648  10.8524   4.1613  -1.0832  -7.1325
+#&gt;    1.4894  -3.5983  -5.5288 -13.8901 -10.3781   6.5673  -5.9233   3.2369
+#&gt;   -3.6380  -4.0456   2.1913  -5.4734  18.1547  -3.7933   5.0162   3.9899
+#&gt;   -0.3314   7.9992   4.3374  -0.0042  -9.5690  -1.3394  17.0652  -4.3626
+#&gt;   -5.2768  -3.1344  -1.1358   1.3520   9.2251   5.6593  -5.9296   9.0785
+#&gt;    9.4193  -6.8123   8.6052  -4.0533   6.9688   1.3662  15.2029  -7.0616
+#&gt;    3.9928  -2.9939  -4.9972  -8.6826 -18.3501 -11.7910   5.2548   3.0088
+#&gt;   -5.7589 -14.4873   7.5612  -9.8233  11.5037   4.0116  11.8311  -4.7630
+#&gt;    4.2859   0.5745   3.4160  -4.1035   3.7191  -4.9911   3.2578 -10.4416
+#&gt;   -4.0827  -9.8458 -10.4471 -12.1190  -1.5617   0.8391 -12.4993   4.9041
+#&gt;   -2.0138  -9.3498  10.1790  10.3041  12.8580  -2.0940   7.0812  16.6040
+#&gt;    1.3709  -9.2536  -3.8364  -1.8204  -9.9328   6.5994   4.5961  10.5244
+#&gt;    6.4022  -8.8969   3.6429   0.9117   0.9441 -10.0114  -6.0480   5.8424
+#&gt;   10.7966   5.0398   1.4386  -5.0239  -1.1596  -8.2526  -3.6071  -0.0900
+#&gt;    3.9841  -3.3743  -4.2212  -1.0025  -4.8112  -5.1390  10.2893   2.6183
+#&gt;    4.2903   8.8365   0.8650  -2.0502  -0.1431   6.2520  -4.8942  -2.7032
+#&gt;    0.3704 -17.7415  -5.7719 -10.0108 -12.9640 -12.5472  11.2287  -0.2070
+#&gt;   -1.0709  17.7364   4.6136   4.4929  12.2768   0.8676  -6.2423  -3.1522
+#&gt;    0.5952   1.1386  14.9558  17.7456  17.0263   1.8315  10.4364   9.7502
+#&gt;    1.5720   1.1694  -3.9902   4.9676   0.4082  23.6846  -3.0325  -1.5709
+#&gt;    1.2193   4.3040  12.7433   8.2747   4.9245  -3.6125  -1.0537   7.9518
+#&gt; 
+#&gt; Columns 9 to 16  -4.7201  -3.1236  -4.3389  -1.9527   4.6023   6.3952 -12.6313 -21.1348
+#&gt;   -0.6620  -3.7689  -4.2902  13.6811  -2.3209   6.6041  -4.8710 -17.3554
+#&gt;    5.1904  10.6001  -6.5149  -4.8909  -9.0926 -11.8929   5.2871   2.2837
+#&gt;    0.7013   4.9385  11.7092   2.8884  13.2337 -17.4017  14.9012   9.3459
+#&gt;    6.8411  -1.4300  -1.6803  -2.5781  10.4844  -4.2093  -3.0250   1.0544
+#&gt;   12.9819  -4.7711 -14.4137   5.0339   8.2432   0.5383 -11.8861  -2.3156
+#&gt;    2.7924   1.7839   2.2759   1.9873  -5.4555  -1.7162  15.9806   6.6636
+#&gt;   -4.0410 -15.5223 -13.1287  -2.8102  -5.5585   0.1104  -2.2128 -12.1074
+#&gt;   17.3425   8.5051   3.8251 -12.2377  -1.1293  -5.4657   0.8050   4.6297
+#&gt;   -4.8510   6.7570   6.4678  10.1318   3.9934  -8.2748  13.6497  13.6396
+#&gt;    9.3899   0.2428  -1.8257   5.4489   5.2872   3.9363   0.1621  -8.0930
+#&gt;   -9.2866  -7.1185   2.7267   0.8633  -1.3314   0.9149  -9.6874   5.3744
+#&gt;   -1.1862  -7.3784  -2.1169   6.4714  -8.7672  -7.9466  -5.3680 -11.5957
+#&gt;   13.7131  -4.5149  10.2006  -6.1466  12.6197  19.8093  -3.2519 -13.6451
+#&gt;   -5.5197  10.4320   0.0603   5.5517  -6.6388  -7.9648   1.7688   3.0486
+#&gt;   -7.3826   0.1136   1.5554  -5.5288   7.0923  -0.7994   1.9077  -8.2893
+#&gt;   -4.4383   0.3800  -2.3695   1.3786   6.1753  -4.8534  12.6246 -18.0979
+#&gt;   -9.0982 -13.8421  -5.1902   6.5919  -3.6211  -3.6110  -0.9803  -8.3294
+#&gt;    8.1154  -3.1195   0.4546  -1.1779  -3.6951  -4.3140  -1.4386   3.9892
+#&gt;   -4.9093  -4.5519   0.8064  15.6809  -2.5004  -1.2447 -16.7001 -13.4800
+#&gt;   11.5612  -2.9265  -1.7209   2.6694  -0.4203  -5.8078  -1.4483   1.3615
+#&gt;   -5.2648  12.7933  -4.3861  -0.3763  -1.6852   4.4957   4.9136   4.4373
+#&gt;   -3.9523  11.9814   1.7973   1.4953  -2.6835   0.2446  -4.5562  17.3886
+#&gt;    5.1934  10.6655   0.6115  11.1418   3.1180  -8.9435   6.6520   3.4674
+#&gt;    5.1238   8.8625   4.3751   3.1573   2.5184  -5.1505   1.8224  10.4806
+#&gt;   -2.4792   6.4549  -6.0669   0.6875   5.4914   3.0087  -2.3207  -5.9153
+#&gt;   -2.4603  -2.0192   0.0122   7.6925   3.2375 -23.4830  10.0626  -7.1549
+#&gt;    4.1439  -5.9518   5.2762  -1.1040  13.5939   0.5049  11.1101 -11.0639
+#&gt;    8.4713   7.5715   0.9777   8.2896   0.3026  10.2661   4.7805   4.8929
+#&gt;    7.5424  -5.8435  -1.1392 -13.0447   1.6035   0.5726   3.4629 -18.9380
+#&gt;   -6.0775   3.4473  -5.3616  -4.7318  -5.5823  -0.2472  14.1388   7.6101
+#&gt;    0.9798 -11.0886  -8.4849  -3.8576  -9.8913   8.9775 -13.6314 -10.7274
+#&gt;    1.0321  -0.2241   4.4221  10.9133  13.4562  14.3194  -7.3366  -0.7615
+#&gt; 
+#&gt; Columns 17 to 24   3.2404  -7.3528  -4.0721   7.6482  -2.2974  -1.6315  -8.5402  -0.2590
+#&gt;    9.0915  -5.5299   7.1584  -6.4244  -6.2699  -4.5453   0.4297   1.3497
+#&gt;    5.0526  -2.3287  16.0916  -8.3235  -1.0199  -0.6070   4.0202   4.3790
+#&gt;   -7.1261  11.7021   0.9335   1.3722  -7.4772  -8.3906  -0.6237  -9.8234
+#&gt;   -2.8154  -1.1493  11.3990   5.7868  -9.0052  -8.9318  -0.5868   1.0273
+#&gt;   -7.2551  -2.0263  -0.2881   3.6759  -5.8995  -0.1478  -3.0632  -8.5266
+#&gt;    3.9098  -1.3945   3.8332   7.5970   3.7336   0.7507 -11.8417  -5.6555
+#&gt;    0.2132   8.4414   3.7995  -9.1395  -8.8469   7.1621   7.9866   5.6705
+#&gt;   -8.7067  -4.8448  -1.2625   3.3080   0.7969   1.8951   1.2556   1.2725
+#&gt;    3.4546   6.4591  -5.8498   4.8281  -0.9510  -5.3729  -1.1176   3.3406
+#&gt;    8.2234 -12.0190  16.8075 -19.5644  -3.8102  -0.2338   3.2206  -3.4571
+#&gt;   -0.3564  -5.0141  -4.0951   7.5192   3.4239   0.3308  -5.1510  -4.0848
+#&gt;   -0.3188   4.4719  10.9368 -10.5394   0.3799  -0.0291   3.2460   0.9790
+#&gt;   10.2404   0.1642  -5.1547   8.4400  -8.4119 -10.9495   2.0886  -1.9074
+#&gt;   -0.9937   5.1533  19.3627  -7.2296  -9.1919  -4.4116   3.1740  -3.1250
+#&gt;    1.2888  -1.0215   8.0473   4.1319  -5.1494 -14.1310  -1.6532   1.7811
+#&gt;    0.3926   6.3051   3.6569   0.1893 -11.3633   5.9868   0.7072  10.7594
+#&gt;   -7.1072  -3.0477  12.2373  -4.7590  -6.2750  -3.4573   7.7633  -8.3014
+#&gt;   -8.7176  -2.8906  -0.7154 -14.9467   5.2296  -7.2645   5.2165  10.9547
+#&gt;    2.6475   0.4443   1.2017 -12.9848  -5.7442   7.7503   1.7651   6.4125
+#&gt;    0.2760 -11.0673  -3.5735   1.7614   8.2979   1.2129  -2.4041  -1.7983
+#&gt;   -5.1471  -7.7904   5.4553  -3.4230  -8.1335   7.8878   6.8528   2.7138
+#&gt;   -2.6071  -6.1638   5.2968   3.1989   1.4251  11.1042   3.8692  -6.5996
+#&gt;  -15.7746  15.8617  -1.6893  -7.4314  11.2806 -12.9195   0.9744  -5.6331
+#&gt;    4.5438  -5.6933  -0.7325   2.9716   1.4225   0.3591   2.3317  -0.8351
+#&gt;    4.9513   8.1065  -9.9296  -2.4630  -5.4124   5.6269   1.6865  12.9014
+#&gt;   -0.4381   7.9463   5.5066  11.0034  -7.7295 -13.9815   6.7824   5.4234
+#&gt;   11.2191  12.7392  -2.9574  -5.2218  -9.9673  -5.6994   4.9155   5.8823
+#&gt;   -5.3755 -14.1145  20.0468  -2.6852   0.5977 -10.9694  -1.0630   6.6494
+#&gt;    0.7967  -1.2790   0.7432  -5.6644  -8.0744  14.7843  -4.1934   3.5868
+#&gt;   -8.6367   3.4889   1.5427  -0.7841   6.2432   3.9866  -7.5451  -3.6884
+#&gt;   -1.1392  12.9018  -9.7556   3.6774   4.3340   1.9292  -0.7439   9.6608
+#&gt;   -0.7898   0.4567  -9.0658   0.7583  -0.5643  -2.7536 -12.0739 -13.5161
+#&gt; 
+#&gt; Columns 25 to 32  -7.7718  -0.9114   2.4102  -2.2426  -8.2447  -8.6054   4.0331   4.6473
+#&gt;    1.8486  -5.2750   7.4340  15.8399  -5.6456  -5.6514 -10.1373   3.1087
+#&gt;   -9.4465   4.7849   1.6913   3.6872  -6.2903   8.9057   6.9255  -5.0254
+#&gt;   14.8307  -4.0019   0.4373 -15.9645  12.4092 -11.6289  -2.6368 -14.3868
+#&gt;   -0.2235   3.8137   3.8534  -1.3829   2.1283  -1.9937   4.9293  -5.9287
+#&gt;   -2.5982   0.9202  11.7816  -7.2020  -6.4888  -4.1582  -5.2661  -7.1377
+#&gt;    4.0172   1.0735   2.9697  -3.6743   1.0773   9.7277   3.3830  -1.5863
+#&gt;    0.8849   1.0239  -5.2432   0.7883   5.8000  -3.4362   8.1262   7.7836
+#&gt;    2.9349  -5.4463   0.5602  10.1004   1.6811  -1.1345   9.2585   5.7046
+#&gt;   -6.0164  -1.3181  -0.4065   1.5494  11.7829 -14.4608   3.9402  -5.7432
+#&gt;  -17.4480   5.4720   1.7400   5.2470  -9.6330   4.0045   4.5097 -11.8818
+#&gt;   -2.0877   2.5754   2.0526  -2.6236  -5.7712   3.4179  -0.6518   3.2757
+#&gt;   -6.4103   3.7287   4.6183 -17.0621 -15.5844   5.8730  -3.4688  -5.3081
+#&gt;   10.6664  -2.7789 -12.8203   3.8798  -2.2326   5.6436  -5.2723   0.9585
+#&gt;    3.0664   1.0020  -7.0297  -2.8026   4.1825   0.0879  -3.6404  -1.6307
+#&gt;   -1.5760  -0.0641   2.7851   8.6238  -5.8379 -13.0112  -4.4457   4.8467
+#&gt;   -3.5930  -0.6990  -7.8559   8.1605  -8.8669   3.5128  -8.1397  -6.4161
+#&gt;    4.2051  -4.6264  -2.4763  -4.6992  -7.7277 -11.2350 -10.4281  -6.7836
+#&gt;   -2.7288   5.3781  -0.7707   3.8182  -4.2097   2.8139  -7.3372  10.2148
+#&gt;   -7.5773  -4.9700  -8.8037   1.7967  -2.2666  -4.4975  -7.7480  15.4940
+#&gt;    4.9859   0.0118   1.7544  -7.8191   4.6211  -0.8371   2.3063   4.5158
+#&gt;    2.3803   3.3274  -6.8530  -3.5117   3.6384  -7.1110   5.9185  -9.9121
+#&gt;  -12.0744  -5.7320  -8.6097   9.6187   6.9884   0.7670   4.1691   3.6504
+#&gt;   -9.9666  -1.6004  -6.3201 -10.2167 -10.4199   1.9198  -1.2735  -9.4717
+#&gt;    1.4589   4.1667  -2.6370  -1.6384   4.4989   4.7677  -0.8968   0.3371
+#&gt;    8.3363   0.9305  -3.7238   6.0712   4.8480  -1.6509  -0.6134   7.1978
+#&gt;   10.3574   5.0577   1.0890 -12.0661  -0.4778   3.2472  -7.8195  -2.8836
+#&gt;   -9.1254  -9.6882  -3.2917  10.9773   0.0025   3.2516  -6.4559  -5.2064
+#&gt;    6.9633  -0.8168 -10.9703  -0.6805   3.4517  -5.9585  -4.0768   1.5045
+#&gt;  -15.8190  -6.2055  11.6044  13.8867 -15.3518 -11.9771  19.2955   3.1466
+#&gt;   -0.1233  -9.5387  -9.2900   5.5027   1.5485   3.0759  10.2926  10.8285
+#&gt;   -9.8313   0.3688  -3.3588   7.8980 -14.6756   7.7726  -2.7930   3.4806
+#&gt;  -11.8913  -9.0171   5.8413   3.9101   1.3863  -8.5099   0.7576  -9.0370
+#&gt; 
+#&gt; Columns 33 to 40   2.9796  -5.8591  14.4235   4.4064  -2.7400   0.5498   7.9668  10.1102
+#&gt;   -9.9294  -5.8406  -4.4334   0.4285   3.8579   2.0006  -2.0403   2.3884
+#&gt;   -1.8515  -4.7375  -1.2654   7.3044  15.8168  -2.7423  -2.4067 -11.8371
+#&gt;    4.4462 -14.5330  -4.3808   3.9995   2.6533  -7.2618 -12.5946   0.2572
+#&gt;    3.2368  -4.2141 -13.9740  -8.8299   7.9667   7.1578  -1.1920  -3.9704
+#&gt;   -3.5651  -0.3440 -16.0170   6.9821   0.7315  -7.4706 -11.1905   2.1866
+#&gt;   -1.2230   1.8032   2.2447  13.5615 -13.7201  -7.6674  10.3916  10.3359
+#&gt;    4.5096  -0.2489  -8.7388  -5.6336   6.9253   2.8208 -13.1318   2.1139
+#&gt;   -2.1393   9.3337  -4.5421  -4.7718  11.8707  -8.6940   6.3966  -8.0506
+#&gt;   -2.9345 -15.1383  10.7131  -7.4241  -6.2026   1.8937   6.6104   3.1484
+#&gt;   -4.1044   5.3933  -2.1359   5.8452  10.8731  -3.4783  -7.0924  -6.3855
+#&gt;   -9.3098   2.1850  -4.6465  -7.2282  -2.5180   0.1917  13.3671   9.2172
+#&gt;   -5.2428  -2.8354  -1.1434  12.6908  -9.3882  13.7698  -7.8651  -0.5879
+#&gt;   -7.4181   5.1397 -13.1685  -9.6985  15.4629 -14.1861   7.3946  10.6126
+#&gt;   -4.1513 -13.3208   4.5188   0.1917  -0.8268  11.2592   3.6528  -0.6625
+#&gt;   -0.6403  -6.9440  -4.0975  -3.8169  12.0874   9.4207 -10.7510   3.7073
+#&gt;   10.7357  -5.8957  -1.9186   8.6922  -3.0942   8.8761 -14.8420   1.1825
+#&gt;  -11.9155  -2.5841 -12.9962   5.0492   4.6539  -0.6236  -4.0959  -4.7469
+#&gt;  -11.6471   4.8921   5.0823  -5.0213   9.7070   3.0254 -12.2953   3.9251
+#&gt;   17.0879  -4.0036  -1.0710   0.4942  -2.4409   6.5400  -0.3668   1.1020
+#&gt;   -4.8131   4.7524  -9.4196  -5.5224  -1.1471 -14.8131   9.7049   4.3751
+#&gt;    0.8249 -11.9130  -4.0199   1.9304   6.6916   6.5608  -3.2226  -4.6872
+#&gt;   13.3971   8.0947  -1.4470   8.0229  -7.0477  -9.1122  13.7528   0.1964
+#&gt;   -2.6919  -9.1373   5.1295  -6.3844   0.0863  -2.1316  -0.3428  -0.3389
+#&gt;    0.2724   3.4743   7.8588  -0.7303  -2.4073   1.6905   4.5028  -7.8179
+#&gt;   10.6832  -0.2296   7.1542   9.0711   2.4966  -3.8835  -5.3003   2.0246
+#&gt;   -8.4005  -7.8312 -15.2336   6.9674  -1.8741  -5.7587 -17.6714  15.3893
+#&gt;   -4.9575  -0.1593  -1.1159   3.2139   7.9358  12.5113  -9.8510  -4.5967
+#&gt;  -10.1662  -6.7715   1.6739  -8.9887   3.1111  -5.8976  13.2658  -4.7742
+#&gt;    0.2688   0.8505  10.0891   4.0398  -2.2841  15.1094   4.5957  -6.2957
+#&gt;    0.2787   5.7893   4.1793   1.8786  -1.3759 -13.6348  14.0891  11.3234
+#&gt;    3.5430  14.9865   0.7801  -0.4608  -9.5619  -6.3796   0.0200  -5.6714
+#&gt;   -4.2869   2.2431  11.6142   1.4366   2.0043  -7.9774  14.1313  -5.3636
+#&gt; 
+#&gt; Columns 41 to 48   2.7537  -8.4081   0.8808   1.8540   4.4716   2.6500  -9.3430   9.6118
+#&gt;   -2.9996   6.7433  -0.1671  -6.4098  -9.4328  10.6736  -6.7612  10.3954
+#&gt;   -1.8288   3.1022  -3.4933  -9.7086  -6.0963  12.0057  -4.3052   4.2917
+#&gt;   13.1396  -4.0272   0.2545 -14.0850   5.3521  -8.7964  -0.6994 -10.0611
+#&gt;    5.0306   6.8330  -0.3310  -1.9005 -12.3920   1.2069  -0.4763  -4.8570
+#&gt;    8.2146  13.0620   8.1760 -10.1780 -11.1487  -3.5516   8.0910   1.5439
+#&gt;   -2.7358  -5.7154  -1.5612  -1.4865  -8.1558   0.7511  -5.1962  -2.5678
+#&gt;    7.7914  12.1014   7.8445   9.9661 -15.9547   1.7632  -3.3189   1.1190
+#&gt;   -0.3257  11.3838  13.9759  -1.6907 -12.5652  -9.6671  -2.0921   0.4294
+#&gt;    5.1802  -7.9128   0.9615 -11.0031   3.9570   4.3288  -0.5359  -6.5904
+#&gt;    2.7734   5.8330  -4.6551   2.0356 -21.9550  20.8268  -5.8366   9.2868
+#&gt;   -1.5492  -6.3833   3.1915   5.4757  -1.5982  -6.8798  11.5828  -2.1211
+#&gt;   -2.1020  -1.9674  -2.8494  -1.0265   3.5978   8.0475   4.5782   2.6527
+#&gt;   -9.5135  -1.2447 -12.3409 -11.5975  -0.7767  -3.5692   1.9104   6.9491
+#&gt;   -9.3450 -11.3317   7.6089 -13.9269 -11.1711  11.9842  -3.0715   7.0820
+#&gt;   -5.8388  -2.1508  -2.8305  -5.4169   9.6731   3.7725   0.5182  -6.3051
+#&gt;    2.8293   2.7246   0.6847  -6.7214  -6.0762   5.8649   1.2158  -1.6557
+#&gt;   -0.9985   2.9063  -2.2778  -4.0852  -3.4789   3.1871  -3.4138   5.5535
+#&gt;    4.2588  -2.1259  -6.4136  10.6392  -5.6241   0.1208   1.3676  -4.4714
+#&gt;    9.0142   7.3940   8.1806   1.3387   3.6750  12.4905  -4.2329  11.7774
+#&gt;    1.2015   2.8161  -5.4220   1.0295  -9.5838  -0.5959   3.4263   8.4852
+#&gt;    3.2337   4.1017   9.9291   2.2344   0.6333  -3.3308   3.4500   4.4569
+#&gt;   -3.7095  -3.7454 -13.8820   8.2878   0.2788  11.7937   0.7563   6.4430
+#&gt;    3.9434   0.0988   2.9141  -5.8733   0.7279   1.4817   9.6820  -0.6106
+#&gt;   -3.4528 -12.7778  -4.5525   4.0766   4.5152  -3.7994  -9.8088   0.2060
+#&gt;   -4.0679  -4.0544   4.9276  -0.5949  -0.2665  -0.3859  -5.7662  -0.2687
+#&gt;    0.1229   1.3327  -2.0212 -10.0097 -17.2825  -2.8027   1.7124  -8.1562
+#&gt;  -14.8658  -6.8483  -3.5400   4.9178  -5.4110   2.2206   8.5238   1.9944
+#&gt;   -3.6567  -6.3353  -7.6350   1.1357  -8.3796   2.2761  -6.0554   9.7495
+#&gt;   -6.2436  -2.9375  10.3187   7.0562  -1.3654   1.9643   2.6747   7.7124
+#&gt;    0.9717  -5.3566  -0.0893   9.3926  -4.3974  -7.2100  -5.8098  -6.3129
+#&gt;    3.7503   5.8549   2.2083   3.3030   3.9939   0.3026   0.6920   5.8119
+#&gt;    3.0689  -2.5120   5.5766  -6.1170   1.9618  -0.7384   1.8105   4.9841
+#&gt; 
+#&gt; (20,.,.) = 
+#&gt;  Columns 1 to 8   2.6317  -3.2704   6.4893   4.0617  -3.9221 -14.5928  -0.2165   0.3166
+#&gt;   -1.7656  12.3500  -5.9235  10.2710  -5.4963   2.4739   7.8553 -11.0974
+#&gt;    4.2000  -3.5453  -0.8064  -4.0630   6.7606 -12.7523  -2.3810   4.4675
+#&gt;   -2.4432   7.5452   5.9066  -4.3508  14.2043  -4.4407 -11.5158   0.9818
+#&gt;    2.1510  -1.3800   5.0692  -2.6042   4.9786 -15.2360   5.3043  -2.0795
+#&gt;    6.1902  -6.8906  -4.6501   0.0899  -6.4293  -5.1811 -13.5472   0.0140
+#&gt;   -8.3217  -5.8681  -2.1790   6.5348  -0.1611 -11.5489  -0.5299   8.3786
+#&gt;   11.3522   8.5959  -1.9855   0.1147  -7.1166  -0.7803  18.1329 -13.3221
+#&gt;   -0.4951   1.8566 -10.2217   2.6549   1.3131  12.8182 -29.6905   9.1980
+#&gt;   -6.8784 -12.1935  10.4695  -6.8811   5.5331   3.3465   0.9023  12.8057
+#&gt;    7.2101   4.4492  -3.0272   1.0221   2.6727 -11.9136  11.3500 -10.3183
+#&gt;    0.3164   1.4806  -0.3643   4.1276  -0.4381   3.7842   5.1534   8.5298
+#&gt;    1.8226   0.0824  13.5183  -1.3857   6.3781 -17.4850   4.9547  -2.4414
+#&gt;   -6.9603  13.8362 -16.1456  -0.7156 -13.0805   6.3018 -11.9658  -1.1504
+#&gt;   -3.5703   4.7417   6.4240   1.6624 -17.1390  -2.1930   4.6340  -4.7743
+#&gt;   -2.9960  10.0738   5.9415   2.8126   2.9826   1.8867   1.4020   6.8104
+#&gt;    0.6208  -2.6765   3.8367  -0.8087   4.7581 -26.0039  -1.0620  -3.7928
+#&gt;   -3.1788  13.3034   7.1760   9.3869  -2.0361   3.8333  -6.9656   2.2902
+#&gt;    1.5968   2.1896   1.7029  12.7194  -3.5679   7.4415  -0.9195  -7.3017
+#&gt;    8.2864   3.4497   4.3539   4.6864 -14.1377  14.5084  -3.1976  -2.3807
+#&gt;    5.5481  -7.7046   0.3324  -1.6636   5.1512   6.7901 -11.8125  -2.2419
+#&gt;    9.1587   1.7614  -5.1246   1.2268  11.3454  -3.3256  10.4029  -4.0478
+#&gt;    0.3763  -6.8253   7.2491  -6.7257   4.1883  -1.1559   2.6184  12.4293
+#&gt;    2.1107  -7.9201  10.0034  -7.4355  -0.6598  -4.3904 -17.2219   7.2302
+#&gt;   -1.0541  -4.4945   4.3513  -0.1292  -3.6126  -1.0940  -5.8077   3.4546
+#&gt;    5.3242  -8.1556   1.4671  -1.5818 -14.3665  11.7792  -6.1844  -5.0912
+#&gt;   -3.0772  -1.5444  10.0528  -2.3464  -1.0673 -25.1990   1.1664   8.0409
+#&gt;    5.6377   4.6678   6.3061  -6.4699   6.3947  -1.2818   4.1777  -8.6689
+#&gt;   -2.0267   0.4467   0.3525  10.9873  -1.3003   0.2035  -5.4983  -4.9964
+#&gt;    3.6723  -0.4729   0.4000  -5.0117   3.4031  -7.4031  10.6463 -10.4689
+#&gt;   -4.9988  -2.3497   5.1630   6.9212  -2.5057  -1.7708   1.4655   9.3307
+#&gt;   -1.1346  -1.0179   0.6928  -3.7315  -5.7301   5.4592 -11.9378   9.1582
+#&gt;    1.1608  -1.4629  -0.4300   3.2595  -3.4088  12.4622  -8.1716   2.4827
+#&gt; 
+#&gt; Columns 9 to 16  12.3189  -7.4661  -4.2392  11.7499   1.0018  -4.1385   4.2201  -5.1919
+#&gt;   -2.3632   3.5253  -4.6502  -1.3243  -3.5071  -4.1476 -13.1182  -9.8020
+#&gt;   -4.7953   1.5255  -0.6499   4.0517   2.5277  -1.7646  -5.2528   5.2599
+#&gt;   -2.0369  -3.6353  -6.1835   8.7470   9.1838  -5.3791   8.8067  20.0277
+#&gt;   -1.2960   5.6209  -3.3121  -4.8769  -3.9266  -5.8039 -12.2958   4.2274
+#&gt;   10.5936  -6.9445  -0.8961 -12.4010  -6.4007  -5.3509  12.7792  -2.2874
+#&gt;    2.5691 -13.2197   0.6038   8.3391  -5.8939  14.4678  -0.5205  -4.0887
+#&gt;    4.1848  11.3388  -1.9578  -9.1297  -9.4681  -5.6152  -6.0008   0.7851
+#&gt;    4.4057  -1.0972   9.0309   1.3224  -3.7618   0.6563  -8.6499   4.4606
+#&gt;   -5.0686  -4.4629   7.5355  -1.2612 -10.0848   2.7247   7.0528   7.2881
+#&gt;   -4.9709  15.5319  -4.2474  -8.5831   6.5794  12.3218  -7.9114  11.4092
+#&gt;   -2.8564  -7.3116  -5.2396  -1.0795  -0.0653   9.1868  -0.2262  -5.8491
+#&gt;   -0.8342  -1.9837   3.4885 -13.1955   5.6293   5.9005  -0.3317   3.5845
+#&gt;   -6.5592   9.3104 -11.2125   8.8877   8.0341  -5.8824  -1.1890   6.4209
+#&gt;   -0.6094  -4.6323   1.4846  -8.3095   3.2218  11.1752  -8.5526  -3.3383
+#&gt;   -9.9859   0.8720  -2.8285  -1.3160   9.5755   1.7600  -6.3442  -4.7246
+#&gt;   -1.9063  13.7199   1.2022 -13.4989  -8.4142  -0.5966   2.7934 -10.4546
+#&gt;  -11.8290   3.8885   2.5128 -10.7260 -14.9850  10.9759   0.1504   1.9472
+#&gt;    6.3647  -0.5885   8.9794   4.3158 -16.9057  -9.6695  -2.0466   6.2499
+#&gt;   12.7378   9.0779   4.4035 -12.7644  -8.6863  11.0101   2.7448   2.6279
+#&gt;    8.2543  -6.9941   4.6757  13.2053 -15.9934  -2.1725  -3.7339   2.6762
+#&gt;  -15.5257   3.3541  -1.4097  -7.8562   7.5251  -3.9706  10.9223  -5.9378
+#&gt;    4.2843   4.0547   3.5963  -3.8833   3.8719  17.3283  17.0613   1.3608
+#&gt;    1.8062   2.1106   3.0785 -10.7370  -4.8876   5.7470  17.1128  20.2215
+#&gt;   -1.0501   1.8343  10.6267   5.2201  -3.1459  -7.6928   2.0141 -13.0849
+#&gt;    9.1640  -0.2273   2.5444  12.4343  -5.9568  -9.7919   0.5788 -16.2262
+#&gt;   -6.0636  -8.2957   0.5474  -7.2691  -3.9940  -7.2372   3.5615  -0.1772
+#&gt;   -0.0116  18.5618  -0.0501   1.4772 -11.5969  -6.3888  10.5140   6.0256
+#&gt;   -6.4140  -6.3266   8.7698   4.9552  -8.6961   2.5264  -6.4764   1.3719
+#&gt;   -2.1428  13.2581   0.0243  -6.2169   2.2053   5.1598  -8.1174  -8.1981
+#&gt;    6.9769  -3.2637  -1.7238  15.3795  -5.4236   9.5380  11.7204   3.4857
+#&gt;    4.2541  -5.3250   8.2638  -8.9295   5.8716  -3.8351  14.4074  -9.4258
+#&gt;    6.9027  -1.9696  -0.0950   6.5029   3.8962   6.0836   7.7747   8.1169
+#&gt; 
+#&gt; Columns 17 to 24   3.1896   7.1136  16.5850  21.3689  14.1253  10.9252  10.6069 -12.9360
+#&gt;    3.1916  -2.3407  10.7862   1.1791  -2.3508  -0.8567  -2.7486  -0.3680
+#&gt;   -8.0882   0.5215  -6.1814 -15.5077  -8.7990   6.2791   3.8403   9.6876
+#&gt;  -17.2918  -3.3978   4.2483  -4.9023   2.4044   0.9558   9.9897   0.8995
+#&gt;   -4.9701   6.9746   3.1969  -1.7714  -6.7167  -6.3982  10.8513   7.5809
+#&gt;   12.1795  22.3433  16.0185   7.4013   8.3001   8.4499  -1.4369  -6.1516
+#&gt;   -8.4451   1.9695  -4.3517  12.2279  -4.1713   1.0889  -4.5477 -12.6213
+#&gt;   -7.2546   1.2788   0.9460   6.0031   3.7693  -3.9883  -3.7087  -3.4668
+#&gt;   -6.9514  10.6885   7.4600   0.1471   4.4466  -0.3061  -0.6925   8.2213
+#&gt;   11.2392   3.6268   8.8963  -4.9476  -0.7547 -13.0547  -8.4332   2.4718
+#&gt;    6.9078   0.5906   1.6236   0.2955 -10.1980   3.3020   3.8726  -2.5545
+#&gt;    5.3922   0.0548  -2.6088   1.4309  -0.8519 -11.7812  -5.1712  -7.9835
+#&gt;   16.3538  -0.2866  -3.9257   4.2461   6.4649   6.9675   0.3583  -0.1692
+#&gt;   -8.3542   3.7870  -5.1359   0.5153   2.7189   7.9143   9.8094   2.3193
+#&gt;   14.3007  -4.8221  10.5206  -3.7007   1.1044  12.1687   2.2237  -6.5298
+#&gt;    6.7927  -0.3696  10.7242   6.0659   2.8485  18.9118  10.8026  -3.7626
+#&gt;   10.4871   3.5258   1.9896  10.0899   2.9323   1.0943  11.1587  -5.3213
+#&gt;    4.1095  11.5633  -3.8133   0.0403  -2.0528  20.9037   1.9324  -0.6095
+#&gt;    0.3955  10.9574  14.2628   6.2530  -1.9365  -3.1222   1.6854  -4.4150
+#&gt;   13.8606   4.9778   6.4612  10.9626   3.8364  14.6639 -11.1712  -4.2862
+#&gt;  -17.4411  15.5994 -10.8096  -7.8068  -2.7533  -5.6480 -10.9167   3.3260
+#&gt;   10.4801   0.7587   1.2777  -0.4488   3.1472   9.4578   1.6560  -1.7689
+#&gt;    9.2851   3.1067  -2.2496   5.5730  -5.7733   7.4467   1.4873 -16.7869
+#&gt;   20.4946   1.6005  10.7375  -8.5792  11.5899  -0.3161  -6.1408   5.1686
+#&gt;    2.6878   7.3988   4.1201  -9.0180   2.5914   1.3089  -4.1484   0.6474
+#&gt;   -1.9797  -2.2724   6.8919   5.8167   0.9384  17.1842  -1.2179  -0.5146
+#&gt;    1.6663   7.3121  14.5475   2.8160   2.4474  -5.1266   8.7430 -11.3458
+#&gt;    5.1560   5.9187  -0.3446   8.4645  -2.7297  -9.8186  14.9239   3.5063
+#&gt;   -1.9313  12.5361   7.1592   1.6826   1.1412  16.7364  -5.3221   0.5639
+#&gt;   15.9264  12.0776  -3.7066  18.7699   9.2437  -3.5412  -1.8549   1.2673
+#&gt;  -12.8781   7.2273   0.9743  14.6515   2.3926   0.9963  -7.2469  -6.9089
+#&gt;   10.2803 -10.6271 -10.5320  -2.2252   6.0630  -1.4732  -3.5993   7.3931
+#&gt;   14.1778   5.7967   7.9909   6.9608   8.6100  -7.6136  -8.2632  -0.3995
+#&gt; 
+#&gt; Columns 25 to 32  -4.9114  -6.5843   7.0307  -4.9146  -3.4639   2.0096   8.6154 -10.3412
+#&gt;   -4.9881 -16.5373   0.8815 -14.8672  -3.2208   0.1541   8.4281  -1.7680
+#&gt;    1.6603  -3.2193   0.9856   0.7298   3.6267 -19.4214   7.9226  -6.4101
+#&gt;    0.8370  -3.7783  -5.5143   7.5889  -0.3336  -3.7922   0.4118   7.4096
+#&gt;    9.0980   0.0702  -6.6039   0.2466   3.8681  -4.9208   5.6830   3.2396
+#&gt;   -9.4309   0.0826 -21.4884 -13.7381  -2.1915  -3.1371   6.9177   1.3061
+#&gt;    3.6304   3.2765   4.7168   4.3914   3.3643  -3.9210  -5.0385   9.1815
+#&gt;    4.5755  -4.3560  -1.8081  -0.4241   7.7188  -4.3440   3.7476  -0.3437
+#&gt;    2.5515   4.0793   0.5688 -14.8879   1.5542  -3.5263  -1.1997  -0.4273
+#&gt;    1.3687  -4.1839  -7.1615  -0.2557   6.2164   2.9262  11.1157   1.1417
+#&gt;    3.3176  -6.9254  -1.2322  -7.1140   4.5201 -15.2834  -0.9299  -4.2925
+#&gt;   -4.0570   3.7860  -1.8286  -5.3160   5.1573   9.9160  -7.1879   1.8805
+#&gt;  -10.5045  -1.4338  -2.9479   6.3257  11.2079  -2.9802   1.2451  -5.4419
+#&gt;    8.1824   5.0542   4.5832   2.3177  -7.6769  18.4706   3.9747   1.6845
+#&gt;    4.4435  -1.0758  11.1831  -0.3116   4.0288 -13.3231  11.6478  -3.9394
+#&gt;    0.9642  -0.9493  -6.7294   4.8991 -14.8633  -0.9488   9.4906   4.4281
+#&gt;   -6.7962   0.2925   1.0403   8.6866   0.5212  -5.6361   0.6018   9.0195
+#&gt;    6.7747  -2.7812  -1.8297   2.4707  -7.2375   5.6537   1.1593   5.4003
+#&gt;   -3.9716   3.7339  -5.5811   0.7016  14.3519  -5.9226   2.1348   1.1818
+#&gt;   -3.7538  -4.7981   2.3779   2.0818   4.0368   1.3383  10.4708 -10.9149
+#&gt;   -2.3645   2.2764   7.5331   3.0271   4.3130  -6.0624   3.7986  -1.1034
+#&gt;    6.1696  -3.0635  -5.1319   4.8253   0.4628   2.3354  -4.9309  -5.5517
+#&gt;   -3.8062   0.3722   3.1567  12.7407 -13.9317  -2.3135  -2.0125  -4.3503
+#&gt;  -11.3840   5.4055  -3.2454  -4.5566   6.4311   1.2773  12.4571  -1.6851
+#&gt;   -3.2685   7.4117  20.0026   1.3566  -8.3665  -6.4926  -3.3746   0.9071
+#&gt;    4.8057   2.7966   7.6597  14.1099  -1.2854  -0.8436   0.8242  -3.6776
+#&gt;  -11.2720   1.0225 -14.1237   4.8509   1.6023  -5.1638   4.0037  15.5045
+#&gt;    3.3805  14.0331  -2.4699   1.1550   9.8972   6.2443  -3.5412   3.7678
+#&gt;   11.9026   2.1099  17.6332  -3.5072  -6.8630  -0.3439   9.0685  -6.9890
+#&gt;   -4.7753  -5.2764   4.8345  -8.9518   8.0896   8.2099  -5.8078  -4.4894
+#&gt;    4.6541   9.9653  13.8764   5.6798   0.0742   6.0066  -5.7651   4.0624
+#&gt;  -12.9363  -4.7503  -6.8934 -10.8562  -9.2596   6.5418   2.2233  -3.3519
+#&gt;   -1.8764  -4.3724  -5.7903 -28.4369  -0.7876   9.9454  -1.7579  -5.5863
+#&gt; 
+#&gt; Columns 33 to 40  -7.9782  -0.1174   9.2081  -0.5138   6.2838   3.0893  -4.8058 -18.1583
+#&gt;   -4.4992   0.1592  -4.9528  -6.1422  -2.1497  11.9840  -6.4663 -16.9782
+#&gt;   14.0196  -7.9512   4.7963  -6.6332  -5.8331   3.4767   2.0578  12.3614
+#&gt;   -3.7671  -4.8483   3.5894   9.3979  -6.9846  -5.1328  16.0085  -1.0437
+#&gt;    8.4310  -3.9356  -4.7848  -3.9968  -1.5736  -8.0616   3.9807  -3.8722
+#&gt;    7.6377   6.1733 -11.0966  -2.6771  -0.5499  -6.9654   1.3978  -3.7241
+#&gt;    1.2850   6.9083   3.2346  -6.3490  -5.6001   3.6724  -7.4045  -5.5348
+#&gt;   -2.1955  -0.1386  -3.9806  -3.2931  -5.8467  -5.0261  -4.8924  -4.6912
+#&gt;    9.8305  12.6788  -7.4050  -5.2257  -9.9318  -1.4535   0.6971   3.5317
+#&gt;    5.0632  -4.2288   2.2580  -1.8263  11.1705   8.9084  -5.8374  -6.0012
+#&gt;    8.1201  -5.6505   0.6060  -6.7071  -0.3699  -6.9170  -7.7779   6.6524
+#&gt;   -4.3955   8.7702  -3.0082   6.5812  -2.8098   0.0620 -10.2726   0.1328
+#&gt;   -0.2795  -6.3769  -0.1139  14.9688  -2.7549  -1.6537  -5.3231   3.2797
+#&gt;   -5.0211  -0.9620   3.7316  -4.8731  -1.6097  -6.1001   9.7407   0.7674
+#&gt;   -2.1829   4.2108  -0.9183   4.6779  -8.4763  -4.7964  -2.1435   1.3281
+#&gt;   -5.8912  -4.4834  -3.0533   2.5049  -3.2299  -9.6439  -1.0027  -6.7984
+#&gt;   -9.0744  -9.8956   9.6744  -9.2164  -2.7103   1.2476   7.4178  -0.1035
+#&gt;   -5.2211  -5.5057  -3.2928  11.1094 -12.1035   4.6906   3.1275   4.5405
+#&gt;    3.7232  -2.3739  -8.5902   0.9798  -7.2881  -9.8486 -16.0251   5.2795
+#&gt;   -9.9477   8.4432  -1.7972   5.5190  -6.1098  -1.9647  -7.1623  -4.2358
+#&gt;    8.4954  12.9459 -10.8776  -2.0965  -1.3022   6.1228  -4.4629  14.5403
+#&gt;   -0.2208  -9.9050   4.5437  -6.8559  -9.4238  -9.3158  -0.3664  -1.7320
+#&gt;    7.6426  -1.2814   7.7386  11.7444   1.8633   2.9149   6.0874   0.1475
+#&gt;    8.4882 -16.9385   8.4649  11.7110   9.7894   4.5368  -6.7920   7.8080
+#&gt;    2.8875   1.8071   2.7394  -8.7541   5.8108   0.4024   7.5638  15.7222
+#&gt;   -6.2697   8.1250   4.2751   1.8647  -5.8885   1.8181   5.5904  -1.6848
+#&gt;   -4.3160   3.0767  -5.3289  -8.2283  -2.4016  -2.6025   4.7989  -1.3792
+#&gt;    0.9026  -8.5282   7.1421   8.1328   1.1274   0.1584   5.5324  -8.1130
+#&gt;    2.6120  -2.0762  -6.0001  -9.1572  -8.5995  -4.7389  -6.9053  10.6624
+#&gt;   -0.1218   1.0712   0.8658  -5.4335  12.2737   6.7308 -10.1385 -27.2439
+#&gt;    2.2173   4.4802   9.8104  -1.4112 -12.2861  10.1242  -5.8051  -2.2389
+#&gt;    2.0607  -7.1254   3.2760  10.5545   4.6498  -2.4794   7.7428   0.6788
+#&gt;    0.9462   1.8375   0.8980   3.4936   8.3570   7.2085  -0.4131 -14.5003
+#&gt; 
+#&gt; Columns 41 to 48   2.9347  -6.5413  -2.7144   7.3507  10.2499  19.5012  11.9704   4.0226
+#&gt;    7.6969  -0.3759  -0.9515  14.8465  -0.4770  11.8782   5.7558  -0.1092
+#&gt;    0.5673  10.7646  -7.6328  -8.9557  -3.4129   4.7266  -8.4608  -7.6704
+#&gt;   12.3682   7.7268  -4.8314 -16.3501  -8.0761  13.1647  14.0554  -3.6347
+#&gt;    2.5149   8.1191   7.5053  -0.6208  -6.2784   5.1073  10.8315  -0.1904
+#&gt;   -7.1793   8.9993  -1.1687   5.0640  10.9705  17.1949  10.9811  15.4482
+#&gt;   11.3650 -10.3334  -1.9698  -1.5696   8.0683   0.7480  -4.9720  -0.0693
+#&gt;   12.6532   5.2129   1.0802   4.8707  -3.9001 -10.7755  -1.1094   5.5256
+#&gt;  -14.9249   4.7129  12.2226  -3.0380  -5.4822   3.5384   4.7636  -6.5954
+#&gt;   -2.6631  -4.3724  -6.3074   2.9076   8.8519  -2.7298  14.7076   0.3063
+#&gt;    8.0633  -0.6514   7.6628   2.0957   8.4992  -3.0437  -8.3949   3.6666
+#&gt;   -4.4896   3.1980   7.0277   4.3710  -2.4215  -6.1094  -0.3414   0.4719
+#&gt;    0.2284   4.9868 -11.0636   3.0958   9.8737   9.6359  -6.2769  -3.0681
+#&gt;    6.8440 -10.4315  -2.9086  -9.5762  -5.9412  16.2668   4.3936 -12.5563
+#&gt;   -2.7370  14.5742 -10.9801   4.9551   7.1912   6.3545   7.9185  -2.8689
+#&gt;   -0.6319   5.5352  -1.6038  -6.4819  16.2483   5.5396   7.8797   5.5145
+#&gt;    0.2896   1.5684   4.5287 -11.5754   8.5899   3.2760  10.2598  -2.6193
+#&gt;    3.8999   3.8732  -5.7280  -6.9490   2.7778   4.4373   2.9833   5.8405
+#&gt;   -3.0089   1.5390   0.4579  12.6652  14.0262   4.2430 -16.2028   6.4233
+#&gt;   -9.0877   3.0460   0.8589   5.1432   6.2836  -4.5454   6.0026  -2.2429
+#&gt;    3.9197  -0.0397   0.7961  -1.7077  -6.5249 -14.8881  -7.8815   0.5807
+#&gt;   -0.6347  14.0922   3.1064  -5.3681   0.8794  -1.9129  -5.5581   0.0186
+#&gt;  -17.1356  -7.0935  -0.7364   4.1804  11.8833  -3.0501  -9.5860  -0.3069
+#&gt;  -15.5952  -0.5495 -18.1164   5.3012  11.3696  -3.1717   2.4292  13.4601
+#&gt;   -9.2839  -1.0520   5.2714  -3.2606  -0.7096  -7.9491  -6.9085   2.7991
+#&gt;    6.4440   2.0277  -9.0858  -7.5276  14.1867  -2.4964   5.7862  -4.2194
+#&gt;    6.3172   0.8966  -1.3876 -12.1631  14.6530  12.8441   6.9321   6.2326
+#&gt;    0.7124 -12.4130  -8.9603  -6.4906  19.0114  13.5405  -3.5782  -0.8632
+#&gt;   -1.8861   1.0826  -0.0976   1.6771  -4.5812   5.2838   6.5466   4.5431
+#&gt;    7.3507  -2.6981   5.3992   2.5461  17.2578   5.4251  -1.5992  -7.3955
+#&gt;    7.2382  -4.5121  -0.9355  -7.8539   0.3607   1.1957  -6.9880   0.4112
+#&gt;  -10.8835  -1.8415  -7.2738   5.6169  -6.4167   2.0098   4.2866  -6.7110
+#&gt;   -9.0879  -6.7933  -0.8936  10.1913   3.3567  10.5202  13.4314   6.9530
+#&gt; [ CPUFloatType{20,33,48} ]</div></pre>
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+    <div class="page-header">
+    <h1>Conv2d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_conv2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Conv2d</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_conv2d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>weight</span>,
+  bias = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(),
+  stride = <span class='fl'>1L</span>,
+  padding = <span class='fl'>0L</span>,
+  dilation = <span class='fl'>1L</span>,
+  groups = <span class='fl'>1L</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iH , iW)\)</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>filters of shape \((\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kH , kW)\)</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>optional bias tensor of shape \((\mbox{out\_channels})\). Default: <code>NULL</code></p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the convolving kernel. Can be a single number or a      tuple <code>(sH, sW)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit paddings on both sides of the input. Can be a      single number or a tuple <code>(padH, padW)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>the spacing between kernel elements. Can be a single number or      a tuple <code>(dH, dW)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-"><a class="anchor" href="#conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-"></a>conv2d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -&gt; Tensor </h2>
+
+    
+
+
+<p>Applies a 2D convolution over an input image composed of several input
+planes.</p>
+<p>See <code><a href='nn_conv2d.html'>nn_conv2d()</a></code> for details and output shape.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='co'># With square kernels and equal stride</span>
+<span class='kw'>filters</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>8</span>,<span class='fl'>4</span>,<span class='fl'>3</span>,<span class='fl'>3</span>))
+<span class='kw'>inputs</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>4</span>,<span class='fl'>5</span>,<span class='fl'>5</span>))
+<span class='fu'><a href='nnf_conv2d.html'>nnf_conv2d</a></span>(<span class='kw'>inputs</span>, <span class='kw'>filters</span>, padding=<span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,1,.,.) = 
+#&gt;   -3.5486  -7.1217  -3.2540  -5.9799   1.1629
+#&gt;   -4.3378   2.2990 -16.4566   9.0228  -5.5777
+#&gt;   -2.6722  -9.2867   4.0511  -4.4372  10.4134
+#&gt;   -4.7778  -0.7326  -1.6019  12.0401  -3.1893
+#&gt;    0.2123  -0.5914   1.1211   3.0260   6.8859
+#&gt; 
+#&gt; (1,2,.,.) = 
+#&gt;   -3.5645  -0.6611   1.0267   0.0105  -2.2313
+#&gt;   -4.8099  10.2621   0.1669   0.9807  -2.2823
+#&gt;    0.7857   0.4305   0.4022   7.5398   0.2195
+#&gt;   -1.5292   7.5032  10.2237  -4.5421  -3.0606
+#&gt;    4.4675   1.8375   9.2117  -1.7632  -6.9053
+#&gt; 
+#&gt; (1,3,.,.) = 
+#&gt;   -4.0560  -3.3721  -3.9417   1.2809   1.6676
+#&gt;    3.7261   1.6556  -7.7465  -0.6448   3.6757
+#&gt;   -1.8130  -5.0859  -3.2322   4.3222   5.7618
+#&gt;   -2.3173  -7.4043   2.5139  15.0216   0.5374
+#&gt;   -1.4209  -1.0366   0.8718   4.0616   3.1472
+#&gt; 
+#&gt; (1,4,.,.) = 
+#&gt;  -9.5668  3.2701  6.5622  9.6684  2.5335
+#&gt;  -4.4349  3.4779 -1.2737  2.9751  1.7985
+#&gt;   2.6595  7.2363  4.4882  0.3747  1.3252
+#&gt;   5.3966 -4.0692 -2.2576  9.7051 -0.5021
+#&gt;  -0.2109  4.6618  0.3269 -6.7229 -2.3171
+#&gt; 
+#&gt; (1,5,.,.) = 
+#&gt;  -6.8665 -0.7471  3.0223  5.9422  0.0773
+#&gt;   0.1951  6.6746  4.5183  0.2309 -3.8129
+#&gt;   4.9832  2.5449 -5.4966  3.8797  3.9093
+#&gt;  -1.2040 -1.1130  7.1904  3.6236  3.7172
+#&gt;  -1.3379 -5.7016  6.7872  7.4608  4.0352
+#&gt; 
+#&gt; (1,6,.,.) = 
+#&gt;   -3.9338   0.3756  -3.0094  -0.7198   3.5040
+#&gt;    0.3583  -1.8715  -4.4996  11.1578  11.3811
+#&gt;   -7.6832  -2.1231  13.6712   7.9567   4.1675
+#&gt;    7.7207   2.5612   9.3426   5.4903  -9.0039
+#&gt;   -1.2806   1.9558   6.0637  -3.7703   2.7139
+#&gt; 
+#&gt; (1,7,.,.) = 
+#&gt;   -3.5015  -2.8558  -3.0872   2.0101   8.1843
+#&gt;   -2.7114  -4.0357   0.2954   1.3045   2.9680
+#&gt;   -0.0320   0.3198  -2.8986  -0.1796  -4.9480
+#&gt;    6.8524  -9.7840  11.4722   0.6094  -8.8633
+#&gt;   -2.3049  -2.9029  -4.5625  -5.2812  -1.8420
+#&gt; 
+#&gt; (1,8,.,.) = 
+#&gt;    4.1203  -6.8209  -7.9792   0.5355  -6.1896
+#&gt;    8.1814 -14.2806   2.7375 -18.4326  10.9079
+#&gt;   -0.8891  -8.0756   0.7869  10.5435   0.2842
+#&gt;  -11.1754   5.4648   9.2422   8.0793  -3.8445
+#&gt;    3.1076   1.0749  -4.5827   4.5922  -1.6976
+#&gt; [ CPUFloatType{1,8,5,5} ]</div></pre>
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Conv3d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_conv3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Conv3d</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_conv3d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>weight</span>,
+  bias = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(),
+  stride = <span class='fl'>1L</span>,
+  padding = <span class='fl'>0L</span>,
+  dilation = <span class='fl'>1L</span>,
+  groups = <span class='fl'>1L</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)\)</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>filters of shape \((\mbox{out\_channels} , \frac{\mbox{in\_channels}}{\mbox{groups}} , kT , kH , kW)\)</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>optional bias tensor of shape \((\mbox{out\_channels})\). Default: NULL</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the convolving kernel. Can be a single number or a      tuple <code>(sT, sH, sW)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p>implicit paddings on both sides of the input. Can be a      single number or a tuple <code>(padT, padH, padW)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>the spacing between kernel elements. Can be a single number or      a tuple <code>(dT, dH, dW)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by      the number of groups. Default: 1</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-"><a class="anchor" href="#conv-d-input-weight-bias-null-stride-padding-dilation-groups-gt-tensor-"></a>conv3d(input, weight, bias=NULL, stride=1, padding=0, dilation=1, groups=1) -&gt; Tensor </h2>
+
+    
+
+
+<p>Applies a 3D convolution over an input image composed of several input
+planes.</p>
+<p>See <code><a href='nn_conv3d.html'>nn_conv3d()</a></code> for details and output shape.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='co'># filters = torch_randn(c(33, 16, 3, 3, 3))</span>
+<span class='co'># inputs = torch_randn(c(20, 16, 50, 10, 20))</span>
+<span class='co'># nnf_conv3d(inputs, filters)</span>
+}
+</div><div class='output co'>#&gt; NULL</div></pre>
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+    <div class="page-header">
+    <h1>Conv_tbc</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_conv_tbc.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Conv_tbc</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_conv_tbc</span>(<span class='kw'>self</span>, <span class='kw'>weight</span>, <span class='kw'>bias</span>, pad = <span class='fl'>0L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>NA input tensor of shape \((\mbox{sequence length} \times batch \times \mbox{in\_channels})\)</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>NA filter of shape (\(\mbox{kernel width} \times \mbox{in\_channels} \times \mbox{out\_channels}\))</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>NA bias of shape (\(\mbox{out\_channels}\))</p></td>
+    </tr>
+    <tr>
+      <th>pad</th>
+      <td><p>NA number of timesteps to pad. Default: 0</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
+
+    
+
+
+<p>Applies a 1-dimensional sequence convolution over an input sequence.
+Input and output dimensions are (Time, Batch, Channels) - hence TBC.</p>
+
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+    <div class="page-header">
+    <h1>Conv_transpose1d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_conv_transpose1d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Conv_transpose1d</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_conv_transpose1d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>weight</span>,
+  bias = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(),
+  stride = <span class='fl'>1L</span>,
+  padding = <span class='fl'>0L</span>,
+  output_padding = <span class='fl'>0L</span>,
+  groups = <span class='fl'>1L</span>,
+  dilation = <span class='fl'>1L</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iW)\)</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>filters of shape \((\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kW)\)</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>optional bias of shape \((\mbox{out\_channels})\). Default: NULL</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the convolving kernel. Can be a single number or a      tuple <code>(sW,)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p><code>dilation * (kernel_size - 1) - padding</code> zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      <code>(padW,)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>output_padding</th>
+      <td><p>additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple <code>(out_padW)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>the spacing between kernel elements. Can be a single number or      a tuple <code>(dW,)</code>. Default: 1</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-"><a class="anchor" href="#conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-"></a>conv_transpose1d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -&gt; Tensor </h2>
+
+    
+
+
+<p>Applies a 1D transposed convolution operator over an input signal
+composed of several input planes, sometimes also called "deconvolution".</p>
+<p>See <code><a href='nn_conv_transpose1d.html'>nn_conv_transpose1d()</a></code>  for details and output shape.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>inputs</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>))
+<span class='kw'>weights</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>5</span>))
+<span class='fu'><a href='nnf_conv_transpose1d.html'>nnf_conv_transpose1d</a></span>(<span class='kw'>inputs</span>, <span class='kw'>weights</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;  Columns 1 to 8  -0.6004   4.0901   4.1162   0.6192   6.3756  12.6002  -2.0543  -8.9684
+#&gt;    2.5562   5.4577  -6.9559   1.3808   0.9724  -2.1635   0.0468  -9.2402
+#&gt;    2.1054  -3.9812   4.3883 -11.3571  -7.2336  -7.9630  -7.3477  -3.1796
+#&gt;    3.4706  -3.2964  -6.8210  -0.9201  -4.3900  -9.7930  -4.8746   6.7569
+#&gt;   -4.4986  -1.9795   0.7281   3.7279   2.8359  -2.9289   4.9091   0.0196
+#&gt;   -0.1566  -1.7666   6.7628  21.8539  13.1175   3.0355  11.8317 -10.9293
+#&gt;    1.9233  -4.0127   5.4726  -2.8186 -15.2970 -14.9055   9.0864   9.7691
+#&gt;   -6.2536  -1.2273   1.0028   2.6480  -3.2984   0.7050   0.2384  -0.4342
+#&gt;   -0.7137  -1.2991  -1.9438  -0.9533  -0.0313   6.3989  -8.8965   1.5028
+#&gt;    2.8515   1.2079   7.5720  11.2757   4.0208   4.0302 -18.4834   2.6042
+#&gt;   -1.2608  -1.9830   0.4157   5.8398  -8.0424   2.8853   1.9586 -11.1629
+#&gt;    0.9166   3.7020   6.3919   5.6656   8.5167  17.4386   9.7697  11.3048
+#&gt;    3.7391   5.0856   3.8721   1.7100  -1.3962  -9.1984   3.3265  -3.9582
+#&gt;   -1.6088   0.2460   0.5347  -2.3824  -9.6884  -0.5348  -6.0122  -0.1030
+#&gt;   -0.6148  -1.2322   1.0224   6.9799  -1.8193  11.8577 -19.9366   2.6108
+#&gt;    1.4129   6.4468  -1.6165  -3.0123 -14.8057  -1.4289  -9.7638  -1.8674
+#&gt;   -2.9941  -9.5087   2.1096   5.6394  -6.3901  -4.2324  -3.9090 -13.8909
+#&gt;   -0.4531   2.0755  -1.2331  -5.6545   8.4489  -3.9393  -8.1968   3.5906
+#&gt;   -0.5256   3.3574  -1.5862   7.9468   9.7259   0.9548  15.2170  12.6929
+#&gt;   -1.0922   2.8931  -0.3569   1.4262  -5.5169  -1.0022  13.1522   6.1245
+#&gt;    4.8329   0.7218  -0.3335  -5.6755  14.5178  -8.6349   4.7855   4.8308
+#&gt;    1.7461  -3.7079  -1.3584   4.6693  -4.8814   9.9969   0.9081   5.7656
+#&gt;   -0.2636  -0.1443  -7.4224  -3.2104  -8.8714  -8.0212  -6.6852   0.9089
+#&gt;    5.7567  -0.5389   7.3149  -8.6823 -13.8152  18.6386  15.1764   7.8335
+#&gt;    0.3944   4.6797  -2.1249  -4.5136  -3.7679   4.9865  -0.3764   0.3817
+#&gt;   -1.5968   2.2421  -3.5420 -13.0538   7.0071   4.8528   1.0440   4.9850
+#&gt;    1.6772   0.7801   6.6048 -10.8922 -14.4722  11.5427  -2.8412   7.4468
+#&gt;   -5.5516  -3.5058   2.0037  -2.6942   0.7408   0.6017  -1.2814  -1.5558
+#&gt;    3.5480  -1.8899  -5.4336  -3.3593  -4.9892  -6.0104  -5.0009 -13.4594
+#&gt;   -8.6330  -0.3369   9.3590  11.0104  -5.5114   8.3425  -6.3278  -5.8439
+#&gt;    2.4533  -2.4106  -7.8900  11.0399 -10.0707   3.2837  13.9738   1.2788
+#&gt;   -2.3656   5.0306   2.3163  -9.2069   8.0224  -4.3115   8.8182   8.4418
+#&gt;    7.1396  -1.4040   2.8544  16.0946  -2.0559  -9.8389  10.0793  -3.5420
+#&gt; 
+#&gt; Columns 9 to 16  19.6296  -1.0571   2.2479   6.0396  17.4604   5.4292  -5.2999  -3.4805
+#&gt;    4.4689   6.2760  -6.7538  10.4388  -5.3242  -4.4771  19.5997  -9.2312
+#&gt;    7.3714   2.9589   3.8303  -3.8989  -1.2139  -7.2902 -12.3022   5.5554
+#&gt;    1.1485  -3.1741 -14.2423  -4.7028  -7.3247   3.6348   0.5571  11.8129
+#&gt;   -3.9914  -9.8326 -10.1469  11.5117  13.0456  -7.4854   8.4747  -4.2242
+#&gt;   -8.1024  11.8125   9.0316   1.8900  19.6748   6.5998  -6.3706   1.9563
+#&gt;   -4.3288  -4.2767   6.9220  -6.2718 -14.5755   6.9661  11.9982 -11.3933
+#&gt;    3.6337  -0.4349  -2.0598  -2.9402  -2.3109  -3.6216  -9.9334  -6.8303
+#&gt;    3.0969   8.0662  -1.9271   0.9911  11.9432 -13.8506  -0.3506  -8.2148
+#&gt;   -0.3625 -10.1219  -3.2581  12.9066  -2.7811  -1.5516   1.4874   0.0285
+#&gt;    4.2390  11.4472  14.2799  -4.3681  -1.2805  -7.7423  -7.4996  -9.5044
+#&gt;    5.9248  17.1279   8.5384  12.4752   7.6657   4.4720   3.8107  18.1527
+#&gt;   -6.5947  -2.0549   6.2950   1.4028 -10.9088  -7.4368  15.4439  -4.2578
+#&gt;    8.6277   2.5786  -2.0515  -2.8812  -0.1932   2.6490   5.3511   2.3057
+#&gt;   -5.8411  -6.6885   1.8910  -0.9538   5.1784  10.1539 -13.0269  13.5722
+#&gt;  -11.6944   4.1298  -3.8437  -2.5096  -6.4828  -6.6850   7.6715   2.7347
+#&gt;   -8.0584   8.5316   2.6845  -5.3957   2.3331  -4.4324   3.8076  -0.7175
+#&gt;  -18.2143  11.8918  10.6286  11.9950  -1.4405  -5.5318   6.0004   2.2942
+#&gt;    8.1489  -2.0809   8.1918  16.5408  13.5879 -19.5593  -3.7664 -10.7886
+#&gt;   11.0072  -6.6765  -4.8078  11.7145  -4.5950  12.0940   4.1818   0.9954
+#&gt;    3.1661  -1.3012   6.0041  -4.4507   7.4049  -0.6932  13.2061   3.2743
+#&gt;    9.5199   7.2679  -3.3639 -11.7749   8.3600  -3.3641  -5.7926   3.3618
+#&gt;   -6.4810 -12.4436  -1.3938   3.0750 -16.1594   1.9717  -5.5333   4.2702
+#&gt;   11.1419   0.0955  19.3511  -4.1583  -0.4624   3.2259   1.0754  -2.9664
+#&gt;    8.9533  -0.9478  -5.3318   8.1153   6.2505  19.1452   1.0230   8.2152
+#&gt;   -2.8706  -8.6784 -12.3010  -7.2931  -2.0150   5.7070  -7.1829  -2.9493
+#&gt;    5.2020  -1.9136   1.4974   1.2598  -0.3027  -1.9155  -8.9421   2.2337
+#&gt;   -7.7501  -9.0444  -9.3237 -10.6197   3.8131  -5.0688 -24.0951   2.3849
+#&gt;   -7.7414   4.0537 -19.9329   0.5265  10.7112  -3.8184  -6.8621  -5.1801
+#&gt;   -4.2732  10.6180 -12.4161   6.6465  -0.6175   0.5263 -16.3148  11.5405
+#&gt;   -6.2823  -6.9256   3.7962  -9.9520  -9.6631  15.2088   6.3811  -9.0371
+#&gt;  -19.0446   0.8602  14.5508  -8.9178  -2.6391 -13.0266  -3.0735  13.5590
+#&gt;   -4.9628   7.5579  22.1511  -1.2725  -2.9494  -4.4844   2.3112  17.1739
+#&gt; 
+#&gt; Columns 17 to 24  -4.1825  21.5207  11.4520  -4.5708  -1.5438  15.3972   3.2125  17.3042
+#&gt;   -0.4366   5.4250   5.0380   4.0624  -3.6303  -3.8972 -14.9252  -4.3104
+#&gt;   -0.7165  -2.3864   3.1508   8.7325  -1.1502 -21.1438  -1.3657 -13.6428
+#&gt;  -10.3651   5.3801 -12.4743  -2.2434  -5.3823  -7.6754  -6.2727   5.5183
+#&gt;   -0.4864  11.9181 -14.7448  -1.3623 -10.4562   2.1550   1.3195  12.3553
+#&gt;   -1.9183  -2.3853  14.9228  18.9550 -17.5423   7.9292  17.5551  -2.3659
+#&gt;    8.5572 -10.7424   1.5513 -10.6038   8.8510  -9.7853  -6.9093  -1.2731
+#&gt;   -3.3836   3.1122  15.9079  -7.9366   4.0856 -15.1774  15.0615 -10.2502
+#&gt;    2.9098   7.0142   3.7753 -14.3875  -5.1446 -10.9741  -6.6907  -0.5269
+#&gt;   11.1435  -2.2287 -18.3215   5.5178   0.0646   1.9037   8.9206   5.6702
+#&gt;   15.9882   5.7715  -3.0261  12.0738 -12.1655   9.7216  10.7748  -6.2806
+#&gt;    8.5648  10.9936  13.3946   6.0727   4.1547  15.0799  19.8217   5.5906
+#&gt;    9.4260  -1.6321 -10.8740   1.2734   6.4871  -3.9937  -5.1651  -6.5331
+#&gt;  -21.2690   2.0586   1.4541  17.1033 -13.2072   0.4539  -0.3309  -7.4712
+#&gt;   10.4926   4.8057  -0.4733  -2.7449   3.5052 -10.6738  22.6786  -1.7802
+#&gt;    6.1693 -14.3340  -7.6485 -10.2864  -0.2380   6.3123   2.6203   3.5415
+#&gt;   -0.6640  -8.3534  12.0272  10.8964   2.9217   6.1839   2.3538   9.3204
+#&gt;   -5.1073  13.8817   7.7285  11.8317   4.1813 -16.0389  19.8230   5.0212
+#&gt;    0.3832  -2.1313  -3.2197   6.2428  -7.2094  -1.7911   0.1555   0.9442
+#&gt;   -1.6238   9.3784  -3.2428   1.1754   8.0022  -9.8045   0.1608  -5.7132
+#&gt;   -2.2772   4.6684   3.3027   1.0269   7.1341   8.5374   3.5203  12.7100
+#&gt;   10.5552  -6.7427   9.7302  -4.9299  -4.2069 -10.8554  -1.9141   0.6340
+#&gt;    3.6097 -13.6879 -19.2162  11.0106  -3.8462   1.1233  -4.3375   2.9411
+#&gt;   -2.6338 -16.1336   1.1961 -16.3511  -2.5227 -17.6820 -18.0181   9.3209
+#&gt;    4.9945  16.4619  -9.6469  -2.7882   5.7165   5.6490 -14.4887  -7.6353
+#&gt;   -9.1514  -8.5315 -15.5044  -5.3980  -3.1871  -6.8574  -0.2664   4.4388
+#&gt;   -9.5559  -2.3091  14.8938  -4.0996   5.5592 -19.4200   1.9836   6.5751
+#&gt;   -8.0059   5.4177   4.6515  -3.9604 -13.8529 -17.5115   6.4735  -8.0561
+#&gt;  -13.0087   7.8845   4.5359   7.1285  -9.0164  -0.6873 -22.5152  -2.0334
+#&gt;   -9.1964   0.6174  18.8030   6.6753  -1.9048  -5.0334  -0.6214  -9.7074
+#&gt;    2.8011  -1.5303   2.3510   1.6620  -4.6654  -3.1248  -1.7060   6.6866
+#&gt;   -8.1370  -1.5563  -0.5429   4.6920   2.2020  -0.8108   9.9542  -7.6876
+#&gt;    3.8961  21.6270   2.4845  -1.5813   6.1576   2.7277  -5.1375   4.4078
+#&gt; 
+#&gt; Columns 25 to 32  -2.3601  -3.4322   9.3053  -8.1667   4.9778 -13.8512  -0.7769  -1.5251
+#&gt;    3.6972  12.7780   3.2833 -11.2094   4.9365   0.8090  -4.1411  11.1159
+#&gt;    2.1876   7.3518 -12.6594   7.8482  -3.3922   7.2875  11.8075  -1.1018
+#&gt;   -2.9391   6.5277   3.6980  -0.4259  -6.1169   5.0371  14.8063  -8.8458
+#&gt;   11.8637  -1.4726  21.8518  12.0867  -8.8219   4.1038   1.4818  -1.6840
+#&gt;   24.4477  -2.4871  -4.6144  14.6080  -6.4686  -1.9640 -10.3194   0.6770
+#&gt;   -1.7146   9.5057   0.6013  -5.5986   7.1479  -2.1409  -8.9335  13.1021
+#&gt;    4.6454  12.8925  -9.2337  17.1118  -3.7436   1.9554   0.4862   4.6698
+#&gt;   -4.8602 -19.4728  -0.3412  12.0404  -2.2184  12.8902   1.5057   1.0611
+#&gt;    4.5539  -0.5962  -8.7740   5.9415 -14.5654  -1.0543   1.0969  -4.6110
+#&gt;  -13.4540  -1.8438 -27.4494  10.4325  12.9727  -5.3659   4.3214  -9.1497
+#&gt;    8.4932  10.4420  -0.1234  -2.1550  -5.8957   1.4477  -0.5030  -4.5601
+#&gt;   -2.8887  10.2157   4.6852  -2.6711   5.0831  -4.4824  -4.4490  12.0549
+#&gt;   10.8636   3.0547 -14.1468 -11.5786  -4.1872  14.1289   3.3529   3.5294
+#&gt;   -5.0578  19.5757 -10.7164  14.3858 -15.5695 -23.5430   0.6562   0.4816
+#&gt;  -17.2403  10.0789   8.6334 -16.7596   5.0725   1.1319  -1.7669   2.2916
+#&gt;   -1.0254   0.8765   1.0145   7.5937   8.4460 -11.7051  -8.6109  -3.9665
+#&gt;    4.5205   6.8878  -2.7549 -11.9323 -22.9036  11.9790 -22.1282  10.1852
+#&gt;  -14.1557  -5.4662   9.6428  -7.9654   0.4415  16.7461   5.6598  14.2657
+#&gt;   11.7360  15.4267   7.0044  -0.2906   3.8354 -16.8175  11.6116   4.2682
+#&gt;    7.5793  -3.2285  -5.5165  -6.5840   2.1186  12.6062  -6.2278  -9.1656
+#&gt;    2.0305  10.7111   2.9078  11.1756   6.8596  -5.1708  12.8105   1.4350
+#&gt;  -19.5372  -9.6168  -6.2032  -6.6678  -0.1795  -2.0621   0.1891  -0.2887
+#&gt;   -6.1471  -4.1238   5.0940  -0.5406   3.4143   4.3145  17.2420  14.2220
+#&gt;   15.2717 -11.6314   0.6753   4.6649  -8.3632  -4.0830  -2.4835   2.9289
+#&gt;   -7.8195  -8.8384  11.2586  -6.0931  10.6657  -1.6245   5.1102  -8.9102
+#&gt;   17.8543   5.0104 -12.4566   0.1958 -16.1957  -0.1449  12.1718   8.7589
+#&gt;   -1.8529  -4.3217  -6.6898   3.4766   3.4081  14.7981  -5.4841  -4.5221
+#&gt;    1.1381   1.2830  11.2350  -3.1520  -9.8428   4.2556   2.6595   8.2908
+#&gt;    1.7770  -4.4640   4.3794  23.1427  -4.7616  -7.9387   2.9123  -2.6110
+#&gt;   -8.3071   4.1271  -4.8753   1.2700  12.8085  -4.8894  -3.6147   3.9395
+#&gt;   -0.0611   6.6493 -13.0310  -1.8303   1.7900  -0.6020  -5.4802 -13.4728
+#&gt;   26.6121   8.2568  -6.9124 -11.3372  -3.8023   4.2649  -4.5585  14.7970
+#&gt; 
+#&gt; Columns 33 to 40 -13.1377  -0.0706   5.6916   6.5058  -3.0752   4.5630  -7.6258  20.9413
+#&gt;   -0.8843   1.0609   3.0657  -0.0612  -3.1638  -3.3998   3.0816   3.0866
+#&gt;    7.3812  -1.7633   7.9183 -13.8968  14.0850 -20.5245   4.3020   6.5696
+#&gt;    4.5379   5.9704   3.7029   3.3116 -22.2722   2.8277   2.2261  -4.4455
+#&gt;    1.5286   7.1339   3.0548   3.2838   2.5137  -0.4061   8.9326  17.9624
+#&gt;    2.3315  -1.2700   3.9492   2.8422  18.2107  -7.2438  21.5324   3.9252
+#&gt;    9.6432   0.4445  -2.2403   4.9207  -0.7474  -7.9729  -0.5129 -15.4101
+#&gt;  -20.4920 -17.6694  -5.8191  13.5121   3.1379  -5.5635  -1.6764  -9.0776
+#&gt;    3.2790  -1.0290  15.0159   6.6719   5.8982  -3.4839  -0.0748  10.8644
+#&gt;   10.9863   9.2873   3.2989   2.4354   9.8572  -2.2714   7.1368   4.1491
+#&gt;   -7.7011   3.7981  -5.6473  -9.6907  14.5500  -3.3643  -1.8682   0.4615
+#&gt;    4.9033 -13.4153   5.4821   1.2573   5.0443  -2.3632   3.0864   5.1709
+#&gt;   -6.3609  11.7518   9.7436   6.3601   7.2588  -2.8778   2.2560   2.2091
+#&gt;    0.6333   9.5380 -16.4339  -7.4011 -12.0568   0.4711   9.7366  -8.5114
+#&gt;   16.3144   8.5388   0.5593  -8.6230  -5.3358  -1.3064  -3.4502  -4.9662
+#&gt;    5.6114  -3.9022   8.0300   3.4313  -1.6205 -13.8910  -4.1147  -7.6525
+#&gt;   15.3002   6.0428  -9.9738 -10.1090  10.0225  -1.4212   0.5714 -10.6084
+#&gt;    6.0626  -5.3162   7.5097   0.3177   0.6618 -12.0088   5.9090 -13.9977
+#&gt;   -2.9831  -4.7394   1.3440   4.8945  17.2124   4.3742  -1.8140  -3.4268
+#&gt;    3.9576   0.6434 -12.3714 -10.8635   9.2416  -4.4522   5.8705  -3.2069
+#&gt;    3.4398  -2.3467  14.6558  -7.6711  -6.7356  -5.1956   5.4958 -12.6438
+#&gt;   -3.3017  -4.6428  -0.7086 -10.4358   6.3154   0.3267  -4.0852  -0.8664
+#&gt;   10.5626   2.0228 -14.4048   8.2696  -0.0745 -10.8902  -6.6894   5.1885
+#&gt;  -12.5296 -12.9663  -4.4452  -2.9155   5.2775  18.9949   2.4824  13.1430
+#&gt;    0.6722  -7.0047   6.4576  -3.7775  -9.1132   6.3353  -3.5569   4.1473
+#&gt;    6.7089  -7.4115  10.6349   5.1406   2.1408   6.6096  -5.9116   9.5312
+#&gt;    7.9793   9.1878 -15.3085  -6.6413   1.8546  15.5129   1.8919   7.0326
+#&gt;  -10.7967  -1.9923 -12.8950 -10.9955  15.2740   7.0095   8.7453   1.6567
+#&gt;   -3.5113  -4.6141   5.5052  -2.7090  -0.7984   4.3020   0.6053  -0.6996
+#&gt;   -2.2838   2.9193 -10.9042   9.5657  -5.6694  -0.0304   8.0606  17.9457
+#&gt;   -3.7685  -2.6690   0.9492  -7.3359   4.6296  -4.3763  -5.5983  -1.0029
+#&gt;   16.1059  -0.4918  -5.9363   4.5314  -2.9774   9.2013   2.6621  -8.6090
+#&gt;   -1.3673  11.2294   0.8330   0.1796  -1.9448  13.2667  -1.8795 -11.9470
+#&gt; 
+#&gt; Columns 41 to 48   5.4623   6.0557  -2.3547 -10.5777  -8.9394 -17.5632  17.0012  -7.7506
+#&gt;   -3.4502   5.7822 -22.9018 -16.1433   3.3162  -7.2222   4.9175   5.3236
+#&gt;   -6.8991  -4.6364  -2.5290 -20.8305  -5.5299  15.0443  -3.6268  12.7072
+#&gt;    9.9013  -1.1473  -9.4256  -2.5440   0.7606  13.8442  -2.8150   8.2952
+#&gt;    0.2988  -2.1047  12.3417   4.8989  -4.7860   6.8126   0.6023   6.9697
+#&gt;    6.4256 -18.5609  12.9657 -13.4234   3.5923  -3.0290 -19.5210   7.9649
+#&gt;    8.5411   7.2910  -1.3473  12.0052  -4.6327  -5.4855  -9.0772  21.2862
+#&gt;   -8.1589  -3.7239  15.3635  -0.2900  -8.4298  -5.8980   8.3588 -31.6000
+#&gt;    9.1120   3.6091   0.5912  13.4243  -0.9888  -7.5009  12.9340   2.7174
+#&gt;   13.6130  -5.7139   2.6131  -4.1449 -13.8080  12.9982   1.1384  -5.4977
+#&gt;    8.9638   6.3303  -9.3563  11.8058   8.1987   8.6516  -2.2029 -17.6940
+#&gt;   -4.9596   7.8049  -4.9706  -1.9639  -1.2503   4.1113   3.3422  -3.7928
+#&gt;   -6.4181   6.0623  -8.2092   0.7996  -6.1743   0.6363  -7.4182  -8.2864
+#&gt;    1.0932  -2.6376  -9.6817  -8.3699   9.8409  12.7849   4.8182  11.9833
+#&gt;  -14.7259 -18.3420  11.0260   6.2939 -17.9582  10.5616   6.2648  -1.0947
+#&gt;  -10.5056   2.1502  -4.2209  -0.2779  12.3265   4.9331  -2.0322  -2.0515
+#&gt;    8.0115  -9.9201 -15.0605   4.7110   2.5659  -7.8833 -16.4231  15.7626
+#&gt;    7.2102   9.5568 -11.0781  -2.8903  -0.3292   7.6369   1.6897   1.3017
+#&gt;   -4.6736  16.3344  13.0339   2.9969   4.7427  -0.4004  -0.6330  -3.7610
+#&gt;  -12.2635 -13.3897  -0.6776  12.6669 -17.6320  -6.1654   0.0280 -10.9851
+#&gt;   -7.5166  -0.4481  -4.1911 -11.3640   3.6991  14.0500 -13.1051   7.0555
+#&gt;    3.5955  -1.8552   1.2731   0.7391 -11.2164  -4.5338  -7.1506  -1.2045
+#&gt;   -7.6455 -12.3654 -11.3886 -10.4583  -8.9162   8.7736  17.7514  16.4935
+#&gt;    7.3194  24.3887   9.7357  -3.6405   0.1455  -5.7188   6.6516  -4.2392
+#&gt;   15.0456  13.1130  12.7170  -3.8100 -19.4943 -16.5896  17.4840   3.2342
+#&gt;   -7.2503   7.9477  19.6389   4.8204   7.8325  -9.6368   7.2113 -12.7309
+#&gt;    4.2267  -4.0082  -7.8041  -1.5592   9.4208  10.1543  16.0493   4.4434
+#&gt;   -4.3093 -18.7923  -1.8592   0.0332   5.8462  -1.1693   6.3583  -8.1359
+#&gt;   -3.6780  -9.1685  -5.1180 -12.9000   9.9408  -2.7031   4.7989  23.9640
+#&gt;    3.3705  -4.7995   2.9122  10.1147  -6.5252 -11.1011  16.6597 -12.7891
+#&gt;    5.2562   6.5468  -9.5708   1.3940  -0.2515  -9.5245 -12.9337  19.8222
+#&gt;  -13.3648   3.8419   3.6211 -19.4950 -16.4319   5.5172   0.6711   7.0542
+#&gt;   10.5676   3.0193 -13.6869 -17.7664   9.5023  12.4828 -11.6906  13.0204
+#&gt; 
+#&gt; Columns 49 to 54  10.4049  -4.3937  -1.2158   1.0199  -1.4881  -1.6759
+#&gt;   -0.5652  -7.3973   0.0691  -5.2152  -3.7536   1.2808
+#&gt;    7.0183   5.8691  -4.2220   4.2102  11.6324   4.5266
+#&gt;    6.6726  -7.8257  -8.2892   5.9208  -1.7354   7.0313
+#&gt;   -0.5905   4.8962  -0.2178   0.9379   3.7152   1.9126
+#&gt;   -6.6017   8.1995  -0.5955  -5.2730  -2.5379   2.6127
+#&gt;   -5.8200  -4.8252   5.8403  -2.0860  -1.0756   1.2638
+#&gt;   11.4338  11.5370  -5.4214   6.2038  -5.4447   4.8322
+#&gt;  -18.9999   2.5189  10.5246  -0.0110   5.5061  -1.5023
+#&gt;    4.4337  -3.9522 -12.7223   7.7476   1.5671   0.0794
+#&gt;  -18.2992  -1.8082  26.3236   1.9072   4.0479   1.4653
+#&gt;   -0.3543  -1.8854   9.7096  -1.0631  -4.9233   3.6277
+#&gt;    4.8155   0.9072  -1.4398  -4.3319   3.4289  -4.0279
+#&gt;   11.1888 -19.1820  -9.4257   0.9476  -2.8059   2.2915
+#&gt;    0.8938  -8.0130  -3.0255  -4.9254  -6.8895   3.7856
+#&gt;    0.6107  -3.4682  -3.0902   8.6203   5.0539  -7.3057
+#&gt;   -6.0682  12.0741   9.0698   2.2445  -7.1664 -12.1935
+#&gt;   -1.4495   0.8049  -3.1648   9.4595  -1.4624   0.4464
+#&gt;    0.0692  -8.6347 -14.3722  -1.6164   5.0661   0.5902
+#&gt;    9.6713   0.1688  -6.0017   1.9842  -6.2445  -7.3938
+#&gt;   21.8469 -12.3380  -8.1051  -1.8002   0.4829   3.4799
+#&gt;  -12.2342  16.0267   3.8473 -11.1799   0.1126  -5.3088
+#&gt;    0.2123 -21.1952   2.5425   9.4203  -1.4694   0.0316
+#&gt;   -3.6046  -4.2468   5.5372  -5.5574   3.6863   3.7841
+#&gt;    1.9404   5.0886   8.4009 -16.4564   0.9253  11.0061
+#&gt;   -6.1988  -1.1603   8.1998  -3.0862  -3.1927  -1.6622
+#&gt;  -14.0336  -7.8097   7.9282   2.5255   2.7384   2.9216
+#&gt;  -10.8568 -11.8154   6.2506   3.0923   1.0809   1.9786
+#&gt;  -28.8676  -2.6244  11.4707   1.6968   0.9274   6.8802
+#&gt;  -18.4692   4.5212  -2.0207   4.3433  -9.6943  -3.0231
+#&gt;    1.0947  -4.4575  -4.9881  -1.8650  -1.7975  -0.3476
+#&gt;    5.9016  -4.6479  -9.4835   8.6000  -5.9623  -2.5017
+#&gt;   -3.7405  -2.3338   1.5422  -2.0316  -3.3168   5.1828
+#&gt; 
+#&gt; (2,.,.) = 
+#&gt;  Columns 1 to 6 -7.7358e+00  2.7064e+00  4.1159e+00  2.3887e+00  9.3367e+00 -1.4353e+01
+#&gt;   8.5316e-01  3.7687e+00 -3.4629e+00  6.8568e+00 -7.2856e-01 -3.5939e-01
+#&gt;   4.5799e+00 -6.9411e+00  2.7158e+00 -5.9324e-01  6.3918e+00  3.3153e+00
+#&gt;   4.7981e+00  8.9044e+00  5.5984e+00  5.0699e+00 -1.4592e+01 -2.0785e+00
+#&gt;  -4.6695e+00 -2.5667e+00  1.3037e+00 -1.0801e+00 -5.1928e+00 -2.5828e+00
+#&gt;  -3.8563e-02 -2.9945e+00 -2.9541e+00  4.8150e+00  7.5176e+00 -5.7202e+00
+#&gt;   1.0594e+01 -1.3108e+01  6.0147e+00  3.7226e+00  1.9505e+00  1.4969e+00
+#&gt;  -7.6931e+00 -4.9361e+00 -2.7434e+00  4.6197e+00  1.1686e+01  9.4690e+00
+#&gt;  -9.7675e-01  4.7890e+00 -6.4019e-01  1.1604e+01 -5.3119e+00  1.1268e+01
+#&gt;   3.5569e+00 -1.6191e+00  1.7762e+00 -3.0177e+00 -1.7011e+01  9.1518e+00
+#&gt;   1.0447e+00 -6.3763e+00 -4.8049e+00  1.2495e+01  7.7488e+00  1.6410e+01
+#&gt;  -7.6849e-01  6.6844e+00 -3.0198e+00  1.0458e+01 -1.2576e+00  1.5203e+00
+#&gt;   4.3539e+00  8.1895e+00 -8.7703e+00  9.5344e+00 -3.5441e+00 -1.4626e+00
+#&gt;   3.7191e-01  4.0707e+00 -3.7622e+00  1.2110e+00 -1.2392e+01 -3.5298e+00
+#&gt;   6.9584e+00  9.4487e+00  2.3289e+00  1.1768e+01 -2.5903e+01 -1.8819e+01
+#&gt;  -2.9398e-02  1.9986e+00 -1.7783e+00 -1.2250e-01  4.4910e+00  2.3489e+00
+#&gt;   4.1574e+00 -1.2356e+01 -1.4853e+01  1.4956e+01  1.0511e+01 -4.9196e-01
+#&gt;   2.0067e+00 -3.8693e+00 -8.2226e+00  1.2391e+01 -1.7505e+00 -5.3938e+00
+#&gt;   2.2505e+00  7.4454e+00 -3.4723e+00 -1.5694e+01  1.0488e+01  7.3954e+00
+#&gt;   7.9690e-02  4.2664e+00  9.6114e+00 -2.6502e+00 -8.2685e+00  4.5040e+00
+#&gt;   2.7958e+00 -7.8079e+00  9.3878e+00 -1.3784e+01  4.6990e+00 -6.3731e+00
+#&gt;   3.5866e+00 -2.8282e+00 -4.5961e+00  1.0287e+00  2.9950e+00 -7.6990e+00
+#&gt;  -3.3657e-01  1.6978e+00 -9.6235e+00  8.0490e-01  4.4091e-01  1.0902e+01
+#&gt;   2.5087e+00  5.0536e+00 -3.8724e+00  1.0526e+01  1.6037e+01 -2.1520e-01
+#&gt;  -6.3437e-01 -1.1003e+00  8.1602e+00 -4.9335e+00 -1.2635e+01  9.8150e+00
+#&gt;  -2.1249e+00  3.7480e+00  7.7365e+00 -6.1236e+00  6.1852e+00 -1.3069e+01
+#&gt;   7.3109e+00  1.2901e+01 -8.8473e+00 -3.7098e+00 -8.9201e+00 -6.5198e+00
+#&gt;  -5.4844e+00 -2.5238e+00 -4.1557e+00 -1.8064e-01  5.5304e-01  6.7819e+00
+#&gt;  -1.9159e+00  1.0311e+01  3.6737e+00 -1.1483e+01 -7.9527e+00 -6.1219e+00
+#&gt;  -1.0295e+01  3.7689e+00 -1.4383e+01  1.2151e+01 -4.4508e+00 -8.2422e+00
+#&gt;   4.2292e+00  3.6646e+00 -1.0031e+01  2.9930e+00  2.1887e+00 -7.5918e+00
+#&gt;   9.7002e-01 -2.7367e+00  7.7447e+00 -1.0757e+01  6.6942e+00 -8.9944e+00
+#&gt;   7.1615e+00  3.2208e+00 -8.1345e+00 -7.2343e+00  1.8508e+00  7.8587e-01
+#&gt; 
+#&gt; Columns 7 to 12  6.1971e-01 -3.5566e+00 -6.0313e+00 -9.2937e+00 -2.9808e+00  6.3143e-01
+#&gt;   6.1591e+00  3.1784e+00 -1.4260e+01 -4.0602e+00  1.9287e+01  4.2227e+00
+#&gt;   4.9860e+00  1.1289e+01  1.4267e+00 -1.4943e+00 -9.9253e+00  2.7712e+00
+#&gt;  -5.2414e+00  1.2068e+01 -8.3227e+00 -5.1654e+00  4.5057e-02 -6.2535e+00
+#&gt;   4.2992e+00 -5.7681e+00  8.0550e+00  7.8340e+00 -7.1266e+00  1.9123e+00
+#&gt;   1.3649e+01 -8.0137e+00 -4.5874e+00  1.6664e+01 -6.1277e+00 -3.3137e+00
+#&gt;  -7.7574e+00  6.1543e+00  7.9361e+00 -3.3967e+00  4.5374e+00 -1.9442e+00
+#&gt;   1.8258e+00  2.5381e+00  4.9820e+00  6.2882e-01 -2.1422e+00 -1.5931e+01
+#&gt;   2.7219e+00 -8.0431e+00  3.3396e+00 -4.2228e+00 -1.0965e+00  3.3755e+00
+#&gt;  -2.8221e+00 -2.8199e+00 -7.3222e+00  2.3526e+00 -1.6953e+00  1.0169e+01
+#&gt;   1.9237e+01 -7.6633e+00 -1.0494e+01 -5.6553e-01 -1.5653e+01 -1.5750e+01
+#&gt;   1.7894e+00 -5.2464e+00 -5.6364e+00  7.6857e-01 -1.0334e+01 -1.7855e+01
+#&gt;   1.8345e+00 -7.4619e+00  6.2523e+00  1.5827e+01 -7.7351e-01 -1.5328e-01
+#&gt;  -5.8410e+00 -7.1343e+00 -4.4032e+00  4.0195e-01  3.6980e+00  6.1992e+00
+#&gt;  -4.7707e-02  1.5093e+01 -1.3516e+01 -4.6621e-01 -1.1089e+01  3.5860e-01
+#&gt;  -1.9751e+01  1.6217e+01  1.2023e+01  2.6548e+00  2.0095e+01 -1.4241e+01
+#&gt;   6.4834e-01  1.0354e+01 -9.9857e+00 -4.7512e+00 -3.9769e-01 -1.2231e+01
+#&gt;  -1.1477e+01  6.8674e+00 -3.0546e+00 -5.0975e+00  2.7640e+00 -7.8965e+00
+#&gt;  -3.2730e+00  7.8723e-01 -4.4877e-02  7.8095e+00  1.6561e+01  4.8170e-01
+#&gt;   1.2527e+00 -8.8599e+00 -1.6237e+00  3.5457e+00 -5.1429e+00 -4.6922e+00
+#&gt;   1.3649e+00  4.6369e+00 -1.6378e+00 -4.3386e+00  7.2234e+00 -7.3588e+00
+#&gt;   9.8566e+00  8.9358e+00 -1.2413e+01 -5.5862e+00 -1.2195e+01  3.5331e-01
+#&gt;  -6.1922e+00  1.8767e+00 -2.6397e+00 -7.5956e+00  4.6306e+00  5.0578e+00
+#&gt;  -1.8053e+00 -9.5046e-01  2.7005e+00  3.5801e+00  4.6201e+00 -1.5657e+00
+#&gt;  -8.2460e+00  6.8687e+00 -1.0745e+01 -1.4851e+01 -6.3045e+00  1.3622e+00
+#&gt;   9.1886e+00 -1.7410e+00 -4.4931e-01 -2.5491e+00 -4.5174e+00  1.1401e+00
+#&gt;  -2.4219e+00  1.1363e+01 -5.0151e+00 -7.2247e+00  3.0972e+00  1.2977e+01
+#&gt;   8.8036e+00  7.9221e+00 -5.6821e+00  5.5599e+00  6.7689e+00  8.8789e+00
+#&gt;   1.2687e+00  1.1066e+01  8.3913e+00 -5.0425e-01  1.0345e+01  1.3019e+01
+#&gt;  -1.8805e+01  4.4200e+00 -3.2719e+00 -3.9947e+00 -8.4469e+00 -6.8574e+00
+#&gt;   1.2520e+00 -1.8530e+00  6.9782e-01 -6.9486e+00 -1.1507e+01  1.4238e+01
+#&gt;   4.3166e+00 -1.6821e+00 -1.5755e+00 -1.6891e+01 -2.0169e+00 -8.6049e+00
+#&gt;   1.1485e+01 -9.6323e-01 -3.6946e+00 -4.7002e+00 -6.9156e+00 -3.2433e+00
+#&gt; 
+#&gt; Columns 13 to 18  7.1991e+00 -3.1020e+00 -1.0215e+01  7.3400e+00 -3.8720e+00 -3.3105e+00
+#&gt;   7.5273e+00 -1.8029e+00  1.1510e+01 -6.4125e+00 -5.0547e+00 -9.4835e+00
+#&gt;   1.0358e+00  9.7088e+00 -2.3843e+00 -1.5570e+01  2.7400e+00 -2.6007e+00
+#&gt;   1.7425e+01 -1.6332e+01  6.0853e+00  6.0137e+00 -3.3011e+00 -1.4179e+00
+#&gt;   9.4562e+00 -1.8261e+01 -7.5022e+00 -7.5939e+00 -7.0870e+00 -7.4055e-01
+#&gt;  -3.9737e+00  1.8817e+00 -8.4407e+00  8.2835e+00 -4.1121e+00  7.4358e+00
+#&gt;   6.4169e+00  1.1986e+00 -1.8904e+01  1.0954e+01 -6.5748e-01 -7.1496e+00
+#&gt;  -1.8261e+00  5.0867e-01 -1.4623e+01 -3.4692e+00 -4.8791e+00 -1.0374e+00
+#&gt;  -5.0341e+00 -1.4850e+01  2.4858e+00  1.1707e+01  5.5279e+00  5.3110e+00
+#&gt;  -2.7140e+00  9.7383e-01  4.0812e+00 -3.3657e+00  6.4222e+00  1.9005e+00
+#&gt;  -1.6522e+01 -1.1063e+01  6.8178e+00 -2.1295e+01  7.5161e+00  3.4128e+00
+#&gt;  -8.0671e+00 -1.9479e+01 -4.5783e-01  1.4737e+00  1.6592e-01  6.2285e+00
+#&gt;   1.0884e+00 -1.0263e+01  1.7437e+01  2.9594e+00 -2.4843e+00  6.6805e+00
+#&gt;   1.6568e+01  8.4857e-01  1.1532e+00 -3.9707e+00  7.1131e+00 -6.0201e+00
+#&gt;   6.9973e+00 -3.9699e+00  2.1605e+00  7.8246e+00  8.5465e+00 -1.2609e+01
+#&gt;  -1.0380e+01  3.2347e+00 -5.6959e+00 -8.4189e-01  6.5300e+00 -2.4554e+01
+#&gt;  -1.1610e+01  4.5740e+00 -3.2509e+00 -1.4320e+01 -2.0172e+00  3.4778e-01
+#&gt;  -1.8972e+00  7.9535e+00 -1.1391e+01  8.0844e-01  9.2623e-01 -7.4139e-01
+#&gt;   3.2082e+00  1.7688e+00  6.5023e+00  6.8089e+00  5.2405e+00 -4.9714e+00
+#&gt;   5.2694e+00 -1.3602e+00  1.5576e+00  1.1241e+01  4.4270e+00 -1.4899e+00
+#&gt;   6.5272e+00 -6.2469e-01  7.4468e-01 -8.0070e+00 -5.7550e+00 -3.5396e-01
+#&gt;  -4.3746e+00  1.2775e+01  7.9490e+00  3.3861e+00  1.5139e+00 -2.3440e+00
+#&gt;   2.9495e+00 -7.8901e+00  4.4748e+00 -5.6787e+00  2.1329e+00 -1.4135e+01
+#&gt;  -1.9857e+00  4.7930e+00  1.0535e+00  1.0709e+00 -3.0928e+00  6.7112e+00
+#&gt;  -6.3406e+00 -3.5523e+00  1.2149e+01 -6.1518e-01  1.1473e+01 -6.0265e+00
+#&gt;  -8.7913e+00  5.0781e+00 -9.3632e-01  1.7481e+01  6.8463e-01  6.5693e+00
+#&gt;  -1.1081e+01 -3.8875e+00 -1.0769e+01 -2.0818e+01  1.7809e+00 -6.9261e-01
+#&gt;   6.7785e+00  9.7152e-01 -7.7956e+00 -1.0056e+01  6.0275e+00  5.2517e+00
+#&gt;   1.1351e-01 -9.0045e+00  9.5697e+00  2.3714e-01 -9.0294e+00 -2.9469e+00
+#&gt;   1.3735e+01  3.8854e+00 -2.9617e+00  1.0569e+00  1.4450e+01  1.2248e+01
+#&gt;  -2.5803e+00  5.4637e+00  1.1467e+01 -4.1106e+00  5.5802e+00 -3.3711e+00
+#&gt;  -5.2019e+00  4.9950e+00 -1.9193e+01  1.9517e+01 -4.0764e+00  1.0695e+01
+#&gt;  -9.5082e+00  1.0037e+01 -1.1583e+01 -8.1655e+00 -1.2235e+01  1.6803e+01
+#&gt; 
+#&gt; Columns 19 to 24  2.2936e-01 -1.6888e+01  1.3698e+01 -6.3099e+00  9.9215e-02 -2.9322e+00
+#&gt;   1.9769e+00  3.4036e+00 -7.5839e+00 -7.6613e+00  1.7599e-02  1.2105e+00
+#&gt;  -1.8907e+01 -2.0247e+00  3.6977e+00 -5.8704e+00 -6.2005e+00  1.5775e+01
+#&gt;   6.5952e+00  7.7901e+00 -1.2322e+01  7.3395e-01 -9.5554e+00 -4.4870e+00
+#&gt;  -1.4227e+01 -2.1848e-01  2.5902e+00 -5.1566e+00 -2.3201e+01 -2.9847e+00
+#&gt;   8.3254e+00 -2.6919e+00 -7.2369e+00 -2.7206e-01 -3.8869e-01 -1.1716e+00
+#&gt;   4.6815e+00 -1.1390e-01 -3.7367e+00  3.8517e+00 -1.4061e+00 -6.6726e+00
+#&gt;  -7.7048e+00  2.2018e+00  5.9940e+00  6.2055e+00  8.6355e+00  4.7104e+00
+#&gt;  -4.9984e+00  1.8139e+00  8.1921e+00 -6.0335e+00 -5.7494e+00 -1.3691e+00
+#&gt;   3.3706e+00 -1.1477e+01  1.1708e-01 -1.2528e+01 -4.0107e+00  6.8041e+00
+#&gt;   2.3757e+01 -7.6485e+00  7.0026e+00 -1.4405e+01  1.2900e-01  6.8069e+00
+#&gt;   1.7010e+00  1.7767e+01 -9.5315e+00 -3.5809e+00 -8.8127e+00 -5.7658e+00
+#&gt;   6.8095e+00  4.8048e+00  7.5958e+00 -1.7835e+00 -5.2495e+00  1.0999e+01
+#&gt;  -1.2062e+00 -1.4216e+01  1.6161e+00 -4.8818e+00  9.8680e+00 -4.7297e+00
+#&gt;   4.6286e+00 -2.3997e+00  9.1565e+00 -1.8091e+01 -1.8798e+01 -1.7613e+00
+#&gt;  -3.3277e+00 -9.2900e+00  6.0133e+00  3.0082e+00 -2.0833e+00 -7.1763e+00
+#&gt;   4.2549e+00 -8.8263e+00 -7.8801e+00  3.9957e-01 -3.5176e+00 -1.8507e+00
+#&gt;  -4.9498e+00  1.5408e+01 -8.1279e+00 -7.4771e+00  2.3541e+00  5.4949e+00
+#&gt;   4.1214e+00 -1.2128e+00  1.2296e+01  7.6001e+00  1.4415e+01 -1.6055e+00
+#&gt;  -4.9501e+00  1.3041e+01 -4.9621e-01  1.8703e+01 -1.8289e+01  1.0630e+01
+#&gt;   1.0798e+00  1.5102e+00 -2.2588e+01 -1.9352e+00  1.3287e+01 -4.5660e+00
+#&gt;   1.4408e+01 -7.8796e+00 -1.0958e+01  3.5238e+00  1.0787e+00  1.1946e+01
+#&gt;  -2.9766e+00 -3.4401e+00  2.6354e+00 -1.8404e+01 -8.8099e+00  8.9852e+00
+#&gt;   1.6289e+01 -6.3441e+00  1.6091e+01  1.5075e+00  1.1968e+01 -2.3290e+00
+#&gt;   5.2946e+00  6.6794e+00 -3.5291e+00  2.2196e-01 -2.1849e+00 -7.8252e+00
+#&gt;  -3.6348e+00  1.4114e+01 -2.7772e+00  8.4282e+00 -1.0159e+01  1.4503e+01
+#&gt;  -7.9663e+00 -1.0334e+00 -3.8956e+00 -9.7743e+00 -7.9716e+00  2.0617e+00
+#&gt;  -2.1056e+00 -1.1841e+01  4.4612e+00  1.1785e+01  9.6248e+00 -2.4593e+00
+#&gt;  -7.4521e+00 -2.6863e+00  6.6458e+00  8.8731e+00 -2.6243e+00 -7.9622e+00
+#&gt;  -1.4993e+01 -7.1811e+00 -1.8982e+00  1.7245e+01 -1.9409e+01 -7.2242e-01
+#&gt;   7.3318e+00 -1.8824e+00  4.5499e+00 -7.1541e+00  1.9782e+00 -3.5518e+00
+#&gt;  -4.1648e+00  1.9168e+01 -1.1092e+01 -1.5164e+01 -3.6274e+00  1.6950e+00
+#&gt;   2.6833e+01 -1.0341e+01 -1.7529e+01 -1.1678e+01 -3.8251e+00 -1.9653e+01
+#&gt; 
+#&gt; Columns 25 to 30  8.9089e+00 -2.2109e+01  1.4243e+01 -8.6281e+00  4.8957e+00  6.5300e+00
+#&gt;  -1.6790e+01  2.2822e+00  1.2295e+01 -1.3033e+01 -3.7050e+00  1.8875e+01
+#&gt;  -1.0381e+01 -3.2040e-01 -7.3908e+00  1.1772e+01  4.1685e+00 -1.2452e+01
+#&gt;  -1.0310e+01  4.5585e+00 -1.0004e+01  1.9990e+00  5.6939e+00  2.0298e+00
+#&gt;  -1.0004e+01  1.0409e+00 -1.3124e+01 -2.0799e+00 -7.1760e+00  7.3562e+00
+#&gt;  -6.0570e+00 -5.3023e+00 -2.1541e+01  1.9820e+00  7.8665e+00  6.3329e+00
+#&gt;   6.2140e+00  3.1022e+01 -1.2335e+01 -1.1288e+01  1.1501e+01 -4.5731e-01
+#&gt;  -1.8300e+00 -1.7596e+01  4.1389e-01 -1.1390e+01  2.6726e+00  2.1959e+00
+#&gt;  -1.4649e+00 -8.5470e-01  5.2110e+00  1.0870e+01 -5.0161e+00 -5.2889e+00
+#&gt;  -8.2089e+00 -8.4885e+00 -7.0544e+00  1.2611e+00  3.7091e+00  6.7700e+00
+#&gt;   7.2513e+00 -5.8998e+00 -8.3026e+00 -3.4304e+00  1.7832e+01 -1.1176e+01
+#&gt;  -9.1592e+00 -8.7092e-01 -1.0351e+01 -1.0681e+01 -1.2339e+01  6.6825e+00
+#&gt;  -6.7008e-01 -4.8440e+00  2.3226e-01 -2.9946e-02  5.6478e+00  1.3199e+01
+#&gt;  -1.2542e+01 -5.9341e+00  1.2123e+01  1.3029e+01 -1.2662e+01 -4.4325e+00
+#&gt;  -6.6169e+00  1.6597e+00 -3.5043e+00 -1.1193e+01  8.6377e+00 -5.4953e+00
+#&gt;  -5.0659e+00  2.7551e-01  1.3744e+01 -3.0966e+00  1.8892e+00 -4.4114e+00
+#&gt;  -8.6836e+00 -2.4412e+00 -1.4585e+01  4.2378e+00 -6.7566e+00 -8.1642e+00
+#&gt;  -1.2766e+01 -6.4137e+00 -8.1060e+00  4.6149e+00 -9.7195e+00 -7.7202e+00
+#&gt;  -1.4313e+01 -6.9195e+00  1.1693e+01 -1.4572e+00 -3.4242e+00 -1.8018e+00
+#&gt;  -1.0537e+01  1.6098e-01 -1.2272e+01  2.7677e+00 -8.9725e+00 -6.2632e+00
+#&gt;  -6.2350e+00  6.9955e+00  4.7859e+00 -2.2685e+00  6.8022e+00  2.9070e+00
+#&gt;   4.5809e-01 -8.3880e+00 -1.9776e+01  5.2140e+00  3.7813e+00 -6.2155e+00
+#&gt;   3.0042e+00  1.4669e+01  6.6397e+00 -1.2993e+01  6.9356e+00  2.2650e+00
+#&gt;   1.1576e+01 -1.2529e+01  6.9593e+00  1.8562e+01  2.0904e+01  9.1805e+00
+#&gt;   3.0764e+00  1.5342e+00  1.4406e+00  2.7671e-01  8.5106e+00  1.1056e+00
+#&gt;   1.0280e+00  4.0112e+00  3.4976e+00 -3.5274e-02 -5.2699e+00 -3.1552e+00
+#&gt;   1.1858e+01 -4.7703e+00  5.7537e+00  2.1920e+01  5.4029e+00  5.0997e+00
+#&gt;   1.2290e+01  6.4362e+00  4.0488e+00  1.4499e+01 -6.0501e+00 -3.2366e+00
+#&gt;   6.2689e+00  7.3161e-01 -1.2452e+00  1.4861e+01 -8.7672e+00 -1.1689e+01
+#&gt;   1.1985e+01 -1.7174e+01 -1.0914e+01  7.4671e+00 -7.0140e+00 -3.4464e+00
+#&gt;  -7.8820e+00  3.4978e+00  3.0521e+00 -1.5181e+01  1.3071e+01 -1.4274e+01
+#&gt;   1.9783e-01 -1.6555e-01 -2.6493e+00  1.3774e+01  4.8866e-01 -1.0697e+01
+#&gt;   7.7743e+00 -1.2427e+01 -5.4104e+00 -1.0587e+01  1.4412e+01  5.0859e+00
+#&gt; 
+#&gt; Columns 31 to 36 -1.1357e+01  1.1981e+01  9.9716e+00  1.1857e+01 -1.8115e+00 -1.4695e+01
+#&gt;   7.3422e+00  1.5548e+01  3.2082e+00  1.0147e+01  1.4100e+01  1.4162e+01
+#&gt;   3.0844e+00 -7.3096e+00  6.9814e+00  2.2799e+00  5.8196e-01 -4.6313e+00
+#&gt;   7.6082e-02 -9.9290e+00 -1.0213e+01 -8.2199e+00 -3.0799e+00 -8.5401e-01
+#&gt;  -3.4770e+00  1.3473e+00  1.4197e+01 -1.0314e-01  9.3214e+00 -4.5633e-01
+#&gt;   1.0109e+01 -1.0152e+01  5.8044e+00  1.2882e+01 -4.3226e+00  1.0007e+01
+#&gt;   7.5746e-01  2.2464e+00  1.1969e+01 -8.5846e-01  1.3138e+01  9.1428e+00
+#&gt;  -4.1221e+00 -2.3758e+00 -6.9433e+00 -7.5041e+00 -6.2797e+00 -4.0670e+00
+#&gt;  -5.4838e+00  5.2215e+00  1.3257e+01  1.2602e+01  8.5995e+00  1.5516e+01
+#&gt;   1.5377e+01  7.5229e-01  2.2312e+00  3.4551e-01  1.2492e+00  5.7592e+00
+#&gt;   9.3748e+00 -4.4612e+00 -1.1006e+01 -2.0006e+00  1.4334e-01 -1.1820e+01
+#&gt;   1.6497e+01 -8.6081e+00 -6.0907e+00  5.8540e-01 -2.5686e+00  2.6386e+00
+#&gt;  -9.0482e+00  1.9906e+01  2.4323e+00  2.7765e+00  2.0396e+00  8.2006e+00
+#&gt;  -3.8722e+00 -8.3080e+00 -2.0545e+01  3.4558e+00 -2.0635e-01 -3.7895e+00
+#&gt;   9.1895e+00 -2.5371e+00  8.2614e+00  4.9813e-01 -5.2952e+00  3.0628e+00
+#&gt;   1.1221e+01  8.7408e+00  1.2788e+01 -4.7302e+00  2.2632e+00  7.0985e+00
+#&gt;  -7.5462e-03  2.7481e+00 -8.8928e-01  1.5583e+00  1.2498e+00  1.1898e+01
+#&gt;  -2.0618e+00 -1.0152e+00  7.9617e-01 -8.6114e+00 -6.5780e+00  2.5092e+00
+#&gt;  -5.6837e+00  3.7266e-01  1.2264e+01 -5.9856e+00  2.4086e+00  1.5265e+00
+#&gt;   9.8641e-01  1.1195e+01 -9.3773e+00 -6.0877e+00 -2.7005e+00 -9.2722e+00
+#&gt;  -5.5643e+00  2.6269e+00  5.6519e+00  2.0768e+01  1.3134e+01 -4.0507e+00
+#&gt;   6.6763e+00 -1.2067e+01  8.3925e+00  4.6361e+00  4.4979e+00  4.3787e-01
+#&gt;  -4.4566e+00  4.6770e+00  7.7471e+00  5.9833e+00 -3.9309e+00 -1.0741e+01
+#&gt;  -1.5834e+01  3.2567e+00  2.3690e-01  9.5044e+00 -1.1210e+00 -1.2841e-02
+#&gt;  -2.4358e+00 -5.8075e-01 -5.3243e+00  1.2629e+01  8.5652e+00  1.7653e+01
+#&gt;   9.1133e+00 -1.1740e+01 -3.6817e-01 -5.1786e+00 -1.1297e+01  4.8665e+00
+#&gt;   8.5231e-01 -1.4404e+01  2.4547e+00  1.9263e+01  3.3342e+00  9.9445e-01
+#&gt;  -6.6792e-01  3.1188e+00 -9.2717e+00  1.3892e+00 -1.7736e+00  1.7867e+00
+#&gt;  -3.4408e+00  7.4662e+00  1.0113e+01 -1.6295e+00 -3.7887e+00 -6.8620e+00
+#&gt;  -5.6302e+00  8.0703e+00  6.8752e-01  1.4518e+01 -9.6824e-01  1.1248e+01
+#&gt;  -1.8732e+01 -4.1066e+00  1.1552e+01  1.5887e+00 -3.4372e+00 -3.8972e+00
+#&gt;   6.2236e+00 -1.1276e+01 -2.0439e+00 -3.1262e+00  1.2531e+00  2.0769e+00
+#&gt;   1.0418e+01  5.4322e+00  1.2515e+00  9.2656e+00  2.2542e+01  1.0728e+01
+#&gt; 
+#&gt; Columns 37 to 42  6.0988e+00 -8.0439e-01  1.8655e+00 -5.1543e+00 -1.9880e+01 -1.4004e+01
+#&gt;  -1.9940e-01 -6.6575e+00  1.6445e+01 -1.5268e+01 -1.3676e+01  2.8681e+00
+#&gt;  -6.7479e+00 -9.1959e+00 -3.7102e+00  1.6594e+01  5.5031e-01  5.4496e+00
+#&gt;  -6.0228e+00  2.1354e-01  8.0222e+00 -6.6496e-02 -6.0220e+00  1.3493e+01
+#&gt;   2.6637e+01  3.2170e+00  7.4214e+00  2.3912e+01 -1.2703e+01  4.4444e+00
+#&gt;   9.1571e+00  1.4790e+00  9.4493e-01  2.1464e+01 -9.7413e+00  6.8209e+00
+#&gt;  -6.4138e+00 -1.7047e+01 -2.1116e+00 -5.2546e+00 -7.3529e+00 -9.4311e+00
+#&gt;  -7.6436e+00  1.5305e-01 -3.3620e+00  1.1230e+01  3.0282e-01 -9.2001e+00
+#&gt;   6.4844e+00  9.1075e+00  5.0587e+00 -8.0096e+00 -4.7839e+00  9.3991e-01
+#&gt;  -1.8610e+00  1.5192e+01 -8.9527e+00  2.0508e+00  6.9849e-01  1.4954e+01
+#&gt;  -1.0395e+00 -3.6823e+00 -7.6426e-01 -6.1870e+00  1.3009e+01  1.7793e+01
+#&gt;  -1.3253e+01 -1.1113e+01  3.0313e+00  7.1198e+00 -2.9371e+00  8.8453e+00
+#&gt;   8.1860e+00 -1.3867e+01  6.3761e+00 -1.4327e+01 -6.9700e-01  1.3793e+01
+#&gt;  -1.2574e+00 -1.6937e+00 -3.5716e+00 -1.3432e+01  2.9394e+00  4.8733e+00
+#&gt;   1.1381e+01  2.4025e+01 -4.6198e+00  5.5551e+00 -9.6417e+00 -1.0214e+01
+#&gt;  -1.2939e+00  4.9727e+00  7.6788e+00 -3.4079e+00  5.5547e+00 -7.1482e+00
+#&gt;   1.2859e+01  6.2153e+00  4.7319e-01  4.6990e+00 -5.7847e+00 -1.0949e+01
+#&gt;  -2.2701e+01  1.3047e+01  5.7216e+00 -7.4328e+00 -1.2135e+01  3.5048e-01
+#&gt;   1.4917e+01 -8.3515e+00 -1.0262e+01  8.2380e-01  6.2886e+00 -9.5574e-02
+#&gt;  -6.3037e+00 -4.2987e-04 -2.0615e+01  2.1270e+01  1.2580e+00  1.0511e+01
+#&gt;  -6.2005e+00 -3.6623e+00  4.5152e+00 -9.2875e+00 -3.2735e+00  6.2376e+00
+#&gt;  -8.5251e+00  5.6726e+00 -1.0867e+01  3.3117e+00  1.0386e+01 -4.3640e+00
+#&gt;   2.3705e+00 -8.4754e+00  7.1369e-01  1.0543e+01  3.1853e+00  8.7148e+00
+#&gt;  -1.0430e+01 -2.7178e+01  5.6021e+00  6.4076e-01  8.2638e+00  1.2219e-01
+#&gt;   1.0713e+00  7.1091e+00 -6.5412e+00 -1.4832e+00  1.4617e+01  8.5215e+00
+#&gt;  -5.7575e+00  5.8882e+00  6.2337e+00  7.9177e+00 -2.3209e+00 -1.0861e+01
+#&gt;  -7.8439e+00 -3.4696e+00 -4.0875e+00 -9.2443e+00 -4.3091e+00 -7.2878e+00
+#&gt;  -1.6487e+00  6.6453e+00 -6.2274e+00  1.2108e+01  7.4702e+00  3.9402e+00
+#&gt;   1.1521e+01  5.7844e+00  1.4997e+01 -6.6601e+00 -7.1250e+00 -5.4158e-02
+#&gt;   1.6169e+01  1.8308e+01 -9.9830e+00  1.2630e+01 -5.8414e+00 -1.6383e+01
+#&gt;   3.4601e+00  4.3866e+00  3.0461e+00  5.2795e+00 -1.1307e-01 -7.0420e-02
+#&gt;  -6.7810e+00 -9.2056e+00 -6.5165e+00  1.7744e+01  6.2753e+00  1.7646e+00
+#&gt;   2.3100e+00 -7.1944e+00 -9.5489e+00 -7.0821e+00 -4.1903e+00  9.0515e+00
+#&gt; 
+#&gt; Columns 43 to 48  1.8651e+01  5.8580e+00 -2.5540e+00  5.9798e+00  1.3616e+01  5.9374e+00
+#&gt;   1.4042e+00 -7.1437e+00  5.3403e+00  4.8087e+00 -4.3136e+00 -4.4749e+00
+#&gt;   4.2473e+00 -3.3378e+00 -5.9252e+00  8.7385e+00 -2.1737e+00  5.5311e+00
+#&gt;  -7.1610e+00  1.5589e+01 -8.4832e+00 -1.0002e+01  8.0409e+00 -1.4458e+01
+#&gt;   9.0307e+00  7.9130e+00 -1.3612e+01  3.4962e-01  8.6784e+00 -4.7658e+00
+#&gt;  -3.8549e+00  2.0299e-01 -7.7250e+00 -4.7844e+00 -1.3173e+01  2.1174e+01
+#&gt;  -9.5103e+00  6.2254e+00  1.5409e+01 -2.4048e+00  8.4910e+00 -7.8213e+00
+#&gt;   1.4605e+01 -1.4826e+01  4.2888e+00 -1.0561e+01 -5.2321e+00 -1.0529e+00
+#&gt;  -7.9933e-01 -3.1178e-02  5.4608e-01  6.5385e+00  9.7176e+00  2.0845e+01
+#&gt;  -5.3888e+00  2.0419e+01 -2.5235e+01  4.9453e-01  4.0366e-01  1.4562e+01
+#&gt;  -1.5129e+00 -1.3035e+01  5.7489e+00  3.9341e+00 -7.8148e+00  2.0222e+01
+#&gt;   1.7552e+01  3.7504e+00  9.3178e+00 -3.3478e+00 -1.0304e+01  1.3753e+00
+#&gt;   1.0735e+01 -9.2614e+00 -4.0927e+00  1.0137e+01  6.5703e-01  6.3034e+00
+#&gt;  -1.8100e+01 -4.4140e-01 -3.2765e+00  1.1385e+01  8.2657e-01 -9.7269e+00
+#&gt;   7.4825e+00  3.7591e+00 -9.3485e+00 -2.8601e+00 -5.7009e+00 -1.0053e+01
+#&gt;   2.8396e-01 -1.3266e+01 -1.0565e+01  4.4111e+00  1.4520e+01 -1.1182e+01
+#&gt;  -1.6583e+01  1.5507e+00 -1.2863e+01  9.1985e+00  1.0691e+01  2.8953e+00
+#&gt;  -1.2593e+01 -1.3589e-01 -9.3040e+00  1.3394e+01 -1.0354e+01  1.3999e+01
+#&gt;   8.2749e+00  7.5436e+00  1.7821e-01  1.1863e+01  6.5062e+00 -5.8205e+00
+#&gt;  -7.1312e+00  1.1181e+01  3.9970e+00 -1.1016e+01  8.4926e+00 -1.1297e+01
+#&gt;  -1.1315e+01 -2.8348e+00  9.9462e+00 -1.4857e+00 -2.0638e+00  6.0903e+00
+#&gt;   1.4766e+01  1.5735e+01  6.4633e+00 -1.0304e+01  1.8137e+01  1.3001e+01
+#&gt;   2.0535e+00  8.0652e+00 -6.2877e+00  1.0530e+01 -3.1022e+00 -1.1258e+01
+#&gt;   1.7652e+01  1.0152e+01 -2.7717e+00  9.9863e+00  1.9430e+01  1.6897e+00
+#&gt;  -3.1389e+00  1.5090e+01  5.4868e+00 -1.5961e+00 -1.1954e+01  2.6257e+00
+#&gt;   2.6598e+00  4.5923e-02  1.9518e+00  3.6742e+00  4.0554e+00 -4.1224e+00
+#&gt;   5.6864e+00  7.4536e-01  1.2337e+00 -9.2960e+00  6.0701e+00  6.4188e+00
+#&gt;  -3.7751e+00  8.2547e+00 -1.3889e+01  8.6632e+00 -1.6368e+01 -8.1808e+00
+#&gt;  -1.3147e+01  1.0182e+01 -9.7701e+00 -6.5206e+00 -1.8821e+00 -3.4522e+00
+#&gt;   3.7844e+00  1.0395e+01 -9.9069e-01 -8.6665e+00  9.4882e+00 -1.9181e-02
+#&gt;  -2.2905e+00 -4.3181e+00  6.0473e+00  7.4193e+00 -1.3721e+01 -1.1281e+00
+#&gt;   5.2884e+00  1.1093e+00  9.2624e+00 -6.7758e-01  1.8620e+00 -6.4113e+00
+#&gt;   8.1688e+00  9.3747e+00  4.6817e+00 -1.0918e+01  1.2850e+01  2.5548e+01
+#&gt; 
+#&gt; Columns 49 to 54 -7.9121e-02 -9.1412e+00 -1.0781e+00  8.9441e-02 -2.5786e+00 -2.5158e-01
+#&gt;   1.3240e+01 -2.2763e+00  4.5066e+00  3.8285e+00  6.0378e+00  4.7827e+00
+#&gt;  -3.1463e+00  2.6070e+00 -8.4117e+00  1.5027e+00  8.7442e+00  4.2795e+00
+#&gt;   1.3520e+00 -5.2432e+00 -2.7057e+00 -9.7851e-01 -8.7705e-01 -3.2616e-01
+#&gt;   5.5362e+00  1.0861e+01  7.3578e+00  1.9651e+00 -1.9482e+00  3.4265e+00
+#&gt;  -1.0524e+01  5.4771e+00  8.4625e+00 -8.9160e-01  3.9857e+00  3.4231e+00
+#&gt;   1.0566e+01  9.0016e+00 -1.3289e+01 -4.3572e+00  1.4488e+00  2.0633e-01
+#&gt;  -1.1994e+00 -1.0704e+01  3.1709e+00 -1.4812e+00  1.5413e+00  5.3184e-01
+#&gt;   8.8366e+00  9.7180e+00  1.0314e+01  1.0961e+01  3.0913e+00  1.0606e-01
+#&gt;  -1.3399e+01  2.8587e+00 -1.0157e+01 -5.0444e-01 -4.7002e+00 -6.9904e-01
+#&gt;   5.0243e+00  2.1031e+00  2.2536e+00  3.3043e+00  3.4988e+00 -1.4744e+00
+#&gt;  -4.8099e+00 -1.8123e+00  5.6880e+00 -5.0763e-01  5.4416e+00 -2.5526e+00
+#&gt;  -4.8819e+00 -1.3431e+00  6.0133e-01  2.1635e+00 -3.5245e+00  2.3077e+00
+#&gt;  -3.7879e+00 -5.3912e+00 -4.9927e+00  7.2770e-01  6.8276e+00 -2.4188e+00
+#&gt;   1.3457e+01  1.4833e+01  2.3735e+00  7.1478e-01 -1.3741e+00 -1.6697e+00
+#&gt;   3.7287e+00  1.2958e+01 -2.6546e-02  9.0811e+00  1.4764e+00 -5.4536e+00
+#&gt;  -7.3302e+00  1.3150e+00 -7.3113e-01  1.1774e+01  4.5585e+00  2.0654e-01
+#&gt;  -7.4635e+00 -3.2945e+00 -1.6004e+01  1.0148e+00  4.5388e+00 -2.1846e+00
+#&gt;   4.0827e+00  4.6811e+00  1.1506e+00  8.8552e-01 -2.1057e+00  2.4387e+00
+#&gt;  -6.3624e+00 -9.1315e+00 -1.6520e-02  4.3562e-01 -4.4460e+00 -1.1886e+00
+#&gt;   5.0401e+00  5.8519e+00  9.1493e+00 -4.4009e+00  6.4286e+00 -1.5992e+00
+#&gt;   2.8538e-01 -9.3507e-02 -3.7667e+00  4.3281e+00  2.3204e+00 -3.5145e-01
+#&gt;   1.6218e+00  5.5460e+00 -7.3334e+00 -5.5465e+00 -5.7455e+00  3.0386e+00
+#&gt;  -2.5979e+00  4.6217e-01 -3.6948e+00 -9.4825e-01  4.1991e+00  2.1228e+00
+#&gt;   7.4229e+00  3.5157e+00  2.8468e+00 -3.2869e+00  3.3928e+00  1.8340e+00
+#&gt;   8.2374e+00  2.4794e+00 -7.6380e-01 -7.9588e+00 -4.9124e+00 -1.4156e+00
+#&gt;  -1.3288e+01 -2.0089e+00 -6.0241e+00  1.8306e+00  3.4282e+00 -1.2858e+00
+#&gt;  -1.3545e+01  1.6046e+00 -1.0394e+00  2.9090e-01  1.8811e+00  5.5214e-01
+#&gt;   2.4362e+00 -1.8237e+00  1.6450e+00  3.1722e+00  4.5288e+00  5.4734e+00
+#&gt;  -1.0636e+00 -3.5529e+00  3.3539e+00  2.2426e+00 -4.1244e-01  1.8183e+00
+#&gt;   1.4988e+00  2.4386e+00  2.3093e+00  1.1566e+00  4.6186e-01  1.3810e+00
+#&gt;   1.6040e+01 -9.8615e+00 -1.0179e+01 -8.8022e+00 -3.5965e+00 -2.8926e+00
+#&gt;   4.9511e-01  4.6599e+00 -1.6281e+00  3.8184e+00  5.6074e+00 -5.0963e-01
+#&gt; 
+#&gt; (3,.,.) = 
+#&gt;  Columns 1 to 8   3.0031  -1.1713   1.7735  -9.0906  -7.7523   1.6255  -8.1038   0.4803
+#&gt;   -1.4395   3.3060  -4.9207   0.5426 -26.5450  -9.1573   1.2341   2.6821
+#&gt;   -1.6181  -4.7474   6.6856  -8.8715  -6.7656  -9.3924  18.3535  16.4979
+#&gt;    2.0125  10.5115   0.4946   1.4673   1.5072  12.2754  -7.2831   1.1050
+#&gt;   -1.0779  -1.3903  -5.9078  -2.7975 -10.6282  -1.9355 -28.0926  -8.5147
+#&gt;    1.3459   1.0233   4.4565  -3.9460  11.7021 -22.0011   3.7328   2.8262
+#&gt;   -7.9638  -3.2818  11.9780  -3.6959   1.7928  -2.5773   2.7235   0.0797
+#&gt;   -5.8846   1.2887  -4.5946  -7.9901 -17.1901  -6.6256  16.0995   3.1597
+#&gt;   -0.3332  -5.5103  -9.8488  -3.2059   0.0323  -9.4431 -18.4452   5.7317
+#&gt;    5.8413  -0.3776   3.9603 -10.9460  -8.4634  -3.9871 -12.5948  -3.5386
+#&gt;    4.0921  -6.9083  -7.3737  -5.5127   1.0160 -10.7198  -0.8637  15.9982
+#&gt;    4.4676   6.3974  11.2690  20.4401   8.4547  -0.3921   2.8117   5.6110
+#&gt;   -0.5407   3.5666  -0.3481  -1.5658  -5.2985  -3.3953  -7.0007  15.7980
+#&gt;    3.4430   1.8497 -11.5248   7.5493  -7.5274   3.9757  13.4175   1.6013
+#&gt;    3.4110   3.6490   7.0635  -6.0675   4.6773  -5.6185 -17.0664  -5.3096
+#&gt;   -5.1638  -3.6366   0.1630  -7.2286   0.7935   8.5049  -1.6619  -2.8189
+#&gt;   -2.0293 -11.2180  -3.9819 -13.4532   7.4374 -11.3310 -10.2651   7.9634
+#&gt;    0.4887   9.5984   6.3274   3.2128   7.1249   7.1410  -0.4603   1.0461
+#&gt;    0.6471   0.3731  -1.7175   8.2021   5.7231   3.8515  -1.8011  -4.4598
+#&gt;    1.6338   3.5299   2.0626   4.3411   2.0501  -2.9471  -5.0114   3.5891
+#&gt;   -6.8317   2.1409  -0.3158  -7.3118  -9.3937  14.5959  15.3571  -1.8564
+#&gt;    0.8886   1.4641   0.5028 -12.2130  -5.3902 -10.1844  11.3079  10.3780
+#&gt;   -3.0151  -6.1362   3.1912  -2.0550  -4.8657   0.0678 -20.8676  13.3277
+#&gt;    5.0685  -0.4177   1.6027  -5.8127 -10.5129  -8.3065  -5.8800  -2.2088
+#&gt;    5.2699  -5.7045  -5.1716   6.8226 -15.0316  -5.0530   0.6497 -12.4067
+#&gt;   -1.5064  -1.0875  -1.5125  -5.5702  -3.2429  -2.1734 -12.1101  -1.8075
+#&gt;    2.8814  -4.0411  -7.4772   1.5799  -8.6595  -7.5146   2.7112   4.5064
+#&gt;    1.8012  -6.0426  -9.7752   2.7970  -1.7619  -5.3261  -3.2915   4.0403
+#&gt;    1.3697   6.3530  -4.4796  10.4673   0.5710 -10.7031  -6.3763   1.9706
+#&gt;   -1.2342   1.2121 -13.5034   2.4156 -13.5557 -21.2035 -15.4164  -4.9243
+#&gt;    4.0965  -2.9095   4.9796  -3.4179   8.6316   2.9157 -10.0892  -8.3838
+#&gt;   -5.2568   2.4127  -7.6201 -11.9952  -0.9887 -11.3262   8.5635   5.2253
+#&gt;    7.8430   2.1430   5.2939  -7.9930  -0.9956  -8.6677   3.6763 -15.1486
+#&gt; 
+#&gt; Columns 9 to 16   4.6593   6.7269   6.6702   7.3907  -7.8672  -4.0630  12.8214  -2.5460
+#&gt;   -0.0965  -5.4140   0.5344   5.3575  -7.3039   0.7799   0.9683  -3.1604
+#&gt;    0.3504   0.1819  -1.6127   9.8124  32.5733   3.7362  -8.9751  -4.3400
+#&gt;    2.6242  -8.9156  -0.2250   1.4677   0.5044   3.6535   2.1986   7.7236
+#&gt;   -1.3133  10.9565  -1.7728  17.1786 -19.9343   9.0192   8.5705  -6.0406
+#&gt;    8.5221  -6.1071   0.2244  -8.0260  -2.7828   3.6833   0.7928   7.3192
+#&gt;   15.0199 -16.7423   6.0564  -5.2131   2.0064  14.0828 -14.1567   1.9929
+#&gt;    2.3899  -3.1128  -5.2550   3.8801  17.2259  13.7856   8.9296  -0.2724
+#&gt;   -5.8980  -4.0879  -1.9533   0.1919  -4.3493   6.1849  10.4726   7.2466
+#&gt;   -3.7371   1.9459  -1.1663   1.6162  -2.5395   0.5327  -0.2901  -3.8612
+#&gt;    2.2095   6.9287 -12.3987   7.1235   4.1101   9.2574  -5.3279  -2.2198
+#&gt;   -7.3193   7.8056   3.6111  -6.5194   6.3612  10.7283   4.3855  11.0293
+#&gt;    2.1896  -5.5781 -19.1569   0.7391  -6.5319   9.0854  -7.2169  -9.5070
+#&gt;   -6.4358  -2.3173  -6.2730   4.3687   0.7691  -6.1351   0.5836  -1.7487
+#&gt;  -18.7487   3.9496   5.3738   0.0837   6.9914 -12.1602  -4.9256   6.7643
+#&gt;   -3.4495 -10.0417  12.7282   7.4786   2.5694  -8.6762  -0.5433   0.1506
+#&gt;   12.5367  -4.5606   8.8003   9.8323  -8.9455 -14.3511   0.7297  22.4463
+#&gt;  -13.3844   5.5442   6.8929  19.5213   3.6610  -9.9020  -5.0455  12.4286
+#&gt;   -4.0541  -8.0233  -8.1308  -1.7781  -2.0019   6.3873   8.4276   2.1452
+#&gt;   -5.3457   2.0029  -2.7451   7.9457  -5.2178   2.0413   6.7447 -14.8059
+#&gt;    5.7557   1.3620   1.3234  17.9600  13.0151   2.1660  -8.7398   3.9648
+#&gt;  -10.6699  -8.9068  -6.6008   3.4348  21.5199  -1.6352   5.8334  11.9984
+#&gt;    5.4020   4.2524   9.1617  12.0283  -8.3139   2.7388 -14.3782  -6.9844
+#&gt;   -8.5022 -32.2319  -7.4838 -10.9457   4.3391  -4.9729  -9.7857   8.1494
+#&gt;  -11.2569  -1.4706 -11.1208 -12.0966  11.7453   8.9875   5.0079  -7.2054
+#&gt;  -19.4426  -4.2547  15.5110  -7.6708  -4.6302  -1.4468   5.8384  -4.6624
+#&gt;    0.7718  -2.8727  -7.0337  -3.5872  18.4001 -19.5692 -16.5489   7.8874
+#&gt;   -7.3169   9.3362   1.5757 -19.8433   2.6673  -4.9955   9.7552 -10.4626
+#&gt;   -1.2032  -3.5806   4.6284  -4.4536  -7.2250  -2.1550  11.7586   6.2467
+#&gt;   -0.7258   3.4492 -22.7294 -15.0464   0.7035  -2.5275  18.2421   8.7096
+#&gt;   -9.1511  -5.0277   6.5897  20.7037  -0.6972  -2.7146  -6.0418   8.4277
+#&gt;    8.5743  -4.4994  -6.9698   8.8198  18.6992   0.6420 -12.9564   2.3944
+#&gt;   14.5578  -1.8890  -8.0158   2.9650  10.0440   9.4335   8.7453  -0.1934
+#&gt; 
+#&gt; Columns 17 to 24  -6.6739  -3.3174   4.8387  -0.9273  -7.7149   0.7350   0.7539 -12.7757
+#&gt;    1.4936   1.5664  -0.7254   8.8269   5.0336  -2.1062  11.5775   3.8742
+#&gt;    4.1964   8.1240  -8.9542   9.2426  -4.2874   1.6129  11.9477 -14.8053
+#&gt;  -17.7976  13.5713  10.5377   6.5788   0.5665   7.3900  -3.5301  -8.3864
+#&gt;   -7.7135  24.4112   8.5829   9.0302   5.6441  11.9017 -15.2928   9.8670
+#&gt;    7.8455 -14.1607  24.9822   7.2399  -3.6282 -10.9977  15.6533  24.1705
+#&gt;   -2.7380   3.3217  -5.2483 -17.4094   0.6183  -3.6812  10.6658 -13.4362
+#&gt;   -0.0283 -14.1412   3.7245 -10.9191  -5.2927  11.3169  34.0355  -6.1253
+#&gt;    0.9896   3.7909  15.6755   2.6959   6.6624   5.3276   5.7858   2.5801
+#&gt;   -0.8955   7.1207   5.7362  -3.7618   2.5286  10.6009  -9.3078   1.7687
+#&gt;   16.1548 -19.1678  -5.6831   7.8994   8.6877   4.6436   4.2476  -0.4299
+#&gt;   -8.1035 -10.2413  11.0808  18.7428  -6.1531   1.4083   1.5032  -1.9261
+#&gt;    3.4789  13.5798 -16.1593   0.7770  -2.3820  -0.6284  -3.4782   2.3536
+#&gt;   21.1276   3.7961  14.4141  -6.9490   3.5795  -0.7582   2.4345   7.0882
+#&gt;    0.3352  -0.0920  -0.4495  -0.4418  -4.9307   5.1436 -14.9609   3.9155
+#&gt;    6.8881  -5.7349 -14.4124  -3.8325  12.5811  -4.6771   0.8841  -3.3465
+#&gt;    8.0628  -6.7419  -3.9527   3.5416  -3.5149  -7.7816   0.2831   9.6582
+#&gt;   14.2947 -15.2795  -2.8609   4.6593   1.2110  10.0167   4.2606 -11.5680
+#&gt;    5.3466   0.4190  -8.5543  10.8854  11.1437   4.1192   3.3871  19.8865
+#&gt;   -0.9948  18.3307  -3.0989   1.2748   5.2957   7.5191  -3.6155   5.4813
+#&gt;  -15.4406   7.2360  -3.9158  -8.4858  -4.7730  -5.4790  -4.4418 -26.1587
+#&gt;   -8.9448 -11.9719   2.7618  -3.9798 -12.0544  15.9367   4.1552  -0.8900
+#&gt;   13.3973  -8.4427 -18.6767   6.8483   8.1360 -23.6531 -16.4057   0.3555
+#&gt;    7.2695  19.2310   8.4524   9.4012  13.6135  10.9855  18.6647   4.0355
+#&gt;    0.2357  -2.5369  10.6670  -2.6963   4.3938   9.4022  11.7677   2.9247
+#&gt;    3.1197  -2.7420  -5.7643   4.8510  -6.9641   2.9854 -12.6033  -3.6164
+#&gt;    6.4381   4.2443  -6.4563  -1.7414 -12.8242 -11.2641  -6.9360  -1.4475
+#&gt;   -5.7302  -2.3058  -2.4990  -9.3361  -6.7829  -4.5237  -4.0134  13.4664
+#&gt;   -0.4313   5.6937   0.0022  -5.6486  -7.6801 -14.7791   3.9556  18.4725
+#&gt;    6.2085  -5.9200   8.8480   2.7069 -17.4849 -15.5185  20.1055  15.0809
+#&gt;   16.2159  -3.3914 -14.8833  21.7363  12.9022  -0.7343  -8.1583  12.4803
+#&gt;   11.2343   1.4257  -5.3833 -12.7218  -2.6865  -2.5140  -6.7985  -2.5359
+#&gt;   -4.0532   2.6016   7.4993  -8.4987   9.5665   3.0245   7.0870  -9.0506
+#&gt; 
+#&gt; Columns 25 to 32   1.1221 -15.1468   8.3570   1.9067 -11.5429 -14.4144   9.4440   2.2931
+#&gt;    2.9613  -5.0968  10.2646   1.3281  -7.5577  -0.9216   7.4214   8.1904
+#&gt;   -8.5052 -17.4922 -10.5043  -0.4389   8.9870   1.2418  -0.9949  -5.0835
+#&gt;   12.2524   6.3351  15.4138  12.0733  -1.9435   0.8878  -7.0024   2.1013
+#&gt;    1.0187  -7.7225  25.5166  18.5899  -5.6871  13.7447  -8.9432  20.9760
+#&gt;   -3.3446  12.4658   6.8752   5.7418  -1.4096   2.0620   6.0016   2.0485
+#&gt;  -14.6137  -1.3447   4.2214  11.4609  -1.4652   3.6985 -15.4739  -4.4912
+#&gt;  -14.5247  -6.0115 -16.5187  17.6410   3.8002  10.4642  -4.3999   0.9559
+#&gt;   -1.8676 -14.0983  -5.6664  -3.0940 -13.4328  13.5993  -5.7134   1.2287
+#&gt;   -0.6292  19.5502   6.4174   4.8042   6.3895  -3.5560   2.3204  18.9714
+#&gt;   -2.3795   5.8969   0.7731  -1.4679  -4.4090  -2.2931   3.7329 -12.4271
+#&gt;   -3.1622  11.9378  16.6575  -6.7612   1.4359 -12.4484 -17.6017  -9.2899
+#&gt;   -1.4348   2.1966  15.6815  -7.2354  10.0188  18.4178  15.3872   2.5706
+#&gt;    3.0502   7.7196 -17.1957 -10.0506   6.4401   6.7961 -15.4598  -2.5249
+#&gt;    2.4114   5.5757   7.4796  11.6125  -6.2794 -11.1033  -1.4154   7.4313
+#&gt;  -12.3398   4.9861   6.3509   4.6999  10.8454  -8.6616   4.3145  -0.3505
+#&gt;    5.1506   1.4426  -4.8630  10.4596  -1.3728   4.9981  -0.5317  -2.3504
+#&gt;  -18.7493   9.9155   6.3305  19.4469  -4.2548  -9.9538 -23.5458   1.5363
+#&gt;   -5.7270 -28.5966   1.4610   2.6186   6.9747  19.2051   6.0497   0.9164
+#&gt;   10.9339  -6.6366  12.2284   3.0518   2.6061  11.2897   0.1941  -0.2602
+#&gt;   19.5594 -11.9835  -1.9872 -15.9810   6.5773  -5.2519   2.7786  14.8894
+#&gt;   -1.3409 -11.1748   1.0937  -3.5925  -0.7549  12.0844   4.0608   1.2738
+#&gt;  -11.5565  -7.7279 -11.3881  -1.5980 -15.1130  -6.5238  -1.9475   0.3795
+#&gt;    0.7774  -9.4843   0.2672 -19.9400   0.8753  -2.3304  -3.0680  -8.8586
+#&gt;    7.9566   3.6271  -5.0917  -6.8523  -9.8662  -2.2442 -16.8183   4.5610
+#&gt;    1.2773 -15.6154  14.5905 -14.0544  11.1515  -6.0772  14.6152  -1.7339
+#&gt;   14.9440  12.5391  -9.0561  13.2410   4.0097 -13.9463   2.5720  -6.4587
+#&gt;   -2.3829  -6.7018 -11.9655  -5.8682   5.5277   0.0934  -6.2478  10.2131
+#&gt;   -3.7337 -10.5398  -7.9042  -4.4180   1.0297   4.7407   5.2402   3.7955
+#&gt;    5.4218   8.4790 -25.0305  20.2131   1.5218   1.6669   1.7754   2.5871
+#&gt;   -2.5454   3.7441  10.6950  -0.2284   2.8383  -7.8144   3.1613 -14.6570
+#&gt;   -4.4937  -1.8892 -11.6622  -7.5895   1.2404  -3.1188  -1.9563  12.0491
+#&gt;   -8.3915  34.2823  -6.9476   7.6439  -1.6938  -6.7918   3.5380  -9.0375
+#&gt; 
+#&gt; Columns 33 to 40   3.3995  -0.3210   2.9188   5.1383  -1.3407   7.5646   4.3350  12.8163
+#&gt;   -4.6758   3.8246  -0.4502  -5.3551  -2.4679  -1.6020   6.7734   6.7087
+#&gt;   -1.8866  -7.8625 -11.9390  -9.3608   4.7640 -17.6644  17.5815  -8.8841
+#&gt;   -1.3425  -8.0310  -8.0548  20.8914   3.6614   0.4057   9.1090  -8.9089
+#&gt;   -6.8993  11.7285  -3.0791   9.8707 -14.5684  -1.5579  -4.5657 -11.7019
+#&gt;   17.2565  -3.6128   2.5843 -14.9282  12.2219 -13.2551 -13.5065   6.2647
+#&gt;    6.6300  10.2063  -5.0556  -5.2237  -5.6799  -5.2010  -1.2620  -4.6160
+#&gt;    1.6600   0.4635 -14.1997  -5.3116   5.8385  -3.3701  18.2749   4.3410
+#&gt;   -2.3960   7.0271   7.9698  -5.0902   6.2283   0.9159  -4.6657   3.5176
+#&gt;    3.0836   3.9876  -4.8300   0.3241  -0.3630   5.0639   4.8222  -4.8342
+#&gt;   -4.8762  -0.1001  10.6387  -7.2324  -4.1635   0.1551   7.3297   6.6931
+#&gt;   10.2620  -1.0456  -2.0783   4.0471   5.1181   8.0840   8.2518  10.5336
+#&gt;   -2.2210  18.0167  -1.2456  -8.7770 -17.6564   7.3704 -13.3693  -9.1483
+#&gt;  -14.8682  -7.7006  -2.0882  18.8748   1.6923   9.6779  13.6605 -12.6538
+#&gt;   16.1572 -16.5525  13.6661   5.4012  11.1833  -5.3037   7.9277   2.4325
+#&gt;   -3.3884  -5.0985   4.8811 -17.6648   5.1383  -0.4609   3.3404   1.5507
+#&gt;   -5.6561  -0.2068   7.1859 -16.5431  -5.6844  -0.4892   2.3343   8.0768
+#&gt;   -5.9822  10.8257  -0.3373   0.9161  -4.8604   5.9253  18.3434  -0.3593
+#&gt;  -12.5291  13.5229   8.5409  -9.3303   4.1437   3.7960  -1.9802   5.5924
+#&gt;   -3.9977  -1.4491  -8.0225   2.4355  -5.1887   0.9020  11.6591  -8.5988
+#&gt;  -10.6864   4.7166 -12.1693  15.2190  -7.3645  -0.4941  -1.6018 -19.0628
+#&gt;    5.8257  -6.8657  -9.8551 -12.6238   2.9744  -7.7327   6.4582   7.8213
+#&gt;   -1.5285  12.3356  -3.8897  -9.5486   8.2061   1.1672  -4.6765  -4.8519
+#&gt;   -9.7547   0.5000  -2.3187  13.6487  -4.9364   0.3583   2.2306   5.9768
+#&gt;    6.6336  -7.8624  11.8713   8.1273  20.9814   1.9781   8.6172   1.3056
+#&gt;   16.1903 -10.9793   9.7522   1.6372  -2.8432   2.4354 -15.3075  -1.4748
+#&gt;   -3.4112  -1.5999   9.6158  17.9750   2.0975  -6.0335   9.8225 -14.3505
+#&gt;    2.6330  -1.7996   6.3781  10.6677  -3.4005 -14.3448  -2.8834  -5.9152
+#&gt;  -10.8092  -2.3071   3.1028  11.0733  -1.5603 -22.6859  -2.0244   7.1346
+#&gt;   18.9459   6.7388  -8.3142  14.8399  17.1676 -13.3548   8.5614  -7.7465
+#&gt;    8.6905  11.5966   5.1644  -5.1298   5.1416   5.4848  -8.0632  -0.1872
+#&gt;   -4.2913  -1.5870   7.7001  11.6801  -4.0722   4.0107   8.4250 -17.5451
+#&gt;   -5.4688   7.5811   1.7385  -0.4735  -8.9413   0.2439  17.5193  -2.7576
+#&gt; 
+#&gt; Columns 41 to 48   0.0465  -3.4862 -13.2738   3.8935  -3.2092  10.1039   5.0745   3.7190
+#&gt;    2.0494  -9.1959  -2.2920  -5.7282   3.3374   8.6497  -8.7164  -0.0291
+#&gt;   -9.8911  -0.7584   2.2297  -5.7634  -4.3136   2.0147  20.0442  -3.9719
+#&gt;    5.5081  -8.8566   2.8731 -13.9006  20.8200  -5.5648   7.5882   5.7305
+#&gt;   -3.1711 -12.6775  -3.9172  -8.7452  14.4456 -12.8824  -1.8546   3.4528
+#&gt;    2.4446  -3.4813   6.2757   0.4589  -4.7091   2.1109  -9.6654  -2.8753
+#&gt;   -6.4858   3.2467  10.2775  -8.8833  -7.7731  -9.0477  -1.6386   4.3995
+#&gt;    1.5574  -6.3637   4.2262  -9.4756  -3.0853  -1.0484  17.7309   3.0610
+#&gt;  -11.3941   4.9077   1.1466  -2.9488   7.8308  -3.5794 -14.2437  10.8045
+#&gt;    4.6599   5.4957  -0.6567  -3.0111  -2.0842  -7.0308  -1.1045  -7.3985
+#&gt;    2.8322 -12.0449   5.3779  -0.5008   5.0249  24.7133   4.6630  -1.7614
+#&gt;   -1.8948  -7.6056  -7.6986  10.9410  12.1283  -5.6003  17.2878  16.8752
+#&gt;   -5.6440  -6.6151   8.7042   8.5497  -7.9938  -2.3105   1.3292   0.2753
+#&gt;   13.1700  -2.1738  -1.6815  -2.9282  -2.1487  -7.5467   2.2566  -3.1159
+#&gt;   -6.7321  21.2448  -4.8457   3.3978  -0.0036   4.4460   1.1738 -13.2747
+#&gt;   -9.0073  17.5459  -4.0688  -5.1694  -2.4779  -4.3687  -1.9631   4.2734
+#&gt;    0.6338  17.4965   0.2533 -18.1077   3.7392  13.4510 -15.2351 -10.5991
+#&gt;   13.9620  18.9477  -1.3132 -15.6907   0.9643   0.4753   2.6462   1.9783
+#&gt;    3.1352  -6.3590 -10.6552 -13.6167  -6.0599   1.4963 -10.2603  11.7957
+#&gt;   15.1292  -5.5503   1.0119  -0.6963   3.3856 -11.4307  13.1196  11.5283
+#&gt;  -12.3759  -6.4238  12.8371   2.5045  -1.7021  -2.0936   4.3229   2.1624
+#&gt;  -12.5627  13.9249  -3.5476 -14.4480  -1.9823  13.2438  -2.1036   3.9360
+#&gt;    8.0561  -6.5157  18.4071 -12.5351   6.8261  -7.4138   9.1498  13.4108
+#&gt;  -12.7752  10.7888 -21.2931  -4.3270  -9.9881  14.7584  12.8067   6.9470
+#&gt;   -6.1149   7.5197   1.2564  14.0175   1.7411   3.7951   5.6573   1.6170
+#&gt;   10.2582  -2.0958  -2.3722  -8.6935  -3.2190  -4.3119   4.8846  -6.9076
+#&gt;  -15.0583   0.4817  -4.4535   4.1261  -9.5967  12.2711  -1.1233 -17.4156
+#&gt;   -2.5126  -6.0924 -11.5254  11.3586   6.2273   2.9722  -9.3137   1.5586
+#&gt;   -2.3737  11.0494  -4.2991  -7.8241   3.8737  -0.8572  -3.2632  -3.1751
+#&gt;   19.0771   1.6400   2.4942  -4.3823  -1.9991  -1.5681  -1.9602  -1.9225
+#&gt;   -9.8879  12.0578  -4.9132   4.8810  -0.5849   8.4618   6.0484  -0.8199
+#&gt;    2.6374  -3.6763  16.9745   1.4207   4.6623  -1.5556   8.6995  -6.1866
+#&gt;  -17.2081   3.8919  -6.3286  12.8504   3.4418   0.1833   1.7541   1.8093
+#&gt; 
+#&gt; Columns 49 to 54   4.2277  15.2095  -5.3106  -3.5254  -2.0239   0.5410
+#&gt;    1.0530  -1.5268  -8.3209  -7.5667  -4.3293  -0.3304
+#&gt;   14.5093  15.5059   3.6394  -7.8419  -0.5077  -2.7458
+#&gt;   -4.2606  -1.3002  -6.4896   4.1244  -7.6892   4.5164
+#&gt;   -3.3746  -5.3938   2.3163  -0.2463  -1.7688  -0.0258
+#&gt;    1.9585 -18.0378   7.8877   0.2530   4.8485   0.6589
+#&gt;    3.0419  10.3141  12.3120  -6.6679 -10.8065  -2.5067
+#&gt;   -4.6297   6.5639 -12.5321  -0.1158  -5.2502  -2.2560
+#&gt;   10.4186  -1.9900   9.9362   8.4835   5.7325   0.9782
+#&gt;    1.6694   9.0592 -15.8919   2.0805   1.6827  -2.1085
+#&gt;    5.7053  11.3829  -7.9181   5.3085   7.7903  -0.2385
+#&gt;   22.8388  -0.1096   1.3239  -8.0848  -2.0209   0.9309
+#&gt;  -16.6073   2.2912  -6.8168  -4.4002   6.6341  -2.6954
+#&gt;   -8.1871   8.7314   3.0309   6.2616  -1.7341  -0.1670
+#&gt;   -7.3059  -1.1614 -12.2037   7.7507  -8.1071   1.1435
+#&gt;    2.9345  -2.6649   2.3461  -2.0491  -1.6806  -2.3369
+#&gt;    3.1239   4.0808  13.8076  -2.9526  -1.5092  -7.7307
+#&gt;    3.0788  18.8719   5.5230   1.3509   3.6166  -5.6440
+#&gt;    4.1107  -5.4855   5.2623   6.4164   1.0479   1.9502
+#&gt;    1.7431   5.8753  -4.8010 -18.8203  -9.6599  -5.9157
+#&gt;   -0.0724 -10.8421  -0.9135  -2.0209  -0.0128   1.9955
+#&gt;   11.8888  -1.6736   8.6527  -6.8223  -2.6404  -4.3233
+#&gt;    7.3335   0.9019 -22.4590   1.2885  -7.3577  -2.0053
+#&gt;   -1.2451   2.6736   7.4570  -4.3535  -1.6255   1.7764
+#&gt;   11.3746  -9.8116   5.5320   2.7358  -6.3190   4.3476
+#&gt;    6.3236 -14.9279   2.5646  -0.5103   3.6606   0.7772
+#&gt;    6.1821  16.4744 -10.2067  -5.7784  -0.8729   1.1509
+#&gt;   -0.3717  -5.3564  -3.1124 -13.4197  -5.1752   2.2853
+#&gt;   -3.0716  -4.4458  12.2943  -3.3567   1.3988   1.0051
+#&gt;  -22.5080  -8.9841   5.5813   7.1742   1.6875  -3.7041
+#&gt;   -9.3930  12.2926 -11.7914  -1.4915  -3.2901  -4.0539
+#&gt;    0.4054  -1.7884  -2.7561  -2.5213  -9.4311   1.7463
+#&gt;    2.3147  26.5946   3.4709  -9.9254  -3.4216   1.8680
+#&gt; 
+#&gt; (4,.,.) = 
+#&gt;  Columns 1 to 8   0.2219   0.5287  -7.2462   3.4678   4.6731  -0.1877 -16.9202   9.5219
+#&gt;   -1.9105   3.5858   5.7166  -1.6073  -1.7945  -8.8200  -5.3217  12.1512
+#&gt;    4.7816   1.2403  -0.7889  -2.0563   6.4095  -0.4141   7.0459 -21.4778
+#&gt;    1.6454  -5.1022  -1.6111  -1.3982   2.7441 -22.8674   8.2726   6.3412
+#&gt;    7.9354 -11.1167   3.1809  -0.3604   3.7264 -11.9987  -1.9954  -0.3730
+#&gt;   -3.4294  -3.1767  -4.2183  -4.3765  -1.3808   4.5756  -0.6131  -1.5962
+#&gt;    0.1901   2.4103   8.1420  -5.3695  -5.3668 -10.9919   6.8256 -16.6919
+#&gt;   -1.6335   0.6493  -0.5593   0.5749  -9.4058  25.1887 -13.2565  -1.3850
+#&gt;    6.2220   2.0828   0.7704  24.1788   8.7479  -5.7883  -0.6454   9.4337
+#&gt;    1.0294  -0.5649  -3.3157  -5.5922   8.3732  -3.2402  -8.2360  -0.5550
+#&gt;   -2.9694  -7.1426   3.2555   5.3949   2.0131  15.1476   3.9756  13.2564
+#&gt;   -1.8526 -10.5077   1.2471  -1.1577 -13.0580  -2.4768  -1.2320   1.6977
+#&gt;   -1.5976   2.1847   4.7230  -6.0342  -0.5115   0.7416  -6.4935  -0.2440
+#&gt;   -0.8131  -3.3292  -1.9453  -2.7024 -12.0865   0.0555  -1.3913   1.2639
+#&gt;    0.9961  -5.2428 -11.8401  11.0223 -13.4765 -10.1120  12.0808  13.4634
+#&gt;    2.8489   6.0960  -1.7192   5.1269   1.7591 -13.9983  -7.1799  11.6296
+#&gt;    1.4397 -10.4571  -4.6499   5.7463   1.5327 -22.5954  12.3886   0.7576
+#&gt;    5.2328 -12.1251 -12.2410  13.5180  -7.4857  -8.4028 -13.6096  -9.3512
+#&gt;    5.8540  -5.6182  -1.6361  10.4952  11.7672   4.1303   0.0729   2.5591
+#&gt;    1.0030   4.6119   1.1199 -16.2927  -7.2099   1.8887  -0.6048 -17.4918
+#&gt;    1.1425  -2.4799   9.2321 -15.8380   2.6032 -11.9649   5.2088  -0.5586
+#&gt;    2.0217   3.5377  -2.2017  10.9276   3.7871  10.5646  21.3250  -5.9608
+#&gt;    4.1862   2.7262  -0.0812   6.6039   8.8524 -19.9248  -1.2731  14.4253
+#&gt;    1.9577   6.5865  -5.6471  11.7944  17.1732  -0.8650   5.9225   0.6377
+#&gt;    0.2850   2.7053   4.6109   9.1281   2.9065   3.1857  14.4020  16.7285
+#&gt;    0.3713   8.4739  -6.7031   4.5455   0.3447   0.4787   7.7604 -12.5854
+#&gt;   -1.7503   7.2190  -8.6510  -3.4702   5.6751  13.9228  -5.3114 -15.2229
+#&gt;    3.4015  -3.7903  -3.1152  14.0049   5.0853   7.4088   7.7160 -17.8688
+#&gt;    0.8898   5.4846  -2.9675  -2.9970   0.9324 -12.9664  15.2617   1.2304
+#&gt;   -1.9995  -0.5858  -4.9704  12.2816  -4.0337  16.3481   5.3325  14.1150
+#&gt;   -3.1097   3.8707  -0.5704   6.0035   2.6616  -4.3465  -4.7927  -6.2101
+#&gt;    1.9746  -2.8249   1.1796   4.5028  -2.0795  10.9674   2.1140  -6.5755
+#&gt;   -3.4156   3.5885   1.1321  -6.4363   5.4823  14.2754  -5.5120  -9.4005
+#&gt; 
+#&gt; Columns 9 to 16  13.2124   2.8994  -5.3735   2.2360  25.7976   3.0137 -11.4937   1.8268
+#&gt;   -2.8935   4.7092  -6.5326  -8.3361   3.1229 -11.7171   2.4599  -8.0837
+#&gt;    3.1654  -6.8736  -3.0040  17.3870  -6.3694  -0.7925  16.5050  -2.2160
+#&gt;   -9.2479  15.3839  -4.0923   1.8941 -11.9576  -3.3405  -3.0182  11.0526
+#&gt;   -8.7475  11.4002  -3.1246   9.1074  -8.3205   5.0534  -1.0137  23.3715
+#&gt;   12.7605 -12.0524   4.9179 -14.8454  11.8215  -6.9595  -3.8740  -3.3183
+#&gt;  -22.4466  30.3703  16.0598 -25.0331  -1.7203   8.4954  18.3378 -19.4747
+#&gt;   -5.5124  -7.1481   0.1281   2.4150  11.9876 -12.5131   5.0769 -19.8594
+#&gt;   -9.5846   1.3561  -8.8571  -5.7119   7.8162  -3.4918   1.9622  20.0733
+#&gt;    7.9827 -11.1137   3.3657  29.9334  -8.8698   3.8945  -0.4167   0.9169
+#&gt;   15.6365 -15.8412 -11.0218   3.1657  -6.8065 -12.0324  -1.1061   5.1023
+#&gt;   12.2327  13.6443   0.3516  12.6832  -1.4542   6.8167  -0.6641  -9.1212
+#&gt;   10.2712  -2.0197  -4.6885  14.2755 -10.2557  -6.5737   3.8165   6.1315
+#&gt;   -4.2317   0.6908  12.1939   6.9378  -3.0615  -2.2660  12.1141  -0.1512
+#&gt;    0.8666  10.2340  -6.8133   9.7195   8.6313  -6.9433  -9.8662   2.2463
+#&gt;   -3.7955  -2.5939   2.8869 -10.2202  -0.2583  -2.9208 -11.6945  -8.5631
+#&gt;  -14.7348   3.9317  20.9995   2.2973   2.0594   2.2833  -6.1081   1.9177
+#&gt;    2.8468   8.1607   3.1404  11.0361   3.6421  12.4354   4.0767 -15.4768
+#&gt;  -14.6342   8.3660   5.1886  -8.6252  -4.2892  -5.4175  16.5140  15.3920
+#&gt;    2.0241  12.6425   1.9053  15.0312   1.9797   3.7726   0.5551  20.0474
+#&gt;   -5.4475  -1.1319   5.2913   3.7935  -8.1448   7.8411   4.2528 -14.5803
+#&gt;   -1.9503  -1.1509  -2.4265   4.8543   2.2002   4.1706  -1.2490   8.4582
+#&gt;   -9.7253  15.4198  13.5754 -10.7198  -9.2916  12.8682   6.8419   0.6993
+#&gt;    1.9818  19.2110 -14.5814 -20.0495  -1.6095  -3.7466  19.6918   9.6997
+#&gt;    3.3216  13.9218  -4.2295  -6.2760   4.0693 -21.0303   3.8601   5.3268
+#&gt;    8.4889  -5.6500   7.9181   3.5116  11.6170  14.6077   0.0909  12.0469
+#&gt;    3.3008   9.4569  -8.8514 -13.8592 -17.5208   3.9864   1.5469 -23.5100
+#&gt;    1.5347   7.3003 -11.3367  -1.6975   8.1932  -9.5387  -1.2249   1.8121
+#&gt;   -8.1982   0.8516 -10.5033  -8.6144  16.8025   0.9546  -9.9261   5.5325
+#&gt;    8.1604  14.4861  -6.1633  -7.9642   6.1587   2.8146  -9.4546   5.0518
+#&gt;   -2.6576   6.2556  -0.7938  -7.1543 -17.7354  18.7747   9.7348  -2.2436
+#&gt;    4.5735   3.3980   0.8378  -2.6990  14.0849  14.1037   4.1190   2.0277
+#&gt;   -6.6686   3.3696   8.5207   8.9040   0.9597  -2.7365   3.4979  -2.5273
+#&gt; 
+#&gt; Columns 17 to 24   4.1596 -25.7686 -15.1404  -8.9288  -1.3671   5.0678  -1.9301  16.9985
+#&gt;  -12.4490   6.7502  -3.9174  -4.8482  19.0792 -15.0144   4.8911  -1.4765
+#&gt;   -8.8776   2.3609  -4.1307  -8.2305   5.7766  19.5054   6.7913   8.6220
+#&gt;    3.4641   3.1098  -7.0934   0.3988   4.3806 -22.5267   0.0561 -17.0301
+#&gt;  -12.8308   2.0799  13.8156   1.8082 -13.9609  -2.5745   7.5044  -5.9666
+#&gt;    0.2498  12.5673   7.5503 -16.1104  -3.4187  -7.2543  -4.3433  -7.1847
+#&gt;   -3.1907   2.8129   9.4351 -13.6088   4.9873   2.4302   5.7697   3.3274
+#&gt;    0.9187   1.9439  -1.7335  -4.2249  -4.8246  -5.5245  -5.0744  -8.4851
+#&gt;    4.3851   2.8204 -13.0176  11.6872 -17.4204 -15.5696   2.8239   5.9843
+#&gt;   11.3976   8.9254 -14.8285   2.2775 -20.1134  -1.8695  16.1060  13.2642
+#&gt;    0.3391  -2.1935  -8.9088   7.7289 -12.7010 -16.2638 -16.0576   0.9651
+#&gt;   -6.3201  18.0630 -12.1047  -4.1500  -3.2474   7.1294 -10.6871 -10.4556
+#&gt;   -7.5286   1.0544   6.0757   6.5414 -12.5292  13.6479  10.5169  12.7154
+#&gt;   -9.8171   6.7652   1.5683  -0.0256  13.5938  -4.6878   1.4223  -3.7993
+#&gt;   12.3174   8.9059   4.7810  11.0903  -9.1580   3.7943 -17.4670 -18.2115
+#&gt;    1.8995  18.5803  -6.1339 -18.1816  -7.4241   4.7667  20.6264  -8.6507
+#&gt;  -13.2026   4.1181  -2.7518  15.8183   4.5620   9.4490  16.4680 -21.5272
+#&gt;   14.1773  11.3816 -31.4019  -0.7659  -6.8353   8.2688  10.8511  -1.9778
+#&gt;   -4.7224  15.2913  18.8708 -15.4398 -13.3489 -17.6808   1.6728  -0.1029
+#&gt;  -20.5853  -5.6671  22.0880   3.5830   2.7769   8.0856  14.4633 -13.8408
+#&gt;    1.3969  -6.0445   1.2313   4.9702  18.8178   4.0873   4.1045   5.1362
+#&gt;   10.2653   1.1919  -5.6667  18.5522  -7.2126  -5.5894 -17.3211  -6.0522
+#&gt;   -8.3299   8.8381   4.4417  -3.4878   8.0462  15.5097  18.6968 -12.0810
+#&gt;    5.5836  -1.8382 -13.5796 -16.5298 -22.5308  -2.2137 -13.6045   3.5380
+#&gt;    0.4382   3.1033  -5.0076  -6.2842  -7.6827 -15.5193  -3.6441   3.2390
+#&gt;    9.8403  -0.5997   2.9366  -9.6024   0.6152   1.8874 -15.6021  -0.0891
+#&gt;   -7.9489 -19.4334 -19.7915  12.7997   5.3265  13.1515 -16.4574  18.4732
+#&gt;   -7.7003  -0.6809   5.8432   7.4024   1.7996 -12.3416  -5.2899  -5.3632
+#&gt;   -8.5651  -3.3111  -9.8433   8.6207  22.2462  -7.7504   0.8410   2.2015
+#&gt;    2.5760   2.7540  -8.8211   8.0310  -4.4091  -4.6158 -13.2911 -11.8524
+#&gt;   -4.8951  -2.8482   2.9793   0.8178 -14.6286   2.1986   5.1434  -7.7913
+#&gt;    2.7216   4.1392  20.0401 -19.5082  15.5115  -0.1485  -4.7362  14.6629
+#&gt;   16.3551  -8.6646   5.3361   7.1782  -4.6457 -13.8959  -5.2264  20.3985
+#&gt; 
+#&gt; Columns 25 to 32  19.2265   2.5813   4.2602  -8.6212   8.0270  14.7208  -2.4313  -6.6042
+#&gt;   -8.4114  20.3017   2.0620  -4.8383   3.4691  -1.5760   4.6170   2.7803
+#&gt;    0.6465  -5.7817 -16.7433 -17.2697  -2.9205   9.8512  -2.9238   3.5182
+#&gt;    8.6801  -3.3657 -11.3030   3.9110  -2.6238 -10.8564 -13.4369  11.0938
+#&gt;    9.6046   1.2772   3.9488  -5.2463  12.9554  -0.5244 -21.8934   4.8245
+#&gt;    6.2853  -1.1873  -4.1437   6.8021 -12.8606  -7.8836   7.3618  22.9472
+#&gt;    8.4518  11.2520 -10.6945  10.1761  11.1940 -17.1313  13.7517 -15.8747
+#&gt;   -0.8503   0.0473 -15.0965  -7.3103  -9.4960  12.9389  -3.5424   4.7263
+#&gt;    0.6302   5.3077  -1.5759  12.8717  -0.6424   3.9268   8.6920  10.4053
+#&gt;   19.6230  -6.9193  -3.5887   1.7106  -1.2620   3.1657  -0.0184   8.3143
+#&gt;    2.9247 -12.4803  -2.2822   4.9216   5.1835  -3.6888  10.2193  19.3383
+#&gt;    0.8281 -12.9886 -14.3478   8.3200   3.7656 -11.4106  -8.8675   9.3077
+#&gt;  -14.8788  -0.5899   7.3588   4.3446   6.0987  -8.5687   2.9230  -4.8289
+#&gt;   10.2902  10.2134   4.1634   1.2769  -5.2478  -5.8166  -0.0322   1.0880
+#&gt;   18.3145 -10.1861   2.7633   6.5026   7.1369   9.8278 -10.0902   0.1051
+#&gt;  -20.0572   3.9915 -14.7786  -1.9932   2.8509   2.7118  -5.6367  -7.3608
+#&gt;    0.6559  -3.5271  -4.6592   7.9450   1.1289  -7.8782   0.4414  -4.6317
+#&gt;    9.6705   3.3123 -22.6803  17.0145  21.1617  -5.5084  -2.1212  -4.3812
+#&gt;  -13.4505   9.5107  -3.5649 -16.0908   2.5337   4.6970  -9.4074   0.9188
+#&gt;  -11.0729 -12.7696  13.6597  -2.0072  -5.0806  18.4795  -5.9133  -9.5014
+#&gt;    4.7037   3.6743   5.2145  -4.1880  -2.6656  -5.2377   6.0863  10.4891
+#&gt;   11.7670 -10.4386  -0.2109  -0.2333 -12.2052   6.4594   5.4473   3.6028
+#&gt;    0.8061  -4.8452  -6.2903  -3.1591  -0.0587 -14.1828 -18.9741  -0.2332
+#&gt;   -0.3267  25.3989  19.3063   2.1349   4.2234  -3.1067   2.6432 -11.7914
+#&gt;   -2.3602   2.0220  12.4222   2.7937  -4.9585   0.8077  -6.2972   6.2894
+#&gt;    6.5054  10.4263  11.3934   6.9248  -3.4319   9.5436   9.2800   2.6421
+#&gt;   11.7031  -9.1718   4.6275   7.3577   3.4125  18.0781   7.2450 -10.2295
+#&gt;    3.3806  -6.7652 -12.1229  -5.9293  -3.1413  14.5744   2.6997   2.5371
+#&gt;    3.0301   8.9022  -7.5954  -6.3892 -10.5618   2.1032  10.6880   5.5672
+#&gt;   12.4864  13.4421   6.6533   3.7224 -13.4527  14.6014 -17.2829  22.0472
+#&gt;   -4.2936  -0.0431   9.9653   3.0915  14.5693 -10.5417   2.3160  -8.4353
+#&gt;   -3.0880  -6.1304  -2.0531  -7.1627 -12.8319  -2.8523   4.6373  10.1443
+#&gt;   -0.0269  -6.6783  -4.6217  -4.0809  12.2070  -0.7288  11.2078  -6.6478
+#&gt; 
+#&gt; Columns 33 to 40   6.4185   4.8604   3.7222   4.1845  -6.9717   6.2573  -8.9588  -2.0797
+#&gt;  -10.8805  10.3958   6.5959   0.2492  15.6693  -2.3024  -5.1993  -5.0015
+#&gt;  -11.0611 -18.4653 -17.0961  -2.1793 -15.8091  -4.8495  -4.0930  -7.7277
+#&gt;  -12.6086  -3.9492   8.4564   3.7170   4.2184   0.4347   1.7343   5.0374
+#&gt;   -3.5199  10.0360  18.4011   6.6154   7.6686  10.3938   8.6690  -3.2892
+#&gt;   11.9896  14.9657  17.3100  13.6816   5.7155  -6.2593   2.5469  -7.6239
+#&gt;   -2.0192 -12.7663  -1.8799  -9.2099  24.5372  -6.6509  -7.4240  -7.4372
+#&gt;    3.2858  -6.3573   0.9446  10.3753   2.7172  -6.0686   2.2428  -4.7485
+#&gt;    2.8410  12.0668  -3.0514   1.8473 -11.1713   1.2479   2.0316  -0.6065
+#&gt;    4.6437  15.0374   0.2794  18.3464   4.3572   0.1622  18.0440  10.0460
+#&gt;    7.2547  14.2274  -9.1562   9.7975 -19.9968 -19.8045  -1.2774   7.8726
+#&gt;   -4.9674  16.0535   1.7580  11.0541  -4.9818  -0.5423 -10.2666   5.8336
+#&gt;    5.3223  13.3867  -5.7377  -0.4243  -2.8942   0.4821  -2.8297   4.5070
+#&gt;  -32.6253   0.2349   4.3426  12.1137   1.6440   7.2439   5.9732   7.4394
+#&gt;   11.8648   0.1440  -2.2802  -1.4521   4.3316   2.3332  12.4300  -0.3200
+#&gt;   -6.3452   9.5664   0.8558 -22.2897  -3.8459   7.0089   5.8969  -4.4485
+#&gt;   -8.6390  -1.5985   0.7078  -6.7466   3.5989  12.8649  -1.7226  -2.5022
+#&gt;  -12.6954   5.1820 -15.4668  -0.1136   9.0383  -1.8060  -2.1011   7.6085
+#&gt;    9.8640  12.0282  15.3925   1.3519  11.9642  12.9207  -1.3220  -7.5618
+#&gt;  -13.5007   1.6434   8.9013   9.6739  -0.7869  16.0522   6.1873  -7.6691
+#&gt;  -13.1869 -10.5223   0.5893 -10.4336 -11.4600   2.9302 -11.7560  -9.4367
+#&gt;    1.0218 -12.6724  -3.8524   0.6912  -3.0898  -4.9917 -17.9133   2.3959
+#&gt;  -10.5114  -3.0623 -21.4408 -11.9590  -4.1554   7.4104   2.1276 -18.3623
+#&gt;    6.8585  23.4705  12.6679  11.8100  -3.3717  -1.6442 -19.9979  11.4009
+#&gt;    7.6246   2.4456  -1.0381   5.7176   5.9947  -3.6588  -2.6176  -2.4339
+#&gt;   -2.1380  11.9104   1.4937  -0.9187  11.4557  -1.7577   1.6467   7.3846
+#&gt;    3.8289   8.6479   8.8680   8.2986   2.5710   9.3239  -2.7676   5.4409
+#&gt;    2.0634   5.6217   8.0566   7.1804  -6.9430  -5.0080  -0.1164  -3.7963
+#&gt;  -22.5194  -0.9365   0.0082 -14.0502   2.1485  -1.5372 -13.6636  -8.0557
+#&gt;    4.1657   6.2588   0.7398  27.1365   6.5433  14.9252  -9.3923  10.3682
+#&gt;    4.6615   2.4031  -6.2987  -6.8812   4.4682   8.5512  -1.3710  -3.4787
+#&gt;    3.2988  -2.1579   8.7593 -13.0047  -9.2239  -4.7590   8.9181   5.5893
+#&gt;    3.6827   0.2190   2.4700 -10.8235  -2.6666  -6.8960 -14.1972  14.2358
+#&gt; 
+#&gt; Columns 41 to 48   6.4534  -9.1895  -8.6435 -14.7488  11.7945   7.9069   6.2200   2.6855
+#&gt;    3.7099   6.7005  -6.4447  14.9308  13.9055   3.2064   3.0763  -3.9926
+#&gt;    1.3041  -1.2253  -5.3743  -0.0238 -11.3995  13.4731   0.8305  -0.6777
+#&gt;    2.3425   4.7941   5.5960   4.5050   9.3140   1.8727   0.0980 -16.1363
+#&gt;   15.9776   6.7659   0.1963  -6.5183   9.9605  -3.8728   2.0315  -4.3087
+#&gt;   -0.4646   7.3267   9.4552  -7.7112 -11.6981   5.7818   2.1668   1.2031
+#&gt;    4.4953  -2.8655   0.4263  -9.0706   6.0559   4.9343   3.4586  -1.6319
+#&gt;    9.0060   6.4304  -1.7217 -12.2141   7.1489   3.2940   1.0976  20.3411
+#&gt;   -6.8470  11.7559  -8.2976   8.8400  -4.5367  -5.4740  -9.5925  10.1248
+#&gt;    3.1792  11.0095  -5.4095   8.5358  -0.0891   7.4820   7.9858  -9.3940
+#&gt;   -4.8595  -5.1274  -6.9625  -6.2827 -11.5960  15.2161   9.9113   4.1383
+#&gt;   -2.7703  -4.5346   1.3240  -4.7023   2.2619   1.5294  -0.7932   5.1330
+#&gt;  -13.8488   3.7725  -7.4467  10.0005   2.3924   7.0655  -4.2412 -10.6243
+#&gt;   -0.5703   2.1365   6.5394  15.4758   6.1899   5.1621  -9.3508  -9.5881
+#&gt;   -1.6777  11.2626   2.7623  -8.8503   4.7320  -9.9948  -2.1906  -1.3836
+#&gt;    2.0351   4.1408  -7.0070   4.6746  15.7931  -0.7728 -12.3294  -0.6707
+#&gt;    4.6545   8.8259  -4.0885 -11.8345   1.9865  11.8464   3.1237   7.3947
+#&gt;    1.1175  -3.9012 -12.5408  13.5431  -3.7331  10.7896  -0.2898   4.3303
+#&gt;   11.6799  16.1524   6.9541  -2.2194   0.3828   6.8236   9.9355  12.6040
+#&gt;   -1.0700  -0.2820  13.9910  11.0524  -3.5197  -5.9382  -7.1684   0.8504
+#&gt;   -6.0114  -3.4103  -2.3126  -3.8858 -10.1844  -6.1619  11.4913  -2.3328
+#&gt;   -6.6570   2.1689   0.3162 -20.9597  -7.9286  15.7913   4.5882   6.8197
+#&gt;   17.7398   9.6953   4.2466  -5.7422  -8.0725  12.1194   6.9694 -12.5887
+#&gt;    4.1084   9.9369  -2.8953   4.8401  22.3587   7.2082 -10.6898 -19.8510
+#&gt;    0.9589 -12.2802  -4.2077   4.4325   0.7391 -14.0510  -5.7468   2.9880
+#&gt;    5.0825   1.6433   2.1427   0.0308  -9.4286  -7.0943  -9.0435   8.6887
+#&gt;   -8.7570  -4.5735  -2.5489  15.2376   4.0394   0.9269 -10.9205  -5.9028
+#&gt;    1.9380  -4.2203  -3.1124   4.4167 -10.2325  -5.5920  -8.7279  -4.0147
+#&gt;   -2.2084   2.4891  -9.7241   4.3068   0.6057 -15.8200 -11.3434 -10.7842
+#&gt;    5.4077   1.6802 -15.9240  -6.0652  -2.5681  20.3799  -8.0014   9.7064
+#&gt;   -5.6949   8.5338   5.7999  -3.7980   5.1719   8.5332   2.3118  -9.0947
+#&gt;    3.2450   2.4662   7.0022  -5.3419   5.7970  -2.7587  -6.6008  -4.9079
+#&gt;   -8.3299 -12.9051   4.1120  -0.4969  15.4392   4.8217  14.1742  -7.5018
+#&gt; 
+#&gt; Columns 49 to 54  -4.7991   0.0500  -8.7191   0.4213   3.1326  -0.7528
+#&gt;  -18.3523   3.6540   0.0161  -1.2858   2.6736  -0.7228
+#&gt;   -2.1636  -4.9768   4.3219  -4.3713   4.8987  -2.0903
+#&gt;   -2.0971  -9.9748   3.3338   7.4499  -2.8762   5.8465
+#&gt;    6.8928  -4.1073   5.6118  12.3544   3.8265   7.2727
+#&gt;    2.9705   1.9568   2.5834   0.8506   1.9960  -2.9966
+#&gt;   -2.9388  -2.1280   1.5803  12.5152  -2.4432  -0.8005
+#&gt;  -12.0056  14.8970  -7.3867  -2.2539  -4.7291   0.8013
+#&gt;    2.5186   1.8716   2.1189   1.9451  -2.0923  -5.3365
+#&gt;  -10.2855  -1.1241   3.2962   4.4069   3.3473   2.6102
+#&gt;   -7.0721  -2.5420  -7.3420 -13.0171  -2.2379  -6.7108
+#&gt;   -3.4175  -3.6209   7.7254  -8.8157  -5.4033   0.7490
+#&gt;   -8.5337   9.6271  -0.0745   4.8559  10.0779  -5.1408
+#&gt;    0.0217   1.3013   4.7639  -2.0329  -0.8737  -1.2641
+#&gt;    5.1340   4.6470  -8.7506  -1.6405  -3.4937  10.2746
+#&gt;   11.9159  22.8571   5.2302  -9.1702  -1.6322  -2.3057
+#&gt;   20.3475   1.4480   3.6028  -0.7666  -0.9609  -8.4161
+#&gt;  -10.5663   6.8000   1.0616  -5.9306   5.6407  -3.3033
+#&gt;   -2.6967  -0.7815  -8.6606   1.8991   2.3074  -1.4894
+#&gt;    0.0664  -9.5043  -8.0940   1.5034 -11.0601   2.7597
+#&gt;    5.8905  10.2688   2.1554  -3.0246   4.5676   1.4414
+#&gt;    6.9913  -4.2888  -9.0173  -2.8255  -3.0490  -5.1996
+#&gt;   -4.3810  -6.9911  -6.0053   4.0219   3.4453   1.5885
+#&gt;  -15.1787  -9.3592   0.6733   5.3358   0.3754  -2.1221
+#&gt;   -3.7403  -7.2926   5.3745   1.8880  -3.5720   0.1754
+#&gt;    5.1834  -0.0021  -0.0035  -0.1351   1.0208   4.0843
+#&gt;   -5.8821   0.7086  10.6882   4.8212   3.1791   6.6074
+#&gt;   -9.5721  -9.9010   6.5255   2.3268  -2.0515   2.9862
+#&gt;   15.4546  -1.7387   9.6608   1.5877   6.8544   0.1361
+#&gt;    1.1707  -2.3197   5.2251   4.3118   1.0230   0.6757
+#&gt;    6.7378  -2.8661 -13.6288   2.6468   3.8596  -1.8382
+#&gt;   -3.3849  -7.5928   5.2608   6.9661  -7.8696   2.8806
+#&gt;   -1.0976  -8.2777  -1.6198   6.4236   0.9753  -4.2247
+#&gt; 
+#&gt; (5,.,.) = 
+#&gt;  Columns 1 to 8   0.1270   3.9969  -8.8403 -17.5017  18.6949 -11.8889   1.7688  23.0865
+#&gt;   -2.0743  -3.4313  -0.5353  -2.2056  -7.4029  -3.6609 -11.6210   7.4690
+#&gt;   -4.2211   3.8374   1.6643   8.0054 -14.7087   3.0723   3.5819  15.7280
+#&gt;    0.3868   7.4607   2.6972   6.7585   1.5712   0.7332   9.5469   1.5579
+#&gt;    1.3800   9.2863 -11.0584  -3.1627   4.3456  -5.3272   3.4245   1.0607
+#&gt;    0.1366   0.8656   5.4564   4.3627 -13.1777   8.9084 -16.2988  -6.1773
+#&gt;   -5.2455  -6.2299   3.7452   2.0602 -12.6547  -3.8861  -0.2208   0.7433
+#&gt;   -0.9262  -0.8542   4.7811   0.1665 -13.8330  -4.0361  10.8870  10.7425
+#&gt;    1.9948   6.3012 -24.9735  14.5074  13.6684 -16.8739  -3.7360   8.0338
+#&gt;   -2.1510   4.7077   2.5011  -3.1044  -5.4948   8.0186  17.5787   2.0567
+#&gt;    0.3403  -5.0138  -2.3260  -5.7637  -0.8710  22.6176  -5.8266  18.9274
+#&gt;    0.1255  -1.3090   5.7327   4.1940 -10.9863  10.3695   8.2213 -12.3300
+#&gt;   -3.2420  -5.2769 -12.7078  16.8987   8.1143  -4.5887  -6.8306   9.4650
+#&gt;    2.1145  -4.0721  15.0845 -18.5331  -5.0826  12.1581 -11.6930  -4.2580
+#&gt;    1.7775   6.6388   3.0794   2.0254  -6.6043  -8.7520  -7.1985  -0.0161
+#&gt;   -1.2429 -10.8730   0.3081  -3.5270  -1.2679   6.2354  -9.5832  -6.1032
+#&gt;   -3.7292   5.2118  -1.6073  -2.0752  -0.5546 -11.9674 -14.3058  -0.1385
+#&gt;    1.5663  -0.2134   1.1244   8.9575 -21.3111   9.7652  14.7048 -16.1164
+#&gt;    5.0537  -5.4365  -4.2810  -3.5680  -0.4654  -0.6910  -1.4555  16.7210
+#&gt;    2.3733   5.7109  -5.0402  14.3269  -3.7080  -0.5469   1.3461   0.0370
+#&gt;  -10.6768   1.6675  -2.6818  -6.6235 -23.3109   4.4723  -2.9306 -15.1552
+#&gt;    1.3749   5.6761   6.1372   0.0540 -15.2654 -10.2012   9.1777  21.9168
+#&gt;    0.0939   1.4988  -5.8306   2.3862   5.4255  -7.5310   8.3593  22.5439
+#&gt;    0.6239   0.0600 -10.5110 -10.8921  20.7994  -3.7823   9.1831  12.2325
+#&gt;    4.4807   1.2006  11.4099   4.1220   4.0172  12.5550  -5.6524  -5.8280
+#&gt;    2.3734   5.6991  -7.4481   6.7318   2.9518  -3.5768   8.0330  -2.3949
+#&gt;    1.4166   3.1967   4.9052  -8.5509  -0.2633   4.0145  -2.5000  -6.6647
+#&gt;    8.5022   1.2671   4.9056  -3.2028  -5.1207  17.0980  12.4183 -21.3307
+#&gt;    3.3560   0.0335   0.1486  10.0135   4.2337 -11.4092  -7.9652  -3.1330
+#&gt;    3.8520   6.2577   5.6189  -4.1615  -5.3337   5.7469   0.9777   1.5486
+#&gt;    4.1924  -3.3068  -6.0355   8.9054   1.5996 -12.0054  -4.6602  14.9232
+#&gt;   -4.0917   6.6781   3.7390  -4.2841   6.7789  11.4633  13.1591 -20.8491
+#&gt;   -1.2403  -5.8101  11.5358 -20.3429  -9.8043  10.3301   5.6508  -9.9822
+#&gt; 
+#&gt; Columns 9 to 16  -5.0519 -15.6536  -5.5278  10.4839   4.8238  -4.2297  -4.9211  10.4956
+#&gt;   -3.2430  -9.5777  -0.2008  11.8599  -3.4722   2.9282 -10.5189 -11.0866
+#&gt;   -8.1572  -0.2395   8.4587 -11.2127  -0.8930   0.3864  -2.3457  -6.3919
+#&gt;  -12.6023  -9.3652 -15.9834   9.0576  -6.4162   8.6291 -16.4968  -5.3234
+#&gt;   -6.2092 -16.6057  -8.0656   4.7572   0.6566  16.6773  -9.9437  12.1628
+#&gt;    3.4973 -15.5520   8.4753   0.7503  13.3989   7.6743   1.1990  -8.3222
+#&gt;    3.2941   0.5389  -4.4373  -8.0926  10.3525   8.2620  -0.3922 -10.5982
+#&gt;    2.0992   2.8307 -11.3777   5.5632 -18.6747 -11.0641  12.3123  -5.2210
+#&gt;   11.6860  -2.3646   6.6022   9.7806   6.7111 -13.4087  -1.0562   4.7586
+#&gt;  -16.2824 -14.1014  15.6424  -2.8804  -1.8653  16.2567  -1.7422   9.6859
+#&gt;    7.2852  -5.1156  -4.8869  -1.8487   0.9758  -6.1300  -9.8608   4.3139
+#&gt;    8.4879 -16.2979 -22.0021  20.8787   3.8001  -2.2933   2.3055  -6.7258
+#&gt;  -11.6384 -10.7877  23.9544  -0.3630  -4.4196  -5.4371   0.1727   0.2736
+#&gt;    8.2331  -0.9939 -10.9051   6.3860 -10.9876  -2.9687  -0.3035  -6.2201
+#&gt;    1.6205  -7.3983   3.2433   9.5837 -15.1221   1.2189 -11.8776 -10.1880
+#&gt;   -8.5982  -2.8860  -1.5523  12.6660  -6.2845  -4.3429   8.6657   4.9786
+#&gt;   10.7378  -2.4435   6.9686 -13.8020   0.4491  12.7697  -4.6896  -0.3179
+#&gt;   12.3907  -9.3405  -8.8491  -3.5816   5.9158  -2.1643  14.6102 -11.7558
+#&gt;   11.3377   7.6532  -9.1046   0.7160 -15.4803   7.0581   8.9869  -3.3178
+#&gt;   -1.8110   0.7145   0.5814  -6.3573  -9.7225  -9.5813  -7.1352   6.4063
+#&gt;   -4.0320   7.8878   5.9525  -5.4456  -2.6309   3.0996   5.5376  -7.9531
+#&gt;    1.6578   5.8366   5.3673 -11.4888 -13.9038 -17.5789  -1.9719  -0.9318
+#&gt;   -7.2453   1.9663   4.3650  -0.3790  -4.2484  14.1084 -14.0829  -5.9840
+#&gt;   -3.2372   2.3716   7.5951  -1.4341   1.9291  -2.3323 -13.2522   0.9026
+#&gt;   17.4940  -8.7963   1.2609   8.2443  -0.4105   2.7368  -0.9383   5.9460
+#&gt;    0.5119  19.2834  -2.7669  -1.6191  -0.8475 -12.9259   8.1604   5.5678
+#&gt;  -15.2675  -2.3728   4.2026   7.0293   6.2566  -2.7296  -3.3734  -2.0884
+#&gt;   10.2617   5.2372  -4.6707   6.7844   4.9344   5.8431 -10.6913  -6.4078
+#&gt;   -0.4118  -5.9068   8.8041   9.4516   3.0169  -2.9376 -12.5616   7.4301
+#&gt;    5.8621   4.0873 -13.9898   4.6101   8.8701  -0.1944 -10.0589  -2.1204
+#&gt;   -8.7893  -4.7496   3.4217 -19.9301   1.4026   5.6201  -9.7724   3.1006
+#&gt;    3.7177  25.8277  -7.9515  10.2657   1.2519 -10.6447  -4.5983 -19.6696
+#&gt;   -3.3835  -6.4632  15.6626  -6.6609   2.5483   8.2629 -11.0463  -8.7981
+#&gt; 
+#&gt; Columns 17 to 24 -15.1061   1.9276 -13.8666  -3.8967  -5.5002  -7.8204  -5.1597 -13.6479
+#&gt;   -1.0532  -4.3306   5.4546  -4.7892  15.1088   8.0288  12.6441   1.9789
+#&gt;    8.0984   1.0747  -1.2822 -10.0374  10.3140  14.7900 -11.8292  -3.7487
+#&gt;    3.2155  -0.4762   6.4662   4.6925  -4.8926  -7.3207   5.0228  13.0175
+#&gt;   11.5186 -11.5963   9.4002  -5.7629  -9.2373 -13.5491  -1.5002  -1.1277
+#&gt;    8.0051  -9.2534  -0.8459 -10.4178  -5.8362   4.5994   0.2911   5.1845
+#&gt;   18.8482  -9.6356   0.5151  -1.5948   9.9338  15.7383  -7.3023   1.8789
+#&gt;   13.5459   5.4399  -7.1362   2.1980  21.2082   1.5343  -2.2570 -28.6290
+#&gt;    3.2007  -6.6374  17.2961   6.0226  -6.0808   1.0173  10.7136 -12.6136
+#&gt;  -13.5292   2.4464  -7.7585  -0.6386   5.8336 -11.2572   1.8914  10.2651
+#&gt;    9.2183  -7.1085 -16.7239 -14.4238 -13.1062  12.2904  -4.7747 -12.3438
+#&gt;   -5.3513  -1.4276 -10.7133  -1.0104 -14.7948 -11.3771 -10.5380 -14.1806
+#&gt;    2.2582   2.5082   3.1395 -10.3802  -3.4846   9.7888   6.3816   8.8894
+#&gt;   -3.3255   8.7914   3.5600  -2.4824   6.4942   3.9103   0.1821  -1.1500
+#&gt;    4.7220   1.1739  -5.6123  -1.4683   0.5952  -0.7315  -0.1751  -5.2895
+#&gt;  -10.7894   0.0108   3.8297   3.5149   5.1771  -7.0988  13.1429   9.9300
+#&gt;    4.9622  -7.1838 -20.6221  -4.5445  -6.3929  14.4627   5.7682  -5.9828
+#&gt;   -1.6371  -2.6708  -7.7051 -12.2669   3.6014   1.2710   1.0021 -16.3431
+#&gt;  -10.6782   5.9850  32.8903  12.4231  12.3848 -13.1794  11.3659  16.1553
+#&gt;   -8.6677 -10.8305   2.6711   5.8227  -3.2384  12.1780  -8.5230  -3.7069
+#&gt;    9.8047   1.7091  -6.1964   9.3361   2.7999  -3.9781 -13.4320  12.7182
+#&gt;    5.2453  -3.3518 -10.8381  -3.0425  19.1940   6.6235  -9.0769  -0.6954
+#&gt;  -17.2991  -1.8443  13.4762   2.0591  -8.9836   2.1469  12.4268   3.2675
+#&gt;    0.7351  22.4695   6.1724   5.6455  -0.3949   1.2288  10.5278  -0.1145
+#&gt;   -2.4389  -3.5736   0.5515  -5.5292  -1.3270  -3.0887 -18.4237   4.8149
+#&gt;   -4.3389   5.3588   7.6871  10.6304   4.6827  -5.4639  -4.1754   6.1267
+#&gt;   11.6110  -3.9579  -5.9381  -4.1842  -5.2819  14.5803   3.7449  -9.4213
+#&gt;   -1.9401   6.1597   5.4595   6.9357  -1.2076  10.2617   7.7148 -11.8499
+#&gt;    0.7595  -0.6014  15.9818 -10.5274  -5.3796   3.8521  20.9919   0.7645
+#&gt;    1.8653  10.0281   6.2075 -15.7851 -14.1920   9.4755  13.5518 -15.5913
+#&gt;   -1.4591   5.5454  -5.1016  -0.7982   4.8222  -3.2032   7.6409  -8.4062
+#&gt;    6.5436   3.7843  -8.6999  -4.8098   5.4861   4.2819   8.1165   7.3718
+#&gt;    4.6000  -5.8893 -23.4787  -4.9795   3.0594  12.3016   3.2690   3.5217
+#&gt; 
+#&gt; Columns 25 to 32   1.7778  -0.1741  10.0144   0.1100 -14.6647  -5.5856  -0.2637  10.2384
+#&gt;   -1.3931   8.4956   1.8278  -9.6910   9.9097   2.5671 -10.6541   4.6897
+#&gt;  -13.2821   3.1024  -6.2520  -8.1819 -10.8312  15.8243  -6.2202  -5.2134
+#&gt;  -10.4369  10.2615  -5.4063   7.5572  11.9416   7.1635  -1.3847  -0.4782
+#&gt;   -3.4464   9.0079   6.4960   4.4430   5.3117   4.7168   5.1332  11.8318
+#&gt;   -1.6587  -3.9442   1.0908  10.2382  -5.6771  12.8907   9.4950   4.1755
+#&gt;   -5.3847  27.2970  -5.2261 -12.2230  22.8968 -14.2896 -15.2572  -4.1222
+#&gt;   13.7583 -10.0067   0.2167 -21.0369   1.2837   8.8987   5.9922  -3.0999
+#&gt;   -4.2537   4.1788  16.4749  -2.8583  -2.1226  -9.5117   2.1922   1.8162
+#&gt;    8.5287  -2.9122  -2.1248  10.5133   4.8937   7.5389   0.2121  -7.8859
+#&gt;   -4.9846 -12.8030  -1.8882   0.1910   1.2181   6.9836  14.2577  22.4123
+#&gt;   -7.4522 -12.7975  -0.8079  -0.8627   8.6350  11.2845  -2.3146   5.7266
+#&gt;   12.1104  14.6532  19.1453  -9.0659  -7.2229  -6.0188  16.3080  10.1372
+#&gt;    4.5465  -9.0641   0.4322  -7.4698   1.1671  11.2206 -14.3092 -11.6783
+#&gt;   17.7936  -0.8289 -13.6353   6.1814  -2.8911   8.2300   5.4011   4.8218
+#&gt;   -7.9323  17.8275  -0.4554  -3.4589   4.4595   3.7615  -5.4832  -1.0095
+#&gt;  -13.9743  16.0892  -2.6171  -3.5611   4.0792  -2.1144  13.8133   4.5982
+#&gt;   -2.6170  -0.6755 -12.1117 -24.0577   1.1589   6.5868  -0.5188   1.1435
+#&gt;   -8.4654  -2.7579   1.2969  -9.0796 -10.1263  12.4411  -3.7248   2.8195
+#&gt;   17.3934  -8.4432  -2.7909   2.7379  -8.4793 -13.8044  10.1999 -10.4044
+#&gt;   -7.7334  -2.5178   5.2465  -9.3406  10.2474  -0.2369 -22.2037   4.8796
+#&gt;    4.0159   6.8714  -7.3192  -4.6508   1.0704   2.8704   6.4501   5.2904
+#&gt;  -11.5786   5.6984   3.3727   3.5654  -7.6489  -6.9025  -9.9804  -4.0167
+#&gt;    1.5916   4.3510  19.0703   3.0556  -2.2498  -7.1878  -0.2999  11.1407
+#&gt;   10.9710 -12.5355  -3.5661   7.9113  -5.3299  -0.6712  -6.0232  -4.9191
+#&gt;    6.9522   7.4351  -1.8514  17.6501  -4.5240  -8.5315  -0.9537  -1.2942
+#&gt;   11.7416  -2.4716   7.3769   2.1249   2.2503  -5.5305  -3.4552   5.2614
+#&gt;    4.4133 -14.9014   2.6159  18.2596  -0.2846  -0.0281  -4.2213  -7.2945
+#&gt;  -14.1361  20.3555  -7.8349  -0.0203 -15.6937  -0.6371  -3.8028  12.6171
+#&gt;   19.4651  -5.1343  14.9903   0.6087 -10.3531   4.2478  19.4004   3.1429
+#&gt;   -2.1876  17.4168  -5.6756  -0.2676  -2.2310  -0.9550  -5.5168   9.5355
+#&gt;   -5.8493 -14.3634  -6.6581  10.0440  -2.8606  -2.0556 -11.0157  -2.0355
+#&gt;  -14.2199  -5.4934  -0.0826   1.4885  16.0165  12.4117  -7.0615  12.5529
+#&gt; 
+#&gt; Columns 33 to 40  10.1500  -2.7168   5.8167 -12.4654  -1.3378 -12.2143  10.4488 -13.0792
+#&gt;   -2.6546  -1.7643  -5.9071 -11.1366  -4.9924  21.9747  -8.3145 -10.3978
+#&gt;  -11.8286  14.7212  11.9621 -10.4938   7.4112  25.7293  24.8885   3.2072
+#&gt;    6.3994   4.2064 -11.9686   9.1049  -4.7787  18.2113   0.4549   6.3143
+#&gt;    1.8633   5.0401 -10.5132  15.4074  -3.6229  13.2258  -1.8025  10.2815
+#&gt;   -8.0038  -5.8750   5.6899   7.1259  -5.7372   5.9912  -1.7020   7.0553
+#&gt;    0.6529  11.6060   5.7158  -5.1726 -16.5611   5.9302   2.3731   4.5060
+#&gt;    7.9616  11.2526   7.7906  -3.9221 -15.5615   6.2789  18.7343   3.2657
+#&gt;   -9.6967  -2.3485  -3.2977   5.0751  11.0015  -3.8198  -4.6981  13.0108
+#&gt;   -6.1833   0.3852 -13.5263 -10.9368  -5.7111  13.8614   3.1115  -2.4188
+#&gt;  -23.3818  -4.6403   1.6466   5.0277  14.6978  11.7769   1.6959  -0.8511
+#&gt;   18.4458  -0.8086   3.2779   7.4938   2.9144  -8.4173  12.3187   1.5119
+#&gt;  -13.7431  -0.5227  -0.7258  -5.3151 -15.2641   4.0030  -0.8535   3.9266
+#&gt;    5.3062   3.4875  -2.3348   3.8489  -0.5105 -10.3150  -0.6041   9.8378
+#&gt;   -1.0193  -8.3886   9.1420   5.2717 -15.8106 -10.3659 -14.0255  -4.1194
+#&gt;   -2.4097 -10.2394  -4.6149  -4.6161   2.0708   6.0866  15.4649 -12.5058
+#&gt;  -14.6599 -14.0379  13.4541  20.2061  -3.6042  -2.7530 -13.5095  -7.4449
+#&gt;    0.3103  -1.0956  -8.0902   1.3008  -3.4313  11.7765 -11.8600   3.8552
+#&gt;   -2.7998   6.2498  -0.1702   0.4605 -13.8449  -8.1785  18.2829  23.7498
+#&gt;    6.0590  20.4857  10.4082  -2.4494   2.3333  -1.4283   0.1231   7.1851
+#&gt;    7.4581   7.6981 -12.6472  -2.2774  -1.0708   1.0064  -2.8286 -11.5675
+#&gt;   -2.5189  14.7925   3.5630  -9.5981   2.0895 -14.7525  16.8936  -3.0053
+#&gt;    1.2041   3.4081   1.7201   2.0051 -10.9110  22.4472  -5.5768   7.2891
+#&gt;    8.0471  -4.4368  10.0651 -10.3965  15.8845 -15.5329  25.6666   3.4862
+#&gt;   15.5102 -10.4686   4.5244 -19.9958   6.8780  -8.7682  -9.2341  18.1238
+#&gt;   15.5442   3.4110  -5.3646  -3.8514   8.0869  -0.3192   3.4853  -9.9307
+#&gt;    0.9164  -5.3659   4.1568  -6.6659  -5.4278 -18.0584   0.3826 -19.3212
+#&gt;    6.0171  -2.7288   6.8094   5.1870   9.5104   1.4394   1.4395   2.5156
+#&gt;   -3.5162   5.0776  -5.7641  -4.2334  -6.3650   5.7626  -3.5435   1.7901
+#&gt;   -1.3093  -1.8369  -2.8865   0.9459 -20.2915 -32.7982  -7.0939  -1.9332
+#&gt;   -4.3264   4.8188   8.0833  -4.7750  -3.5830   1.9510  -1.8569  11.8234
+#&gt;   14.5494   4.4043 -10.1908   4.0622   0.1435   3.1325  11.6746 -11.3710
+#&gt;   -0.5474 -17.7964  -5.1160  -7.5432  10.3011   2.1522  -2.5519  -6.5769
+#&gt; 
+#&gt; Columns 41 to 48  -7.7728  13.0417  -6.2141   7.5805 -10.5306   2.4039  -2.6211 -10.8351
+#&gt;   -5.7943   1.4155  -0.0184  -6.7851  -2.8573   5.4923  14.2143  -4.9217
+#&gt;  -10.2013  17.2115  -5.5376  -0.1993  -0.0483  -1.8005  27.7821   2.4018
+#&gt;   -0.1497 -10.1295  20.7939  -2.0661   7.0674   4.5289  -5.6561 -18.6444
+#&gt;    2.6511   7.1128 -12.3257   2.0484  12.3790  -4.2943   9.5533 -16.7716
+#&gt;  -12.8534  -8.1058  -1.1198   1.3922   3.3961 -13.1037   8.0641  -0.8374
+#&gt;    2.6301  -4.3767  11.9773 -15.8264   0.5274  13.3549   0.8531 -15.4280
+#&gt;   -0.4952  -4.6740   6.5916  -6.6276  -3.5350  -1.0065   8.1074   3.3948
+#&gt;    6.4617   4.4943   3.4736   9.9566  10.4772  -0.0510   6.4968  18.4616
+#&gt;   -5.7202  -5.0081  -2.1739   9.4466  -5.9768  -1.9245  -4.2107 -12.0566
+#&gt;    6.8396   9.8853  -4.6098   8.1681  12.2168 -11.0984   0.6794   6.4963
+#&gt;   -6.9582 -14.8138   6.2660  14.0955   1.1016   8.0584  -5.1369  -3.8343
+#&gt;    3.1336   2.0385 -10.6221   1.4631  -8.8730 -11.5658  -1.9890  -2.1705
+#&gt;   -3.2012  -3.2624   0.0124   5.5875  -5.9079   3.6307   9.1475  -5.5163
+#&gt;  -16.6238   1.3657  13.1135 -18.5553  19.3835   3.0428 -11.0630  -6.1474
+#&gt;    1.9540 -12.7126  17.6025   9.4565 -13.1545  15.5756   5.4707   2.3039
+#&gt;   -2.2161  -2.3298   4.7162 -23.5897  -1.6494  14.3266   2.2557  10.9696
+#&gt;   -0.9794  -0.4933   2.9129  -6.6436  10.5483  12.2195   3.2601   3.5748
+#&gt;   15.1612  -3.5543  -3.3447  12.1451  -9.2359 -12.5165   8.5579   5.8165
+#&gt;    2.1669  -5.3160 -11.2051  -5.0594   3.0964  -6.5364  -2.7889   0.1063
+#&gt;    1.6183   8.3523   0.4388   3.9910  -6.5673   2.6309   7.4823  -3.9073
+#&gt;    3.9793   9.4371   4.4418  -7.3555  -1.5988   0.2323  -1.8985   1.3787
+#&gt;   -8.1983  -4.2067  11.3807   3.0023  -6.7143   0.3628   8.3436  -5.4336
+#&gt;    2.1477  16.8822  -6.4990   4.1027 -12.6306   5.3138 -21.9907 -14.3988
+#&gt;  -19.9449  14.1068  -1.2428   4.0588  19.1461  -7.3755   1.1968   6.9337
+#&gt;   10.8277   5.7167   1.2530   0.4044  -8.9044  -2.3803 -22.9909 -10.8675
+#&gt;   -8.5395  15.7964 -10.0465 -10.4169   0.0549  -0.2287 -14.4526 -14.1527
+#&gt;   -9.4452   4.3918 -11.0792  -5.8634   6.1970  -3.8583 -18.6164   3.9002
+#&gt;   -9.7997   3.0892   7.0320 -14.7294   4.5604   1.5742  12.2626   3.5791
+#&gt;   -0.0732 -13.5142   6.8419 -11.9071   1.5294   5.4908  -1.9896  11.3291
+#&gt;    1.4363   9.0709   3.7630  -4.3767  -0.1546   1.2374   5.7477  -3.1649
+#&gt;    2.7391  -9.0105   3.2614   4.6591   1.7479  -4.8373 -15.5717   0.2825
+#&gt;    3.0020   6.7660  -1.4641  -1.4705  18.1625   7.9863  -2.3651  -5.9829
+#&gt; 
+#&gt; Columns 49 to 54 -10.3971   8.8077  -4.6116   0.1249  -1.6678   2.0824
+#&gt;   -2.2096  -8.2924  13.3718   6.6673   1.4004  -1.3016
+#&gt;    2.5277   1.7145  -0.9568  -5.3421  -2.9864   1.5047
+#&gt;   -6.6332  -8.7000   7.2478   1.0313   0.7790  -3.3170
+#&gt;   -3.6021  10.2331 -10.4041   4.7000   6.0417  -7.5707
+#&gt;   -1.3524   0.1230   4.3883  -6.7944   4.1890   1.4641
+#&gt;   -2.5252 -11.2596  -0.6901   6.4817   5.9568  -4.7086
+#&gt;   -7.5398   3.9761   2.6150  -2.4091   3.3055   0.5490
+#&gt;   -0.6691   5.7260  -2.5497   1.0488   4.6558  -0.9856
+#&gt;    9.7165  11.1001 -10.1059  -2.6323  -0.2516   5.0828
+#&gt;   -1.5492   4.6172   8.0683  -9.1370 -10.2819   4.1062
+#&gt;  -14.9461  -1.1056   4.2353   2.6166  -2.3585   3.9567
+#&gt;   -2.1073  -2.8609   0.2629  12.1419   0.1297   0.5681
+#&gt;    0.1319  -6.9208   7.5661  -4.5516  -1.4141   0.7718
+#&gt;   -5.0699  17.0210   6.4682   2.1013   5.7954  -3.9657
+#&gt;    8.7692  -9.5887  -5.7504   2.7774  -1.2659   0.4100
+#&gt;   -2.1969   1.8587   0.1379  -7.1127  11.8105   5.3066
+#&gt;   -5.8700  -6.6889  -5.8325   0.0023  -2.7781   9.8854
+#&gt;   11.8987  -5.3737 -11.4409   1.0054   2.0471   0.0693
+#&gt;   -8.3490  -3.2622  -2.3483   9.3945  11.1448  -5.8516
+#&gt;   13.3893   9.0998   6.4305   0.8584  -6.0464  -0.4977
+#&gt;  -10.9009  24.4256  12.4352  -9.8091   3.5869  -3.4174
+#&gt;   12.5356  -5.5965  -0.5642   5.0351   3.2751  -1.5896
+#&gt;  -11.1168  -3.6532  -3.4759   7.6531  -0.7342   0.2704
+#&gt;    7.4699  11.7731   6.1229   3.4599   3.3510  -0.4367
+#&gt;   -4.2270  -2.4345   2.1732   2.9828   0.3095  -2.4458
+#&gt;   -9.8393  14.6494  10.6712  -6.4012  -0.0743   1.9862
+#&gt;   -1.1818  -4.4589   0.8286  -7.5110   1.1673   0.7609
+#&gt;   -1.1280  -5.8980   6.6434   0.7556  -1.9274   5.3015
+#&gt;   -9.2338   3.2889  10.3630   3.2861   9.8004   2.4265
+#&gt;    3.9075   5.8852   3.2487   6.3270  -4.0139   4.3934
+#&gt;   15.9540   1.2406   1.6622   8.0269  -0.0123  -0.9241
+#&gt;    2.7061  11.8639   1.6918   2.4294  -7.4127   6.2571
+#&gt; 
+#&gt; (6,.,.) = 
+#&gt;  Columns 1 to 8  -2.3933  -2.8231 -14.5649   1.2265   6.9484  16.7138   2.5152  -3.8714
+#&gt;   -2.3410   0.0383  -1.8482  -1.2086  21.3870   2.8062  -8.6456   0.0021
+#&gt;   -1.8909   3.2607  -0.2578   0.7585   1.2088   4.6651 -14.7558  -7.0397
+#&gt;    1.6005   3.0437   2.7254   0.4729   0.4200   2.9602  -5.7026   7.4910
+#&gt;    7.7097   5.1390  -4.2629  -5.3246 -13.7337  25.3440  -7.3598   4.1057
+#&gt;    8.0588  -5.3984   7.8002  -8.1057  -0.1618  24.6554 -12.0122   3.7527
+#&gt;   -2.1816   7.3111 -13.8940 -13.4551  19.3834  -5.2697   6.9472  -6.4791
+#&gt;    4.0208  -3.4722 -17.0254   7.7089  -7.8556   7.9077   1.4991 -18.5755
+#&gt;    4.4408  -3.0581   2.1746   0.8543  -2.8558   7.9674   8.2400  19.3803
+#&gt;    2.6803  -8.3320  -0.8502  18.1305   3.0918  -4.5918  -7.5745   2.3753
+#&gt;   -1.4478 -10.6514  -0.9545  -0.7664   1.4152  -5.6242  19.3267  14.1202
+#&gt;    9.0945  -8.8592  -4.3406   0.4397  -4.6462   6.7036  -5.7391  24.0809
+#&gt;    1.9166  -6.8628  -7.8494   3.6546  17.2094   3.9769  -4.0957   3.9199
+#&gt;    1.7243  -9.9460   6.4939   4.1563  10.2148 -24.9290  -1.3239  -3.4306
+#&gt;   -0.1722 -10.1212  -3.0323   5.8537 -12.9172   1.2957   9.8134  -4.4496
+#&gt;   -0.8825   1.3276   5.5673   7.1959  13.0362 -25.3318 -13.5864   6.1727
+#&gt;    1.5064  -4.7732   4.0040 -23.9419   3.9104  -2.8041   3.1461   1.7080
+#&gt;    7.4478   0.0707 -10.1757   2.4835  -1.7016  -3.2533   1.0278   6.0833
+#&gt;    1.6632  14.5217   1.0237  -7.3828   0.9577  13.4193  -0.7450 -11.0810
+#&gt;   -0.3358  -0.7283  -2.9503  -3.8002  -5.4520   9.4111  -3.8151   6.5682
+#&gt;  -12.6043   3.4720   4.7416  -0.9385  -0.6316   5.4884  -2.0362  -7.3382
+#&gt;   -8.0593   7.6517  -8.7342  -5.1107  20.6218   9.9912  16.3979  -8.4791
+#&gt;    1.8494  -0.2744  10.7613  -6.2050 -19.5197  -5.7177  -5.4184  -5.1411
+#&gt;   -3.0168   6.6093  -3.3874  -2.2436  18.0242  -1.7924   1.4984   6.4660
+#&gt;  -12.2303 -14.3616  16.1126   2.0701  11.4154   0.3789   4.9664  -0.8457
+#&gt;   -4.4937   8.3347 -13.6119   7.7609 -20.5116   0.1745  -0.5136  15.9122
+#&gt;   -7.5482  -0.6359  -0.0841  -5.4823   0.1912   0.8122  20.5052  -2.4496
+#&gt;    6.8388   0.3139   7.5516  -9.9683 -10.5476  -2.7888  -1.1222  -9.8913
+#&gt;   -3.1357   9.2803   7.8254 -11.2691  -0.8377   2.4385  -8.8452   4.2665
+#&gt;    0.1097  -9.1326  -7.8229 -12.0185   2.6693  23.2933  20.2058  -1.6425
+#&gt;    0.5991   3.7670   0.2063  -1.4881  -1.1862  13.1080  -3.0750  10.8134
+#&gt;   -8.0925   2.0068  -6.0206  21.5375 -20.4440   3.3259  -3.7572 -20.7331
+#&gt;    0.4287   2.6760   2.0979  -4.7913  11.6869  12.6154   1.7529   7.2859
+#&gt; 
+#&gt; Columns 9 to 16   1.5429 -16.2952  -4.6099   4.4046  -3.0855   6.6220   1.6809 -15.7730
+#&gt;    4.9859 -10.9777  -9.2018  -1.2260  11.0141  17.4103 -11.0088 -14.0926
+#&gt;   -5.6609   1.2191  -2.1708  12.3986  10.5315   3.7087  14.3801   0.3327
+#&gt;    0.4658  15.3275   5.7982   1.7107  -2.0811  -0.2691  -4.6511 -22.7007
+#&gt;   12.6620 -10.0261  15.5672 -11.6777  -9.5031 -12.7180   1.2024 -19.7086
+#&gt;   -6.3520  -7.2483  -7.2158  -5.1041  -6.8909  -7.9554  -0.5690   7.9753
+#&gt;    2.5586   1.4266  -5.7646 -22.6115  14.7957   6.7313 -12.9693   6.5472
+#&gt;    2.1179  -6.2491   6.2176   3.6738  -3.9064   6.0592  17.8334  -1.8069
+#&gt;    5.5736 -10.2409   9.7896   9.8987   5.8255   9.5428   3.6325   2.3045
+#&gt;  -11.4319   7.3671  -8.7400 -20.3050 -16.0279 -13.2162  -0.9830 -18.5106
+#&gt;    1.1721   0.5274 -10.7990  15.6938   5.4564   0.7109  -6.9359  22.1753
+#&gt;   -8.8085   9.1855   9.5483   1.9305 -12.1039   1.4839  -9.9639 -15.0440
+#&gt;   -2.3187 -12.9011  -8.9538  11.1161  -0.4570  -9.3071 -13.1429   4.4905
+#&gt;    2.0216   8.9996   8.6279  19.9109  -6.0012  11.1547   5.3846 -15.7855
+#&gt;   -5.7400  11.2579  -7.9448  -0.6473 -16.4727 -14.3253 -12.2542  -4.0223
+#&gt;    9.3313  22.7818 -15.1444   2.1813   6.9884  -2.3573   2.4577   7.7630
+#&gt;   -7.4960   5.6692 -14.7068  11.3074  -1.1772   1.8731  -1.3718  19.7326
+#&gt;    3.1983  -1.7400   0.2044   0.1885   5.6679  -7.9948   6.3472 -15.2114
+#&gt;    7.0347  10.1483  11.5145  -8.7661   0.6672  15.2777   8.6046  -5.1687
+#&gt;  -15.9128  -1.2608  -3.7305  -6.6174  -0.8022 -12.1907   0.5581  -6.8652
+#&gt;   -2.6660  -8.1604  13.6352 -12.5818   4.3933   0.8350  -0.8670   6.9186
+#&gt;  -14.4307   4.6077   1.2247   8.5692   7.3176  -7.7655  11.5433  26.7380
+#&gt;    0.2708   1.2055  -9.8883  -8.4861   0.2955   1.5498  -8.4476  -3.7971
+#&gt;    4.1670   4.8443   3.1621  19.7748  17.0420  10.3664  -0.9168   3.7278
+#&gt;   -2.1689   7.2192   8.4080  -3.2654  -4.2774  14.1565  -3.8137   7.5986
+#&gt;   -1.7891  -7.4096  -4.3061 -13.8280  -5.9388 -12.7266  11.5695 -13.9269
+#&gt;  -12.7099  -0.0428 -14.1989  -5.1375  -8.5218  -9.1595 -16.8514  -6.4152
+#&gt;   -7.9016   4.6911 -10.4946 -12.6796  -4.2166  -1.3278   1.8187   1.4088
+#&gt;    0.2709   1.4040  -2.1233   0.3287  17.2023 -10.9112   5.0172  -3.8812
+#&gt;   -9.1995   8.2871 -14.2229  15.7726 -10.5637 -11.2286  15.9094   2.9421
+#&gt;    2.8056  -0.9664   6.7691   7.8808  10.5534  -9.2041  -1.0348   3.5583
+#&gt;    7.8407 -16.9434  -1.9920 -17.0384  -4.8415  -8.9256  11.2713 -15.5950
+#&gt;   11.6981   0.1152   0.9315   1.8805   8.5721 -14.7573   0.4268   9.2058
+#&gt; 
+#&gt; Columns 17 to 24   1.6778  -6.4415  -0.1352  -8.3186   2.3479  -2.9124  -3.0235   5.4116
+#&gt;    1.7527   1.8941  -7.2753  -6.6662  -0.6948   5.5409   7.5639 -11.6145
+#&gt;  -19.2897   2.8418   7.8411   9.0814  12.8986   2.8308  10.1728   5.7908
+#&gt;   -1.6884  14.0491  19.4663 -11.5493   1.3748   4.0061  10.2249  -6.3584
+#&gt;    1.6027  -2.9045   7.8118  -5.7253  -2.4294   1.6924   8.8773   2.7202
+#&gt;   -6.7086 -17.3329   7.9259  -2.4976  -7.2582  13.8021   1.8096   8.7034
+#&gt;  -12.4561   6.8747  -3.2511   4.0770   1.9898  13.9476   0.8878 -12.9841
+#&gt;    4.3855  -2.7669  -3.9468  -3.1256 -11.9641 -10.6414  -4.2919  -4.2538
+#&gt;   -3.9837   0.9678 -11.9469  -3.0423   3.9294  16.7164  -1.3809   6.7290
+#&gt;   -2.8724  -9.0192   2.6321   0.5427  17.7220  -3.3728   0.8575  10.2964
+#&gt;   22.2418  -9.5127  -9.0471 -19.1665  12.1969  -8.3111   6.4907   3.1734
+#&gt;   -0.2076  -2.4305   5.1785   2.1002  -0.7725   1.4751   1.8629  -5.4309
+#&gt;   -5.0746  -0.7222   4.2633   6.0595  -1.4383   6.2370   2.5049   6.4484
+#&gt;   -2.7657   6.0351  -9.5651 -15.2493   6.4888  -9.2095   4.7027  -5.8996
+#&gt;    9.5221   4.1215  11.3843   1.7492  -3.3621 -11.1883  -7.6068   1.9288
+#&gt;   -0.0211   8.0418   4.4088   7.3769  15.1228 -22.4525 -11.0030   4.5516
+#&gt;   -7.2525   5.1525  -8.8931  -5.2451   4.2944  -7.1268  -4.7372 -10.3392
+#&gt;    1.4353   4.9326  -9.9241   6.8887  -8.8230   5.2892  -2.2666   2.2917
+#&gt;   -1.3109 -19.8390 -10.8671  -9.9451  -1.5207 -11.8419   2.0018  12.9587
+#&gt;    0.4461 -10.1880   3.2137   2.4221   5.3363   0.4954 -15.3489   6.2938
+#&gt;   -4.6830   3.2804  -1.1812  -8.2964   3.7031   2.6834   4.3869 -19.4382
+#&gt;   -9.0388  12.4784  -3.2806  -7.6163   1.6500  -1.9572 -12.4062  -7.6018
+#&gt;    7.8623 -13.5411  18.3099  14.9309   9.6770 -14.5300   0.7799  -1.9263
+#&gt;  -18.3736   3.3309 -10.0669 -12.7049  -3.2633   1.2947   3.9347   9.7269
+#&gt;   11.3697   3.8972   6.4741  -3.7142 -12.9091   6.1751  -3.9448  -7.4640
+#&gt;    8.2126  -0.8895   4.4201   7.0476 -13.7706   1.3601  -4.8520   6.2506
+#&gt;    8.5005  18.1041 -16.0235  -2.0557   6.6932  10.8115  -3.1100 -13.5942
+#&gt;    7.9085  -2.9537   4.6969  13.3141   6.4515  -1.0102  -2.8777   5.5790
+#&gt;   -7.1045  15.9441  13.3113   4.8020   8.2366   4.4381   8.0431  -7.1640
+#&gt;   -8.8677  -2.9996   9.1404   2.6122 -21.6991 -16.3302   2.8424   6.2128
+#&gt;   10.3883  -5.7031  -1.9179 -10.6424  -7.1544   2.9624  -6.7363   8.1942
+#&gt;   -0.9672  -1.5058  -4.0205   6.8180   1.1208   9.9072   3.4915 -13.4807
+#&gt;   -7.6882   1.6917  -7.1852 -13.2910   8.2148  22.9102  -9.2222  -9.8807
+#&gt; 
+#&gt; Columns 25 to 32  -5.2330  -1.3815   2.1893 -13.3959 -20.0664  -1.1699   2.4693   0.6031
+#&gt;    3.2243  -1.5599   8.9246  -5.4088  -5.8966 -15.8264   3.9623  -7.8535
+#&gt;  -12.6833   2.4453  10.2644   6.5495  -2.7492   3.6596   1.9944   7.6586
+#&gt;   -3.8781 -10.5531  -3.0427  -4.5520   4.2960   9.7992  -3.2453   6.4634
+#&gt;    0.0360   1.1191  -0.1727   6.7454  -9.5639   5.6739   2.7641   2.8032
+#&gt;    2.8271  -7.2727  -0.0424   9.8217  -3.7367 -21.3563   0.2294  11.2744
+#&gt;   10.2004   0.9974  -3.1559  -1.7444  11.6059  -4.6158  12.4775  -5.4706
+#&gt;    4.2351   3.3644   4.5505 -12.7553 -10.2433  -4.5546  -1.5327  21.5162
+#&gt;    4.4139  13.4896   1.2470  -2.8440   0.5755   4.0851  16.5470   3.8477
+#&gt;   -4.0246  -4.0716 -12.7473   3.2761   4.2678  -6.4974 -14.6987   2.1921
+#&gt;    3.4868  12.5677  -2.0901   2.2527   5.1330  -0.4987   4.6309   2.8002
+#&gt;   -7.3434   1.1411 -22.2636  -4.9744  -1.3532   1.0651 -18.1702   9.8706
+#&gt;    4.9933  -8.0530   6.8392  -4.6851   2.0099  -0.3594  16.5478   1.6143
+#&gt;   -6.7089   1.2296  -3.3766 -22.8374  -6.2673  -0.4287  -0.7818  -2.8487
+#&gt;  -20.3780 -10.1946 -14.1414  -1.8622   0.5909  -3.7518   1.6533  -5.9114
+#&gt;   -7.5912  -1.6410 -11.5662  13.6936   8.9424  15.1482   8.7913 -13.7951
+#&gt;   -0.9836  -3.2381 -11.7469  -7.2732  -3.1640 -25.6097  -7.5313  15.5874
+#&gt;   -4.0414   9.6809 -10.4774  -4.9385   1.1550  -7.6655 -16.8005  -0.4686
+#&gt;   11.9079  12.2473   6.1715  -0.1022   0.0843  11.3742   3.1708 -18.4931
+#&gt;   -6.2798  -6.1154  17.0070 -10.1745 -13.4848  -1.4222  -6.4125 -15.9544
+#&gt;    4.5880  -1.8172   1.3404  -0.7641   9.0390  -0.8292   8.8150  -0.0080
+#&gt;   -8.6819  13.4015  -8.5101   2.4812  10.9497   0.8663   9.6707   3.3289
+#&gt;    0.1464  -8.4763   0.8412   2.7965   2.5752  -0.5562 -12.5688  -0.9830
+#&gt;   15.1069   4.9995  16.4681  -0.5231  -7.8215 -13.6747   6.4876   0.6850
+#&gt;   -8.4894  -3.5768  -6.4240  -3.0812  15.4772  12.1769  10.4209  -3.3678
+#&gt;    3.8084   1.9189  -1.7310   8.7086  -3.6002   2.5440  -0.2780   4.0019
+#&gt;   -1.3956   2.6794  -9.5926  -6.8309   6.3613  -9.5152  -0.6415   8.0382
+#&gt;   -2.3512  -6.4113  17.4262   1.2770   2.9487   0.0967  -1.5846   5.8918
+#&gt;   -7.3301   2.1242   0.9905   2.8800  -5.8558  10.1734  -3.6074   3.0594
+#&gt;  -13.2812 -16.7233  -9.6330 -11.3087  -9.2737   1.5179  -0.8227  28.9040
+#&gt;   -2.7239   6.9531  -0.7233   4.6741   4.4453   3.3907   6.6621  -4.8867
+#&gt;   -5.7248  -7.8697  -9.8186  -2.3132  -7.1376  -3.2057 -12.4708   8.3674
+#&gt;   -4.3927   1.4272  -8.7889   3.6932  10.4733  -8.7547 -10.5809  13.0088
+#&gt; 
+#&gt; Columns 33 to 40  -0.1658  -4.2741 -13.0167   2.8125  19.4319  -5.4442   8.7345  11.3106
+#&gt;   -5.4128   4.5412 -14.4241  -6.9806  20.9891   1.9883  -8.5468 -11.6336
+#&gt;    3.9230  -5.9303   1.0094  -2.0760 -18.3764   2.7971  -9.8910 -17.1911
+#&gt;   -0.7150  -1.6136   5.7454   4.3717 -10.7253   1.1466   0.7195  -8.8246
+#&gt;    4.0325  -3.4147   6.5115   6.8854 -28.3982   9.8396  -4.9240   6.0618
+#&gt;   -0.7968   7.2536  -8.6915  -4.6818  -6.5899  11.5288   8.7270  26.6901
+#&gt;   -4.0894 -10.4742  -2.8013   3.5833   2.8203   5.2279  -3.2011 -19.0499
+#&gt;   -7.7248  -1.2338  -5.4393  13.5333   1.4814   6.0945   0.7191  -8.0978
+#&gt;    7.6185  -0.8284  -4.9946  13.8686  -2.9363  -3.2145   6.9183  -2.4251
+#&gt;    5.0350 -11.5492  14.6761   9.1763  -7.2025  -0.9546   6.7639   9.4197
+#&gt;   16.2627   1.5523 -14.3638  -2.5518  -6.0636  -0.8085   1.3869  -2.2191
+#&gt;    2.2576  -7.7883  -0.2024   3.2101   2.8854  11.1889  -3.9979   2.4221
+#&gt;   -5.9915   4.2164  -8.7803   4.5399  19.8924  -5.1557  -5.6777  -5.8437
+#&gt;    7.3394   6.0241  -1.8508  -1.9891  10.3307   4.8951 -12.2203  -3.0790
+#&gt;  -22.0107  -2.2640   7.6870   9.3464   0.2916  -5.7537   7.5604   3.1492
+#&gt;  -10.7479  -3.7193  16.7831 -15.1875  13.6446  -8.0983 -22.4715  -2.1607
+#&gt;   11.9150  -5.6264  -2.5958 -14.5199  -7.6892   2.8243   6.1238  13.7542
+#&gt;  -12.1732   1.5765  -3.3213  -6.7741   1.4289  15.2627  -7.1139  -3.3994
+#&gt;   14.9497  12.3898  13.8701  10.1220  -3.4746  14.4940  23.5101   4.9444
+#&gt;   -7.4806   3.8434 -10.1420  10.8480  -3.5622  -4.8281  -7.2726  -2.8693
+#&gt;   -4.7255  -2.2326   5.0139   0.5783  -5.6597   5.1953 -15.6661  -0.2395
+#&gt;    8.3898 -11.8495   7.7439  12.7849  -1.7928  -6.8798  10.0592  -1.0226
+#&gt;   -4.4024  -0.1014   9.6252  -3.3212 -20.1394  -5.8556   4.4151   3.6770
+#&gt;    8.4042   9.1342   9.2365  -7.5792  24.0859   0.5709  12.2008 -14.5676
+#&gt;    0.0516  -2.5967  -7.9509  20.5446  -4.4098  -3.6914   0.0778   4.5274
+#&gt;   -7.2622  18.3369  13.0771   0.7538   5.7499  -6.8232   7.2616   9.0808
+#&gt;  -12.1754  -9.0076  11.1918   1.9653  11.7068  -2.9894   4.2650 -11.6029
+#&gt;   13.0535   1.4470  -8.0668 -17.0397  -5.0373  12.8577   6.7251  -4.5107
+#&gt;   -3.0594   2.9478   3.9375 -13.9612   1.8279  -0.7990  -6.5206  -1.8909
+#&gt;    4.2958   2.5674  -5.0706   3.6540   7.2688  10.8935  35.1408   6.1412
+#&gt;   -7.2251  -0.6509   7.0742  -7.2755   6.0282  -2.5625  -7.4393   1.4161
+#&gt;    5.6039  12.7617  -3.2069  -7.9473   7.4579  20.2680   0.9503  -9.4715
+#&gt;    1.8726  -0.4505  -8.4528  -3.1456  22.2555   8.8612 -14.3986   1.5937
+#&gt; 
+#&gt; Columns 41 to 48  -7.0365  -7.1911  -6.9832  -6.6034   1.8335   7.5551   3.6796   8.7989
+#&gt;   -3.6209  -3.4856  -6.2510   2.3313   1.1667  -8.5825   9.9867   1.9490
+#&gt;  -12.8983  -1.8999   2.1977   4.7087  24.5768   8.9532   3.6447 -12.3067
+#&gt;   -0.1780   2.2829  -1.9048  13.2251   2.4473  -1.6018 -13.6998  -0.8499
+#&gt;   -4.5863   8.7913  -1.2143  -1.1927 -10.1472   7.0486   7.8177  -2.5055
+#&gt;  -10.8818 -10.3920  -4.0015 -27.1391   4.7582  -6.4043  12.1196   2.8603
+#&gt;   -3.2827   6.1637   3.9089  -1.5718  -0.1650   2.7290   5.3034  -7.1740
+#&gt;   12.8172  -3.8016   1.2016  -2.3019  -5.9471  -3.1962   3.1356  -7.3677
+#&gt;   -2.7111  -1.8917  -2.2459 -13.3483   7.5332   7.3395  15.2006   3.9319
+#&gt;   -4.5157 -11.7697  -5.2488   5.5137   2.6628  10.3019  -2.9456   2.4325
+#&gt;   -8.0180 -11.1056  -4.5557  -1.5262   9.2000  -6.9966   7.3665 -15.5740
+#&gt;   16.5981   2.1450 -13.4648   6.9906  -3.8754  -6.5307  -2.1259  -2.9880
+#&gt;  -14.1285  -9.4364  -1.0108   7.9575  13.1723  -3.3897   3.5740  -9.5083
+#&gt;    4.1621  -1.5070  -2.2087  21.1738  -1.1345 -12.7376  11.6858  12.5840
+#&gt;   10.2081  -7.1385  -0.2708  -9.7277  -2.5300   8.6155   1.8450   1.3057
+#&gt;   13.8926   7.6847  -7.3537  -0.5823   9.2490  -7.8143 -11.8197   2.1618
+#&gt;   -7.0815  -0.0924   1.7060   0.2990   3.1005  -2.8214   0.9185  18.8770
+#&gt;   10.3080  -2.8859 -18.1575   0.8361  14.2335 -10.9023 -10.3132   6.2573
+#&gt;   -2.3226  10.1153  -9.8714 -11.1489   8.7382  11.8088   3.5335  -8.6061
+#&gt;    0.9529   6.2439  13.7589   2.4170 -15.8774  -0.7123   7.9430  -5.7493
+#&gt;    3.0389  -8.2618   0.8278  -5.5897   2.5569 -11.8408   5.3491   3.7439
+#&gt;    0.4842   9.9147  -1.0470  -2.6416  20.6687  -2.1343 -11.0162  -4.9360
+#&gt;  -12.7470   9.6998   6.0791   5.8153  15.2451  23.3747  -4.8672 -11.0663
+#&gt;  -21.7379   2.9156   6.4470   3.1172   8.1243   8.6528 -10.7619   4.5328
+#&gt;   -1.5000  -0.2806  16.6390   0.5521 -15.4529  11.5029  -1.5393   1.3152
+#&gt;    1.4420  10.6928   4.3994 -12.0668  -0.2689  -1.5219  -2.0803   8.6118
+#&gt;   -1.3304   5.7595   8.6643  -0.6670  -0.9824   5.3312  -9.3878  -6.1657
+#&gt;   -2.6928   5.0323   4.3580  -2.5496  -8.2724   4.0027  -8.0189   9.2038
+#&gt;  -14.3821   7.1752   1.4434  -7.9288   3.9231   6.6579  -7.0609   2.5534
+#&gt;    0.6453  -4.4102  -4.5158   4.6314   9.5885   2.2223   2.2485  -1.6284
+#&gt;   -3.5814  -5.0103   3.0189  -1.3147  -7.1761  -6.2938  -8.0212  -0.3543
+#&gt;    7.2415   6.0512   3.7243  10.1745   9.9101   0.0936   9.8109   6.2233
+#&gt;   -8.6651  -7.9083  -5.1292   5.5781   5.1312  -1.0708  -4.8641  11.3148
+#&gt; 
+#&gt; Columns 49 to 54 -10.8975  11.6077  -6.2775  -1.0497 -13.6208  -1.8142
+#&gt;   13.1456  -9.1939   0.3886  -4.1949  -1.6206  -4.2013
+#&gt;    6.0395  -7.3898   7.8786  14.0081  -3.9137   0.4371
+#&gt;    2.2743  -9.5347   1.2135 -10.0645  11.8385   2.1302
+#&gt;   -0.4494  16.9023  -1.9057  -6.1568   3.9840  -3.3445
+#&gt;    0.9947   2.2809  -8.2821   9.3761  -6.5058  -5.3371
+#&gt;    0.0837  -7.2570  10.4998  -5.4291   2.0629  -1.2072
+#&gt;    2.0734  -4.8856  22.5101   0.4016   2.8442  -2.9386
+#&gt;   12.2798  -4.8926   1.5630  -0.9470   4.9285   0.0782
+#&gt;  -10.5178  16.6957   1.3643  -8.1838  -7.0226  -4.0855
+#&gt;    8.0503  -0.6926  -5.9212  16.0828   1.3463   1.9198
+#&gt;    4.4199  -8.7152  -7.8884   2.5225  -3.3634   1.3823
+#&gt;    7.1209  -6.7880 -12.6276 -14.1402   7.5011   3.6135
+#&gt;    2.4809 -16.6612   3.1206   1.3862   8.9676   1.8443
+#&gt;  -17.2778  -1.3190   5.9414  -9.6632  -3.6013   2.3111
+#&gt;   -1.8095   1.7647   5.5546   9.7994  -1.9943  -0.8855
+#&gt;    4.7578  -7.2273   2.8811   0.5475  -9.1510  -3.1228
+#&gt;    3.7045  -0.2151  17.4081  -3.1675  -5.3228  -5.7981
+#&gt;    9.6104  13.4691  -1.4277   1.9960   6.9995  -3.7394
+#&gt;  -13.1755   1.6854   9.4084  -9.5875  -0.8298   4.6526
+#&gt;   -0.5333 -10.8859   2.6966  -0.2496   5.6243  -0.3994
+#&gt;  -11.5627 -13.2616  10.7873   4.5478   2.5188   5.3648
+#&gt;   14.5499   9.6646 -10.2373   1.7955  -1.3717  -3.4287
+#&gt;   13.2639   0.8773   1.1604   1.2519   3.5976   8.2300
+#&gt;  -17.1914   0.5858  -4.3062   1.3593   6.9147   7.4865
+#&gt;   -4.8861  20.8843  -4.2372   3.0550  -6.0051   1.4374
+#&gt;   -3.9068 -33.6806   1.2267   0.0336  -3.5000   3.5497
+#&gt;  -10.3744  -0.5291   7.1012   4.2670  -4.3717  -0.9353
+#&gt;   -1.4874 -10.1441  -6.8394  12.4491  -1.1372  -2.3226
+#&gt;   -0.8102 -29.3352   5.3444  -8.5302   3.6247   1.2677
+#&gt;   10.1890   5.5535  -6.2732  13.2793   6.5547  -1.0918
+#&gt;   -2.8983  -3.8226   1.3922  -5.4163  -2.0137   0.7241
+#&gt;  -13.1667  -1.6609  -2.3496  -2.3967  -2.1740   3.1770
+#&gt; 
+#&gt; (7,.,.) = 
+#&gt;  Columns 1 to 8  -1.1510   3.3446  -4.6907  -4.0923  -9.0119  -1.8544 -11.5465  10.2266
+#&gt;    2.1530 -10.3229 -13.4920 -10.5353  -7.1878   4.3803  -1.0222   1.2178
+#&gt;   -0.0279   2.5698 -14.4998   0.8784  10.8162  -5.7057   7.7050  -9.5477
+#&gt;   -4.2213  -5.9699  -0.4114  -2.1312   6.4255   7.8751  -3.3435   0.9462
+#&gt;   -1.5995   8.4146  -2.0530   4.2347   3.0521  -4.4703  -8.0160  -9.5067
+#&gt;    5.6889  -3.7806  -3.1521  -3.1988 -12.7691  -0.8307   9.4835 -12.2073
+#&gt;    4.7189   1.5370  -4.2539   1.2660   7.0476  11.2019 -15.6511  -4.4649
+#&gt;    5.5624   3.4705  -2.2476 -13.6931   9.5659  -6.6446  -3.9418  -8.1081
+#&gt;   -7.8403  -4.5030   0.9928 -18.9725  -0.6898  -3.9355   6.6413 -12.1244
+#&gt;   -2.3357   0.3881  -5.6700  -0.9241   3.7925 -16.9245   4.3452   0.9876
+#&gt;   -0.0763   3.1268  -3.8460  -4.8019 -14.2220 -17.3226   7.2250  -0.6345
+#&gt;   -2.4367   6.0777   4.7913 -12.2375  -6.7339   0.4671   9.0299   3.9063
+#&gt;    0.6186  -2.9219  -2.7057  -0.7764  -2.6864  -7.8872   4.4441  10.2005
+#&gt;   -0.5241  -2.6075  -1.9883  -0.3281   4.3225   6.6077   7.0158  -7.8009
+#&gt;   -3.4496 -11.0826 -11.6018  -4.6082  25.9934 -16.3178 -11.9316  -8.0340
+#&gt;    1.5328  -4.1684  -6.1294   4.3586   5.1483   0.1453  -6.2353  -2.7218
+#&gt;    3.5766   6.2953   1.1756  -2.9681 -14.6940  -2.2812  -2.3563  -2.7273
+#&gt;   -2.8772   0.3871   2.0151  -0.8548  10.4352 -24.0016   3.5732   3.9069
+#&gt;   -4.8925  -2.5335   5.6550   5.4676 -15.1168 -12.5531   0.4016   9.9141
+#&gt;   -1.1719  -0.4005  -3.1729   2.5641  -3.1845 -10.5227 -14.5495  -7.9963
+#&gt;    0.0419   2.4212 -12.3028  10.4835  -0.1816   6.0758  -1.5183   7.0821
+#&gt;   -0.2427  -4.8821   1.2099   2.4364   0.6832 -11.2929 -12.2063  -6.9524
+#&gt;   -1.1091  -2.1504  -1.6837   5.4961   1.8894  -3.6969   7.8218  -4.1573
+#&gt;   -2.8983  -1.7186   6.0291  -6.4366  -7.9518   0.8214 -14.5556   7.1323
+#&gt;   -6.7137  -2.2434  -6.1717 -17.8276  -1.7784   9.0319   3.7935 -14.0787
+#&gt;    2.4205  -0.9703   4.8595   9.9742   0.5819  -7.0176 -16.9618  -4.5669
+#&gt;    0.8702  -2.8651   8.0752   1.4832  19.5654  -0.9157  -2.8576  -7.4101
+#&gt;   -0.2374   6.4131   4.4602  -5.2020   4.4795  -0.2797   7.7103  -2.0048
+#&gt;    0.3967  -5.9701   1.5705   4.2041   3.2667  12.0663  -2.2622 -15.7162
+#&gt;    2.2880  -3.6944   5.7249 -23.6550   3.7084   8.1872   3.2680 -27.8372
+#&gt;   -4.2591  -4.9057   4.0272   1.2348  -9.4757  -9.8123   1.3069   5.6520
+#&gt;    1.8418  -9.5065  -8.6097   3.2440  -5.6186  -1.7234   4.7165   5.6540
+#&gt;   -5.0394  -3.8266   1.3010  -4.1619 -13.8733  -5.5402   8.6290   6.4715
+#&gt; 
+#&gt; Columns 9 to 16   7.9968   3.1824   1.6528  -5.3501   2.8699  -6.5961   5.7740 -12.2310
+#&gt;    0.7302  -4.2759   1.8779  -4.9163  -3.0430  -7.2339  11.5894  -6.2907
+#&gt;   10.8629  17.9219  -1.4833  19.6404  -5.1833   3.2871  -3.9377   2.2159
+#&gt;    3.5275  -4.0493   0.6487 -13.8285  -3.1606 -10.5714   8.2817  -5.4528
+#&gt;    2.0357  -2.3497  -8.4855  -2.4340  -0.7470   0.5274   6.4336  11.4348
+#&gt;    0.1513   0.0801   2.9735   9.0915   5.5422   6.9197   1.6482   4.7752
+#&gt;    2.4132   6.8459  -3.9542  12.0085  -9.6651  -3.9433   7.8995   5.2293
+#&gt;    5.2293   2.7419  20.1011  -7.3417   7.4816   2.0838  -1.1586   5.7965
+#&gt;   -9.5940   0.7334   5.8397  11.7022   9.7054   9.1951  -2.6624  -2.2839
+#&gt;   -5.4442  -7.4897   5.2764  -9.4087   3.7028  -4.2671  -6.9070  -0.4682
+#&gt;   -5.3623  19.0221   7.5789 -11.4834  -3.0362  10.1072   9.7035 -12.8669
+#&gt;   -6.9003   8.0125   1.4583  -3.7150   1.7329  17.1508  -2.5087  -7.2431
+#&gt;   -9.6946  -9.9120  -0.9855  12.4596  -5.2041  -3.8580  -1.7943   0.2785
+#&gt;   -5.7165  -4.5681   8.4780 -17.5609  -6.8587   0.9480  -0.0522   0.0123
+#&gt;   -1.7467  11.0275  -7.7045 -11.1939  -0.5519  -0.3445  -1.7720 -18.8731
+#&gt;   -3.1978  -5.6833   8.4781  12.3317   2.4258  -2.4807   7.0808  -4.3378
+#&gt;  -13.6104   7.2438   4.5246   8.7591 -20.6504   1.5312   0.3517  12.4284
+#&gt;   12.5804  -1.3469  16.2424  -1.5184 -10.0994   5.1005  -9.0594  11.6394
+#&gt;   11.3501  -2.1988   2.7424  12.4550  15.6389   3.3059   6.8598   2.2757
+#&gt;   11.3390  -3.5430  -2.9019  -0.1729  -2.5298  -6.6461   6.2712  -2.0054
+#&gt;   -1.1417   9.1742  -3.3956  15.1536  15.2132   5.6723  -0.3860   9.3219
+#&gt;    9.2356   9.3205   0.0930   5.9758 -12.2520   3.8998 -10.7852   8.9788
+#&gt;    2.4628   3.8309  -6.3892  -0.0402  16.0499 -17.4290   4.0783 -12.4398
+#&gt;   -4.7559  -7.5739 -16.7031  -4.0543 -18.5902  -1.6052  16.7595   1.5219
+#&gt;   -4.2533  -5.4680  -2.1122  -4.1514   6.1736   0.0462   6.5320 -11.1835
+#&gt;   -0.7842  -4.2493   7.3948  -7.3262   7.4715  -8.2523   4.5708  -4.9115
+#&gt;   10.5087   5.8179 -25.7325 -25.3737  -9.0901   2.1340  -2.1222   1.4530
+#&gt;   -2.2761  -6.2733  -7.1621  -6.9965  -1.7172   3.2972  10.9736  -4.0085
+#&gt;   12.1618  -6.2517  -6.5980   2.9502   4.2635  -4.8189  -5.1003  -4.1420
+#&gt;    3.9728 -11.8250  15.6391 -13.9557 -14.7237 -11.7873  -5.1508   4.1413
+#&gt;    9.6275  -2.4659 -10.3977  11.2501   3.9619   1.8273   8.9549  -1.4237
+#&gt;    1.5820  -7.8791   2.5443   8.2473   9.9800   4.2415  -4.5965  10.8618
+#&gt;   10.0454 -13.3391  -6.2946  12.4930 -10.3633  -1.6016   2.9378  10.6521
+#&gt; 
+#&gt; Columns 17 to 24  -0.2017 -14.2036  10.9189  -6.3093  13.3421  -5.2021 -10.8571   5.6784
+#&gt;   -3.7236   4.2107  11.5695   4.1625  12.5397   3.5341  -1.0868   3.1356
+#&gt;   12.3779 -15.8945 -11.7514   6.5719   7.3708  12.7398  -4.8171 -10.1031
+#&gt;   -6.3732  -3.1501  -0.9881   9.8138   7.9670  -6.8364   2.6885   6.0887
+#&gt;  -17.8622  13.3396  -1.6779  -2.7707   9.6506  -6.1295 -11.8552  12.9470
+#&gt;    5.1236   5.7030  -4.1452  -5.1171  -0.0168   8.9045   9.7367  14.3302
+#&gt;   -9.8137   4.5833 -17.7446   3.9888   4.8591  21.3616  -9.9362  -9.1715
+#&gt;   15.0867  -4.7968  -8.5767 -16.3302   0.7973  23.9033  -5.7737   1.1635
+#&gt;   -8.7811  -3.0575   2.9939 -10.3879   9.7657   9.5343   3.0429  20.9961
+#&gt;   10.0502  -8.2041   2.8456  11.3417  -2.4953   6.4162  16.7315  -4.6014
+#&gt;   22.0820 -21.9904 -11.3016 -22.8116 -23.9028   3.8842  22.1187  -0.2680
+#&gt;   -1.6472   1.6026  -7.8115  -3.6156  -3.2747  -7.2421   5.1116  -1.7513
+#&gt;    5.3359   2.4528   0.3550   1.3230   7.9741  -1.6141   2.0854  12.3013
+#&gt;   13.7491  -6.5297  11.0942  -6.7257  -3.1055 -16.7902   9.7864  -7.6694
+#&gt;    8.0004   0.4933  32.4629   4.3232  -5.2497  -1.7230 -17.6472   2.9151
+#&gt;    6.4136  -0.2973  -0.9971  -1.3520   3.8680  13.8084  -2.5657 -16.9238
+#&gt;   -7.1824  -2.1916   4.0267   4.1113 -13.9599   9.5921   5.3595   3.1066
+#&gt;   -5.2619  -8.1361  -7.5799  -2.7969  -0.9957 -13.9166   4.1316  -8.2160
+#&gt;   11.3372  16.3689   8.8371  -7.3994  -2.0990   3.0968  10.8980  15.3794
+#&gt;   -3.8021  10.2220  -1.2202  14.9014   3.2765   3.3371 -11.9771  -2.4916
+#&gt;   -6.8762   4.6891   2.5929  -0.4768   9.1211  -9.8519  -9.6648 -11.6314
+#&gt;    9.7460  -6.4685  -4.7942  21.3611  -3.0056  17.9657   1.6230   1.0961
+#&gt;   11.9743  -0.9533  11.4663  -6.1106   2.7248   3.4914  -5.2735 -14.6978
+#&gt;   -1.5166  -8.1734  -6.6487  -5.7655   2.7949  -8.5387  -3.7835  29.8814
+#&gt;   -4.0876   4.7392   1.2242 -15.8987   7.1728  -9.1029  11.9292   8.2534
+#&gt;   -8.9881  -4.8746  -9.5954  21.2173  10.0572  -1.6628   1.2289   5.8971
+#&gt;    7.0142  -4.1670   5.6467  15.3771 -12.8819 -14.6869 -11.2740  -0.9921
+#&gt;   -0.2972  -2.1018   6.0177   0.5221   1.4981  -6.0632  11.9915  -3.5423
+#&gt;   -3.3258  10.5987  -5.7706  -3.9644  12.1994  -9.6323  -1.2799   8.8518
+#&gt;   11.6141  -4.6315  -6.4642  -3.2025  -0.1698  17.3934   3.8328  13.8618
+#&gt;   -2.5127  -4.7518   6.7672  -2.5339  -1.2967  -2.0569  -1.7243   6.5882
+#&gt;  -16.7856   4.9965  -0.3747   5.9526   2.7530   9.1097   7.1798   2.2725
+#&gt;   -5.8005  -2.7035  -3.8047  -0.9284   5.5126   2.2023   8.9701  -2.5880
+#&gt; 
+#&gt; Columns 25 to 32  -3.3465 -12.1665  -4.7266   3.3576  -3.1950  -2.8802  17.6598   4.7404
+#&gt;  -10.6723 -15.0681   1.1665   5.6881  -4.7752  14.3467   4.8009   5.4295
+#&gt;  -17.6142  -1.5378  -2.4484  -7.1934 -10.2677   1.5343  12.1779  -3.2596
+#&gt;    6.8188   7.3524  -2.2905   4.4959   1.7009  -2.8864 -18.7653  -4.7065
+#&gt;   16.6051   6.1646  -1.4842   0.5813   1.0516  11.0768 -12.5964  14.5830
+#&gt;   16.7302  -7.0112   3.0949  -5.1746  22.0134  -4.1310   8.7279  24.2889
+#&gt;  -10.7381  -3.4775   6.6372   6.4803  -3.1785  -5.5057  -2.6962   9.3441
+#&gt;   -1.8757 -11.1710   9.7184  -5.2805  -8.8909  -5.4596   0.0158 -14.2830
+#&gt;    1.7568  -7.5622  -1.3491   2.7702 -10.3292  -1.1636   2.6564   4.2344
+#&gt;    0.7000   1.7821  14.5931  -9.0773   5.2029  14.6774   7.8485   2.8135
+#&gt;    1.6623   1.7492   1.3100   2.6948 -13.5191  -4.6533  10.4787  15.1358
+#&gt;    7.3247   4.5906  -4.4544   2.1359   4.3850  -3.7317  -1.3749  13.7357
+#&gt;    0.8120  14.9744   2.7189  10.4039  -2.4289  12.5968   2.3494   2.9934
+#&gt;    2.5820  -5.7150  -8.7422   5.8390  -3.7284  -3.0733   0.3706  -9.7306
+#&gt;    4.4213 -16.0351  -3.1989   7.5213  -1.6001   0.9968  -4.5627   0.9700
+#&gt;   -2.7907  -7.1278  -2.0649   4.2140   4.5857   5.0167  -4.1840 -13.4642
+#&gt;   10.4783  -7.9112 -17.1973 -10.2099  -3.1797 -10.2205   1.3935   8.0176
+#&gt;   -0.7982   2.0720  -6.4560  -8.0288  -6.7375   3.6489  -0.0936  -5.3324
+#&gt;    4.8929   8.4467   5.4012   3.9065   0.2363  16.5182  -2.2986  -0.8222
+#&gt;   -4.1151   8.6142   1.8951   6.1967  -1.7194  -2.3320   5.2856  -4.6069
+#&gt;  -19.3944  -1.7963  -4.2656  -8.2972   2.7705   5.6570   6.9059   4.4057
+#&gt;   -0.4050   3.9011  -2.3972  -3.1239 -17.2196  -5.5653  12.2193  -0.9968
+#&gt;   -5.3592  -3.0987  -7.6986  -7.4511  -4.2069  -3.4384  -4.8321  12.0722
+#&gt;   20.0403  19.5729  15.6391   6.2954   0.4169  -3.8861  12.2240 -12.1667
+#&gt;    3.5800 -11.0232  14.0919  -0.2808  -7.4968 -12.0439  -4.0667  -9.9398
+#&gt;   10.0156  11.4773   6.1229  -1.3124  -8.8166  -8.6302   1.0571 -11.2056
+#&gt;    1.6166   6.8320   0.6153   6.1363  -8.2500 -11.0222  11.3847   1.4746
+#&gt;   -0.7793  -0.4208  -0.2259  -6.4614   9.4015  -4.7347   2.8738  -6.9280
+#&gt;   -1.6318 -12.4125   3.9271   7.4695  -5.8701 -11.2740  -3.0332   1.2802
+#&gt;   28.0953   2.1393 -24.2158  -4.0061   7.0950  -8.6099   2.1225  -2.6146
+#&gt;    3.7211   9.1366 -11.3096  -4.2825  -2.1420  -2.1255 -12.4218   9.6057
+#&gt;   -1.3382  -5.4725  -1.8357   0.5935  -9.3416  -3.5055  -1.9799 -12.6972
+#&gt;  -11.4266   8.7223   2.7088  -8.5663  -2.2837  -0.0327   6.6363   6.6428
+#&gt; 
+#&gt; Columns 33 to 40   6.7635   0.2676  -0.4159   9.0724  -7.2259  -4.9980  -5.8763   8.4493
+#&gt;    4.4937   2.5311   1.9138   0.3078   1.9588  -3.5497  -1.1907   0.5561
+#&gt;  -10.2568  -3.1322  11.5117  -4.3087  -4.6816  17.6844   8.7753   3.3748
+#&gt;   -5.3756  -7.6747   2.2086   0.9428   3.6217   1.3725   7.7266   3.1351
+#&gt;   14.0019  -4.5984  14.8477   0.3603  -5.1469   1.7272  16.7233 -14.4129
+#&gt;   -5.7418 -11.3068   6.4387   4.5919  -6.4510  -4.8739   1.7584  13.3482
+#&gt;   11.4781   1.1217   3.0755   7.8919   6.0445  -1.6499   9.7943 -12.8667
+#&gt;    0.0311 -16.3330   5.5842   9.4925   2.7538  -3.7333  10.3775  -1.5636
+#&gt;   -0.8140   3.1831   5.1449  -2.5414   6.0609  -1.0483  10.0245  -6.0115
+#&gt;  -10.3292  -5.1663   4.7419  -2.7025 -10.1061   1.4991  -2.2201   3.2466
+#&gt;   -7.9465 -14.7259   9.1298  -5.0068 -12.2494  12.7274   7.7615  -3.5029
+#&gt;   10.7479  -8.9968   6.5722   3.0814  -1.4771  -5.4336   1.0488   6.0063
+#&gt;   -6.4348   0.7132   4.3070  -8.7904   8.6975   1.1075  -2.0351  -5.8650
+#&gt;   -4.7090  10.8575   2.0938  -4.5925  13.6765   5.7960 -10.5568  10.8240
+#&gt;    4.3676   6.4377  -5.5873  -2.7393  -1.0691 -10.3165   7.6410  -2.0931
+#&gt;   -7.1456   5.1314   9.0001  -1.0220   1.6024   7.0429 -20.3988  11.5544
+#&gt;   -9.4779   8.7472   4.8977  -4.8484   1.3657  -1.3088  -2.8118  -0.2669
+#&gt;   -1.8931  -0.8126   5.4710  -1.1423  11.9743   2.1789  -1.0370   2.7930
+#&gt;   12.8539  -7.2875  -3.3640   4.9971   9.4708   5.3407   0.1027   3.0712
+#&gt;    5.3685  13.3009  -0.5570  -1.6243  12.8898  -6.8893  -4.9286   5.7364
+#&gt;   21.6231   7.1604   9.1344  -1.7378  -3.5171   1.3859  -0.7098  -8.5414
+#&gt;   -8.8463  -5.5049  -3.2001  -1.2804   4.5352   1.3429   7.7656 -17.9811
+#&gt;   -3.8347  -4.9080  -4.4590 -21.8495  -7.2955   6.4522  -7.3021  11.5973
+#&gt;   -4.6035   6.9409   1.1151   7.4165   9.8849   3.9993  -3.3113  -7.1636
+#&gt;    0.8386   3.7905 -14.2038  14.4263 -17.5220 -10.7280   8.3832 -17.0463
+#&gt;    4.4654   5.3002   3.3907  -0.2470   3.5849  13.1716 -12.6129   0.5816
+#&gt;   -0.3431   5.6900  -5.8313  -2.5538   3.6540   3.3511  -0.2439 -17.6064
+#&gt;   -6.1437  -9.6946  -9.5116  -0.1163  -5.2854   0.0935   9.3468  10.3278
+#&gt;  -19.4557  -1.8373   2.0779  -1.2904  -2.0416  -1.6558  11.6877  -1.9486
+#&gt;  -12.2806 -12.7014 -19.7658  -2.3969   5.6329  -8.3815   2.2392   0.5735
+#&gt;    1.0984  12.8486  -2.2627  -1.3134  12.2106   5.7082  -9.0247  -9.3327
+#&gt;    7.9066  -3.8783 -10.4517 -15.9973  -3.3150  -6.4859 -11.3980  13.3674
+#&gt;    3.4618   3.1344  -6.4312   3.0740  -4.2376  -2.6805  12.8825  -7.3342
+#&gt; 
+#&gt; Columns 41 to 48  -8.2446 -14.1066   7.4180   5.9619  -4.1717  -2.6857  -4.5057  -2.5795
+#&gt;   -0.3471  14.8749   0.6670   5.6553 -10.9413   2.5896  -3.8698   4.2730
+#&gt;    8.1345  -4.1757   3.7532   6.9645 -14.8808   1.3076  -9.9735  -9.2789
+#&gt;   -5.8473  13.6811  -7.2703  -5.9008  -4.0263  -8.5845   3.9876   3.7570
+#&gt;   11.3226   6.9115   4.8852  15.7906  -0.8682   8.0937   8.1273   5.7852
+#&gt;   17.3444  -6.6595   6.7296   5.8521   9.9824  -1.5831   3.1717  -4.8209
+#&gt;  -16.4776  10.5050   2.9079  -8.5284   2.8132 -16.2037   8.6382   0.7495
+#&gt;  -23.1697  -3.9381 -13.5591   5.6852  -0.6891  -3.2555   8.2294  -8.8434
+#&gt;    3.8975  10.2780 -12.6152   5.3798 -15.7025  -3.5821  10.9657  -7.8737
+#&gt;   15.2915  -0.5255   1.8512  13.8459   2.2610   3.5411  -1.5479  -1.5811
+#&gt;   -8.6926  -1.1466  -7.9095  -5.5129   3.6154  -5.7410 -18.9909   3.5314
+#&gt;    0.0673   7.6417  -4.5786  -8.5491   2.4879  -8.6047 -16.9503   3.3925
+#&gt;    6.1347  -0.2703   0.6698   2.9947 -18.5610   6.9394  14.4577   5.3886
+#&gt;    6.0268  12.8496   4.6240 -11.4068   6.4780   1.9538 -23.4217   2.5520
+#&gt;   -2.0623  -2.9128 -12.5985  18.0719  -3.6771   9.9461  14.1066  -9.3032
+#&gt;   10.3474   0.1668  -9.4097   4.7920  -0.7543   0.7548   9.4571   7.3780
+#&gt;    8.5396  -8.9624 -13.7220   6.6342  26.1063  -2.2718  12.9850 -12.8724
+#&gt;    4.9769  -0.4265   1.8554   6.0727  -5.0484   3.0449 -11.8768 -16.2155
+#&gt;    3.4199  11.6511  10.9404   5.5802   2.0642  -3.1614 -32.3605  -4.1784
+#&gt;   -4.5305  -7.2991   9.0261   1.8573  -2.2770  17.8930  -1.7181  -2.7132
+#&gt;   -5.0601   7.4311   6.1313   0.2610  -3.9911   9.5264  -9.0407   1.1036
+#&gt;  -14.7860  -6.6657 -13.7383   0.9878  12.0193   9.0834  -0.4269  -0.0988
+#&gt;    5.7226   1.2954   4.7022  12.1605  -8.3241 -11.2832   3.1006  -1.2237
+#&gt;   -2.4029   4.1849  -4.9961  -8.9011 -17.0678 -17.9874  -4.4997  -1.9155
+#&gt;  -12.5505  12.1856  -3.0765  -2.0942   0.7412  16.8770  -4.1587  -3.8859
+#&gt;   -2.1647 -20.1726  -5.2616  -6.9617 -11.7598  13.4308   4.2533   1.5311
+#&gt;   -5.6661  -1.5919 -12.8315   6.9896   2.5813  -7.4786  -2.9170  -0.3050
+#&gt;   -6.6409  -7.6123  -2.1276  -1.4862  -4.7415  -1.6506 -15.4718   8.7143
+#&gt;   -1.0364   8.5024 -13.1561  -5.9855  -8.4445  -3.3230  10.1388   2.4691
+#&gt;  -13.9208  -7.7000 -11.4377   3.8244  -0.5969  -7.5562   7.3189   1.4867
+#&gt;    9.7942  -4.7978   3.3158   9.0595  -9.0613   1.8362   3.8362   1.1675
+#&gt;    1.0413   0.4598  13.2523 -17.2823   3.7930  -3.0105   2.8211  -2.3156
+#&gt;   -2.8977   7.6209   9.4072  -5.2823  11.0450 -18.1353  -5.5680 -11.8955
+#&gt; 
+#&gt; Columns 49 to 54  -2.5928  -7.1004   6.6081   1.7156   5.4287   0.3410
+#&gt;   13.2924   3.9968  -1.9209   8.0944   2.1424   3.7392
+#&gt;    6.7899 -10.5478  -0.7002  -1.6988  -6.1060  -0.1987
+#&gt;    7.4364  10.9952   3.2161   2.4305  -4.5592  -6.1626
+#&gt;   -9.6062  -3.1601   0.2585   3.1764  -6.7485   1.2784
+#&gt;   -3.3731   1.0436   4.3733  -4.6247   4.0325   2.0421
+#&gt;   -4.4729  -0.6966   4.3825   8.6256  -3.5688  -8.9680
+#&gt;    5.6425 -15.1321  -2.3662   0.1096   2.5494  -4.4745
+#&gt;    1.9892  -0.5806  -9.2635   2.6563   5.5949   3.1520
+#&gt;   10.0269   8.4024   7.9782  -0.0012  -3.3929  -4.4011
+#&gt;   -3.4735   0.3273   1.0878   9.8868  12.0104   5.5299
+#&gt;   -5.3334  -3.3646   2.0360   2.3895   6.0884  -1.9941
+#&gt;   -8.2128   9.0170   6.6366   1.8423  -5.0797   2.4894
+#&gt;   -1.3292  -2.9701   2.3141  -7.0520   0.3502  -4.1201
+#&gt;    2.2892   0.8809   8.7419   8.5965  -1.6637   2.2177
+#&gt;    0.5825  -2.6708  -1.1449   0.7359 -10.7928  -3.5240
+#&gt;  -19.0753  -6.4115  -2.3333   0.4257   1.2642   9.3979
+#&gt;    6.4100  -9.9433   0.5144  -0.9337   6.1790  -7.8487
+#&gt;    7.3200  -6.5671  -8.7311   1.1663  -4.1018   1.6795
+#&gt;   -4.5269   5.5922  10.9591  -1.6388   1.5133   5.6238
+#&gt;   -3.2997  -5.9542  -4.4557  -3.3275   1.7602  -5.1058
+#&gt;   -7.5134  -0.7009   5.4777  15.1911  -0.0899   3.9860
+#&gt;    4.1985   1.1996   0.2030  -4.6156  -3.7712   0.6755
+#&gt;    8.4728  19.2328   3.1302 -10.3603  -1.4621  -0.7851
+#&gt;   13.4330  -4.9662   7.7158  -7.3931  -4.1295  -6.4962
+#&gt;   -8.0304   8.5032  -9.3079  -5.2790   2.8347   2.4643
+#&gt;    7.0726  22.4254   3.3912   5.3902   8.3798   1.6767
+#&gt;   21.0437   3.2168  -6.0675  -4.4073   3.2828   0.1357
+#&gt;   12.8162   6.2139 -10.2245  -6.9193  -3.1154   1.8901
+#&gt;   -2.3231   0.6678   9.1898   6.6339   4.3099   5.7301
+#&gt;   -5.9029  -2.0468  13.5363  -1.7687  -7.4800   2.0132
+#&gt;    0.1635   7.0316  -4.7992 -14.0503   4.2662  -0.6726
+#&gt;    4.5819   2.0853  11.7331   0.3567   9.5702  -6.5673
+#&gt; 
+#&gt; (8,.,.) = 
+#&gt;  Columns 1 to 8  -2.7529   0.8192  -7.1341  -6.5282  11.1871   4.4511 -21.7879  -1.4713
+#&gt;   -3.1048  -7.8195  -0.2788  -6.0910   1.3162  -9.9899 -16.0470  -0.5912
+#&gt;   -2.9547  -5.6337   4.0594   6.9994   6.0116   4.2885  12.9769   0.2763
+#&gt;   11.0393  -3.4285  -3.4719   3.9043   6.3892  -3.4250  14.3331  -7.9237
+#&gt;    9.0916   1.6536  -3.8375  16.7278   3.2863   8.3293   5.2747   5.0463
+#&gt;   13.7368  -2.6661   1.8664   3.0571   4.3028   0.9034 -16.2985   1.4940
+#&gt;   -8.0692  -9.9216   5.6677  -6.9962 -15.2937  -7.3063  15.3760   3.5604
+#&gt;   -9.0523   8.6461  -0.4937   3.0509   5.6696   2.0694  -5.2931   3.3585
+#&gt;    2.3426  -9.7887 -13.0725  -3.5629  -4.1340  -4.8556  -3.6626   4.4876
+#&gt;    8.1377  -3.9488  -4.9067  -6.2834   3.1234  -3.5747   4.6790  -8.5427
+#&gt;   -3.0277   0.6315   8.4442   7.6313   4.6818  -7.2317 -26.7969  -3.1051
+#&gt;    9.9994   4.5279   2.0965  10.3779   7.8810  -0.4459  16.5216  11.3792
+#&gt;   -2.8980  -9.2737  -1.2585   1.7333   6.8224 -17.7776 -10.4174   3.9152
+#&gt;   -3.9673  -0.4021  -2.0732  12.1576 -10.3663   1.5133   4.0214   4.7282
+#&gt;    7.0511   0.4957   4.1995  -5.9918   0.0764 -12.3378  14.4978  -5.6393
+#&gt;    0.2674  -7.7723   6.0367  -7.7422  -0.1882  -5.4300 -14.0436  12.4318
+#&gt;    0.9067 -13.9798  15.4094   6.1903   8.0087 -11.8253  -4.7356  12.0953
+#&gt;    1.1894   4.2628  -1.1142   2.2573  -7.1488 -11.9867  -3.1376  -4.5180
+#&gt;   -1.3142  -6.1627 -11.0111 -10.1632   5.6541  -1.5477 -22.9148  -1.1882
+#&gt;   -3.0296   4.3970   1.8265  -2.7886   0.0352  16.6297  15.4871   1.1961
+#&gt;   -5.0692   1.5222  -4.8863   6.1280  -4.2142   4.1118  10.0311  -7.1992
+#&gt;   -2.7119   0.2040  -0.3274  -2.1352  10.6831   2.7113   2.9998   2.2773
+#&gt;    4.0870  -8.3630   1.3345   5.7558   8.6777   6.6120   4.7221  12.0395
+#&gt;   -3.0057 -11.6187  -1.4478  -0.9246   3.5884  -3.1255   4.0088  -2.2778
+#&gt;    0.9240   4.9592   7.4613   6.5776  -2.6666  14.3401  17.3487  -2.4898
+#&gt;   -2.3642  -2.1005  -9.3355  -9.3243 -10.9523   6.9057   7.6943  -7.1152
+#&gt;   -3.1120  -1.7855  -4.4008 -13.6525  -6.0453  -0.5619   8.5483 -13.9939
+#&gt;    2.8317   2.0840   1.2822  -0.4408  -2.4619  14.0995  -2.7047 -13.0475
+#&gt;    7.0575  -5.8074  -4.1218   1.6133  -3.2632  13.8478  -4.2430  -8.0296
+#&gt;   -0.8498   5.2949   6.6301  -0.3180  12.0754  -5.2642 -18.0740  -1.4874
+#&gt;    1.4737  -7.5670   5.6318   2.6489  -0.7847  -5.5680   3.2172  -1.7407
+#&gt;   -5.1628   8.3471  -3.3556   6.5424  -9.1413  10.8724  14.4525  -8.6271
+#&gt;    6.2300   0.4581  -3.5530   2.6457  -8.0018   3.6802  -4.7906  -3.5306
+#&gt; 
+#&gt; Columns 9 to 16   2.6059   0.2068  18.7862   2.4858   5.5476   0.0574   8.6758  10.5389
+#&gt;   -5.0830  10.2386  11.1307   0.6531   6.4362  -3.3191   0.2904  -6.1147
+#&gt;   15.8550  17.1665  -3.4921   3.4039 -17.2131   1.2187  -0.2304  -0.9924
+#&gt;   -9.5623   2.8004  -8.9082   2.4214  -2.3780 -14.2012  -8.7973   4.5452
+#&gt;   -2.8062  -6.0448 -13.6052   3.1826 -10.9059 -17.3714   4.2268   1.8483
+#&gt;  -27.0679  -5.9070   5.8950   0.8538  -6.8051   7.9535  -3.5834  13.9089
+#&gt;   -9.0877   3.0419   6.0590   5.6717  -0.5998  -3.2637  -8.2851   9.2411
+#&gt;  -14.4284   7.5758  -1.0864 -11.7635   2.7582 -10.3796 -12.3267   6.0934
+#&gt;   -7.5945  12.7614  12.6524  -0.1052   4.6192   5.3419   6.2143   7.6067
+#&gt;   -6.7993   5.3109 -10.7815  15.6736  -7.0126   1.1654 -10.1354   1.9374
+#&gt;    2.6210  18.7662   2.4249 -17.9167  -8.3412   1.4214  10.6310   1.9863
+#&gt;   -2.9500   3.1937   7.4129   3.2962  14.4996   0.4553  -0.1157   5.2943
+#&gt;    5.7296  -1.6762   2.8099  -2.4858  -2.4683  -5.6878  -6.6655   3.5194
+#&gt;   -8.0123   1.1176  -3.9421   1.1844   5.9917  -4.3421  -8.6695  -1.2122
+#&gt;  -16.6057   5.8500  -2.5749  -3.0693  14.9980  -1.7176   6.8648  -9.9549
+#&gt;    4.9229 -12.4192  -3.1418 -18.3803   3.1458  -5.5795  -6.2845   2.4021
+#&gt;   -9.7384  -9.2445 -14.0000  -6.0651   1.8076   7.7087   3.2962  13.9044
+#&gt;   -5.8195  12.8527  -4.2801  -4.9660   2.3438  18.3197  -7.5903   2.4078
+#&gt;   -3.8759  -1.0662  10.1111   2.1849   3.8999   0.2588  -8.7367 -11.6584
+#&gt;    9.5571 -12.2928  -2.4735  10.2014 -12.1671   1.6716  -0.6683  -6.9441
+#&gt;    5.0652  10.4032 -11.4672   3.9434   0.9181  -3.1731  -6.7213   3.8030
+#&gt;    5.0064   4.5650   7.9435  -7.7492   5.5196   8.4759  -6.5715   2.1932
+#&gt;   -6.2972   2.7103  10.7781   7.4434 -12.7144  -2.8269   2.7386  -7.9616
+#&gt;   12.6700  25.3529  21.4739  -0.1103  -7.4641   3.7377   0.1204  10.8745
+#&gt;   -6.6823  19.7743  13.1868  15.4460   1.7699   1.6169  14.1696 -10.2873
+#&gt;   -5.2075 -23.1905   4.7842  -5.2366  -8.8474   9.9627   0.4570  -7.2879
+#&gt;   10.5321  15.3019  10.4495  12.4939 -11.4734  -2.0136  16.9252  -5.5005
+#&gt;   -3.2051   0.4821  -8.7248   2.0482 -11.4909   8.4767  -4.1454  -6.0090
+#&gt;   -2.6472   7.5251   8.0112  -1.4287  -3.5853   0.8019   6.6144  -6.8130
+#&gt;  -16.1993  -7.3859  14.0124   8.9923   5.5679  -0.9503   8.3413   3.4190
+#&gt;   -0.9727 -14.9218  -5.1464   0.0045   0.5696  -5.9744   2.7713  -0.7118
+#&gt;    4.2479   6.4606  -0.9434  -4.8116  -8.2512   6.2986   3.6989  -2.9903
+#&gt;   12.5828  15.4578  -2.0097   7.4678  18.1141   2.4997  -9.8066   7.8747
+#&gt; 
+#&gt; Columns 17 to 24  -9.6185  -5.2363  -6.4971   2.9259   5.3478  -1.3745 -11.1241   5.3987
+#&gt;    5.0377 -10.5867 -14.1546   1.7548 -25.1766  -3.7241  14.0057   1.3400
+#&gt;    7.3620  -7.2763  -4.2834  -3.2599 -12.6782  -1.8999   0.6878   0.6934
+#&gt;    3.7993  -3.0477  -6.4918  -2.9106  12.9166 -10.5429  19.3072  -3.5060
+#&gt;    1.1725  -6.3711   2.1429   5.8187   0.5333   1.2548  12.6958  -7.0119
+#&gt;    7.1364   1.7381  -5.0821   2.3576  -1.2286  11.1923 -10.4377 -13.2910
+#&gt;  -12.4025   1.6296   7.5249 -17.2821 -11.1699   6.4293 -10.1163  -0.0761
+#&gt;   -1.7264   8.4885   4.0593   4.8025  11.4839  -7.3611   1.4249  -6.4636
+#&gt;    3.5115   6.1677  -3.1524   3.9877  -9.3612   0.2320  -6.9287   0.9210
+#&gt;    1.7669  -1.4957  -1.1088  -5.6035  12.9229  -4.6847  -8.2598  -2.7438
+#&gt;    4.8460  -4.1944   5.4778  14.8436  -2.7482   1.0736  -4.6544   6.0900
+#&gt;   16.3952  -1.7126  -2.9432  -0.8184   6.0616   2.6004   3.9056 -10.1400
+#&gt;   -4.8629  -2.0349  -4.2152  11.5647 -15.5042  -6.9312   5.8201   7.9488
+#&gt;    2.3042 -10.7469  -1.8377  -7.0127 -10.5825  -5.2462   5.5303   1.1535
+#&gt;  -14.1828  -6.2652 -12.6412   7.8766   3.5697  -0.5576  -9.0542   6.4479
+#&gt;   -1.8594 -12.0191   1.1954  -3.4492   2.6362  -1.5502   1.0217  11.0366
+#&gt;   -2.3127 -12.8279   1.9349   5.3822  -0.3430  -8.6638  -9.1417  -1.8167
+#&gt;    0.4679  -2.3985  -4.0016   7.9150   3.4129  -0.2558  -6.5079  -0.5395
+#&gt;   13.7021  14.7185  -0.9215  -2.1865  -5.6755   3.5487  -1.3128   0.8928
+#&gt;   -5.6018   0.6378   5.0673 -22.4938 -12.6288  -2.9217   6.7909  -6.8253
+#&gt;   -5.8383  -3.5588  -1.0980  -5.1938  -6.0518   2.1598   3.9049  -2.2670
+#&gt;    8.3973  -2.8062   3.6439  -7.4556   7.8676   0.9450  -9.9529  19.5965
+#&gt;   -6.7550  -6.3885  -4.9750  -1.8393   1.2241   6.1629  10.4999  -8.8812
+#&gt;    1.9692  -6.6736  -4.2791  -6.9886  -0.1644  -3.6476  -1.0289  19.9012
+#&gt;   14.5276   6.4475   3.3525  12.8286  -7.6074  20.2843  -3.5258   6.6068
+#&gt;    1.7833  13.3814   0.1390  -5.2190   8.0605  -5.4690   0.5839 -14.1667
+#&gt;  -10.1046   3.2568   4.2568  -0.8551   9.1603  -7.0637   8.3551  19.1710
+#&gt;    5.4195   3.1397   0.0737   2.5032   8.6055   6.7515  -1.5177  -0.5559
+#&gt;   -7.0993   0.6425   1.4658  -0.1229  -0.3991  -2.2585   6.4073   8.8977
+#&gt;    5.6671   3.0705   3.2044   3.6582  31.1265 -14.4046   2.2149  -6.1952
+#&gt;   -5.4701 -14.7604   8.3713   9.8865 -24.9880  11.3028   1.5293   4.6248
+#&gt;    0.1451   5.1354  -3.6193  -6.1743  28.6728  -9.9950  10.5469  -1.0057
+#&gt;   -0.6977  -5.1401   1.7695   1.4553  -2.8253  -2.7118   2.4154  24.9040
+#&gt; 
+#&gt; Columns 25 to 32  -7.1700  11.6366   7.2286   8.8610  -3.0368  -3.5862   7.6909  15.8158
+#&gt;    7.5102  10.8181   2.6158  19.5021  -4.5559  -1.4966  -5.9985  -7.0599
+#&gt;    3.9965  -4.6997  -5.6602 -11.8554  18.1068  -2.0255 -15.3478  -3.8725
+#&gt;   15.7267  -8.1247   1.8175  -6.2360  -1.4675  -4.8595   5.2477 -14.5281
+#&gt;   -8.9276   5.8084  10.6656 -21.9621   7.2696  -9.8578  10.2391  -8.6043
+#&gt;   -5.5958   4.3886   3.6833  -3.8290   1.8972   0.2591  -2.6312   9.3042
+#&gt;   16.8863   6.3746 -14.4350   5.9461  -7.5033   5.9999 -13.7993  -2.1955
+#&gt;    4.5430  -1.1801  -5.7656   3.3432   0.2830  31.4724 -10.8935   5.0580
+#&gt;   -2.5888  -4.2127  28.1604   6.3434 -21.9281  -1.4052  10.1537   5.8521
+#&gt;   11.1758  -4.5972  -3.2592  -2.2157  11.2609  -4.3342   8.0417   4.7088
+#&gt;  -10.4686  -9.2667  -5.1960   7.8169  14.3069  -7.7104  19.9064  16.9445
+#&gt;   -1.9276 -11.7381  -1.7933 -17.9961   5.6561  -0.1927  21.0128   0.4839
+#&gt;   -0.4672   8.2286  -2.6962  -1.3400  -7.7344  -9.1882  10.3880  -0.9670
+#&gt;    8.2809   9.7850   1.1968   8.3133  16.6780  -8.3523  -2.4638   0.1139
+#&gt;   11.1578   3.0020   6.4177  -8.3652  -7.9631  17.1988   8.0300   8.5071
+#&gt;   -0.0551   5.9983   5.7762   2.2087   9.6836  -9.9556   6.5206  -2.1632
+#&gt;   -3.6828   6.6844  19.4924  15.1757   4.4342  -9.5140  17.7458  17.2699
+#&gt;    0.4415 -11.3196   3.8629   2.5370  15.5115  -5.7184   6.3163  13.6840
+#&gt;   -1.1127   1.6238  -3.6081  -1.9011   3.7526  -3.0333  -2.1946 -10.1399
+#&gt;   10.7830  -5.8261  -6.1259 -10.7859  -0.4588  11.1663  -6.9900  -4.2215
+#&gt;   -1.4840   4.4005  -0.6102  -6.3680  17.9664   8.3212 -18.3523   4.9031
+#&gt;    3.2974 -10.7969  -0.3268  -9.9017   7.4522   6.7771  -3.5755   3.8225
+#&gt;    7.3126  -3.5130  -7.6412  -2.8489  -1.1771  -8.6129  22.5971 -16.1773
+#&gt;   -0.3023 -13.9086 -10.5268   5.9208 -12.7641  -0.8612 -13.7925   4.4451
+#&gt;   13.9948  -7.1070   4.2837 -14.2782 -14.0873  22.4218  -6.5989  -5.5982
+#&gt;   -3.4005  -8.0452  -0.1647   3.6283   8.3792  -6.1744   3.5500   0.9821
+#&gt;    5.4092 -11.6146 -20.8671   3.7039   5.2048  -0.4829  -6.9013  13.6288
+#&gt;   -6.7347 -13.6019   2.6732 -11.2069  -1.1424   5.3381 -22.5442  -1.3596
+#&gt;    4.3021  -3.2848   6.6782 -12.2661   1.3848  -8.1107  -0.6805 -17.9858
+#&gt;   17.8665   6.8305  15.7989  -0.8882   0.4430  14.3679   9.7999  -4.2770
+#&gt;  -13.5004   3.0550   9.7904  12.1239  -6.2687   0.7774   8.3725   3.0753
+#&gt;   10.9506  -5.6546 -27.5954   5.8827   9.0555  -2.9388  -2.4872  10.3958
+#&gt;   -1.1340  -7.0988 -14.1755   1.7169  14.1141   0.9794  -3.7148  12.2845
+#&gt; 
+#&gt; Columns 33 to 40   1.4384  -4.6102   7.9683   2.7660   7.9938  -1.0803  -8.9664  -1.3981
+#&gt;   19.5743   0.8177   3.9418   5.5161   9.0790  10.3763   1.0911  -3.6978
+#&gt;   -5.6234   7.9174  -1.2428  13.7931  -5.4066  -0.4459  15.9044  -4.9218
+#&gt;    7.1198  -5.6939  -1.3782  -2.8288  -7.2263  -1.5326  -8.7972  -7.8809
+#&gt;    2.0581   5.2294  10.4372   1.7771  17.5576  -2.2725 -11.5410 -10.8612
+#&gt;  -14.4360  16.1779  -8.2027   8.4059  -3.5090  -2.6810  -7.2530   5.8010
+#&gt;   11.7753   1.6925   0.0918  15.1680 -12.1380   0.0158 -10.3996  -7.2048
+#&gt;   -7.0974  10.7667 -10.2725   7.3891 -17.3839  -2.9920  -2.4067  -1.1251
+#&gt;  -18.5467   4.7870   9.5273  13.4591  19.5929   1.9708  -6.4323   3.8563
+#&gt;  -13.7966  10.4357   2.8547   0.2278  -6.0680  -6.4969   7.0030  -6.4925
+#&gt;  -15.3292  -8.6361  -4.0396   5.9759  -8.9914 -28.1967  -2.3788  21.7079
+#&gt;    4.3733  -4.7229  -4.6953   8.4878   0.0928  -9.9990  -1.3342  -1.0123
+#&gt;    1.7047  -0.8910  16.3733 -11.1787  -1.2181 -17.9266  -9.3722  12.1319
+#&gt;    3.6155  -6.4393 -10.4984  14.2492   0.6283  19.7343  17.0469  -1.6854
+#&gt;   -3.7397  -4.4730  10.8904   1.0943  -2.3979  -6.4401  -2.0084   0.4883
+#&gt;    2.3665   9.4115   3.8417  16.7768   1.0740  -2.6458   1.6131  -7.3026
+#&gt;  -10.7354  -7.7811   5.5553  -1.1074  -8.8395  -2.0963   6.2890  -2.4415
+#&gt;    3.8500   1.9622  -7.5663  12.1152  -9.1200  -2.7134   2.1331  -7.6049
+#&gt;    6.3665   7.5608  -5.2248   0.8428  15.7291  16.6072  10.2988  -1.4232
+#&gt;   -7.8167  19.1128  -2.7300  -3.5795  -5.0596   5.3191   7.9266   3.7644
+#&gt;   10.8489   8.5284  -1.8161  10.5279  -3.5506   3.3824   3.4306  -8.5454
+#&gt;  -13.2690  -0.5293  11.1187  -4.5714 -14.9835   4.9575  21.8038  17.2836
+#&gt;   -4.4791   7.3667  -4.0882   9.4668   4.9189  -6.9242  -1.7868  -3.7555
+#&gt;   12.5676  -4.2350  14.2625  -5.9949  -1.6661  15.3602   5.4294  10.8306
+#&gt;    0.6979 -12.6459   2.3800   8.3869   0.8838   5.4597  12.7520   6.8345
+#&gt;   -4.8699  13.0221  -1.8689 -20.3537  -4.7671   1.1076  -5.6562  -1.5576
+#&gt;   -5.1534 -12.7627   7.0405  -8.1219 -15.5294  -5.6776   3.9444   0.8268
+#&gt;   -2.5128   1.5007 -11.5941  -0.8027  11.0840   2.9976  -9.7512  -9.2726
+#&gt;   21.1139 -10.3481  10.7656   5.1777  -2.5764   2.7313  -1.9686  -6.8200
+#&gt;   -7.9482  -3.3359  -0.4480   9.9027  -4.0077  -1.9207  -1.9934   9.1539
+#&gt;    0.7318 -13.0766  10.2638   0.4785  11.2678   8.6835   3.3261   4.6726
+#&gt;   -2.1868   6.1275  -9.7815   2.8962 -16.2180  -6.2915  -4.5034   3.0573
+#&gt;   15.5121  -6.8184  10.7959  12.5375 -13.7759   4.4653  -0.9482  -0.9945
+#&gt; 
+#&gt; Columns 41 to 48  15.1234 -10.3035  18.9469  -9.3543  -7.7956 -18.9362  -0.6373 -16.9786
+#&gt;  -19.6301  -9.1963   0.4785  15.1186  -9.2118   6.7499   7.7051  -9.3235
+#&gt;  -11.8282 -12.6039 -13.7831   3.7439 -23.2749   4.1191  -1.8422  22.8505
+#&gt;   -6.4394   3.4854 -12.3427  10.9700  -5.3929   0.1003  -1.4660  15.0701
+#&gt;   -0.5888  -1.2878  12.4392  16.2797   4.4355  10.1608  -3.6143   9.7532
+#&gt;  -11.4246  18.0569  21.2602   4.3748   2.1658 -14.8113 -24.5796   2.4979
+#&gt;  -14.2909   4.8997 -17.6585   0.7266  16.5683   3.5429   1.4633 -10.7058
+#&gt;  -13.0987  -7.3477  -3.6767 -15.0189 -17.3225  -2.4886  -1.2141   6.8781
+#&gt;   -4.2019  -7.1041   6.8834   3.1884 -12.3768  -3.9347 -16.8851  -4.9041
+#&gt;    1.1614  15.5545   6.9877  -7.2775  -7.2149  -2.0622  -5.2004  15.0375
+#&gt;   17.3684   5.4120  -3.9649 -26.9709 -22.3064  -3.7620  -0.4840  -7.7502
+#&gt;    8.2134  16.4462   9.2899   4.5362  -3.4135 -17.8783 -23.2531  -8.3197
+#&gt;    4.2102  -8.7115   5.7801  -1.2040 -16.3119  16.9851  -4.2366   3.0893
+#&gt;   -7.9770  -1.9856  -1.2772   4.4906  -0.2718   3.9672  12.5172  -9.9648
+#&gt;    0.6586   9.1921  12.7662   8.2750 -10.7659  -4.8818   4.7952   3.0726
+#&gt;  -13.0078  -7.4784 -15.6910   2.9226   3.6097  -7.4876  23.6680   2.0568
+#&gt;   -7.8754   5.0710   4.8404   2.5667   3.2970  19.4876   2.7635   2.3988
+#&gt;   -7.3073  -5.4642   2.1707   0.9878 -13.8835  -4.3143  -0.7010   0.1638
+#&gt;   -6.1368   8.6022  -2.3794  -4.9842  -2.6207 -16.6344  -0.5803 -23.0859
+#&gt;   -1.4737  -6.8409   9.4013   3.6841  -2.5767  10.2542 -13.6277  14.4757
+#&gt;  -12.7047  -0.9505  -7.3066  -5.3821  -0.2884  -0.8365  15.3252   2.6350
+#&gt;    8.7984  19.0534   3.6122 -15.4152 -12.9271  -0.1066  -6.9598   8.6080
+#&gt;    2.0097   2.7893  -0.8227   4.1339  -0.6433   9.9833   1.5379  22.7720
+#&gt;   22.9658   2.7287   0.0924  -7.3743  -8.6120 -10.5792  -8.3092 -34.4353
+#&gt;    8.3216   0.8046  -2.7417  -2.8410  12.2403 -14.6068   2.8725 -18.7368
+#&gt;    5.1215  -0.3085   6.0280 -10.1927  -2.0305  -1.7860 -14.4816   7.4972
+#&gt;    3.4380   4.4268  -8.7352   4.2027  -5.2833   9.8693 -19.4086  -7.5258
+#&gt;    2.5557   0.1169  -2.1515   7.2013   3.8568  -4.1783  -1.7116   2.7032
+#&gt;  -16.7468 -16.8727  -1.2258  18.1821  -4.3218  -5.5793   4.0499  -5.7701
+#&gt;   15.1284  14.2322  -1.3899  17.1513  -5.2165  -4.9669 -25.0727  20.5660
+#&gt;   -0.5518 -23.9666 -10.6307 -12.3171  -6.0767   7.3313   4.0299 -18.5027
+#&gt;    6.5236   7.7597   2.4812  -4.6051   0.3597  -4.3789   6.9711   9.4499
+#&gt;   13.7426   8.1034  -3.4679 -12.3309   2.7366  -7.2719  14.5037 -21.5581
+#&gt; 
+#&gt; Columns 49 to 54  28.2266 -24.0744  -3.0613  -9.8396  -4.7171   8.8575
+#&gt;   11.6383   2.1390  -7.0953   1.6003  -7.2498   0.8229
+#&gt;   20.9584   2.1395   5.4265  12.1389  -4.7847   8.8648
+#&gt;  -17.1530  16.8176 -12.7773   3.5272   1.3383  -2.3195
+#&gt;   15.4277   2.0832   5.0761   4.7348 -12.5190   2.1667
+#&gt;    9.4932   3.9052 -16.7069   0.1884 -13.9748   6.2520
+#&gt;  -20.4911  21.7655  -0.5098  -0.9561  -2.9878  -0.4902
+#&gt;    5.1038   6.4373 -13.7096 -10.1529  -1.5253  -2.9657
+#&gt;   -0.1040 -13.4354   5.6028 -10.4792  -0.9341  -0.8847
+#&gt;    6.3219  -4.8316  -6.8600  -8.3210   3.8692   3.7136
+#&gt;   13.8827 -26.3527  -8.5919  -7.6520   3.4166  -2.0100
+#&gt;   -6.1717  11.1735  -1.6515 -11.3219  -0.4028  -5.4764
+#&gt;    4.0893 -19.1908   6.6321   6.2797   5.8809  -0.7183
+#&gt;   -4.3332   4.8074   8.4485  13.3534   1.9774  -8.3769
+#&gt;   13.6668   7.4613 -10.1284   6.3912  -4.4749  -7.1188
+#&gt;    0.8212   4.6318 -10.7527 -10.8872   2.0984 -10.7213
+#&gt;   -4.6114   8.9248   7.3142   2.4720   3.0747  -8.1015
+#&gt;  -15.3217  19.8732 -13.4930  -7.6570   6.2652  -3.6485
+#&gt;   -0.9852  -9.7691  -1.8700  -3.5571 -12.5527   0.9310
+#&gt;  -12.0288  20.0358  15.8906   4.8544  -3.7699  -5.6404
+#&gt;    1.3941  -0.1866  11.3678  -6.3311  10.9359  -7.0841
+#&gt;   12.0489   3.4691   5.6454  -3.1639   3.1077  -4.2794
+#&gt;   11.4797  13.1703   8.9269   9.0371  -7.2471  10.1417
+#&gt;    6.3791 -32.7896   6.8775  -5.3002   2.7534   2.1391
+#&gt;   -7.3106   7.0380 -16.5145   8.6710 -12.6582   3.1825
+#&gt;  -17.3828   5.1435  -6.8716   3.5323  -5.5878  -1.0485
+#&gt;   -6.2563  -2.6207  10.9603  -5.9135   3.8629   1.1014
+#&gt;   10.1823   7.6165   0.5025  -4.5793  -2.8579   0.7155
+#&gt;   18.8798  10.5151  -3.2269  10.4497 -10.7137   3.3351
+#&gt;  -12.9150  14.6824  -9.3680 -12.7423   3.8019  -5.0127
+#&gt;   18.3241 -10.5744  21.1866   7.0562 -11.9339  -3.2777
+#&gt;  -18.0871  16.1602   5.9427   1.3406   6.7964   3.2303
+#&gt;    9.2389   4.0168  -4.3945  -2.0490   0.0984   1.5683
+#&gt; 
+#&gt; (9,.,.) = 
+#&gt;  Columns 1 to 8   4.0875 -13.1660   6.1413   1.6866  -7.9687  11.6420   0.5888  21.6606
+#&gt;   -2.9234  -7.8627   3.5386   6.7541   8.4469  -0.6668  10.9694   5.8437
+#&gt;   -2.8451   9.0504  -2.0656  -3.7975  15.4361 -10.9013   9.2741  13.7776
+#&gt;   -1.3023  -4.6213   1.9568  -8.2512  13.9255  -7.4534  11.3668   7.4748
+#&gt;    1.5525  -2.3667  -5.8136  -7.4960   5.6232  -1.6982   0.8556  11.8351
+#&gt;   -5.5165   1.7084  -7.7292   5.4986   3.2420   7.6540  -0.2872   9.7683
+#&gt;   -2.9122  13.6950 -12.6256   7.8668  13.7894   1.3318  -6.7552  -0.6951
+#&gt;    2.8501  -1.6932   3.2410   0.0887  -4.4175   3.7085  -3.0652  11.5549
+#&gt;   -1.7622  -6.1368   2.4826   7.4410   9.7392   3.6960  15.6597   6.1769
+#&gt;   -7.6209   1.6972  -3.0448   1.9954  -8.1176   2.7204  13.5791  -2.6416
+#&gt;    5.6467   2.8106   4.7776  -9.6193   2.1398   6.4787  13.7958  19.0501
+#&gt;   -0.1869  -5.5733   4.4560  -7.0290  11.0339  -1.5122  17.4960  10.0469
+#&gt;   -0.5857 -10.2198  14.7713  16.3169  -1.5068   1.1987   4.6137 -15.1676
+#&gt;   -1.8894 -15.2043   7.0605 -11.0119 -11.8196   1.7829  -0.6664  -5.7275
+#&gt;   -7.3169   6.6425   3.8902   3.2093   6.3221  -1.0500  -2.6227  -9.2949
+#&gt;   -0.2854  -6.3065  10.3913   8.1978  -4.6598  -6.8949   7.0462  -0.3631
+#&gt;   -2.6381  13.2912  -1.1430 -12.5023   8.5150 -10.3241   5.7787  -1.6207
+#&gt;   -3.0306   0.1186 -11.0713  11.8305 -11.7191  -8.0379  20.6490  -3.5747
+#&gt;   -3.7223  -4.9261  -7.8515  -3.3555  -0.5600   4.2985  -4.3782   6.1615
+#&gt;   -0.7275  -6.7427   6.0927  -0.9234  -3.6294  -4.5123  -4.4903 -12.0694
+#&gt;    1.7222   6.4943   8.4215  -5.9419  12.6630   7.3062   5.2832  -1.2977
+#&gt;    0.4108  15.7560  -8.1166   1.0658   1.7338 -23.5575   3.8808  -4.2467
+#&gt;    2.3169  -0.3698   6.6242 -16.5452   5.7667   2.0703  -6.2268   8.1523
+#&gt;   -0.0676  -4.9680  -3.6224   0.9647   3.0948  -3.4271   1.3068   5.9702
+#&gt;   -3.0543  12.9846   3.5692  -9.7936   6.0392   1.1089  -6.0544  -0.5150
+#&gt;    0.9283   4.9139  -8.5166   8.3903  -1.7509  -5.2080   7.8338  -6.4984
+#&gt;   -3.3726   5.7619  -6.9525  -0.1470  19.7022  -3.8281   0.6373  10.6404
+#&gt;   -0.5416   1.2048 -10.0297  -3.0471   4.5664  10.2175   0.1318  -5.1289
+#&gt;   -2.1869   0.0989  -1.0279   7.7973  11.0315  -9.5611  -5.9728   8.5446
+#&gt;    3.6401  -5.9616  -2.8115 -12.7283  13.7908  -3.5134  -8.3821  -3.8347
+#&gt;   -1.2762  -0.9875  -2.2588   5.1875  -8.8331   6.0745   3.8728  -0.2223
+#&gt;   -1.7755   2.7929  -3.2278   3.5319   2.0656   9.9338  -0.1135  -2.6921
+#&gt;   -0.7828   2.1875  -2.6695   1.7830  -1.5953  19.4810   6.1483  15.0575
+#&gt; 
+#&gt; Columns 9 to 16   4.2088 -23.9762  -6.8043   8.8212   8.7422  -2.3903   3.2339  -5.2097
+#&gt;   -7.2594   4.7735 -12.3461  -4.8847 -10.4494   7.4665   8.4385 -12.5377
+#&gt;  -11.8828   4.3953  11.8320  21.5989  11.6513   6.9019  -6.6922  -2.8721
+#&gt;    6.7395  10.5906  -5.7747   7.1411 -16.0808  -6.9254  -5.4521 -10.9185
+#&gt;    8.2466   1.1895  -0.1548  -2.3253 -28.0649   1.2423  -9.5628  -0.1640
+#&gt;    3.5897 -17.9191 -17.7579 -27.1033  14.4983  -2.0833   1.3654  15.6925
+#&gt;   10.1009   8.5532  -0.2808  -0.2258 -16.1121   9.4849   3.8841  -3.2685
+#&gt;    1.0302   2.9111  -1.6666  23.5359  -4.0120   0.3524 -16.0500   0.5214
+#&gt;   -1.5642  -4.4428  -2.7665   1.2616   7.2632   7.8903  -8.8398  -5.6303
+#&gt;   -6.0943 -19.7397  -0.9850  -0.2281 -13.3369   1.8731   2.2916   9.9167
+#&gt;   -6.0780  -1.6448   7.8172   2.0459   7.7387   2.5029 -23.2533 -12.3586
+#&gt;   13.2757  11.1168  -9.2176  -5.5516   0.8257  -8.8642 -13.2948  -7.4019
+#&gt;   -9.3961 -10.9207   8.7869   1.3486 -11.6727   6.0785  -0.1468   1.0374
+#&gt;    4.1470   8.7967   9.1593   8.6602  15.7938  -2.6806  10.9040  -1.8152
+#&gt;    6.2388  -2.0511   0.3299 -10.8950 -11.9330   2.8885   0.4726   9.5734
+#&gt;   -4.9775  19.8541  15.4489  -0.7692  -9.5588 -11.9333  12.7164 -12.2642
+#&gt;   -5.1962   9.7119  14.1155  -5.6186  -3.1858  15.5836  12.0592   2.3122
+#&gt;   14.0698  19.7998   3.3043   3.4178   0.8533  -0.0373   5.3411   2.4784
+#&gt;    5.4941   5.4324   4.7054  10.5663  -9.1261  -4.5124   7.2460  -6.0025
+#&gt;   13.6831   8.8033  -6.7323  11.9500 -30.1266   4.7476 -18.0578  10.4861
+#&gt;   -6.9986  -4.8844  -8.5996   7.4400  -0.4392   3.8846   5.0058   3.8753
+#&gt;   -3.3200  -5.5672   1.5313  17.5748   3.4141  -7.4009 -13.9479   3.4515
+#&gt;  -10.8194   6.7758  -1.8326  -5.6283   1.0390  20.6476   1.7635 -15.6967
+#&gt;    9.8613  -2.6945   4.2391  -7.6302  -0.9557  -6.1139  -7.9384  -6.0483
+#&gt;   11.0115   4.5217  -7.0642  -9.2148   7.0387  -8.5065   0.6995  -6.8530
+#&gt;   -5.7435   4.7510  -8.7562  -3.7314  -0.9758  -3.0986  -6.8076   8.7105
+#&gt;   -8.1038  -2.4940  -4.7496 -17.7449   6.4212  -9.0613 -17.9790   7.3966
+#&gt;   -6.8502 -18.9183  -7.4141  -2.1069   7.5214  -5.9213  -2.8063   7.5368
+#&gt;    1.5855  -9.5122  -0.6550  -0.7084  24.4028   1.6516   0.6475 -11.2705
+#&gt;   -9.6477  -0.4903 -17.6532   5.7543  18.2415  -4.9971  -3.8286  -3.2842
+#&gt;   14.1147   9.0895   6.6478 -18.3653 -13.5341  10.5265  -6.7646 -13.0001
+#&gt;  -16.9131  -3.4685 -19.9777  -4.9386  -1.7271   4.9551  12.4286   7.8517
+#&gt;    4.1537 -11.3263   3.8526  -6.3522   3.6299 -13.6234  12.4613  -4.8751
+#&gt; 
+#&gt; Columns 17 to 24  -4.2706   9.6318 -14.4805   6.3895  -3.7929   8.0710   2.0545   1.8292
+#&gt;    7.8162   6.6203  -5.8975  -1.1224   1.9272  -0.8045   6.8998  -3.2840
+#&gt;   -6.8472 -17.6737   7.2496  -3.0438   8.6990   3.2873  -2.7906   2.0976
+#&gt;   -2.9556  -0.9176  17.6425  -7.5155  -2.5665 -10.1610   3.8583  -6.7622
+#&gt;    1.1791  13.9903   6.0277   3.7011   9.8324  -2.8115  -1.3943   7.6187
+#&gt;    0.9433  16.6836  17.6236 -18.7736  -6.3167  -4.2612   8.3085  -3.4196
+#&gt;   -3.4771  -1.7288  19.2656  10.8092   3.1097   2.7039   6.5544  13.4471
+#&gt;   -2.5811  -7.8217 -10.2289   7.0625  -8.1599  -0.8722 -11.0821  -5.3660
+#&gt;   -0.3557   0.1654 -27.0798   7.3281   9.1698   4.4319   7.1142  11.8328
+#&gt;    2.6711  12.0621  26.9639   3.9798  -1.4104   7.9621   2.4684  -6.6909
+#&gt;    4.1163 -10.5928 -14.7403 -12.4781 -14.6933  10.3249 -18.3088   4.6522
+#&gt;   -0.0078  -4.2932   4.7690   0.9550  -3.7099  -4.5541  -7.9861  -6.3120
+#&gt;    4.3695  13.4931   2.4103  11.5745   7.9793   8.5670  -1.6531   1.4744
+#&gt;   -5.4559   5.6987 -10.2102  -7.2425   0.9817  -2.2712   6.4125   3.5513
+#&gt;   -1.8864   2.3376   6.6529 -13.3615 -10.9942  -2.3597   0.7256  -9.1127
+#&gt;    0.1496   1.5402  -2.3539   3.0123   9.9166   5.6713  -8.0756   3.9683
+#&gt;   -0.4101   9.1029   2.3711 -10.7000 -15.2477 -17.3745  -7.0343  -9.0581
+#&gt;    1.8222  -8.6733   6.3450  13.1310   1.8973  -3.9703   9.2409   0.7033
+#&gt;   -6.0650  -3.3313 -14.9866  13.3368  21.1127  25.5643  12.0376  15.6202
+#&gt;   -6.1982   0.8577   7.0105  -3.4303   5.7572  -0.5078  -4.1691 -14.3487
+#&gt;   -4.9384  -2.2306  12.4091   9.5809  -7.9701  15.5971  -6.1461   3.4497
+#&gt;  -14.6010 -19.7534  -3.0943 -15.2183 -10.4810   3.3483  -6.5617 -10.0276
+#&gt;   -1.8306   1.1335   2.9998  -4.5367   0.8741  -3.0733  -2.7563   4.2682
+#&gt;   -5.0458   1.9771 -33.9085  -3.2428   1.2707  16.8337  14.7150  10.6823
+#&gt;    2.4681  -7.3226 -22.9366  -3.9452 -18.8797 -18.5527  -2.8076  -8.6607
+#&gt;    9.9208   3.2776  10.6686  -4.7430  14.8613  -3.8122   4.8039  -4.9235
+#&gt;    9.9328  -2.9447  -0.5335  -0.8264   0.1861   9.5124   5.4681 -14.0606
+#&gt;   -3.1846   8.4049  -1.4289  -6.1339  -9.3869  -2.7514   0.4952 -11.9531
+#&gt;   18.0310   2.2193 -14.0216 -13.0492  22.2719  -4.2757   4.5596 -10.6983
+#&gt;    7.9559   1.0702  -5.5950 -17.2573 -13.7766 -13.7111   0.9190  -4.5991
+#&gt;   -0.3905  -3.3458   1.7576   6.3945   5.7497  -2.7466   2.4251  13.0997
+#&gt;   -4.5891   0.2517   7.7112  -8.0639  -1.7789  -8.1066   0.9122 -13.0374
+#&gt;   -5.3413  -4.9465   5.5711   6.2203  -0.3533   2.2089  -8.5250   1.5109
+#&gt; 
+#&gt; Columns 25 to 32  -2.5417   0.8453   4.7376  -2.9612   1.8473   1.4810   4.5415   7.2610
+#&gt;   -4.2711   2.9126  -7.8663  -0.3670  -9.6876 -15.3951   3.4643  -1.2463
+#&gt;  -15.1669  -2.1372   9.1019  -1.4552   4.6120   4.7544  -4.4678   2.5946
+#&gt;   -8.8096  -2.5299   0.3544   3.5773   1.0737  -6.6828  -2.0851   1.3127
+#&gt;   -7.1648  -0.3307  -9.3747   4.5719   3.2879   7.4797  11.3753   8.1376
+#&gt;    4.2475   2.2863   4.1592   2.2073  18.8083   1.7558   5.6452 -11.2770
+#&gt;    1.8582 -17.5141  -9.8492   6.6420   0.0380  -7.6875  -7.2550   6.8817
+#&gt;  -12.4302  -8.5575  13.1428   6.6443   5.4586   4.6629  -0.2422   5.3181
+#&gt;   11.7403   1.0674  -9.5835   1.3690  -7.6413  -6.8457   7.1888 -11.0833
+#&gt;   -8.1675 -11.4043  -6.3836  -7.3862  -8.4281  -5.4854   7.3219  -0.0165
+#&gt;   -0.8766  21.4948  12.8014  14.8860  26.1611   7.7665 -12.3399 -11.0969
+#&gt;   -6.5066  -8.8206   3.9323   7.7160   7.8918   5.5574  10.4558   9.2808
+#&gt;   -6.7053  -1.6588   1.5652  -2.6664  -4.8871   1.6638   4.5137  -3.4380
+#&gt;    0.3392  14.3931  -0.6118   0.2134  -6.2186  -5.7362  -4.2839   4.3567
+#&gt;  -13.5350  -6.2299   0.3904 -16.4546  -7.2557  -0.6942   9.1569  -9.9394
+#&gt;   -4.5590  -2.8492   0.1492  -0.4079  -4.7893  -0.5491  17.3962   3.4775
+#&gt;    2.0336   5.1708 -12.4232   0.2248   8.3460   1.0982  -0.4847  -4.7426
+#&gt;   -5.0570  -7.8702   3.1474  -8.8002  -2.3591   1.0060   1.5078  -5.2314
+#&gt;   16.9447  12.7382 -13.2697  -0.0683   5.2825  -3.0754  -7.3953  -5.5983
+#&gt;   -7.7573  -5.0490  -4.0720   1.8675  -6.4763  -2.1763   3.5788  14.7622
+#&gt;   -4.0019   6.1244   5.4763   0.6726   1.2970   4.2389   2.8034   4.1622
+#&gt;   -0.9995   6.5293   2.1186  10.6913   2.2119   4.5758  -7.5272  -8.9917
+#&gt;   -8.7199  -8.4839 -18.1779  -9.1905  -4.0585   1.9482   3.9999  -7.3518
+#&gt;   12.7110   9.5307  15.4975  13.9518  -9.2565   2.2380  -5.5922  -5.2402
+#&gt;    4.5704 -11.6175  -7.3932   6.6459   4.7025  -2.2518   8.3495  -2.0091
+#&gt;   10.7717   2.8472   6.3731  -5.9258   0.8502  -2.0102   1.9226  -6.5894
+#&gt;  -14.1405  -2.1212   6.4797 -14.1395 -16.3247   7.2465  -6.4626  -4.0388
+#&gt;    4.1977  -2.3055  -2.6956   0.9332   2.7282   2.3741  -8.7264   8.0648
+#&gt;  -12.1615  -6.7460  -9.2023  -3.4595 -13.0378  -0.4108  11.2732  -8.4836
+#&gt;   -9.0775 -24.4088   0.1750   5.2929  -2.1573   3.3092 -13.1942  -8.2572
+#&gt;    3.6687  12.6269   0.1908  -6.8676  -1.9316   6.3263  -9.2124 -11.4998
+#&gt;  -17.3930  -2.7537  -1.4206  -6.4436  -3.9444 -11.5823  -4.6937   7.5235
+#&gt;  -11.4818  -5.7549   5.9569  -1.7157  -7.7764   6.1034 -10.4827   3.0300
+#&gt; 
+#&gt; Columns 33 to 40  -4.0546   5.7033  -1.9717   4.9453  -4.0232  21.8872  -3.3472   2.4886
+#&gt;  -10.6006  -6.1495   1.9815   4.9314   1.9719   6.6286   6.7427   2.7484
+#&gt;    5.8264 -10.3392  18.2553  -9.3866 -14.3963 -13.8339   0.8560 -11.0095
+#&gt;   -7.8975  11.0141 -15.8547   3.1282  -5.9673   8.7539  -0.4567  -4.7988
+#&gt;    7.7529  -1.0745 -16.7453 -11.1365  -7.0175   0.1077 -11.2151  -0.8434
+#&gt;   -0.7133  -7.7374  -0.7937 -10.8897  16.1370 -13.8290  -0.1160   3.4583
+#&gt;   -1.5516   8.6448  -4.6002   1.4309  -3.2235   2.6007  12.0963 -13.2540
+#&gt;   -7.9362  -4.9958  26.8412 -13.2264  -6.6898 -11.1547  -2.7267  -0.6085
+#&gt;    4.1175  -7.4804  -3.3964 -10.2496  22.5975 -22.6124  -4.8042 -10.3592
+#&gt;   -8.3552  -2.6763  -0.5044 -19.3676  10.0768   3.1593  15.9853  -3.9306
+#&gt;   -4.9406  -2.3733  22.2437 -21.2701   4.0356 -19.9860 -16.7914   8.4032
+#&gt;    9.8218 -10.7498  10.6639   1.5719   0.2460  -3.3337  -1.8820  14.1830
+#&gt;  -11.1259  -1.1850   0.7274  -3.5176   4.5723  -1.3078   3.5844   4.4614
+#&gt;   -9.0342   1.3072   0.6087   5.9559  14.0140   2.3856  11.1323  -6.3673
+#&gt;   -0.9597   6.0675   3.4484 -16.5896  14.6504   2.3404  13.2214   3.1287
+#&gt;  -15.1154 -12.5263   6.2127  -7.7274  -0.1448   3.1026  -2.2499 -11.8948
+#&gt;  -10.4774  -1.3816  -4.5550 -23.3595  11.2419  -2.9943   2.6173   5.4653
+#&gt;   -1.0691   5.1118   5.7607 -16.5816  14.0878  -3.2260  17.9101  -9.2509
+#&gt;    4.7743  -1.2142  -1.9710  12.9453  -5.1697  -6.0383 -12.7658  -3.3180
+#&gt;   -4.7639  -9.1992   5.1599   2.0450  11.3600  -2.9904  -4.9618   1.1367
+#&gt;   -0.2035   7.4995   6.7237   9.6282 -15.1450   0.0953  -1.5054  -3.1165
+#&gt;    9.3922   6.3088  10.0187  -6.8202   0.7742  -5.7718   9.5027  -9.4457
+#&gt;   -7.3586   9.0690  -9.2550 -11.7036   6.6068  -4.6989  -0.0246  -0.4834
+#&gt;    8.0758  11.4354  -0.4742  16.5706  -1.5196  -2.9838  -5.4655 -11.9395
+#&gt;   16.8395  -8.0241  -7.3224  20.5876  14.2268 -10.0591  16.2582  -3.5242
+#&gt;    6.7963  10.5324 -16.6560   5.6012  -9.8153   2.5755 -13.8786   3.6098
+#&gt;   -4.7407   3.8492  14.7185  -4.0867  17.5908  16.4011  11.1086 -21.1926
+#&gt;   13.4565 -10.2485  -0.9491   5.4584   7.1630 -14.4297  -6.8817   1.7544
+#&gt;    4.9086   3.5352 -14.2149  16.3630   4.2086   3.2492  -4.4649  -5.6819
+#&gt;   -2.4664  -7.4547   7.8175 -12.1694  21.7147  10.8425  13.5847   6.1566
+#&gt;   14.5458   1.8772  -4.1889 -15.3383  -8.2980   6.9765   3.1014 -15.1226
+#&gt;   -1.3925   3.0163 -11.4281  19.1352 -12.2901  -1.0417 -13.7466  13.6779
+#&gt;   -0.7463   1.0728  -0.6218   9.9406  -9.6542   3.4510  20.0130  -7.8743
+#&gt; 
+#&gt; Columns 41 to 48   7.7196   0.3849  -8.3918  -7.7087   9.4628   6.3112  -4.8614   0.7898
+#&gt;  -10.5400   6.7290  -3.3070  11.4925  17.5507   2.6617  10.3062  -3.3887
+#&gt;    2.3790   1.5194   5.8885  -2.5441   5.1343  -5.0835 -19.2448  -6.2814
+#&gt;  -13.8772  -7.0727   5.0106  -2.6973  10.7691   4.7806   1.1937   1.7533
+#&gt;   -0.1879  11.4678   7.5956  16.2828   2.8012   5.0949   5.4573   7.6978
+#&gt;   18.0733  16.1860  10.2843  -3.6556   7.2638   8.1729  11.3880 -15.7216
+#&gt;   -9.6233  -3.4684  -1.5559   2.1601 -13.1356   7.2361  16.7344 -10.8216
+#&gt;  -16.5723  -7.2646 -11.1144  -3.9776  -0.2065  13.8502  -8.2284  -9.6323
+#&gt;    1.5238  12.3028   8.9714  17.9027  -4.7627  -8.3126  16.8880   7.9462
+#&gt;   10.9103  -5.3810  -5.5318   5.7035  21.4334   2.3475  -2.6262 -12.9918
+#&gt;    8.0442   8.9567 -11.8374  -5.4994   7.8793  -1.9821   3.3344   9.2903
+#&gt;  -12.1435   6.2812  10.7557  -8.9740  -0.2044  10.1391   0.1696 -13.2730
+#&gt;    1.4029   9.6779  -5.9944  21.5273  12.4168  -2.9909  -7.2323  -0.2837
+#&gt;  -15.4937 -15.8609  -4.5449 -11.5226  10.0661 -19.1849 -13.9259   4.5161
+#&gt;   11.5241   4.0590 -15.7215  -2.1419  -1.3034   5.4464   3.6719  15.3133
+#&gt;   -4.4282  -7.1290  -0.5408  10.7173   9.4064 -10.2900  -1.1956  -0.3817
+#&gt;   13.4626  14.8415 -15.4962  -9.9346 -15.0116 -11.9595  17.3005   7.6668
+#&gt;   -0.6313  -3.6673  -7.9908 -18.4216  -0.2426  -4.6290  -3.6879  -7.4714
+#&gt;  -10.8215  -7.7815   6.3899  14.1215  -1.5517  -4.1354  17.5691  -0.2966
+#&gt;   11.1281  -7.7009   6.5719  10.8587  -6.1595  -8.7532  -4.3086  15.5671
+#&gt;   -9.7915   8.7561   1.0023  -2.0281  -1.7689  -4.6823 -12.4498  -6.9113
+#&gt;    7.7028   4.9294  -7.8488  -6.5750  -9.0153  11.6397   4.5690  -0.1016
+#&gt;    7.1106  -1.6929  -1.8737   2.1624 -13.8151  -5.3098  15.0948   0.1795
+#&gt;   -1.3814   9.2974   5.5842  13.2400  12.6897  12.6315   3.4940  -2.6392
+#&gt;  -11.3209  -8.2444  16.8397  17.8638   5.5211  16.4997  15.2528   0.0082
+#&gt;    3.0446  -4.4829   4.3880 -10.7411  -0.3064 -13.2129   2.5039   9.2481
+#&gt;   -6.7606  10.0870   5.7216  -5.6511  -6.1712  -2.0900 -17.3603   8.3145
+#&gt;    6.8814  -0.1777   5.1503   0.9947   5.6574   2.5916   0.6318  -2.3832
+#&gt;   -0.4918   0.3861   4.7932  -4.0897   4.0422  -1.6061   2.8181  -8.4493
+#&gt;    5.8768 -11.2762   5.9376 -21.8132 -10.8796  26.4854   3.2273  -6.3154
+#&gt;    2.7235  -7.0789  -3.0443   2.1171  -7.4423   2.0577   9.3470  26.1017
+#&gt;   12.9806  -5.9052   5.8160  -3.9925   4.2319 -11.9046 -10.8885  -0.4372
+#&gt;    7.3300  -9.5418   7.7864   2.6138  10.5347  17.3636 -21.5352   0.4739
+#&gt; 
+#&gt; Columns 49 to 54 -14.8691  -9.1324  10.1655   0.7449   2.1667   5.3363
+#&gt;   -4.4574  -0.1098  -3.1628  -9.3421   1.4818   0.8749
+#&gt;    4.6459   9.2058  -0.2434   0.4508  -7.6855   3.1409
+#&gt;    4.7861  28.0300  -0.7577   3.9693  -3.3897  -2.4329
+#&gt;  -21.8895   4.7810  -9.7748 -11.4574   1.2092  -4.2950
+#&gt;   -4.4833   5.0633   4.8649   8.0878   0.2127   2.9271
+#&gt;   -5.7122   0.5988  -9.7677   0.9075  10.6970   0.2351
+#&gt;   -1.1009  15.3324   9.8893 -11.4473   4.1047   0.4827
+#&gt;  -11.1070  -3.0292   2.0740  -3.7679   4.7414  -1.2884
+#&gt;   -8.6583  15.6834  -9.2855   8.0586   0.4443  -0.7787
+#&gt;    0.2194   4.4984  11.6936   7.5706  -2.2380  -5.1833
+#&gt;    4.4206  17.4562  11.1093  14.2875   3.0627  -1.2111
+#&gt;    0.6527   2.9872  14.8202  -1.0611  -1.8122   1.5132
+#&gt;   14.1045  -4.4124  -9.2904  -4.5478  -2.2182  -0.7207
+#&gt;   -1.4828   3.6784   3.2015   2.2996 -12.7189  -7.8033
+#&gt;    5.9095   8.7084  -5.1051   0.3851  -1.0407  -5.2680
+#&gt;   -6.3020  -4.3540  -4.5427  -1.1850  -3.6061   2.5747
+#&gt;   -1.0570   4.7170   2.8429   7.9308   2.2215  -0.9558
+#&gt;  -12.0819   4.4918   7.6590   2.5101   9.8169   4.9874
+#&gt;    0.1378   8.4419  -1.8811   0.1350  -1.8110   1.3977
+#&gt;    7.6449  -6.2310 -13.1248  -9.3925   5.5972   0.2361
+#&gt;    9.6474   5.3826   7.5009  -9.5686  -8.0677   1.1340
+#&gt;  -32.7488  -2.4500  -8.0628  12.2802  -0.2749  -3.8348
+#&gt;    8.0790  -6.9282   3.2445  -1.6245  -2.1593   2.9972
+#&gt;   -0.7125   0.4441  -7.0608  -6.4584  -8.6841   0.2525
+#&gt;    3.5664  -0.2363  -2.8050   7.3713  -2.8613  -0.4915
+#&gt;   17.1820 -11.7188  -3.8804  -3.7392  -4.7100  -1.1502
+#&gt;    8.2372  -7.1018  -3.2752   3.9507  -2.3532   1.3612
+#&gt;    0.1603   4.1866   2.4981   3.8943  -6.4633  -2.6680
+#&gt;    1.7882  -4.5337   6.1890  -0.3154 -10.4658  -5.8276
+#&gt;   -7.1726  -8.9011   0.1020  -3.4402  -8.8854  -3.4632
+#&gt;  -10.3952  -2.3793  -0.3542   3.8725   4.9056   2.3551
+#&gt;    7.1893   7.1117   1.6026  -7.5178  -0.4649   2.7714
+#&gt; 
+#&gt; (10,.,.) = 
+#&gt;  Columns 1 to 8   2.2363  -2.1157  -3.9890  -1.4472 -12.1587   1.9112  -0.6772   2.6816
+#&gt;   -0.5097   0.8234   2.5852  -2.9076  -6.4124  -4.2266  -6.0710   1.3366
+#&gt;   -5.7555   2.2560   4.2838  -1.0270   8.4408  16.8556  -2.1342  -1.6465
+#&gt;    0.5127  -2.0896   1.1144  -0.6333  -3.0143  -9.4323  -1.9678   1.8573
+#&gt;    1.4213  -2.9222   6.7214  -9.3739   3.3599  -8.5956  -6.5851  -9.5308
+#&gt;   -4.4239   7.0383   1.5490  -1.8766 -19.2846  -8.9789   6.5690 -12.5547
+#&gt;   -4.9949   2.4635  -9.9984   3.4919   3.7359  -2.8006  -7.1007  19.4063
+#&gt;    0.9228   1.9885  -0.7494  10.7508 -14.2172  14.6552   7.3359  -7.5264
+#&gt;    8.4246  -6.7451   0.3721   4.4351   3.0836  -5.5432   9.8858  13.3133
+#&gt;   -4.4414   2.3311   8.9908 -10.3337 -15.8835  -5.9958  -0.2566 -16.4942
+#&gt;   -6.2991  -1.0376   8.1352   2.4895   7.1721   5.1369   5.8482  -9.7590
+#&gt;   -0.2901 -10.0629  -0.6162   4.4312   3.4176  -8.6422   5.5362  -2.1296
+#&gt;   -2.1814  -7.6086   5.7908  -6.4812  -8.6060   5.2281   4.8243  -3.4728
+#&gt;    3.2813  -1.3609  -0.0201 -12.3297  -2.0040  -1.9826  -3.4319  15.8522
+#&gt;    0.5173  -4.4348   2.7485   4.3449 -20.0676 -13.4316   6.2142 -15.6838
+#&gt;    1.4934   1.6267  -5.8460  -4.2022   7.2496  -0.9272   0.6064   4.2118
+#&gt;   -7.5285   1.3303   3.2797   6.9902 -10.4241 -16.2242   9.1904   4.0080
+#&gt;    5.1461  -5.2510   0.6457   1.7364  -2.2899 -17.4661   4.1976  10.2075
+#&gt;    5.8689  -0.0052 -11.2868  -1.8224  19.4149   8.4131  -1.6432   4.1972
+#&gt;   -1.6723   5.1238  -1.1278  -8.6816  -5.3589  16.0121  -6.3449 -11.0641
+#&gt;   -3.6626  -7.9673   3.6362  -7.0441   6.2639  15.9587 -14.8696  14.3943
+#&gt;   -1.4947  -3.7085   3.2552  10.2461   2.8955   0.1107  13.2408  -2.0492
+#&gt;   -0.9754   5.7900  -2.2947   0.9517  18.2419  -2.0235  -7.2679  -3.8560
+#&gt;    3.6322  -3.7929  -5.4700   2.1984  -3.2789   8.3245   5.7207  18.7523
+#&gt;    5.2059  -0.2214  -2.8451   1.5933  -0.0468  -8.6477   0.1390   1.3842
+#&gt;   -1.2361  -0.2643  -4.9882  -0.0226   8.1592  -3.9915  -1.6402  -2.3398
+#&gt;   -0.0395  -5.7144  -8.8134 -10.7175  -6.4796   0.8209   4.4671  -2.2380
+#&gt;    4.9342   4.5964   1.2061   0.4309   2.9231   0.1605  -1.3336  -8.6909
+#&gt;    6.0918  -0.9102  -4.8624 -10.4539  10.7629  -5.7115   8.8977   1.2112
+#&gt;    4.2715  -0.6758  -2.4629   7.5929  -9.4385 -23.0817   6.5401   0.1794
+#&gt;    1.2896   1.9627  -0.7723   5.5046   3.7965   0.8929   1.7333 -12.3365
+#&gt;   -1.6969   0.4768  -2.0244   8.8202  -0.3477  -9.0021  -7.3399  15.2483
+#&gt;   -5.3663  -2.8496   1.1370   5.4573  -9.0503   2.6561   5.2536  -5.2445
+#&gt; 
+#&gt; Columns 9 to 16  -1.9999   0.2756  -3.0347 -12.2593   3.9656 -27.9082   6.1364  -6.9188
+#&gt;    2.1805   3.2268 -20.1301  -2.3668  -1.9788  -0.9791   7.7772 -13.9050
+#&gt;   10.4914 -14.8245   5.8770  -2.1505   4.3295  17.9494   3.2332  15.6589
+#&gt;  -11.3138  20.1980   2.4907   1.7703   1.7816  12.1020 -16.8125  19.6667
+#&gt;  -12.8666   9.0573 -20.4490   7.0089 -22.6402 -28.0731 -14.0704   2.2834
+#&gt;    3.1369 -21.4233  -1.3923  -9.6448  -9.6338   5.1032 -20.7964 -10.3669
+#&gt;    4.1008   9.9791   9.0406  -1.1264  -4.4379   8.5063  -1.5124   8.5893
+#&gt;  -11.0239  -6.4762  10.6674   2.0926  -1.1683  15.1673  10.6290  -4.3064
+#&gt;    9.1525   0.5704   3.5861   7.8905  16.3161 -14.5311   7.6131   1.7053
+#&gt;    5.6472 -29.9151  -6.5300 -20.4768 -10.8918  -3.2348 -23.2803 -10.3696
+#&gt;    9.7585 -10.2933  -3.6008   7.3111   9.5163  11.7164   0.6670  -3.8617
+#&gt;  -17.8701  -2.2579   4.9358  -3.7360  -7.5334   2.2365  -1.2352 -15.2224
+#&gt;    4.6645   8.1105 -11.1601   3.5535 -20.6257 -13.1826  15.6582  -9.6127
+#&gt;   -1.1510 -13.3487  -2.8594  -3.8370   4.1328   7.3028  -8.5192   0.5595
+#&gt;   -3.8685 -14.6638  10.8199   2.5929 -21.6798 -18.7749 -20.0722  -6.4741
+#&gt;   -3.6035 -13.9078  16.3609  15.9312  25.7936  -5.2368  -4.4059   1.2649
+#&gt;   -8.0685 -13.5076 -16.3485   9.0274   3.7127 -14.7530 -16.8515  -5.1507
+#&gt;    1.5580 -15.9283   5.5596  -4.7943   7.0539   2.2376  -9.1937   4.7592
+#&gt;    2.2859   8.7128  -2.9579  -8.4410   1.2170 -10.6162   7.5178  -5.7933
+#&gt;  -18.0433  -5.6030  -8.8691   8.4996 -26.8533 -17.3229  -0.6320   5.0746
+#&gt;   -7.9684  18.0999  -9.5078 -11.4769   1.3021  13.0954  -2.8317  -3.7281
+#&gt;   -2.6961   2.3346  -4.2845 -11.5774  -4.2805  -2.6057   3.8918  -0.9071
+#&gt;    3.4042 -13.9383  -8.8197   3.8370 -11.1391   3.3404  -2.8338  21.0822
+#&gt;   16.8104  11.1530  13.4996  -9.7915   8.7716  -2.5054  12.0095 -11.5617
+#&gt;   14.1501   1.8761   6.3314 -10.1340  -7.7371  10.3086  13.3223   0.9572
+#&gt;   -0.6895   9.7227   9.2780   1.8902  -8.7706  -9.8212   3.1535 -13.4027
+#&gt;    9.2930   3.7463   1.2078 -19.1367  -7.3601   7.2493  -4.3065   4.2799
+#&gt;   -3.2989  -6.0517  -4.5894   3.2898  14.5887   4.2458   4.4147  16.0410
+#&gt;    5.0267   8.3714   0.1451   6.8633  16.1347  -1.1124   5.0697  20.6016
+#&gt;   -3.2677 -17.3143  -7.0599   0.8349 -13.2482 -17.2869 -13.9283   8.5212
+#&gt;   21.3014  -3.3553   2.7272  -0.7305 -11.4003  -4.8428   9.4332   1.6574
+#&gt;  -15.5308 -11.2574 -10.1778 -22.1764 -18.2551   6.8741 -14.3040   7.3803
+#&gt;   12.2890  -8.7037  -0.7001 -30.3416  -4.4090   7.3282 -19.0024   5.6816
+#&gt; 
+#&gt; Columns 17 to 24  11.3415   1.8475   1.1421  -1.9459 -20.8720  -4.1786  15.6824  -8.0598
+#&gt;   -9.8086   1.3116   8.6122  -8.0612  -3.7797  -2.9260  -5.7208   0.0336
+#&gt;    5.2273  11.1455   1.5187   3.8744 -12.5833 -14.4831 -12.2603   7.3923
+#&gt;  -25.1665   6.8883 -12.9580   0.7983   6.3732   0.4855  -4.4769   0.0224
+#&gt;  -23.7427   8.5282 -13.7325   2.0021  -7.4680   6.8203   1.4800   2.3046
+#&gt;  -10.6971  -2.8438  -5.5806  25.1138 -11.0431  15.1867  -4.4431  15.7058
+#&gt;   -9.2288   0.0881  -3.0729 -16.7718   0.6031  -6.5455  -2.6865   1.0175
+#&gt;    4.0480  10.4326  11.0936 -11.2328  -8.6291   4.2397  -7.3878   0.1333
+#&gt;    1.5080   1.1348  12.0883  -6.0027 -18.7562 -11.7351   7.0525  -7.5212
+#&gt;  -16.6178  14.5494  -7.9325   1.3820  -6.5049   3.9571   2.1005  -2.2390
+#&gt;   30.8940  13.3603  -0.6191   2.7103  -6.4713  11.2503  -9.1462  -2.0153
+#&gt;   -2.8805  -5.2401 -11.1418 -16.3078  -3.2876   6.3194   4.8781   2.4407
+#&gt;    7.1361 -22.5099 -10.3275   2.2974   8.9481  12.9058   9.1893  -6.4378
+#&gt;  -23.3701   5.0497  -1.9278   4.7373  -1.8211  -5.9550 -10.0134 -10.5139
+#&gt;  -11.5440 -10.2881  -7.1974  -0.4086  14.8722  15.4445   5.4690  -6.9529
+#&gt;   10.5349   8.7367  -5.6713  -8.3642   4.2299   4.1340   2.8754 -13.9507
+#&gt;   -0.5988   5.8704  -8.2566   5.5887 -14.3388   7.6471 -11.1970   3.2961
+#&gt;   -0.2537  10.4046  -9.4422 -10.4438  -7.7936 -14.9798  10.1950 -15.7083
+#&gt;  -21.3917   6.3735   6.5893  -2.8601 -14.1835   2.0417  -2.5771 -13.8505
+#&gt;  -23.9282 -19.6325  -5.5975  -8.8023   1.6138   0.0271   5.3163   4.2856
+#&gt;    2.1600  -5.2791  -5.4355   4.8082   1.0244   2.1854  -3.3372  16.7090
+#&gt;    6.8382  -2.4120  21.7143   4.5327  -0.8197   4.1578   7.4679   3.8636
+#&gt;    4.2070  18.5672  -7.0864   5.2927 -12.6891  -6.0291   0.3712   6.0586
+#&gt;    8.6083  -6.0751   1.5819  -4.1973   6.8597  -7.1708   2.9634  -7.9239
+#&gt;   -9.8341  17.0530  13.1495   1.2774  17.5114   1.3631 -12.2239   7.6702
+#&gt;    0.1338 -17.9398   5.5986  -1.6880   3.3359 -13.7695  15.4135  -8.5532
+#&gt;   19.4245 -18.9081   5.8314  -7.6547   8.9253  -9.3756   5.7626  13.6244
+#&gt;    4.7026  22.9031   8.1282   5.4685  -7.1700  -1.5186   2.0961   5.5197
+#&gt;  -14.1119   6.1434   1.1908  25.2888  -1.2966 -10.8851  -0.1169  12.7462
+#&gt;    5.1816  -5.7916  11.5887   1.7730  15.3466   9.7472   7.4484   6.8338
+#&gt;    1.6397 -10.5599 -10.0957  11.6892   8.6562  -9.3152  -3.9449 -10.9035
+#&gt;   -4.4536   4.4691  15.2725   0.5402   3.1361  -6.1849   2.5591  -6.1711
+#&gt;   -1.8157  10.1398  -5.5183   1.7201  -1.8657  -3.3718   1.1055   2.2464
+#&gt; 
+#&gt; Columns 25 to 32   6.4815   5.6893  -0.9227  -3.1664  -6.2062  -4.3568   4.5600  -3.0158
+#&gt;   12.8531   2.1956   1.1480   9.7768  -3.6533  -9.2790  -7.2291  -0.9515
+#&gt;    4.8664  -1.7881  -7.7357  17.3084  -8.7027  13.6272   1.0505  20.4756
+#&gt;  -17.7767  14.0274  -2.2175   1.9212  -6.6565   5.5522  -2.8884  -1.8843
+#&gt;   -0.8330  11.0690   8.7098   1.9187   0.7351   3.5510   4.4887   5.0016
+#&gt;   -4.3257   8.1221   2.6468   0.4250   0.2066   2.7744 -10.2055   5.2186
+#&gt;    1.2346   0.7154   1.0156   6.1360   4.9314  -6.6754  13.3389   1.6581
+#&gt;   -1.7103  -5.5087  -3.7728  10.8461  -4.9028 -10.6431  -5.0634  -2.1074
+#&gt;   -0.2228  -0.4752  -7.8371   1.3122 -15.3493  -8.4393 -11.0277   8.3516
+#&gt;   -5.9093   9.9790  -1.4842   1.6851   1.4589  12.7874   4.4788   9.0027
+#&gt;   -0.4178 -19.4052   1.5148  -3.4644  -5.5994  -8.1708 -26.6034   5.4712
+#&gt;    3.9157  -0.2716  19.1154  -0.7381  -3.5439   6.2129  19.1589   4.5414
+#&gt;    7.1570  11.1482  -7.5456  -4.1460  -4.8931  13.3967  -3.7882   7.5528
+#&gt;   -1.6330  -0.4864   1.6889  -9.7207  10.3118  -3.4479  -9.1503  -2.1856
+#&gt;    5.0068  12.6816 -11.8438   4.0864   0.2849   7.5290   8.5680  -6.5796
+#&gt;    1.9399   2.8008  -0.7312 -21.1728   5.3786  -3.8231   7.9454   1.1585
+#&gt;   -4.7803  18.4195  -2.4929  -5.0381  12.4804  -1.6395  -1.5868  -1.6330
+#&gt;  -13.5353  -7.7027   0.2612   3.4031   2.1050   9.2101   3.0357  -8.1287
+#&gt;    2.4584 -16.7182  -4.7704  -2.7177  -5.0968 -22.1300 -13.1682   5.2942
+#&gt;    8.8140   6.0968 -12.3037  20.1467   3.0207  16.8412 -15.3083  24.1607
+#&gt;    2.2434  -1.5179   9.1971  -1.3209  -6.3701  10.1950   4.6083   7.7751
+#&gt;   -8.9829  -4.7765   7.2443  -5.7954 -16.3653  -1.3180  -6.4603  -1.6012
+#&gt;   -2.6718  13.0925 -13.5202   7.0508   4.7404   7.2232   4.7078  12.7123
+#&gt;   -0.9451   7.0915  -2.9803  -0.7490  -5.6806 -15.7917   0.3480  -9.7282
+#&gt;   22.9171  -2.5859  -2.9637  12.2590   3.6550  -3.5028   8.7403  -2.8165
+#&gt;   -6.8268   1.9172  -9.0970   4.3419  -7.7849   7.8244  -9.3282  -0.7085
+#&gt;  -11.6038   0.0854   1.1722   3.9985  -2.3884  10.6390   4.8932  -1.6232
+#&gt;    6.2525   1.6452   2.3449  10.9179   2.4850 -11.0971  -8.7406  -2.9657
+#&gt;   -4.8382  -4.9508   1.7669   5.4140  10.2555   0.8560   5.6142  -5.5381
+#&gt;  -12.4554   7.3132   3.8855   2.8679 -12.0267   1.9528 -11.5046   0.5150
+#&gt;    1.5025  -1.3696 -11.2275  -4.3624  -0.0759   0.8373  -6.3970  -9.6812
+#&gt;    4.5267   8.3538  -7.7346  -3.5750   1.7459   3.5132  -3.8898  13.1212
+#&gt;  -18.6642   5.1290   7.8952  -2.3550   4.7407  -7.8264   5.7572 -14.2221
+#&gt; 
+#&gt; Columns 33 to 40   8.0496  -4.3295   1.8407  -8.9827   7.7951   0.9815  -3.3443 -10.9863
+#&gt;   -3.0410  10.0320   5.7510   3.9766  -7.4743  -2.5651   2.2061 -10.4112
+#&gt;  -10.6715  -1.7391  -1.1629   1.3610  -5.0009   0.7881   7.1177  11.1729
+#&gt;  -18.8247   6.3556  -7.0350  11.2825  13.5470  17.5848  -3.1697   3.9358
+#&gt;    0.6382   2.6878   3.0333  10.8931  -6.3605  29.8396   6.9935   7.7576
+#&gt;    9.3820   1.4815  -0.3291  -6.9713  -3.5959   9.0425   5.1229  -5.8787
+#&gt;    1.1551   6.9779   7.5625  -5.4963   3.3594  -3.2003  -8.1196   3.4297
+#&gt;   -9.8684  12.0555 -15.4573   1.9966  -8.1726  15.2350   8.3469   1.2325
+#&gt;    9.0008   5.6839   1.3163  -6.8948 -17.9637   1.1142  -2.0898  -4.6909
+#&gt;   -3.6163   2.3786   3.5571  -3.2093   1.1328  16.8561  -1.2516   7.7491
+#&gt;   -1.7443  -8.1475  -6.9335  21.6469   0.9863  -0.8011   5.3470 -11.8187
+#&gt;   -4.3721   7.7496 -15.3290 -14.7002  -4.6889   9.6844   5.8437  -5.3226
+#&gt;    9.4806  -3.6792  -5.3814   2.6639   0.3295   9.1594 -12.4843 -13.4395
+#&gt;  -15.7484   6.3044  -6.4635  10.6292  -1.2039   1.8374   6.8766   6.9814
+#&gt;   -4.5046  -9.6416  -6.1771   1.9989  12.0743   0.4090   9.4904  -8.5855
+#&gt;  -19.5573 -13.9309   4.8088 -15.0457  15.1453   7.5878   1.0427  -0.1884
+#&gt;   -8.1178 -12.3319  10.5809   0.4396  -3.8857   7.0447  21.8247   2.4879
+#&gt;  -10.7616  21.6806  -9.9731  -0.2034  -8.8837  13.3950   0.4176   8.9088
+#&gt;   -1.9661   7.1422   7.4603  -2.7760 -14.4412 -18.2499   3.2360  10.8341
+#&gt;   -3.7387  -1.8276  -4.5835  -1.3713  -8.7792   8.4404   4.5047   5.8481
+#&gt;    0.9969   2.9623 -11.4680  -2.5941  -0.0900  -2.7210 -19.6152   3.4699
+#&gt;   -7.1210 -11.8914   1.7412  -5.4167   4.6137 -12.9696   2.6714   5.5981
+#&gt;  -17.3747   1.1312   1.7433   8.5802  -1.5415  14.1470  -9.4081   3.8514
+#&gt;    9.3312  -0.7556  22.4715  -8.4808   4.1353 -12.0296 -13.0816 -17.7008
+#&gt;    1.0289 -14.4718   4.8107  13.3372  -2.9651 -10.1529  -4.4134 -15.8602
+#&gt;   -7.1483   6.0918   6.7277   0.3636   5.1824   0.2863  -7.3600  16.8108
+#&gt;    8.6143  12.9924  17.0444   6.9125  -0.3173 -12.9158  -9.7363 -15.0327
+#&gt;   12.7596   7.4542   4.2434   9.1233  -2.8078 -10.4957  16.8506   4.0370
+#&gt;    0.6383  -4.2978   6.9013   2.2868  -4.1186 -13.9069   3.5185  12.9860
+#&gt;  -27.9366  11.4565  -8.4703   7.0572  -0.2669   2.7892  11.4372  -5.5336
+#&gt;    2.1539 -15.5910   4.9511   5.3579   5.9842  -6.1158   2.0469  -9.6203
+#&gt;   -3.7822   7.1565  -5.9621   6.0425  -6.5943   1.6693  -0.4180   5.4690
+#&gt;   16.8042  -5.0099   5.0895  -8.8010   0.1436  -1.9357   3.0880 -13.2745
+#&gt; 
+#&gt; Columns 41 to 48   0.3146 -14.3353  -5.9365   7.4027 -17.7053  -7.8412 -10.0810 -18.0308
+#&gt;    4.0662   7.7610   1.7881   9.0038   8.5724   7.1975  -7.4737  -9.6039
+#&gt;    4.9590  -4.5158  -6.8833   4.6137  18.8929   2.1048  -5.4436   1.9336
+#&gt;   -4.8528  -3.6431  13.7987  12.6808   9.2454   0.6520   3.5415   7.5007
+#&gt;   -0.6831   6.4074  18.4871  25.3673   5.4122  15.7172  23.4501   4.4716
+#&gt;   16.4760  -2.8680   0.7216   4.0668  18.1909   3.2577   2.4170  20.2340
+#&gt;   -8.1073  -0.9868  13.7834  -1.9514  -3.2242   2.6656   3.5993   0.2573
+#&gt;    3.5294  16.4313 -18.7772   4.2040  -2.7209   8.4672   8.0065  -2.4085
+#&gt;   -1.4619  -3.0826   8.1454   5.4698  -2.2318  -1.3324  12.1368  -9.4877
+#&gt;    4.4847 -13.7462  19.8069  15.7085  13.8782   1.7101  -5.4297   1.1614
+#&gt;   -4.5525   7.3460  -3.3067  -9.1236   1.7664  -1.3738  -2.8044  -0.9467
+#&gt;  -27.4670   1.2062 -18.4750 -15.0367   6.5579  -3.3533  -0.4274   1.1769
+#&gt;   12.5707  16.2844   5.2190  -4.8410  -0.9088   5.8897   1.0212  -7.7595
+#&gt;   10.1050   3.4524  -1.3630  -3.5027   0.3427  -5.4786  -1.7661  -1.0505
+#&gt;    4.3743  -3.2571  -2.2518   7.7369   0.3507   5.3110   0.2836 -12.8981
+#&gt;   -4.3187   6.1857  -5.9451  -8.2904   6.1723  17.5522   9.5769  -9.2986
+#&gt;   10.3207  -4.8456   6.7989   5.4208   4.2143  -0.1745   3.9681  -5.4716
+#&gt;   -8.8992  -6.6920  -4.0081 -19.4168   4.1258   0.2592   7.3254 -13.1612
+#&gt;   -5.0748  -3.2694   3.9104  -5.0094  -4.3356   9.6339  17.6926   2.7489
+#&gt;    7.9159   1.2296   5.7530   5.2686  -4.5178   7.0554   9.1664   8.6424
+#&gt;    8.9341 -11.6695  -4.1183  -4.7487   3.2787  -8.7734  -8.8676   2.4187
+#&gt;   -0.5846  -7.5384  -8.2017  -3.2226  -1.1003 -13.9250  -9.9109 -12.9002
+#&gt;   -7.1770  -5.6694  13.1071  12.3576  -5.2566  -2.9248 -14.2614   1.8318
+#&gt;    6.0011   8.2952  10.7749   2.2634 -13.4215  -1.2809   6.6806   2.5869
+#&gt;  -12.1651  -4.9027  -9.3990  -7.3810 -19.8350  -3.1701 -17.9258  -5.2328
+#&gt;   -3.2122   0.5319  12.5485   8.6093   4.8249   6.0053 -13.9898  13.6141
+#&gt;    2.4600   6.9859  -0.7049   2.6028 -12.7993 -13.0092   2.9656  12.3087
+#&gt;   -7.3083  -3.6930  15.8727  10.6552  -6.9516  -5.3436   5.7612   1.9576
+#&gt;    5.3848  -2.5760  -9.2433  -0.3591  -3.4518   1.0722  10.9195  -8.5985
+#&gt;   -8.0646   4.3434  -2.1281  -1.4199 -16.7954 -12.6490   0.7447  -8.1730
+#&gt;   13.3801   4.9655  -2.5100  -0.3978  11.2956  -0.5729   5.2475   5.3386
+#&gt;   -6.9883   9.6422  18.8881  -9.8414  -0.1467  -2.5258 -16.4064  -2.6196
+#&gt;   -5.7340  -4.1890  -1.8319   4.0902   1.0645  -4.4699 -11.0413   4.2681
+#&gt; 
+#&gt; Columns 49 to 54 -15.0626   4.6630   2.1379  -3.9945  -4.5883   1.5795
+#&gt;   17.6983  -1.3270  -3.1154   2.2603   2.6928  -0.7149
+#&gt;   -0.2805  16.9501  10.5462 -11.3031  -4.7032   6.6998
+#&gt;    7.2113  20.6352   1.0219   1.2684   1.2694  -4.6234
+#&gt;    7.4568  -4.2505   2.3414  -5.1908  -2.3375  -5.6689
+#&gt;  -18.9978 -11.5822  -6.5452   2.4236   2.7432   0.1641
+#&gt;    3.4828   6.0013   3.6968 -10.0672  -6.4488   1.8463
+#&gt;   -1.6269   8.8388   1.7039  10.8039   4.8676  -0.8757
+#&gt;    8.0023   7.3665  -3.6373   0.8060   4.4465   2.0906
+#&gt;   14.0379   5.0369  -8.3252   1.9085  -1.2375  -0.7666
+#&gt;   -8.2903 -10.8521   5.5031  -3.2708   5.1663   9.9725
+#&gt;   -4.5692   0.8189  -5.1834   1.5868   1.9432   1.3214
+#&gt;   12.6362  -6.4726   5.5729  -9.4261  -0.5406   2.7630
+#&gt;    6.3896   4.7119  -2.1385   0.5686   2.1703   0.6736
+#&gt;   -5.1342  -2.3181  -4.9941   5.9961  -0.5455  -3.2953
+#&gt;    2.0037  10.6868   5.2003   7.3448  -5.1892  -0.2359
+#&gt;   -5.1612  -9.5109 -19.9095   5.0390   8.7671   4.5186
+#&gt;    6.5665   0.5478  -0.2521   2.9676   0.2838   2.1890
+#&gt;   -2.5895  -3.7767  13.7813  15.5430  -1.4989  -2.7487
+#&gt;   -6.8314  -2.9919 -14.5160  -7.0359   6.2852   5.7361
+#&gt;   17.4565  -9.9268  -0.5212  -2.3981  -5.5776  -4.8160
+#&gt;   -4.4211  -1.5263 -10.7164   1.1898   7.4499   6.3166
+#&gt;   -0.1586   1.6893   3.3332  -7.7713   0.1963   5.0846
+#&gt;  -10.3228 -10.7535   5.2761  14.6063   2.9479   2.6619
+#&gt;   11.5348   7.4959   1.8269  16.0055   4.7889   0.3962
+#&gt;   -4.4697  -7.9666 -13.3593  -4.0063  -2.3265  -0.5683
+#&gt;    9.0687   0.1223  -1.3498  -4.1347   1.9401   0.9255
+#&gt;  -10.6185  -4.0579  -4.0332  -3.4902  -6.2295  -0.2965
+#&gt;    0.9226   7.3937   2.3543   1.6309  -2.5689  -1.2707
+#&gt;  -19.4518   6.9227   3.6227   9.0081   8.0081   1.1943
+#&gt;    5.5501   3.1205  11.6577  -3.1306   2.7364   2.7878
+#&gt;  -13.3373   6.9455  -4.3216  -6.7553   2.7839   0.4027
+#&gt;   -0.9990   5.7631   0.4375  -1.4641  -1.9611   1.0508
+#&gt; 
+#&gt; (11,.,.) = 
+#&gt;  Columns 1 to 6 -3.8360e+00  1.5939e+00 -3.7936e+00  1.8678e+00 -1.8081e+01  4.4164e+00
+#&gt;  -1.1471e+00 -2.8029e+00 -2.5762e+00  5.1902e+00  6.6967e+00  7.1688e+00
+#&gt;   2.5485e+00 -4.8021e+00 -3.1657e+00 -1.4638e+01  1.5666e+01  3.7014e+00
+#&gt;  -1.6291e+00  1.9018e+00 -4.8757e+00  1.5393e+00 -2.5465e+00  1.8616e+00
+#&gt;  -1.8028e+00 -2.3302e-01  5.7494e+00 -1.0798e+00  1.0129e+01  9.0177e+00
+#&gt;  -6.6964e-01  1.0868e+00  6.3221e+00  5.0807e+00  4.7175e+00  2.8995e+00
+#&gt;   3.0178e+00 -3.2517e+00  5.1202e+00  5.3788e+00  1.1414e+01 -1.3915e+01
+#&gt;  -4.1800e+00  1.3642e+00  3.0657e+00 -1.0666e+00 -4.3976e+00 -3.3562e+00
+#&gt;  -3.0942e+00 -9.7947e+00  9.8732e+00 -1.2089e+01  9.8079e+00  4.0579e+00
+#&gt;  -3.1702e+00  4.5015e+00  2.2437e+00 -7.6928e+00 -7.5949e+00 -1.4488e+00
+#&gt;   2.8843e+00 -2.5495e+00  5.6960e+00 -4.4285e+00 -6.2185e+00 -2.1271e+00
+#&gt;  -6.1734e-02 -2.3013e+00  4.0962e+00 -2.6230e+00 -5.5801e+00  6.3505e+00
+#&gt;   5.1560e+00  2.2354e+00  7.4479e+00 -2.7796e+00  6.7787e+00  7.4205e+00
+#&gt;   8.6645e-01 -4.0719e+00 -1.4128e+01  5.4836e+00 -1.8982e+01 -1.4326e+01
+#&gt;  -1.6762e+00  1.3226e+00  6.9057e+00  7.8310e+00 -4.7439e+00 -4.6881e+00
+#&gt;   3.0498e+00  7.4968e+00 -6.1214e+00  8.7065e-01  3.5176e+00  1.2662e+00
+#&gt;   1.5084e+00 -3.7030e+00  5.4233e+00  1.1332e+01  9.5553e+00 -3.7449e-01
+#&gt;   1.7620e-01 -7.8269e+00 -1.3969e+01 -5.6421e+00 -2.0002e+00 -4.6152e+00
+#&gt;   3.1169e+00 -8.0233e-01 -3.1288e+00 -1.1862e+01 -6.6224e+00 -3.2739e+00
+#&gt;  -2.7442e+00  2.3985e+00  3.2201e-01 -6.7745e-01 -4.5126e+00 -7.6190e+00
+#&gt;   3.8572e+00  1.3669e+00  4.1118e+00 -1.1800e+00  1.6259e+01 -7.0737e+00
+#&gt;   3.7801e+00 -1.9738e-01  9.7668e+00 -3.9714e+00  4.9906e+00 -1.9493e+01
+#&gt;  -1.6236e+00  6.5810e+00 -6.8311e+00  3.3564e+00 -2.2126e+00  1.1171e+01
+#&gt;   7.3322e+00 -2.2437e-01  5.6662e-01 -1.2073e+01 -9.4299e+00 -4.0470e+00
+#&gt;  -1.7680e+00  1.6458e+00 -7.3639e-01 -6.0911e+00 -1.2497e+01  1.1654e+00
+#&gt;  -7.3167e-01  1.8133e+00 -2.0562e+00  5.6627e+00 -3.3552e+00 -6.1999e+00
+#&gt;   5.3507e+00  2.7325e+00  4.9057e+00 -3.1438e+00  9.1261e+00 -6.7998e-01
+#&gt;  -3.0363e+00 -8.1935e-01 -3.0917e+00  5.6052e+00  6.7996e+00  4.9521e+00
+#&gt;  -1.8488e+00  3.3741e+00 -8.2848e+00 -5.4631e+00  1.0128e+01  5.8941e+00
+#&gt;  -1.2998e+00  1.3414e+00 -4.5637e+00  4.8459e+00 -8.2052e+00 -1.0843e+01
+#&gt;  -5.1679e-01 -2.2994e+00 -7.7908e+00 -5.5937e+00 -1.0556e+00  2.5691e-01
+#&gt;   3.5901e+00 -2.1805e+00 -6.4358e-01  6.8101e+00 -1.3151e+01 -3.8822e+00
+#&gt;   3.8115e+00  4.7139e+00 -3.8519e+00  2.4953e+00 -2.6490e+00  1.2095e+01
+#&gt; 
+#&gt; Columns 7 to 12  1.1355e+00  8.0373e+00  9.8857e-01 -3.5068e+00  6.9162e+00  9.3497e-01
+#&gt;   2.8756e+00 -2.6017e+00  1.1818e+01 -8.8997e+00 -3.1929e+00 -7.8767e+00
+#&gt;   9.8390e+00  8.1572e+00  1.4665e+00 -1.1117e+00 -4.8860e+00 -3.9219e-01
+#&gt;   7.8412e-01  2.0463e+01  3.2234e+00  2.9016e+00  5.3400e+00  3.6160e+00
+#&gt;   5.6648e+00  1.6924e+01  1.1407e+00  4.5146e+00  8.7476e+00 -3.6236e+00
+#&gt;   1.2562e+01  7.6311e+00  5.9790e+00  5.1021e+00 -4.0275e+00  6.5509e+00
+#&gt;  -1.3679e+01  1.5848e+01  9.6090e+00 -1.3240e+01 -9.9157e+00 -1.6351e+00
+#&gt;   4.1676e-01  4.6696e+00  2.1223e+01 -1.5720e+01  1.4410e+01  4.6688e+00
+#&gt;  -9.9709e+00  5.2292e+00  5.8353e+00  7.9057e-01  3.5686e+00  2.8194e+00
+#&gt;   1.9564e+01  1.1953e+01 -1.2233e+01  1.0940e+00 -8.2733e-01  2.0802e+01
+#&gt;   7.7502e+00 -7.1128e+00  7.5082e+00 -2.4273e+00  6.4676e+00  4.0435e+00
+#&gt;  -5.3417e+00  1.2981e+01  8.5225e+00 -1.3927e+00  1.4157e+01  3.2939e+01
+#&gt;   9.7841e+00 -7.4163e+00 -6.2632e+00  1.8813e+01 -4.8736e+00 -5.7193e+00
+#&gt;  -5.2379e+00 -1.6308e+01  8.9360e+00 -2.1223e+00 -9.0632e+00 -8.2404e+00
+#&gt;  -1.5874e+00  2.3141e+01 -2.1215e+01 -1.1144e+01  6.3798e+00  7.7378e+00
+#&gt;   2.7432e+01 -6.5000e+00 -9.0771e+00 -1.0587e+00  5.5313e+00  7.7932e+00
+#&gt;  -4.2467e+00 -2.2803e+00 -1.1580e+01 -7.6551e-01 -1.0155e+01 -3.6816e+00
+#&gt;   3.7888e+00  1.7301e+00 -8.4693e+00 -3.7254e+00 -6.6073e+00  1.8567e+01
+#&gt;   2.5772e+00 -1.0706e+01  9.7069e+00  4.7409e+00  5.5050e+00  6.3782e+00
+#&gt;   2.6113e+00 -6.4904e+00 -1.4243e+01  1.3646e+01  1.1248e+01 -1.3836e+01
+#&gt;   1.9483e+00  8.2244e+00  5.3361e+00 -1.8463e+00  3.8943e+00 -5.7493e+00
+#&gt;  -1.2684e+00  1.0993e+01 -6.9079e-01  9.9783e-01  1.4274e+00  1.8779e+01
+#&gt;  -3.4843e+00 -4.9284e+00 -2.8626e+00 -1.5959e+01  3.7211e+00 -1.3182e+00
+#&gt;  -1.2014e+01  2.7731e+00 -8.7801e+00  1.4702e+01  1.3477e+01  2.8389e+00
+#&gt;  -7.1466e+00  1.6777e+01  1.2249e+01  6.4850e+00  1.4657e+01 -3.4285e+00
+#&gt;   3.8257e-01 -9.9398e+00  2.2121e+00  6.9386e+00 -1.5108e+00 -7.0370e+00
+#&gt;  -1.9808e+00  1.1421e+01 -1.3566e+01 -1.3428e+01  5.7615e+00  1.9043e+00
+#&gt;   9.5193e+00  5.8011e+00 -1.1230e+01 -2.5744e+00 -9.9539e-01 -1.8106e+01
+#&gt;   7.9076e+00  6.8125e+00 -1.8321e+00  5.3211e+00 -4.6525e+00 -8.1244e+00
+#&gt;  -3.5788e-02 -2.8294e+00  1.1478e+01  4.6395e+00  6.3767e+00  2.9761e+00
+#&gt;  -8.7438e+00  8.3134e+00  5.7363e+00  1.3889e+01 -5.7648e-01  2.6462e+00
+#&gt;   1.5458e+01  4.3068e+00 -4.7382e+00 -2.4897e+00 -9.8575e-01  1.3976e+00
+#&gt;   9.4647e+00  1.0196e+01 -9.4177e+00  9.1721e+00  3.3925e+00  1.1460e+01
+#&gt; 
+#&gt; Columns 13 to 18 -1.0379e+00 -5.1405e-01  7.7139e+00 -1.0642e-01  8.3741e+00  1.9785e+00
+#&gt;  -7.2977e-01 -1.2779e+00 -8.6253e+00  5.1659e+00  2.0163e+00 -7.9648e+00
+#&gt;   1.9047e+01 -7.8211e+00 -1.7251e+01  1.0182e+01 -3.4768e+00  3.5280e+00
+#&gt;  -6.8537e+00 -8.3516e+00 -1.0993e+01  6.2705e+00 -1.7694e+01 -4.0229e+00
+#&gt;   1.1758e+00  5.2104e+00 -6.0128e+00 -1.2200e+01 -1.4170e+00  1.2333e+01
+#&gt;   1.8981e+00 -7.7969e+00 -8.2999e+00  7.0828e+00  6.2507e+00  2.2976e+01
+#&gt;   1.0308e+01  2.0559e+00 -1.7678e+01  2.1769e+00 -3.3622e+00 -1.1646e+01
+#&gt;  -8.8872e+00 -1.0547e+01 -1.0122e+01 -1.6779e+01  4.8407e+00 -1.0983e+01
+#&gt;   1.9676e+00  6.8608e+00  1.3503e+01 -1.0649e+01 -3.7704e+00  6.5000e+00
+#&gt;  -5.4224e+00 -2.0540e+01 -3.5309e+00  3.0255e+00 -1.4995e+00  1.4269e+01
+#&gt;   3.9895e+00 -6.3869e+00  5.0963e+00 -4.7499e+00  4.6711e-01  5.8398e+00
+#&gt;   1.1262e+01  1.1528e+01 -4.1378e+00 -8.8242e+00  2.8453e+00 -4.3191e+00
+#&gt;  -7.2129e-02 -2.3884e+00 -2.0270e+00  5.6990e+00 -2.8940e+00  1.8328e+00
+#&gt;   9.1767e+00 -4.4345e+00  1.6328e+01  2.1799e+00 -7.5807e-01 -2.0310e+01
+#&gt;   2.5884e+00 -1.7368e+00 -1.2152e+01  1.0830e+01 -7.4779e+00  3.1142e+00
+#&gt;  -1.3374e+01 -1.4053e+01 -4.6780e+00 -1.5641e+01 -1.0669e+00  2.0404e+00
+#&gt;  -1.4194e+01  1.7131e+00  6.0105e+00  7.9512e+00 -6.1213e+00  1.9179e+01
+#&gt;  -5.0826e-01 -6.4392e+00 -3.9512e-01  4.7914e-01  8.4086e+00 -4.8526e+00
+#&gt;   1.5437e+01  1.6998e+01  1.0508e+01 -8.6180e+00 -3.1337e+00 -7.2217e+00
+#&gt;   2.4477e+01  1.0709e+01 -9.2398e+00 -5.8075e+00 -1.3620e+01  2.6313e-01
+#&gt;  -3.0655e+00 -1.0128e+01 -6.8721e+00  4.9362e+00 -1.2731e+00 -8.4537e+00
+#&gt;   1.6656e+01 -1.2754e+01  1.5588e+01  1.4962e+00 -1.3077e+01 -1.4743e+00
+#&gt;   2.4511e+00 -2.1280e+00 -1.1724e+01  5.4683e+00  9.7342e+00  8.6476e+00
+#&gt;   1.1642e+01  5.3199e+00  1.3054e+01  8.3679e+00 -1.0100e+01 -1.1214e+01
+#&gt;   1.3779e+01  8.0466e+00 -1.1280e+01  1.2072e+01  1.1482e+00  1.6226e-01
+#&gt;  -3.8898e+00 -1.2344e+01  6.9595e+00 -4.3888e+00 -1.5284e+00 -5.0525e+00
+#&gt;   9.0238e+00  1.5506e+01  4.0511e+00 -7.3665e+00 -1.4392e+01 -1.0761e+01
+#&gt;   8.3401e+00  2.9931e+00 -9.2663e+00 -7.3426e+00 -2.0989e+00  1.3918e+01
+#&gt;  -5.0650e+00  3.7366e+00  1.7401e+00 -3.5033e+00 -1.0104e+01 -7.8859e-01
+#&gt;  -3.7941e+00  9.6133e+00  5.1849e+00 -5.6589e+00  7.7796e+00  2.6745e+00
+#&gt;  -1.9627e+00 -5.5493e+00 -8.1682e+00 -4.2417e+00 -2.1606e+01 -2.9949e+00
+#&gt;   7.0163e-01 -1.0841e+01 -8.8551e-01 -8.6048e-01  1.1358e+01 -1.1409e+00
+#&gt;   1.2621e+01  1.4819e+00 -3.9977e+00 -4.2345e+00 -1.2149e+01 -6.6803e-01
+#&gt; 
+#&gt; Columns 19 to 24  4.0309e+00 -9.1221e+00 -3.9438e+00 -1.7986e+01  1.8854e+00  9.0591e-01
+#&gt;   5.4623e-01  5.4963e+00  2.4605e+00 -4.4006e+00  1.0538e+01  1.6970e+01
+#&gt;  -1.0713e+01  6.1633e+00 -2.0267e+00  3.7437e+00 -1.0923e+01  8.9042e+00
+#&gt;  -1.1127e+01  8.0173e+00  2.9128e+00  3.2007e+00  4.7226e+00 -9.0789e+00
+#&gt;   6.1319e+00  1.1253e+01  6.9746e+00  2.8675e+00  5.8655e+00  1.0044e+00
+#&gt;  -1.6951e+00  9.3632e+00  6.8477e+00 -7.9658e-01 -5.9129e+00  1.8410e+00
+#&gt;   7.1308e+00 -2.0794e+00 -7.7926e+00  8.8771e+00 -4.4297e-01  8.4418e+00
+#&gt;   3.3092e+00  2.3140e+00  6.0336e+00 -4.7366e+00 -7.6794e+00  4.8340e+00
+#&gt;  -8.0594e+00  3.9725e-02 -9.1456e-01 -4.6298e+00 -3.2125e+00  1.3890e+01
+#&gt;  -8.9300e+00  1.3681e+01 -9.5088e+00  1.2522e+01  7.4292e-02  6.4323e+00
+#&gt;   2.0328e+00  2.6531e+00 -1.2640e+01 -9.4904e+00 -1.5841e+01  5.4049e+00
+#&gt;  -4.0114e+00  1.4736e+00 -1.1151e+01 -8.2712e+00  4.5151e+00 -7.3063e+00
+#&gt;  -4.3667e-01 -2.3232e-01 -3.1493e+00  1.7114e+01  7.8338e+00  1.8030e+01
+#&gt;  -4.7537e+00  2.0193e+00 -1.0430e+00 -1.0014e+01  3.9959e+00 -2.3063e+01
+#&gt;  -9.8200e+00  1.0670e+01 -5.9706e+00  2.4227e+01 -2.7277e+00  5.0799e+00
+#&gt;  -6.7298e+00  4.2245e+00  2.7938e+00 -2.8428e+00  2.2324e+01 -6.9202e-01
+#&gt;   8.1541e-01  1.5880e+01 -1.3547e+01 -9.0919e+00  4.8563e+00 -1.3871e+01
+#&gt;  -1.3599e+01  1.1264e+01 -2.1159e+01 -3.1379e+00 -7.7998e+00  9.0941e-01
+#&gt;  -1.5343e+01 -2.8572e+00 -5.8122e-01 -3.8335e+00 -9.4521e+00 -4.6786e+00
+#&gt;   7.6362e-01 -2.8310e+00  9.1508e+00  6.4683e+00  5.6846e+00  4.6314e+00
+#&gt;   9.5910e+00  3.8809e+00  8.4984e+00 -3.5116e+00  1.0446e+01 -1.0756e-01
+#&gt;  -4.8337e+00  9.8921e+00 -5.1602e+00 -7.1428e+00 -1.0196e-01 -7.2319e-01
+#&gt;  -1.2248e+01  1.1094e-01 -5.7928e+00  7.0802e+00  5.0713e+00 -3.1212e+00
+#&gt;  -2.4178e+00 -1.0519e+01 -2.3732e+00 -2.7023e+00 -5.9592e-01 -7.1338e-01
+#&gt;   6.3050e+00 -3.9132e-01  7.2173e+00  9.4026e+00 -2.7707e+00  9.6987e-01
+#&gt;   2.0358e+00 -1.3545e+00  7.1738e+00 -3.6405e+00  3.7646e+00 -1.6337e+01
+#&gt;  -6.3457e+00 -1.4147e+00 -6.5676e+00  1.1348e+01  1.1023e+00  9.8485e+00
+#&gt;   1.5842e+00  3.5686e+00  5.5394e+00 -1.1322e+00 -3.6309e+00  5.8952e+00
+#&gt;  -8.2128e+00  9.2595e+00  1.7783e+01 -4.6057e+00  3.9662e+00  1.4638e+00
+#&gt;  -1.2341e+01  2.5213e+00  1.2736e+00 -2.4336e-01 -1.6478e+00 -1.7396e+01
+#&gt;  -9.3529e-01  2.8352e+00 -6.1613e+00  1.5112e+01 -1.2392e+01  1.0438e+01
+#&gt;   1.2253e+01 -4.4346e+00  6.0388e+00 -2.3199e+00 -1.7197e+00 -7.9958e+00
+#&gt;   1.8004e+01 -3.0359e+00 -1.5593e+01 -4.7352e+00 -9.6357e-02  1.7268e+01
+#&gt; 
+#&gt; Columns 25 to 30  4.5417e+00  9.9356e+00 -7.9734e+00  8.6843e+00  1.9243e+00  7.8274e+00
+#&gt;   8.7007e+00 -4.7809e+00 -3.4190e+00  7.8836e+00 -5.8560e+00  4.9606e+00
+#&gt;  -2.3389e-01  1.1928e+01 -9.1595e+00  5.2450e+00  1.2959e+01 -8.0261e+00
+#&gt;  -2.3453e+00 -1.1489e+01  3.8974e+00 -2.7657e+00  6.5121e+00  8.7784e+00
+#&gt;  -2.8829e+00  7.7365e+00 -2.3080e+01  1.0360e+01  4.4827e+00 -1.6566e+01
+#&gt;   1.7922e+00  1.4484e+01  3.0660e+00 -1.2048e+01  7.5874e+00  6.2713e+00
+#&gt;   5.5566e+00 -1.4929e+01  5.3574e+00 -5.9776e+00 -9.7370e+00  1.1398e+00
+#&gt;   2.4957e+00 -1.4012e-01 -2.0386e+00 -2.8146e+00  1.4249e+01  2.2646e+00
+#&gt;  -4.2497e+00  1.3767e+01 -3.7657e+00  7.9206e+00  2.9652e+00 -9.6100e-01
+#&gt;  -1.6080e+00  1.4514e+01 -2.0387e+01 -1.7081e+01  1.1001e+01  1.1186e+01
+#&gt;   2.7661e+00  2.3567e+01  9.5477e+00  1.2551e+01  8.0121e+00  3.5173e+00
+#&gt;   1.3002e+01 -6.6859e+00  1.1111e+01  4.0720e+00  2.4169e+00  9.7231e+00
+#&gt;  -7.4153e+00 -5.0619e+00 -1.1198e+01  1.4229e+01 -6.1807e+00  4.0081e+00
+#&gt;  -1.9085e+01 -7.4257e+00 -1.2958e+01  8.1068e+00  2.7206e+00  2.1854e+00
+#&gt;  -1.0049e+01  5.6731e+00  8.5600e+00 -1.7086e+01  8.3425e+00 -8.1282e+00
+#&gt;  -1.0292e+01 -1.0067e+01 -2.2118e+00  8.2715e+00  6.0819e+00 -3.0324e+00
+#&gt;  -3.3625e+00  4.6885e+00 -9.0741e+00 -1.1488e+01  6.0181e+00 -2.8482e+00
+#&gt;  -7.8853e+00  6.8509e+00  4.0404e+00  5.2101e+00 -3.8899e+00  3.3171e-01
+#&gt;  -9.4386e+00  1.4857e+01 -2.6685e+00  1.1545e+01  9.6147e+00  1.5497e+00
+#&gt;   2.8378e+00 -1.1177e+01 -1.1782e+01  3.5629e+00  1.0285e+01  4.6724e+00
+#&gt;  -1.3464e+00  3.1451e+00  1.8565e+00  1.0258e+00 -1.0872e+01  9.4655e+00
+#&gt;  -1.1968e+01  6.8576e+00  7.9288e+00 -1.9535e+01  1.2262e+01  1.3630e+01
+#&gt;   5.4541e+00  7.6256e+00 -1.1775e+01  8.9696e+00 -1.0420e+01 -1.0184e+01
+#&gt;  -7.0127e+00 -1.0383e+01  5.1228e+00  1.2812e+01  1.4420e+01  6.0488e+00
+#&gt;   4.6218e+00 -4.0049e-01  1.6324e+01  2.4842e+00  9.8292e+00  5.9595e+00
+#&gt;   5.6140e+00 -1.7067e+01  6.4086e+00 -3.8410e+00 -6.4977e+00  4.8475e+00
+#&gt;  -4.9237e+00 -6.8551e+00  7.2600e+00  9.0261e-01  7.7543e-01 -1.7415e+01
+#&gt;   7.6608e+00  1.7461e+01 -9.9003e-01 -1.1081e+01  1.3593e+01 -2.8464e+00
+#&gt;  -4.0654e+00  1.7042e+00  1.9005e+00 -2.1048e+00  2.3070e+00 -8.9824e+00
+#&gt;   4.1790e+00  2.9341e+00 -4.1916e+00 -8.8421e+00  1.0892e+01 -9.1511e+00
+#&gt;  -6.2952e+00  7.3589e+00  1.1996e+01  1.7767e+01 -1.4724e+01 -6.8176e+00
+#&gt;   1.6905e+01 -5.8042e+00 -2.2659e+00 -1.0988e+01  1.2340e+00  5.7880e+00
+#&gt;   2.8737e+00  3.9366e+00 -6.4715e+00 -8.6918e+00  7.6655e+00  8.2046e+00
+#&gt; 
+#&gt; Columns 31 to 36  4.9854e+00 -3.8726e-01  6.5628e+00  6.7788e-01 -5.2493e+00  4.1982e-01
+#&gt;   1.4268e+00  2.0383e+00 -2.1152e+00 -4.2073e+00  9.1627e+00  1.5472e+01
+#&gt;   8.2502e+00 -1.0515e+01 -1.5214e+01 -6.1506e+00  5.3685e+00 -1.2504e+01
+#&gt;   5.2293e+00 -9.7807e+00  4.1479e+00 -2.4804e+01 -5.1699e+00  7.7294e+00
+#&gt;   5.3212e+00  1.3778e+00 -3.7450e+00 -1.1298e+01  4.2642e+00  4.4505e+00
+#&gt;   1.5083e+01 -1.1646e-02 -6.4796e+00  9.1971e-01  7.9833e+00  1.7602e+01
+#&gt;  -2.6859e+00  2.7046e+00  9.2215e+00  2.6360e+00 -4.5803e+00  2.3114e+00
+#&gt;  -1.6276e+00 -9.2005e-01 -2.5471e+00  1.0154e+01 -1.1071e+01  2.7301e+00
+#&gt;  -4.7220e-02  1.0298e+01  5.7568e+00  3.7165e+00  5.3836e+00  1.2636e+01
+#&gt;   1.3372e+01 -4.1079e+00  3.2401e+00 -1.5440e+01  1.0938e+01 -2.2595e+00
+#&gt;  -6.2048e+00  1.0810e+01 -4.6557e+00 -5.7423e+00  1.9922e+01  6.8378e+00
+#&gt;   1.6257e+01  3.3141e+00  2.6023e+00 -7.5886e+00 -5.4325e+00  1.6676e+01
+#&gt;  -1.5218e+00  1.2508e+00 -2.9083e+00  1.2418e-01  1.8454e+01 -3.9098e+00
+#&gt;  -7.6743e-01 -1.0442e+00 -6.1045e+00 -7.6160e+00 -7.2777e+00 -8.3719e+00
+#&gt;  -5.6189e+00  1.0683e+01  4.7618e+00 -1.0877e+01  1.6728e+01  2.3661e+01
+#&gt;  -3.1498e+00  4.8652e+00  7.7276e+00 -1.7810e+00  5.4526e-02  1.3808e+01
+#&gt;  -1.6673e+01  2.2490e+00  4.5205e+00 -7.0093e+00  2.0609e+01  5.7754e+00
+#&gt;   1.0943e+01 -1.8617e+00  1.0474e+01 -7.6102e+00 -1.5107e+01 -2.9932e-01
+#&gt;   2.8436e+00  2.7909e+00  2.2022e+01  1.6944e+00 -1.2976e+01 -3.5025e+00
+#&gt;  -5.4795e+00  3.9904e-01 -6.4733e+00  6.7021e+00  7.2860e+00 -2.8783e+00
+#&gt;   2.6436e+00 -5.1108e+00  3.1331e+00  1.4062e+01  5.9850e+00  6.8239e+00
+#&gt;  -8.2778e+00  5.1245e+00  3.0526e+00  6.2872e+00  1.4696e+01  1.1970e+01
+#&gt;   8.6024e+00 -5.6751e+00 -9.3626e+00 -1.4129e+01 -3.8140e+00 -1.1897e+01
+#&gt;  -4.4061e+00 -2.3178e+00  9.2872e+00 -8.1672e-01  1.2030e+00 -1.2162e+01
+#&gt;   5.3659e+00  7.7213e+00  5.7185e+00  9.0630e+00 -5.1373e+00 -3.1592e+00
+#&gt;   3.2050e+00 -2.3414e+00 -6.9714e+00  7.1941e+00 -9.8566e-01 -3.3091e+00
+#&gt;  -1.6919e+01 -1.2293e+01 -6.6814e+00  3.7901e+00  5.9487e+00  8.8619e-01
+#&gt;  -1.2029e+01 -7.6166e+00 -4.6589e+00 -1.7603e+01 -2.6050e+00  4.8997e+00
+#&gt;   2.4186e+00 -5.6908e+00 -2.5564e+00 -1.6754e+01  8.0733e+00 -1.1196e+00
+#&gt;  -8.7796e-01  6.5197e+00  6.5565e-01 -8.6806e-01 -2.6335e+00  6.8496e+00
+#&gt;   1.1316e+01  2.8948e+00  4.4362e+00  4.8354e+00 -2.3211e+00 -1.2829e+01
+#&gt;   8.5893e+00 -3.7059e+00 -8.4572e+00  6.7882e+00 -8.4576e+00  8.2972e-01
+#&gt;   1.6636e+01 -8.8189e+00  6.4427e+00  6.5319e+00  6.9739e+00  1.1096e+01
+#&gt; 
+#&gt; Columns 37 to 42  4.4878e+00 -2.3642e+00 -9.1210e+00  1.5661e+01 -1.9917e+01 -6.3785e+00
+#&gt;   1.1384e+01  3.5865e+00 -2.5288e+00 -1.7164e+01  8.7337e+00 -1.0503e+01
+#&gt;  -6.2520e+00  3.9174e+00 -1.2509e+01 -5.3240e+00  3.7293e+00 -1.5637e-01
+#&gt;  -1.9859e+00 -6.6096e+00  1.2515e+01 -1.4296e+01  3.2204e+00 -6.3126e+00
+#&gt;   1.6918e+00  2.0285e+01 -3.0068e+00  3.2718e+00 -1.4610e+01 -1.4036e+01
+#&gt;   9.0918e+00 -5.9544e+00  7.9972e+00 -1.1101e+01 -1.3772e+01 -1.9439e+01
+#&gt;   1.8324e+00 -3.4902e+00  1.1935e+01 -2.5032e+01  1.8524e+01 -5.9759e-01
+#&gt;  -3.1921e-01  3.2242e+00  2.3243e+00  1.0866e+01  5.6447e+00 -4.0742e+00
+#&gt;   2.0699e+00  1.4610e+01 -1.7303e+01 -2.6887e+00 -1.1823e+01  1.4420e+01
+#&gt;   5.1856e+00  2.0304e+00 -7.1594e+00 -5.7598e-01 -1.5643e+01 -1.7092e+01
+#&gt;  -5.6571e+00 -6.3264e-01 -1.8718e+01 -1.6022e+00 -1.6949e+00  8.5261e+00
+#&gt;  -2.5168e+00 -3.4719e+00  6.6238e-01  7.8183e+00 -7.9532e+00  7.6461e+00
+#&gt;   5.2890e+00  4.8125e+00 -9.6915e+00 -3.7230e+00 -5.9298e+00  6.9522e+00
+#&gt;  -2.5441e+00  7.1421e+00  2.4809e+00 -3.4307e+00 -1.7882e+00  6.1988e+00
+#&gt;  -7.7466e+00  9.6058e-01  1.4210e+00 -8.9925e-02 -5.7440e+00 -1.0791e+01
+#&gt;  -5.5046e-02 -1.0465e+01  1.9446e+00 -4.2025e+00  5.9414e-04 -7.0922e+00
+#&gt;  -1.1868e+01  2.7056e+00  1.4955e+01 -4.6315e+00 -2.0707e+00  1.0120e+01
+#&gt;   2.2510e+00  5.3352e+00 -6.9040e-01 -3.6846e+00 -4.9931e+00 -2.6069e+00
+#&gt;   3.9388e+00  3.7917e+00  1.4283e+01 -2.5320e+00 -5.9300e+00 -9.6440e+00
+#&gt;   9.9951e-01  1.2395e+01 -1.7485e+01  6.0842e+00  4.0708e+00 -1.4335e+00
+#&gt;  -5.4947e-01 -1.1178e+01  1.4815e+01 -4.7548e-01  1.6524e+00 -6.1346e+00
+#&gt;  -1.8547e+01 -3.5868e+00  5.7109e+00  1.0381e+01  5.3679e+00  4.0875e+00
+#&gt;  -6.2024e+00 -3.8099e+00  3.1032e+00  8.7504e+00 -2.4905e+00 -3.1567e+00
+#&gt;  -5.7587e+00  2.6403e-01 -6.2622e+00 -3.2774e+00 -3.4080e+00  1.7630e+01
+#&gt;  -6.5531e+00 -5.1616e-01 -2.5381e+00  4.3673e-01  1.3827e+00 -1.7608e+00
+#&gt;   1.3258e+00  7.4871e-01  7.2413e+00  2.9663e-01 -6.5212e+00  7.2607e-01
+#&gt;   7.7659e+00  7.0330e+00 -1.2670e+01  8.9590e-01  1.4491e+01  1.8087e+01
+#&gt;   6.2934e+00  8.0264e+00  3.6147e-01 -7.0904e+00  7.8345e-02  2.3515e+00
+#&gt;  -1.1642e+00  1.2683e+01  6.3582e+00 -1.7169e+01  6.1291e+00 -2.4972e+00
+#&gt;   1.1013e+01  1.7106e+01  7.4904e+00  7.7218e+00 -1.7584e+01  4.4086e+00
+#&gt;  -1.2509e+00  1.5248e+01 -3.4874e+00  1.3855e+01  5.7941e+00 -9.0559e+00
+#&gt;   2.0705e+00 -1.1685e+01  3.1573e-01 -1.4187e+01  6.1962e+00  1.0098e+01
+#&gt;   4.0630e+00  7.0444e+00 -6.7645e+00 -1.2123e+01  1.0104e+01  6.0825e+00
+#&gt; 
+#&gt; Columns 43 to 48 -1.3225e+00  7.5155e+00  3.5457e+00 -7.7401e-01 -7.9913e+00 -2.8263e+00
+#&gt;  -4.4239e-01 -6.5029e+00 -5.3231e+00 -1.2749e+01  1.2061e+00  1.3572e+00
+#&gt;   6.6092e+00  7.3148e+00  4.0908e+00 -1.6144e+01  4.0651e+00  9.7613e+00
+#&gt;   1.6555e+01 -4.9039e+00  2.0471e+00  2.6363e+00  1.6037e+01  7.5261e+00
+#&gt;   5.6720e-01 -1.8519e+01 -1.4613e+01 -1.0380e+01  7.9935e+00 -2.4992e+01
+#&gt;  -9.9498e+00 -1.4021e+01  3.0135e+00  9.9928e+00 -1.4162e+01 -7.7319e+00
+#&gt;   4.5391e+00 -9.1612e+00  8.2764e-01  1.4789e+01  8.5675e+00 -4.3570e+00
+#&gt;  -1.1531e+01  5.0008e+00 -1.0593e+01  6.8532e+00 -7.9979e+00 -2.3096e+00
+#&gt;  -6.9519e+00  9.2419e+00 -5.3410e+00  4.6456e+00  1.2305e+01 -1.4149e-01
+#&gt;  -5.0381e+00  4.8369e+00 -9.1629e+00 -1.7132e+00 -5.3706e+00 -1.9919e+01
+#&gt;  -2.1738e+01 -6.7851e+00 -8.8066e-01 -8.6924e+00 -1.1466e+01  9.4515e+00
+#&gt;  -9.2470e+00  4.1282e+00  6.4433e+00  1.4437e+01 -5.9255e+00  5.0156e+00
+#&gt;  -3.8086e+00 -8.3930e+00 -8.6682e+00 -2.9186e+00  8.8949e-01 -7.8708e+00
+#&gt;   9.3219e+00 -2.1789e+00  2.8101e+00  1.1117e+01  3.1088e+00  3.8468e+00
+#&gt;  -4.7248e+00 -1.1072e+01 -4.4787e+00  1.2252e+01  2.8345e+00 -1.1620e+01
+#&gt;   2.5926e+00  1.7134e+01 -7.0157e+00  7.1150e+00 -1.0001e+01  7.1872e+00
+#&gt;  -9.4637e+00 -1.9046e+01 -4.2980e+00  5.0236e-01  8.4141e+00 -5.4341e+00
+#&gt;  -8.8937e+00  2.2311e+01 -1.7071e+01  9.9567e+00  1.3399e+01 -7.8803e+00
+#&gt;   9.9015e+00  7.5980e+00  1.0382e+01 -6.2705e+00 -5.7278e+00 -1.9555e+00
+#&gt;  -1.4069e+01 -1.9179e+01 -8.3042e+00 -3.1122e+00  1.2979e+00 -1.2923e+01
+#&gt;   3.4753e+00 -3.3074e+00  6.5502e+00 -2.8901e+00  4.0644e+00 -1.2498e+00
+#&gt;  -8.5863e+00 -1.1009e+00  4.9903e+00 -9.2786e+00  8.5524e+00  4.5484e-02
+#&gt;  -2.3117e-01 -6.9398e-01 -2.4986e-01 -7.5937e+00 -1.0116e+01  1.9911e+00
+#&gt;   9.7825e+00  3.6422e+00  8.0094e+00 -2.3208e+00 -7.2454e+00  1.1305e+01
+#&gt;  -3.8630e+00  8.8709e+00  7.7070e+00  2.0446e+01  1.2637e+00  8.0608e+00
+#&gt;  -6.2783e+00 -9.8522e-01 -1.4196e+00 -8.4213e+00 -2.2625e-01 -1.6038e+00
+#&gt;  -8.0083e+00  7.4533e-01  2.5935e+01 -1.0613e+01  4.8281e-01  2.1464e+01
+#&gt;  -2.3609e+00 -1.1517e+01 -3.8818e+00 -1.6465e+01 -5.4758e+00  3.3605e+00
+#&gt;   1.6953e+01  1.1260e+01  9.7832e+00 -1.2325e+01 -4.8735e+00  1.1821e+01
+#&gt;   5.7424e-01  9.8175e+00  2.3017e+00 -1.5989e+00  3.4220e+00  1.0485e+01
+#&gt;  -6.5441e+00 -8.2770e+00 -4.5998e+00  1.2889e+01 -2.1546e+00 -1.7968e+01
+#&gt;  -5.9971e+00 -1.7395e+01 -8.5698e+00  1.1068e+00  6.6695e+00 -4.2924e+00
+#&gt;  -1.5701e+00  9.1711e-02 -1.4028e+01  9.8882e+00  3.9283e-02  1.3230e+00
+#&gt; 
+#&gt; Columns 49 to 54 -3.8444e+00  1.1858e+00  4.9792e+00  9.6627e-01  1.2525e+00 -1.7345e+00
+#&gt;   7.1986e+00  2.9036e+00 -1.3004e+00  1.1473e+01 -4.6877e+00  1.6839e+00
+#&gt;   3.4666e-01  3.0801e+00  1.3069e+01  1.7583e+01 -8.5806e+00  4.5497e+00
+#&gt;  -2.2805e+00  1.7387e+01 -1.5286e+01 -5.8295e+00  4.2371e+00 -2.7819e+00
+#&gt;  -2.4679e+00  7.7251e+00 -2.3069e+01  8.4689e+00 -3.3133e+00 -8.6648e+00
+#&gt;  -3.6375e-01 -2.5602e+01  1.0000e+01  3.5638e+00  4.2016e+00  9.5066e-01
+#&gt;  -1.1269e+01  6.4835e+00  3.1930e+00 -4.3767e-01 -1.6418e-01 -6.3701e+00
+#&gt;   2.7810e+00  5.8144e+00  4.0933e+00 -1.3157e+00  1.1435e+01 -3.0485e+00
+#&gt;   8.1508e+00  1.4555e+01 -2.8499e+00 -2.8760e+00  8.9614e+00  2.1232e+00
+#&gt;  -8.3261e-01  2.7100e+00  8.7359e+00 -8.2285e+00 -6.2578e+00 -5.3856e+00
+#&gt;   2.2066e+00  1.0378e+01  9.5569e+00  9.5588e+00  1.6320e+00  1.0084e+01
+#&gt;   1.4588e+00  9.2064e-01 -7.1439e+00 -5.2017e+00 -2.4140e+00  5.3526e+00
+#&gt;   1.3612e+01  5.6620e+00 -1.1620e+01  2.3625e+00 -1.1330e+01  1.5248e+00
+#&gt;  -3.5426e+00 -7.0384e+00 -2.9504e+00 -9.5061e+00  4.3853e+00  4.7988e+00
+#&gt;  -9.7679e+00  1.7907e+00 -7.9331e+00 -6.1811e-01  5.9971e+00 -3.7143e+00
+#&gt;   7.6239e+00  6.9541e+00  5.5589e-01 -4.7093e+00  2.4645e+00  5.1278e+00
+#&gt;  -9.8085e+00  8.3869e+00  8.0053e+00 -9.6987e+00  8.2273e+00  5.5820e+00
+#&gt;  -9.3834e+00  6.4200e+00  1.0142e+01 -8.7463e+00 -4.5796e-01  3.4890e+00
+#&gt;   7.2693e+00  2.4162e+00 -4.3967e-01  4.9801e+00 -6.2719e+00 -4.2498e+00
+#&gt;   5.4299e+00 -4.0730e+00 -4.8699e+00 -3.7527e+00  2.8188e+00 -6.4849e+00
+#&gt;  -1.6949e+01 -7.3988e+00 -6.1241e+00  7.4695e+00  1.5441e+00  1.4237e+00
+#&gt;  -9.0940e+00  6.1137e+00  9.6657e+00 -2.4854e+00  7.8322e+00  2.6870e+00
+#&gt;   1.3036e+01  1.8695e+01  2.8623e+00  1.2134e+01 -8.4328e+00 -2.1355e+00
+#&gt;   1.8717e+01  7.8995e+00  7.2458e+00 -7.6360e+00 -9.9172e+00 -2.4202e+00
+#&gt;  -3.4935e-01 -4.2851e+00 -3.0568e+00  1.6756e-01 -2.4791e+00  2.2609e+00
+#&gt;   8.0906e+00 -7.4999e+00 -4.3320e+00  1.7309e+00 -6.8967e+00 -3.5653e+00
+#&gt;  -1.0714e+01 -1.1555e+01 -2.1318e+00 -1.1573e+01  1.0170e+00  1.3795e+00
+#&gt;   2.5068e+00 -1.0478e+01  1.8676e+01  2.4759e+00  1.5531e+00 -4.1684e+00
+#&gt;   8.1488e+00 -8.4433e+00 -7.5819e+00  1.6040e+01 -4.2868e-01  4.2592e+00
+#&gt;  -1.5774e+01 -4.5053e+00 -2.0056e+00 -1.8684e+01  1.1285e+01  5.9708e+00
+#&gt;   1.0074e+01 -1.4951e+01 -4.2046e+00  1.2449e+01 -3.6492e+00  5.5716e+00
+#&gt;  -5.2416e-01  1.1741e+01  1.2132e+01 -4.0776e+00 -2.5397e+00 -3.4564e+00
+#&gt;  -7.4641e+00  3.1837e+00  1.8403e+01 -2.4848e+00 -5.9302e-01 -9.3820e-02
+#&gt; 
+#&gt; (12,.,.) = 
+#&gt;  Columns 1 to 6  5.6711e+00 -9.7715e+00  8.0139e+00 -3.1456e+00 -8.2597e+00 -8.6188e+00
+#&gt;   4.5534e+00 -2.6602e+00  5.7101e-01  4.5924e-01 -7.1426e+00 -7.6855e+00
+#&gt;   2.3944e+00 -1.8833e-01 -1.2372e+00  1.3183e+01 -1.1774e+01 -1.2614e+01
+#&gt;  -3.7058e+00  1.2332e+01 -7.1135e+00  1.0252e+01 -1.1324e+01  1.5886e+00
+#&gt;  -1.3778e+00  1.3744e+01  1.0130e+01  1.7991e+01 -9.5907e-01  4.4537e+00
+#&gt;   4.3923e+00  2.0173e+01 -1.1748e+00  2.2084e+00  1.0591e+01 -2.1620e+01
+#&gt;  -4.6225e+00 -4.0510e+00  4.9409e+00 -8.6606e+00 -4.5434e+00  4.4733e-01
+#&gt;  -1.6025e+00 -6.5477e-01  1.8544e+00 -2.0565e+00 -2.4117e+00  3.3357e+00
+#&gt;   2.8552e+00 -1.0342e+01  1.0375e+01  4.7829e+00 -7.2180e+00 -8.8688e+00
+#&gt;   1.0235e+01  9.7207e+00  3.1417e+00  4.0030e+00 -7.3955e+00 -6.9170e+00
+#&gt;  -5.7200e-01  5.5964e+00 -1.5976e+01  8.3666e+00  6.3403e+00 -7.6915e+00
+#&gt;   3.4703e-02  3.5169e+00  3.8781e-01  1.0636e+00  4.0983e+00 -2.7044e+00
+#&gt;   4.6174e+00 -2.4054e-01 -8.3332e-01 -1.0104e+01  5.7178e+00  5.9626e-01
+#&gt;  -3.6530e+00  6.3614e+00 -1.4573e+01  1.8428e+01 -1.0996e+01  7.7436e+00
+#&gt;  -7.8050e-01  1.7219e+00  1.0551e+01 -7.6440e+00 -1.9886e+00 -2.9778e+00
+#&gt;   3.0868e+00 -2.7772e+00 -5.4846e-01 -4.6270e+00  7.8908e-01 -1.3109e+01
+#&gt;   2.8907e+00 -1.3217e+00 -1.0009e+01  1.4561e+01  4.7109e+00 -3.6655e+00
+#&gt;   2.1374e-01 -3.3225e+00  1.0521e+01  3.3724e+00 -1.4473e+01  1.0237e+01
+#&gt;   2.7409e+00  1.5870e-01  8.3594e-02  1.3877e+00  6.6924e+00 -2.4924e+00
+#&gt;  -2.1034e-01  1.1392e+01  4.4399e-01 -2.0182e+00  6.3715e+00 -1.6278e+00
+#&gt;  -2.7978e+00  2.1835e+00 -3.4727e+00 -1.1099e+01  1.1308e+01  1.9844e+00
+#&gt;   1.4916e+00 -4.6658e+00  7.5224e+00 -4.0165e-01  4.3843e+00  2.9431e+00
+#&gt;  -4.8740e+00  3.3266e-01 -1.0033e+01  2.4769e+01 -1.0689e+01  5.7757e+00
+#&gt;   9.7244e+00 -2.0223e+01  1.5571e+00 -6.1857e+00 -4.8121e-01  5.3037e+00
+#&gt;   2.1985e-01 -6.2206e+00  1.8928e+00  4.3969e-01 -4.0883e+00  1.2525e+01
+#&gt;   3.6974e+00 -6.7978e+00  1.4884e+01 -1.1140e+01  1.9589e+00  2.8536e-01
+#&gt;  -2.5475e+00 -1.5448e+01 -9.8222e+00  2.7567e-02 -1.6978e+01 -1.3631e+01
+#&gt;  -2.5236e+00  7.1482e+00  9.9684e-01  1.6637e+00 -1.8525e+00 -3.7578e+00
+#&gt;   6.8408e-01 -4.5971e+00 -2.3067e+00  1.6949e+01 -1.8636e+01 -1.2232e+01
+#&gt;  -6.7232e+00  9.1325e+00  2.0426e-01  2.6954e+01 -8.9083e+00 -1.2419e+01
+#&gt;   2.1591e+00 -1.0398e+01  1.2408e+01 -2.9166e+00  6.0366e+00  1.0637e+01
+#&gt;  -2.0474e+00  9.3563e+00 -5.2706e+00 -7.9547e+00  7.8486e+00  1.2689e+01
+#&gt;   3.8286e+00  6.1758e+00 -7.4604e+00  2.8888e-02 -1.1185e+00  1.1341e+01
+#&gt; 
+#&gt; Columns 7 to 12 -1.1826e+01  2.2621e+01  5.7299e+00 -1.0211e+01  2.0500e+00 -2.0580e+00
+#&gt;   2.2837e+00 -4.1392e+00 -1.3015e+01  4.3746e-01  1.4448e+01  4.2647e+00
+#&gt;   1.8809e+01 -5.0689e+00  1.1920e+01  4.9381e+00 -1.1874e+01  1.6306e+01
+#&gt;  -7.0689e+00 -8.3381e-01  1.5031e+01  5.1379e-01  9.0282e+00 -1.0080e+01
+#&gt;   9.0968e-01  6.8403e+00 -1.2286e+00 -7.2483e+00  1.4132e+01  1.1597e+01
+#&gt;  -1.2094e+01 -3.4152e+00  2.6146e+00  1.4391e+01 -4.1691e+00 -3.0251e+00
+#&gt;   1.3840e+00 -6.1495e+00 -6.8200e+00 -7.8748e+00  1.6628e+01  8.4340e+00
+#&gt;  -1.9125e+00  8.5565e+00 -3.7415e+00 -9.7756e+00 -1.5671e+01 -1.0357e+01
+#&gt;  -2.6787e+00 -3.3692e-01 -1.3210e+01  4.1409e+00  5.5250e+00  6.2864e+00
+#&gt;  -6.5676e+00  1.9990e+00 -4.1325e+00  1.8645e+01  4.8182e+00  6.1783e+00
+#&gt;   1.2527e+01 -9.8162e+00 -3.1660e-01 -4.4354e+00 -1.9086e+01 -7.7893e+00
+#&gt;  -2.1272e+00 -1.3353e+01  7.4139e-01  1.5879e+01  1.2691e+00  8.5015e+00
+#&gt;   4.1590e+00  4.9067e+00 -1.2046e+01 -1.4323e+00  1.0125e+01 -2.5866e+00
+#&gt;   3.5672e+00  4.8377e+00  3.1483e+01  4.4150e+00  2.4836e+00 -7.8096e+00
+#&gt;  -4.1977e+00 -1.7131e+00  5.3965e+00  6.3721e+00  6.7158e+00  1.0346e+01
+#&gt;  -5.3131e+00 -3.7630e+00 -3.8306e+00 -2.7599e+00  5.5997e+00  1.4647e+00
+#&gt;   1.8103e+00  6.5942e-01  7.3887e+00 -9.9311e+00  8.3289e-01  5.5044e+00
+#&gt;  -4.5116e+00 -5.5039e+00 -1.5688e+00  1.6182e+01 -3.9394e+00 -1.0381e+00
+#&gt;  -2.6093e+00 -3.5263e+00 -5.2329e+00 -3.5045e+00  5.7408e+00 -3.6470e+00
+#&gt;   1.3142e+01  1.6011e+00  4.2480e+00 -1.1670e+01  1.3931e+01  2.5416e+01
+#&gt;  -4.3017e+00 -2.4624e+00  8.3654e+00  6.6246e+00  3.9868e-01 -1.0769e+00
+#&gt;   6.2551e+00 -3.5745e+00  7.6361e+00 -5.0444e+00 -7.1267e+00 -1.1137e+00
+#&gt;   2.0551e+00 -9.9614e+00 -1.9466e+00  7.4334e+00  1.4679e+01 -4.0151e+00
+#&gt;   4.6821e-02  1.1432e+01 -3.3222e-01  4.6008e+00 -7.8557e-01 -1.3359e+01
+#&gt;  -2.3926e-02  6.8382e-01 -4.8464e+00  7.1137e+00  2.9912e+00  4.5205e+00
+#&gt;   4.2275e+00  6.5084e+00 -1.7213e+01  8.4103e+00 -5.6102e+00  3.6258e+00
+#&gt;   1.3469e+01 -2.8784e+00 -8.9576e-01  2.5106e+00 -5.1594e+00  1.0925e+01
+#&gt;   2.6589e+00  2.8767e+00  3.8252e+00 -1.2938e+01 -2.2467e+01 -3.1135e+00
+#&gt;   5.5026e-01  2.3104e+01  1.6266e-01 -8.8421e+00 -2.3897e-01 -1.3208e+01
+#&gt;  -3.0139e+00  7.5189e+00  2.7617e+01  1.6454e+00 -2.2950e+01 -7.1717e+00
+#&gt;   2.6843e+00  1.2358e+00 -8.7613e-02 -1.3074e+00  1.4932e+00 -4.6459e+00
+#&gt;  -7.2866e+00  6.1055e+00 -1.9969e-01  1.4076e+00 -7.0442e-02  8.3251e+00
+#&gt;  -7.3165e+00  6.8355e-01 -6.6699e+00 -9.3733e-01  1.8232e+01  2.1962e+00
+#&gt; 
+#&gt; Columns 13 to 18  1.9015e+00 -7.1054e-02  6.5036e+00  1.8471e+01 -7.8308e+00 -1.3398e+01
+#&gt;   1.4368e+01 -8.2049e+00  8.0801e+00 -3.8359e-01  7.3189e-01 -1.1775e+00
+#&gt;  -1.4420e+01 -4.6961e+00 -1.6440e+00 -7.7410e+00 -6.0583e+00 -3.1094e+00
+#&gt;   3.3701e+00 -3.4220e-01 -1.1748e+01 -5.6438e+00 -3.8476e+00  1.4843e+01
+#&gt;  -9.6171e+00 -6.9606e-01 -4.0281e+00 -1.1368e+00 -6.0511e+00 -1.3137e+01
+#&gt;  -9.2864e+00 -2.6392e+00 -1.1496e+00 -1.6844e-01  1.0979e+01  7.7849e+00
+#&gt;  -9.9460e-01  1.0206e+00  4.4142e-01  1.1463e+01 -7.1979e+00 -1.1835e+01
+#&gt;  -8.3785e+00  2.4509e-01 -4.0796e+00  3.0417e+00 -3.3664e+00 -1.6919e+01
+#&gt;  -2.7345e+00  6.1984e+00  3.6193e+00  1.6176e+00  1.8195e+01 -8.8061e+00
+#&gt;  -6.9595e-01  1.5538e+01 -5.7967e+00  4.6592e-01 -7.5308e+00 -2.6751e+00
+#&gt;  -8.8344e+00  1.1063e+00  1.5160e+01 -2.0454e+01  3.6625e+00 -7.1725e+00
+#&gt;  -5.0083e+00  1.1612e+01 -1.2413e+01 -1.8029e+00 -1.0512e+01 -4.8151e+00
+#&gt;   7.4919e+00  7.1667e+00  1.0053e+01 -3.3491e+00 -1.1306e+01  6.5037e-01
+#&gt;   2.3728e+01 -8.9091e+00 -1.0776e+01 -8.8852e+00  4.8242e+00  5.6458e-01
+#&gt;   8.7586e+00 -9.7872e+00  1.0937e+01  2.2949e+01 -7.2551e+00 -6.7513e+00
+#&gt;   9.4950e+00  1.3832e+00 -1.6443e+01  2.3965e+00  4.3156e+00  4.0602e+00
+#&gt;  -3.6678e+00  3.3257e+00  4.8965e+00  1.3294e+01  6.1973e+00 -1.0398e+01
+#&gt;  -2.4726e+00  2.3567e+01 -2.0121e+00  4.4666e+00 -2.9066e+00 -2.0574e+00
+#&gt;   6.9333e+00  1.1008e+01  9.4940e+00  2.6360e+00 -2.3520e+00 -9.7747e+00
+#&gt;  -6.8040e+00 -6.3517e-01 -1.3560e+01  3.8996e+00  4.3230e+00 -1.5269e+01
+#&gt;   5.5237e+00 -5.9878e+00 -4.8637e+00 -5.7554e+00  3.2201e+00 -9.9667e-01
+#&gt;  -1.5524e+01  4.4229e+00  1.0867e+01 -6.8404e+00 -8.2170e-03 -1.1930e+01
+#&gt;  -8.9235e+00  2.8391e+00  7.8580e+00  5.2690e+00 -1.1646e+01 -1.6284e+01
+#&gt;   1.1135e+00  2.0755e-01 -5.3085e-01  4.4674e+00 -2.2772e+01  1.2490e+01
+#&gt;   3.5108e+00 -1.7097e+01 -1.0687e+00 -6.3410e+00 -1.7460e+01  3.0247e+00
+#&gt;  -1.5075e+01  4.4179e+00  2.9446e+00  4.5553e+00  3.9893e+00  1.0847e+01
+#&gt;  -3.8475e-01 -3.0224e+00  1.3595e+01 -6.9657e-01 -1.1146e+00  7.6620e+00
+#&gt;  -2.6613e+00 -1.5902e+01 -9.7376e+00  5.7318e+00  5.6692e+00  1.2321e+01
+#&gt;   5.4724e+00 -1.1878e+01 -4.7547e-01  9.2209e+00 -3.4451e+00  1.6410e+01
+#&gt;  -2.8532e+00  3.8302e+00  1.2172e-01 -6.0661e+00 -4.2698e+00 -1.0130e+01
+#&gt;   1.6465e+00 -5.3229e+00  7.0761e+00 -1.5705e+00 -7.0562e+00 -2.7597e+00
+#&gt;  -6.1864e-01 -3.3426e+00 -9.9110e+00 -9.6252e-01  7.0601e+00  6.6784e+00
+#&gt;   1.9348e+01  3.2684e+00 -5.1465e-01  3.5049e-01 -1.0837e+01  9.5411e+00
+#&gt; 
+#&gt; Columns 19 to 24 -3.8948e+00 -2.6723e+00 -6.6318e+00  1.6838e+00 -3.7660e+00  7.2476e+00
+#&gt;   1.1216e+01  8.1438e-01 -3.6275e+00 -2.4376e-01 -9.8312e+00 -7.3129e+00
+#&gt;   1.5957e+00 -1.2974e+01  1.8953e+01  9.7969e+00 -1.1822e+00  4.6118e-01
+#&gt;   1.1569e+01  2.7565e+01  6.6624e+00 -1.0608e+01 -4.3190e+00 -8.5527e+00
+#&gt;   1.7083e+01  4.3328e+00 -4.9356e-01  3.5066e+00 -7.9629e+00  1.8458e+00
+#&gt;   6.6433e+00  2.2971e+00 -1.3667e+01 -4.0295e+00 -1.0085e+01 -4.5433e+00
+#&gt;   1.5841e+00 -2.4033e+00  5.9013e+00 -2.2349e+01  1.7666e+00 -1.9011e+01
+#&gt;   8.8770e+00  1.1447e+01  4.4222e+00  2.0156e+00  3.1334e-01  6.5320e+00
+#&gt;   4.4325e-01 -9.0440e+00 -5.9457e+00  3.4535e+00 -1.6740e+01  5.7113e+00
+#&gt;   5.1010e+00  6.8260e+00 -8.1804e+00 -2.4639e+00 -1.3020e+01  4.3330e+00
+#&gt;   1.1201e+01  2.3794e+00  2.9129e+00  1.5070e+01 -1.1008e+01 -5.5706e+00
+#&gt;   4.8113e-01  8.7237e+00  1.0651e+01 -1.0053e+01 -1.4475e+01  9.6086e-01
+#&gt;   4.8966e+00 -4.8608e+00 -4.7656e+00  4.5974e+00 -5.3435e+00 -3.1645e+00
+#&gt;  -2.8056e+00  6.0239e+00  4.9811e+00  9.3587e+00 -1.2518e-01  1.4811e+01
+#&gt;   1.5243e+00  5.9463e+00 -1.9694e+01  2.4770e+00 -7.4116e+00  4.2682e+00
+#&gt;   1.7486e+01  5.6142e+00 -2.6774e+00 -5.4607e+00  3.1753e+00 -7.7612e+00
+#&gt;  -4.7767e+00 -4.1876e+00  8.2306e+00  1.8335e+01  1.6702e+01 -6.1712e+00
+#&gt;  -1.0461e+01  7.7642e+00  9.8448e+00 -3.9824e+00 -1.4456e+01  1.0921e+01
+#&gt;  -3.0338e+00 -3.8039e+00  3.7088e+00  1.1555e+01 -6.6480e+00 -7.0763e+00
+#&gt;   1.2589e+00 -9.1476e+00 -1.0267e+00 -9.8251e+00  1.3203e+01 -1.1121e+01
+#&gt;  -7.1104e+00 -2.1209e+01 -4.1617e-01 -1.6712e+01  7.2966e+00  8.6806e-01
+#&gt;  -1.3472e+00  2.4518e+00  2.5488e+00  5.1634e+00  8.3433e+00 -6.9640e-01
+#&gt;   4.9083e+00  1.1894e+00  1.6084e+01  1.8053e+01 -8.3202e+00  5.9620e+00
+#&gt;   1.4155e+00 -1.2155e+01 -8.0382e+00  1.2269e+00 -2.4019e+00 -4.7523e+00
+#&gt;   5.7603e+00  2.8282e+00 -6.3438e+00  9.6194e-01  4.2417e+00 -8.6298e-01
+#&gt;  -6.2398e+00 -1.3997e+01 -1.0611e+01 -1.2185e+01  5.8461e+00 -1.5036e+01
+#&gt;  -9.8415e+00  7.8770e-01 -7.4182e+00 -8.3712e+00 -5.5750e+00  5.2374e+00
+#&gt;  -6.6236e-01  8.8197e+00  2.5809e+00  9.2737e-01 -2.7482e-01 -3.0782e+00
+#&gt;  -4.8489e+00  1.2334e+00 -8.1420e+00  8.0714e+00  1.8021e+00  1.0030e+01
+#&gt;   2.2816e+00  2.9167e+01  5.3001e+00  1.2647e+01 -1.0302e+01  1.7506e+01
+#&gt;   4.0581e-01  4.3354e-01  4.6884e+00  2.4039e+00 -7.0114e+00 -2.3394e+00
+#&gt;  -1.7465e+01 -2.9286e+00 -3.3655e+00 -5.9884e+00  1.8096e+01  4.9700e+00
+#&gt;  -8.3931e+00  9.1925e+00 -6.2284e-01 -3.7626e+00  7.2228e-02 -1.4662e+00
+#&gt; 
+#&gt; Columns 25 to 30  2.0364e+00  3.4924e+00  8.8576e+00  1.9560e+01 -5.3159e+00 -6.7273e-02
+#&gt;   8.5199e+00  1.2460e+01  1.0849e+01 -1.2488e+01  1.1534e+01  8.3744e+00
+#&gt;  -5.2998e+00 -1.3045e+01  7.0058e-02 -1.6556e+01 -5.1436e+00  1.2320e+00
+#&gt;  -1.9245e+00  2.6752e+00  2.5943e+00  3.8957e+00  1.9414e+00 -4.5554e+00
+#&gt;  -1.7785e+01  3.6812e+00  1.3851e+01  7.9270e+00  1.4260e+01 -5.3344e+00
+#&gt;   1.7070e+01  7.5528e+00  2.5537e+01  2.0648e+00  9.1443e+00  4.2659e-01
+#&gt;  -5.8135e+00 -1.1264e+00  5.3336e+00 -7.6384e+00  7.7942e-01 -1.4103e+00
+#&gt;  -6.6164e-02 -1.1689e+01 -5.0414e+00  2.4737e+00 -8.3804e+00  2.4184e+01
+#&gt;  -1.2746e+01 -1.4828e+00 -5.7543e+00 -5.3110e+00  5.5415e+00 -1.6850e+01
+#&gt;   3.1513e+00  1.5225e+01  6.6620e+00  1.6091e+01  1.0120e+00  7.5204e+00
+#&gt;  -1.2891e+01  7.4804e+00  8.0265e-01 -1.2574e+01  9.4596e+00  7.2928e+00
+#&gt;  -4.2098e+00  4.1588e+00  6.9672e+00  2.2453e+01  9.1519e+00  1.8179e+01
+#&gt;  -4.1001e+00  1.8540e+01  4.0887e+00 -1.0073e+01  1.8330e+00 -1.4200e+00
+#&gt;   5.6480e+00 -1.3350e+00 -1.2036e+01 -9.5990e+00  7.4656e-01  3.2980e-01
+#&gt;   3.4966e+00 -1.6570e-01  1.8722e+01  2.3947e+00 -2.9675e+00  2.2816e+01
+#&gt;   2.8179e+00 -3.1789e+00 -1.1038e+01  5.5216e+00  5.2880e+00  4.8121e+00
+#&gt;  -1.4641e+00  7.2584e+00  7.5047e+00 -1.4729e+01  1.1529e+01  4.0212e+00
+#&gt;  -2.1840e+01  1.6311e+01 -2.2091e+00  8.8685e+00 -2.6726e-01  1.0857e+01
+#&gt;  -6.0878e+00 -1.9527e+01 -2.7790e+00 -2.3459e+00  9.2915e+00  3.6225e+00
+#&gt;   3.0910e+00 -8.8166e+00 -1.2194e+00  2.1275e+00  1.4776e+01 -6.3646e+00
+#&gt;   3.4392e+00  1.2337e+00 -2.4477e+00 -4.2940e+00  1.2196e+01 -8.3100e+00
+#&gt;   2.1095e+00 -1.6299e+01 -1.8262e+00 -8.5428e+00  5.4610e+00 -1.8177e-01
+#&gt;  -8.0396e+00 -3.5281e-01 -4.7305e+00 -2.0184e+00 -1.3290e+00 -1.2946e+01
+#&gt;   6.7542e+00  2.7995e+00 -2.6588e+00 -5.7228e+00 -1.7821e+01 -1.2070e+01
+#&gt;   8.5798e+00  1.3482e+01  5.4124e+00  4.5472e+00  4.2665e-01  9.1178e+00
+#&gt;   7.9867e+00 -1.7613e+01 -3.2339e+00  4.7845e+00 -1.2815e+01  5.4119e-01
+#&gt;   6.9358e+00 -7.6092e+00  8.9106e+00  1.4474e+01 -5.1735e+00  6.0925e-01
+#&gt;  -3.7007e+00 -1.1889e+01  1.2766e+01 -4.3393e+00 -8.2446e+00 -5.4101e+00
+#&gt;   1.4022e+00 -2.8613e+00 -1.5519e+01  5.5836e-01 -5.3816e+00 -1.6839e+01
+#&gt;   3.9563e+00 -1.7790e+00  1.2531e+01  2.5413e+01 -6.9938e+00 -5.0654e+00
+#&gt;  -1.3314e+01  1.3742e+01 -5.1880e+00 -2.6514e+00  7.9159e+00 -3.9425e-01
+#&gt;   5.2115e+00  5.2123e+00  8.2073e+00  4.7590e-01 -1.2351e+01  6.1773e+00
+#&gt;  -1.2535e+01  1.6295e+01  5.8316e+00  1.8009e+01  7.4288e+00  4.5441e+00
+#&gt; 
+#&gt; Columns 31 to 36  1.2023e+01 -6.7178e+00 -5.8166e+00  1.1401e+01  1.5205e+01  1.9883e+01
+#&gt;  -1.2687e+01 -8.2098e+00  5.3369e+00 -8.2628e+00  1.0745e+00 -1.1268e+01
+#&gt;   9.6230e-01 -1.3118e+01 -6.6789e+00  1.8343e+01  4.4205e+00 -7.8813e+00
+#&gt;   4.5552e+00 -2.2099e+01 -1.1815e+01 -8.8782e+00 -3.4950e+00  4.7670e-01
+#&gt;   1.8077e+01 -6.6238e+00 -1.3619e+01 -7.2434e+00 -8.6265e+00 -7.9528e-01
+#&gt;  -1.0967e+01  9.8506e+00 -2.7321e+01 -1.0223e+01  6.2490e-01 -5.6411e-01
+#&gt;   1.4522e+01  8.1797e-01 -8.0892e+00  4.3848e+00  1.1477e+00 -3.4365e+00
+#&gt;   4.7297e+00  6.4393e+00 -8.8589e+00  6.6986e+00  1.0247e+00  3.6755e+00
+#&gt;  -1.7666e+00  7.6427e+00 -2.5547e+00 -4.3410e+00 -1.5351e-01 -1.1309e+01
+#&gt;  -3.9239e+00 -2.5789e+01 -8.5965e+00  1.2510e+01 -7.7351e+00 -1.1317e+01
+#&gt;  -7.5335e+00 -1.1775e+01 -6.7104e+00  1.6478e+00  1.5399e+01  3.0482e+00
+#&gt;  -1.0282e+00 -3.0531e+00 -1.1335e+01 -1.5999e+01 -1.5555e+01  1.6338e+01
+#&gt;   7.3122e+00 -8.6837e+00  6.5041e+00 -1.4024e+01  4.4772e+00 -5.9621e+00
+#&gt;  -2.8027e+00 -5.2603e+00  1.3327e+01  5.9999e+00 -5.2299e+00 -3.3551e+00
+#&gt;  -2.3727e+00 -1.3826e+01 -1.2738e+01 -1.9202e+01 -7.7574e-01 -1.4906e+00
+#&gt;  -8.8552e+00 -2.1012e+00  1.3027e+01  8.1915e+00 -6.9022e+00 -4.1185e+00
+#&gt;   1.3801e+01  1.0830e+01 -5.0325e+00  4.9789e-01 -2.2028e+00 -1.6712e+01
+#&gt;   1.0335e-01 -3.7909e+00 -6.2674e+00  1.4032e+01  1.0125e-01 -4.1395e+00
+#&gt;   9.5018e+00  1.9901e+00  1.2336e+01 -1.1491e+00 -1.4146e+01 -9.2964e+00
+#&gt;   1.6288e+01 -1.8714e+00 -1.2278e+01 -6.2869e-01 -2.4788e+01 -1.2325e+01
+#&gt;   1.3845e-01 -4.5797e-01  6.1840e+00  8.4224e+00  9.5258e+00 -1.0124e+00
+#&gt;   1.2613e+01 -1.3998e+01 -1.5340e+01 -4.1053e+00  6.9608e+00  4.7141e+00
+#&gt;  -5.0590e+00  1.6985e+00  9.5863e+00  8.1763e-01 -6.1154e+00  5.2919e+00
+#&gt;   4.0963e+00 -1.0605e+01 -5.2540e-02  6.2528e+00  1.6638e+01  1.9486e+01
+#&gt;  -1.9335e+01 -3.2307e+00 -2.5775e+01  2.1714e+00  9.3489e+00  5.1945e+00
+#&gt;  -1.1523e+00  6.8666e+00  6.6111e+00  1.3814e+00 -8.8345e+00  8.1187e+00
+#&gt;   2.5454e+00 -1.1054e+01 -3.9274e+00  1.4365e+00  7.5614e+00  2.1234e+00
+#&gt;  -1.0611e+01  3.2993e+00 -4.9833e+00  5.5129e+00 -1.5963e+00 -3.0104e+00
+#&gt;   3.0948e+00  7.7989e+00  2.8320e+00 -6.8328e+00  5.3871e+00 -7.3793e+00
+#&gt;   1.0356e+01  1.0498e+01 -2.1031e+01 -2.4123e+01  2.2733e+00  1.2569e+01
+#&gt;  -5.8569e+00 -2.2212e+00  8.6687e+00 -1.8951e+01  1.2854e+01  9.3725e+00
+#&gt;  -1.1501e+00 -1.2831e+01 -6.0599e+00  6.2460e+00 -4.7948e+00 -3.0693e+00
+#&gt;  -2.7834e+00 -1.5981e+01 -2.3720e+01 -5.7574e+00  4.2840e+00  2.6247e+00
+#&gt; 
+#&gt; Columns 37 to 42 -9.1028e+00  1.2591e+01  1.0310e+00 -5.8421e+00 -1.1338e+01  4.1153e+00
+#&gt;   3.1706e+00  2.9428e+00  1.4845e-01  8.9454e+00  1.3134e+01  6.5534e-01
+#&gt;   4.5841e+00 -8.7716e+00  8.0809e-01 -1.6475e+01  4.4807e+00 -1.0819e+01
+#&gt;   2.3524e+00 -5.6207e+00 -2.0095e+00 -4.4384e+00  1.3316e+01 -6.8045e+00
+#&gt;  -2.4933e+00 -3.9729e+00 -7.7103e+00  1.1894e+00 -1.1831e+01 -5.8958e+00
+#&gt;   4.1788e+00 -4.9825e-01  1.6073e+01 -3.8185e+00  1.1832e+01 -8.7843e+00
+#&gt;   8.6549e+00 -4.8510e+00  2.8659e+00  1.5565e+00  5.0214e+00  3.4118e+00
+#&gt;  -7.3109e-01  9.8369e+00 -4.7401e+00  9.5182e+00  5.5939e-01 -4.8440e+00
+#&gt;   5.2137e+00  1.8856e+01 -3.0772e+00  1.2977e+01 -1.7254e-01  3.3656e-01
+#&gt;   1.0825e+01 -4.8495e+00 -1.2025e+01  1.1839e+00 -1.6483e+00  1.5632e-01
+#&gt;   7.4011e+00  1.1780e+00 -8.3667e+00  3.9612e+00 -1.0989e+00 -1.1149e+00
+#&gt;   7.2148e+00  9.1705e-01  1.3965e-01  1.2729e+01 -8.3906e+00  6.4803e+00
+#&gt;  -8.2265e+00  3.5863e+00 -6.5682e+00 -4.1911e+00 -4.0294e+00  1.6310e+01
+#&gt;   6.1826e+00 -8.1974e+00 -1.2904e+00  1.0439e+01 -7.9703e+00 -7.8956e+00
+#&gt;   5.9539e+00  1.3534e+01 -3.9589e+00  6.4846e+00 -3.7262e+00 -9.1065e+00
+#&gt;   7.2161e+00 -2.5541e+00 -9.6471e+00  2.1303e+00 -5.7376e+00  3.6745e+00
+#&gt;  -5.3257e+00  4.5439e+00 -1.3490e+01 -5.4855e+00  9.1892e+00 -9.2638e+00
+#&gt;   1.5159e+01 -8.0027e+00 -1.8171e+01  3.2348e+00 -1.1226e+01 -1.2237e+00
+#&gt;   4.0426e+00  4.7529e+00 -3.9383e+00  4.2050e-01  7.3034e-01  1.5667e+01
+#&gt;  -1.0191e+00  1.3406e+01  8.5509e+00 -4.3237e+00  1.7792e+01 -1.0651e+01
+#&gt;   9.1773e+00 -5.7015e+00  5.4302e+00 -3.8808e+00 -7.5434e-01 -8.2778e+00
+#&gt;   1.1293e+00  1.3398e+01 -6.2309e-01  1.1985e+00  1.2024e+01 -1.1281e+01
+#&gt;   4.1953e-01 -1.8520e+01 -1.6476e+01 -2.2505e+01 -8.8054e+00 -3.2068e+00
+#&gt;   1.7972e+00  1.9958e+01  2.3866e+00  1.9869e+01  5.1840e+00  2.0026e+01
+#&gt;   7.1083e+00  6.2996e+00  2.2023e+00  5.7702e+00 -3.2933e+00  1.0233e+01
+#&gt;   3.9714e+00  2.4181e-02  1.1250e+01 -1.2165e+00  1.1519e+01 -5.1492e+00
+#&gt;  -4.7458e+00  2.2553e+00  9.3586e+00  7.0260e+00 -8.9473e-01  3.7660e+00
+#&gt;  -9.3217e-01  2.3749e+00  2.4645e+00  7.2412e+00 -1.8842e+00 -3.2332e+00
+#&gt;  -3.2802e+00  1.5417e+00  6.4068e+00  1.9257e+00  2.9942e+00 -1.0548e+01
+#&gt;  -8.2871e+00 -2.6355e+00  4.1110e+00  4.1654e+00  9.5214e+00 -1.0637e+01
+#&gt;  -8.8577e+00 -1.2026e+00  3.2324e+00 -1.0275e+01  8.1027e-01  5.3804e+00
+#&gt;   1.2246e+00 -4.9161e+00  6.2280e+00 -1.3336e+01  3.3344e-01 -1.7871e+01
+#&gt;  -2.5499e+00  3.3478e+00  6.1585e+00 -9.2499e-01 -4.8937e+00 -7.7639e+00
+#&gt; 
+#&gt; Columns 43 to 48 -1.8949e+00  3.1792e+00 -2.1589e+00  6.3949e-01  3.2063e+00 -1.3578e+00
+#&gt;  -1.0469e+01 -5.6708e+00  7.5980e+00  9.6664e+00  6.7643e+00 -6.5708e+00
+#&gt;  -6.1512e+00 -2.0264e+01  3.4358e+00  6.0738e+00 -1.3076e+01  2.5472e+00
+#&gt;   7.7560e+00  1.4065e+00  1.2090e+00 -8.3934e+00  1.8032e+00  8.1045e+00
+#&gt;  -1.9726e+00 -3.6610e+00 -2.0917e+01 -5.1021e+00 -8.5341e+00  9.6339e+00
+#&gt;   3.1744e+00 -1.5621e+01  3.4864e+00 -1.6545e+00  4.0293e+00 -5.3260e+00
+#&gt;  -8.8298e+00  6.7961e+00  1.1437e+01 -2.5266e+00  3.7172e-01  9.4313e+00
+#&gt;   3.0545e-01  1.8151e+00  9.5635e+00 -1.7820e+01 -6.6283e+00 -8.8928e+00
+#&gt;   1.2522e-01  4.0336e+00 -7.4177e+00  9.1586e-01 -4.6258e+00  3.6882e+00
+#&gt;  -1.2605e+00 -9.0548e-01  8.1084e+00 -6.0548e+00 -1.6634e+01  2.2741e+00
+#&gt;   1.8113e+00  1.5702e+00  5.0019e-01  2.8852e+00 -4.3100e+00  2.9644e+00
+#&gt;   7.3638e+00  3.9861e+00 -3.0309e+00 -2.8950e+00 -3.3484e+00 -6.0369e-01
+#&gt;   7.5221e-03  3.2168e+00 -1.2645e+01  1.7435e+01 -3.2664e+00  3.6868e+00
+#&gt;   3.1162e+00 -5.5322e+00  1.7958e+00 -8.8683e-01  2.2310e+00 -1.3305e+01
+#&gt;  -1.9391e+00  3.5524e+00  6.6669e+00 -3.1837e+00 -1.0453e+00 -9.0635e+00
+#&gt;   2.0948e+00  1.3118e+00 -6.5282e+00 -1.7253e+01  2.3108e+00  8.9559e+00
+#&gt;  -1.7999e+01 -3.3407e+00 -5.3289e+00  4.9456e+00 -1.0145e+01  8.4892e+00
+#&gt;   2.7427e-01  8.0205e+00  1.4450e+01 -1.3079e+01 -1.2436e+01  4.7463e+00
+#&gt;   4.4269e+00  5.9370e+00  9.3929e-01 -1.3290e+01 -1.3577e+01 -9.1268e+00
+#&gt;   1.6066e+01 -8.7729e+00 -2.0591e+00  4.5703e+00  4.9376e+00 -7.4957e+00
+#&gt;  -1.2894e+01 -1.7018e+00 -6.3262e+00  2.8053e+00  3.8833e-01  1.0766e+01
+#&gt;   2.5407e+00  1.3492e+00  5.4623e+00  4.9909e-01 -1.1941e+01 -3.1936e+00
+#&gt;  -4.5454e+00 -4.9026e+00  7.8692e+00  1.5897e-01 -8.2075e+00  7.7568e+00
+#&gt;   1.6057e+00  1.8280e+01 -6.0971e+00  1.3906e+01 -2.5730e+00 -3.1180e+00
+#&gt;  -2.2184e+00  2.2779e-02  6.5701e-01  2.4582e+00  9.5279e+00 -3.7017e+00
+#&gt;   2.0165e+01 -1.5902e+01  9.6445e+00 -7.0694e+00  7.1113e+00 -1.9141e+01
+#&gt;   6.5520e+00  9.2279e+00  8.5650e+00  1.9217e+01  4.2980e-01 -2.1048e+00
+#&gt;  -1.4847e+00  7.6447e+00 -1.0691e+00  6.1191e+00  4.5094e+00 -4.1965e+00
+#&gt;   4.9171e+00 -3.5977e+00 -5.1303e+00  9.4302e+00  1.0969e+01 -2.8175e+00
+#&gt;   1.7654e+01  1.2303e+00 -1.8604e+00 -6.7042e+00  8.0041e+00  2.6355e+00
+#&gt;   3.4652e+00 -8.8835e+00 -2.4696e+00 -6.1194e+00 -2.3170e+00  1.8372e+00
+#&gt;  -3.2396e+00 -1.2917e+01  1.1964e+01 -2.1103e-01  1.5640e+01 -9.4318e+00
+#&gt;  -7.8141e+00  4.2299e+00  2.8658e+00  7.7334e+00 -2.7448e+00 -8.3782e-01
+#&gt; 
+#&gt; Columns 49 to 54  1.2333e+01 -1.3767e-01 -3.5458e+00 -2.4091e+00 -4.3892e+00 -1.8596e+00
+#&gt;   3.0590e+00 -8.6098e+00  1.2912e+01  1.1340e+01 -2.3048e+00  1.8209e-01
+#&gt;  -1.4247e+00 -1.1627e+01  2.7382e+00  2.6747e+00  1.1848e+01  8.3825e+00
+#&gt;  -1.0018e+01  6.1788e+00 -8.4593e+00  7.7510e+00  2.9908e-02 -1.5930e+00
+#&gt;  -6.7280e+00  1.1571e+01  9.2223e+00  4.1258e+00 -8.2129e+00  7.0230e-01
+#&gt;   1.0891e+01  9.3680e-01 -7.9255e+00  4.5878e-01 -7.3924e+00 -4.2198e+00
+#&gt;  -9.4668e+00 -5.4795e+00  1.5067e+01 -3.1557e+00 -4.1782e+00 -1.7167e-01
+#&gt;   3.4064e+00 -4.4980e-01  1.1592e+01  7.2165e+00 -2.5246e+00 -2.8478e+00
+#&gt;   1.0886e+01 -1.5934e+01  1.2154e+00 -6.4077e+00 -4.7183e-01 -4.6793e-01
+#&gt;   1.0714e+01  4.9262e+00 -1.0091e+01 -4.0105e+00 -2.2166e+00 -6.6209e+00
+#&gt;   2.1358e+01  4.1649e+00 -2.9043e+00 -6.3058e+00  2.6100e+00 -5.6352e-01
+#&gt;  -4.0338e-01  1.6002e+00 -8.1261e+00 -5.7080e+00 -2.5969e+00 -1.8784e+00
+#&gt;   7.2134e+00 -4.0635e+00 -1.1044e+01 -7.3213e+00  4.8594e+00  2.2916e+00
+#&gt;  -8.6441e+00  2.0574e+00 -2.0262e+00  8.5201e+00  9.9343e+00 -2.0685e+00
+#&gt;   8.1453e-01 -3.3945e+00  1.0309e+01  6.3051e+00 -2.7420e+00  8.4013e-01
+#&gt;   5.6428e+00  1.0729e+01  6.6803e+00 -2.7419e+00  1.7320e+00 -1.8016e+00
+#&gt;   1.6561e+00  1.4647e+00 -7.4047e+00  9.6098e+00  7.3993e+00 -9.3957e-01
+#&gt;   6.3324e+00  6.9388e+00 -5.6508e+00 -1.0262e+01  6.1348e+00 -3.7880e+00
+#&gt;   2.7018e+00 -4.5579e+00 -5.6484e+00 -1.1570e+01 -1.1231e+01 -6.9494e+00
+#&gt;  -1.4790e+01 -5.7864e-01  5.0066e+00  8.4038e-01  9.4816e+00 -3.1154e+00
+#&gt;  -1.1103e+01 -2.8144e+00  8.4787e+00  7.7905e+00  6.3268e+00  8.5337e-01
+#&gt;  -5.4284e+00 -9.9734e+00  8.3666e+00  4.5437e+00  4.6585e+00  2.5157e+00
+#&gt;   1.3540e+00 -1.0940e+01 -5.3762e+00  1.2806e+01  1.4043e+00  7.3552e-04
+#&gt;   6.4825e+00 -8.2156e+00 -4.1786e+00 -1.4547e+01 -8.1273e+00  6.3691e+00
+#&gt;  -1.3291e+00 -2.9907e+00  4.6872e+00 -4.9224e+00 -8.3817e+00  5.2724e+00
+#&gt;  -3.5581e+00  2.5445e+00 -7.8640e-01 -2.6496e+00 -6.5729e+00 -4.9490e+00
+#&gt;   5.3952e+00 -1.0344e+01  1.2800e+00 -1.8468e+00  3.4351e+00  2.9873e+00
+#&gt;  -5.5920e+00  3.8213e-01  9.8507e+00  2.6040e+00  2.8464e+00 -1.8464e+00
+#&gt;  -1.4649e+01 -1.0690e+00  5.6015e+00  1.5896e+01 -5.8986e+00  8.7044e+00
+#&gt;  -1.1121e+01 -4.9316e+00  2.6215e+00  2.8442e+00  4.1814e+00 -4.4011e-01
+#&gt;   3.7989e+00 -9.5559e+00  1.2084e+00  6.3876e+00  7.4033e+00  5.4886e+00
+#&gt;  -1.5470e+01  5.8021e-01 -1.7981e+01 -6.3462e+00  5.2296e+00 -3.3604e-01
+#&gt;  -2.3717e+00  8.1532e+00 -7.8498e+00 -2.2843e+00 -3.7729e-01  8.6050e+00
+#&gt; 
+#&gt; (13,.,.) = 
+#&gt;  Columns 1 to 8   0.6225  -1.5633  -1.0825   5.1088   5.7446  -3.3708   3.6607  11.8080
+#&gt;   -5.9414   4.0864  -1.2822  13.1553 -15.7463   6.3882   1.5634 -11.4881
+#&gt;    3.8365  -8.4567   4.2103  13.8929  -3.8816  -6.1134  -0.6663  -8.0852
+#&gt;   -3.0222   6.8643  -2.3826  -0.7312  -5.0444  10.4230   3.6401  -5.1320
+#&gt;    2.9885   0.6388  -1.7581  -0.5176   8.0534   9.4546  17.4677   2.8854
+#&gt;   -0.0614   2.0208 -14.5500   0.9292  16.2407   1.4846   5.7644   0.9823
+#&gt;   -5.0461   4.9234   2.7604   1.9248  -0.2402  -1.4744   6.1263 -18.5446
+#&gt;    1.5126  -6.5236   7.4569  -6.4902   0.9539 -34.3821   4.7376  -6.4473
+#&gt;   -1.8858  -1.7096  -1.9603   0.5967  -2.9680  -0.6489  -4.0181  -0.8944
+#&gt;   -0.4119  -1.9274   1.7104   6.9449   2.2967  14.1728   4.3677   5.9133
+#&gt;    3.1806   0.2433  -6.9227  -6.6097  -3.4068  -1.5102   1.1768  -7.3246
+#&gt;    5.1976   3.0144   2.7979  -7.2828   7.7405   4.1381   7.9900  10.3155
+#&gt;   -1.6468   0.6064  -5.1478   9.8157   2.6589   6.8723  -2.0592  11.5436
+#&gt;   -0.2124  -2.1939  -5.1560  13.0934  -6.1557   5.6263 -11.1988   0.6105
+#&gt;   -0.7770   5.0216  -9.4610   3.9703   5.4409  11.2737  12.1204  -2.3762
+#&gt;   -4.3581   6.0825   2.3620  -0.5420  -0.5396  -2.1976  -2.9201 -17.2563
+#&gt;    1.2618   5.1261  -7.6242   3.7173   9.8331  17.3887   2.6580  -6.2977
+#&gt;   -1.8163  -2.6399   9.1186  -5.1573   2.7076  10.3487  -5.5950   6.4227
+#&gt;   -2.2549   2.9415  -1.3807  -0.5117 -13.7537  -9.9439  -7.9778 -11.1709
+#&gt;   -0.0283  -3.4669   0.2005   4.9303   1.9195 -14.3671  -4.5576   6.9302
+#&gt;    1.2863   6.2312   5.7416  -1.8370  -6.7823  11.3672   5.8564   1.9807
+#&gt;    1.8187   2.6548   4.8856  -0.5090   3.4795  -2.6129  -6.3327   3.1360
+#&gt;    0.8995   7.3851  -0.9977  -1.0382  -0.0063   7.6577  -0.6447 -18.8161
+#&gt;    0.1038  -3.5217  -9.7995   3.8576 -10.7382  -3.8209 -21.4523  -5.3858
+#&gt;    0.1988  -0.2407   2.9182   9.4107  -7.8575   8.6792  11.2470   1.5597
+#&gt;    1.0691  -7.2791  10.4935 -11.5572   2.8200 -11.1891   0.6554   4.3099
+#&gt;    3.9875  -1.9605   1.6879   8.9195   6.2382  12.0618  -4.7136  -1.3624
+#&gt;    1.0449  -2.2370  -5.4446   3.2154  -7.8951  -3.5772 -16.4724  -0.0223
+#&gt;   -4.2487  -0.0201  -5.0634  15.8200   3.4258   2.2750  -6.9639  -1.5070
+#&gt;    7.8249  -2.3258 -11.5096   6.5237  25.4575  12.2537  -2.4187   9.4461
+#&gt;   -4.5060   3.7490  -4.8310  -6.0221  -7.5922   4.1831  -8.2478   1.6914
+#&gt;    4.5197  -6.3969   6.6634  -7.9376  11.1415  -1.7244   3.6967  -5.8130
+#&gt;    1.6796  10.2047   2.9963   0.4611  15.4367   8.6640  12.3652   9.3268
+#&gt; 
+#&gt; Columns 9 to 16  -1.2855   0.0185  -9.0798  23.0098   1.5778   7.3526  15.9490  -2.5839
+#&gt;   12.9727  -1.6580   6.0191  -1.8907   3.2326  -4.4916 -12.2917   9.3610
+#&gt;   -9.4650  -3.3754   9.2407 -20.8298  -4.0272  -4.0452  -8.5894 -13.6104
+#&gt;    4.6112 -15.6477   8.9300   6.9903  -3.0968   0.7586  -4.6015 -13.4000
+#&gt;    8.4996  -4.4138  -5.4577  -7.4059 -19.6451  -5.3132 -17.6285  -8.1284
+#&gt;   17.0900 -12.0884   7.6606  -5.2643  -3.9708   8.9101 -12.8181   1.2724
+#&gt;   11.7912   3.3413   1.8484 -10.1811  -3.7404   5.9754  12.8199   3.5822
+#&gt;    9.0085   0.0326  -8.7861   4.5516  -3.4405 -11.4276  -1.4082 -14.7301
+#&gt;   14.3183 -13.1309  -5.4163  -2.6748 -15.5476  -1.9758  -9.0136   7.4097
+#&gt;   -2.9459   1.3235  14.2310  -4.8895   1.0256  18.3548   3.3083   8.2479
+#&gt;  -10.8172  -6.8999 -12.8204 -11.6486  -5.6255   2.9987   7.2506 -16.9556
+#&gt;    8.6517   2.0668  -5.2789   2.6444  16.9798   5.7512  -6.2904   9.3246
+#&gt;    8.5083  -4.5114   2.7383  -3.9136   2.9081  11.4299  -2.0102   4.7976
+#&gt;  -14.5124   2.3619  11.0900  13.8907   5.7813  -8.9663   6.9377  15.0322
+#&gt;    9.4700  -5.7718  21.7840   1.7330 -13.4566   9.4243  11.2065   3.2765
+#&gt;    7.1058  -3.1070 -12.9466  -0.9314   6.2793  -5.7981  -1.6923   4.2464
+#&gt;    9.8869   2.1072   0.9882  -8.5688  -6.7740   5.2935  -1.1322  -6.0721
+#&gt;   13.6170  10.1805  -0.6006   5.0214  24.5619  17.5630  12.9840   8.9247
+#&gt;    0.7140   1.3764   5.9720   0.2036   2.1168  20.9245   2.4649 -10.0140
+#&gt;   -5.4863  -4.5560   6.1671   2.2992  -9.4693   3.2962   8.1224  -7.1518
+#&gt;    4.0313  -3.1694  -3.7682   8.7248   6.9283  -6.7974   1.0717   3.5391
+#&gt;  -12.6850 -11.1544  -3.5832 -17.0292 -10.2922  10.5102  -8.5805 -16.6701
+#&gt;    1.6295  -9.6518  -9.2382 -21.4327  -4.1098  14.8623   6.2514 -18.4324
+#&gt;   -4.7173 -13.7172 -15.1778 -13.1720 -12.6725   4.9590  -3.7975  -8.5846
+#&gt;    4.4336  -0.8218   7.3717  -5.4434  -4.8480 -20.0243  -2.8340   7.2669
+#&gt;   -3.9407  -5.8612  -9.2493   2.7658  -6.3236   7.3682   0.8614  -2.2647
+#&gt;   -5.1057  15.3606  17.8084   0.8658  -4.1440  11.4043  27.9573  -0.7385
+#&gt;  -14.7314   4.1165  11.2977 -13.5234 -15.3198 -13.3219 -12.4830  -9.5864
+#&gt;   12.5962  -2.0848  20.9790   6.7316  -7.9422  -7.3325 -11.7881   2.1356
+#&gt;   -2.2800   5.7363  11.4628   7.3722  10.9769 -12.4952  -6.5846   4.3698
+#&gt;   -5.5631   6.5627  -2.8050  -8.0543  13.7225   5.1217 -13.3229 -10.1688
+#&gt;    1.1927   8.0427  -9.9975   1.4130 -20.1412  10.6100  13.6041  -6.3356
+#&gt;   -3.2324  13.7741  -1.9013  -4.1233  -2.3054   2.5272   6.2873   4.8383
+#&gt; 
+#&gt; Columns 17 to 24  18.1636   2.6890   5.8660  -0.9663  -7.8354  13.3825  -5.7938  -3.0844
+#&gt;   14.9101  -8.1264   9.3529  -5.6501 -13.8379  -5.1529  12.0903   6.6171
+#&gt;  -12.9787  -4.5172  -3.5492  -0.1676  -0.3024 -20.2456 -17.2140  -3.7271
+#&gt;   15.0217 -17.9985  -2.7245   0.7354 -10.6781  -4.0954  16.4370  -3.9958
+#&gt;   19.8714 -15.6207   3.7955 -11.6631   0.5926   3.2357 -10.5831  -6.8354
+#&gt;    7.3107  10.1958 -12.5282 -15.6849   9.5947  -0.6776  -3.5694  -6.8568
+#&gt;    4.1721   6.1826  -3.5171  -1.6282  -4.5980 -12.7351   2.4134   1.4011
+#&gt;   -3.8929   0.8567  -2.3181  10.7230  -0.4437  -1.1194  -2.6472  -1.0988
+#&gt;    0.5700  -4.2833   0.8950   9.9760   0.1999  -7.7351   8.4031  -1.9698
+#&gt;    8.6987   1.8773   5.9658  -6.3321  10.9693   8.2531  -7.0048 -16.8536
+#&gt;  -25.2377 -12.1537  14.2061  -8.3470   7.8966  -2.2503 -19.2228   6.2746
+#&gt;   -4.2712  -3.8228  -5.6966 -14.1824 -10.1671 -10.6292  -2.9392  -2.9507
+#&gt;   -1.0011  -7.9093   8.0571 -11.0527  12.1593  -3.7356 -10.7960  -6.1334
+#&gt;   -0.5150 -10.1990  13.7810  -3.9238  -9.3145  12.6934   7.1857   6.6778
+#&gt;    6.2490  -4.6729  -2.4496  -4.4163   1.3302  24.1066  -2.6552 -12.0586
+#&gt;    3.7691   3.7795  -1.8822   9.3887  -7.6862  -0.9493   0.4402  -6.8497
+#&gt;    7.8804  20.2961   4.4132  -0.6763   1.9556  -9.0676  -0.4367  -9.1325
+#&gt;   -8.1008  11.3290  19.9934  18.0099 -12.1368 -15.7701  15.0487  -3.7897
+#&gt;    3.9090 -12.1362  11.0174   8.8886   1.6636   4.9241 -14.1295   5.4046
+#&gt;    2.5688  -3.1138 -14.4614   0.4418 -14.4975   8.2753 -17.5903  -9.1665
+#&gt;    9.6189  -2.4555   4.4813 -10.1254  -0.2373  -9.0948   8.7192   4.3740
+#&gt;  -16.1791   5.0023  -5.0693  12.8547  -3.6058  11.3588  -5.5056  -5.0991
+#&gt;    5.2873  -8.9815   3.3710 -13.0669  14.1989  -8.1130  -9.1110   9.0331
+#&gt;  -23.5598 -30.5964 -14.4885   4.7473   2.7410 -11.7885  -4.4209  -2.4463
+#&gt;   -0.0130 -15.6218 -14.0751   1.8073  -3.6559   1.4849   7.6913   4.5255
+#&gt;    4.9514  -0.2254  -3.8016  15.9771   1.8918  12.4013  -6.2868  -1.3038
+#&gt;  -19.2015   2.9579  -6.1137  19.9605   3.5282 -10.5849   6.1530   0.7995
+#&gt;   -0.4617  -3.9831  -4.4857  -0.1848   1.6324   9.0417  -0.4186 -10.2384
+#&gt;   -1.0973 -10.0601  -0.4049   9.9800   4.7209 -10.4516  18.6407   1.3664
+#&gt;    2.4310  13.5651 -10.4308  18.9115   8.8863  17.2386  15.7466   5.9440
+#&gt;  -19.0119  17.7831  11.2511  -6.0253  -4.8593  -1.7501  -1.0230   5.3960
+#&gt;    3.3324  -6.8259 -16.2877  11.6091  16.1198   6.0207  -9.4439   6.7558
+#&gt;   -8.2590   8.0797  -6.6256  10.1228  -0.1918 -10.1314   1.6924 -16.9398
+#&gt; 
+#&gt; Columns 25 to 32   9.7602  -7.7804   8.1099 -10.5406  -0.8016   4.2707   1.6632   6.3764
+#&gt;   -6.5471 -11.5912  -6.5146   0.6060  -2.6289   6.8809  -8.7830  14.0514
+#&gt;   -8.0664  -0.9994  24.0894  -6.0419  11.4410   7.0581   0.7835  15.4865
+#&gt;  -12.7290   8.7368 -21.3734  12.8849  -0.0553   3.9926   3.6383   1.3704
+#&gt;  -13.4071   3.8879 -24.2288  11.2635   1.5915   7.5209   6.7797  -0.2068
+#&gt;  -17.5532 -16.1468  -2.0649  10.6989  -0.0317  -5.5492  -0.6623  -1.1360
+#&gt;   -5.5422  15.5088 -10.3694  -7.2312  -0.0041  -6.3378  14.6600 -10.6514
+#&gt;   -7.5865   1.1804  -1.6392 -22.0979 -11.5349  -7.3807   9.2519   7.6128
+#&gt;  -10.6179 -12.5250   1.7685   8.5996  14.2387  -8.7196  -6.3942   6.2967
+#&gt;   -4.5517   3.1659   1.0517   5.2230   1.6644   7.6189   7.8894  -9.2089
+#&gt;   -0.7227  -8.7862  21.3328 -13.5036   9.0519  -3.5375 -10.7146   7.8418
+#&gt;   -7.3776  -9.2339  -9.0334   4.7565   9.8160   3.7270  15.0688   7.1635
+#&gt;   11.3902 -10.1863   1.0542  -6.8610   2.8108   3.0329 -15.4997   3.2724
+#&gt;   -1.3498   1.5421   4.7036   2.1384   1.6352  -6.2726   8.5540  15.2884
+#&gt;    4.6787  -2.6138  12.6692   5.2700  -5.3515   0.3652  10.9815  -3.3373
+#&gt;   -2.6148  11.6006   1.1225  -5.9275   5.0149  -0.7353   2.5467 -10.4715
+#&gt;    3.8180  12.0771   8.0940   0.3466 -15.9737 -17.4497   7.3648   3.8229
+#&gt;   -1.6993  19.6717  -6.1632  -9.6537   8.7153  -3.2533  13.1750   5.6769
+#&gt;   -8.5416 -11.7091  -9.0161 -15.5721 -10.8837   2.5474   8.6095   1.1659
+#&gt;    8.6536   2.1465   4.0729  -7.8629 -11.2442  10.7150   0.3810  13.0305
+#&gt;  -23.0618   1.2090   1.6410   8.9639   2.0674   0.9350  -4.3420   6.6411
+#&gt;   -5.8685   3.1908   9.4136 -13.6394 -15.1120 -19.4785   5.9027  11.2434
+#&gt;    9.5898   2.5199   9.4495   4.7774   8.1902  15.0195  -1.8043   7.9227
+#&gt;   11.5086 -11.9990   0.6903 -15.1623  -4.1851   0.9766   6.7075  16.3212
+#&gt;   21.3647 -18.4335  -2.4270   7.1990   4.3983   2.1114   0.8842  -0.2500
+#&gt;    3.3664  -6.8320  -6.3752   8.8395  -9.9285   1.5100  -6.8595  -5.1740
+#&gt;    9.0478  -0.2612  10.8768 -13.0471  -8.4355  -2.3569  -8.9594  -6.3812
+#&gt;    6.6571  -4.9372   4.6812  -3.2220  -4.3514   8.6508  -2.3734 -10.8352
+#&gt;   -9.3337  13.9356 -11.9485  16.9562  -3.8194   1.4336 -10.1326   4.5468
+#&gt;   11.5920  -1.3552   0.0446  -6.0608 -11.8932  -7.2355 -17.2701   6.8176
+#&gt;   13.2213  -3.6443 -12.2204 -14.1070   7.7651   0.5954   2.4866  11.5992
+#&gt;  -23.3829  12.7079  -3.0813   7.9698  -1.7549  -0.0543   3.8429  -3.8380
+#&gt;  -11.4183  11.7327  -9.6460 -18.0117  17.6143  -1.6256   9.8787  -2.0024
+#&gt; 
+#&gt; Columns 33 to 40  -3.9341  -2.0305   0.6524  -5.1456   0.6766   5.5498   1.0476  -0.1857
+#&gt;   -6.8117   2.9403   7.8930  -7.9923   3.1811  -1.0809   1.7309  -3.3199
+#&gt;    2.2975 -11.8925   3.8148  10.5640  -6.8398  25.4739   1.6435  14.8361
+#&gt;  -10.5531  -3.5991  -4.7588  -8.2663   7.6058   2.9165   0.2993 -17.7117
+#&gt;   -8.8943 -11.8832  10.5742 -11.1564   0.5177  -2.3430  -7.9058  -8.9088
+#&gt;    4.5182   4.0905  -3.4065 -13.8010 -11.5456  -3.7047 -10.3269  11.0212
+#&gt;  -11.1254   3.4482  14.1089 -15.2183   2.6742   4.0473   3.2420 -20.2393
+#&gt;    0.8347   2.2787   4.5881   2.3749   8.7257  -4.3932   7.1358  -8.3630
+#&gt;    5.0211   9.5129 -15.4526  -4.9501  10.4043  12.4970  -3.7509  23.3894
+#&gt;  -10.2102   3.5111 -12.3805   3.7743  -6.8626  -0.0747  -2.9689   9.1161
+#&gt;    8.2601   3.4696 -12.2933  -0.6306  -1.6229   4.7930  -7.3460  32.7231
+#&gt;    2.5878  10.6950 -19.4801  -2.9348  -1.4626  -0.3699   2.5573   5.6626
+#&gt;   -6.8752   0.9755  -4.6640 -17.8662  -0.8453   3.5109  -4.6187   9.9316
+#&gt;    1.6028  -0.1389  -1.2994  -6.2128  10.0624  -2.3837   2.1566   8.8314
+#&gt;  -14.0722   1.5607 -20.0004  22.8786  -0.1401   2.0176 -14.8383 -13.5920
+#&gt;  -18.6510  -0.4177  -2.9582   4.9505  12.8327   7.7842   1.7678  -8.5907
+#&gt;    7.1897  -2.1071   1.5479   6.3302   1.3973  -2.8158  -8.7517   1.4023
+#&gt;   -6.8289   3.7599   1.2994  -1.9434   0.1248   5.1686   1.8590   8.0154
+#&gt;   -1.5625   2.7974  14.1518 -10.1855 -17.9860  -7.5408   8.3709  10.3564
+#&gt;    0.7383  -5.3753  11.1572   9.0830  -4.7921   5.3966 -17.6511   0.3852
+#&gt;    1.2908 -10.6373   9.6046   0.0826  -3.8387   0.5976   2.8609  -9.5399
+#&gt;   -2.6528  -1.6465  -1.9558  13.9648   7.2380  -0.6112 -14.5066   2.6307
+#&gt;   -2.3397  -0.2024   0.8239  -6.0347   4.0913  11.3913  14.4397   4.5889
+#&gt;   11.2896  10.6101 -10.7042 -15.5441  -9.2189  -9.8412   5.1248  32.5710
+#&gt;   11.0244   5.5595  -7.1076  16.0597   6.2505   6.3358  -0.2796  -2.1579
+#&gt;   -9.9230   0.2472  -9.2166   5.0312   3.5981  -7.8504  -7.2184  -1.3071
+#&gt;   -2.4879 -11.3545 -12.4110  24.2905  -8.7519 -17.0857 -10.2919  -3.4404
+#&gt;    4.1187  -2.6143   1.0428  10.6671 -13.0915  -3.5546   0.5629   6.4200
+#&gt;   -3.3104 -12.0760  -0.7076  -3.0841  24.2404  -3.1691  -6.9119   1.7941
+#&gt;    3.1939  -8.4331 -17.0943  10.9742   6.5465   8.0851   0.8856  -8.9553
+#&gt;  -14.2499  13.0122  -2.6418  -2.2588  -0.4159  14.4637  -3.2607   8.2385
+#&gt;   18.2165  -3.9107   0.1991  -8.0249   0.6290  -2.4179  11.3165   6.8118
+#&gt;    3.9876  -3.3598  14.0990  -5.3588 -12.7347 -11.6581  -0.1995  11.9736
+#&gt; 
+#&gt; Columns 41 to 48   2.6384  12.6728  -3.7734 -20.3231  -0.2065  -1.2895   5.8673  -0.0072
+#&gt;   -0.5856  -7.6191   9.3754  -7.0757  -1.0990  -0.6045  -2.5725   0.2833
+#&gt;  -11.6955   1.5578  -5.1756  -6.8691 -13.8330   1.5411  -2.2008  13.6485
+#&gt;    5.0361 -17.7714  10.7466  -6.2763  13.0006  -3.0142   2.9823   1.0376
+#&gt;    8.8774 -10.9829   0.9405  -9.5145   2.8328  -4.1716   0.8532   2.6348
+#&gt;  -10.9172   3.7905   8.3874   7.9075  -8.7231   7.7682  -9.8785  -1.4428
+#&gt;    4.1869  -4.6672  -1.5456  -1.7447  -8.1386   3.2080   0.5161   8.1082
+#&gt;   -0.7679   5.4786   5.5345   8.9032   5.1916   1.9881  -1.1946   8.5584
+#&gt;    2.3608  22.4209 -18.1195  -9.3485  -0.4505   0.1381 -21.7665  -0.1648
+#&gt;   -8.8235   4.9310  -6.2566 -16.0138   3.0802  -1.5473   1.1070   1.7021
+#&gt;    1.9942   9.4574   4.0311  11.4089  -5.2419   4.3092 -11.9660  12.5830
+#&gt;   -2.6881   3.7668   2.4198  13.9039   5.7139  14.8638  12.3989  -1.7952
+#&gt;  -22.4898 -14.8158  -6.6019  -5.1961   5.4858  -0.3881 -13.2620  -0.4163
+#&gt;   -4.5293   8.1611   5.8838  -9.0136  -5.5433 -14.5050  -8.3331   9.2668
+#&gt;    7.0383   0.8013   0.0123   7.6101  25.3196  -5.0556  -8.8034   6.1687
+#&gt;  -13.9259  -4.7097  -6.8706   4.4354   4.7897  29.0184  -1.8586  -9.1406
+#&gt;   -8.2563   5.5888  -0.1159   8.7976  -2.7876  12.0876 -13.8446  21.1724
+#&gt;   -4.6292   2.1923  -8.3140   1.8247   4.4025   0.4105  -1.3992   6.8583
+#&gt;    7.0735   2.5204   0.8192  -3.7221 -21.1281  -4.5568  -1.9114 -10.8041
+#&gt;    0.4631  -5.1953   3.0787   2.2137   9.7525  -2.0934  -5.1761  10.7299
+#&gt;    3.2992   2.7955   6.4101   6.6697 -15.3544  -2.3311  10.5766  -5.4545
+#&gt;   -4.7215  12.9134   5.2489  11.9368 -10.3907   0.9388  -7.1882  20.6407
+#&gt;   11.2167  -1.1713  -4.6229 -26.7602   8.4682   6.1601   7.4882  -2.4842
+#&gt;   11.0823  13.9282  -1.9220  -9.2037  -4.9446 -20.9689 -21.6739  -3.2960
+#&gt;   22.2801  11.3753   4.0951  17.2048   6.1801 -12.2547  10.9081  -2.8286
+#&gt;    6.4324  -4.8378  -0.7057  -3.4801   0.3702 -13.0156   3.2830 -12.0592
+#&gt;   -4.2670   7.1882  -2.7934   2.9694  10.5591  -1.4846   6.7703  20.9930
+#&gt;    3.9284  -4.1591  -8.8767  -2.8856  -2.4679  -2.1686  12.8365   9.9197
+#&gt;    1.9951 -20.0595  -4.7230 -15.3246  16.8989   2.5943  -2.6376   3.5854
+#&gt;    0.8159   7.6094 -14.7591  -1.7713  14.2379   1.4623  -9.3418   0.7935
+#&gt;   -5.7499   8.6248  12.7241  -6.9128  -4.7761  -6.7299  -4.8388   0.9684
+#&gt;   24.4516  -7.3553  -1.6329   0.4585  -2.1951 -10.1090   9.4520  -1.1079
+#&gt;    2.9964  -5.9975  10.7465   8.8183  -7.0952  -0.7106   4.2629   1.4456
+#&gt; 
+#&gt; Columns 49 to 54   8.2625   0.9033   1.5123   3.2150  -4.9727  -0.7974
+#&gt;   -9.1411  -6.7768  -3.3312  -7.6552  -5.8629   1.5708
+#&gt;   19.4400  -2.8266   0.6595   2.9002   2.2003  -3.4884
+#&gt;   10.0413   0.8295   9.7387  -5.7218   2.9886   1.8164
+#&gt;    0.7244  -4.3088  15.7654  -5.2667   6.0944  -3.1314
+#&gt;   -3.9781  -0.5492   3.8910  -8.0746   9.1950   0.4971
+#&gt;   -4.2548  -0.8059   1.0549   7.2977  -5.5230  -1.4808
+#&gt;    6.5204  -2.4275   3.5413  -2.1586  -8.4760   1.7143
+#&gt;  -17.1326   3.4917   3.2997   2.7240   0.5655   3.8225
+#&gt;   -7.7256  -0.1063   4.6172  -5.3888   7.1422  -8.5851
+#&gt;    7.6347  -2.0811  -0.6710  -2.1723  -7.2139   1.1386
+#&gt;   -1.6509   1.1366  16.1597  -1.2614  -3.2961   1.4994
+#&gt;    4.6051  22.8175   1.6403  -9.0837   7.6439  -2.1571
+#&gt;   -3.5945  -6.0262 -16.3896   0.2016   0.9746   1.5790
+#&gt;   -7.6154  -7.2821   4.4517   5.6029  -6.9573  -4.2717
+#&gt;  -10.4473  -0.8336  -9.6946   9.4865  -7.1611   1.8194
+#&gt;   11.3759  -6.8616 -11.5624   3.5180  -1.9766  -3.6941
+#&gt;    2.0064  -1.6333   4.0976   0.6205  -2.6042  -4.9661
+#&gt;  -12.8823  -7.8360   1.0782   5.6972   5.6660   2.1111
+#&gt;    1.9252  -9.6661   8.4916  -9.2061  -4.0192  -8.4211
+#&gt;    6.6718  -3.4963  -4.0314  -7.0760   5.7587   3.5493
+#&gt;   12.1766  11.8530  -6.2429  -4.2156  -5.7470   1.1385
+#&gt;   -3.2129   3.4765   6.8705   2.2789  -1.6691  -9.8592
+#&gt;   -3.0113   9.5897  -7.1651   3.0243   4.2135  -0.0837
+#&gt;   -7.6214   2.8420   1.4394  -0.0282  -3.7063   6.0629
+#&gt;    8.3831  -2.2977  16.5964  -4.5028   2.6921  -2.9995
+#&gt;    7.3325  -0.6589  -8.6266   0.7577  -7.1119  -4.5866
+#&gt;   -5.9435 -12.6229   2.5341   8.3034   5.0543  -2.7943
+#&gt;    1.8347   6.9768   7.7201   0.5520   6.6773   1.3603
+#&gt;   11.4995   9.5357  -4.6271   4.5563  -4.1167  -1.1788
+#&gt;    1.0763   0.1444 -15.0228  -0.1753   2.3464  -6.2756
+#&gt;   11.7925 -10.6398  18.1778   6.3685  -4.0435  -2.9618
+#&gt;    6.0329  12.3226  -8.7054   2.7213   1.1492   1.0349
+#&gt; 
+#&gt; (14,.,.) = 
+#&gt;  Columns 1 to 8  11.9867  -8.2984  -1.3146  -3.6354  -8.8529   0.2349 -13.9622  -3.0493
+#&gt;    6.4384   6.9374  -6.6913   7.1586 -13.3554   1.9107   0.1068  -1.9499
+#&gt;   -1.5772   0.2219  10.3118 -10.6105  -2.3904   0.7299   8.2559   3.1453
+#&gt;   -0.2696  18.1811   0.1068   4.8140   7.3397  -9.6986   2.2253   4.3820
+#&gt;    6.5211   9.0709  10.7426  21.6991  15.5631   1.3897   4.9629  -9.8507
+#&gt;    4.8956  11.2489  10.0215  12.3291   6.3059 -13.7399 -11.5145 -16.4382
+#&gt;  -13.6097   1.8697   4.8802  -6.9369  -8.3503  -5.2314  -4.0539  16.6210
+#&gt;   -8.3542  -3.8929   5.4088  -9.4494   1.2373   2.9799  -0.9539   5.0020
+#&gt;    7.5931 -16.0784  -0.6130   4.4867   1.8511 -10.0700  -6.7818 -12.6482
+#&gt;    6.8347   7.8955  15.9790   5.6663   0.2463  -7.2013   0.4226  -2.7929
+#&gt;   12.1881  -6.6606 -13.5196  -0.0920  -4.2267   3.3949   3.1965   1.4301
+#&gt;    1.1594  -2.0000   7.4615  -1.4799  10.2481 -19.5450  -6.4259  -7.2719
+#&gt;    6.6965  -1.9487  -3.2567   2.7485  -5.5610  -3.7478  17.9432   9.7405
+#&gt;    9.2307   7.2232 -16.9909  18.2683  -5.5177  18.6778   6.8506   8.0292
+#&gt;    1.0844   5.2928  12.9493   8.5347   5.1063 -22.1321  -3.8124 -10.9738
+#&gt;    0.1242  -0.8362   0.9491  -2.1039   9.1767 -10.5851 -22.4580   0.0073
+#&gt;    4.1889  -6.7578   0.6158  10.6993  20.2475  -2.6803   1.4228   2.6368
+#&gt;    2.6003  12.7629  -5.6436 -10.5305  16.5167 -12.4023  -1.8523  -2.2946
+#&gt;    1.3399  -0.8541  -0.2925  -1.9588  -3.1782   7.4115  -7.8541  -7.5948
+#&gt;    1.3034   5.2238   8.1993  11.3116   1.9543   6.8484  19.9934   1.7398
+#&gt;   -9.7040   1.3257   0.3924 -16.1826   0.2296  -2.0072  -7.8176  16.1278
+#&gt;   -2.4648  -0.0334   8.7678  -7.9293   4.9946   2.8806  12.8632   3.9208
+#&gt;   -2.9757  -2.2769   7.5569   5.6977 -13.9843   3.7280   0.5950   5.5230
+#&gt;   17.0316  -4.8927  -7.4138 -23.9129 -16.3091  15.0418  12.8346   4.5024
+#&gt;    0.2592  -3.1293   5.7996   9.5155 -17.7701  -0.6414  -0.9153  -3.0268
+#&gt;   -4.5513  -0.0853   3.2202  -2.6910   4.5919  -0.7635  16.7898  -1.5109
+#&gt;   -1.3973  -1.0692 -12.9523 -30.2056 -19.6100   5.0399  13.8209 -17.2502
+#&gt;    2.2549   2.7606  -5.4813   0.8905   5.9473   8.5687  -7.6555 -13.5678
+#&gt;   -0.9458   7.3816  -7.9265   2.9466  -7.6597  -5.3167   8.1822  -8.8753
+#&gt;   -1.8768   8.9373   6.3757   4.5212   5.9159   0.5309  -3.3872  -5.6588
+#&gt;    4.2539  -4.2977   2.2383   4.8838   3.9870   5.2324  12.4818  -3.0790
+#&gt;   -4.5559  10.2216  -4.1422  -6.7525   7.4266   1.5538   1.6988  16.4077
+#&gt;    4.8749  15.8087  -3.5180 -10.5315  -0.0794  -6.0009   1.2058   3.4717
+#&gt; 
+#&gt; Columns 9 to 16  17.1385  -1.7777  -3.5087 -12.1099  -3.3899   5.2232  10.7039  -0.3877
+#&gt;  -13.5326 -20.5020   3.1858  -1.0639   2.6542   6.7054 -14.8185   4.5870
+#&gt;    5.9097 -11.6857 -10.4692  11.6955  -0.4011  -2.0465  -2.4632   6.9488
+#&gt;  -14.1932   9.3081   3.2745   8.7878   5.5004   0.1761 -14.6211 -10.5968
+#&gt;    1.7818  -3.1959   3.9106  -5.2226   0.7281  -2.4754 -10.0146 -12.3474
+#&gt;   -6.9109  10.7561   4.2927   3.9453  -0.1506  -6.4972  -4.0949  17.0473
+#&gt;  -10.4787   8.9851  -4.0980  -3.8323  18.6653  14.4851 -15.8948  -3.3505
+#&gt;    2.8277 -15.8193  -3.6551   1.9299   0.3159  23.0975   6.4151   5.7881
+#&gt;    7.1573  -2.5123  11.9745  -6.2904 -11.5491  -2.1794  -3.4662  10.7510
+#&gt;   10.7676 -11.3751   1.2635  -1.9772   5.0680 -24.8550   3.4418  -7.2806
+#&gt;   14.6815   9.9974  -4.7321   6.8144   4.9209  -5.8303  -0.1228   2.7059
+#&gt;   -7.8457   2.3963   8.4782  11.1909  11.9533   5.7583  -7.5739   0.8734
+#&gt;   17.4703 -11.4663 -10.2138  10.8576  10.6437  -6.2552   7.0891   6.3207
+#&gt;  -17.5415 -19.9175   6.7340   2.6164  -5.2055  -2.9767   3.3248  -7.6270
+#&gt;   13.8038   8.1952  15.0012  -1.9493 -11.4842  -2.7522  -1.7889   7.3613
+#&gt;   -9.8399  -5.3707  15.4129  13.8695  17.0482   4.0208 -13.5116   0.9707
+#&gt;   -9.0796   2.1351  -9.5623 -11.4923   1.1438  -4.7025  -0.7771  20.3234
+#&gt;    1.8303  -4.7797   9.7657  -7.4804  14.0735 -10.1588   8.9940   2.3020
+#&gt;    8.9459   3.2066  15.0946 -13.5423   8.3755  28.6378   0.6761   3.6107
+#&gt;    2.0416 -12.4461  -8.3948  12.2424  -6.6186   2.3483   6.4522  -8.7031
+#&gt;  -23.7058  11.7056  20.3069  -2.6221   8.4900   9.8045   7.4044  -1.5314
+#&gt;    1.4330   6.2013 -19.5130  -3.8664   3.1176   6.3897   3.1219  22.7205
+#&gt;    7.3159  -3.7498  -0.9872  -9.3042  -9.2403  -1.6763  -0.1357  -9.7366
+#&gt;    2.7242  -2.1545  -1.2852  -8.8201   2.2035  -5.8164  -9.1770   7.0053
+#&gt;   -3.7575 -21.8548   7.5362 -27.8420  -9.4957  -0.5877 -18.0524 -18.7607
+#&gt;    3.0822  21.1661   3.2644  13.9712  -8.8555  -9.6856   3.4375   0.3204
+#&gt;   -7.6758   1.7168 -13.5409  -0.6890  -6.3293 -11.9538  -7.7964   3.2132
+#&gt;   -1.1651  -1.6261   8.0642 -12.0310   7.2915  -9.8800  -0.7667   0.9424
+#&gt;  -10.3043   3.9737  -5.0173 -13.3134   5.6635   5.2256  -6.4463   3.3400
+#&gt;    6.5242 -11.1280 -29.3445 -12.3845 -20.2978  -2.3998  -5.4099   6.7119
+#&gt;   12.9018   3.5089  -1.3941  -5.3138  -3.4071   3.0327   9.5516   0.7164
+#&gt;   -7.0746   5.9619  -4.6661   8.6978 -15.0780  -2.8786  -3.4132  -4.3365
+#&gt;   -8.3428   5.4275  -7.3953   7.4961  24.9181  -2.1467  -8.6705  -3.6659
+#&gt; 
+#&gt; Columns 17 to 24  11.5171 -13.5963   2.5003   6.5864 -14.5384   6.9039 -18.7580   0.8669
+#&gt;   -1.2107  10.7887   6.0819 -11.5975  -2.2031   6.5728   2.1513  -7.7375
+#&gt;   -0.8223   3.6664   2.4372  -5.6217  12.5742  19.4965   0.0526   6.5964
+#&gt;   -6.2581   3.5735   1.2482   8.0885  10.9134  -4.5984  10.8286   3.3268
+#&gt;   -3.3290  -4.1021  11.1832  16.9330   4.6907  11.7326  29.6726  -0.0969
+#&gt;    0.5262   4.0599  14.4213 -10.2620   6.5553 -11.4401  20.3248 -26.7666
+#&gt;    2.4857  11.3926 -14.0582  -2.2133   8.6209  -7.9137   4.1798  -1.4231
+#&gt;    5.2495  -0.5675  -2.2643  -7.5104  10.9990   3.7068  -6.7966  13.7726
+#&gt;  -19.1619  10.3072   6.5310  -1.0618   5.0327   5.1669  14.8004  21.6915
+#&gt;  -16.9673  -8.3238 -10.4902  18.6426  11.9763 -15.1956  -3.9650   2.7984
+#&gt;  -11.7504   0.0668   8.7361   0.3543  -6.3230   8.1647  -6.3996   3.7345
+#&gt;   -7.2738   0.4172   5.9568  -0.7624   4.1710   0.5172   3.5015  -5.8163
+#&gt;  -15.7977   1.5926   6.3499  -7.6268 -13.1398  14.6137  -6.5688  -8.8103
+#&gt;    0.7629   4.0633  -4.5180  -0.3671   5.7061  -1.2815  -5.2170  -8.4716
+#&gt;  -11.7767   0.9209 -18.8888   4.4665   9.5710  -6.4211   1.0147   1.5405
+#&gt;   -8.4317   9.7537   1.9881   4.6121   0.4938  -6.5393  -0.0713   4.9697
+#&gt;  -15.2060   4.7632   1.2563  12.2544   6.4372 -11.2441   5.9420   0.2902
+#&gt;  -20.6139  -0.3935 -16.5252  -0.3189  15.2685 -21.3963  -1.8786   9.5508
+#&gt;   -1.3409  -5.6721  -2.4587   4.6961 -12.9813  -0.7488   4.6960   6.8340
+#&gt;    1.4624   1.1889  -4.2159   4.0801   2.9847  -1.3100  12.9584   1.6323
+#&gt;   23.1831   2.5809  -5.2848  -6.6509   9.1711  -1.1559 -11.4886   1.3686
+#&gt;   -0.6514   6.4197  -7.5416   6.3921  15.6665   0.7941  -8.1033  14.6386
+#&gt;  -12.0915  -0.6876  13.3686   5.8638  -0.8935   3.7644   7.6196   0.2200
+#&gt;   -1.6452  -1.5316   7.2437   4.4482 -12.4975  17.8050 -11.5380   9.0727
+#&gt;    7.0956   3.6777  -3.2342  12.4985   9.9739   5.1889  13.3412  -8.5417
+#&gt;    6.1458 -12.5611  14.1390 -12.5054   9.4268  -0.0469   2.0785   0.5694
+#&gt;   -2.4983  -8.0480  -4.1426   1.6966  -8.7688  -7.3796 -19.6699  10.0829
+#&gt;    9.6098   8.0111   7.6299   5.3213  -1.2586  -9.8363  14.0288  -0.7746
+#&gt;    6.7143   0.5243   1.7446  -9.3635  -3.6307  -1.0245  14.2477  -7.5727
+#&gt;    0.0423   7.0274   4.0858   9.4900  -4.4992  -6.8116   8.3830  -0.1123
+#&gt;  -13.3977   7.0101   2.2001   0.4743  -2.4360  19.8296  -4.3704   5.9854
+#&gt;   17.4984 -14.0824   0.6152   4.4569  16.7575  -3.1909  -0.1884  14.3114
+#&gt;    1.5785 -11.2978  -3.4116  15.3848   3.2945  -3.2251   1.2248   5.3961
+#&gt; 
+#&gt; Columns 25 to 32  -8.1145  -8.5795   5.9131   2.4377   7.5201  -4.6986  -7.5430  -9.7138
+#&gt;    3.7041   3.2672 -11.4226  -7.4969   2.8387  -8.5854  -3.1510  -4.9110
+#&gt;   26.2928   3.8416  -3.0306   1.4455 -18.7729   5.4855  -0.0242  -3.6069
+#&gt;    7.4172  -7.8073  13.4746  -4.8996   8.2154  11.3917   6.2756  -6.2200
+#&gt;   -6.6342   8.8344   6.9771   1.2821   8.9269  15.9903  12.5617  -2.2922
+#&gt;   -6.2727   0.8454  -2.9785  16.1220  12.7200  -8.8158  13.0746 -13.5324
+#&gt;   -4.1055  -2.8899  -2.6837 -10.1672 -14.6019   4.6622   2.9432   6.0218
+#&gt;  -11.6356  14.2121   1.5131  -4.1166  10.9975  -2.0590 -14.1106   7.0641
+#&gt;    1.2110  12.9263  -2.7073   9.8011  -4.0078   4.8110  -5.7098  -7.0139
+#&gt;    2.5019  -4.6459  14.4936   6.9029   6.8220  -6.1545   4.6982 -13.7960
+#&gt;    3.8880  24.0579   5.2231  22.2702  10.4620  -6.8659  -7.0574  10.2661
+#&gt;   -4.2881   5.0802  -7.6389   1.0890   5.0370  18.1272  -6.3395  11.5115
+#&gt;   -6.9992  13.3076  -3.4046  -7.0203   2.2997  -4.9772   3.6725   1.6278
+#&gt;   15.8015   5.4040  -0.5921   1.0171  13.0780  -0.1570 -21.2774   4.6421
+#&gt;  -14.5559  -4.4831   4.5585  -9.3380  16.2462   0.5818  12.1105   6.9620
+#&gt;    1.8914   4.9895   2.9694 -11.0574 -16.9952  12.5293 -15.0569  18.8390
+#&gt;   -4.6414  -9.7090  15.4887   5.8955  -1.0994   2.9043   1.4137   4.9682
+#&gt;   -2.6412  -7.0216   5.2076 -16.7756  10.3232  17.7661 -11.6975   3.4358
+#&gt;   10.6992   9.7291   6.3405   7.0945  -3.1234  -4.5210 -12.6039   2.7055
+#&gt;   -0.9061  -3.8552  -3.4819  -5.2309  -2.0386   7.6870   8.0089   5.3179
+#&gt;   -9.2466  -1.9734 -17.7327   1.4467  -4.5935  -2.4652  -7.3554  -4.5967
+#&gt;   -0.0229  -3.8766   6.8379  15.7671  -4.2786   8.3920  -1.8504   0.6503
+#&gt;   13.0757  -6.5117   6.0628  -2.8543  -5.9146  -3.3948   7.3113  -8.8372
+#&gt;   12.8777  10.6938  11.7491  13.2491  -6.4082  14.3670   8.2762 -10.5485
+#&gt;   20.8602   5.2665  -2.1849   8.4760   4.6720  -9.7380  14.8252   2.4705
+#&gt;   -2.9191   2.4862   5.1356   3.3433   5.7164  10.1240  10.1418 -12.5545
+#&gt;    7.7613 -17.3256 -14.2300   3.0357  -8.0037  -9.3167   5.4464  -9.5051
+#&gt;    0.3402 -13.6247   8.6591  -8.0319  -4.1533  -5.3817  -8.5196  -2.2487
+#&gt;   12.3752 -11.3022  -5.1591  -7.1980  -4.7129  -1.5999   6.0342   0.5003
+#&gt;   -8.3133  -7.0496  17.7399  11.1410   0.1447   5.0498  -7.6757 -12.1452
+#&gt;   11.3473  -1.1404   0.2236   3.8494   6.2571  -3.1258   6.3751  -2.4485
+#&gt;   -6.3385  12.0757   2.5867  -5.1963   3.9730   2.3733  -4.1170  -6.3596
+#&gt;    6.0022  -3.2219   4.8640   2.0869   3.6794   0.9510   3.1537  -5.3880
+#&gt; 
+#&gt; Columns 33 to 40  -9.7750   4.3122   7.7314  -0.3517   6.2544  -6.1334   0.5192  -9.4471
+#&gt;   -8.7052   3.9934  23.8465   8.6498   2.4528  -4.0451  -4.4595  15.7021
+#&gt;   10.9452   6.8185  23.5919  10.0206   7.8168  -9.7620   0.6737  12.7527
+#&gt;    8.2015  -5.9087  11.5235  -4.9371  -8.2185  -6.3917  -5.5924   2.4774
+#&gt;    8.4680   6.6133  -5.3330   5.5582  11.6515  19.1548  10.5538   9.0030
+#&gt;   -9.4199   2.2284   6.7835   8.8719  -8.4643  -8.7587  -6.3010  -1.5074
+#&gt;   -1.1034  -8.4749  -5.3583  14.8149   8.7404  20.1333  -4.1000   1.9190
+#&gt;   -7.1595   4.5246  -1.5435  -8.6677  -2.4809 -10.2686   4.5958   1.0697
+#&gt;   -3.0526   5.9754  -1.7177   0.9364   7.2218  15.6521   5.0914  15.7620
+#&gt;   -2.3993   3.3825  17.5335   7.8474  18.1009  -8.3220   6.9126   1.3761
+#&gt;   -9.2370   5.9037   4.8368 -10.3760   1.5467   3.6940   5.9009   2.6261
+#&gt;   -6.8671  -5.5725  11.4637   1.8629  -4.3842  -8.8654  -5.1225 -19.9867
+#&gt;   -2.1253   8.3315  -0.9771   7.8220  11.6228   3.0225 -10.3436   4.4569
+#&gt;   -0.0098   0.1681   7.3899 -14.7242 -10.5119  -3.0313  13.5913  16.3598
+#&gt;    2.0848  13.5177  -1.9371  -9.8813   7.2109   0.1098   2.8727   5.3730
+#&gt;    9.2950  -5.0439   1.8789  -8.6116   0.4375   0.2704  -4.6135  -0.4481
+#&gt;  -15.4528   6.8408   4.1062  -1.2382  -1.1624  14.0390  -3.2953   9.6390
+#&gt;   -2.4949 -13.5640   2.6893  -2.2827   2.5954  -0.7560  -3.5461   7.4404
+#&gt;   -4.1343 -22.1728 -12.7734  -8.1149   0.2042  -6.0266  -0.7888   5.2665
+#&gt;   18.2782   1.1584   9.0241   2.2745  21.5432   4.8197  -8.2883  -2.4910
+#&gt;    9.1289   5.9599   6.8255   7.6997  -0.3532  -4.1977   8.8119   0.5369
+#&gt;  -10.6763   6.4529   2.8220   1.1191   1.3069  -0.0376  -0.4241   1.5943
+#&gt;    3.2870   1.2273  15.5045  11.5385  12.0046   2.5891  -2.5266  10.8700
+#&gt;  -10.8855   2.7544 -10.3731  -7.6112   5.5987   2.8348   0.2626   4.2871
+#&gt;    1.8254   3.8367   3.7787   0.6154   1.4018  -6.2460  -8.3149  -3.8919
+#&gt;    4.5782 -14.2862  -4.3419  -2.8506  12.8187   6.0623   9.9037  -8.7852
+#&gt;   -7.6454   7.2889  -7.3449 -14.5525  -4.5972   8.2140  -0.5723  -8.6042
+#&gt;    8.7800   0.2766   6.5277  -5.1133   1.4634  -2.1700   5.2418  -1.3187
+#&gt;    4.1663   6.2197   9.3275  -3.1985  -8.7473   2.1206  -2.6255   6.9047
+#&gt;   -7.2054   0.4252   0.9457 -13.5936  -9.6846   5.9752   9.3577   7.8062
+#&gt;   -7.9872  -9.4947  -3.2777  -1.0915   7.9006   4.6527   2.2801  -0.2176
+#&gt;    9.3088  -7.8706   0.1679  11.2275   8.6029   8.0369  -5.1506  -6.3863
+#&gt;   -9.3878   3.4855  -3.5517  18.5091   4.3952  13.0829  -0.8334  -5.4597
+#&gt; 
+#&gt; Columns 41 to 48   6.5011 -16.1431  -4.3466 -14.9900 -11.7130   7.8898  -2.9533  -2.2251
+#&gt;   -0.9887  -5.6208   3.8264   6.5192  -9.3852   7.1299   3.5867  -6.3774
+#&gt;    0.2947  -3.7010  10.4453  -2.2354  -0.0995   9.3341  13.2049   6.7727
+#&gt;   -6.2542   8.5937   1.3642 -10.2065  -0.6303 -10.4907  -6.5015  -0.1814
+#&gt;   11.8759  14.5775   7.2311  -4.1882  -3.3403  -1.6617 -13.8284 -15.8210
+#&gt;    8.9016  -4.4975  -2.8111  -3.9421  -8.8392 -15.6729   5.8049  -9.0963
+#&gt;  -22.4988  -3.1013   5.6064  -1.3716  -0.4750   4.9586  -0.3411  -1.9196
+#&gt;  -11.7136  -0.1444   0.6934   4.2718  -4.5249  26.0992  -3.3704  -2.0736
+#&gt;    3.3940  -4.8608  -6.7398   2.2891 -11.9959  19.4911  -4.5567   7.0563
+#&gt;   -2.8931   0.6912   2.3908 -20.5148  -8.7039 -21.1667  -5.3784  -4.7638
+#&gt;    3.5246   2.6905  -2.7401  12.7459 -13.7531  -1.9057 -24.9776  -5.5714
+#&gt;   -8.2436  12.1626   8.5889   0.0052   1.3230 -19.5498  -0.2498  -2.6468
+#&gt;   -4.6138  -4.6218  -2.2591   8.9042  -4.3583   6.2058  -3.3368   1.2475
+#&gt;   -8.1050   7.6328 -15.1233  -5.7670  -5.6250  -2.8747 -12.5963  17.1892
+#&gt;    3.3816   9.4714   9.2402   4.2461  -1.8082   1.3889 -18.4537  -7.4553
+#&gt;   -6.8785  -3.8493   4.7792   8.7750   5.6983   3.3413  -2.4371   7.8816
+#&gt;   14.1797  -8.5735   1.2438  11.8582 -10.3231  -2.7905   7.6571  -1.2268
+#&gt;  -18.1333   1.1585  -0.1916  -7.3058   3.1073  -9.1316 -14.7963   4.7654
+#&gt;   -8.7561 -18.2628 -10.8891   4.9068  10.5885  -1.6268  -4.1198   6.0831
+#&gt;    0.5767  -5.7960  14.2352  -6.2464  -0.0318   4.7683  -3.9952 -16.9078
+#&gt;    4.2374   6.0610   8.8447  -2.9822   9.9090   7.2188   8.9347   9.0626
+#&gt;   -1.2002   2.3952   6.9179   6.7029   0.9641  11.7896   4.9396  10.7570
+#&gt;    8.7479   3.4975   6.1097   3.5737  -9.6930   2.4751  -0.9593   6.4604
+#&gt;    8.7248  -4.2146   0.1575  -8.3680  -4.5994  -3.6923 -11.4719  -6.3495
+#&gt;    8.2636   8.0527   1.2133  -4.7065   7.7905   4.3437   1.3388   6.6275
+#&gt;   11.6542 -13.8952   6.7065 -26.8512   8.7871 -20.8686  13.1924 -11.7947
+#&gt;    3.9899   0.6227 -15.7883  -3.1894  -3.9283  -0.7139  -5.5691 -12.3483
+#&gt;    3.3880  -5.5641  -5.1218  -7.5778   7.3389   1.1221  15.2900   1.8776
+#&gt;    0.4520  -0.0513  -6.6910  -1.2088  14.5641  14.3009   8.1152   2.6959
+#&gt;    2.3548   1.1817 -16.2080   1.7125 -10.1196   9.7649   3.8981   3.3544
+#&gt;    7.7892   8.2897   0.6544   8.3422  -3.1110  -0.1419   2.9984   8.1290
+#&gt;    2.9468   7.7314   1.1350 -15.7203   7.8031 -26.0266   4.3703  14.3819
+#&gt;    6.5798  21.6411   6.7600  11.7019  -0.3172  -9.8948  -6.3248   9.0993
+#&gt; 
+#&gt; Columns 49 to 54   5.0734  -3.1985  -3.1484 -18.5449   3.5854   2.6389
+#&gt;   -9.9198   1.8784  -2.8820  -2.0754  -2.6528  -2.0361
+#&gt;   13.2967  -9.1146 -10.2911   8.6274   2.0238   0.7990
+#&gt;    4.9978   6.5459   5.7172   1.0634  -1.8348   0.2181
+#&gt;   -6.6560  -6.9395  -2.7945 -13.4165   0.3720  -5.3638
+#&gt;  -13.8443  17.2028   1.6859  -3.0717   7.0974  -2.4310
+#&gt;  -11.1912   0.7051   0.4575  -1.5135  -5.2907   3.6489
+#&gt;   -0.9601  -1.2169   5.9644 -14.7754  -2.9408   9.7640
+#&gt;    6.9968   3.5194  -5.5172  12.0147   0.2977  -1.2645
+#&gt;    4.0465 -13.0005  -4.5254  -6.8678  -5.6679  -0.6868
+#&gt;   11.8746   9.7976  -6.3857   0.3301   6.3049  -1.9373
+#&gt;   -4.6632  10.6690  11.6423   1.4945   0.5620   1.4400
+#&gt;   -3.6426  -3.7228  -9.2963   2.9672   8.7875   0.7035
+#&gt;   -8.5415  -6.0032   8.7938  11.3102  -3.4259   1.2885
+#&gt;   -4.5487  10.1005   8.8807 -16.3164 -13.8255  -7.4006
+#&gt;    4.4433 -13.8975  -6.4573   1.1518  -4.9269   3.9875
+#&gt;  -16.5790 -15.7375   1.8982   9.4079  -4.0299   2.0403
+#&gt;    5.4469 -11.0663   3.1077   5.7961 -17.1680   6.1061
+#&gt;   11.3496   7.0669   8.9131   1.8658   6.6558   4.8192
+#&gt;   -1.0470 -21.5752  14.9542   8.4510  -1.2142  -0.3483
+#&gt;  -10.2189  -3.1078  -1.9010  -6.4678   1.8478  -1.3810
+#&gt;   11.3472   6.0519   4.0031   1.0326  -0.1843   5.3557
+#&gt;    5.0970  -4.6407   5.5243   0.9926  -4.7836  -8.5118
+#&gt;    5.8961  23.3211   7.8782   7.2337  11.0661  -2.1141
+#&gt;   -1.7034  11.4659   6.8905  -1.7727   1.0258   4.5055
+#&gt;    3.9860  -2.7932   6.7847  -3.8188   0.9939  -9.3229
+#&gt;   -5.9320   4.4995 -13.8126  -5.9319  -6.1371  -5.0455
+#&gt;    7.7796  -4.0133   4.3013   2.0061  -6.8397  -7.0406
+#&gt;   10.1231   5.2064 -10.2621  -0.2242   0.5774  -5.2299
+#&gt;    3.2700   5.6559   3.3086   2.3718 -15.8170   2.4293
+#&gt;    3.7895   3.7239  -2.7195  -0.8528  -0.1359  -2.3084
+#&gt;   -2.4946   0.8061  17.5376   4.8175  -4.9913  -5.3384
+#&gt;   -6.9893  11.9924  -3.7919  -3.3553   0.5683   0.4901
+#&gt; 
+#&gt; (15,.,.) = 
+#&gt;  Columns 1 to 8   3.1122   3.8611   4.8205  -1.6298   6.9329   9.8906   2.1740  -4.2024
+#&gt;    8.5203   9.7147  -1.5809  -0.6421  -2.0104   2.4745 -14.1152  -2.4426
+#&gt;    7.0298   6.1547  -9.4351  -0.5382 -19.4606 -12.2878  -1.7764  -3.0312
+#&gt;    0.2865  -0.9511   5.3513   4.3796  -2.7623 -13.8982 -14.1224   0.6164
+#&gt;    0.0973  -0.4816  -4.8435  -3.1812   0.4628  -2.6683  -0.5602  22.2579
+#&gt;    0.4775   3.2235  -1.8869   0.2404  13.6895  -0.6795  15.2676   5.6317
+#&gt;   -2.1784  -1.1375   4.5558   3.1385  -4.9571   3.7874  -5.9118  10.8517
+#&gt;   -4.7388  -6.9078 -10.4307  -2.2684   6.6817  -1.7626 -18.4548 -10.9733
+#&gt;    6.7687  13.5322  -5.5984 -12.1420  -1.2412   5.6165 -13.5941  -2.0271
+#&gt;    2.0455   9.0392   1.6625   6.0172   3.8340   7.0512   1.2465  -1.9772
+#&gt;   -2.1886  -6.4816   4.6749   5.9960 -10.1334  -6.3482  -2.7780  -6.9412
+#&gt;    5.0527   4.9251   2.5347   7.6254  -1.3561   3.7016  -3.2117 -11.3581
+#&gt;    7.7865   4.6978  -7.2020  -4.0605   8.9861  15.5755 -12.3648  11.8017
+#&gt;    8.7464 -11.9171  12.6224   5.7931  -9.1825   1.7586   8.2715 -19.4382
+#&gt;   -3.1150  -6.2088   0.4635   8.2511   8.0410   4.9534   2.9653  17.6959
+#&gt;    0.9174  -0.7699  -4.1018   7.9598   0.7056  11.8100  10.3111 -20.1756
+#&gt;   -5.3045  -6.8303  -2.2864  -6.5579  11.0886  22.3864   7.9265  -1.5356
+#&gt;    4.6490  -1.5498   1.7408  14.1294   2.5618  18.0424  -1.0681 -17.9160
+#&gt;    3.5954   9.3813  17.2237  18.8808  -6.6940   8.5774  -5.3845 -30.3545
+#&gt;    5.2688  -3.6840  -5.1910  -5.7160 -11.5041   4.3481   3.6597   7.6335
+#&gt;   -1.1897   0.6484   3.4448   4.7273  -4.5146  -2.8914   7.4103  30.1499
+#&gt;   -4.3721   1.5688   2.7589   0.5297   1.1048   0.7333  -2.0578   0.0610
+#&gt;    5.4195   2.0839  -1.5410 -10.0713 -13.5664   1.3268  -0.3629   4.4647
+#&gt;    9.1930   9.5706  -7.9102  -7.7028 -10.7286 -22.2398 -20.2083 -15.1622
+#&gt;   -5.6316   3.1994   6.1807   0.7756   1.6676 -14.9653  -9.9982  -4.2857
+#&gt;   -5.1895   3.7136 -17.8547   3.9746   1.8439  -4.8682  12.8033  -7.1466
+#&gt;    2.1943   9.0551  -0.9147  -4.1940 -11.3143 -11.8204  12.1720  16.6048
+#&gt;    2.7969  -7.6488  -2.1236  -4.0005 -30.9301 -11.7749  10.6926  -4.1824
+#&gt;   -1.0040   2.9021  -5.1487 -15.2700   2.6248  -2.1149   6.4104   3.2766
+#&gt;   -3.7081  -1.6625   4.2692  -1.5709  11.0265  10.0323  11.2276  -8.9382
+#&gt;    2.6578   5.1897   0.1735  -5.7386   5.8531 -11.7441 -14.0351  17.3496
+#&gt;   -2.1352   6.3114  10.2460  -8.7306 -20.9668  -3.4366  25.2204  -8.0201
+#&gt;    5.8776  15.5592  22.1419  -8.1323   9.0823 -11.0189  -2.7949  18.9450
+#&gt; 
+#&gt; Columns 9 to 16  -2.4820   8.6576   5.9839  -1.8184  -9.2920   5.6428  13.7748   8.7812
+#&gt;   21.4926   4.5923 -11.8291   0.6987  11.9017   6.1472   3.6525   2.2308
+#&gt;   -4.7901  22.6197  -4.5075 -11.4693  -5.4537   5.0686   5.0970   9.4705
+#&gt;    7.0716   0.2369  -2.7498   1.5082  -6.0825 -12.4953  -6.6059  -1.0555
+#&gt;   -2.6514  12.4401  14.5083   4.0852   4.0618   4.1491  26.6751   3.6393
+#&gt;   -3.9949  11.5821   4.5012   7.7274  12.3697   3.9179  -5.3370   3.5276
+#&gt;   12.0472 -18.0762   7.6975   5.1272   3.4334  -6.3926  -1.2136  -6.6883
+#&gt;    4.8552 -11.6885  -1.4379  12.8208   0.1779  -9.7202  -6.0333   5.4248
+#&gt;    5.1050   5.3917  -5.1519   1.0647  -4.9101 -10.4036   2.9865   5.9236
+#&gt;   15.4829  12.2093  -8.3707  -0.1868  17.1047  -2.8015   6.6041  -1.1927
+#&gt;  -10.6184   4.6587  -6.7819  -5.7780   4.3709  -7.5517   1.9509   2.6140
+#&gt;   17.4269   5.2521  -7.8666   3.7005   0.9033  -2.5157  -3.4188   5.0107
+#&gt;   -0.0271  20.8237  -0.5649  -6.5968   5.7183   8.3035  -5.4941  -7.4708
+#&gt;    2.0521   5.0497  -8.4153   2.5045  11.8565  -9.7868  -6.0838  -5.0893
+#&gt;    2.5230  -5.0851   4.8680  -1.1900   0.4753   0.8048   5.0609 -11.2180
+#&gt;    6.2523   2.1532  -4.1158  -8.2963   5.3402  -1.7555   5.6635  -5.3381
+#&gt;    8.4795  10.2285 -10.0182  -3.3915  14.9372   7.8887   9.9017  -7.2846
+#&gt;    6.7871   2.0844  -7.8338   3.5193  14.5492   2.6393   7.4061 -12.4265
+#&gt;   -8.6737   9.2819  15.6804   4.1469   1.2852 -19.8180  -7.3381  10.9151
+#&gt;  -10.3532   8.9571  -0.5642  -3.9206   2.9838   9.9372 -15.1253   1.8576
+#&gt;    7.4981  -7.1064   3.3215   9.7494   2.7893   3.1182  -2.1001  25.5508
+#&gt;   -4.8811  -3.8547   0.6671  -8.4423 -12.9163 -11.9388  -7.4008   9.0730
+#&gt;   -0.4422  -5.3445 -14.6563   5.8467   6.8341  -4.3484  21.7684  -0.6717
+#&gt;  -18.2219  -7.6869  16.1875  -7.0942 -22.3077  -7.1760 -10.6216  -3.2437
+#&gt;    4.1118 -20.1772  -7.1432  -6.9128 -18.2796   2.3830   0.2257  -3.6572
+#&gt;   -3.8686  11.1459   2.9378   0.2278 -14.0622   9.4110 -20.2442   1.3321
+#&gt;   -6.3837  -4.3450   9.4582   7.1413  -9.9865  -0.6224  -0.6129  -0.0896
+#&gt;  -13.8273   0.5218  13.4575 -12.8272  -4.1247  -1.7526   7.2794   0.0497
+#&gt;  -16.4962  -2.7740  19.6613   0.7536 -15.6204  -9.4792  17.8453  -7.5782
+#&gt;  -14.1994  -2.2635  12.2250  11.7569 -18.9640  -5.6786   2.5413   4.4004
+#&gt;   -6.9980  -4.5628   2.6912  -0.7503  -9.6685  -0.6845  15.9196  -2.9514
+#&gt;    3.3739  12.9420  -3.2897 -14.8992  -4.0071  17.6254 -12.3500  10.5809
+#&gt;    4.4941  -1.2783   2.5828  -7.5568   4.0072   6.3986   7.0076  -7.2597
+#&gt; 
+#&gt; Columns 17 to 24   2.7415 -14.5243   7.7590 -11.1564  -9.3308  -7.2492  -1.2720   2.2529
+#&gt;  -11.5159  10.8135 -16.2901   4.3608   8.0637  -9.7066  -8.7165   1.8619
+#&gt;    0.0860  12.3243 -31.5053   4.8606   2.1148   0.3605  -5.5557  -0.4323
+#&gt;  -11.2532  -3.9996   0.5845   3.8652  22.2795  -1.9149   6.9725   8.2957
+#&gt;   -2.9669  11.4869  -0.4343  -9.9054  -2.8446  -8.0256   1.0610   9.3770
+#&gt;   10.0457  -3.4253  -4.7768  -7.3880  -3.9299   8.4463  -1.8519   6.3592
+#&gt;   -1.4612   4.7071  -2.8134   3.0198  10.9095   1.7908  -3.9836   5.6276
+#&gt;    6.5276   4.2167   5.7684  -0.7467   0.8570  -4.0737 -14.7975   9.9143
+#&gt;   -5.9155  16.2076  -8.2101   8.2188  -2.8018   7.0341   2.4280   7.5394
+#&gt;    1.4553 -11.1364 -17.4621  10.4478  -7.7759  12.9649   5.3697  -5.1533
+#&gt;   -4.2117   7.7752 -15.3058  13.0305  -7.9705   0.6638 -10.2953  23.3320
+#&gt;   -5.2965   1.2251  -1.9521 -11.8184   1.4711   0.9470   0.6935  -0.8830
+#&gt;   -4.1606   4.4501 -20.9371  14.0476  11.0796   3.0351   2.3615  -5.1264
+#&gt;    2.8865 -12.0706  -5.3796  -0.6273 -10.8796   4.0407 -12.8834  -2.8364
+#&gt;    2.2295  -2.5573   2.4217   7.7431  -0.0052   5.6104   0.5390   5.1543
+#&gt;  -10.9820  -3.3330   7.8870  20.0087  -1.9390  15.2596   6.6805   3.3873
+#&gt;   -3.5583   0.9338  -2.4335   5.2367  -8.3011  -2.0619 -16.8000   8.4627
+#&gt;    5.1808   1.4008 -11.4610  12.9522  -1.9121  -5.4007  -1.5541   4.0131
+#&gt;   -5.7829  10.3096   6.7852  -5.5373 -18.5659  10.4546  -6.8009  -3.4478
+#&gt;   -5.0784  10.0420 -11.7954   2.7051 -16.1865   3.7774 -13.4981 -10.3071
+#&gt;    3.2760   5.2329  -1.7290  -3.1013   2.8472  -4.8237   1.2745  19.0922
+#&gt;    6.6286   6.4568   0.0930  18.2459 -13.5861  -7.7898  -8.3763  -5.8059
+#&gt;   -9.0000 -10.3056 -13.3352  -3.0509  -3.2558   3.6195  -7.3976  10.1592
+#&gt;    1.1644  -4.5979  -6.7606  -2.0620   1.7103 -17.1890  -7.9710 -19.0924
+#&gt;    9.3919  16.9688  15.8126   0.2450   7.8224   1.2130   2.5086  -3.5090
+#&gt;   -1.9533   1.1459   0.4736  10.3822   1.3048  -6.4972  13.3227 -29.6419
+#&gt;   -7.0148   1.7177   0.0313   6.3580   9.1552 -11.2252   7.7353   7.3579
+#&gt;   -4.7833  -0.1028   2.7229  -5.2943 -12.8086  18.4304  -4.4705  -3.4158
+#&gt;   -9.2536   4.5833   5.5145  -0.7567  23.3196 -10.2063  17.2270   3.5223
+#&gt;  -15.1389   8.7041  12.2054 -12.8302  -0.7823  13.7584   2.4872  -0.4881
+#&gt;   14.4617   3.9974  -7.9709  21.8895  -4.8486 -19.2757   6.6048   5.4127
+#&gt;    6.0082 -18.5477   2.9759 -20.5861   8.7631  10.0609   4.0358 -11.9522
+#&gt;   14.4334 -10.0376   8.1364 -16.9371  15.9354 -13.9389   7.9507  10.4880
+#&gt; 
+#&gt; Columns 25 to 32   4.4647  -7.0875   2.3327   1.8307   7.3989   0.4803  -3.6664  -6.0721
+#&gt;    5.8742  -8.4197   6.2609  -6.9642  -2.8786   2.0274  -7.7869  10.8764
+#&gt;  -14.0883   8.3286  -1.4974  -4.1067  10.7286   3.1396   0.8001  -1.4272
+#&gt;   -8.3975   1.1112 -14.3248  14.9886   0.6196   3.2767  -2.5909   4.2835
+#&gt;  -13.5545   9.2322  -5.9542  -2.5594   1.4618  16.6292  -8.4196  12.4791
+#&gt;   -0.7116  11.6965 -10.5454  -3.5316   4.3152   9.0918   5.8082 -13.3541
+#&gt;  -11.4994   4.4063  -5.8573 -17.3401  -6.0474  18.5556  -1.1967   6.8481
+#&gt;   12.0583  18.5907   9.8987  -8.1644   0.4287   2.6380   3.0048   0.5976
+#&gt;   -7.0870 -14.3711   0.4314 -16.2165  -2.3792 -12.6777  -4.3406   6.0900
+#&gt;   -5.8610  11.7489  -7.8149  -6.6605   9.2336   2.3260   7.7857  -4.9880
+#&gt;    0.3623  -1.4200  13.0666  -2.4665  -4.7151  -3.9452  12.5038   3.2693
+#&gt;   13.9317  -6.6125  -2.8563   9.5944   0.0860   4.3587  14.8142  -6.9758
+#&gt;  -13.4904  -2.4979   6.6735 -11.6457   0.9460  -1.9097 -13.3650   8.9596
+#&gt;    1.9304  -4.0284   0.7302  14.4181  -7.0430 -10.5987   7.4161   6.4617
+#&gt;   -0.0161  -4.2945   2.9026   1.7768   3.9085  -6.9946  -4.5485  -2.9448
+#&gt;    6.8562 -10.8369  -5.6905 -11.0385   0.1285  -4.7716   2.3382  -0.4609
+#&gt;   -6.1776  19.8724  18.4406  -8.7792 -20.6198   5.8089   7.3201   0.5096
+#&gt;    1.2028   3.0662  -5.1415  -0.9428   4.9533  -6.4426   5.5798   0.8762
+#&gt;    4.4632 -17.6349 -13.0296  -4.2663 -16.4941  -6.0344  -5.5801   8.4649
+#&gt;   -6.9736  13.5637   2.4916  -1.7900  11.5020   8.7405   2.0377  -4.7403
+#&gt;   -6.1378  -4.8473  -9.7515   2.3663  -1.1794  -0.7174  -5.0061   2.4843
+#&gt;   -3.4997   3.5160   9.5409  -3.1535 -11.6830  -3.5705   2.6792   1.4836
+#&gt;  -11.8763   4.6721  -1.8193   3.2386  11.2250   7.8626  -1.8528   0.4861
+#&gt;   -8.0426 -14.7109   4.3708   2.2663  13.2169   6.3566  -9.9510   6.0865
+#&gt;   11.0725  -3.9107   0.6830  -4.5783  -1.6197  -2.3960  10.7319 -13.7979
+#&gt;    7.3039   0.8473  -9.9158   6.4912   4.9668  -9.9030  -4.0687  -9.6390
+#&gt;    0.3819   1.3212  18.2018   8.2165   6.2052   4.6291   5.0011   0.7982
+#&gt;    6.8205  -2.5124   9.6381   9.0118   2.8916  -1.5941   9.1080  -7.1449
+#&gt;  -13.4964  -8.6642  14.5336  15.9082  -0.8775 -10.1957 -18.6116   3.5536
+#&gt;    0.9977   4.0760  11.8884   2.6569   0.1379  -7.2319  -0.1400 -11.4456
+#&gt;   -9.2941   1.0872   5.3523  -7.4826   2.3029  -2.4002  -1.3751  -1.8256
+#&gt;   -3.2221   5.9747  -5.6958  20.7800  -4.3516   4.8361   0.0648   4.8459
+#&gt;  -24.3830   2.8290   1.2183 -13.2337 -16.3716   6.1607   7.4295  -0.7610
+#&gt; 
+#&gt; Columns 33 to 40   1.8205  -1.9031  12.3617  -5.0926  -3.0377   7.9483  11.3565   5.3461
+#&gt;   -0.4750   7.1601  -2.3730  -7.5397 -10.2509  -7.0522  -0.8622   7.4636
+#&gt;   16.1951  -2.6339  10.4763   6.7128  -2.4033   1.5973   0.5560 -12.5422
+#&gt;   -5.8209 -14.4871   6.3812 -20.7239   6.6207   4.5128  -6.8785  -0.2301
+#&gt;   10.9282   8.5717   6.2680   2.4384   0.5621  -0.7995 -20.6212  -2.2964
+#&gt;    9.4476 -14.1702   4.5617 -12.0078  -8.1397  -2.5639  -2.7928  11.8655
+#&gt;  -12.6462   2.1810   1.2845   9.7156 -20.5358  -5.9804 -10.9644  -0.5939
+#&gt;    3.5793   5.2351  -6.4354  -4.9213  -9.9919   7.4415  -3.4633  -9.8310
+#&gt;  -12.8611   2.8465   0.1158   0.1859  -1.2493   9.6240   1.1403  -0.6776
+#&gt;   -0.3449   7.4077  -0.7959  -7.9119  11.8331  -4.9754 -14.2622   6.4361
+#&gt;    0.7595 -17.6125  -5.8273  -3.2121   8.0339   2.5602  12.3164  -3.8753
+#&gt;   -1.0412  -5.4503  -3.5347 -13.2825   1.0322   6.5559  13.9156  15.9307
+#&gt;   -9.2140   6.1835  -6.6971  -3.4164   2.1543 -10.5855  -3.5077   6.1390
+#&gt;    4.4808 -10.5445   7.2821  -3.9587   9.7382  -2.9853   2.7431  -9.6809
+#&gt;   -2.5871  -4.7222  -5.5093  -2.2494   0.6249   9.5892  -3.0757  10.6005
+#&gt;    1.6629  -3.8746   5.5493   5.2350 -16.2316 -10.5616  -7.7546   3.6809
+#&gt;   -0.4978  -3.4466  13.3552 -10.9518  -5.6727 -18.6570 -15.8404  -3.5805
+#&gt;  -13.1766  -0.4777   0.2826  -0.6377  -9.3905  -3.6232  -9.2420   4.7829
+#&gt;   -3.6613  -5.0635   2.4324  11.3758  -8.1947  11.6599   0.3691  -0.2076
+#&gt;   12.5498   0.2379  11.0158  -4.5225  -6.0512  -6.8703  -1.2930  -6.1242
+#&gt;    4.9730  -2.3177  -4.2980   5.2178  -5.1310   0.4080   2.9159  -8.6934
+#&gt;  -11.7598   6.6139   2.1697  -0.6532  10.4587  21.6341  -3.2036  -3.5018
+#&gt;   13.5214  -2.9392  21.8901   4.7567   0.3415  -9.1058  12.1258   9.6166
+#&gt;   -8.3603   1.6233  -2.6932   7.0846  15.2218  17.6835  15.8797  10.3380
+#&gt;    2.6707   0.4024  -3.1447   2.7802   9.8796  16.6959  23.8264   7.0454
+#&gt;    1.6237  -1.3032  -4.3692   1.1793   9.3805  -1.8181  -2.0411  -1.3207
+#&gt;   -0.9425   6.4807  -5.7085   6.3386  10.6126   2.3979  -8.2275   1.4569
+#&gt;   11.1681  -1.0505   0.3317   1.7951  -0.9978   7.5406  -8.7477  -3.5775
+#&gt;   -5.3785   7.9169  11.0730 -17.4246  -5.1151   6.9251 -13.2934  -0.3874
+#&gt;    5.2756  -3.2304   1.5981 -14.7328  11.5731   7.4060   9.7360  -6.7432
+#&gt;  -15.2574   4.5115   7.5608  14.3726  -2.1682 -10.3855  -5.8989   9.8928
+#&gt;   -0.3856  -8.3256   2.6750   5.4766   2.3158   6.9830  10.7369  -5.5621
+#&gt;    0.8368  -4.4529  -2.9222 -10.4655   7.5028   3.0049 -13.9050   6.4207
+#&gt; 
+#&gt; Columns 41 to 48  10.6267   2.0566  -0.1899  -4.2216  10.1488   4.8102  -8.9333 -12.9299
+#&gt;    2.8428  -1.9617  12.4254   7.3913   4.2177   2.8855  -6.2039   1.6922
+#&gt;   -4.9332  -2.4444  -0.4150   4.8136   8.5222  -3.2814  -2.5849  -4.5969
+#&gt;    2.6271 -12.1750  -6.9400  -3.1252   9.6071 -13.4722  10.1572   2.5616
+#&gt;   -5.8844  -6.8112  -7.7014  -8.0022   1.7783   4.1806  -2.3968 -17.2372
+#&gt;   -6.7355   1.5915  -9.2817  -0.8666   6.2460  14.7628  11.8597 -12.6760
+#&gt;   17.6917   5.5680   1.8349  -1.1989  -4.6840   7.0459  -3.3207  10.4828
+#&gt;    5.6398  -6.0632   1.6924   0.6557  -4.7924  10.7048  -0.6390  -3.2781
+#&gt;   15.3371   2.9338   8.0760  13.9686   1.7372  -3.0089  -2.3561   0.3753
+#&gt;  -11.5604  -2.6880 -11.4365   2.3654   7.7114   2.9627  13.5443 -12.7850
+#&gt;    9.6262   9.4812  -0.5602  -2.8509   0.3019  -4.0123  16.3873  13.6085
+#&gt;   20.5345  -8.0568 -11.0704  -9.2712 -10.8319   0.2600   9.1865  -6.3293
+#&gt;  -11.1006 -12.9762   4.1232  -8.3061  -6.4398  -0.4549 -17.1532   6.3581
+#&gt;   -7.1097   4.8705  16.1682  -2.3171   4.2617 -22.1824   5.9422  -4.8015
+#&gt;  -10.4708  -1.7752  -7.4766 -12.3306   1.3784  -6.3603   3.2971  -7.3621
+#&gt;   -1.7025 -12.1037   9.3636   7.3521   4.8636   7.5377   2.7529  10.1537
+#&gt;   11.0573   9.7915   7.8233  -2.0144  -3.4713  -2.7266  16.8523  -8.3005
+#&gt;    2.3746   1.5750   5.2558   7.2855  -5.3842  -2.6478   9.7472   3.5639
+#&gt;    5.4006  -0.8139  -6.0416   7.4403   6.9999 -15.6390  -5.8958  10.2864
+#&gt;    5.7947   3.8600  -1.4406 -14.5901  -4.3508  -9.8071 -14.7991   1.8049
+#&gt;   -5.0717 -16.6773   7.0603  -2.3248  -4.0577  -4.1785   0.2248   0.3476
+#&gt;    7.0438  -5.2534  -8.0476  -2.4107 -17.2631  -6.7523   1.3226  -2.7569
+#&gt;   -1.7567  -9.6459  -2.5314  -1.1619  10.4042 -12.3261  -9.5224  -4.6514
+#&gt;   12.4365   2.8640  10.7389  -0.2652  -2.4801 -11.0003 -14.6895  -1.4004
+#&gt;    4.1451   0.1326 -14.8814 -10.9389  14.0706  -7.5585   4.3588  -4.7046
+#&gt;  -12.1984   7.8382  -6.0839   3.8610  -9.9043   7.9125 -14.7297   2.9360
+#&gt;    5.7610  14.6969   7.7319   7.3925  -3.0844   4.3962   4.0488  14.4855
+#&gt;   -0.1482   8.7862   1.5423  -5.4913  -0.2252  -7.1675  13.4628 -12.1098
+#&gt;  -12.1577  -7.5669  12.8937   7.8985  -0.8192  -7.9449  -0.0794  -7.7434
+#&gt;   -9.4886  -0.4424  -1.1893 -10.5959  -9.3650  -1.0334   1.2311 -10.4145
+#&gt;  -16.8748  -7.4350  -2.3928  -9.6226   3.4923   3.5035  -0.6490   1.1591
+#&gt;    0.8123   9.7618  -9.3335  -0.5018 -11.4629 -11.9040  -5.3682   7.9294
+#&gt;    2.4693   5.7884  11.3076 -19.0914 -11.9091  -3.3234  10.0314  -6.7216
+#&gt; 
+#&gt; Columns 49 to 54   0.4406   1.6129  11.1615  -6.4500  -0.8354   0.9156
+#&gt;   -8.4724   3.7934  -0.6207   7.8873   1.1957   0.5047
+#&gt;    1.6362 -12.6187   8.1702   9.1539  -0.2782   4.0106
+#&gt;    1.5858  13.5181   3.5678  -1.2366   7.3036  -0.1232
+#&gt;    9.1165  15.2435  -6.9428   1.9598  -6.4923   3.1891
+#&gt;    9.3808  -9.5047  -2.5205   5.0731  10.0928  -2.0794
+#&gt;  -14.7524  16.4280   6.8513   0.7544  -8.9306   1.7082
+#&gt;  -11.2358  15.5635   3.2996   1.5688   1.8301  -1.2618
+#&gt;   -1.0093   0.3102  -0.9837   1.1016  -3.9838  -0.2361
+#&gt;   12.1988  -5.1664  -0.1060   0.1254   0.0174   0.3351
+#&gt;   12.4086  -1.4730  -5.9202  -0.6916  -7.4567  -2.5951
+#&gt;    5.1866  12.7100  -2.6120  -5.1527   2.4302  -4.9550
+#&gt;   23.0999  -1.2934  -6.9460   1.9001   1.8792   2.4004
+#&gt;  -13.5533 -15.6596  -1.9504  10.2901   6.8115  -5.0187
+#&gt;    0.1487  21.4774  -5.9941  -1.9582  -7.3697  -3.1072
+#&gt;    0.5098   7.4638   3.3007   4.9267   1.0696   2.9138
+#&gt;  -13.9605 -15.3375   2.3721   4.2856   7.6262   0.1114
+#&gt;   -0.0946  -1.9983   7.4403  -1.6219  -3.5262   0.3071
+#&gt;   -3.5941   6.7896   1.7399   4.9699  -0.1605  -0.2439
+#&gt;  -10.2240   2.4551   6.6242 -12.3264  -2.7923  -6.0393
+#&gt;    4.6394  -4.9876  -2.6554   5.6472   1.9260  -3.1263
+#&gt;  -12.1955   8.9774  -6.0791  -7.6644   2.5696  -1.0618
+#&gt;    3.4853  -8.6396   4.8650   4.8160  -5.9536   5.4277
+#&gt;    5.7871  -7.7097  -5.2413  -6.2673  -2.1149  -1.7612
+#&gt;   -6.8536   1.3883  -7.2248  -8.3984   0.0079  -0.3945
+#&gt;    4.2400  -1.7337   3.1773  -7.3190   2.1686  -9.1689
+#&gt;   19.0523  14.2516   7.0283  -3.9777 -12.5752  -2.5832
+#&gt;  -11.7205  -0.0090  11.9184   2.2404   0.3422  -3.7061
+#&gt;    4.4009  -0.8376   5.5358   9.0462   0.6890   3.6592
+#&gt;  -13.7356   4.7588   4.9515  -8.4374   4.7421   1.8016
+#&gt;    2.9980 -13.3701  -1.4301   3.5365  -8.7095   5.2190
+#&gt;  -13.4774  -7.1882  -0.0140  -5.5037   2.3036  -1.9311
+#&gt;   12.4994  -2.9208   4.1533  -4.6218  -6.7373   2.3593
+#&gt; 
+#&gt; (16,.,.) = 
+#&gt;  Columns 1 to 8   5.9169   1.1962  10.7501  -8.9790   4.1484  -0.2881  -2.9131   8.8346
+#&gt;    1.6393  -2.6889   5.0957  -4.3603 -10.1045   4.5340  -2.5791  -9.9538
+#&gt;    2.0981   1.8680  -2.1006  13.5482  -2.9988  -9.8013   2.6953  -3.9434
+#&gt;   -6.9594  14.0226  -6.9573  13.5674  -4.3679  -6.6665  -3.0626 -10.0226
+#&gt;   -4.8158  17.7432  -5.9582   0.1271   5.2878  -2.4874   4.2246   7.6162
+#&gt;   -1.8618   3.9992 -14.8552  -0.6717   2.1653  -0.0528  -0.5305   9.8745
+#&gt;   -6.0365   5.1183   3.4952  -4.1085   2.3846   7.8327   4.9367  -2.7905
+#&gt;    1.3103  -4.7215  -2.1793   4.6908  -3.7660   5.4065  -9.4307  -6.7514
+#&gt;    4.4123   0.1263   6.5382 -10.5020   3.3511   4.1023   9.8486  -0.5677
+#&gt;   -1.5796   0.2654  -6.1811  12.6362  -3.4534   5.1137   0.9683   4.5397
+#&gt;    6.5069   5.2308  -1.0415   3.2129   3.6340  -3.6598   2.7702  -4.7992
+#&gt;    1.8092   2.6591  -5.5465   0.3925   6.2483  -0.5837   4.6502  -8.0858
+#&gt;    6.4339  -4.2291  -5.4463   1.3123  12.0333  -0.3098   3.8704  -5.4030
+#&gt;    6.8779  -5.5842   3.2166  17.6905  -1.6929  -5.4125   4.0909   6.6283
+#&gt;   -4.6292   0.9189 -11.1377  -4.3370  -5.0335  11.1261   0.1276   8.0493
+#&gt;   -2.5658  -3.2492  -2.2783   0.5461  12.7220   2.3526  -4.2149 -18.9606
+#&gt;   -4.9198   7.3656  -4.8668  -0.3271  -0.8615   0.2618  14.6728   2.8823
+#&gt;    0.3484   0.3916  -8.9037   6.6532  -2.4009   6.8033   1.5034 -17.2913
+#&gt;   -1.1485   5.0058   4.2632  -2.1971   3.7979  10.3216   7.6086   1.4941
+#&gt;    0.4886   3.0998  -2.3149   6.9763  -3.5498 -11.9070   0.7731   2.6125
+#&gt;   -5.4350  -4.2100   6.8747 -11.3444   3.5116  -6.8341   3.3729   9.3017
+#&gt;   -0.1810   0.0186  11.3952  -3.4121   6.9950   3.7474  -9.6391   4.4926
+#&gt;   -5.7175  13.6596 -11.6647   1.7630  -8.6601   3.9700   8.8271 -19.6870
+#&gt;   16.2903   2.8260  23.8470  -2.6987  14.3097   5.0003 -10.3122   4.4237
+#&gt;    0.7487  -4.2665   9.0854  -0.7235 -13.1581  12.4358 -19.0049 -16.0640
+#&gt;    0.0781   1.3698   9.2325   4.6129   5.4748  -4.3969   0.6132   9.7189
+#&gt;    5.8890  -1.4452   5.4483 -10.7550 -18.6493  -0.3483   2.3382   5.1563
+#&gt;   -0.0916   5.1133  -2.3725  -7.2628 -11.0122  -9.0826   1.5038   6.7757
+#&gt;   -1.1501   0.0571  -6.8683  -0.1019  -5.7275  -0.2904  -3.8972  -6.5371
+#&gt;    1.7825   3.4269 -11.7473   3.5808   7.4465   5.7079  -0.9744 -11.0135
+#&gt;    3.0592   1.6905   3.4647  -1.9238 -10.8315   8.9985  -2.4843  -8.3443
+#&gt;    0.2737   1.7902  -6.4242   2.6640 -10.9324 -10.8223  11.0115   4.6460
+#&gt;    1.6822   3.0623   0.2343   2.7980  -4.0485   7.8199   1.1451  12.8511
+#&gt; 
+#&gt; Columns 9 to 16 -13.7071  -0.7653   5.5538  -2.4353  -5.7447  -0.6709  -7.2778   6.2663
+#&gt;   14.0684   8.3597  -5.1964  -0.3258   6.5183 -12.6385   1.4381 -19.2791
+#&gt;   -4.4823  -5.3254   3.3574   7.8482   6.4927  19.5868   1.6706   5.6892
+#&gt;   11.6002  -4.3351   4.4546  -8.9352  -0.6136  -0.0973   8.0068  -8.3215
+#&gt;   -2.1117   7.8508   4.9740  -4.2024   8.0122  -4.6648  -3.8557   6.6858
+#&gt;   19.8712   5.1868 -11.2870   0.4251   6.9703 -12.9883 -13.2475  18.2691
+#&gt;   -9.1562  10.4813  -1.2635  11.8747  -4.2531  -1.0797  11.2160 -19.6530
+#&gt;   -0.0567  -4.9667   9.7458   2.3701  10.9538   1.9237  -1.9627  13.9311
+#&gt;    1.3140   8.7365  -0.1949  -8.5719  -7.5008  -2.7805  -0.9558  -5.4660
+#&gt;   -6.1256  -3.5703  -2.3028  -9.0828  -8.1802   3.4247 -11.0404  14.6100
+#&gt;  -12.9374 -12.2777  -3.5171  -5.9202  13.1994   8.6371  -5.0024  -0.5268
+#&gt;   12.8851   3.0432   2.3660   5.1235   5.7813  -8.8750  -0.5470   4.2925
+#&gt;   -2.6762  -1.8033  -3.3926 -17.6865  -2.9754  -8.5596  -1.3519 -10.5077
+#&gt;    7.7198  -7.3484   3.8967   2.3075   7.3826 -11.7406   6.7200   3.9621
+#&gt;    6.3456  -5.4186  -5.6147 -19.3007  -6.5337  -8.0445  -1.3140  11.4247
+#&gt;   -3.4923   8.3719   1.3613  -0.5927  -7.4595   4.1947  12.5511   2.8711
+#&gt;    2.5924  -1.3840  -6.6778  -1.6072   6.5471  11.2356  -6.4557   3.2157
+#&gt;    6.1776   5.3566  11.3179   0.2979   3.6626  14.9784   0.3117 -15.7688
+#&gt;  -11.2680   6.5108   7.6255   2.8750 -15.6441 -11.5801   4.3952  -6.3325
+#&gt;   -5.5521  -5.5296   5.4242   2.3838   2.0523   0.7012 -18.1893  -0.5384
+#&gt;   14.5744  -0.8031  -4.6487  10.3956   5.4341  -4.0979  -0.9086  -0.3972
+#&gt;  -11.0830 -22.4040 -12.0306 -11.2804  -2.2865   3.0656 -12.6764  -6.4913
+#&gt;  -16.6999   4.3738   4.0502 -10.0046   3.9659   2.0011   9.7486  -4.4500
+#&gt;   -8.8754  -5.8829  -2.1642  10.0776 -10.7525 -10.7539  -0.5335 -16.6962
+#&gt;   -3.3995 -13.1827  -8.8088   7.4763  -1.9271  11.0060  17.0620   9.4130
+#&gt;    3.0052  -0.0882  -7.0467  -5.8017 -14.0206 -15.3782  -9.5878   8.9883
+#&gt;   -3.8261  -5.8923  14.9247  11.1826   5.4076   6.7611  10.0125  -4.7802
+#&gt;   -4.5243  -8.8796  -3.0152   7.7899  -9.3244   3.0303 -14.7213   3.7061
+#&gt;   13.4022  -0.5678  -2.1548   1.3116  -4.6149  -1.8015  13.2173  -2.9844
+#&gt;    6.0882 -17.9818   0.5556 -23.7100   0.1930  -4.3171   1.8997  -3.6025
+#&gt;   -7.7793   9.4382   3.9473  -2.6181   0.2232   8.3356  -3.1850  -7.8183
+#&gt;   -4.7888  -1.6125  -8.2620  -6.1865  -4.4570  -9.0457   1.7380  -9.7770
+#&gt;   -2.7259 -10.7252  -7.6792   2.6634  -2.2631   3.8497 -15.9251 -20.6218
+#&gt; 
+#&gt; Columns 17 to 24  -7.8796  17.9775  -9.3992  -4.8710 -10.0097  -0.4248  12.5003 -13.9830
+#&gt;   10.9893   3.6070   5.4122  -4.1925 -10.8265  11.4295   4.7736   3.5215
+#&gt;   -9.8660   6.7317   8.8891  -2.7798  -4.1303  -5.8500   2.5645   3.4559
+#&gt;   12.4319  -7.9728  -2.8910  -1.3465  -2.0578  -2.5140  -7.0054   8.4268
+#&gt;   -0.9489  -7.8873  -3.4151   0.7461   1.9786  -4.1699   3.1521   5.1590
+#&gt;   -7.7628  14.5598  -3.7605  -2.4563  -4.8887   4.3739  -1.3184  12.0368
+#&gt;   -3.3868  -9.5283  23.5325  12.5333  -6.4736  -6.5903   7.1718  10.8270
+#&gt;   12.0811   0.8471   3.1682   7.7866 -11.2046  -0.6210  -5.9061   6.1771
+#&gt;   -2.5386   6.6288 -21.1735  -7.4060   8.6651  16.5632  -3.3783 -14.9110
+#&gt;    6.4171  12.3525   3.2848  -7.7431   6.7203  13.0827   2.9858   3.2090
+#&gt;  -14.8683  -2.8080  -0.1957  10.3795  -7.0103  15.6995 -15.7298  -8.2846
+#&gt;   -0.5186  -0.5380  -1.4989  -1.5238  -1.6552  -0.1317   3.3847 -10.4952
+#&gt;    1.0079  -0.7870  -3.5615   3.2061  10.0451  17.6399   3.1742  -9.9340
+#&gt;    8.9299  -8.4056 -10.5260  -3.7712 -14.4148   0.4000 -15.8502  13.8139
+#&gt;   -4.4611  -6.0424  -8.9023  10.5212   4.2197   7.3905 -12.1776  11.8185
+#&gt;   -9.9123  -1.9639 -10.0037  -0.6602  -2.5114   6.8629   1.1878  -6.3813
+#&gt;   -0.0282  -2.9304  -9.4680  -8.3135  -6.3681  -4.3604  -7.0652  -3.8044
+#&gt;    4.8215  22.9106   7.5484  -2.5532  -2.5809   3.1510  -2.8192  -4.3745
+#&gt;  -13.7029 -13.7980  -6.9890  16.0019  -1.9355  -5.9001   9.0478  20.1010
+#&gt;    3.2395   0.1785   3.6520  19.3297   5.5748  -2.6324   0.0679  -6.5968
+#&gt;    8.1041  -7.6415   4.9666  -0.1479   6.9535  -1.0444  -1.1585  13.3160
+#&gt;  -10.1763  -5.5170   0.5665   1.8729  -0.9565  -3.9006  -0.9908  -0.9861
+#&gt;  -22.9678   2.1149   1.5818   9.4493   1.6239   2.5172  -7.3676   5.2019
+#&gt;  -15.0124   3.9553   4.8910  -3.0632   4.0516  -6.1032  -3.0397 -16.0125
+#&gt;    4.1737  -6.5068  -0.9279   4.9228   5.0661   7.6071  -5.4581   6.6090
+#&gt;  -15.5276  15.1106   2.8998 -10.0532  15.5078 -12.6153  10.8250 -12.8329
+#&gt;  -14.1448  -0.5968  25.9646  -1.1691   6.4707   4.0210  -4.2684  -3.9871
+#&gt;    4.7639   3.5075  -4.7696  -0.6260  -0.1757  -1.9947  14.4427  11.0419
+#&gt;    2.0681   9.3647  10.1557  -3.0895 -13.3740   0.8708   2.6143   5.3274
+#&gt;    8.0578  -6.6474  -0.7361   4.9535  -6.1931   2.7753  -4.5427  11.5385
+#&gt;  -11.6095   3.9669   5.9931  11.6033  -5.9513  -1.0784  -0.9202   2.6523
+#&gt;   -0.9995   9.6205   4.9987  -3.6116   6.1728  -1.6928   6.5078   6.2526
+#&gt;   -5.6990  10.3888  -4.3727  -2.2719  -4.1629  15.1052   5.0346   4.0389
+#&gt; 
+#&gt; Columns 25 to 32  -3.3179   8.9114  10.0572  -1.2587   2.0931  -2.0530   6.5594  10.9616
+#&gt;   -4.7203  -7.3808  -6.6912 -12.0078 -11.0555   5.6575   7.7934  18.6705
+#&gt;  -19.2785 -15.9866   1.3843  -0.8469  15.9078  -0.2410   7.6636   8.9358
+#&gt;  -11.1760  14.2224  -3.0896  16.3962  -5.9672   8.0302 -13.1924  -1.9202
+#&gt;  -11.5296  10.4649  -3.0178   2.9463   8.7748 -14.6963   4.4653   2.7594
+#&gt;   -7.3800  -5.3960 -17.0513   0.9882   0.4144  -4.9462  18.2287  -5.4529
+#&gt;   -4.0838 -15.5619  11.6511   8.8858 -12.9532 -13.0284  -0.6605   8.6867
+#&gt;   -9.4081  -3.5767  -5.3829   2.1063   6.0295  -9.8064  17.7653  -5.9768
+#&gt;  -12.7177   4.9938  -7.4952 -13.0969   8.9979  -0.0998  22.3145   1.7401
+#&gt;   -5.8592   4.4302  -6.8574   3.3468 -11.9368  -0.0754  11.5460   0.0574
+#&gt;   -4.4656   5.4609   3.4491 -10.8961   7.8371   3.5985   7.8025   2.8597
+#&gt;   -3.9979  -0.6659  -5.5074  15.7577   5.8280   6.6209  15.0434  -1.5492
+#&gt;   10.2756   9.7925   1.1668  -2.0181  -2.6645   5.3276  -1.1349  -4.2664
+#&gt;    1.6781  -0.7389  -0.0576   0.9104   0.7526  -4.0417 -13.5008   0.9477
+#&gt;    9.2704   6.1267   2.2287  14.4398 -15.2803   8.1811 -15.1956  -4.7375
+#&gt;   -0.8798   2.0092  -5.3028  -3.4708  -1.6346   7.8313   4.4540  -8.5716
+#&gt;    6.6299  13.0193  -4.3130   6.2526   1.2605  -7.0203   4.4134  -8.3241
+#&gt;   -4.6752   5.8241  -1.0570   6.9613  -6.6649  -9.4870  15.5721  -8.3716
+#&gt;    2.1473 -12.8502 -18.3775 -20.7149   5.0487  -0.8780  -9.4605   4.9170
+#&gt;    7.3915   0.3985   4.3123  -3.5218  -0.3456  -9.1704  -0.8021   1.1542
+#&gt;    2.5691  -6.5108   3.8852  -2.2915   9.4971  -0.9335   8.3368 -17.9244
+#&gt;    8.7376   4.6082   6.3856   0.9325   0.8904  -2.0705  -6.0855   1.2072
+#&gt;   -4.9679   9.4473  -8.5908  14.6577   9.2367   2.4137  -5.8935  -3.3195
+#&gt;   -8.3799 -11.7817   8.2112  -1.3504  -3.6656  14.2825  -1.3165   6.0722
+#&gt;    1.7320 -12.4078  11.2184   0.9507  -8.4417  12.5083   9.1634  12.5494
+#&gt;   13.9281   3.4773  13.7172  13.0891  -9.7119  -8.2638  -8.7622  -8.8073
+#&gt;    6.4454   5.8153   5.1188   1.5204 -21.2378  -1.3159 -18.2051   5.7055
+#&gt;   -4.9967  -9.7521  -2.4479  -0.7929   5.7172  -4.6572  -1.0606  10.8816
+#&gt;   -2.7617   4.1111  -7.0416   0.6337   0.8852   2.1034  -5.1787   8.5571
+#&gt;    8.7010  12.4941  -5.7907  16.1933   0.8232  -8.7499 -21.4545  14.7660
+#&gt;    3.5851   8.8012  -3.2526   0.5185  -1.6775  -6.9595 -13.1621   6.2687
+#&gt;   -7.7303 -11.4324  19.5842  10.0752   1.3555   1.9197   8.3356   4.5247
+#&gt;   -8.8562  -2.9946   6.5604  -1.4950  -7.3620  13.2178  17.4251  11.9080
+#&gt; 
+#&gt; Columns 33 to 40  11.3978 -11.5616   0.6751   5.4931 -21.9684   2.6750   8.7489  24.5304
+#&gt;   -6.9687 -10.7086 -13.1778 -13.8945   8.1555   6.2153  16.2339 -10.6723
+#&gt;   -8.1479  -2.2703   4.3881  -1.6490  -1.7727   1.6611  19.1424  -9.4395
+#&gt;    1.6611   6.1769  -3.1931  -0.8321   6.1536   5.8515  -5.7873   4.9654
+#&gt;   12.7029  -4.0359   3.3536   7.6909 -10.7435  21.4226   5.7366  34.2568
+#&gt;    9.1381 -17.1925  -5.0425  14.0283  11.5891  -0.8054   6.9017  -3.6643
+#&gt;    1.6606   3.6179 -16.8114 -10.9507   3.6880 -12.8685  -8.8030 -13.1678
+#&gt;    0.9630   3.8960   1.9926   5.5745   6.0234  -7.7944   2.0300  -8.8346
+#&gt;   11.5074   6.9423   6.5628  -7.9312  -0.3811   2.8890   3.3496 -13.8868
+#&gt;   -9.8659  -9.3607  -5.8141  -3.7807  -1.1790   7.4838   8.1174  15.1448
+#&gt;   -4.9651   9.1740  14.4638   3.6277   9.6858   8.8757   4.3408 -11.4244
+#&gt;    8.6441  -1.5692   8.1847   0.1239   8.9716  -2.4765 -10.7623   7.3977
+#&gt;  -11.0943 -16.6178   2.2971 -21.6193  -2.5390 -10.3741   9.8438  -5.6642
+#&gt;   -9.2277  14.6166  -0.9204  14.9913  10.7844  13.5524   2.8325  -8.9513
+#&gt;   -2.6473  -5.8741   4.6406  -5.3104   4.9784  -7.5659   7.1071  -9.0259
+#&gt;   -1.1382   0.5198  -2.9474   5.7955   6.4414  -5.7807   3.7976 -12.1651
+#&gt;    6.7978  -6.5910   1.2208   6.1146   7.8964  16.9533   8.0562  -5.8046
+#&gt;    4.1896  14.8452  -4.5841  -7.3017  -0.0823   4.4017   4.8627  -7.9858
+#&gt;    7.3247  18.5870   7.4510  -3.6479 -25.6386  -0.6795  -4.1227 -13.5621
+#&gt;  -10.5895 -10.6946   8.3915  -3.0894  -2.1166  -8.1934   3.9229  11.6619
+#&gt;    3.3745   3.2772 -12.0464   0.5004  -2.8965  -5.3103 -15.6017  10.8435
+#&gt;   10.7255   3.9382   5.0526   6.5678  -8.1970 -12.1692  -4.3047   2.8854
+#&gt;   -5.4066   0.8166  -0.8165 -12.6045  -2.5846   7.5421  19.8140  -4.3701
+#&gt;    7.6552   7.8124  14.6502  -2.3297 -11.4268 -10.0371   1.9409  12.4794
+#&gt;    1.4078   9.8272   7.0404  -1.8239  10.8952  11.4932   1.9102 -10.4790
+#&gt;    9.4703  -6.6227  12.2637  -6.4715 -15.9110 -14.3271  -7.6908  14.2522
+#&gt;   -0.8424  -4.3798   9.7508  -1.0919  -3.3694 -15.9257  12.9108   2.7339
+#&gt;    1.0147  -4.9658  -6.1954   5.5696  -5.3329  -1.1068   4.5829  -5.8869
+#&gt;    9.7037  -5.5950 -16.5860   7.1283  -6.5315  -2.6857   2.3856   8.0047
+#&gt;   14.0389   2.8653   0.3209   4.5985  11.4294  -2.1647  10.7592 -13.9907
+#&gt;   14.3837  19.9179  -1.5585 -13.3698  -8.6395  -1.7041   3.9278   2.5171
+#&gt;  -13.9244  -0.2977  -3.2740   4.1151  -4.9771  11.6901  -6.4768   6.6685
+#&gt;    5.1153  -0.3700 -17.0048   8.4165  -6.7035  -6.2654  -1.5954   9.6956
+#&gt; 
+#&gt; Columns 41 to 48 -22.1942  -7.2350  -2.4917 -11.1559  -3.0166 -15.0017  -1.1812  -3.4713
+#&gt;    0.2388   8.1962  -9.7739   9.0305   8.3463   2.1590  -5.6769   5.0629
+#&gt;   16.7023  -3.2802   0.8704   2.1747  14.1589  -8.2684  -3.2724   7.1870
+#&gt;    4.6840  -7.5805   9.2810   2.9401  -2.8086   6.2953  -9.3358  -1.4327
+#&gt;   -0.5781  10.5564  19.4567  -8.2992   1.6499   1.0847   2.9731  10.7564
+#&gt;    9.4773  -0.5292  16.9066  -6.3917 -11.6539  15.5057   4.0474   7.7430
+#&gt;   15.5874   1.3924 -16.0389   4.0388   5.7586   4.8510  -7.7798  -2.4974
+#&gt;   21.3204 -14.2573   3.3693   5.9481 -10.7813  -0.9151  -5.0470 -10.1517
+#&gt;    5.6014  -5.1494  19.4992   6.3122  -8.5826  -4.0738   2.6674   7.9877
+#&gt;    0.5577   6.1221  16.2732  -1.8582  -3.3096  -1.8466   8.7034  20.5187
+#&gt;    3.1797  10.5174 -15.2399  -2.6417   6.3028   1.2196  18.4807  -4.0511
+#&gt;    3.0401  -8.5193  -1.1578   0.3379  -1.0156  -7.5664 -18.8893  -6.9766
+#&gt;   -8.8674  11.4856  -8.3224  -7.0834   7.2036   6.2827  18.5061   9.4553
+#&gt;   -0.7081   3.2447 -13.5620  -3.5952   5.2871  -7.9405 -12.0735   1.1528
+#&gt;   15.1621  -0.6657  17.3880  -4.1401  -5.4776   1.6688  -5.8632   9.7832
+#&gt;   -4.7534  14.7511  -7.6977  -1.0605   6.7663   8.0379  -1.9791   2.8543
+#&gt;   11.4958   0.2522 -19.5168 -14.8940  -3.2251   9.1275   9.5365   5.7808
+#&gt;   16.8602  -8.5958   1.7279   0.3534 -15.7748   1.2065  -5.9608  -1.8861
+#&gt;  -11.9294  23.8705  10.0658  -9.9190  11.2422  13.1915   2.9278  -8.3528
+#&gt;   20.0953  -0.7472  17.5934  -3.3260  18.8082  -8.5177   0.2490  -6.8630
+#&gt;  -12.7300  -2.4564  -1.6036  -1.8352  -7.7443  -7.1272 -13.3813  -3.6956
+#&gt;    8.1264 -13.6377  10.9397  -3.0560   6.5067   2.8503  -4.2379 -10.5363
+#&gt;    0.5117  11.3553  -9.0868  -2.7140   1.1550   5.6160  11.9133  14.7809
+#&gt;   -3.9240   5.4078 -13.7294  -4.3688   6.5080  -3.5070  -1.7219  -4.4902
+#&gt;   12.5034 -12.8467   2.1763   5.1926   3.1938  -0.7747 -20.0967  -9.0441
+#&gt;   -4.2370   4.1992   7.6872  -5.2593  -1.7053 -11.8188  -0.1682  -4.0788
+#&gt;   -5.0200 -27.1574  12.7201  20.7038   0.9286 -12.9077   0.3077  10.2574
+#&gt;   -6.0969   0.4821  11.8856   5.4467   0.9379   3.4836  -5.4542   6.9657
+#&gt;  -18.5719  -0.9974  -5.4086  15.3446  -2.6354  -2.4166 -15.3805   5.1253
+#&gt;    6.2883 -29.3754  -1.2569  -1.7976  -6.8801  -0.9421   3.6779   3.4648
+#&gt;   14.4022   7.4876  -4.4341  -0.7004   5.4160  -1.7521  -5.0956 -13.7839
+#&gt;    7.8083   2.2676  -7.3100   7.3198  -1.0796  -7.9814   2.6069  -0.7932
+#&gt;   -2.4366 -11.5112   3.4781  12.4499  -3.4112 -10.5143 -14.6968   5.0345
+#&gt; 
+#&gt; Columns 49 to 54  -8.9649  15.0480  -3.5127  -1.3708  -6.2487   0.3050
+#&gt;   14.9347   5.8413   1.5031  -1.6481  -4.4734  -2.3108
+#&gt;   -6.8708  -4.6939  13.0038  -4.7202   2.7074  -7.4918
+#&gt;   -1.4283  11.7403   9.1098  -4.2097   5.4451  -4.1161
+#&gt;    7.9117  21.6279  -2.7317  -0.8931  -9.2567  -3.0612
+#&gt;   17.0366 -33.7473   4.8260   0.1720  -1.2427  -4.2173
+#&gt;    9.5700 -15.3509  12.6820  -0.3305   0.5376  -6.5525
+#&gt;  -13.2608 -18.3225  18.4981  14.1568  -3.1979 -10.7866
+#&gt;    8.7527 -11.6705 -10.1354 -10.2437   4.1236   5.4367
+#&gt;    7.5527  -4.3760   9.8673  -0.5702   4.7787  -5.6126
+#&gt;    1.2647 -21.6524 -21.0066   7.6477   2.8037   3.0143
+#&gt;   -8.7994 -14.5644 -19.7484   6.1574  -0.9049  -1.3891
+#&gt;   -4.8802   0.4612  -3.7767   2.4743   1.4301   0.2956
+#&gt;    7.3854  10.7632   8.3053   1.8451   6.9022  -4.2529
+#&gt;   -3.2762  -6.7379   8.6647   5.6959  -1.1013  -0.0037
+#&gt;    0.3988  -6.0855  12.6957   1.2420  -4.7745   3.7277
+#&gt;   12.0424 -22.9109   0.8896   5.6065   4.0492  -0.3889
+#&gt;  -12.7498  -0.4841  11.0614  -5.0944  14.2974  -8.2520
+#&gt;    3.4900   2.0673  -2.8354   1.1162  -1.4959  -4.3088
+#&gt;   -7.9860   7.1940  -1.3905   5.9913  -1.7375  -5.5433
+#&gt;    1.8201   5.1878   9.1053  -6.9162  -5.2749  -1.3472
+#&gt;  -13.8737 -16.1515   4.4713  11.9625  -2.8959  -2.3175
+#&gt;    7.7018  -2.5956   0.8244   3.0606   5.3660  -4.8304
+#&gt;  -20.7461  -7.6867  12.4397  -6.9317  -7.0717   6.5692
+#&gt;   -3.1832  -9.8930  -9.0189   2.7933  -0.3160  -0.1822
+#&gt;   -2.9797  16.1053  -1.1040   4.7778  -8.6608   3.9642
+#&gt;   -7.8118   1.3081   0.3643 -13.3631  -4.6473   1.9124
+#&gt;   16.6642   3.7141 -11.1340 -17.0547   4.8123   2.1086
+#&gt;    1.4257   8.5336   7.0028 -12.1989  -3.7266   4.4054
+#&gt;  -13.9857 -10.3642   0.7977   5.1235  10.0793  -1.8005
+#&gt;  -11.1381  18.8368   9.4772   8.2808   1.2748  -3.9059
+#&gt;    5.8320  -3.8253  -8.4454  -7.3230   2.7275   3.6790
+#&gt;   -0.2858  -1.3838  -1.1742 -20.6285   5.9709   7.5458
+#&gt; 
+#&gt; (17,.,.) = 
+#&gt;  Columns 1 to 8  -0.0036  -3.3190  -7.9587   3.2906 -13.0091   4.0434  11.5652 -12.1427
+#&gt;    0.1164   0.8379   4.9234  -1.3137   0.0675  -5.0679  -4.4452  -4.7952
+#&gt;    2.5500  -3.7011   0.9437   1.4541   1.8920 -14.1025   0.3019  14.6240
+#&gt;   -1.2880   4.0459   1.9433   3.9558   2.5960   2.6825  -9.3180  14.3707
+#&gt;    1.4861  -3.1195  -1.5166  -4.0141  -7.8198  -5.3168  16.8417  13.6179
+#&gt;   -2.4961  -1.8708  -3.0573  -7.6208  14.6806  10.5994   3.2349 -19.1036
+#&gt;    1.1798   0.3461   9.7883  -3.9783   3.7118  -5.1532 -10.9065  17.8243
+#&gt;    3.5643  -8.1262   0.6550  16.2425   7.3746   0.6471  -4.3637  16.9330
+#&gt;    8.2370   0.4671   8.6061  -3.5247  -2.3181   0.9235  -2.5817  -4.7410
+#&gt;    1.2592   0.1341  -8.5860 -10.3599   1.3681   3.0343  -3.2627  -4.4536
+#&gt;    2.4246  -4.6030  -0.9959   1.6058  -1.8115  10.3210  11.3543 -14.6947
+#&gt;    1.3383  -1.8282  -1.9980  -7.7775   0.9999  10.5046   7.7484  -1.0406
+#&gt;    6.5106  -5.1481  -8.9645   2.7458  13.4778  -1.9987  -8.3724  -6.8411
+#&gt;   -6.7584  -5.6272  -3.1131  18.0075  -6.0840 -12.9692   4.0645   0.7947
+#&gt;   -4.2316   5.9853   7.8798  -3.3417 -11.0788  -4.6102   7.5990  -4.8012
+#&gt;    2.0482   2.4839   2.4645  -0.5146   7.5531  -6.1914  -5.2242   5.2656
+#&gt;    2.3681  -6.2423  -1.4921  -1.4588  13.6384  11.2711   6.0663   0.3827
+#&gt;    1.7283   0.8490  -4.8233  -8.1522   1.8019  -3.3521   1.7968  13.4382
+#&gt;    1.9979  13.1230   7.3525 -13.3938  -4.5078  -1.8459 -14.6914 -14.1974
+#&gt;   -1.2166  -5.0405   1.4720   3.4205 -11.9439 -16.7012   5.8988   9.3834
+#&gt;    2.9653  -4.7849  11.0924  -3.6218   6.1173  -2.4609 -12.5736  11.4327
+#&gt;   -3.2606  12.5382   9.5843  -9.3089   4.5541   2.0092 -20.0066  -9.3231
+#&gt;    0.5372   6.2045  -6.6611 -15.5596  -8.4444   7.8671  -0.7634  -4.9848
+#&gt;    1.0054   0.0214  -4.3680   3.8120  -2.7707  -7.4050  -8.1512  -9.1003
+#&gt;   -7.9268  -6.0573   9.4077   1.9014   0.7067   1.0413  14.1705 -11.5789
+#&gt;   -3.5318   8.0966   9.8679  -7.0404  -7.6394   3.9163  -2.4689   0.5888
+#&gt;  -11.6240   2.5042  14.4123   2.6386 -19.1348   4.2073  11.3132   4.3702
+#&gt;   -4.9533   1.3656   2.4042  -2.3242  -2.4680  -8.1332  13.3980  -0.8918
+#&gt;   -7.9514   6.3341   5.5485   2.2275   1.4123  -1.7811   3.2591   3.3837
+#&gt;   -6.7042  -2.2874 -10.2507  20.4313   9.5276   8.1279  -1.0739  12.8549
+#&gt;    2.1646   6.3275  -8.4244   7.2597  -2.9179 -12.6174  12.2693  -3.4996
+#&gt;   -2.9582   1.8840  -1.1082   4.9597  -8.9087   5.5940 -13.2627  15.1192
+#&gt;    3.8620  15.6053 -14.6896   8.3630   5.0803 -12.6752  -7.6925   2.4458
+#&gt; 
+#&gt; Columns 9 to 16  18.2032 -19.6725   5.0508 -16.0836   9.1070  -3.1893  -5.8347  -3.0840
+#&gt;   -5.6912  -0.4025   4.0813  -1.5855  -9.3055 -12.0094 -11.9593  -5.9454
+#&gt;    6.7649  18.8722  -8.4775  10.3950  -4.3235  -0.8341  13.1874   3.2388
+#&gt;   -8.8735  16.5639  -1.0106   5.3047  10.7215  -8.9410  15.9507   2.6645
+#&gt;   -0.4064  15.6966   1.1777   7.8093  18.6075   0.4738   1.0298 -12.1063
+#&gt;   -2.6083  22.5461 -18.7206  15.4192 -32.0458   7.3132  -8.8752 -11.7511
+#&gt;  -13.7117 -11.5101  -0.8774   1.9782  -4.6365  11.1330  -1.2761   7.9712
+#&gt;    0.2302  13.4904   1.5463  -1.8252  -9.0678   9.8361  -8.8496   1.8389
+#&gt;    5.2399  -2.1986 -10.1448  -6.6668 -12.4374   9.5085  -8.5832   3.4156
+#&gt;    1.6309  11.2047   5.7111   5.6381 -10.8640  -0.8878   3.1294  -7.3463
+#&gt;    0.7705   9.0933   0.9354  -3.5888 -14.0555 -11.2154  15.2315  -3.7600
+#&gt;   -1.7876   8.2053   1.9111  -0.1420   7.0392  -7.9765   4.7696   2.9816
+#&gt;   -0.0192 -12.5377  -9.9169   2.0928  12.4499   2.4241  -1.6729  -4.4325
+#&gt;    8.1069  11.8041   0.5192   7.5075  -3.9413  -1.4742   8.3041   6.3053
+#&gt;  -13.3357   2.6357   6.9307   9.0956  -4.7934  -2.9089   8.3363 -13.9510
+#&gt;    7.2659 -13.8078  -1.1089 -15.7769   9.4236  -5.1112  -6.0908   2.2405
+#&gt;    7.9741  -7.2104  -7.6181  20.0668 -10.3272  -1.1638  11.3504 -12.6043
+#&gt;   -9.1118   9.9215  -0.0853  15.8825  -8.3135  -1.1377   3.6858   0.1344
+#&gt;    8.6729   1.6374   1.3439  -9.6331   1.6315  -8.2283  -9.1082   4.5495
+#&gt;   -5.4776   6.7521  -3.4657  10.1412  11.8968  14.7285  -1.7380 -11.2804
+#&gt;   -2.5355  -4.1345   2.2051 -18.1531   5.0060  -6.3627   4.9419   4.7983
+#&gt;    9.2168  -8.3501   1.9289  11.9154  -9.8096  11.2362   9.2350  -8.4550
+#&gt;    4.9740  12.1595  -3.6848  10.5441   7.1024  -6.7502  10.8825  -5.2812
+#&gt;   21.5238 -15.0916  12.0990  -2.5791  12.4567   1.5288  -7.3818  16.1056
+#&gt;   -0.0058   2.3107  13.5580   7.0735   4.1138  10.0373  -8.5884   9.2899
+#&gt;   -1.8384  -0.6716  -4.3395   4.3135  13.5270   2.1079   2.9250 -11.1521
+#&gt;   -0.6385 -10.9262  -4.9343   5.1412 -14.9500 -13.1119  13.7721   2.5448
+#&gt;    2.3601   5.2188   0.3091   0.2517  -6.2330  -9.7139   7.5875  -0.9963
+#&gt;    1.1930   7.7077 -11.2370   3.9249   7.5846  -2.9404   1.1477  -1.9224
+#&gt;   -2.4618   3.0866  -5.5732  13.2863 -11.5555   8.1338  -1.0781  -8.0933
+#&gt;    4.2506  -6.1249  -2.8451   6.1572  13.4412   4.0567  -1.8001  -1.1474
+#&gt;  -16.7791  13.2047   4.3998   5.3425  -3.4872   4.9933  11.7567  -7.1929
+#&gt;   -4.3003  -4.2831  10.9765   5.8159  -5.9722  -1.2655  -5.2305  18.9444
+#&gt; 
+#&gt; Columns 17 to 24 -12.1367   1.4567  -1.6269   8.3249  -1.6047   3.3990 -11.6582  -5.8878
+#&gt;   -2.3760   0.4142   9.0234 -13.6288  -8.3573  -6.6818   7.0287  -2.7305
+#&gt;   -9.1737   0.7451  20.8649  17.2731 -19.0187   9.8993   7.9144   1.6249
+#&gt;    9.1767  -5.3467  -4.6508  -9.8663   1.7865  -8.4075   5.7143  -0.6771
+#&gt;   10.6411 -19.1595   0.1464  -0.1775  -1.1914 -10.3426  -1.2584  -7.9563
+#&gt;  -16.1971 -13.8179  -9.4703  20.5316  -9.9628 -20.8515   9.6431 -10.2868
+#&gt;   -9.6250  10.1539  14.2932 -11.5516  -4.8499  -2.0416  12.4956   8.9299
+#&gt;  -12.3655   0.1540   9.0695  -9.6254  -7.8712 -19.9817  20.3502   4.4879
+#&gt;    1.8061  -8.6229  -6.4227  17.4155   4.0062   4.4123 -12.7788 -12.0072
+#&gt;   -4.9458  -5.6366  -4.4784   0.7285  -4.5531 -13.3600  -4.7004 -15.0519
+#&gt;   -0.2497  -0.2428  -8.2563   3.6055 -14.8665  -2.6090 -15.8119   5.3822
+#&gt;    4.3693  -6.9149 -12.2015   4.5604  -0.3401   5.8007 -17.0388   0.4493
+#&gt;   15.0418  -0.5247   5.2963   3.6107  -8.5075 -11.3623 -12.6868   1.0413
+#&gt;   -4.0378   8.6373   2.0527  -0.9846  -0.3666  13.2075   6.5006   9.9071
+#&gt;   -0.7308   7.5805  -3.0739  -0.0865 -14.7430 -19.0587  12.9800 -11.4091
+#&gt;   -6.2874  16.6758   7.1346  -7.2085  -4.7202   7.4124   3.7254   5.7929
+#&gt;  -11.5627   6.5587  -4.1144  19.9498  -6.9683 -10.0468  -7.7720  15.8065
+#&gt;  -12.7454   8.7868  12.2793 -10.7621  -4.4990   2.5450  -0.2208 -17.3568
+#&gt;    1.5817   1.2042   8.9885  -2.1994   4.8149  15.1747   2.5702  -2.2010
+#&gt;    3.3618   3.3082  -4.8614  -7.8732 -11.8926  -7.8178  -5.5999  -2.6310
+#&gt;   -7.0714  -4.9754   6.3195  -3.3233   5.6727  17.1982   2.4234   7.0407
+#&gt;   -0.0079   4.2773  -2.5152  -5.1012  -6.6415   7.7634  -1.7209  15.0072
+#&gt;    0.7598  -0.0868  11.9345  15.8469   4.9130  -2.6162  11.1826  -5.4076
+#&gt;    3.9734   2.6924  -6.5429   4.9290  -8.8703  13.0501   1.0853  -1.9093
+#&gt;    0.0759 -10.3995 -20.7333  -6.7558  -3.4664  -5.0556  -3.4500  -2.6451
+#&gt;   11.9588  -0.9773  -5.5220  -9.4147   9.2083   2.0006  10.9240 -26.0863
+#&gt;   -8.0222  -7.1364  -6.2283  -4.9439 -18.3233 -12.8074   0.2920   3.7361
+#&gt;  -14.7121   1.6619  -5.7169   9.3542   3.5858   1.4770  -0.7576  -9.8484
+#&gt;    1.3114  -0.9283  18.1555  -3.2016   4.1839  -9.9645   2.9604  -3.2908
+#&gt;   -5.9759  -8.9707  -0.6552   9.0356  -7.6296 -30.9177  -8.4567   9.6805
+#&gt;   -7.4921  -3.0522  -4.5990  -1.9557   0.6606   3.0873  -0.2393  -6.4692
+#&gt;   -1.3348  -6.8173  -5.2836  -9.4862  13.9301  -0.0991   6.0113 -13.2417
+#&gt;    4.5449   5.1664 -15.3847   1.6871   7.4429  -1.8639 -16.2640   4.9284
+#&gt; 
+#&gt; Columns 25 to 32 -14.1407  14.2955   3.8695  -6.0790  -5.2760   6.6175   3.1503  -5.2982
+#&gt;    2.2688 -11.4986  -2.2534   2.4166  13.8815  -4.4936 -12.1703  -4.5053
+#&gt;   -1.4144  -5.8853  19.2525  14.6247  19.1070  11.7330   5.7779  -4.3484
+#&gt;   -3.9388  -1.7344 -10.0980   9.1879  -8.3052  11.6017 -14.8255   3.6860
+#&gt;  -10.8255   7.7143  13.6104  -3.6169  -5.8611  -6.1461  10.0281  -2.9510
+#&gt;    3.6030 -19.8067   4.5132  -1.7495 -11.6521 -12.9786  19.7649   3.0201
+#&gt;    5.1040 -18.6112  10.5612   5.6103  -2.6788   0.9387   1.4030   1.6843
+#&gt;   -1.2084 -16.5771  15.1098   9.0082   0.5725   0.1737  -1.0725 -11.1100
+#&gt;    8.9186  -5.9483  -3.8864  -6.2679  17.5105   7.6420   1.4482 -30.5893
+#&gt;   -5.3877  -5.3120   1.5567  -4.8999 -12.9138   5.5266  -4.2428  12.0746
+#&gt;    4.3041  -1.7397  -6.7371   0.3417  -0.7745  -5.9680  -4.7485  -9.4796
+#&gt;   -1.8727 -14.7426 -10.1967   8.3783  -5.9343  -5.3843  10.4344  -4.4727
+#&gt;    1.0782   4.0079   0.5302 -19.9129  13.8336  -5.9290   2.6223 -10.6001
+#&gt;    3.2687  -4.3255  -7.6249  -5.0333   3.5369   2.8440   1.9130  13.2635
+#&gt;    0.9298  -3.1962   4.1605  -0.2121 -19.9007  -2.3541  -9.4520   1.5965
+#&gt;   -9.8351  13.3726  -3.0548   1.2052  -6.0823   8.8489 -14.4292   4.4013
+#&gt;   13.7231  23.7698  -9.7120   5.3621 -10.5620 -14.4767  -2.1569   3.0922
+#&gt;   17.7819 -11.9477   8.1932 -11.3599   5.3066   3.3444   5.0477   1.9190
+#&gt;  -11.2310 -17.4655 -21.1093 -16.8335  11.1529  14.9433  -1.9176   0.8984
+#&gt;   -8.4986   1.0614   5.4820  -7.7777  -7.8456  -2.3261   6.5574   0.4245
+#&gt;   -5.7616  -5.6309  18.0811  10.8869   4.5236  -8.1555 -11.5226  -2.2178
+#&gt;   -6.5431   0.4128  -2.1879   8.4302  -3.1771  -6.8854  -6.7822 -16.3649
+#&gt;    3.8306   9.3733   2.6196  13.6446   6.9131   2.6113 -12.4660   7.1945
+#&gt;  -20.6794  -3.3141 -15.1161 -15.1085   6.8808  24.0385  13.2143  -3.2077
+#&gt;    9.8282 -19.6173 -10.8193  -0.2638   8.3347  14.2335  -5.1957   0.3102
+#&gt;   -3.0323   9.2271   1.8307   0.7336  -7.8705  -2.3976  -3.2060   3.4770
+#&gt;    3.7052   3.8880   4.7929  -8.8234  -3.5462   5.8435  11.7265   5.0594
+#&gt;   11.1393  -4.2118   3.1103  -1.0103   5.0755   5.7154   5.7432   5.4446
+#&gt;   -2.5627  11.9907   5.3111   3.7991   6.3624  26.5475  -4.0819   5.1281
+#&gt;    4.9443   1.2031 -10.6381   1.1163 -11.6207  -7.0394   7.4165  15.7235
+#&gt;  -14.2614   3.5230  -7.0514   4.8836  11.7337  -6.1559  -7.5084  -3.4756
+#&gt;    0.3203   9.2310   4.0731   2.0453  -3.5192   7.8414 -11.4932  -3.4787
+#&gt;   -1.9585  11.0028  -7.8681  -5.2722  -4.7085  -0.3444  -8.8240   2.5753
+#&gt; 
+#&gt; Columns 33 to 40 -16.2086   3.6009   4.1534  -4.0031   0.7665   6.8490   7.3541   8.2326
+#&gt;  -11.6117   0.8177   2.1269   3.1290   4.0927  12.8487  -2.8535   9.8357
+#&gt;  -20.3408   2.3152   4.2274   8.3990  -4.7943 -18.4804  21.1509  -7.6883
+#&gt;    0.1038  12.0059 -14.0866   7.1420  -2.2766   2.9557  -9.2178  -4.7261
+#&gt;    4.3252   7.8233 -10.1021   0.0185   6.5683  -8.0528  -3.2560   0.2315
+#&gt;   -8.5887 -11.0928 -14.6416   3.7763   4.4483 -10.3446   1.0966   1.6011
+#&gt;   11.9414  -6.7054  -2.7639  -4.9038  -6.6649   3.8405   5.4326   0.9827
+#&gt;  -19.0930  -8.3548  13.6712  -4.4705  -7.1463   8.9296  -1.0585  20.3146
+#&gt;   16.2528  -3.6523   0.9896 -21.1536   2.3888   4.4423  -6.8638   6.4089
+#&gt;   -1.6519   6.3646   3.1551   9.5345  -4.8651   5.2906  18.3544  -2.8488
+#&gt;    2.6591   4.9864 -21.2580 -10.1801   6.1868   3.7901   9.3343 -12.3642
+#&gt;   -1.5676  -9.3029 -11.8483  -2.9516  -1.4575  -6.7623   6.5269  -3.6121
+#&gt;   -2.2296  -7.6398   6.7909  -5.8588   6.1594  12.8406  -4.1241   9.5473
+#&gt;   11.0382  10.7851  -4.9545  11.5578  -4.3485 -11.3629  -4.7165  -5.0466
+#&gt;    2.5669  -4.2435   8.9381   0.1753   8.1956  -0.5499   2.4431  21.6045
+#&gt;    4.7957 -14.3638  -1.0203   5.1330  -5.1298  -4.5777   4.2896  -5.9634
+#&gt;   24.2212 -21.3843  -6.0477  10.0261   4.5586   2.9284   4.3612  -9.6834
+#&gt;    3.6108   2.4465   4.1788 -12.8152 -10.0255  19.3243   9.6198   7.5142
+#&gt;   -8.3931  -8.3866 -14.1537  -3.7318  -8.6589 -18.7726   8.6055  17.5604
+#&gt;    0.3024 -15.7799  -0.8323   5.2878   1.8991  -9.5312   3.1859   2.5497
+#&gt;   -1.8618  12.1423  -3.1689  -2.1018   6.6899  -7.0607  -4.2293   0.3692
+#&gt;    6.0903 -19.3583   0.6555  -4.0463   1.7142   1.0252   5.1535  -2.7416
+#&gt;    2.6232  16.2810  -4.7329  12.3208  -6.1677   5.3871   1.9002  -0.7168
+#&gt;  -10.6743 -21.4390   1.2288   1.0133   1.3441  -7.4375  -8.6747  -3.8495
+#&gt;   -2.5638   5.9309   4.8542  -4.5298  23.0909  -7.1586  -0.3635   1.0118
+#&gt;   -7.4935   9.6163  -7.2168   7.3669 -16.4921   3.3259  -2.4198  10.2347
+#&gt;    4.2367  -0.5784  16.5164   4.3086  -7.5247   1.8854   3.8563  -7.8103
+#&gt;   -9.3610  -8.2013  -2.5560  12.9635  -3.8509 -10.7876   1.4986  -1.3885
+#&gt;  -14.0475  -2.9250  -2.8980  14.5107  -5.2680  -4.4482  -5.0714  14.8558
+#&gt;   -6.7825 -16.8933   9.7776   1.8856   4.4857  17.3968  -0.1486  13.6344
+#&gt;   -4.0811   1.1356  -6.8292 -15.4074  11.5754  -3.7018   8.3617  -1.3083
+#&gt;  -11.2235   4.8559   3.5214  23.2757  -6.6173   3.2491  -0.4854   9.0660
+#&gt;  -13.1656  -2.8782   9.3092 -10.9921  10.8435   1.7970  18.7206  -7.0084
+#&gt; 
+#&gt; Columns 41 to 48  -0.4794  -4.7200   2.1935  -2.2066   4.2906  -7.6101  10.5657   6.5193
+#&gt;  -10.3014 -12.9307   1.8835   2.5713   2.2012   2.0539 -13.8011   7.5363
+#&gt;  -17.2547 -12.5349 -20.9318  -4.4617  -0.9685  -0.3390   5.2662  -6.5757
+#&gt;   -3.1898   5.6737  -7.1999  14.6965  -5.5819  -2.1300   0.7077  -3.1309
+#&gt;  -18.0966   1.1532 -11.4858   7.5103  -4.0690   8.0907  -7.2170  -7.7530
+#&gt;    7.6633   1.8808   5.7849   7.3424  -3.0240  -9.6336  -4.3158  -4.6648
+#&gt;   -8.0560   1.5696   3.1842 -11.2185   7.8985   8.3951  -3.0128 -21.7698
+#&gt;  -10.0482  14.3059   5.0131  -2.6909   9.0579   1.8615  20.8154   9.7676
+#&gt;   -1.6295   4.7432  -4.8663  -3.7719   1.1519  -3.5064  15.8951   2.9470
+#&gt;   -5.0773  -6.4014   0.0620  10.7953  -1.3257   3.5800  -8.8108   3.2778
+#&gt;   -7.8998   4.0090  13.3705 -15.3735  -3.4188   3.6468  -1.2002   8.7656
+#&gt;   -2.2991   7.6728  -7.0008  19.0875 -10.4308  -5.2981  -6.5476 -10.0805
+#&gt;   -7.6098  -5.1771   5.7833   4.8127  -7.2318  -4.9819 -16.0469   1.4642
+#&gt;   -1.1119   2.1865   5.0103   0.7753 -13.7961  -3.5765  11.4363  11.7848
+#&gt;    5.3285   0.6544  -4.3014  -3.7732  -6.6757  -4.6680   9.2389  -3.0111
+#&gt;   14.9243 -18.9852   1.5592   5.8168   3.2338   7.5046  -9.2199  -1.9998
+#&gt;    5.8316 -16.0814  24.0642   1.9585  -3.5264  -6.1538  -9.1210  -2.4992
+#&gt;  -13.5651  -5.3863  -7.1816  17.6731 -13.0657   7.3824   1.5581  -5.1607
+#&gt;   13.1493   4.8888  -5.5545   0.1581  -0.9223  11.4728   1.0332  -0.4469
+#&gt;    1.6263   3.3358   0.6984  -6.3469  14.6469  -9.9027  -5.6818   1.4462
+#&gt;    7.5497   7.6437 -17.7139   3.6806 -12.6042   4.5378   7.5226 -16.3628
+#&gt;   15.6895 -10.2874  11.3661  -2.3743  -5.0022   2.3989   6.9662   1.3720
+#&gt;  -14.7766  -5.7658  -9.8210  -3.8484   9.2184  -5.0199 -10.5909   4.3750
+#&gt;   -3.3873   3.1920  10.2297   8.2557  -5.2131 -17.5189   8.5251  12.7148
+#&gt;   -4.4102  13.5598 -20.2984 -13.7089  -0.8403   3.0563  10.2671  -2.3559
+#&gt;   15.3842   5.0043  14.6081   9.0073   4.0625  -3.2320  -2.1856  -2.8255
+#&gt;   -6.7683   7.2567   4.1482  -2.9104  -3.1228  -1.1391  10.5654   6.2337
+#&gt;    4.1170   8.3056   0.6752   6.1380  -1.4899   6.1811   7.6111  -5.8355
+#&gt;   10.8976 -17.5127 -13.1384   4.9627 -13.8710  14.4422  -2.8103   3.5432
+#&gt;    7.4061   7.1341  -0.5488   2.8806   0.9175  -2.6211  14.1834  31.8526
+#&gt;   12.3116   0.2204   1.3383 -13.2747  -2.8086  10.5885  -3.7188   6.7941
+#&gt;    0.7298  13.8047  -3.9216  -1.4572   7.6106   1.1530   8.8140 -11.3650
+#&gt;   -5.5953 -14.6633   2.4963   2.2599  -9.8951   4.3039  -9.2876  -5.9474
+#&gt; 
+#&gt; Columns 49 to 54  -7.3718   0.1724  -6.6997   9.3962  -6.5077  -3.1607
+#&gt;   23.4490  -5.6180   1.4565  -7.3384  -1.8619   4.1763
+#&gt;   -0.4912   2.9085  -4.5126  -6.7124   5.1127  -3.9138
+#&gt;   -6.0853   3.5843   3.1338  -1.4118   0.0586  -5.0726
+#&gt;   -0.1282   6.5454  -0.7282   3.4190   2.0427   5.7622
+#&gt;    4.9669  -6.5563 -12.0750  -7.4261   1.5339   1.5126
+#&gt;    8.9537   5.2251  -4.8157  -0.5032   3.1482  -3.5577
+#&gt;    0.7154   0.0666  -7.2519   2.2708   5.7407  -4.2322
+#&gt;    0.7558  -4.7962   7.9415  11.3416  -3.6383   4.6629
+#&gt;   -3.2890  -0.2584   6.8779  -1.2153   3.1597  -6.3844
+#&gt;   -8.5711  10.4096   9.5683  -2.4923 -11.0658 -11.7025
+#&gt;    2.7361   6.0141   1.2901  -1.3441   6.5208  -3.1641
+#&gt;   -6.9538  -8.9816   1.3693   2.0008  -4.1646   2.1112
+#&gt;    0.4658  10.2723   5.8848   5.8259  13.0186  -2.1762
+#&gt;  -11.7348  10.4812  -4.3369   7.0428  11.4794  -2.2028
+#&gt;    7.4112 -19.0299   7.3612   1.7996  -5.1023   3.9962
+#&gt;    4.9686   9.4521   8.7250  -0.0540   1.7540  11.1828
+#&gt;   -8.2419 -10.3812   8.1248   3.6823  12.9724  -3.7185
+#&gt;    2.8024 -12.8766   3.1866  10.2768  -4.1207   5.6507
+#&gt;   -1.8880  14.1398   4.2122 -11.5026  11.6045  -3.2251
+#&gt;   17.1062  -8.0658  -5.3075   0.5834  -0.4006  -2.4423
+#&gt;    8.6359   3.7877   3.2643   3.3035  -3.9406   0.0275
+#&gt;   -6.1192  17.6782   7.8204  -6.9942   0.3734  -7.6414
+#&gt;   11.0851   6.6772   3.3842  12.7506   5.1887   7.7000
+#&gt;   14.8332  12.8735   2.1239   3.1732   5.5896   2.0718
+#&gt;    7.2465  -5.4545   3.8073  -0.9183   7.1958  -0.9900
+#&gt;   -5.3976   8.9492   1.4537   1.1187   2.3930  -5.6091
+#&gt;   -4.3078   4.0652  -8.3012 -19.9772  -1.4070  -4.8790
+#&gt;    1.6937 -16.0303   0.0988  -2.5211  -4.9906   9.1394
+#&gt;    0.4467  17.8078  17.1774  -6.6270  12.9747   0.3961
+#&gt;  -11.5709  12.6492  13.1732   3.6568  -3.5198   0.5396
+#&gt;   12.2177   7.3196  -6.3694  -1.8782   3.0797  -0.6957
+#&gt;   -2.2324  14.6047  -0.8001  13.8972  -4.3510   5.3483
+#&gt; 
+#&gt; (18,.,.) = 
+#&gt;  Columns 1 to 8  -4.3911   5.4973   1.8484   1.9810  -5.6260  -0.5801 -15.8868   6.4607
+#&gt;   -5.0212  -5.5476  -7.6658  -1.4139  -0.4396   2.2836  -5.9576  -3.4828
+#&gt;   -5.4325  -0.4088   2.7157  -8.4452   1.3914  -0.2093  11.4034  -4.1957
+#&gt;   -0.7190  -5.5509  -2.5918 -12.2629  18.8802   5.8711   1.6982   2.9475
+#&gt;    0.4850  -0.5469  -7.6596  -6.5053   8.6592  13.8677 -21.4914   5.5691
+#&gt;   -1.4620  -6.3371  -3.4570 -11.5751   7.5142  -8.2088  -6.2354 -10.5874
+#&gt;    3.2021  -4.3906  -7.1871   6.7673  10.5294  -7.6768  -3.2767  -2.1408
+#&gt;    5.6072   1.9384   3.5318   2.1610  -5.8641   2.2806   9.2333 -28.8971
+#&gt;  -13.9020   7.9718  -2.5898  -4.9725  -1.6510  -4.6305 -11.0958   1.3548
+#&gt;   -2.8662  -1.9706  -2.5360  -8.9197 -11.1579   2.4577  11.8483 -11.4991
+#&gt;   -4.1481  -2.0496   9.5735  -1.7490 -13.7465  -1.5257   5.2256  -3.2159
+#&gt;    4.4538   2.4108 -11.2044  -3.1763  -8.6365   4.4801   8.9262  -7.8778
+#&gt;   -5.8780   0.8098  -5.3211   2.7571 -13.6454  -0.8512  -0.6691  23.8480
+#&gt;    2.6878   0.7378   0.9408  -2.5093   5.4975  -2.4101  10.2283  -5.4472
+#&gt;   -3.9325   1.6233  -8.9639  -7.7578   5.8783   2.2209  -9.6604  -4.5376
+#&gt;   -0.4879   1.8802  -7.2441  -4.4474  -0.9615   6.2233  10.8690 -14.4598
+#&gt;   -0.7505   5.6795   7.6239 -14.6193  -3.4399  14.3743  -3.5018  -0.3198
+#&gt;    0.3104   9.1646  -9.0805  -7.8947  -5.8951   6.6030  17.4737  -4.6253
+#&gt;   -0.9207   7.8309  -8.4844   0.3523 -14.6567  -8.1897  -1.8762   0.6771
+#&gt;   -0.2594  -6.4661   2.0367  -5.6595  13.3032 -10.9836  -9.3047   7.9829
+#&gt;    0.5242  -3.2823   0.8238   8.1593  -0.9138  -7.1425 -15.7148   2.3371
+#&gt;   -5.6616   8.2627  -0.1257   6.7354   5.7825  -9.0814  10.6219  -0.4017
+#&gt;   -0.0744  -2.6918   8.6814 -16.0938  13.5337   4.3540  -6.6646   9.3643
+#&gt;  -16.0383  11.2291   1.4746  11.9791  -1.7396  -6.4750  -7.7020  10.6767
+#&gt;   -3.9449  -7.1955   2.8869   4.3371   7.5388  -6.7228   3.2023   5.3479
+#&gt;   -0.9995   1.3863   4.3533   4.3314  12.2726 -15.1262  -0.1044  -1.0410
+#&gt;   -5.1356   3.6454   7.3923   1.7739   0.4303   6.9459  -2.3443  -5.2192
+#&gt;    2.5240   0.3547  -3.5044   4.3585   7.6539   3.9435  -0.9854   1.7310
+#&gt;   -5.0543   3.4230   0.4311  -1.0042  11.0358  -8.9949   3.6246  18.5413
+#&gt;    6.2018   1.1566   0.8363  -9.6308   8.2723  10.8877  11.3666 -13.1488
+#&gt;   -5.3042   4.6787  -2.5811  -0.9704  -1.7178  -2.1207  -6.6445   6.3538
+#&gt;   -1.3022  -8.0480   8.6952   1.9588   3.4376 -23.2013   0.1420   5.0201
+#&gt;   -1.6073  -7.1981   0.7532  -1.0488 -11.1509  -5.6928  18.6251   1.7090
+#&gt; 
+#&gt; Columns 9 to 16   7.6927   1.4596  13.0779   5.9595   0.2990   5.2401  -9.1685  -4.2340
+#&gt;  -19.2284  -8.5529  -1.4149  11.4336  10.2557  -4.8607 -16.2310   3.1786
+#&gt;    5.2186   0.3449  -5.2681  -1.7545   6.1023   1.9320   2.4020   7.8770
+#&gt;   -9.2335   9.5286  -9.9603  -1.4513  -8.5965  12.4769   2.8153   6.8837
+#&gt;    1.7237 -10.9426   4.0296  -7.5040   1.0439  12.2765  11.8449  -5.4733
+#&gt;   -8.3496 -20.7904  19.8178 -11.1970   4.5073  31.3651 -11.1798 -22.0608
+#&gt;    5.2126   3.1247 -12.3427  -2.4787   3.8732 -15.9318   6.2172   2.2279
+#&gt;   11.4959 -15.5201   4.8571   5.7709   6.6421 -19.3220   8.6169 -13.8630
+#&gt;   -6.1861  -8.2190  11.6082  -3.3027  12.0702 -15.4140  -8.6867  -8.4738
+#&gt;  -10.8348   2.5051 -12.9579  -0.2584  -6.0464   3.2162   3.6795  -5.0257
+#&gt;   11.7900  -7.5695   7.3021   8.0940  -0.3820  -3.9853  -7.1611   1.7104
+#&gt;   -3.5033   4.5399   8.8900  -1.7946   7.6678   3.3881  13.1164  11.0010
+#&gt;  -15.3460   4.2485  -2.2991  -9.0732 -13.1693  -5.2471   1.1429   3.8441
+#&gt;   -5.8969   2.7695   5.1643   1.2429  10.3890  -2.7055  -6.0126  16.3083
+#&gt;    8.5507  -1.9368  -6.1374   1.8580 -17.3128   9.9511   5.4091  -5.9266
+#&gt;   -5.6076   2.7743  -1.6566  11.0053 -11.9033 -10.1063   9.6658   6.6356
+#&gt;   -9.3610  -7.9023   0.7382   2.5932   3.6247   9.8894  -2.4512  -8.8385
+#&gt;    8.9837  -6.6969 -10.0175   4.2795  13.5702  -6.6152  17.8442  -2.8948
+#&gt;  -15.7365 -10.9606  -4.4099 -13.5879   1.5271  -2.8249 -17.4113   4.3666
+#&gt;  -17.7783  -4.8231  -1.6511  -9.8570  -1.3040  10.0096   5.7238   5.7183
+#&gt;    6.9739  16.3830   1.5270   4.4500  -2.1711  -5.9195   0.3354  -5.8155
+#&gt;   -8.6017   2.4775  -6.2702  -2.7926   4.4337   1.5416   7.3396   9.1336
+#&gt;    9.5212   2.5359   2.9281 -11.4843  -7.7582   4.5839  -7.2885  -9.0561
+#&gt;  -17.8258  13.1264   0.3094   1.1877  21.5266  -2.0017  -9.3248   1.4888
+#&gt;    4.2441   2.4671   1.9099   3.5147  19.1303   0.3357   1.9493   0.9496
+#&gt;    1.1641  -8.2187  -1.0420  -9.8006 -15.3996  12.6898 -12.9848   1.7504
+#&gt;    5.8278   8.0116 -16.8496  -6.5744  -1.8002   2.4645   7.1946   2.5312
+#&gt;   -6.0108  -4.0062  -7.3233  -2.1383  15.2241   4.4813 -12.1431  -2.0238
+#&gt;    0.3557   4.6276  -8.4960   0.1130  11.3615   2.8329  -9.3739   2.4401
+#&gt;    5.9013   1.5485  -7.0356   0.5447   1.4222   0.1700   6.2146  -8.5471
+#&gt;   -1.7002   3.5084 -14.1795   7.6799  -6.8140  13.4071  14.9627  -9.0716
+#&gt;   15.5815  -4.9196   4.5648 -13.9866  -3.6228  -2.3111 -12.6678  -5.1569
+#&gt;   -3.9958   7.5002  -8.8081   8.3486  11.7740  -7.7243  19.9508  -6.5645
+#&gt; 
+#&gt; Columns 17 to 24   1.1347   2.5347  -2.8868  -3.3789  -5.0710   8.2973 -12.6579  16.1731
+#&gt;  -11.5549  -4.9196  -7.8892  -5.2357  -6.8317  -5.2695  -6.6647  -5.2959
+#&gt;    2.1417 -11.1165   5.8018  -9.2976  -4.1183  -7.0978  -5.2528 -15.3420
+#&gt;    2.0741  -5.3868   0.3100   0.1789  25.6531 -13.9063  17.0234 -10.6949
+#&gt;    5.3593  16.3898   1.4933  15.7945   2.9275 -12.2839  18.2287   1.2301
+#&gt;   19.4208   6.8820  -1.1850  -5.9584  -0.1833 -13.4068  30.5236 -17.4262
+#&gt;   -5.4856  -7.0090   2.3754  -5.4628  -8.2741  -9.6361  -3.6279  -5.0407
+#&gt;   -6.5071   3.6426 -22.6544  -4.3219  13.8968   8.3234 -15.4233  -7.8661
+#&gt;   -3.1956   9.5999 -14.0066   7.7170 -12.2087 -14.1807  -3.1584   6.2416
+#&gt;    3.7422   4.2694   7.8953  -1.7984   1.1673   4.2236  15.3837 -13.6411
+#&gt;   14.2686  -2.6712 -18.7982  -5.5391  -2.4245   4.4291  -3.1279  -3.5791
+#&gt;    9.1189   0.0225 -11.5063  -5.8858  -5.8044   1.0564  23.4499  -4.9465
+#&gt;    1.7194  10.7878 -15.6375   9.9320  -2.6833  21.7980  -1.9116  -0.0044
+#&gt;    3.9943 -17.6837   9.9010  -0.0358   6.4139  -3.3084   0.7509 -13.5968
+#&gt;   -5.7291  12.0397  -1.5095  -5.5217  -5.2337   6.6550  14.6906   0.7526
+#&gt;   -5.1364 -18.5721   4.7661 -10.3700   6.6646  -7.4164 -13.3842  -5.5470
+#&gt;   -1.0711   6.4061   5.4447  -6.3443  -1.3465 -15.8051  25.1032  -5.0133
+#&gt;   -6.1450  15.4360  -8.0648  -7.5083  -6.3784 -12.4713   3.3846  -5.0549
+#&gt;    4.9954   3.8794   6.9410  -0.0043  -1.6452 -11.6989  -7.0418  -4.5863
+#&gt;    6.1630  -2.5573   1.2800   4.9611  -8.7608  -5.2573   4.6300   2.7334
+#&gt;   -4.4718  -5.3688  -1.9845   5.1600   0.4501  -7.0452 -16.2698   5.8914
+#&gt;    0.2358  -0.7003   5.2938   9.5373  10.0164  14.2044   3.2263   3.9779
+#&gt;   -3.0353   0.2633   8.0138 -12.7800  -3.9198  -3.9936  17.1907  -3.8080
+#&gt;   -2.8725  -9.3989  -8.8084  -7.4599  -4.0630  18.1741  -5.8362   3.5222
+#&gt;   -3.4204 -19.2696   3.7916   8.3952   2.4625  10.1270   1.0981   4.5824
+#&gt;   -8.6086  -2.0380  13.7854   7.8583   7.4181   2.7249  -1.5048  -2.7189
+#&gt;   -4.1792  -1.9535  -0.2705  -0.0452 -15.2096   3.8035  -8.7664   5.4589
+#&gt;   -0.6465  -2.6124   8.4671  -2.1038  -4.6120  -9.2987  16.5657  -7.5394
+#&gt;    4.7889 -10.3359   1.2910  -3.1306   8.0027 -12.9061   9.8752  -2.3037
+#&gt;   -1.2102   9.4415   4.3168  -4.2345   9.6880  19.6927  29.5038   6.3476
+#&gt;  -13.8122  -3.7279  -6.0201   7.7620  -3.1934  -0.1139 -16.0485   8.4671
+#&gt;   -5.2801   9.7130   7.0902 -12.6219  18.6469   1.0631  10.1957  10.0229
+#&gt;   13.3858  -6.0519   5.5020  -3.9310   5.2673  -0.1442   2.0037  17.0299
+#&gt; 
+#&gt; Columns 25 to 32  -3.8738   3.9220 -12.5185   6.4561   0.0799   5.5301   1.6065   5.2487
+#&gt;    1.4690  -6.9025  -2.4601  -4.2432   4.1212   1.4848   0.4622   1.9091
+#&gt;   -0.8780   2.4436  -8.1781 -10.4016  -8.1332   8.3900   4.3642   9.5500
+#&gt;   -1.2560  -3.2221  -0.0284   6.5983   1.0221  -5.9329   5.6549   7.3524
+#&gt;    0.7555  -4.4902 -11.1791 -23.5908   7.3431  -8.1840   8.0723   0.2110
+#&gt;    1.8144  -1.8614  -1.3185  -4.0768   6.4284  -3.2070 -11.3596   4.9718
+#&gt;   -0.1717   1.2119 -11.3041  -4.1853  15.7426  -1.5315  12.3409 -11.2353
+#&gt;   -2.0192  -1.1751  10.6557  -5.3755  -8.8156   0.9094 -11.1758  -7.5134
+#&gt;    9.2353  -9.6842  -9.6485  -2.3479   9.3271  -5.3431  10.0440   8.1175
+#&gt;   -4.5267 -11.8635   6.1133  -2.1379   6.6027   1.5822  -5.5570  -7.7038
+#&gt;  -11.3767  -6.1185  -0.4371  -6.2906  -8.4045  -3.0140  -5.9909   9.3692
+#&gt;    5.3097  17.9685  -5.6408   2.5726   0.1373  -2.0362  -4.4878  13.1866
+#&gt;   -0.2003 -18.0279   2.8840   7.6310   5.8091 -16.0159   2.7507  -9.0756
+#&gt;   -4.3898   6.3232   9.0166   0.3971 -13.0539   9.7434  -0.7697  14.1789
+#&gt;    4.1869  -5.1755   2.1565  -3.0330   4.2686  -5.8388  -1.7538 -22.2728
+#&gt;  -13.1864  -5.5980   2.3033   1.6994  -5.2467   4.6051  -3.7793   2.0549
+#&gt;   -2.9787 -10.3587  17.2497 -18.6068   2.2088  -2.4777  -5.9609  -0.2677
+#&gt;    6.8232  -0.2212  -1.2915  -2.6751   8.7935   0.6610  -1.5027   7.6642
+#&gt;    0.4888   3.7794   8.7645   1.5360  -6.1666  -8.0935  -0.3406   3.8793
+#&gt;    2.7377  -5.2790   0.5362  -1.7392  -6.0426   4.2360  12.0210 -16.1900
+#&gt;   -2.1322   2.6928  -3.7036  -4.7929  -3.1372  -2.6002 -12.2420   8.5769
+#&gt;    2.5564  -7.6670   9.2444  -8.3447  -1.8659 -14.2068  -6.3827 -13.5205
+#&gt;   -8.6856  -5.2431 -15.0486  -4.4206  11.4039  -5.5736   4.4667  13.0206
+#&gt;   -8.1077  -0.0292  -2.1540   1.7868  -6.3482  -1.5963  13.8036  11.8692
+#&gt;    7.5198   6.7072   7.6203  -0.7738 -11.0714   6.2842  -9.9921  -5.9641
+#&gt;  -13.2923  17.4194 -21.3287   2.7288   5.8553  -2.0353  -6.1919   3.6219
+#&gt;    7.8293  10.1106   8.7589  -6.9789   9.7184  10.9510   7.1282  -3.0372
+#&gt;    1.8077   0.6001   3.8597  -9.2054 -12.7888  12.6369   1.9466 -11.6437
+#&gt;    7.4352  -3.4234   4.3904   7.3170  -8.2280  11.0989   4.0127  -4.6733
+#&gt;    3.7689  -1.8453  18.5053  -8.0752  -7.2611   6.8307 -10.3481  -6.0274
+#&gt;    4.2616  -8.9127  -6.4516   1.1904  -7.8745 -11.4709  15.2262  -2.8091
+#&gt;  -11.1368  12.1557 -11.0857   6.0072  -4.2969   8.1991  -7.0327  -9.7476
+#&gt;   -1.2019  -2.4516  -1.3659  -0.5371 -10.7445   3.9925 -10.0558 -12.4318
+#&gt; 
+#&gt; Columns 33 to 40  -8.5350   0.0875   9.6567 -15.5151  12.1594   0.2972  -6.5993  -4.7356
+#&gt;   -5.1730 -10.0375  -5.3178   8.7592   3.4126  -6.6100  -0.4462   2.7282
+#&gt;   19.0838   6.3240   1.1039  -7.6161 -12.7377   5.3932 -16.2631  10.2438
+#&gt;    4.3135  -3.5741  -5.6618  -5.5723  -6.1734  -9.6130   2.0577   5.0947
+#&gt;   -1.6806   0.4366  -7.4311  -0.9253  -9.2197  -4.2929  -7.8661  -4.0315
+#&gt;   -6.7793   6.6410  -2.3139  -6.6337  -5.3094   0.3094  -5.8473  -0.0352
+#&gt;   -1.0434   1.2943  -9.3600   5.2054   4.6067  10.3721   0.2044  -1.7094
+#&gt;   -2.7223  -0.6669   2.0470   9.0565  -3.1020   3.7929  -4.7129  -4.6957
+#&gt;    1.7211   3.1714  -3.0869   5.9535  -1.7719   6.4213  -1.8118   2.8516
+#&gt;   18.6640  -4.9960   2.3894  -0.6455   0.7528 -16.8513 -22.7559  -6.7013
+#&gt;   -1.3260  21.0229  11.0511  11.1625 -12.5773  -8.3422   1.8465   5.4793
+#&gt;    3.0718   3.7418   1.0446  11.1606  -5.0686 -10.1680   1.5955 -10.0100
+#&gt;  -10.1795   2.2387   1.0678   2.0980   0.5267  -4.3053  -2.7337   4.6885
+#&gt;   -1.4100  -0.6188  -1.5873  -9.6573 -17.2998  -1.8995   6.9750   6.5807
+#&gt;   -2.1986 -12.3286  -0.7730  -2.9716   6.0537  19.9523   4.4846 -20.3818
+#&gt;    2.2540 -10.7448   6.4450   5.5551 -14.0252 -12.9895  10.7741   0.6383
+#&gt;   -3.2762  -6.2054 -12.4799  -2.6375 -16.2244   4.1103  15.7387   0.7658
+#&gt;    2.2558  -1.1088   8.7188   8.7920 -14.1842  -0.3710  -2.6653 -16.4752
+#&gt;    9.9595   0.9145  -7.1987   0.1316  15.5304 -11.5121 -17.1075 -18.9906
+#&gt;    0.7257  11.0916  -5.9431 -12.6732  10.7616   7.0296 -18.1772   0.6022
+#&gt;   -8.4137  -3.2743   4.7066  -0.6106  -1.2597   6.7818  -0.1954   8.4503
+#&gt;    2.2677   2.4595  -2.5608   7.4922  19.8550   7.0307 -10.3801  -6.9346
+#&gt;    3.2337  -5.8513 -10.3497   3.4862  -2.5652  -1.0337  -8.7844  -4.6261
+#&gt;   17.0239  -3.5383  19.6143   2.4119   1.9370 -10.6873  -7.2749   7.7795
+#&gt;   15.5725   4.1913   0.5852  14.9739   7.9995  16.0967  -4.3813   2.8909
+#&gt;   -6.8186  -3.1106  -4.5133 -11.0466  13.4662  -5.2009   0.3870   0.9202
+#&gt;   -0.6736 -15.3876   9.1537  -6.8619  -6.4745  16.5738   6.1942   0.9360
+#&gt;   -3.0737  -8.4914  -6.8561  -9.2497   2.5473   5.0432   9.7131  10.9237
+#&gt;   -7.1638 -11.7686  -5.0851   2.8317   4.6247   6.7577   9.9449   7.3291
+#&gt;  -18.6639 -18.1522  -1.8617  -2.1253   2.2573  12.0296  19.0306  -1.1478
+#&gt;    3.9248  -7.9585  -0.6073  -8.6901  -2.2986   4.8878   9.5865  -7.5159
+#&gt;   -6.4216  -1.9025  -1.6467  -9.5660   0.5932  -0.9738  -7.3584   0.2400
+#&gt;   -1.2851   0.1969  19.3340  12.5870  -6.2235 -10.5510 -13.2139  -0.5947
+#&gt; 
+#&gt; Columns 41 to 48   2.0407   2.9510   2.1377  -3.1482  -3.5517  -1.6021  14.0215   5.6081
+#&gt;   -2.8634  -1.2369   5.2037   9.4677  -2.8090   8.5956   7.9898  -0.5333
+#&gt;   -0.7450  -0.5756  31.8037  -5.6067   4.3648  -9.8439   9.4060   0.9765
+#&gt;    5.5924   4.6208  -4.7390   1.3896   5.1584   8.9603 -19.1332  11.0267
+#&gt;   -7.9844  -1.0559  11.0491   1.2988  -5.2304  -8.4710  -5.0629   5.1838
+#&gt;   -6.3647  -8.2324  -8.2835   9.3915   6.6549   2.4155 -17.9305   5.7850
+#&gt;   -7.2929  -3.4562  10.0331  -1.7906  -5.8117  -7.5337   3.8928  17.7967
+#&gt;    0.0798   9.0012   8.6507   9.0891 -11.2270  -6.5867   3.6516  18.7555
+#&gt;   -6.5055  -3.0581   0.1773  -7.4135   8.0395  -6.7013 -16.4906   0.7733
+#&gt;    6.1016   2.0575   2.5795   0.0249   8.2600   5.5781  -1.7872   6.3839
+#&gt;   16.5275  -0.9233   7.0861  -4.6934  -7.0417   2.9589  -4.6347   3.9824
+#&gt;   -0.4278   8.0586  -5.4852   2.8071  14.5673  11.0391   1.6705  14.5835
+#&gt;   -2.8442 -12.2410   2.2064 -10.4224  -3.4541   0.6471   8.3707  -9.5317
+#&gt;    5.1208  13.7774 -21.4315  16.4837   4.1118  -1.6831  -6.3328  -3.8604
+#&gt;   -3.4582  -5.1599  11.4076  21.8845   8.7755  -3.6688  -0.1227  22.9978
+#&gt;   -4.8443  19.5697  -7.3775  -2.9987  -1.4183   2.7953  -1.1646  -5.3746
+#&gt;   10.0052  -6.8885  -7.1253  11.0419   9.9484  -7.6375  -7.7870  10.2694
+#&gt;   -1.6002  23.8070  -6.2378  -9.8259   5.4546  11.1698  10.0522   5.9604
+#&gt;  -19.6452   2.8931  -2.6409 -10.8057  -8.9000  -0.9451   1.7805   0.4884
+#&gt;   -0.8447  -8.7494  13.6706   9.4764   7.5104  -7.8797  16.1753  10.0951
+#&gt;    1.0271  -1.0085   5.8542  -2.4994   3.6851   1.7053   1.4021  -0.8466
+#&gt;  -13.5364   5.9332   9.0672 -12.8885   0.8499 -11.0641   3.9007  16.8223
+#&gt;    7.2468  13.2686  21.9044   3.8828  -4.1238   3.4955 -10.0625   6.3312
+#&gt;   -7.9251  -1.5882   8.9027 -10.5558 -13.2154 -10.4163  10.8657 -12.6363
+#&gt;    4.4245  -8.0527   0.3378  19.5951  -3.7173 -10.0441   1.2527  -1.2587
+#&gt;   -6.4083   0.5048  -2.7162  -9.1202  -9.5379  -1.4137  11.4220 -13.2740
+#&gt;   -2.2673  -4.0698  17.3785   1.1086  -5.3697   2.8517  16.9722  15.4926
+#&gt;    4.7849   0.3172 -14.7810  12.6112   5.7173  -9.8020  -8.7283  -3.6317
+#&gt;  -12.4094   8.4149  -1.3401   4.7318  -2.2228  -6.4433  -2.3998  12.2818
+#&gt;   -1.0780   1.9622 -18.8152   7.4658  -4.2106 -13.1520 -15.2373  22.1706
+#&gt;   -8.8288  -0.7321  15.0267  -6.7328   2.3793  -3.1096   7.7510 -11.4905
+#&gt;   12.4244   2.2718  -4.3188  13.4401   3.7834   2.9043   3.9654  -4.6028
+#&gt;    3.2978  -2.8197  -1.1388   1.0209   6.0064   8.2707  11.6162  -5.2744
+#&gt; 
+#&gt; Columns 49 to 54  15.6366  -2.3198 -10.2642   1.5620   8.4289  -4.5042
+#&gt;  -13.2333  16.8606   5.2800   3.8640   1.8557  -1.4473
+#&gt;   -8.5576  -8.3046  -3.4812   0.9038  -0.0236  -3.5858
+#&gt;   -4.5940   3.1661   4.2015   8.0996  -4.4305  -1.9093
+#&gt;  -14.5974  -7.1334  -1.0637   3.5828   1.2339   2.5546
+#&gt;   15.3339  -4.6737   7.5107 -15.1962  10.1448  -1.9309
+#&gt;   -1.2980  -5.4444   1.6974   6.4495  -4.8496  -8.1780
+#&gt;   10.3719 -23.1087   9.7942   6.6577   2.8608  -4.1214
+#&gt;   16.3583  -0.6505  -6.5720  -1.6248   1.3788  -0.4460
+#&gt;   -0.9532  -7.5573  12.7045  10.1062   2.0182  -1.4121
+#&gt;    1.0502  -1.7704  -5.8809   6.1531   0.9003  -0.1029
+#&gt;    4.9284  -3.1047   6.9632  -4.9418  -7.1467  -0.6355
+#&gt;  -10.0474  11.3646  -8.2997   0.2088   4.3690   0.6673
+#&gt;   -6.8664  13.9372   4.2527  -3.8616  -2.4935   0.8499
+#&gt;   -2.9825  -6.4267  -2.6215   1.2597   4.8453   5.8249
+#&gt;  -12.8910   2.5260  14.1257   1.6857  -5.3704   4.9627
+#&gt;    3.3596  -1.9308   1.8196   6.9653   3.4050   7.2779
+#&gt;    9.6907  -0.6020  12.7136  -3.0734   2.4047  -0.6476
+#&gt;   -0.0868  -5.7963  -6.1393   5.7635   3.1106  -5.9301
+#&gt;    3.9145  -6.2789  -2.0478   0.5966   2.0519  -1.6803
+#&gt;   -1.7202   1.0289  -2.8785  -9.7041  -9.5608   3.8213
+#&gt;   -2.7978 -21.7646  -8.9645  14.0012  -1.5626   0.9232
+#&gt;   -6.1989  -0.9096  -6.5068   3.1514   6.2610  -1.1243
+#&gt;  -13.9085   2.9254  -2.3032   1.3641  -5.4963  -8.0123
+#&gt;   -2.2217   3.5599  -0.3479  -0.7171   4.3949  -8.4972
+#&gt;    6.1102  -6.4740   6.4523 -12.9943   0.6849   0.2070
+#&gt;    0.6102   6.2528   8.6411   1.7952  -4.9242   1.0878
+#&gt;   -0.0187  -8.1872   8.5016  -5.4746  -3.8439  -3.6778
+#&gt;   -9.5806   1.2556  -1.5308   0.2019   2.0435   1.0044
+#&gt;    3.9516  -3.5034   0.2421   7.7355  11.8162   5.8535
+#&gt;    0.2947  13.0730 -19.5379  -0.3044   5.2665   7.0298
+#&gt;    9.6607  -5.9342  12.9901  -2.8921  -7.3424   0.0711
+#&gt;  -10.5470   6.4672  13.2806   1.4521   0.7254  -5.5319
+#&gt; 
+#&gt; (19,.,.) = 
+#&gt;  Columns 1 to 8   1.0767   5.5533  -4.7036   9.9412  -1.1217  -2.2419   1.8039  -5.7265
+#&gt;   -8.6722  -5.3671   9.6119  -5.1046   0.2392   1.5390  -1.9327 -11.1522
+#&gt;   -1.5390  -9.6345  -7.3554  16.1637  -0.7063  -2.6086  -5.6490  13.4125
+#&gt;   -1.3161   6.6821   9.2491   9.2681  -1.5992 -10.0013  -5.6260 -14.3100
+#&gt;    4.1478   1.8199 -13.8644   9.2892 -11.3088 -17.8347  12.9773   1.0151
+#&gt;    4.2774   0.4376 -12.1486  -1.4250 -11.3629   1.0010  14.5349   2.7159
+#&gt;   -9.5957 -10.0090   9.3964 -11.6175   9.9439   1.5080  -4.2625 -14.0303
+#&gt;    1.8417  -3.3999   3.4323  -9.7912  13.9317  -0.4858  -2.4816   3.5579
+#&gt;   -1.1186   3.8074 -10.4497   0.0204   0.5021  14.5979  12.0954   9.1226
+#&gt;   -0.0734   2.5938  -1.4849   3.2009 -11.6996   4.1094  -8.8224  16.3809
+#&gt;    0.7290   0.7407 -13.0315   9.0456   7.0214  18.5311 -14.6049  21.0846
+#&gt;    6.7994   5.7353   9.3056   5.6686  -1.1127  -3.9791  -8.0180  -9.2956
+#&gt;   -4.5172  -9.8966  -1.6582   2.0723  -8.5300   8.9774  -4.6183  -8.2331
+#&gt;   -1.9031   5.4573   3.7323   9.5858  -2.3931  -3.7597 -14.8737 -11.7221
+#&gt;   -1.2665   7.6661  -8.5460   7.0667   0.4723 -13.6319  -6.8802  10.5505
+#&gt;   -1.7393  -4.5569   8.2047  -5.7268  -5.8031   6.0560  -1.6962  -2.7832
+#&gt;   -2.9019  -9.2061 -13.3871   3.5014   0.2425   4.3444   2.0498   3.8161
+#&gt;   -2.4064   0.5608  10.4820  -0.3968  -4.0915   9.8314  -9.2122  -0.0182
+#&gt;   -4.4806   0.9583   1.7148   6.4891  -3.7685  12.5118  11.5523   1.7418
+#&gt;   -4.1795   4.3758   2.3889   3.8275   5.5741  -2.8142   5.4183 -10.0492
+#&gt;   -2.5554  -4.5916  -0.9749  -7.9303  -7.7465  -1.9804  -3.6414  -1.7364
+#&gt;   -0.2133  -3.2456  -1.4034  14.3690  17.6698   8.4782  -5.1666  18.6391
+#&gt;   -2.6984   1.6757  -1.8009   9.5193  -5.9498   1.4157   9.3605  -1.7934
+#&gt;    2.0156   3.8572   0.4427  14.9490   8.4395  -0.4469  -6.9477 -14.0243
+#&gt;    5.4003  21.3595   0.1373   1.2464   6.0059 -10.9409  -4.1660  -4.8872
+#&gt;    4.0334 -10.4847   5.6587  -4.9414   3.0759  -4.8128  12.8768  -1.0747
+#&gt;    1.5269  -4.5568  -2.0269   5.0052  -1.8830 -16.4411 -14.7579   6.1195
+#&gt;    1.8340   9.0677 -12.6624   1.7791  -0.0315  -4.7311   8.5713   5.6756
+#&gt;    2.8952   3.3245 -10.5208  -1.0060  -3.9409  -7.1061  -1.5845  -4.5052
+#&gt;    9.5655   2.2102 -10.4304   5.0988  13.1694  -2.3234 -10.7082  -4.1711
+#&gt;   -3.5340   9.0905   5.9303  12.0301 -16.5272   4.6653  -3.9841   3.0749
+#&gt;    3.4951  -6.8877   7.5635  -1.8781   4.6857   2.5944  13.6946  -0.5487
+#&gt;   -2.0527   3.1539  -6.7138   6.3938  -1.3217  13.6770  -8.9408   6.8614
+#&gt; 
+#&gt; Columns 9 to 16 -14.0036  -6.9333  -7.5914  -7.3397  21.1018 -20.7922  -7.4453  -2.9514
+#&gt;   15.3105   2.2971   0.2982   4.3908  -0.2667  -6.4674 -13.3505  -1.4617
+#&gt;   -6.1115   2.5714  -7.5683  11.4539   1.3155   3.3049 -21.5070  19.7832
+#&gt;    4.9959   1.4983   9.3627   3.7017 -10.2146  26.5324  -2.2526   5.2295
+#&gt;  -12.8752  19.7129  -0.4196  -7.5762  -2.0572  13.2759  -4.4405  -9.2029
+#&gt;   10.0756   2.7735 -23.5013  12.2906  -5.4782  21.6938 -17.0761  -4.0252
+#&gt;   14.6526   6.0746   4.7952  13.9198  -3.3412  16.1222 -16.5424  -3.8929
+#&gt;  -12.3913  -2.0690 -13.4521   7.9808   6.5945 -14.5495  -2.6231  -0.5771
+#&gt;   -9.7536   9.1040   9.0998   4.7534  -6.1598   8.4980  -7.8844  -9.6756
+#&gt;   -0.1418 -16.9912  -0.5980  16.0907   0.1257   8.7807   2.0439  -1.3475
+#&gt;   -3.1518  -2.1738  -6.6052   6.8677   1.5873 -14.2726   1.7766 -10.5044
+#&gt;   -2.8683  -5.1831 -10.5308 -18.5447 -12.5690  -1.3005  -3.5881 -11.1699
+#&gt;    3.7412  16.8748   3.9050   9.9851  15.3700  -2.9613  -5.7636  10.6608
+#&gt;   -4.2591 -12.5602  -3.5333   3.8479   8.8281  -7.0624   6.9942   5.4780
+#&gt;    8.9278  -4.0972   0.0137  -4.8261  -4.0591   3.0358  11.8793   4.4076
+#&gt;    8.2928   4.9787   6.3531   4.0125  -0.8324  -8.5832   0.3094   7.7585
+#&gt;    7.5487  -6.4172 -12.2168  14.1711 -11.3847  16.0068  -8.3022 -10.8496
+#&gt;  -10.7014  -9.5926  -0.8300  -0.6476   3.2910   5.1632  -0.4048   2.1139
+#&gt;   -5.1289   5.3220   8.1721   0.3316  -0.0183  -1.2238  11.9751   4.5372
+#&gt;   -0.4399   7.5658  -5.5506  -0.7805 -14.4911  -0.9694  -9.7239  15.7855
+#&gt;    6.4175   4.7623  -4.1142  -0.3659  -2.9082   0.1668  -7.6900   3.3325
+#&gt;   12.2541  -4.5770  -4.3425  -3.8493  -7.1113 -17.3472  -5.5664  -3.4450
+#&gt;   -7.3949   2.0393  -7.3935   0.2332  -7.9718  13.4079  -3.7224  -2.4832
+#&gt;   -4.1343 -18.4931  -1.6467 -11.0502  -0.3584 -12.0707  -9.2867   4.7150
+#&gt;    2.5687 -21.6220   4.2097  -8.2748 -15.1219  -3.1143 -11.4862 -14.7020
+#&gt;    3.6943   0.4919   7.0880   8.1900   2.3499   4.6997  11.1992  16.1555
+#&gt;   11.3095   2.7934  11.0052   9.1714   1.3916  -6.2684   4.4728 -10.0944
+#&gt;    4.7105  -5.1521   2.2129  -2.0156   1.4167   3.6537  -3.3794   0.4796
+#&gt;    5.8399   3.3100   8.1257   4.9947  11.2183   4.3283  -7.2079   8.7814
+#&gt;   -7.1103 -10.6905   7.1750   1.3918   1.9821   9.5506  -4.6820 -24.4189
+#&gt;   -8.6839   9.8079  -4.0095 -13.7002  16.0389  -5.6545   7.1707  -1.1640
+#&gt;    4.9739  -0.7280 -13.5759   0.1589  -8.2647  -3.7519   9.5167 -11.3627
+#&gt;    4.5451   0.6956 -10.7291  -7.4561  -4.6505  -7.3631  -4.7628 -28.0731
+#&gt; 
+#&gt; Columns 17 to 24  -3.8539  -2.0230  -4.8859 -11.9436 -22.1095  -5.6628  -5.5931 -13.9555
+#&gt;    9.4386 -11.0877  -7.7920   1.4645   9.1822  -4.6735   3.8736  13.9348
+#&gt;    1.1258   3.4778   3.1203  -1.8828  16.5443  14.8341  -2.4198  13.1858
+#&gt;   -1.4624   3.6322   7.5670  24.5240  18.2129   0.3216  -0.8468  -6.3034
+#&gt;    5.6501  -0.1130   7.7071  24.8891   7.6820   5.8292  -2.0203  -7.4934
+#&gt;    7.5183  12.9281   9.2345  12.9773 -14.5725  15.2097  -5.0126   6.9257
+#&gt;   10.3476  -9.5865   4.2492  -0.4222  -1.4051 -22.3373  21.0602   0.2441
+#&gt;   13.8562  -4.1246 -11.0882   6.7276   1.1437  14.5107   9.8191  -8.3293
+#&gt;   22.9158 -10.9553   1.8614  -6.1393 -14.1027   3.3065  -4.3564  15.2460
+#&gt;   -2.5361   5.8835  10.0634  10.9973   1.2540  11.9511 -12.0903  -2.0412
+#&gt;    7.4133   5.8439   7.5822  -8.0505  11.4378  -2.9357  -7.5842  -0.7917
+#&gt;   -1.2279   1.6481  -0.9516   6.5344   5.2117   4.4362  -8.1969   0.8317
+#&gt;    8.7589 -13.4882   5.3295  -5.6887  -3.2925   5.1488   5.2311  11.4647
+#&gt;  -14.5725   0.6747  -9.5241  -7.4326  10.4742  -2.8029  -6.1621   0.9784
+#&gt;   -2.6426 -23.0778   0.8946  12.7232   1.5908  -0.3250 -11.8063 -12.0918
+#&gt;    4.6674 -16.3255   0.2027   5.4052  10.1676 -15.8522  -3.5531  -9.6944
+#&gt;   -6.6342   5.1806 -11.5974  -1.7326  -1.8143 -11.4650  -5.1670  12.2587
+#&gt;   -1.6934 -12.3489   0.2702   3.2438  -8.1793 -12.0089 -19.8978  12.2621
+#&gt;  -11.0990  -2.8589  -5.5567 -11.1047  10.5503  -6.1740   0.0674   1.6390
+#&gt;   -6.9366   5.6578   6.1890   1.4972   9.6568  24.4663   0.9538  15.1331
+#&gt;   -4.2098  -9.1040 -11.4043  -5.8215  -4.2459 -18.2579  12.9748  -2.4634
+#&gt;  -16.1518  -5.9413  -5.0830  -8.7746   6.3358   1.4073  15.6339  -5.8825
+#&gt;    5.7514   2.2376   8.7201   6.7791  -7.1000  -1.2431  -6.1248  14.9285
+#&gt;   18.2804   4.7080   8.9722   3.6387  -0.8141   5.1707   8.3539   7.4838
+#&gt;   -6.5839  12.5141  -3.7244  -6.3505  10.4050  -3.5125 -13.8111  19.2087
+#&gt;    3.7694   3.4359  -3.1777   4.5373 -13.3302  10.7501  -3.6951 -17.2893
+#&gt;   -3.1303   0.0702   2.6647  -9.0818  -6.5239  -7.3000 -10.3534   4.1345
+#&gt;   -1.6505  30.5225  14.7744  -2.7572   1.8382  22.8058 -15.6121   2.9228
+#&gt;    6.1693  -2.7620  -0.2159  -3.6507  -9.1278  13.1196  10.4311   0.6940
+#&gt;  -15.8986   3.7884 -11.8322 -23.4437 -10.0117  12.9315  -7.4701   2.8522
+#&gt;   -5.2591 -14.5160  12.0403  11.5505  -9.5525 -14.6422   1.0221  14.5150
+#&gt;   -2.2123  23.6488  -8.6307  -2.5041  -1.4410   1.0480  -1.4282  -8.2588
+#&gt;  -10.4613  -1.3295   1.1473   6.4935  -6.8058  -4.4212  -4.8149   1.6436
+#&gt; 
+#&gt; Columns 25 to 32  -9.6950   5.8274  -2.7130   7.8220   0.2628  -8.4575  -0.8611  -2.0891
+#&gt;   -0.6262  -7.6070  10.3993   2.3214  -4.4756 -17.3426  -5.3815   3.6312
+#&gt;   -3.7904  -8.9074  11.7960 -15.4559   2.9198  -3.6039 -18.5744   2.5687
+#&gt;    1.0892   5.1254   1.9500  -0.2883  -5.9760  -1.8075  12.4245   1.2843
+#&gt;   -3.4794 -13.5702  10.2551 -18.4022   4.0738   6.1874   4.3489  10.5939
+#&gt;  -13.8473  -8.3958  20.2520  -2.2035  11.2639 -12.5962  10.4556 -23.7736
+#&gt;    5.4967  -0.0896   1.8114   5.0066   9.4052   3.1489 -13.4869  21.5895
+#&gt;  -14.4500   2.4206   5.2329  -3.3601 -14.6554 -11.6877  -4.2139  10.0467
+#&gt;   -4.6730  -7.9645   8.5485  12.7164   0.3668  -3.0959  -2.8246  -2.3478
+#&gt;   -2.8326  19.0015   2.2847   1.0802  -2.5495  -7.1782   5.1540  -5.5282
+#&gt;   -7.0435 -18.7726  -0.9276   2.9793   7.8034  12.0036  15.0883  -8.5713
+#&gt;  -10.3140  10.2727   9.5996   8.0028  -9.0517  -2.4783  -1.7422  -0.5073
+#&gt;   12.4743  10.3130   3.6449   3.9975   1.7386   4.4202   1.0105   3.8690
+#&gt;  -26.0719  -2.5164 -14.8735 -10.9506  -7.5253   1.9838   8.8550  -7.7213
+#&gt;    3.2727   2.7772  15.2995  -7.4337 -21.3232 -17.8804  11.6360   0.8503
+#&gt;    0.1637  11.9308  -3.0914  -3.8390   5.9435  -6.4313  -0.5033   2.3434
+#&gt;  -19.7685 -25.8448 -22.1217  10.0720   6.8253  -7.4429   3.5742   8.5442
+#&gt;  -12.8470  -3.7712  -0.5459   9.1840   0.1786 -23.6156 -13.7719   7.9354
+#&gt;   14.0543   3.6764   8.9798   2.1178   8.6580  11.0799  -3.9781 -10.0693
+#&gt;   -6.5107  16.1062   1.6875  -5.3284  -9.7218  -9.3557  -3.5293   0.6473
+#&gt;   11.4312  -2.4190  -6.2085  -5.4153  -8.2941  10.3688  -9.1526  17.0744
+#&gt;   -8.0095   4.4340   9.9769   8.8788 -12.9031   7.2673  14.6156   8.1925
+#&gt;    3.3411 -13.2837  -0.9161  -9.1904  11.4688  -3.5172  11.5384  -8.2214
+#&gt;    0.6083  11.5753 -15.9066  14.4976   1.0073  19.7245  -9.7316  -5.6104
+#&gt;    5.7921  14.3940  -1.5066  -1.9038   3.0477   5.1525  -6.0519 -14.2574
+#&gt;    5.7377   0.6755   0.4734  -2.9928   1.3565   4.9964  10.1169  -1.9241
+#&gt;   11.6454  11.2528  -1.9511  11.1411  -7.6800   0.4682   3.3893   7.9758
+#&gt;   -8.2559   4.2834   8.2124   7.4970   0.2958   4.1579  -6.5515 -10.6424
+#&gt;   -0.0748 -10.6938  20.9447  -6.5912   3.3622 -14.7842  -8.1799  -8.8367
+#&gt;  -29.9325   2.2621  -1.3697  21.0621   1.0445  -4.1829  10.6821 -12.7333
+#&gt;   10.7188  -6.6811   2.9166 -17.8202  15.5555  -5.1319  -6.5339  -5.7573
+#&gt;   17.2279  -6.4359 -11.4349   4.5974   9.9781  -0.8728 -17.6340  -4.9185
+#&gt;    9.8801   8.1995   7.8229  -4.0455   6.4705  -8.6501  -6.0530  14.8059
+#&gt; 
+#&gt; Columns 33 to 40 -16.8134   0.9345   6.0130   3.2657   4.0334  12.5669  -3.8526  -7.4433
+#&gt;    4.8040  -7.5728  -8.6687  -4.9897  10.4568  -0.3818   2.9275   7.0264
+#&gt;   17.1487  17.1811   1.8131  -1.6268   3.2504  -1.6103   2.1148   4.8444
+#&gt;    4.3901 -14.4819   4.4570  -3.3131  -4.5546   9.1669  -4.3040   5.5706
+#&gt;   -5.8960  -3.2672   8.1288  -3.7381  -2.1924  22.5451   2.3667   9.7284
+#&gt;  -18.2572   5.4731  11.3168  13.8111 -14.0473   0.3453  14.3634   3.4614
+#&gt;   11.5613 -18.0741   2.7858   6.7078   4.9347  -8.2687  12.4922  -6.4614
+#&gt;   -5.2524  -8.8967   4.0937  -0.9864 -10.7034  18.0523  -4.1173   2.9440
+#&gt;   -7.9853  -3.3826  -1.6270  -9.9387  -0.3615   0.6658  -3.2412 -16.5801
+#&gt;  -20.2413  -3.5176  13.3472  -8.2069   1.5136   7.9762  -8.3682  10.7648
+#&gt;   -3.7595 -11.8931  -1.3305  -8.0434 -15.8344  -6.0567   1.9021  -2.7385
+#&gt;   -5.2238 -10.1297  -4.3264   1.0212   0.9195  -4.5202 -14.3401  -1.9053
+#&gt;    1.1637  19.7997  -1.2302  -3.6157  10.0791  -6.5270  -0.1198   0.9702
+#&gt;   21.7248  -9.0051   0.7113  -6.1913  15.0238  15.0909  -7.3654  10.6735
+#&gt;    7.9912  -5.0314  10.6472   5.0058 -11.3351   3.3964  -3.5761  12.9362
+#&gt;   -3.6125   4.4261   6.5276   1.2179   9.4282  -2.5485 -10.9319  -1.7716
+#&gt;   13.3769   3.6996   2.7557   6.6103  -3.1640   5.6600  15.0018   2.5374
+#&gt;    4.6061  -6.1245   1.1185   4.9069   3.9026  -9.1939 -12.4780  17.3670
+#&gt;   -3.2972   4.5357  -3.6831 -12.5809   5.1991   7.6571  -7.7334 -16.0696
+#&gt;    4.4995  -3.4061 -18.2221   0.1657 -17.2780  18.2298   4.7350  -1.9094
+#&gt;   -4.8294   9.7842  11.0807   7.1513  14.7856 -11.5080  11.0651  -5.7143
+#&gt;    1.3501  -6.8370 -11.7194  -5.1865 -18.3552  -2.8589  14.2047 -23.2365
+#&gt;    7.9918   1.8035   3.4218   3.9900   5.4208   6.7179  -4.7383  31.7702
+#&gt;   -9.9291  -6.0875 -19.0885 -27.4261  11.9650   0.2927   4.1253 -10.5055
+#&gt;   -5.5741 -25.3679  -2.6794  -6.6270  -4.9728  -0.6329 -14.6550 -16.3996
+#&gt;   -8.0340  20.9211 -18.7240   3.0184   0.9541   8.7990  -0.4756   2.7910
+#&gt;    9.2895  -9.3025  -8.1933   0.8929   4.0431  -3.9183  -4.1492  -4.6099
+#&gt;    1.2121   1.7955  -3.7963   7.0246  -2.8241   7.2822  10.2972   6.1966
+#&gt;    9.2500  21.6554  10.0764  -3.8195   1.0120  -0.5537  -0.8079 -14.6708
+#&gt;    7.1617 -10.3057   3.1645   3.6164 -22.4676  10.5733  -3.8868  -2.0448
+#&gt;    1.6206  -5.9916 -17.1144   5.1716   5.7639 -15.1969   4.0864   4.3495
+#&gt;    5.8028  -2.6665   2.5533   8.6684   2.9800   7.9136   2.4781  10.7375
+#&gt;   11.7868 -12.8855  -3.9318   0.7253  -8.3808 -15.1319  -0.2693  10.1590
+#&gt; 
+#&gt; Columns 41 to 48 -16.5444  -4.6287   2.0314  -1.7905  -4.3152 -15.1268  -3.5579   8.1316
+#&gt;    8.2578  12.1657  16.9449  -4.7423  10.7516  -5.3044 -13.8603  -8.9339
+#&gt;    5.1447   3.9740  11.7261   1.3244   3.9746   7.0768  -6.8797   4.1232
+#&gt;    7.0068  -9.2230   0.2787   0.7377   7.8000   0.7086  16.1432  -8.6581
+#&gt;   18.8627   6.4956  16.8406  17.1304   3.2918   3.1755  -3.4407   4.3053
+#&gt;   -4.4693  -1.4799   6.3924   1.7607  -1.3521  -4.0490 -14.7393  -5.2026
+#&gt;    1.0006   1.6647 -13.1156  12.6615   8.3567  -6.5948 -15.2573   7.6170
+#&gt;   -3.1967   4.9423  -4.7073  11.9696   9.9641  -9.7513  -0.7074   6.3584
+#&gt;   -0.4424   8.1667  -5.0007   0.7535  -9.9669 -12.6861 -11.3693  22.1647
+#&gt;   11.3647  -1.3075   7.4077   0.7178  -2.3886  -5.3439   3.9297  12.7206
+#&gt;   -2.3143   2.2302  -7.5687 -31.6458  -2.5112  18.9413   4.5091   9.2022
+#&gt;   -6.8183  -8.6902 -19.0367  -7.5001 -12.1462  -5.3157  -3.3004  -7.1680
+#&gt;   -6.7658   2.4393   6.3413  -8.4221  -5.2463  10.0096   4.4987   4.1712
+#&gt;   -7.3988 -14.1535  11.3764  -4.7804  18.2793  12.4318  11.0025 -13.3344
+#&gt;    4.3526   2.2977  11.8006  -1.2596 -24.6957   3.3288   7.7652  13.5084
+#&gt;   32.4653   8.8435   9.7412   8.9345  -2.9832  -1.6264   4.4315  12.2145
+#&gt;   18.7072  -0.1537   7.3249   1.2747   6.8130  15.6451  -5.6066   1.3597
+#&gt;   -1.5957  -5.7835  -7.2907  -4.1485  -2.1856  -5.6710   6.6938   0.1075
+#&gt;    2.2760   2.9900  -7.1284  -5.2640   0.3314   5.4664  -5.5271   3.6673
+#&gt;    1.9755  -3.5697   5.8237   4.3192   2.5900   1.1106  -3.2682  -3.8377
+#&gt;    1.5259  -1.8319   7.3504   1.0233   8.1935  -9.2719  18.0310  -7.7718
+#&gt;    3.9253  -0.7988 -11.2892  15.1197  -1.6806  11.3325   3.1717  15.6728
+#&gt;   16.1292  10.9454  15.4373 -15.8961   0.9757  -9.1515   8.7367  13.7797
+#&gt;  -19.1597  -8.2421 -16.0136  -7.3302 -11.1742  -0.3500  -1.6897  -6.1324
+#&gt;  -18.3563   7.7518  -7.3949  -7.3812  -2.3338 -13.3431  -7.0959 -10.0065
+#&gt;  -11.7076  -5.5947  -0.5833  -1.9684  -8.2818   1.4432  12.8440  -6.4148
+#&gt;  -14.6965 -15.2207 -16.3022   1.4870 -10.0094   0.8704  -8.3260 -19.1158
+#&gt;    0.3915   0.7392  13.2677   7.1700  -0.7309  -7.1851 -14.4830 -18.1360
+#&gt;    2.5043   2.7492  12.0607   4.7828   6.7693  -7.3077  -5.7348 -11.0223
+#&gt;   -7.0619 -29.3620  -5.8536  13.0872   7.5924  -0.6150  19.7299  -5.6692
+#&gt;  -11.1114   4.6274   8.7869   6.7250  15.6530  23.9817  -3.5366   6.9809
+#&gt;   -5.1315   8.5302  10.0534  -6.3178 -12.8647 -10.8650  13.3111 -11.5817
+#&gt;    4.6134  12.7501 -11.3046   3.7991  -6.4595   5.7412  -5.2063   1.9086
+#&gt; 
+#&gt; Columns 49 to 54  -5.3282  -3.2266 -24.8687 -10.5104  -4.4439   0.3163
+#&gt;   10.6467  -5.4864   2.7542  -0.7171   0.6800   2.0922
+#&gt;   11.7002  10.2993   6.8480   0.6578  17.3164   6.8616
+#&gt;    3.6185 -18.4867   4.6401   4.8705   8.8435  -0.9595
+#&gt;   15.3718   6.8593  14.3551  -5.4822   5.1366   6.1417
+#&gt;    3.1160   9.3470  -6.9606  -0.0294  -3.2393  -4.2674
+#&gt;   15.7196  -1.8938  -7.7399   0.0166   2.4686   3.5718
+#&gt;    6.9156   4.8775  -7.8181   1.5310  -2.6594  -0.0888
+#&gt;    3.7727   7.0841   3.0279  -0.1965  -8.6360  -1.8248
+#&gt;   -8.9784 -22.4677  -8.8660   0.3609   9.7901  -3.4502
+#&gt;   -4.3600 -10.9410  -5.0836  -3.6610  -5.5090  -5.2339
+#&gt;    9.3056 -11.7218 -16.2098  -2.7711  -2.8203  -7.1593
+#&gt;   -3.8441  -9.5755   2.5076   1.8440   6.0984   3.8825
+#&gt;   -3.3463   6.1568  11.1819  16.0915   8.8665  -5.5455
+#&gt;    0.6073   0.7520  -7.7763  -8.8121  -3.8470   1.5911
+#&gt;  -11.1212   5.0537   2.1586   9.1666  -4.0692  -2.6149
+#&gt;   -4.3383  20.6349   7.5382   1.8032  -7.1276  -2.8183
+#&gt;    6.8676  -6.5512 -13.4673  -7.1652   2.2067  -3.7488
+#&gt;    7.9024  13.9121  -0.7281   0.6378  -1.9312  -0.9425
+#&gt;    1.3689   8.0473  18.6780 -11.5955   9.1147  -0.9635
+#&gt;   14.7714  -2.3066   3.0062  11.6449   2.5655  -0.1592
+#&gt;    1.5034   1.5028  -8.2844   1.8364   0.1551  -0.7391
+#&gt;    3.1903  -9.6047   8.1067   1.0414  14.3937   0.3468
+#&gt;  -16.7015  10.6327   1.9860   5.6200  -2.7926   4.6682
+#&gt;  -15.6331 -11.5799  -9.7693  -5.3290  -3.0923   0.8328
+#&gt;   13.5332   2.7533  10.4474  -6.1639   1.1230  -7.9291
+#&gt;  -13.7635  -8.4313  -3.9145  -5.8836  -5.1160   1.8951
+#&gt;   -0.4143  18.4338   8.0957  -5.2611  -2.2945  -2.4783
+#&gt;   -4.6864   9.1464  19.1237  -1.0431  -8.9225   2.5674
+#&gt;  -14.4443  -4.0471  -0.9250 -16.8284 -11.6586  -4.4408
+#&gt;   -4.8694   3.6092  10.6813   0.2885   4.8268   5.6979
+#&gt;   12.0734   2.5061  -1.6331   1.3433   4.1259  -4.5298
+#&gt;    0.4723   3.8604 -15.6149   2.0348  -7.0461   7.8540
+#&gt; 
+#&gt; (20,.,.) = 
+#&gt;  Columns 1 to 6  1.1531e+01  2.3331e+00  6.3103e-01 -1.0747e+01 -1.5133e+01  4.4643e+00
+#&gt;  -2.2385e-01 -2.7180e-01 -6.3996e+00 -6.3756e+00  2.2921e+00  5.5969e+00
+#&gt;   2.5445e+00 -3.3252e+00 -1.3379e+01 -2.0287e+00 -1.2538e+01 -2.3544e+00
+#&gt;  -7.9704e+00 -2.9126e+00  4.3689e+00 -9.9908e+00  1.1437e+01  4.4595e+00
+#&gt;   2.0039e+00 -1.3223e+00  3.4075e+00 -1.4926e+00  3.8605e+00 -1.1802e+01
+#&gt;  -5.0724e-01  1.2890e+01  4.9779e+00  1.1625e+01 -2.1969e+01 -4.6666e+00
+#&gt;  -1.3738e+01  4.1919e+00  9.3695e-01 -1.6508e-02  4.2423e+00 -3.7811e+00
+#&gt;   5.0110e+00  2.4779e+00  4.7327e-01  4.6535e+00 -1.9481e+01 -6.0639e+00
+#&gt;   1.1933e+00  3.6992e+00 -7.0678e+00 -8.9165e+00  2.4942e-01  6.7458e+00
+#&gt;   5.3256e+00  1.0658e+01 -4.6348e+00 -8.8527e+00 -4.4973e+00 -7.1829e+00
+#&gt;   8.4373e-01  7.9508e+00 -7.1050e+00  9.3471e+00 -9.7752e+00 -7.6119e+00
+#&gt;   4.2762e+00 -3.7394e+00  1.2157e+01 -4.3657e+00  5.3120e+00 -5.2498e+00
+#&gt;   4.2000e-01 -1.4188e+00 -3.5735e+00  6.4012e+00  1.5363e+00  6.0558e+00
+#&gt;  -8.0048e-01 -6.7814e+00 -5.5306e+00  3.3690e+00 -1.9144e+00  1.9936e+01
+#&gt;  -1.0017e+01  3.4138e+00 -7.7884e+00  6.4376e+00  1.3389e+01 -4.7419e+00
+#&gt;   2.2364e-01 -3.2301e+00 -7.9332e+00 -1.2841e+01  4.2582e+00  1.8374e+00
+#&gt;  -4.4040e+00  4.5913e+00 -6.1392e+00 -1.2316e+01  3.4954e+00 -1.1173e+01
+#&gt;  -2.2540e+00  3.2320e+00 -5.9081e-01 -5.3758e+00  3.4184e+00 -5.9921e+00
+#&gt;  -2.6445e+00  9.4472e-01  6.2066e+00  6.4064e+00  2.4575e+00  3.5394e+00
+#&gt;   2.5899e-01 -2.1468e+00  1.3507e+00  6.7354e+00  5.0850e+00  3.9325e+00
+#&gt;   7.8227e-02 -5.2020e+00  2.0918e+00 -4.1192e+00  2.2587e+00 -3.3492e+00
+#&gt;  -3.0478e+00  1.3071e+01  4.8683e+00  6.8381e+00  3.9535e+00 -8.2438e+00
+#&gt;  -3.8922e+00 -7.1828e+00 -1.1203e+01 -7.5899e+00 -1.4261e+00  1.1600e+00
+#&gt;   7.7494e+00  7.2217e+00  7.2037e+00  5.6197e+00  6.8696e+00  3.1197e+00
+#&gt;   2.1424e+00  5.3235e+00  1.3030e+00 -9.0769e+00  1.5741e+01  1.3477e+00
+#&gt;   3.4640e+00 -1.1975e-01  6.6389e+00 -4.4436e+00  1.1040e+01 -1.5087e+01
+#&gt;  -7.3618e+00 -6.3839e+00 -3.2193e+00  9.0840e+00  1.5088e+01  9.2531e+00
+#&gt;  -1.0281e-01  1.0142e+00  1.4126e-01 -1.8113e+00  6.3071e+00 -1.0526e+00
+#&gt;  -1.0200e+00  9.5171e-01 -4.2450e+00 -7.9963e+00  1.2206e+01  1.0523e+01
+#&gt;   3.7093e+00  9.6528e-01  4.0630e+00  1.1129e+01 -3.9470e+00  5.5017e+00
+#&gt;  -3.9856e+00  3.6659e+00  1.6750e+00  1.0511e+01 -8.8842e+00 -3.3586e+00
+#&gt;   3.0463e-01  4.3400e-01 -2.9486e+00 -3.8748e+00 -8.4585e+00 -1.4113e+00
+#&gt;   6.9056e-02  5.3960e+00 -2.7156e+00  7.2227e+00 -6.3782e+00 -1.2635e+00
+#&gt; 
+#&gt; Columns 7 to 12  7.9277e+00 -3.3597e+00 -1.2598e+01  6.1640e+00  8.6454e-01 -1.4289e+01
+#&gt;  -1.8540e+00  4.6359e+00 -4.1865e+00 -5.1530e+00  2.3492e+00 -8.1270e+00
+#&gt;   9.1591e+00 -9.1336e+00  2.2569e+00 -2.5140e+00  1.1702e+01 -9.2587e+00
+#&gt;  -7.6002e+00  6.2050e+00 -1.3418e+01 -7.7016e+00  1.2713e+01 -2.0433e+00
+#&gt;  -3.8421e+00  1.9191e+00 -4.5832e+00 -2.3329e+00  9.7887e+00 -2.1843e+01
+#&gt;   6.0568e+00 -2.4789e+00  7.5743e+00 -9.3153e+00 -6.3102e+00  5.5473e+00
+#&gt;   9.7806e-01  1.3823e+01 -3.1965e+00 -6.0133e-01 -3.1926e+00  1.0389e+00
+#&gt;  -1.1084e+01  9.0128e-01 -8.2534e+00 -1.1053e+01  5.0405e+00  9.5454e-01
+#&gt;  -1.9832e+01 -1.6358e+00  7.7623e+00 -1.1648e+01  1.3623e+01 -1.1985e+01
+#&gt;   5.8391e+00  3.5531e+00 -3.1517e+00  1.0902e+01  6.9349e+00 -1.1707e+01
+#&gt;  -1.2187e+01 -1.9061e+01  2.7532e+00 -8.7982e-01 -1.2811e+00 -1.1784e+01
+#&gt;  -9.6197e+00  1.4408e-01 -3.8304e-01 -1.0293e+01 -4.2343e+00 -9.3015e+00
+#&gt;   2.1816e+01 -1.0358e+01  4.4754e+00  4.4965e+00 -3.9147e+00 -8.8297e+00
+#&gt;  -1.3955e+01  7.2483e+00 -2.5442e+01 -7.0836e+00  5.3737e+00 -1.0677e+01
+#&gt;   7.2140e+00  2.3756e+00 -4.7037e+00 -4.8433e+00  1.1872e+01 -8.0447e+00
+#&gt;  -1.3389e+00  1.1117e+01 -1.4307e+01  9.4055e+00  1.2201e+01 -1.6476e+01
+#&gt;  -4.9345e+00  8.2168e+00 -3.9515e+00  4.4601e+00 -1.3724e-01 -1.4649e+01
+#&gt;  -1.0054e+01  1.1957e+01 -9.0555e+00 -1.1360e+01  1.1955e+01 -1.2255e+01
+#&gt;  -6.8150e+00  6.2035e+00 -7.0091e+00 -4.7487e+00  1.4248e+01 -1.3114e+00
+#&gt;   1.1368e+01 -7.3924e+00  9.4632e+00 -9.4340e+00  4.1829e+00 -1.3339e+01
+#&gt;  -1.4158e+00  1.2500e+01 -8.5006e+00  6.2598e+00 -2.9507e+00 -2.1564e-01
+#&gt;   2.3398e+00 -2.4304e+00 -7.3535e-01  2.8813e-01  6.6898e+00  1.7336e+01
+#&gt;  -3.1147e-01  1.6517e+00  1.2610e+01  7.1454e+00  9.4211e+00 -1.4088e+01
+#&gt;  -7.4251e+00 -8.1059e+00 -2.5371e+01 -1.2935e+01 -1.6045e+01 -2.7882e+00
+#&gt;  -1.8432e+01  7.2453e+00 -5.2321e+00 -1.3105e+01 -9.3911e-01 -1.2354e+00
+#&gt;   9.3714e+00 -1.6181e+01  1.5188e+01  7.1519e+00 -1.1995e+01  2.7365e+01
+#&gt;  -1.7714e-01 -1.3419e+01  6.6561e-01 -8.0800e+00 -1.1812e+01  1.2695e+01
+#&gt;  -7.3460e+00  9.6275e+00  2.8878e+00  4.3720e+00  1.5972e+01  4.4419e+00
+#&gt;  -6.1104e+00  1.5898e+00 -7.9875e+00  9.2666e-01  5.0628e+00  7.6020e+00
+#&gt;  -5.2511e+00 -5.0651e+00  5.6202e-01 -1.8023e+01 -1.7160e+00  1.4579e+01
+#&gt;   4.1947e-02 -1.7834e+01  2.7451e+00  1.2405e+00  1.3470e-01 -1.2129e+01
+#&gt;  -5.9979e-01 -3.5876e+00  5.7801e+00  3.4267e+00 -6.0223e+00  2.1237e+01
+#&gt;  -7.8867e+00  3.8303e+00 -4.2517e+00  1.7581e+00  2.2147e-01 -9.6697e+00
+#&gt; 
+#&gt; Columns 13 to 18 -1.3265e+01 -1.3388e-01 -6.2657e+00  1.8457e+01 -2.0316e+01 -8.9335e+00
+#&gt;  -8.2219e+00  2.7945e+00 -1.0981e+01 -1.2172e+01 -4.2599e+00  6.7829e+00
+#&gt;   1.5608e+01  8.9277e-02  3.2390e+00  7.8762e+00  1.2230e+01 -6.7688e+00
+#&gt;   8.7358e+00  5.5304e+00 -5.5587e+00  3.9099e+00 -4.6915e+00 -1.2642e+00
+#&gt;   1.0147e+01  3.6444e+00 -9.1277e+00  1.7335e+01  3.6767e-01  4.4335e+00
+#&gt;   5.7939e+00 -1.8102e+01  1.1411e+01 -8.8106e+00  1.4680e+01  6.0197e+00
+#&gt;   7.5897e+00 -4.5091e+00 -9.6216e+00  1.3621e+01  3.3132e+00 -2.2411e+01
+#&gt;  -7.9518e+00  5.3377e+00 -8.7286e+00  1.1977e+01  1.1974e+01  4.4068e+00
+#&gt;   5.2793e+00  1.2346e+00  1.2539e+01  2.2888e-01  1.6566e+00 -4.3003e+00
+#&gt;   6.2172e+00 -1.6921e+01  4.8339e+00  4.0403e+00 -3.6163e+00  6.3318e+00
+#&gt;   3.7147e+00  1.1907e+00  1.7518e+01  9.2517e+00  9.1858e+00  7.7552e+00
+#&gt;  -1.0452e+01 -2.0280e+00  6.5720e+00 -1.0286e+01 -6.1103e+00  6.2985e-01
+#&gt;   1.8934e+01  1.2379e+01  1.6522e+01 -8.6650e+00 -3.7289e+00  1.0112e+01
+#&gt;   2.5255e+00 -1.0635e+01 -2.0998e+01 -9.0092e+00  1.6594e+01  1.1383e+00
+#&gt;   1.7404e+01 -1.6339e+01 -1.1579e+01 -6.3186e+00 -1.3261e+01 -1.9673e+00
+#&gt;  -9.4890e+00  3.6748e+00  6.6153e+00  7.0064e-01 -1.4821e+00 -5.0903e+00
+#&gt;   1.5503e+01 -9.9915e+00  3.1989e-01 -4.1594e+00  6.4457e+00 -4.3074e+00
+#&gt;   7.8736e-01 -8.5897e+00 -2.0359e+00 -1.3591e+01 -4.8745e+00 -2.1896e+01
+#&gt;  -6.6362e+00 -9.8757e+00 -5.5616e+00  1.8116e+01  7.5799e+00 -1.7781e+00
+#&gt;   8.3797e+00  6.0021e-01 -1.7778e+01  4.7960e+00 -5.5892e+00  1.2583e+00
+#&gt;  -5.1661e+00  5.4751e+00 -8.1088e+00 -5.9602e-01  6.1550e+00  3.6812e+00
+#&gt;   1.0024e+01 -1.3289e+01  4.1283e+00  1.8308e+00  1.7409e+00 -3.9152e+00
+#&gt;   1.5095e+01  1.9088e+00  6.1760e+00  2.3120e+01 -1.4459e+01  1.5964e+00
+#&gt;  -4.3251e+00  1.3200e+00  5.6500e+00  1.0793e+01  1.1772e+01  7.8633e+00
+#&gt;   3.0207e+00  8.6253e+00  3.7148e-01 -8.2103e+00  2.1514e+00  3.3441e+00
+#&gt;  -4.2380e+00  8.7565e+00  5.1626e+00  1.0956e+01 -1.1340e+01  1.2452e+01
+#&gt;  -4.7337e+00 -1.4291e+01 -8.9942e+00 -1.3272e+01  2.6020e+00 -8.6399e+00
+#&gt;  -2.9628e+00  1.7688e+00  9.4542e+00  1.3147e+01  7.1706e+00  3.1746e+00
+#&gt;   6.3297e+00  1.0754e+00  2.7610e+00 -4.0563e+00 -7.0332e+00  1.6860e+00
+#&gt;   2.2331e+00  4.8680e+00  1.0615e+00 -1.3488e+01  3.2870e+00  3.1170e+00
+#&gt;   1.5144e+01 -8.8461e+00  3.1123e+00 -4.4244e-01 -4.8833e+00  4.7693e-01
+#&gt;  -6.3397e+00  1.5592e+01  1.5269e+01  1.5849e+01 -6.6470e+00  2.9379e+00
+#&gt;  -3.5580e+00  2.0140e+00  2.5515e+00 -1.6820e+01  5.1384e+00  9.3253e+00
+#&gt; 
+#&gt; Columns 19 to 24 -4.3144e+00  4.6143e+00  3.7621e+00  1.0236e+00  9.1765e+00  1.1586e+01
+#&gt;  -3.2139e+00 -2.9037e+00  1.1098e+01 -4.6513e+00  1.6938e+00  4.7404e+00
+#&gt;  -1.2711e+01  6.3123e+00 -4.1875e+00 -1.0495e+00 -1.1496e+01 -8.3215e+00
+#&gt;   1.1090e+01  7.4233e-01 -3.5049e+00  4.8077e+00 -4.0711e+00 -1.1453e+01
+#&gt;   1.6252e+01  8.5940e-01  1.1081e+01  4.6098e+00  2.5550e+00  3.6903e-02
+#&gt;  -1.2179e+00  1.0425e+01 -3.1774e-01  1.9092e+01 -1.3186e+01  9.3608e+00
+#&gt;   3.7483e-01  4.5561e+00  6.0095e-01  9.6300e+00 -2.3309e-02 -4.5975e+00
+#&gt;  -1.2796e+01  2.8253e+00  4.3625e+00  7.1202e+00  3.4291e+00  8.9793e-01
+#&gt;   7.0558e-01 -1.2783e+01 -1.1601e+01 -5.2674e+00  4.8640e+00 -8.5976e+00
+#&gt;   1.7004e+00  1.4138e+01 -2.1057e+00 -2.3106e+00 -1.0992e+01 -2.4524e-01
+#&gt;  -9.8371e+00 -1.7429e+00 -1.6777e+00 -7.0428e+00 -4.9677e+00 -1.0101e+01
+#&gt;   8.7128e+00  1.9727e+00  7.3768e+00  6.1230e+00  5.9434e+00  8.9918e+00
+#&gt;   1.5984e+00 -4.9178e+00  2.1932e+01 -5.4599e+00  1.1959e+01 -2.2573e+00
+#&gt;  -6.9175e+00 -4.1474e+00 -2.1975e+00 -5.3939e+00 -2.2144e+00  7.2748e+00
+#&gt;   1.0882e+01  2.0940e+01  3.1680e+00  3.3280e+00  4.6097e+00 -4.0240e+00
+#&gt;   7.1740e+00  6.6605e+00  5.2177e+00 -8.3093e+00 -1.1249e+01  1.3216e+00
+#&gt;   5.9196e+00 -4.6431e+00  2.4043e+00  1.3990e+00  2.1708e+00 -1.1351e+01
+#&gt;  -9.4173e+00  7.0889e+00 -6.5662e+00  3.4494e+00 -1.8142e+01 -2.3038e-01
+#&gt;   6.4866e+00 -4.0087e+00  6.9798e+00  1.2574e+00 -2.6856e+00  1.7630e+00
+#&gt;  -6.4867e+00  8.2717e+00  3.6461e+00  6.8531e+00  8.2888e+00  1.5702e+01
+#&gt;  -1.4982e+00  1.2453e+01  2.7548e+00  8.9527e+00 -1.0024e+00  4.8150e+00
+#&gt;   1.5337e+01 -3.2413e+00  5.8117e+00  1.6994e+01  6.7664e+00 -1.1449e+01
+#&gt;   4.7731e+00  1.0507e+01 -1.6246e+01 -1.7924e+01  7.1068e-01 -3.8325e+00
+#&gt;   1.8137e+00 -7.0561e+00  7.9566e+00  4.3107e-01  1.0817e+01 -5.4474e+00
+#&gt;  -4.6797e+00  1.4865e+00 -1.6564e+01  1.5662e+00 -3.3163e+00 -3.9403e+00
+#&gt;   3.1976e+00  2.9680e+00 -7.3438e+00  1.4924e+01 -2.1774e+00  9.0913e+00
+#&gt;  -3.2800e-01 -6.5956e+00  7.9274e+00  6.7508e+00  1.2148e+01 -9.3619e+00
+#&gt;   4.5274e-01 -2.6912e+00 -7.2021e+00  4.2023e+00 -3.9267e+00  2.5256e+00
+#&gt;   4.3481e+00 -1.2686e+01 -1.1956e+01  1.0051e+01  5.9468e+00 -2.1925e+00
+#&gt;   2.0461e+00 -1.3921e+01 -5.5452e+00  5.2529e+00  2.0601e+01 -2.6521e+00
+#&gt;  -4.6468e+00  1.4440e+01 -7.3033e-01 -9.9996e+00 -8.8389e+00 -1.5808e+00
+#&gt;  -1.2480e+00  9.1248e+00 -2.8179e+00  1.0731e+01 -1.5468e+01  1.7226e+00
+#&gt;   1.3306e+00  8.6867e+00  4.8440e+00  9.5960e+00 -1.1540e+01 -3.9837e+00
+#&gt; 
+#&gt; Columns 25 to 30  5.4699e+00  5.6998e+00 -3.4012e+00 -1.1067e+01  8.6554e+00  6.6413e+00
+#&gt;   1.4069e+01 -9.7582e+00  3.2213e+00 -2.2708e+00 -2.1654e+01 -1.5022e+00
+#&gt;  -3.8468e+00  1.0437e+01 -1.3451e+01 -1.7239e+01  1.4509e+00  7.5753e+00
+#&gt;  -5.2113e+00  2.8510e+00 -6.9533e+00  9.5826e+00  1.6959e+00  4.5609e+00
+#&gt;  -1.4356e+00  1.7240e+00 -2.1492e+01  6.4118e+00 -1.2253e+00 -2.2039e+00
+#&gt;  -2.2775e+00  4.8589e+00 -4.2910e+00 -5.1652e-01 -1.4957e+01 -5.9976e+00
+#&gt;   1.0456e+01  1.3280e+00 -3.5628e-01  2.1212e+00 -8.3177e+00 -3.2242e+00
+#&gt;  -5.4305e-01 -1.2461e-02 -4.7155e+00 -6.2646e+00  3.0596e+00 -1.0798e+01
+#&gt;   6.1351e+00 -6.4744e+00 -3.7152e+00 -7.4972e+00 -3.5417e+00 -6.1697e+00
+#&gt;  -1.1345e+00  5.3915e+00 -1.0067e+01 -4.4847e+00 -5.1633e-01 -1.4962e+00
+#&gt;  -1.7299e+01  7.8617e+00 -5.3225e+00 -5.7595e+00  9.3727e+00  2.8566e+00
+#&gt;  -3.7784e+00  6.6955e+00  2.7104e+00  6.3596e+00  1.0636e+01  7.4172e+00
+#&gt;   3.5782e+00 -2.2840e+01  1.7752e+00  5.2072e+00 -1.1504e+01 -2.6621e+00
+#&gt;  -1.4476e+01  5.4797e+00 -1.4041e+00  3.9727e+00  6.1982e+00  3.7617e+00
+#&gt;   5.4233e+00  1.8527e+00 -1.8342e-01 -6.8265e+00  1.1266e+01 -1.5702e+01
+#&gt;   4.4169e+00 -1.5739e+01 -1.1726e+01  7.3365e+00 -7.8890e+00 -8.5225e+00
+#&gt;   1.2108e+01 -3.1495e-01 -1.5868e+01  6.5639e+00 -8.2699e+00  1.5419e+00
+#&gt;   1.1193e+01 -4.3754e-01 -5.9623e+00 -4.0880e+00  7.9017e+00  8.8718e+00
+#&gt;  -9.3012e+00  1.3934e+00 -5.8818e+00 -1.1752e+01 -8.1547e+00 -5.0979e+00
+#&gt;   1.4485e+00 -5.6431e+00  8.8961e+00 -4.3433e+00  1.0669e+01 -6.7620e+00
+#&gt;   7.8333e+00  1.4045e+00 -1.4081e+01  6.5516e+00 -1.7546e+01 -6.2451e+00
+#&gt;   8.1935e+00  3.1147e+00  2.5687e+00 -8.3399e+00 -3.0574e+00 -7.1151e+00
+#&gt;   1.3414e+00  4.2013e+00 -1.7803e+01 -5.1214e+00 -5.5807e+00  1.5435e+01
+#&gt;   2.8192e+00 -2.4385e+00  1.2972e+01 -3.1511e+00  1.5503e+00  9.6873e+00
+#&gt;  -7.7992e+00  9.0596e+00  2.0192e+01 -7.8699e+00  7.2253e+00 -6.7786e+00
+#&gt;  -2.9993e+00 -1.3195e+01  1.2465e+01  1.7358e+00 -3.1596e+00  3.3080e+00
+#&gt;   6.0420e+00  1.3294e+01  1.7468e+01 -4.0664e+00  1.5949e+00  9.8797e+00
+#&gt;  -3.8004e+00  1.5496e+01 -1.4409e+00 -8.1662e+00  1.1723e+01  1.1117e+00
+#&gt;   3.1524e+00 -2.1490e+00 -1.3218e+00  1.4153e+01 -2.0530e+01  8.6345e+00
+#&gt;  -9.4948e-01 -3.9658e-01  9.1029e+00 -5.6274e+00  1.4035e+01  9.4233e+00
+#&gt;   5.4670e+00 -5.4394e+00  7.3929e+00 -1.1360e+01  2.4601e+00 -4.9093e+00
+#&gt;   1.7102e+00 -4.0448e-01 -8.3279e+00 -4.1029e-03 -1.1221e+00 -1.2746e+01
+#&gt;   1.0214e+00  9.5807e-01  1.8192e+00 -1.2437e+00 -1.7461e+01 -5.0998e+00
+#&gt; 
+#&gt; Columns 31 to 36  1.6438e+01 -1.3438e+01 -2.0719e+00 -1.0913e+01 -7.5714e+00  1.8081e-01
+#&gt;  -8.8542e-01  2.0675e+00 -8.0859e+00  6.9595e+00 -8.2521e+00 -1.8931e+00
+#&gt;   1.0092e+01  1.7633e+01  5.2209e+00 -1.6351e+01 -1.9979e+01  9.8331e+00
+#&gt;  -6.9253e+00  1.1116e+01 -1.1913e+01 -4.0747e+00  4.2845e+00  2.2116e+00
+#&gt;  -1.9262e+00  1.0957e+00 -4.9534e+00 -5.6227e+00 -1.0123e+00  1.1315e+01
+#&gt;   4.2937e+00 -2.6627e+00 -1.2887e+01  4.3937e+00 -5.1109e-01  1.0921e+00
+#&gt;  -7.9952e+00  1.4324e+01 -3.9196e+00 -2.7443e+00  6.3004e-01 -4.6276e+00
+#&gt;   9.4714e+00 -4.8978e+00 -1.0007e-01 -2.0158e+01  1.6750e-02 -1.0488e+00
+#&gt;   3.1494e-01 -6.2173e+00  6.5896e+00  2.9818e+00 -1.0756e+01 -4.3337e+00
+#&gt;   1.3194e+01 -1.4014e+00  2.6727e-04 -1.6177e+00  2.1996e-01  1.5266e+00
+#&gt;   1.9788e+00 -8.5207e+00 -3.9199e-01 -1.0973e+01 -1.4129e+01  1.7517e+00
+#&gt;   9.3051e+00 -2.2612e+00 -7.2882e-01 -1.8655e+01 -1.1064e+01 -5.8109e+00
+#&gt;  -1.1440e+01 -3.3624e+00 -3.3130e+00  9.9564e+00  1.2247e+01  7.4611e+00
+#&gt;   1.4041e+01  8.0288e+00  4.4719e+00  6.5836e+00  5.6681e+00  4.5749e+00
+#&gt;  -1.0175e+01 -2.4834e+00  1.7094e+00  6.6462e+00  9.4967e+00  5.3757e+00
+#&gt;   4.1961e+00 -2.3064e+01 -1.0312e+01  5.6536e+00 -2.5044e+00 -4.9450e+00
+#&gt;  -2.5085e+00 -4.3374e+00 -8.5296e-01  2.7185e+00 -5.6022e+00  6.4684e+00
+#&gt;   1.0241e+01  9.5646e+00  6.8356e+00 -1.2182e+01  4.2921e+00  5.7431e+00
+#&gt;   1.3466e+01  1.3720e-01  1.0346e+01  2.1120e+01 -5.1694e-01 -5.4295e+00
+#&gt;   3.2274e+00  2.0233e+01 -1.4584e+00 -4.0477e-01  6.6957e+00 -4.2500e+00
+#&gt;   4.9743e+00 -2.1034e-01 -1.1772e+01 -5.7535e+00  8.7506e+00 -7.1696e+00
+#&gt;   9.0055e+00  8.2756e+00  5.8166e+00  4.2791e+00 -2.4567e+00 -6.4340e+00
+#&gt;   3.1091e+00  2.3341e+00 -1.7254e+00  2.9272e-01 -1.0347e+01  6.9994e+00
+#&gt;   7.0724e+00 -1.0171e+01  1.3105e+01  1.9351e+00 -1.1987e+01 -6.6013e+00
+#&gt;  -1.6740e+01  1.5330e+00  1.0160e+00  4.8406e+00 -1.2461e+01 -3.5074e+00
+#&gt;  -9.7528e+00  1.1982e-01  1.5609e+00  1.0209e+01  5.4214e+00 -5.4774e+00
+#&gt;  -3.3251e+00 -4.8822e+00  4.5186e+00 -1.5024e+01 -1.3310e+01 -6.2242e+00
+#&gt;  -2.7370e+00 -4.1817e+00 -3.3404e+00 -7.9251e+00 -7.3348e+00  1.0770e+01
+#&gt;  -5.2601e+00 -7.2051e+00  5.9309e-01  1.5419e+01 -1.4580e+01 -6.6892e+00
+#&gt;  -1.4980e+01 -7.9538e+00  1.0906e+01 -3.5722e+00  1.7001e+00  1.7724e+01
+#&gt;  -7.8612e-01 -7.4339e+00 -1.1043e+00  2.8421e+00 -7.2769e-01  2.5934e+00
+#&gt;   1.2884e+00  1.1799e+01  2.6829e+00 -1.6889e+00  1.7691e+01 -9.7053e+00
+#&gt;   1.0168e+01  4.0578e+00  1.3561e+01 -4.0530e+00 -4.0119e+00 -9.7104e+00
+#&gt; 
+#&gt; Columns 37 to 42 -8.2735e+00  3.1403e+00 -1.1959e+01  1.1864e+00 -8.7998e+00 -2.9534e+00
+#&gt;   9.1179e+00 -6.3446e+00  2.2182e+00 -1.5197e+00 -1.2004e+01 -1.0454e+01
+#&gt;  -8.7442e+00 -3.2043e-02  1.6638e+00  5.4491e+00  1.7899e+00 -1.4846e+00
+#&gt;  -1.8708e-01 -7.6960e+00 -5.4669e+00 -7.6985e+00 -7.7320e+00  1.1031e+01
+#&gt;  -2.1632e+00 -7.0626e+00  1.4560e+00 -6.1482e+00  4.3042e-01  1.4981e+01
+#&gt;   5.2970e+00 -1.5019e+01  2.8836e+00 -6.4169e+00  1.1324e+01 -1.0338e+01
+#&gt;   9.4684e+00 -1.3241e+01  4.9726e-02 -2.9331e-01  1.3627e+00  2.9866e-01
+#&gt;  -1.2529e+00 -3.2748e+00  1.9241e+00  9.2193e+00 -1.0986e+01 -2.7363e+01
+#&gt;  -4.8738e+00 -7.7993e+00 -4.7592e+00 -2.0297e+01 -8.3224e+00 -2.2505e+01
+#&gt;  -1.0972e+01 -2.9503e+00  1.1717e+01 -1.6971e+00 -5.3677e+00 -2.8579e+00
+#&gt;  -8.9193e+00 -6.8548e+00 -1.2901e+01 -1.0337e+01  6.7866e+00  3.4333e-01
+#&gt;  -1.5797e+01 -5.8071e+00 -1.9483e+01 -8.7037e+00  5.0029e-01 -1.0541e+01
+#&gt;   3.2845e+00  4.1856e+00 -3.9064e+00 -3.7897e+00  2.1681e+00  7.0478e+00
+#&gt;   1.4918e+00  6.1159e+00  4.3892e+00 -7.7357e+00 -8.0483e+00 -6.6913e+00
+#&gt;  -2.9716e+00 -1.3224e+01  3.6103e+00  5.5090e+00  7.1380e-01  7.3106e+00
+#&gt;  -3.1293e-01  6.9401e+00  7.3920e+00 -2.7220e+00  9.2464e-01  5.9456e+00
+#&gt;   1.5518e+01  1.1428e+01  7.0763e+00 -2.1403e+00  9.3160e+00 -3.3511e+00
+#&gt;  -1.1750e+01 -5.4353e+00  7.5489e-01  4.7460e+00  1.0737e+01 -6.3573e+00
+#&gt;   9.4763e-01  2.6830e-01 -3.0907e+00 -1.5246e+01 -1.6409e+01 -2.3621e+01
+#&gt;  -3.2955e+00  2.3546e+00  1.1154e+00 -2.8251e+00 -8.3575e+00  5.5872e+00
+#&gt;  -2.2356e+00  1.0417e+01  6.6118e+00  1.0563e+01 -4.5935e+00  9.5886e+00
+#&gt;   2.5937e+00 -9.0757e+00 -1.9578e+00 -6.8779e-02 -1.0149e+01 -1.8089e+01
+#&gt;   5.6172e+00  2.3983e+01  5.6968e+00 -3.7683e+00  1.6258e+01  1.9859e+01
+#&gt;  -6.4705e+00 -2.5923e+01 -2.7608e+01 -1.9793e+01 -2.2804e+01 -1.3724e+01
+#&gt;  -3.4172e+00 -1.4107e+01 -9.9054e+00  4.4123e+00 -1.1000e+01  5.2239e+00
+#&gt;   2.5845e+00  2.8300e-01 -3.3345e+00 -1.4225e+00 -9.2023e+00  1.4570e+01
+#&gt;  -5.9657e+00 -2.1567e+01 -2.3819e+01  1.1180e+00 -1.8332e+00 -1.8851e+00
+#&gt;   5.3897e+00  3.4841e+00  3.8876e+00  3.3017e+00  5.1111e+00  3.0648e+00
+#&gt;   1.4952e+01 -5.3567e+00 -2.6438e+00  9.7077e+00  8.9029e+00  6.6048e+00
+#&gt;   1.3372e+01 -5.9119e+00 -1.4201e+01 -9.9048e+00  4.3797e+00 -1.7422e+01
+#&gt;  -3.7871e-01  2.2780e+00 -5.7702e+00 -2.5621e+00  1.3025e+01  1.0764e+01
+#&gt;   3.2211e+00  4.6307e-01  1.1810e+01  5.7527e+00 -5.7293e+00  6.3445e+00
+#&gt;  -2.5170e-01 -1.1096e+01 -1.0406e+01  5.4917e+00 -1.1964e+01 -5.6287e+00
+#&gt; 
+#&gt; Columns 43 to 48  1.0823e+01  1.0227e+01  5.2452e+00 -7.6024e+00  1.0346e+00 -7.9165e+00
+#&gt;  -6.8778e+00 -8.0179e+00 -3.0957e+00 -5.3885e+00 -7.4486e+00 -5.7790e+00
+#&gt;   7.6706e+00  2.3105e+01  7.8097e+00 -1.3647e+00 -1.2281e+01 -1.5098e+01
+#&gt;   2.6246e+00  6.9635e+00 -1.8214e+01  5.7573e+00 -1.0707e+01  7.4098e+00
+#&gt;   2.3787e+00  4.8133e+00 -4.4525e+00  5.3442e+00 -1.4970e+01 -3.1860e+00
+#&gt;  -1.7354e+01  1.6283e+01  1.5564e+00  1.3106e+01 -6.1499e+00 -4.8105e+00
+#&gt;   5.7187e+00 -1.2279e+01  7.5568e+00  1.6225e+00  6.1519e+00  2.5067e+00
+#&gt;   5.5771e+00  7.8960e+00  3.8538e-01  1.5720e+01 -6.6727e+00 -1.1263e+01
+#&gt;  -1.6229e+00 -3.2439e+00  3.1027e+00 -2.5326e-01 -8.8451e+00 -8.2672e-01
+#&gt;   9.0183e+00  4.2075e+00 -1.4198e+01  6.5227e+00 -1.2879e+01 -8.1087e+00
+#&gt;   6.6603e-01  3.8175e+00 -2.9692e+00 -1.5503e+00  1.2798e+00 -7.5683e+00
+#&gt;   1.8157e+01  1.5004e+01 -2.7700e+00  1.1028e+01 -4.5021e+00  4.6181e+00
+#&gt;   7.3547e+00 -5.4684e+00  3.7659e-01 -1.4494e+01  3.1610e-01  1.0328e-01
+#&gt;  -5.1568e+00  1.1580e+01 -1.5136e+01 -1.8530e-01 -7.0306e+00 -5.5930e+00
+#&gt;   7.5433e+00 -1.9105e+01 -6.2412e+00 -5.3192e+00 -3.9712e+00  1.0104e+01
+#&gt;   1.3997e+01 -7.2630e+00 -1.5452e+01 -1.4429e+01 -2.8080e+00  2.6409e+00
+#&gt;   4.5980e+00 -2.1559e+01 -1.2334e+01 -4.2134e-01  7.6498e-01  3.6497e+00
+#&gt;   1.3544e+01  6.7151e+00 -1.9943e+00  8.4802e+00 -1.3441e+01  3.5801e+00
+#&gt;  -2.1211e+01  7.9966e+00  5.1467e+00  1.9884e+00  1.4655e+00 -1.7772e+01
+#&gt;  -6.7888e+00  9.6563e+00  2.9522e+00 -3.2235e+00  4.3726e+00 -6.4796e+00
+#&gt;   1.1923e+01  1.0458e+01  1.1862e+01  6.3710e-01  4.6522e+00  3.3642e+00
+#&gt;  -2.0412e-01  2.3011e+00  7.5140e+00 -6.2739e+00  1.2391e+01  6.0583e-01
+#&gt;   8.2744e-01  4.1622e+00 -1.2777e+01 -6.6249e-01 -2.0946e+01 -5.7807e+00
+#&gt;  -4.2178e+00 -9.3935e+00  1.4077e+01 -1.0468e+01  2.5169e+00 -1.2825e+01
+#&gt;  -7.4545e+00 -1.7324e+01  1.0080e+01  4.3010e+00  6.2536e+00  5.2832e+00
+#&gt;  -1.7112e+01  7.4615e+00 -5.2570e+00 -4.3795e+00  4.2586e+00  1.0613e+01
+#&gt;  -4.2153e-01 -1.6174e+01 -3.5876e-02 -2.6960e+00  5.8841e+00  1.0271e+01
+#&gt;  -1.0999e+01  9.6090e+00  4.4693e+00  3.4537e+00 -4.9400e+00 -5.7933e+00
+#&gt;  -1.1098e+01  4.3504e+00  4.1122e+00  6.6750e+00 -1.3501e+01  5.7151e+00
+#&gt;  -1.8164e+01 -7.8379e+00 -1.6745e+01 -2.6554e-01 -1.3939e+01 -4.3230e+00
+#&gt;   4.1970e+00 -1.3924e+01  8.9203e+00 -1.8577e+00  3.2587e+00  3.2568e+00
+#&gt;  -9.2823e+00  2.3534e+01 -8.9547e+00 -4.0144e+00  1.0853e+00 -1.2542e+01
+#&gt;   6.3050e+00 -1.6179e+00  3.5026e+00  3.5845e+00 -2.6009e+00 -7.2738e+00
+#&gt; 
+#&gt; Columns 49 to 54 -1.2046e+01 -7.2920e+00  6.6618e+00  3.8642e+00  1.1839e-01 -1.1737e-02
+#&gt;  -1.9295e+00 -1.2324e+01  4.3648e+00 -4.7373e-01 -2.2393e+00 -2.6499e+00
+#&gt;  -4.9235e+00  9.2022e+00  8.5990e+00  5.1345e+00  2.7203e+00  1.3626e+00
+#&gt;   1.1801e+01 -3.4244e+00  9.3624e+00 -2.5441e+00  4.0397e+00 -1.0988e-02
+#&gt;  -7.1344e+00 -1.4162e+01 -1.0796e+01 -1.5963e+01  4.3136e-01  5.5208e-01
+#&gt;  -2.7459e+01 -7.8029e+00 -4.7287e+00  7.1100e+00  8.4058e+00 -8.3266e+00
+#&gt;   1.7347e+01 -2.7476e+00 -5.8147e+00  1.0619e+00 -2.6772e+00  3.0231e+00
+#&gt;   7.3309e+00 -3.1074e+00  1.9109e+01  6.2488e+00 -6.4734e+00 -4.7125e-01
+#&gt;  -4.8922e+00 -7.5932e+00 -2.2855e+00 -1.2168e+00 -1.4721e+00 -4.0499e+00
+#&gt;  -1.8002e+01 -4.7983e+00  6.1407e+00 -6.8202e+00  3.7508e+00 -6.9611e-01
+#&gt;   1.5803e+00  2.1600e+00  9.1819e+00  2.6416e-01  1.6545e+00 -6.8457e+00
+#&gt;  -2.5037e+00 -3.5136e+00 -1.2750e+01  9.4648e+00  2.0263e+00 -7.7318e-01
+#&gt;  -5.8539e+00  7.2161e-01  3.3986e+00  2.6831e-01  8.4629e+00  1.0556e+00
+#&gt;  -8.1779e-01 -9.5382e+00  8.8504e+00 -7.6203e+00  1.3994e+00 -2.9230e+00
+#&gt;  -3.5740e+00 -1.7280e+00  1.4569e+01 -1.1737e+01 -8.8975e+00  3.7210e+00
+#&gt;   1.1795e+01  3.3146e+00  1.2242e+01  2.9391e+00 -1.8201e+01 -1.2430e+00
+#&gt;  -1.8295e+01 -4.6633e+00  5.9964e+00 -4.3451e-01 -6.5197e+00  3.5632e+00
+#&gt;   3.7127e-01  7.4454e+00  1.1682e+01  2.6667e-01 -1.9586e+00 -3.3837e+00
+#&gt;  -1.0365e+00 -8.2232e+00 -8.4112e+00 -9.1053e+00 -3.6521e+00 -2.2473e+00
+#&gt;  -2.5694e+00  4.3786e-01 -1.0536e+01 -8.8205e+00 -3.6897e+00  1.0798e+01
+#&gt;   8.6545e+00 -5.0410e+00  7.0885e+00  1.8632e+00  6.6410e+00 -1.3708e+00
+#&gt;  -5.7480e+00  8.0330e-01 -3.3123e+00  5.2791e+00 -5.4046e+00  4.0533e+00
+#&gt;  -3.6363e+00 -8.6738e+00  3.7204e+00  1.1624e-02  1.8064e+01 -1.6268e-01
+#&gt;  -2.2105e+01 -1.7262e+01 -1.0033e+01 -9.1466e+00 -5.0783e+00  6.1272e+00
+#&gt;  -1.2115e+01 -2.3348e+00 -1.0168e+00  5.4170e+00 -1.1375e+00 -1.1276e+00
+#&gt;  -1.1370e+01  9.9577e+00 -1.5178e+01 -3.5892e+00  2.8437e+00  9.4898e-01
+#&gt;   1.3018e+01  1.0340e+01  2.0948e+00 -2.8574e+00  8.2862e-01  3.8906e+00
+#&gt;  -1.7084e+00  2.7070e+00  3.6188e+00 -2.3062e+00  8.2864e+00  5.3085e+00
+#&gt;   1.5275e+00  6.4327e+00  1.8934e+01  7.2108e+00  1.6752e+00  1.9821e+00
+#&gt;  -1.6915e+00  8.5454e-01  7.1428e+00  1.0604e+01 -7.7169e-01  9.2157e-01
+#&gt;  -1.8514e+00 -1.1039e+01 -4.7102e+00 -7.0633e-02  5.1042e+00  5.8931e-01
+#&gt;  -7.9077e+00 -5.2208e+00 -4.4968e+00  7.9002e+00  1.5707e+01  6.7454e+00
+#&gt;  -4.4708e-01 -2.1211e+00 -2.3933e+00 -2.8860e+00  2.2992e+00  4.1969e+00
+#&gt; [ CPUFloatType{20,33,54} ]</div></pre>
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new file mode 100644
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+    <h1>Conv_transpose2d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_conv_transpose2d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Conv_transpose2d</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_conv_transpose2d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>weight</span>,
+  bias = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(),
+  stride = <span class='fl'>1L</span>,
+  padding = <span class='fl'>0L</span>,
+  output_padding = <span class='fl'>0L</span>,
+  groups = <span class='fl'>1L</span>,
+  dilation = <span class='fl'>1L</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iH , iW)\)</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>filters of shape \((\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kH , kW)\)</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>optional bias of shape \((\mbox{out\_channels})\). Default: NULL</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the convolving kernel. Can be a single number or a      tuple <code>(sH, sW)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p><code>dilation * (kernel_size - 1) - padding</code> zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      <code>(padH, padW)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>output_padding</th>
+      <td><p>additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple <code>(out_padH, out_padW)</code>.      Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>the spacing between kernel elements. Can be a single number or      a tuple <code>(dH, dW)</code>. Default: 1</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-"><a class="anchor" href="#conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-"></a>conv_transpose2d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -&gt; Tensor </h2>
+
+    
+
+
+<p>Applies a 2D transposed convolution operator over an input image
+composed of several input planes, sometimes also called "deconvolution".</p>
+<p>See <code><a href='nn_conv_transpose2d.html'>nn_conv_transpose2d()</a></code> for details and output shape.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='co'># With square kernels and equal stride</span>
+<span class='kw'>inputs</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>4</span>, <span class='fl'>5</span>, <span class='fl'>5</span>))
+<span class='kw'>weights</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>8</span>, <span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='fu'><a href='nnf_conv_transpose2d.html'>nnf_conv_transpose2d</a></span>(<span class='kw'>inputs</span>, <span class='kw'>weights</span>, padding=<span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,1,.,.) = 
+#&gt;   -3.6222  -7.9970   2.8105   3.1026   1.7725
+#&gt;   -4.7404  12.8775   1.0415  -0.8881   4.1451
+#&gt;   -2.2161  -0.0100  -7.8568   2.5352   7.7713
+#&gt;    1.3625  -3.1941  -6.9461   6.7935   2.9976
+#&gt;   -3.9660   0.9707  12.1132  -7.1898  -5.3734
+#&gt; 
+#&gt; (1,2,.,.) = 
+#&gt;   5.5750  4.9565 -1.2831 -9.3766  2.7933
+#&gt;   0.3677 -1.5841 -1.1948  2.0401  1.6757
+#&gt;  -1.1554 -7.9480  2.0611  3.8100 -2.3715
+#&gt;   3.6882  5.7345  7.8422 -10.5027  4.2258
+#&gt;   6.4450 -2.5137 -14.2397  6.3799  2.2110
+#&gt; 
+#&gt; (1,3,.,.) = 
+#&gt;   2.3055  1.7193  1.5106  5.7209 -0.4044
+#&gt;   5.4206 -5.3050 -1.7869  8.7362  4.7972
+#&gt;  -1.1797 -8.2598  0.6797 -5.7008  2.3267
+#&gt;   8.3444 -1.9313 -3.6077  1.1224  0.3162
+#&gt;  -0.1740 -4.8076 -2.2376 -3.3701 -7.0435
+#&gt; 
+#&gt; (1,4,.,.) = 
+#&gt;  -2.1321 -0.6399 -0.5702  6.2185 -6.6191
+#&gt;  -6.7085 -1.7849  0.0567  0.2547  4.4343
+#&gt;  -0.1791 -13.0960 -1.1711 -11.7881  7.9123
+#&gt;   1.6398 -8.7353  5.6190  2.7717  2.2932
+#&gt;   6.8436  5.9709  0.8596 -5.4111 -4.1101
+#&gt; 
+#&gt; (1,5,.,.) = 
+#&gt;    6.3795  12.9273   3.7923  -2.6331  -6.7760
+#&gt;   12.3485  -7.3105   3.6371  -0.5407  -1.6971
+#&gt;   -6.8453   8.1406   8.8767  -0.0699 -10.0042
+#&gt;    0.8129  -1.6930   3.5113  -0.9711   2.3083
+#&gt;   -1.1577  -2.1860  -1.9327   3.4738  -2.3536
+#&gt; 
+#&gt; (1,6,.,.) = 
+#&gt;    2.8265  -4.2878 -10.9216  -1.4770  -3.4568
+#&gt;   -8.9188  -7.0011  -0.9053  -2.3985  -0.2689
+#&gt;   -0.8018  12.4512   0.5999  -5.5394   1.6234
+#&gt;    4.3200 -10.4630  -4.2572   7.0144  -0.1696
+#&gt;   -4.6365  -0.5886   7.6997  -3.6797   2.4974
+#&gt; 
+#&gt; (1,7,.,.) = 
+#&gt;  -2.3134  1.9207  6.9010 -2.2058  2.9683
+#&gt;  -2.6449  5.9937 -0.7752 -2.3005 -1.7128
+#&gt;   3.1190 -11.4434  2.3856  3.4017 -0.6764
+#&gt;  -2.5913  6.5472 -0.4979  2.9762 -7.4267
+#&gt;   1.9456  3.5234 -6.3143 -3.6686 -0.6943
+#&gt; 
+#&gt; (1,8,.,.) = 
+#&gt;   -0.3478   4.7669   7.3665   6.3556  -2.5469
+#&gt;   14.4336   2.1104   2.8604   0.8508   7.2790
+#&gt;   -9.4439  -5.6837  -8.8314  10.9166   0.5517
+#&gt;   -8.0535   6.8200   4.4783  13.7326   3.1114
+#&gt;   -0.0064   8.4882  -0.5751   1.8667  -4.2371
+#&gt; [ CPUFloatType{1,8,5,5} ]</div></pre>
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+    <div class="page-header">
+    <h1>Conv_transpose3d</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_conv_transpose3d.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Conv_transpose3d</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_conv_transpose3d</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>weight</span>,
+  bias = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(),
+  stride = <span class='fl'>1L</span>,
+  padding = <span class='fl'>0L</span>,
+  output_padding = <span class='fl'>0L</span>,
+  groups = <span class='fl'>1L</span>,
+  dilation = <span class='fl'>1L</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>input tensor of shape \((\mbox{minibatch} , \mbox{in\_channels} , iT , iH , iW)\)</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>filters of shape \((\mbox{in\_channels} , \frac{\mbox{out\_channels}}{\mbox{groups}} , kT , kH , kW)\)</p></td>
+    </tr>
+    <tr>
+      <th>bias</th>
+      <td><p>optional bias of shape \((\mbox{out\_channels})\). Default: NULL</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>the stride of the convolving kernel. Can be a single number or a      tuple <code>(sT, sH, sW)</code>. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>padding</th>
+      <td><p><code>dilation * (kernel_size - 1) - padding</code> zero-padding will be added to both      sides of each dimension in the input. Can be a single number or a tuple      <code>(padT, padH, padW)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>output_padding</th>
+      <td><p>additional size added to one side of each dimension in the      output shape. Can be a single number or a tuple      <code>(out_padT, out_padH, out_padW)</code>. Default: 0</p></td>
+    </tr>
+    <tr>
+      <th>groups</th>
+      <td><p>split input into groups, \(\mbox{in\_channels}\) should be divisible by the      number of groups. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>dilation</th>
+      <td><p>the spacing between kernel elements. Can be a single number or      a tuple <code>(dT, dH, dW)</code>. Default: 1</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-"><a class="anchor" href="#conv-transpose-d-input-weight-bias-null-stride-padding-output-padding-groups-dilation-gt-tensor-"></a>conv_transpose3d(input, weight, bias=NULL, stride=1, padding=0, output_padding=0, groups=1, dilation=1) -&gt; Tensor </h2>
+
+    
+
+
+<p>Applies a 3D transposed convolution operator over an input image
+composed of several input planes, sometimes also called "deconvolution"</p>
+<p>See <code><a href='nn_conv_transpose3d.html'>nn_conv_transpose3d()</a></code> for details and output shape.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='kw'>inputs</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>20</span>, <span class='fl'>16</span>, <span class='fl'>50</span>, <span class='fl'>10</span>, <span class='fl'>20</span>))
+<span class='kw'>weights</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>16</span>, <span class='fl'>33</span>, <span class='fl'>3</span>, <span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='fu'><a href='nnf_conv_transpose3d.html'>nnf_conv_transpose3d</a></span>(<span class='kw'>inputs</span>, <span class='kw'>weights</span>)
+}
+}
+</div></pre>
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diff --git a/static/docs/reference/torch_cos.html b/static/docs/reference/torch_cos.html
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Cos</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_cos.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Cos</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_cos</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="cos-input-out-null-gt-tensor-"><a class="anchor" href="#cos-input-out-null-gt-tensor-"></a>cos(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the cosine  of the elements of <code>input</code>.</p>
+<p>$$
+    \mbox{out}_{i} = \cos(\mbox{input}_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_cos</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.9950
+#&gt; -0.6881
+#&gt;  0.9608
+#&gt;  0.4292
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/torch_cosh.html b/static/docs/reference/torch_cosh.html
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+  <a href="../reference/index.html">Reference</a>
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+
+      
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Cosh</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_cosh.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Cosh</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_cosh</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="cosh-input-out-null-gt-tensor-"><a class="anchor" href="#cosh-input-out-null-gt-tensor-"></a>cosh(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the hyperbolic cosine  of the elements of
+<code>input</code>.</p>
+<p>$$
+    \mbox{out}_{i} = \cosh(\mbox{input}_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_cosh</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.6794
+#&gt;  1.0414
+#&gt;  1.1420
+#&gt;  1.4987
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/torch_cosine_similarity.html b/static/docs/reference/torch_cosine_similarity.html
new file mode 100644
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--- /dev/null
+++ b/static/docs/reference/torch_cosine_similarity.html
@@ -0,0 +1,368 @@
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+    <div class="page-header">
+    <h1>Cosine_similarity</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_cosine_similarity.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Cosine_similarity</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_cosine_similarity</span>(<span class='kw'>x1</span>, <span class='kw'>x2</span>, dim = <span class='fl'>2L</span>, eps = <span class='fl'>0</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>x1</th>
+      <td><p>(Tensor) First input.</p></td>
+    </tr>
+    <tr>
+      <th>x2</th>
+      <td><p>(Tensor) Second input (of size matching x1).</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int, optional) Dimension of vectors. Default: 1</p></td>
+    </tr>
+    <tr>
+      <th>eps</th>
+      <td><p>(float, optional) Small value to avoid division by zero.        Default: 1e-8</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="cosine-similarity-x-x-dim-eps-e-gt-tensor-"><a class="anchor" href="#cosine-similarity-x-x-dim-eps-e-gt-tensor-"></a>cosine_similarity(x1, x2, dim=1, eps=1e-8) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns cosine similarity between x1 and x2, computed along dim.</p>
+<p>$$
+    \mbox{similarity} = \frac{x_1 \cdot x_2}{\max(\Vert x_1 \Vert _2 \cdot \Vert x_2 \Vert _2, \epsilon)}
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>input1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>100</span>, <span class='fl'>128</span>))
+<span class='kw'>input2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>100</span>, <span class='fl'>128</span>))
+<span class='kw'>output</span> <span class='op'>=</span> <span class='fu'>torch_cosine_similarity</span>(<span class='kw'>input1</span>, <span class='kw'>input2</span>)
+<span class='kw'>output</span>
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0.0839
+#&gt; -0.0532
+#&gt; -0.0722
+#&gt;  0.0385
+#&gt; -0.0419
+#&gt;  0.0142
+#&gt; -0.1544
+#&gt;  0.0472
+#&gt;  0.0008
+#&gt; -0.0617
+#&gt;  0.0141
+#&gt; -0.1420
+#&gt;  0.0335
+#&gt;  0.0430
+#&gt;  0.0615
+#&gt;  0.0353
+#&gt;  0.1243
+#&gt;  0.0716
+#&gt;  0.0977
+#&gt; -0.0064
+#&gt;  0.0749
+#&gt; -0.0833
+#&gt;  0.1121
+#&gt;  0.0322
+#&gt;  0.0634
+#&gt; -0.0648
+#&gt;  0.0626
+#&gt; -0.0684
+#&gt;  0.0730
+#&gt; -0.0172
+#&gt;  0.0471
+#&gt;  0.1222
+#&gt;  0.0414
+#&gt;  0.0982
+#&gt; -0.0227
+#&gt; -0.0359
+#&gt;  0.1331
+#&gt;  0.0087
+#&gt; -0.0901
+#&gt; -0.0007
+#&gt;  0.0342
+#&gt; -0.0128
+#&gt; -0.0778
+#&gt;  0.0089
+#&gt;  0.0861
+#&gt;  0.0460
+#&gt;  0.2530
+#&gt; -0.0914
+#&gt; -0.0440
+#&gt; -0.0222
+#&gt; -0.0150
+#&gt;  0.0758
+#&gt;  0.0366
+#&gt;  0.0954
+#&gt; -0.1439
+#&gt; -0.0192
+#&gt; -0.0154
+#&gt; -0.2044
+#&gt; -0.0923
+#&gt;  0.0788
+#&gt;  0.0914
+#&gt;  0.1129
+#&gt; -0.1281
+#&gt; -0.0538
+#&gt;  0.0407
+#&gt; -0.0087
+#&gt; -0.0040
+#&gt;  0.0872
+#&gt;  0.0249
+#&gt; -0.0875
+#&gt; -0.0190
+#&gt; -0.0206
+#&gt;  0.0033
+#&gt; -0.2125
+#&gt; -0.2117
+#&gt;  0.0331
+#&gt;  0.1047
+#&gt; -0.0187
+#&gt; -0.0631
+#&gt; -0.0723
+#&gt;  0.0119
+#&gt; -0.0522
+#&gt; -0.0242
+#&gt;  0.1630
+#&gt;  0.2203
+#&gt; -0.0939
+#&gt; -0.0853
+#&gt; -0.0385
+#&gt;  0.0749
+#&gt; -0.0212
+#&gt; -0.1387
+#&gt; -0.1505
+#&gt; -0.1320
+#&gt;  0.0642
+#&gt;  0.0524
+#&gt; -0.0435
+#&gt; -0.0708
+#&gt;  0.0286
+#&gt;  0.0018
+#&gt;  0.0362
+#&gt; [ CPUFloatType{100} ]</div></pre>
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diff --git a/static/docs/reference/torch_cross.html b/static/docs/reference/torch_cross.html
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+    <div class="page-header">
+    <h1>Cross</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_cross.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Cross</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_cross</span>(<span class='kw'>self</span>, <span class='kw'>other</span>, dim = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor) the second input tensor</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int, optional) the dimension to take the cross-product in.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="cross-input-other-dim-out-null-gt-tensor-"><a class="anchor" href="#cross-input-other-dim-out-null-gt-tensor-"></a>cross(input, other, dim=-1, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the cross product of vectors in dimension <code>dim</code> of <code>input</code>
+and <code>other</code>.</p>
+<p><code>input</code> and <code>other</code> must have the same size, and the size of their
+<code>dim</code> dimension should be 3.</p>
+<p>If <code>dim</code> is not given, it defaults to the first dimension found with the
+size 3.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span>
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>3</span>))
+<span class='kw'>b</span>
+<span class='fu'>torch_cross</span>(<span class='kw'>a</span>, <span class='kw'>b</span>, dim=<span class='fl'>2</span>)
+<span class='fu'>torch_cross</span>(<span class='kw'>a</span>, <span class='kw'>b</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.0051  1.1323 -1.5101
+#&gt;  0.1088 -0.3737  1.1852
+#&gt; -0.4150 -0.6857 -0.1986
+#&gt; -1.1004 -2.0375  0.8405
+#&gt; [ CPUFloatType{4,3} ]</div></pre>
+  </div>
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+    <div class="page-header">
+    <h1>Cummax</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_cummax.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Cummax</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_cummax</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the dimension to do the operation over</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="cummax-input-dim-gt-tensor-longtensor-"><a class="anchor" href="#cummax-input-dim-gt-tensor-longtensor-"></a>cummax(input, dim) -&gt; (Tensor, LongTensor) </h2>
+
+    
+
+
+<p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the cumulative maximum of
+elements of <code>input</code> in the dimension <code>dim</code>. And <code>indices</code> is the index
+location of each maximum value found in the dimension <code>dim</code>.</p>
+<p>$$
+    y_i = max(x_1, x_2, x_3, \dots, x_i)
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_cummax</span>(<span class='kw'>a</span>, dim=<span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt; -1.3457
+#&gt; -1.3457
+#&gt; -1.2500
+#&gt; -1.2500
+#&gt;  0.6911
+#&gt;  0.6911
+#&gt;  0.6911
+#&gt;  0.7823
+#&gt;  0.7823
+#&gt;  0.7892
+#&gt; [ CPUFloatType{10} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt;  0
+#&gt;  0
+#&gt;  2
+#&gt;  2
+#&gt;  4
+#&gt;  4
+#&gt;  4
+#&gt;  7
+#&gt;  7
+#&gt;  9
+#&gt; [ CPULongType{10} ]
+#&gt; </div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Cummin</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_cummin.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Cummin</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_cummin</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the dimension to do the operation over</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="cummin-input-dim-gt-tensor-longtensor-"><a class="anchor" href="#cummin-input-dim-gt-tensor-longtensor-"></a>cummin(input, dim) -&gt; (Tensor, LongTensor) </h2>
+
+    
+
+
+<p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the cumulative minimum of
+elements of <code>input</code> in the dimension <code>dim</code>. And <code>indices</code> is the index
+location of each maximum value found in the dimension <code>dim</code>.</p>
+<p>$$
+    y_i = min(x_1, x_2, x_3, \dots, x_i)
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_cummin</span>(<span class='kw'>a</span>, dim=<span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt; -1.4730
+#&gt; -1.4730
+#&gt; -1.4730
+#&gt; -1.4730
+#&gt; -1.4730
+#&gt; -1.4730
+#&gt; -1.4730
+#&gt; -1.4730
+#&gt; -1.4730
+#&gt; -1.4730
+#&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></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/torch_cumprod.html b/static/docs/reference/torch_cumprod.html
new file mode 100644
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--- /dev/null
+++ b/static/docs/reference/torch_cumprod.html
@@ -0,0 +1,276 @@
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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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_cumprod.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Cumprod</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_cumprod</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, dtype = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the dimension to do the operation over</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to <code>dtype</code> before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="cumprod-input-dim-out-null-dtype-null-gt-tensor-"><a class="anchor" href="#cumprod-input-dim-out-null-dtype-null-gt-tensor-"></a>cumprod(input, dim, out=NULL, dtype=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the cumulative product of elements of <code>input</code> in the dimension
+<code>dim</code>.</p>
+<p>For example, if <code>input</code> is a vector of size N, the result will also be
+a vector of size N, with elements.</p>
+<p>$$
+    y_i = x_1 \times x_2\times x_3\times \dots \times x_i
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_cumprod</span>(<span class='kw'>a</span>, dim=<span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0.2882
+#&gt;  0.0701
+#&gt;  0.1595
+#&gt;  0.0911
+#&gt;  0.0191
+#&gt;  0.0297
+#&gt; -0.0133
+#&gt;  0.0059
+#&gt; -0.0030
+#&gt; -0.0012
+#&gt; [ CPUFloatType{10} ]</div></pre>
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+
+      
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+<div class="row">
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+    <div class="page-header">
+    <h1>Cumsum</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_cumsum.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Cumsum</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_cumsum</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, dtype = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the dimension to do the operation over</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to <code>dtype</code> before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="cumsum-input-dim-out-null-dtype-null-gt-tensor-"><a class="anchor" href="#cumsum-input-dim-out-null-dtype-null-gt-tensor-"></a>cumsum(input, dim, out=NULL, dtype=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the cumulative sum of elements of <code>input</code> in the dimension
+<code>dim</code>.</p>
+<p>For example, if <code>input</code> is a vector of size N, the result will also be
+a vector of size N, with elements.</p>
+<p>$$
+    y_i = x_1 + x_2 + x_3 + \dots + x_i
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_cumsum</span>(<span class='kw'>a</span>, dim=<span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.5764
+#&gt;  0.8705
+#&gt;  0.0415
+#&gt;  0.2468
+#&gt; -0.7231
+#&gt; -2.5011
+#&gt; -3.5835
+#&gt; -2.3688
+#&gt; -3.0899
+#&gt; -2.3232
+#&gt; [ CPUFloatType{10} ]</div></pre>
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+    <h1>Det</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_det.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Det</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_det</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor of size <code>(*, n, n)</code> where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is zero or more                batch dimensions.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>Backward through `det` internally uses SVD results when `input` is
+not invertible. In this case, double backward through `det` will be
+unstable in when `input` doesn't have distinct singular values. See
+`~torch.svd` for details.
+</pre>
+
+    <h2 class="hasAnchor" id="det-input-gt-tensor-"><a class="anchor" href="#det-input-gt-tensor-"></a>det(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>Calculates determinant of a square matrix or batches of square matrices.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='fu'>torch_det</span>(<span class='kw'>A</span>)
+<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>, <span class='fl'>2</span>))
+<span class='kw'>A</span>
+<span class='kw'>A</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/det.html'>det</a></span>()
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.2158
+#&gt;  1.0192
+#&gt; -0.1403
+#&gt; [ CPUFloatType{3} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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+    <li>
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+    <div class="page-header">
+    <h1>Create a Device object</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/device.R'><code>R/device.R</code></a></small>
+    <div class="hidden name"><code>torch_device.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>A <code>torch_device</code>  is an object representing the device on which a <code>torch_tensor</code>
+is or will be allocated.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_device</span>(<span class='kw'>type</span>, index = <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'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='co'># Via string</span>
+<span class='fu'>torch_device</span>(<span class='st'>"cuda:1"</span>)
+<span class='fu'>torch_device</span>(<span class='st'>"cpu"</span>)
+<span class='fu'>torch_device</span>(<span class='st'>"cuda"</span>) <span class='co'># current cuda device</span>
+
+<span class='co'># Via string and device ordinal</span>
+<span class='fu'>torch_device</span>(<span class='st'>"cuda"</span>, <span class='fl'>0</span>)
+<span class='fu'>torch_device</span>(<span class='st'>"cpu"</span>, <span class='fl'>0</span>)
+
+}
+</div><div class='output co'>#&gt; torch_device(type='cpu', index=0)</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Diag</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_diag.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Diag</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_diag</span>(<span class='kw'>self</span>, diagonal = <span class='fl'>0L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>diagonal</th>
+      <td><p>(int, optional) the diagonal to consider</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="diag-input-diagonal-out-null-gt-tensor-"><a class="anchor" href="#diag-input-diagonal-out-null-gt-tensor-"></a>diag(input, diagonal=0, out=NULL) -&gt; Tensor </h2>
+
+    
+
+<ul>
+<li><p>If <code>input</code> is a vector (1-D tensor), then returns a 2-D square tensor
+with the elements of <code>input</code> as the diagonal.</p></li>
+<li><p>If <code>input</code> is a matrix (2-D tensor), then returns a 1-D tensor with
+the diagonal elements of <code>input</code>.</p></li>
+</ul>
+
+<p>The argument <code>diagonal</code> controls which diagonal to consider:</p><ul>
+<li><p>If <code>diagonal</code> = 0, it is the main diagonal.</p></li>
+<li><p>If <code>diagonal</code> &gt; 0, it is above the main diagonal.</p></li>
+<li><p>If <code>diagonal</code> &lt; 0, it is below the main diagonal.</p></li>
+</ul>
+
+
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+    <div class="page-header">
+    <h1>Diag_embed</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_diag_embed.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Diag_embed</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_diag_embed</span>(<span class='kw'>self</span>, offset = <span class='fl'>0L</span>, dim1 = <span class='op'>-</span><span class='fl'>2L</span>, dim2 = <span class='op'>-</span><span class='fl'>1L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor. Must be at least 1-dimensional.</p></td>
+    </tr>
+    <tr>
+      <th>offset</th>
+      <td><p>(int, optional) which diagonal to consider. Default: 0        (main diagonal).</p></td>
+    </tr>
+    <tr>
+      <th>dim1</th>
+      <td><p>(int, optional) first dimension with respect to which to        take diagonal. Default: -2.</p></td>
+    </tr>
+    <tr>
+      <th>dim2</th>
+      <td><p>(int, optional) second dimension with respect to which to        take diagonal. Default: -1.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="diag-embed-input-offset-dim-dim-gt-tensor-"><a class="anchor" href="#diag-embed-input-offset-dim-dim-gt-tensor-"></a>diag_embed(input, offset=0, dim1=-2, dim2=-1) -&gt; Tensor </h2>
+
+    
+
+
+<p>Creates a tensor whose diagonals of certain 2D planes (specified by
+<code>dim1</code> and <code>dim2</code>) are filled by <code>input</code>.
+To facilitate creating batched diagonal matrices, the 2D planes formed by
+the last two dimensions of the returned tensor are chosen by default.</p>
+<p>The argument <code>offset</code> controls which diagonal to consider:</p><ul>
+<li><p>If <code>offset</code> = 0, it is the main diagonal.</p></li>
+<li><p>If <code>offset</code> &gt; 0, it is above the main diagonal.</p></li>
+<li><p>If <code>offset</code> &lt; 0, it is below the main diagonal.</p></li>
+</ul>
+
+<p>The size of the new matrix will be calculated to make the specified diagonal
+of the size of the last input dimension.
+Note that for <code>offset</code> other than \(0\), the order of <code>dim1</code>
+and <code>dim2</code> matters. Exchanging them is equivalent to changing the
+sign of <code>offset</code>.</p>
+<p>Applying <code>torch_diagonal</code> to the output of this function with
+the same arguments yields a matrix identical to input. However,
+<code>torch_diagonal</code> has different default dimensions, so those
+need to be explicitly specified.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+<span class='fu'>torch_diag_embed</span>(<span class='kw'>a</span>)
+<span class='fu'>torch_diag_embed</span>(<span class='kw'>a</span>, offset=<span class='fl'>1</span>, dim1=<span class='fl'>1</span>, dim2=<span class='fl'>3</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;   0.0000  0.3358  0.0000  0.0000
+#&gt;   0.0000 -3.1130  0.0000  0.0000
+#&gt; 
+#&gt; (2,.,.) = 
+#&gt;   0.0000  0.0000  0.4778  0.0000
+#&gt;   0.0000  0.0000  0.6605  0.0000
+#&gt; 
+#&gt; (3,.,.) = 
+#&gt;   0.0000  0.0000  0.0000 -0.0378
+#&gt;   0.0000  0.0000  0.0000 -0.3726
+#&gt; 
+#&gt; (4,.,.) = 
+#&gt;   0  0  0  0
+#&gt;   0  0  0  0
+#&gt; [ CPUFloatType{4,2,4} ]</div></pre>
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+
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+    <div class="page-header">
+    <h1>Diagflat</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_diagflat.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Diagflat</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_diagflat</span>(<span class='kw'>self</span>, offset = <span class='fl'>0L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>offset</th>
+      <td><p>(int, optional) the diagonal to consider. Default: 0 (main        diagonal).</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="diagflat-input-offset-gt-tensor-"><a class="anchor" href="#diagflat-input-offset-gt-tensor-"></a>diagflat(input, offset=0) -&gt; Tensor </h2>
+
+    
+
+<ul>
+<li><p>If <code>input</code> is a vector (1-D tensor), then returns a 2-D square tensor
+with the elements of <code>input</code> as the diagonal.</p></li>
+<li><p>If <code>input</code> is a tensor with more than one dimension, then returns a
+2-D tensor with diagonal elements equal to a flattened <code>input</code>.</p></li>
+</ul>
+
+<p>The argument <code>offset</code> controls which diagonal to consider:</p><ul>
+<li><p>If <code>offset</code> = 0, it is the main diagonal.</p></li>
+<li><p>If <code>offset</code> &gt; 0, it is above the main diagonal.</p></li>
+<li><p>If <code>offset</code> &lt; 0, it is below the main diagonal.</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_diagflat</span>(<span class='kw'>a</span>)
+<span class='fu'>torch_diagflat</span>(<span class='kw'>a</span>, <span class='fl'>1</span>)
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_diagflat</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0.7924  0.0000  0.0000  0.0000
+#&gt;  0.0000 -0.6579  0.0000  0.0000
+#&gt;  0.0000  0.0000 -1.1050  0.0000
+#&gt;  0.0000  0.0000  0.0000  0.5352
+#&gt; [ CPUFloatType{4,4} ]</div></pre>
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+    <div class="page-header">
+    <h1>Diagonal</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_diagonal.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Diagonal</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_diagonal</span>(<span class='kw'>self</span>, <span class='kw'>outdim</span>, dim1 = <span class='fl'>1L</span>, dim2 = <span class='fl'>2L</span>, offset = <span class='fl'>0L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor. Must be at least 2-dimensional.</p></td>
+    </tr>
+    <tr>
+      <th>outdim</th>
+      <td><p>dimension name if <code>self</code> is a named tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim1</th>
+      <td><p>(int, optional) first dimension with respect to which to        take diagonal. Default: 0.</p></td>
+    </tr>
+    <tr>
+      <th>dim2</th>
+      <td><p>(int, optional) second dimension with respect to which to        take diagonal. Default: 1.</p></td>
+    </tr>
+    <tr>
+      <th>offset</th>
+      <td><p>(int, optional) which diagonal to consider. Default: 0        (main diagonal).</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="diagonal-input-offset-dim-dim-gt-tensor-"><a class="anchor" href="#diagonal-input-offset-dim-dim-gt-tensor-"></a>diagonal(input, offset=0, dim1=0, dim2=1) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a partial view of <code>input</code> with the its diagonal elements
+with respect to <code>dim1</code> and <code>dim2</code> appended as a dimension
+at the end of the shape.</p>
+<p>The argument <code>offset</code> controls which diagonal to consider:</p><ul>
+<li><p>If <code>offset</code> = 0, it is the main diagonal.</p></li>
+<li><p>If <code>offset</code> &gt; 0, it is above the main diagonal.</p></li>
+<li><p>If <code>offset</code> &lt; 0, it is below the main diagonal.</p></li>
+</ul>
+
+<p>Applying <code>torch_diag_embed</code> to the output of this function with
+the same arguments yields a diagonal matrix with the diagonal entries
+of the input. However, <code>torch_diag_embed</code> has different default
+dimensions, so those need to be explicitly specified.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_diagonal</span>(<span class='kw'>a</span>, offset = <span class='fl'>0</span>)
+<span class='fu'>torch_diagonal</span>(<span class='kw'>a</span>, offset = <span class='fl'>1</span>)
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>5</span>, <span class='fl'>4</span>, <span class='fl'>2</span>))
+<span class='fu'>torch_diagonal</span>(<span class='kw'>x</span>, offset=<span class='op'>-</span><span class='fl'>1</span>, dim1=<span class='fl'>1</span>, dim2=<span class='fl'>2</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;   0.6147
+#&gt;   0.7596
+#&gt; 
+#&gt; (2,.,.) = 
+#&gt;  -0.9285
+#&gt;  -0.0531
+#&gt; 
+#&gt; (3,.,.) = 
+#&gt;   0.8622
+#&gt;  -0.1970
+#&gt; 
+#&gt; (4,.,.) = 
+#&gt;  -1.2797
+#&gt;   1.6829
+#&gt; [ CPUFloatType{4,2,1} ]</div></pre>
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diff --git a/static/docs/reference/torch_digamma.html b/static/docs/reference/torch_digamma.html
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+
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+    <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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_digamma.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Digamma</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_digamma</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the tensor to compute the digamma function on</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="digamma-input-out-null-gt-tensor-"><a class="anchor" href="#digamma-input-out-null-gt-tensor-"></a>digamma(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the logarithmic derivative of the gamma function on <code>input</code>.</p>
+<p>$$
+    \psi(x) = \frac{d}{dx} \ln\left(\Gamma\left(x\right)\right) = \frac{\Gamma'(x)}{\Gamma(x)}
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>0.5</span>))
+<span class='fu'>torch_digamma</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0.5772
+#&gt; -1.9635
+#&gt; [ CPUFloatType{2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+
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+    <div class="page-header">
+    <h1>Dist</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_dist.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Dist</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_dist</span>(<span class='kw'>self</span>, <span class='kw'>other</span>, p = <span class='fl'>2L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor) the Right-hand-side input tensor</p></td>
+    </tr>
+    <tr>
+      <th>p</th>
+      <td><p>(float, optional) the norm to be computed</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="dist-input-other-p-gt-tensor-"><a class="anchor" href="#dist-input-other-p-gt-tensor-"></a>dist(input, other, p=2) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the p-norm of (<code>input</code> - <code>other</code>)</p>
+<p>The shapes of <code>input</code> and <code>other</code> must be
+broadcastable .</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>x</span>
+<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>y</span>
+<span class='fu'>torch_dist</span>(<span class='kw'>x</span>, <span class='kw'>y</span>, <span class='fl'>3.5</span>)
+<span class='fu'>torch_dist</span>(<span class='kw'>x</span>, <span class='kw'>y</span>, <span class='fl'>3</span>)
+<span class='fu'>torch_dist</span>(<span class='kw'>x</span>, <span class='kw'>y</span>, <span class='fl'>0</span>)
+<span class='fu'>torch_dist</span>(<span class='kw'>x</span>, <span class='kw'>y</span>, <span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; 2.48136
+#&gt; [ CPUFloatType{} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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+</div>
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+    <li class="dropdown-header">Beginers guide</li>
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+    </li>
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+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/optim.html">optim: optimizers in torch</a>
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+    </li>
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+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
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+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Div</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_div.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Div</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_div</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Number) the number to be divided to each element of <code>input</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="div-input-other-out-null-gt-tensor-"><a class="anchor" href="#div-input-other-out-null-gt-tensor-"></a>div(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Divides each element of the input <code>input</code> with the scalar <code>other</code> and
+returns a new resulting tensor.</p>
+
+
+<p>Each element of the tensor <code>input</code> is divided by each element of the tensor
+<code>other</code>. The resulting tensor is returned.</p>
+<p>$$
+    \mbox{out}_i = \frac{\mbox{input}_i}{\mbox{other}_i}
+$$
+The shapes of <code>input</code> and <code>other</code> must be broadcastable
+. If the <code>torch_dtype</code> of <code>input</code> and
+<code>other</code> differ, the <code>torch_dtype</code> of the result tensor is determined
+following rules described in the type promotion documentation
+. If <code>out</code> is specified, the result must be
+castable  to the <code>torch_dtype</code> of the
+specified output tensor. Integral division by zero leads to undefined behavior.</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p>Integer division using div is deprecated, and in a future release div will
+perform true division like <code><a href='torch_true_divide.html'>torch_true_divide()</a></code>.
+Use <code><a href='torch_floor_divide.html'>torch_floor_divide()</a></code> to perform integer division,
+instead.</p>
+<p>$$
+    \mbox{out}_i = \frac{\mbox{input}_i}{\mbox{other}}
+$$
+If the <code>torch_dtype</code> of <code>input</code> and <code>other</code> differ, the
+<code>torch_dtype</code> of the result tensor is determined following rules
+described in the type promotion documentation . If
+<code>out</code> is specified, the result must be castable
+to the <code>torch_dtype</code> of the specified output tensor. Integral division
+by zero leads to undefined behavior.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_div</span>(<span class='kw'>a</span>, <span class='fl'>0.5</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>b</span>
+<span class='fu'>torch_div</span>(<span class='kw'>a</span>, <span class='kw'>b</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.6647  0.7626 -0.4095  0.1570
+#&gt;  0.7859 -0.3618  0.0104 -0.7726
+#&gt;  0.3775 -0.4202  0.9625  0.0194
+#&gt;  0.4176 -0.7826 -0.3309 -0.8345
+#&gt; [ CPUFloatType{4,4} ]</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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_dot.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Dot</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_dot</span>(<span class='kw'>self</span>, <span class='kw'>tensor</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>tensor</th>
+      <td><p>the other input tensor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>This function does not broadcast .</p>
+    <h2 class="hasAnchor" id="dot-input-tensor-gt-tensor-"><a class="anchor" href="#dot-input-tensor-gt-tensor-"></a>dot(input, tensor) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the dot product (inner product) of two tensors.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_dot</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; 7
+#&gt; [ CPUFloatType{} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Torch data types</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/dtype.R'><code>R/dtype.R</code></a></small>
+    <div class="hidden name"><code>torch_dtype.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Returns the correspondent data type.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_float32</span>()
+
+<span class='fu'>torch_float</span>()
+
+<span class='fu'>torch_float64</span>()
+
+<span class='fu'>torch_double</span>()
+
+<span class='fu'>torch_float16</span>()
+
+<span class='fu'>torch_half</span>()
+
+<span class='fu'>torch_uint8</span>()
+
+<span class='fu'>torch_int8</span>()
+
+<span class='fu'>torch_int16</span>()
+
+<span class='fu'>torch_short</span>()
+
+<span class='fu'>torch_int32</span>()
+
+<span class='fu'>torch_int</span>()
+
+<span class='fu'>torch_int64</span>()
+
+<span class='fu'>torch_long</span>()
+
+<span class='fu'>torch_bool</span>()
+
+<span class='fu'>torch_quint8</span>()
+
+<span class='fu'>torch_qint8</span>()
+
+<span class='fu'>torch_qint32</span>()</pre>
+
+
+
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diff --git a/static/docs/reference/torch_eig.html b/static/docs/reference/torch_eig.html
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+<div class="row">
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+    <div class="page-header">
+    <h1>Eig</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_eig.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Eig</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_eig</span>(<span class='kw'>self</span>, eigenvectors = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the square matrix of shape \((n \times n)\) for which the eigenvalues and eigenvectors        will be computed</p></td>
+    </tr>
+    <tr>
+      <th>eigenvectors</th>
+      <td><p>(bool) <code>TRUE</code> to compute both eigenvalues and eigenvectors;        otherwise, only eigenvalues will be computed</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>Since eigenvalues and eigenvectors might be complex, backward pass is supported only
+for [`torch_symeig`]
+</pre>
+
+    <h2 class="hasAnchor" id="eig-input-eigenvectors-false-out-null-gt-tensor-tensor-"><a class="anchor" href="#eig-input-eigenvectors-false-out-null-gt-tensor-tensor-"></a>eig(input, eigenvectors=False, out=NULL) -&gt; (Tensor, Tensor) </h2>
+
+    
+
+
+<p>Computes the eigenvalues and eigenvectors of a real square matrix.</p>
+
+  </div>
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+    <h1>Einsum</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_einsum.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Einsum</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_einsum</span>(<span class='kw'>equation</span>, <span class='kw'>tensors</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>equation</th>
+      <td><p>(string) The equation is given in terms of lower case letters (indices) to be associated           with each dimension of the operands and result. The left hand side lists the operands           dimensions, separated by commas. There should be one index letter per tensor dimension.           The right hand side follows after <code>-&gt;</code> and gives the indices for the output.           If the <code>-&gt;</code> and right hand side are omitted, it implicitly defined as the alphabetically           sorted list of all indices appearing exactly once in the left hand side.           The indices not apprearing in the output are summed over after multiplying the operands           entries.           If an index appears several times for the same operand, a diagonal is taken.           Ellipses <code>...</code> represent a fixed number of dimensions. If the right hand side is inferred,           the ellipsis dimensions are at the beginning of the output.</p></td>
+    </tr>
+    <tr>
+      <th>tensors</th>
+      <td><p>(Tensor) The operands to compute the Einstein sum of.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="einsum-equation-operands-gt-tensor-"><a class="anchor" href="#einsum-equation-operands-gt-tensor-"></a>einsum(equation, *operands) -&gt; Tensor </h2>
+
+    
+
+
+<p>This function provides a way of computing multilinear expressions (i.e. sums of products) using the
+Einstein summation convention.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>))
+<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='fu'>torch_einsum</span>(<span class='st'>'i,j-&gt;ij'</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>x</span>, <span class='kw'>y</span>))  <span class='co'># outer product</span>
+<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>,<span class='fl'>5</span>,<span class='fl'>4</span>))
+<span class='kw'>l</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>,<span class='fl'>5</span>))
+<span class='kw'>r</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>,<span class='fl'>4</span>))
+<span class='fu'>torch_einsum</span>(<span class='st'>'bn,anm,bm-&gt;ba'</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>l</span>, <span class='kw'>A</span>, <span class='kw'>r</span>)) <span class='co'># compare torch_nn$functional$bilinear</span>
+<span class='kw'>As</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>,<span class='fl'>2</span>,<span class='fl'>5</span>))
+<span class='kw'>Bs</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>,<span class='fl'>5</span>,<span class='fl'>4</span>))
+<span class='fu'>torch_einsum</span>(<span class='st'>'bij,bjk-&gt;bik'</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>As</span>, <span class='kw'>Bs</span>)) <span class='co'># batch matrix multiplication</span>
+<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='fu'>torch_einsum</span>(<span class='st'>'ii-&gt;i'</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>A</span>)) <span class='co'># diagonal</span>
+<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='fu'>torch_einsum</span>(<span class='st'>'...ii-&gt;...i'</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>A</span>)) <span class='co'># batch diagonal</span>
+<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span>))
+<span class='fu'>torch_einsum</span>(<span class='st'>'...ij-&gt;...ji'</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>A</span>))<span class='op'>$</span><span class='kw'>shape</span> <span class='co'># batch permute</span>
+
+}
+}
+</div></pre>
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Empty</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_empty.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Empty</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_empty</span>(
+  <span class='kw'>...</span>,
+  names = <span class='kw'>NULL</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>...</th>
+      <td><p>a sequence of integers defining the shape of the output tensor.</p></td>
+    </tr>
+    <tr>
+      <th>names</th>
+      <td><p>optional character vector naming each dimension.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="empty-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-pin-memory-false-gt-tensor-"><a class="anchor" href="#empty-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-pin-memory-false-gt-tensor-"></a>empty(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, pin_memory=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a tensor filled with uninitialized data. The shape of the tensor is
+defined by the variable argument <code>size</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_empty</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.9205e+31  1.8891e+31  6.3375e-10
+#&gt;  1.8169e+31  4.4726e+21  8.4843e+26
+#&gt; [ CPUFloatType{2,3} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Empty_like</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_empty_like.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Empty_like</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_empty_like</span>(
+  <span class='kw'>input</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>,
+  memory_format = <span class='fu'><a href='torch_memory_format.html'>torch_preserve_format</a></span>()
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>memory_format</th>
+      <td><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="empty-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"><a class="anchor" href="#empty-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a>empty_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns an uninitialized tensor with the same size as <code>input</code>.
+<code>torch_empty_like(input)</code> is equivalent to
+<code><a href='torch_empty.html'>torch_empty(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)</a></code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>,<span class='fl'>3</span>), dtype = <span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>())
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.2885e+10  0.0000e+00  0.0000e+00
+#&gt;  0.0000e+00  1.7180e+10  1.3700e+02
+#&gt; [ CPULongType{2,3} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_empty_strided.html b/static/docs/reference/torch_empty_strided.html
new file mode 100644
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--- /dev/null
+++ b/static/docs/reference/torch_empty_strided.html
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+<div class="row">
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+    <div class="page-header">
+    <h1>Empty_strided</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_empty_strided.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Empty_strided</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_empty_strided</span>(
+  <span class='kw'>size</span>,
+  <span class='kw'>stride</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>,
+  pin_memory = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>size</th>
+      <td><p>(tuple of ints) the shape of the output tensor</p></td>
+    </tr>
+    <tr>
+      <th>stride</th>
+      <td><p>(tuple of ints) the strides of the output tensor</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>pin_memory</th>
+      <td><p>(bool, optional) If set, returned tensor would be allocated in        the pinned memory. Works only for CPU tensors. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="empty-strided-size-stride-dtype-null-layout-null-device-null-requires-grad-false-pin-memory-false-gt-tensor-"><a class="anchor" href="#empty-strided-size-stride-dtype-null-layout-null-device-null-requires-grad-false-pin-memory-false-gt-tensor-"></a>empty_strided(size, stride, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, pin_memory=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a tensor filled with uninitialized data. The shape and strides of the tensor is
+defined by the variable argument <code>size</code> and <code>stride</code> respectively.
+<code>torch_empty_strided(size, stride)</code> is equivalent to
+<code>torch_empty(size).as_strided(size, stride)</code>.</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p>More than one element of the created tensor may refer to a single memory
+location. As a result, in-place operations (especially ones that are
+vectorized) may result in incorrect behavior. If you need to write to
+the tensors, please clone them first.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'>torch_empty_strided</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>), <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>))
+<span class='kw'>a</span>
+<span class='kw'>a</span><span class='op'>$</span><span class='fu'>stride</span>(<span class='fl'>1</span>)
+<span class='kw'>a</span><span class='op'>$</span><span class='fu'>size</span>(<span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; [1] 2</div></pre>
+  </div>
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+    <div class="page-header">
+    <h1>Eq</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_eq.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Eq</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_eq</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the tensor to compare</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor or float) the tensor or value to compare
+Must be a <code>ByteTensor</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="eq-input-other-out-null-gt-tensor-"><a class="anchor" href="#eq-input-other-out-null-gt-tensor-"></a>eq(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes element-wise equality</p>
+<p>The second argument can be a number or a tensor whose shape is
+broadcastable  with the first argument.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_eq</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>3</span>,<span class='fl'>4</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>, <span class='fl'>2</span>, <span class='fl'>4</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1
+#&gt;  0
+#&gt;  0
+#&gt;  1
+#&gt; [ CPUBoolType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Equal</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_equal.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Equal</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_equal</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>the other input tensor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="equal-input-other-gt-bool-"><a class="anchor" href="#equal-input-other-gt-bool-"></a>equal(input, other) -&gt; bool </h2>
+
+    
+
+
+<p><code>TRUE</code> if two tensors have the same size and elements, <code>FALSE</code> otherwise.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_equal</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>)))
+}
+</div><div class='output co'>#&gt; [1] TRUE</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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diff --git a/static/docs/reference/torch_erf.html b/static/docs/reference/torch_erf.html
new file mode 100644
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+++ b/static/docs/reference/torch_erf.html
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+    <div class="page-header">
+    <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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_erf.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Erf</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_erf</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="erf-input-out-null-gt-tensor-"><a class="anchor" href="#erf-input-out-null-gt-tensor-"></a>erf(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the error function of each element. The error function is defined as follows:</p>
+<p>$$
+    \mathrm{erf}(x) = \frac{2}{\sqrt{\pi}} \int_{0}^{x} e^{-t^2} dt
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_erf</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='op'>-</span><span class='fl'>1.</span>, <span class='fl'>10.</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.0000
+#&gt; -0.8427
+#&gt;  1.0000
+#&gt; [ CPUFloatType{3} ]</div></pre>
+  </div>
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+    <div class="page-header">
+    <h1>Erfc</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_erfc.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Erfc</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_erfc</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="erfc-input-out-null-gt-tensor-"><a class="anchor" href="#erfc-input-out-null-gt-tensor-"></a>erfc(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the complementary error function of each element of <code>input</code>.
+The complementary error function is defined as follows:</p>
+<p>$$
+    \mathrm{erfc}(x) = 1 - \frac{2}{\sqrt{\pi}} \int_{0}^{x} e^{-t^2} dt
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_erfc</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='op'>-</span><span class='fl'>1.</span>, <span class='fl'>10.</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.0000e+00
+#&gt;  1.8427e+00
+#&gt;  1.4013e-45
+#&gt; [ CPUFloatType{3} ]</div></pre>
+  </div>
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+    <div class="page-header">
+    <h1>Erfinv</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_erfinv.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Erfinv</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_erfinv</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="erfinv-input-out-null-gt-tensor-"><a class="anchor" href="#erfinv-input-out-null-gt-tensor-"></a>erfinv(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the inverse error function of each element of <code>input</code>.
+The inverse error function is defined in the range \((-1, 1)\) as:</p>
+<p>$$
+    \mathrm{erfinv}(\mathrm{erf}(x)) = x
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_erfinv</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>0.5</span>, <span class='op'>-</span><span class='fl'>1.</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.0000
+#&gt;  0.4769
+#&gt;    -inf
+#&gt; [ CPUFloatType{3} ]</div></pre>
+  </div>
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Exp</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_exp.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Exp</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_exp</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="exp-input-out-null-gt-tensor-"><a class="anchor" href="#exp-input-out-null-gt-tensor-"></a>exp(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the exponential of the elements
+of the input tensor <code>input</code>.</p>
+<p>$$
+    y_{i} = e^{x_{i}}
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_exp</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fu'><a href='https://rdrr.io/r/base/Log.html'>log</a></span>(<span class='fl'>2</span>))))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1
+#&gt;  2
+#&gt; [ CPUFloatType{2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+<div class="row">
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+    <div class="page-header">
+    <h1>Expm1</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_expm1.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Expm1</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_expm1</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="expm-input-out-null-gt-tensor-"><a class="anchor" href="#expm-input-out-null-gt-tensor-"></a>expm1(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the exponential of the elements minus 1
+of <code>input</code>.</p>
+<p>$$
+    y_{i} = e^{x_{i}} - 1
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_expm1</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fu'><a href='https://rdrr.io/r/base/Log.html'>log</a></span>(<span class='fl'>2</span>))))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0
+#&gt;  1
+#&gt; [ CPUFloatType{2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
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diff --git a/static/docs/reference/torch_eye.html b/static/docs/reference/torch_eye.html
new file mode 100644
index 000000000..9bb24caef
--- /dev/null
+++ b/static/docs/reference/torch_eye.html
@@ -0,0 +1,280 @@
+<!-- Generated by pkgdown: do not edit by hand -->
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+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
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+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
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+    </li>
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+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
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+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
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+      <a href="../articles/using-autograd.html">Using autograd</a>
+    </li>
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+</li>
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Eye</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_eye.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Eye</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_eye</span>(
+  <span class='kw'>n</span>,
+  m = <span class='kw'>n</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>n</th>
+      <td><p>(int) the number of rows</p></td>
+    </tr>
+    <tr>
+      <th>m</th>
+      <td><p>(int, optional) the number of columns with default being <code>n</code></p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="eye-n-m-null-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#eye-n-m-null-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>eye(n, m=NULL, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a 2-D tensor with ones on the diagonal and zeros elsewhere.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_eye</span>(<span class='fl'>3</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1  0  0
+#&gt;  0  1  0
+#&gt;  0  0  1
+#&gt; [ CPUFloatType{3,3} ]</div></pre>
+  </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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+
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+
diff --git a/static/docs/reference/torch_fft.html b/static/docs/reference/torch_fft.html
new file mode 100644
index 000000000..78ddfe283
--- /dev/null
+++ b/static/docs/reference/torch_fft.html
@@ -0,0 +1,614 @@
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+    <div class="page-header">
+    <h1>Fft</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_fft.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Fft</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_fft</span>(<span class='kw'>self</span>, <span class='kw'>signal_ndim</span>, normalized = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor of at least <code>signal_ndim</code> <code>+ 1</code>        dimensions</p></td>
+    </tr>
+    <tr>
+      <th>signal_ndim</th>
+      <td><p>(int) the number of dimensions in each signal.        <code>signal_ndim</code> can only be 1, 2 or 3</p></td>
+    </tr>
+    <tr>
+      <th>normalized</th>
+      <td><p>(bool, optional) controls whether to return normalized results.        Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>For CUDA tensors, an LRU cache is used for cuFFT plans to speed up
+repeatedly running FFT methods on tensors of same geometry with same
+configuration. See cufft-plan-cache for more details on how to
+monitor and control the cache.
+</pre>
+
+    <h2 class="hasAnchor" id="fft-input-signal-ndim-normalized-false-gt-tensor-"><a class="anchor" href="#fft-input-signal-ndim-normalized-false-gt-tensor-"></a>fft(input, signal_ndim, normalized=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Complex-to-complex Discrete Fourier Transform</p>
+<p>This method computes the complex-to-complex discrete Fourier transform.
+Ignoring the batch dimensions, it computes the following expression:</p>
+<p>$$
+    X[\omega_1, \dots, \omega_d] =
+        \sum_{n_1=0}^{N_1-1} \dots \sum_{n_d=0}^{N_d-1} x[n_1, \dots, n_d]
+         e^{-j\ 2 \pi \sum_{i=0}^d \frac{\omega_i n_i}{N_i}},
+$$
+where \(d\) = <code>signal_ndim</code> is number of dimensions for the
+signal, and \(N_i\) is the size of signal dimension \(i\).</p>
+<p>This method supports 1D, 2D and 3D complex-to-complex transforms, indicated
+by <code>signal_ndim</code>. <code>input</code> must be a tensor with last dimension
+of size 2, representing the real and imaginary components of complex
+numbers, and should have at least <code>signal_ndim + 1</code> dimensions with optionally
+arbitrary number of leading batch dimensions. If <code>normalized</code> is set to
+<code>TRUE</code>, this normalizes the result by dividing it with
+\(\sqrt{\prod_{i=1}^K N_i}\) so that the operator is unitary.</p>
+<p>Returns the real and the imaginary parts together as one tensor of the same
+shape of <code>input</code>.</p>
+<p>The inverse of this function is <code><a href='torch_ifft.html'>torch_ifft</a></code>.</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p>For CPU tensors, this method is currently only available with MKL. Use
+<code>torch_backends.mkl.is_available</code> to check if MKL is installed.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='co'># unbatched 2D FFT</span>
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>3</span>, <span class='fl'>2</span>))
+<span class='fu'>torch_fft</span>(<span class='kw'>x</span>, <span class='fl'>2</span>)
+<span class='co'># batched 1D FFT</span>
+<span class='fu'>torch_fft</span>(<span class='kw'>x</span>, <span class='fl'>1</span>)
+<span class='co'># arbitrary number of batch dimensions, 2D FFT</span>
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>, <span class='fl'>5</span>, <span class='fl'>5</span>, <span class='fl'>2</span>))
+<span class='fu'>torch_fft</span>(<span class='kw'>x</span>, <span class='fl'>2</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,1,1,.,.) = 
+#&gt;   3.8970  3.1487
+#&gt;   3.5089 -8.0428
+#&gt;  -4.2355  4.7304
+#&gt;   5.1422  8.4612
+#&gt;  -0.2829  6.5526
+#&gt; 
+#&gt; (2,1,1,.,.) = 
+#&gt;  -6.4914  0.7626
+#&gt;  -9.6798 -6.4868
+#&gt;   4.7413 -6.3207
+#&gt;   3.1433  6.0861
+#&gt;   1.4183  8.1368
+#&gt; 
+#&gt; (3,1,1,.,.) = 
+#&gt;   4.1796 -0.8876
+#&gt;  -2.7845 -0.2090
+#&gt;   6.4877 -2.0553
+#&gt;   1.6739 -1.4434
+#&gt;   6.7934  3.7603
+#&gt; 
+#&gt; (1,2,1,.,.) = 
+#&gt;   -8.0135  -3.4585
+#&gt;    3.2928   1.1881
+#&gt;    1.0519   7.3132
+#&gt;   -2.0995   0.7831
+#&gt;   11.4499   0.4932
+#&gt; 
+#&gt; (2,2,1,.,.) = 
+#&gt;   2.4901  2.9753
+#&gt;  -8.2115  4.1514
+#&gt;   6.0281  1.2984
+#&gt;  -7.5086  1.3346
+#&gt;   3.6055 -0.8337
+#&gt; 
+#&gt; (3,2,1,.,.) = 
+#&gt;    4.5424  -0.5391
+#&gt;   10.2617  -3.2754
+#&gt;   -1.8136   5.1917
+#&gt;   -4.6990  -0.9120
+#&gt;   -0.9396  -2.4928
+#&gt; 
+#&gt; (1,3,1,.,.) = 
+#&gt;  -4.4424 -3.1498
+#&gt;  -0.4156  0.1798
+#&gt;  -13.2501 -4.7913
+#&gt;  -0.4209  2.1745
+#&gt;  -0.4077 -2.1715
+#&gt; 
+#&gt; (2,3,1,.,.) = 
+#&gt;   1.8846  7.9342
+#&gt;   3.5554 -4.6065
+#&gt;   2.1314 -1.0630
+#&gt;  -1.8522  5.3271
+#&gt;  -4.2557 -6.4940
+#&gt; 
+#&gt; (3,3,1,.,.) = 
+#&gt;  -5.2358 -6.1865
+#&gt;  -13.5679  8.0294
+#&gt;   5.8208 -5.6350
+#&gt;   6.6610 -3.3221
+#&gt;  -2.0237 -1.4322
+#&gt; 
+#&gt; (1,1,2,.,.) = 
+#&gt;   3.3358  4.7716
+#&gt;   3.1996  3.2956
+#&gt;   2.6306  7.2663
+#&gt;  -2.2276 -8.0122
+#&gt;   2.0598 -2.9997
+#&gt; 
+#&gt; (2,1,2,.,.) = 
+#&gt;   0.5116  0.5725
+#&gt;   2.0175 -4.3590
+#&gt;  -1.4288  0.5337
+#&gt;   2.3421  0.0760
+#&gt;  -7.5872 -0.3140
+#&gt; 
+#&gt; (3,1,2,.,.) = 
+#&gt;   5.6348  1.3832
+#&gt;   6.6151  0.4225
+#&gt;   6.0133 -5.4770
+#&gt;  -7.6286 -6.4727
+#&gt;   3.6738  4.4512
+#&gt; 
+#&gt; (1,2,2,.,.) = 
+#&gt;   2.8345 -7.9496
+#&gt;  -1.2934  6.3027
+#&gt;  -2.5656  3.4925
+#&gt;  -4.2269  8.1202
+#&gt;   7.0657  8.9951
+#&gt; 
+#&gt; (2,2,2,.,.) = 
+#&gt;  -2.5830  2.6838
+#&gt;   1.3960 -2.1490
+#&gt;   3.6903 -4.3413
+#&gt;  -4.8245  1.9661
+#&gt;   2.4275 -0.8489
+#&gt; 
+#&gt; (3,2,2,.,.) = 
+#&gt;  -2.3136 -3.0029
+#&gt;   6.4553  2.9853
+#&gt;   1.0790  6.2816
+#&gt;  -2.3066  3.1048
+#&gt;   2.4425 -2.4980
+#&gt; 
+#&gt; (1,3,2,.,.) = 
+#&gt;   5.0888 -1.9831
+#&gt;  -3.3846 -3.1375
+#&gt;   0.4764  0.9040
+#&gt;  -0.2155 -3.9354
+#&gt;  -0.2403  9.0833
+#&gt; 
+#&gt; (2,3,2,.,.) = 
+#&gt;   3.2478  0.6904
+#&gt;   4.8131 -4.7587
+#&gt;   4.4327 -2.0460
+#&gt;  -0.1583 -1.1435
+#&gt;  -0.8185 -3.7916
+#&gt; 
+#&gt; (3,3,2,.,.) = 
+#&gt;   -4.8215  -5.7823
+#&gt;   -4.3197   2.5006
+#&gt;   -7.6764  10.5907
+#&gt;    5.7097   9.1375
+#&gt;   -6.5421   5.3465
+#&gt; 
+#&gt; (1,1,3,.,.) = 
+#&gt;   6.8857  1.6267
+#&gt;   0.6538 -2.9130
+#&gt;   4.2404 -5.3240
+#&gt;   5.6476  5.1706
+#&gt;   1.4154  2.3173
+#&gt; 
+#&gt; (2,1,3,.,.) = 
+#&gt;  -0.5342 -1.8588
+#&gt;  -6.3230 -2.3969
+#&gt;  -2.6561 -1.8841
+#&gt;   2.2272 -2.2146
+#&gt;  -7.2355 -9.8852
+#&gt; 
+#&gt; (3,1,3,.,.) = 
+#&gt;    6.9817  -1.9044
+#&gt;  -10.6065  -3.0032
+#&gt;    6.2727  10.5124
+#&gt;    2.0892  -0.2945
+#&gt;    1.4965   3.6209
+#&gt; 
+#&gt; (1,2,3,.,.) = 
+#&gt;  -2.7138  0.5805
+#&gt;   3.8351 -13.1060
+#&gt;  -2.0923  8.4118
+#&gt;   6.7002  2.8014
+#&gt;  -2.0329 -5.4260
+#&gt; 
+#&gt; (2,2,3,.,.) = 
+#&gt;  -4.7038  4.4041
+#&gt;   0.6079  6.9384
+#&gt;  -2.4581 -2.0337
+#&gt;  -2.4631  5.5419
+#&gt;  -2.4616 -2.3076
+#&gt; 
+#&gt; (3,2,3,.,.) = 
+#&gt;   1.0971  1.5684
+#&gt;   4.6985  3.6660
+#&gt;   1.7358  2.5220
+#&gt;  -2.9059 -3.8360
+#&gt;   8.5191 -0.6597
+#&gt; 
+#&gt; (1,3,3,.,.) = 
+#&gt;   1.2912 -4.1150
+#&gt;   5.9200 -9.2185
+#&gt;   2.7708 -11.2848
+#&gt;  -5.2274 -9.0754
+#&gt;  -5.9556 -8.3723
+#&gt; 
+#&gt; (2,3,3,.,.) = 
+#&gt;    3.6284  -9.7751
+#&gt;   -6.4936   0.4896
+#&gt;    1.0783   3.2584
+#&gt;   -1.2375   4.0910
+#&gt;    8.7281  11.1889
+#&gt; 
+#&gt; (3,3,3,.,.) = 
+#&gt;  -2.9392 -0.8457
+#&gt;  -5.1092 -3.4291
+#&gt;  -6.7226 -2.2762
+#&gt;  -6.7354  0.7375
+#&gt;  -3.6834 -10.3393
+#&gt; 
+#&gt; (1,1,4,.,.) = 
+#&gt;   -0.4641   5.1642
+#&gt;   -4.9807  -2.8183
+#&gt;   -0.9600   1.1617
+#&gt;   13.1615  -5.7607
+#&gt;   -5.8192   6.1872
+#&gt; 
+#&gt; (2,1,4,.,.) = 
+#&gt;   -0.6880  12.9307
+#&gt;   -8.0335  -1.1752
+#&gt;    6.2135   2.0547
+#&gt;    8.6658  -5.6305
+#&gt;   -7.1765  -4.2632
+#&gt; 
+#&gt; (3,1,4,.,.) = 
+#&gt;  -3.0057 -3.1364
+#&gt;   4.9857  4.4488
+#&gt;   1.3519  9.6340
+#&gt;  -9.3729  4.8508
+#&gt;   0.7863  3.1354
+#&gt; 
+#&gt; (1,2,4,.,.) = 
+#&gt;  -5.5472  0.7396
+#&gt;   5.5568 -2.7190
+#&gt;   2.1487  3.5182
+#&gt;   2.2908  5.8565
+#&gt;  -2.7511  0.1199
+#&gt; 
+#&gt; (2,2,4,.,.) = 
+#&gt;   0.1053 -9.7559
+#&gt;   2.6366  2.9785
+#&gt;   5.8546 -8.3557
+#&gt;   2.3308 -0.3229
+#&gt;  -0.1616 -2.8580
+#&gt; 
+#&gt; (3,2,4,.,.) = 
+#&gt;  -8.8217 -4.0197
+#&gt;  -1.3547  2.0932
+#&gt;   5.0738 -2.1672
+#&gt;  -1.6119  7.2245
+#&gt;  -1.4150 -0.6509
+#&gt; 
+#&gt; (1,3,4,.,.) = 
+#&gt;  -1.2559  2.1416
+#&gt;  -0.0231  4.1475
+#&gt;  -3.8181  2.3299
+#&gt;   3.7565  0.7653
+#&gt;   1.8683 -3.8644
+#&gt; 
+#&gt; (2,3,4,.,.) = 
+#&gt;    0.4839   6.4445
+#&gt;    3.1534  -8.8380
+#&gt;    3.7889   6.3242
+#&gt;    9.0306  11.1551
+#&gt;    5.7409  -1.1564
+#&gt; 
+#&gt; (3,3,4,.,.) = 
+#&gt;  -2.2988  8.8577
+#&gt;   4.7663 -5.2979
+#&gt;  -4.7160  9.3649
+#&gt;  -4.3578 -7.4509
+#&gt;  -4.3874  2.7093
+#&gt; 
+#&gt; (1,1,5,.,.) = 
+#&gt;  -0.4679  1.3350
+#&gt;  -1.4923 -5.4231
+#&gt;  -2.3291  0.3742
+#&gt;   2.6760 -0.3634
+#&gt;  -3.0602  8.7696
+#&gt; 
+#&gt; (2,1,5,.,.) = 
+#&gt;  -2.1765  4.7492
+#&gt;   7.2170  3.6409
+#&gt;  -6.5629 -2.2038
+#&gt;  -5.8527 -0.0878
+#&gt;  -4.5430  0.7514
+#&gt; 
+#&gt; (3,1,5,.,.) = 
+#&gt;   8.4563  6.2536
+#&gt;  -7.6490 -0.3940
+#&gt;  -2.2804  0.5806
+#&gt;  -9.5464  4.4593
+#&gt;  -5.9173 -3.0061
+#&gt; 
+#&gt; (1,2,5,.,.) = 
+#&gt;    5.1614  -5.6453
+#&gt;    4.8296 -15.8688
+#&gt;    1.8169  -0.8523
+#&gt;   10.0722  -2.0120
+#&gt;    0.8588   2.5174
+#&gt; 
+#&gt; (2,2,5,.,.) = 
+#&gt;  -6.1863 -1.2811
+#&gt;  -5.0314  3.6125
+#&gt;  -6.2021 -2.8083
+#&gt;  -0.3612 -3.9482
+#&gt;  -3.0259  0.6619
+#&gt; 
+#&gt; (3,2,5,.,.) = 
+#&gt;   5.1903  0.0372
+#&gt;  -1.9769  1.8278
+#&gt;   4.1834 -11.1886
+#&gt;  -3.8679 -4.4589
+#&gt;   2.2054 -3.4290
+#&gt; 
+#&gt; (1,3,5,.,.) = 
+#&gt;  -9.9394  5.2093
+#&gt;  -6.9233 -3.8417
+#&gt;   7.3800 -2.2016
+#&gt;   1.4532  4.9600
+#&gt;  -2.6033 -3.4185
+#&gt; 
+#&gt; (2,3,5,.,.) = 
+#&gt;  -6.9207  1.5530
+#&gt;  -3.9120  0.2946
+#&gt;  -0.9112  5.2904
+#&gt;   3.2102 -2.0272
+#&gt;   7.6431 -0.3506
+#&gt; 
+#&gt; (3,3,5,.,.) = 
+#&gt;   3.1340  6.7375
+#&gt;   1.4610  3.8716
+#&gt;   2.8714  3.5657
+#&gt;  -4.2452 -3.6816
+#&gt;   5.5423  2.7491
+#&gt; [ CPUFloatType{3,3,5,5,2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
+  </div>
+</div>
+
+
+      <footer>
+      <div class="copyright">
+  <p>Developed by Daniel Falbel, Javier Luraschi.</p>
+</div>
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+  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
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diff --git a/static/docs/reference/torch_finfo.html b/static/docs/reference/torch_finfo.html
new file mode 100644
index 000000000..cb9ed67f8
--- /dev/null
+++ b/static/docs/reference/torch_finfo.html
@@ -0,0 +1,239 @@
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+<title>Floating point type info — torch_finfo • torch</title>
+
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+
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+    <div class="page-header">
+    <h1>Floating point type info</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/type-info.R'><code>R/type-info.R</code></a></small>
+    <div class="hidden name"><code>torch_finfo.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>A list that represents the numerical properties of a
+floating point torch.dtype</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_finfo</span>(<span class='kw'>dtype</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>dtype</th>
+      <td><p>dtype to check information</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/torch_flatten.html b/static/docs/reference/torch_flatten.html
new file mode 100644
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+
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+    <div class="page-header">
+    <h1>Flatten</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_flatten.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Flatten</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_flatten</span>(<span class='kw'>self</span>, <span class='kw'>dims</span>, start_dim = <span class='fl'>1L</span>, end_dim = <span class='op'>-</span><span class='fl'>1L</span>, <span class='kw'>out_dim</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dims</th>
+      <td><p>if tensor is named you can pass the name of the dimensions to
+flatten</p></td>
+    </tr>
+    <tr>
+      <th>start_dim</th>
+      <td><p>(int) the first dim to flatten</p></td>
+    </tr>
+    <tr>
+      <th>end_dim</th>
+      <td><p>(int) the last dim to flatten</p></td>
+    </tr>
+    <tr>
+      <th>out_dim</th>
+      <td><p>the name of the resulting dimension if a named tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="flatten-input-start-dim-end-dim-gt-tensor-"><a class="anchor" href="#flatten-input-start-dim-end-dim-gt-tensor-"></a>flatten(input, start_dim=0, end_dim=-1) -&gt; Tensor </h2>
+
+    
+
+
+<p>Flattens a contiguous range of dims in a tensor.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>t</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>), ncol = <span class='fl'>2</span>))
+<span class='fu'>torch_flatten</span>(<span class='kw'>t</span>)
+<span class='fu'>torch_flatten</span>(<span class='kw'>t</span>, start_dim=<span class='fl'>2</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1  2
+#&gt; [ CPUFloatType{1,2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_flip.html b/static/docs/reference/torch_flip.html
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+
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+    <div class="page-header">
+    <h1>Flip</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_flip.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Flip</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_flip</span>(<span class='kw'>self</span>, <span class='kw'>dims</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dims</th>
+      <td><p>(a list or tuple) axis to flip on</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="flip-input-dims-gt-tensor-"><a class="anchor" href="#flip-input-dims-gt-tensor-"></a>flip(input, dims) -&gt; Tensor </h2>
+
+    
+
+
+<p>Reverse the order of a n-D tensor along given axis in dims.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0</span>, <span class='fl'>8</span>)<span class='op'>$</span><span class='fu'>view</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>, <span class='fl'>2</span>))
+<span class='kw'>x</span>
+<span class='fu'>torch_flip</span>(<span class='kw'>x</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;   6  7
+#&gt;   4  5
+#&gt; 
+#&gt; (2,.,.) = 
+#&gt;   2  3
+#&gt;   0  1
+#&gt; [ CPUFloatType{2,2,2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+
+      
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+    <div class="page-header">
+    <h1>Floor</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_floor.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Floor</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_floor</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="floor-input-out-null-gt-tensor-"><a class="anchor" href="#floor-input-out-null-gt-tensor-"></a>floor(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the floor of the elements of <code>input</code>,
+the largest integer less than or equal to each element.</p>
+<p>$$
+    \mbox{out}_{i} = \left\lfloor \mbox{input}_{i} \right\rfloor
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_floor</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0
+#&gt; -1
+#&gt; -1
+#&gt; -1
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
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+
+
diff --git a/static/docs/reference/torch_floor_divide.html b/static/docs/reference/torch_floor_divide.html
new file mode 100644
index 000000000..10c57ef95
--- /dev/null
+++ b/static/docs/reference/torch_floor_divide.html
@@ -0,0 +1,263 @@
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+    <h1>Floor_divide</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_floor_divide.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Floor_divide</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_floor_divide</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the numerator tensor</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor or Scalar) the denominator</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="floor-divide-input-other-out-null-gt-tensor-"><a class="anchor" href="#floor-divide-input-other-out-null-gt-tensor-"></a>floor_divide(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Return the division of the inputs rounded down to the nearest integer. See <code><a href='torch_div.html'>torch_div</a></code>
+for type promotion and broadcasting rules.</p>
+<p>$$
+    \mbox{{out}}_i = \left\lfloor \frac{{\mbox{{input}}_i}}{{\mbox{{other}}_i}} \right\rfloor
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4.0</span>, <span class='fl'>3.0</span>))
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2.0</span>, <span class='fl'>2.0</span>))
+<span class='fu'>torch_floor_divide</span>(<span class='kw'>a</span>, <span class='kw'>b</span>)
+<span class='fu'>torch_floor_divide</span>(<span class='kw'>a</span>, <span class='fl'>1.4</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  2
+#&gt;  2
+#&gt; [ CPUFloatType{2} ]</div></pre>
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+    <div class="page-header">
+    <h1>Fmod</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_fmod.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Fmod</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_fmod</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the dividend</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor or float) the divisor, which may be either a number or a tensor of the same shape as the dividend</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="fmod-input-other-out-null-gt-tensor-"><a class="anchor" href="#fmod-input-other-out-null-gt-tensor-"></a>fmod(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the element-wise remainder of division.</p>
+<p>The dividend and divisor may contain both for integer and floating point
+numbers. The remainder has the same sign as the dividend <code>input</code>.</p>
+<p>When <code>other</code> is a tensor, the shapes of <code>input</code> and
+<code>other</code> must be broadcastable .</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_fmod</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>3.</span>, <span class='op'>-</span><span class='fl'>2</span>, <span class='op'>-</span><span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>)), <span class='fl'>2</span>)
+<span class='fu'>torch_fmod</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1.</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span>)), <span class='fl'>1.5</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.0000
+#&gt;  0.5000
+#&gt;  0.0000
+#&gt;  1.0000
+#&gt;  0.5000
+#&gt; [ CPUFloatType{5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+    <div class="page-header">
+    <h1>Frac</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_frac.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Frac</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_frac</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="frac-input-out-null-gt-tensor-"><a class="anchor" href="#frac-input-out-null-gt-tensor-"></a>frac(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the fractional portion of each element in <code>input</code>.</p>
+<p>$$
+    \mbox{out}_{i} = \mbox{input}_{i} - \left\lfloor |\mbox{input}_{i}| \right\rfloor * \mbox{sgn}(\mbox{input}_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_frac</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2.5</span>, <span class='op'>-</span><span class='fl'>3.2</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.0000
+#&gt;  0.5000
+#&gt; -0.2000
+#&gt; [ CPUFloatType{3} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+    <div class="page-header">
+    <h1>Full</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_full.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Full</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_full</span>(
+  <span class='kw'>size</span>,
+  <span class='kw'>fill_value</span>,
+  names = <span class='kw'>NULL</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>size</th>
+      <td><p>(int...) a list, tuple, or <code>torch_Size</code> of integers defining the        shape of the output tensor.</p></td>
+    </tr>
+    <tr>
+      <th>fill_value</th>
+      <td><p>NA the number to fill the output tensor with.</p></td>
+    </tr>
+    <tr>
+      <th>names</th>
+      <td><p>optional names of the dimensions</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="full-size-fill-value-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#full-size-fill-value-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>full(size, fill_value, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a tensor of size <code>size</code> filled with <code>fill_value</code>.</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p>In PyTorch 1.5 a bool or integral <code>fill_value</code> will produce a warning if
+<code>dtype</code> or <code>out</code> are not set.
+In a future PyTorch release, when <code>dtype</code> and <code>out</code> are not set
+a bool <code>fill_value</code> will return a tensor of torch.bool dtype,
+and an integral <code>fill_value</code> will return a tensor of torch.long dtype.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_full</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>), <span class='fl'>3.141592</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  3.1416  3.1416  3.1416
+#&gt;  3.1416  3.1416  3.1416
+#&gt; [ CPUFloatType{2,3} ]</div></pre>
+  </div>
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+    <div class="page-header">
+    <h1>Full_like</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_full_like.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Full_like</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_full_like</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>fill_value</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>,
+  memory_format = <span class='fu'><a href='torch_memory_format.html'>torch_preserve_format</a></span>()
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
+    </tr>
+    <tr>
+      <th>fill_value</th>
+      <td><p>the number to fill the output tensor with.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>memory_format</th>
+      <td><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="full-like-input-fill-value-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-"><a class="anchor" href="#full-like-input-fill-value-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-"></a>full_like(input, fill_value, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, </h2>
+
+    
+
+
+<p>memory_format=torch.preserve_format) -&gt; Tensor</p>
+<p>Returns a tensor with the same size as <code>input</code> filled with <code>fill_value</code>.
+<code>torch_full_like(input, fill_value)</code> is equivalent to
+<code><a href='torch_full.html'>torch_full(input.size(), fill_value, dtype=input.dtype, layout=input.layout, device=input.device)</a></code>.</p>
+
+  </div>
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+    <div class="page-header">
+    <h1>Gather</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_gather.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Gather</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_gather</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, <span class='kw'>index</span>, sparse_grad = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the source tensor</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the axis along which to index</p></td>
+    </tr>
+    <tr>
+      <th>index</th>
+      <td><p>(LongTensor) the indices of elements to gather</p></td>
+    </tr>
+    <tr>
+      <th>sparse_grad</th>
+      <td><p>(bool,optional) If <code>TRUE</code>, gradient w.r.t. <code>input</code> will be a sparse tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="gather-input-dim-index-sparse-grad-false-gt-tensor-"><a class="anchor" href="#gather-input-dim-index-sparse-grad-false-gt-tensor-"></a>gather(input, dim, index, sparse_grad=FALSE) -&gt; Tensor </h2>
+
+    
+
+
+<p>Gathers values along an axis specified by <code>dim</code>.</p>
+<p>For a 3-D tensor the output is specified by::</p><pre><span class='kw'>out</span>[<span class='kw'>i</span>][<span class='kw'>j</span>][<span class='kw'>k</span>] <span class='op'>=</span> <span class='kw'>input</span>[<span class='kw'>index</span>[<span class='kw'>i</span>][<span class='kw'>j</span>][<span class='kw'>k</span>]][<span class='kw'>j</span>][<span class='kw'>k</span>]  <span class='co'># if dim == 0</span>
+<span class='kw'>out</span>[<span class='kw'>i</span>][<span class='kw'>j</span>][<span class='kw'>k</span>] <span class='op'>=</span> <span class='kw'>input</span>[<span class='kw'>i</span>][<span class='kw'>index</span>[<span class='kw'>i</span>][<span class='kw'>j</span>][<span class='kw'>k</span>]][<span class='kw'>k</span>]  <span class='co'># if dim == 1</span>
+<span class='kw'>out</span>[<span class='kw'>i</span>][<span class='kw'>j</span>][<span class='kw'>k</span>] <span class='op'>=</span> <span class='kw'>input</span>[<span class='kw'>i</span>][<span class='kw'>j</span>][<span class='kw'>index</span>[<span class='kw'>i</span>][<span class='kw'>j</span>][<span class='kw'>k</span>]]  <span class='co'># if dim == 2</span>
+</pre>
+
+<p>If <code>input</code> is an n-dimensional tensor with size
+\((x_0, x_1..., x_{i-1}, x_i, x_{i+1}, ..., x_{n-1})\)
+and <code>dim = i</code>, then <code>index</code> must be an \(n\)-dimensional tensor with
+size \((x_0, x_1, ..., x_{i-1}, y, x_{i+1}, ..., x_{n-1})\) where \(y \geq 1\)
+and <code>out</code> will have the same size as <code>index</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>t</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>3</span>,<span class='fl'>4</span>), ncol = <span class='fl'>2</span>, byrow = <span class='fl'>TRUE</span>))
+<span class='fu'>torch_gather</span>(<span class='kw'>t</span>, <span class='fl'>2</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>1</span>), ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>), dtype = <span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>()))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1  1
+#&gt;  4  3
+#&gt; [ CPUFloatType{2,2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+    <div class="page-header">
+    <h1>Ge</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_ge.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Ge</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_ge</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the tensor to compare</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor or float) the tensor or value to compare</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="ge-input-other-out-null-gt-tensor-"><a class="anchor" href="#ge-input-other-out-null-gt-tensor-"></a>ge(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes \(\mbox{input} \geq \mbox{other}\) element-wise.</p>
+<p>The second argument can be a number or a tensor whose shape is
+broadcastable  with the first argument.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_ge</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fl'>1</span><span class='op'>:</span><span class='fl'>4</span>, ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>)), 
+         <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>4</span>,<span class='fl'>4</span>), ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1  1
+#&gt;  0  1
+#&gt; [ CPUBoolType{2,2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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diff --git a/static/docs/reference/torch_generator.html b/static/docs/reference/torch_generator.html
new file mode 100644
index 000000000..e205f8f22
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@@ -0,0 +1,246 @@
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Create a Generator object</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/generator.R'><code>R/generator.R</code></a></small>
+    <div class="hidden name"><code>torch_generator.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>A <code>torch_generator</code>  is an object which manages the state of the algorithm
+that produces pseudo random numbers. Used as a keyword argument in many
+In-place random sampling functions.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_generator</span>()</pre>
+
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='co'># Via string</span>
+<span class='kw'>generator</span> <span class='op'>&lt;-</span> <span class='fu'>torch_generator</span>()
+<span class='kw'>generator</span><span class='op'>$</span><span class='fu'>current_seed</span>()
+<span class='kw'>generator</span><span class='op'>$</span><span class='fu'>set_current_seed</span>(<span class='fl'>1234567L</span>)
+<span class='kw'>generator</span><span class='op'>$</span><span class='fu'>current_seed</span>()
+
+
+}
+</div><div class='output co'>#&gt; integer64
+#&gt; [1] 1234567</div></pre>
+  </div>
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+    <div class="page-header">
+    <h1>Geqrf</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_geqrf.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Geqrf</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_geqrf</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input matrix</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="geqrf-input-out-null-gt-tensor-tensor-"><a class="anchor" href="#geqrf-input-out-null-gt-tensor-tensor-"></a>geqrf(input, out=NULL) -&gt; (Tensor, Tensor) </h2>
+
+    
+
+
+<p>This is a low-level function for calling LAPACK directly. This function
+returns a namedtuple (a, tau) as defined in <code>LAPACK documentation for geqrf</code>_ .</p>
+<p>You'll generally want to use <code><a href='torch_qr.html'>torch_qr</a></code> instead.</p>
+<p>Computes a QR decomposition of <code>input</code>, but without constructing
+\(Q\) and \(R\) as explicit separate matrices.</p>
+<p>Rather, this directly calls the underlying LAPACK function <code>?geqrf</code>
+which produces a sequence of 'elementary reflectors'.</p>
+<p>See <code>LAPACK documentation for geqrf</code>_ for further details.</p>
+
+  </div>
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+    <li class="dropdown-header">Torch Mechanics</li>
+    <li>
+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/neural-networks.html">Neural networks</a>
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+<li class="dropdown">
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
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+      <a href="../articles/loading-data.html">Loading Data</a>
+    </li>
+    <li class="dropdown-header">Autograd</li>
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+      <a href="../articles/using-autograd.html">Using autograd</a>
+    </li>
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+      <a href="../articles/extending-autograd.html">Extending autograd</a>
+    </li>
+  </ul>
+</li>
+<li class="dropdown-header">Examples</li>
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+  <a href="../reference/index.html">Reference</a>
+</li>
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+
+      
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Ger</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_ger.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Ger</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_ger</span>(<span class='kw'>self</span>, <span class='kw'>vec2</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) 1-D input vector</p></td>
+    </tr>
+    <tr>
+      <th>vec2</th>
+      <td><p>(Tensor) 1-D input vector</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>This function does not broadcast .</p>
+    <h2 class="hasAnchor" id="ger-input-vec-out-null-gt-tensor-"><a class="anchor" href="#ger-input-vec-out-null-gt-tensor-"></a>ger(input, vec2, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Outer product of <code>input</code> and <code>vec2</code>.
+If <code>input</code> is a vector of size \(n\) and <code>vec2</code> is a vector of
+size \(m\), then <code>out</code> must be a matrix of size \((n \times m)\).</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>v1</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>5.</span>)
+<span class='kw'>v2</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>4.</span>)
+<span class='fu'>torch_ger</span>(<span class='kw'>v1</span>, <span class='kw'>v2</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;   1   2   3
+#&gt;   2   4   6
+#&gt;   3   6   9
+#&gt;   4   8  12
+#&gt; [ CPUFloatType{4,3} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
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+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
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+      <a href="../articles/using-autograd.html">Using autograd</a>
+    </li>
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+      <a href="../articles/extending-autograd.html">Extending autograd</a>
+    </li>
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+
+      
+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Gt</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_gt.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Gt</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_gt</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the tensor to compare</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor or float) the tensor or value to compare</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="gt-input-other-out-null-gt-tensor-"><a class="anchor" href="#gt-input-other-out-null-gt-tensor-"></a>gt(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes \(\mbox{input} &gt; \mbox{other}\) element-wise.</p>
+<p>The second argument can be a number or a tensor whose shape is
+broadcastable  with the first argument.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_gt</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fl'>1</span><span class='op'>:</span><span class='fl'>4</span>, ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>)), 
+         <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>4</span>,<span class='fl'>4</span>), ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0  1
+#&gt;  0  0
+#&gt; [ CPUBoolType{2,2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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diff --git a/static/docs/reference/torch_hamming_window.html b/static/docs/reference/torch_hamming_window.html
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+    <div class="page-header">
+    <h1>Hamming_window</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
+    <div class="hidden name"><code>torch_hamming_window.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Hamming_window</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_hamming_window</span>(
+  <span class='kw'>window_length</span>,
+  periodic = <span class='fl'>TRUE</span>,
+  alpha = <span class='fl'>0.54</span>,
+  beta = <span class='fl'>0.46</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>window_length</th>
+      <td><p>(int) the size of returned window</p></td>
+    </tr>
+    <tr>
+      <th>periodic</th>
+      <td><p>(bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.</p></td>
+    </tr>
+    <tr>
+      <th>alpha</th>
+      <td><p>(float, optional) The coefficient \(\alpha\) in the equation above</p></td>
+    </tr>
+    <tr>
+      <th>beta</th>
+      <td><p>(float, optional) The coefficient \(\beta\) in the equation above</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). Only floating point types are supported.</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned window tensor. Only          <code>torch_strided</code> (dense layout) is supported.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>If `window_length` \eqn{=1}, the returned window contains a single value 1.
+</pre>
+
+<pre>This is a generalized version of `torch_hann_window`.
+</pre>
+
+    <h2 class="hasAnchor" id="hamming-window-window-length-periodic-true-alpha-beta-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#hamming-window-window-length-periodic-true-alpha-beta-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>hamming_window(window_length, periodic=TRUE, alpha=0.54, beta=0.46, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Hamming window function.</p>
+<p>$$
+    w[n] = \alpha - \beta\ \cos \left( \frac{2 \pi n}{N - 1} \right),
+$$
+where \(N\) is the full window size.</p>
+<p>The input <code>window_length</code> is a positive integer controlling the
+returned window size. <code>periodic</code> flag determines whether the returned
+window trims off the last duplicate value from the symmetric window and is
+ready to be used as a periodic window with functions like
+<code>torch_stft</code>. Therefore, if <code>periodic</code> is true, the \(N\) in
+above formula is in fact \(\mbox{window\_length} + 1\). Also, we always have
+<code>torch_hamming_window(L, periodic=TRUE)</code> equal to
+<code>torch_hamming_window(L + 1, periodic=False)[:-1])</code>.</p>
+
+  </div>
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+    <div class="page-header">
+    <h1>Hann_window</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
+    <div class="hidden name"><code>torch_hann_window.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Hann_window</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_hann_window</span>(
+  <span class='kw'>window_length</span>,
+  periodic = <span class='fl'>TRUE</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>window_length</th>
+      <td><p>(int) the size of returned window</p></td>
+    </tr>
+    <tr>
+      <th>periodic</th>
+      <td><p>(bool, optional) If TRUE, returns a window to be used as periodic        function. If False, return a symmetric window.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). Only floating point types are supported.</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned window tensor. Only          <code>torch_strided</code> (dense layout) is supported.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>If `window_length` \eqn{=1}, the returned window contains a single value 1.
+</pre>
+
+    <h2 class="hasAnchor" id="hann-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#hann-window-window-length-periodic-true-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>hann_window(window_length, periodic=TRUE, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Hann window function.</p>
+<p>$$
+    w[n] = \frac{1}{2}\ \left[1 - \cos \left( \frac{2 \pi n}{N - 1} \right)\right] =
+            \sin^2 \left( \frac{\pi n}{N - 1} \right),
+$$
+where \(N\) is the full window size.</p>
+<p>The input <code>window_length</code> is a positive integer controlling the
+returned window size. <code>periodic</code> flag determines whether the returned
+window trims off the last duplicate value from the symmetric window and is
+ready to be used as a periodic window with functions like
+<code>torch_stft</code>. Therefore, if <code>periodic</code> is true, the \(N\) in
+above formula is in fact \(\mbox{window\_length} + 1\). Also, we always have
+<code>torch_hann_window(L, periodic=TRUE)</code> equal to
+<code>torch_hann_window(L + 1, periodic=False)[:-1])</code>.</p>
+
+  </div>
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+
+      
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Histc</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_histc.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Histc</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_histc</span>(<span class='kw'>self</span>, bins = <span class='fl'>100L</span>, min = <span class='fl'>0L</span>, max = <span class='fl'>0L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>bins</th>
+      <td><p>(int) number of histogram bins</p></td>
+    </tr>
+    <tr>
+      <th>min</th>
+      <td><p>(int) lower end of the range (inclusive)</p></td>
+    </tr>
+    <tr>
+      <th>max</th>
+      <td><p>(int) upper end of the range (inclusive)</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="histc-input-bins-min-max-out-null-gt-tensor-"><a class="anchor" href="#histc-input-bins-min-max-out-null-gt-tensor-"></a>histc(input, bins=100, min=0, max=0, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the histogram of a tensor.</p>
+<p>The elements are sorted into equal width bins between <code>min</code> and
+<code>max</code>. If <code>min</code> and <code>max</code> are both zero, the minimum and
+maximum values of the data are used.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_histc</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1.</span>, <span class='fl'>2</span>, <span class='fl'>1</span>)), bins=<span class='fl'>4</span>, min=<span class='fl'>0</span>, max=<span class='fl'>3</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0
+#&gt;  2
+#&gt;  1
+#&gt;  0
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
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+    <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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_ifft.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Ifft</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_ifft</span>(<span class='kw'>self</span>, <span class='kw'>signal_ndim</span>, normalized = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor of at least <code>signal_ndim</code> <code>+ 1</code>        dimensions</p></td>
+    </tr>
+    <tr>
+      <th>signal_ndim</th>
+      <td><p>(int) the number of dimensions in each signal.        <code>signal_ndim</code> can only be 1, 2 or 3</p></td>
+    </tr>
+    <tr>
+      <th>normalized</th>
+      <td><p>(bool, optional) controls whether to return normalized results.        Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>For CUDA tensors, an LRU cache is used for cuFFT plans to speed up
+repeatedly running FFT methods on tensors of same geometry with same
+configuration. See cufft-plan-cache for more details on how to
+monitor and control the cache.
+</pre>
+
+    <h2 class="hasAnchor" id="ifft-input-signal-ndim-normalized-false-gt-tensor-"><a class="anchor" href="#ifft-input-signal-ndim-normalized-false-gt-tensor-"></a>ifft(input, signal_ndim, normalized=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Complex-to-complex Inverse Discrete Fourier Transform</p>
+<p>This method computes the complex-to-complex inverse discrete Fourier
+transform. Ignoring the batch dimensions, it computes the following
+expression:</p>
+<p>$$
+    X[\omega_1, \dots, \omega_d] =
+        \frac{1}{\prod_{i=1}^d N_i} \sum_{n_1=0}^{N_1-1} \dots \sum_{n_d=0}^{N_d-1} x[n_1, \dots, n_d]
+         e^{\ j\ 2 \pi \sum_{i=0}^d \frac{\omega_i n_i}{N_i}},
+$$
+where \(d\) = <code>signal_ndim</code> is number of dimensions for the
+signal, and \(N_i\) is the size of signal dimension \(i\).</p>
+<p>The argument specifications are almost identical with <code><a href='torch_fft.html'>torch_fft</a></code>.
+However, if <code>normalized</code> is set to <code>TRUE</code>, this instead returns the
+results multiplied by \(\sqrt{\prod_{i=1}^d N_i}\), to become a unitary
+operator. Therefore, to invert a <code><a href='torch_fft.html'>torch_fft</a></code>, the <code>normalized</code>
+argument should be set identically for <code><a href='torch_fft.html'>torch_fft</a></code>.</p>
+<p>Returns the real and the imaginary parts together as one tensor of the same
+shape of <code>input</code>.</p>
+<p>The inverse of this function is <code><a href='torch_fft.html'>torch_fft</a></code>.</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p>For CPU tensors, this method is currently only available with MKL. Use
+<code>torch_backends.mkl.is_available</code> to check if MKL is installed.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>, <span class='fl'>2</span>))
+<span class='kw'>x</span>
+<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_fft.html'>torch_fft</a></span>(<span class='kw'>x</span>, <span class='fl'>2</span>)
+<span class='fu'>torch_ifft</span>(<span class='kw'>y</span>, <span class='fl'>2</span>)  <span class='co'># recover x</span>
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;  -1.1864  0.4079
+#&gt;  -0.7702  0.2462
+#&gt;   0.1156  1.5071
+#&gt; 
+#&gt; (2,.,.) = 
+#&gt;  -1.1418 -0.3572
+#&gt;  -0.6047  0.3412
+#&gt;  -0.5722 -1.1495
+#&gt; 
+#&gt; (3,.,.) = 
+#&gt;   0.3688  0.8228
+#&gt;  -0.6308  0.5165
+#&gt;   1.4051  0.6681
+#&gt; [ CPUFloatType{3,3,2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Integer type info</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/type-info.R'><code>R/type-info.R</code></a></small>
+    <div class="hidden name"><code>torch_iinfo.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>A list that represents the numerical properties of a integer
+type.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_iinfo</span>(<span class='kw'>dtype</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>dtype</th>
+      <td><p>dtype to get information from.</p></td>
+    </tr>
+    </table>
+
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/torch_imag.html b/static/docs/reference/torch_imag.html
new file mode 100644
index 000000000..8f35c0bc1
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+++ b/static/docs/reference/torch_imag.html
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+
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+    </li>
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Imag</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_imag.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Imag</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_imag</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="imag-input-gt-tensor-"><a class="anchor" href="#imag-input-gt-tensor-"></a>imag(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the imaginary part of the <code>input</code> tensor.</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p>Not yet implemented.</p>
+<p>$$
+    \mbox{out}_{i} = imag(\mbox{input}_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='fu'>torch_imag</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>1</span> <span class='op'>+</span> <span class='fl'>1i</span>, <span class='op'>-</span><span class='fl'>2</span> <span class='op'>+</span> <span class='fl'>2i</span>, <span class='fl'>3</span> <span class='op'>-</span> <span class='fl'>3i</span>)))
+}
+}
+</div></pre>
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Index_select</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_index_select.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Index_select</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_index_select</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, <span class='kw'>index</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the dimension in which we index</p></td>
+    </tr>
+    <tr>
+      <th>index</th>
+      <td><p>(LongTensor) the 1-D tensor containing the indices to index</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>The returned tensor does <strong>not</strong> use the same storage as the original
+tensor.  If <code>out</code> has a different shape than expected, we
+silently change it to the correct shape, reallocating the underlying
+storage if necessary.</p>
+    <h2 class="hasAnchor" id="index-select-input-dim-index-out-null-gt-tensor-"><a class="anchor" href="#index-select-input-dim-index-out-null-gt-tensor-"></a>index_select(input, dim, index, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor which indexes the <code>input</code> tensor along dimension
+<code>dim</code> using the entries in <code>index</code> which is a <code>LongTensor</code>.</p>
+<p>The returned tensor has the same number of dimensions as the original tensor
+(<code>input</code>).  The <code>dim</code>\ th dimension has the same size as the length
+of <code>index</code>; other dimensions have the same size as in the original tensor.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>4</span>))
+<span class='kw'>x</span>
+<span class='kw'>indices</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>), dtype = <span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>())
+<span class='fu'>torch_index_select</span>(<span class='kw'>x</span>, <span class='fl'>1</span>, <span class='kw'>indices</span>)
+<span class='fu'>torch_index_select</span>(<span class='kw'>x</span>, <span class='fl'>2</span>, <span class='kw'>indices</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.1471  0.1196
+#&gt;  1.7271  0.1422
+#&gt; -0.8387  0.1700
+#&gt; [ CPUFloatType{3,2} ]</div></pre>
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+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Inverse</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_inverse.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Inverse</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_inverse</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor of size \((*, n, n)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is zero or more                    batch dimensions</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>Irrespective of the original strides, the returned tensors will be
+transposed, i.e. with strides like `input.contiguous().transpose(-2, -1).stride()`
+</pre>
+
+    <h2 class="hasAnchor" id="inverse-input-out-null-gt-tensor-"><a class="anchor" href="#inverse-input-out-null-gt-tensor-"></a>inverse(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Takes the inverse of the square matrix <code>input</code>. <code>input</code> can be batches
+of 2D square tensors, in which case this function would return a tensor composed of
+individual inverses.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_rand.html'>torch_rand</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'>torch_inverse</span>(<span class='kw'>x</span>)
+<span class='kw'>z</span> <span class='op'>=</span> <span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='kw'>x</span>, <span class='kw'>y</span>)
+<span class='kw'>z</span>
+<span class='fu'><a href='torch_max.html'>torch_max</a></span>(<span class='fu'><a href='torch_abs.html'>torch_abs</a></span>(<span class='kw'>z</span> <span class='op'>-</span> <span class='fu'><a href='torch_eye.html'>torch_eye</a></span>(<span class='fl'>4</span>))) <span class='co'># Max non-zero</span>
+<span class='co'># Batched inverse example</span>
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'>torch_inverse</span>(<span class='kw'>x</span>)
+<span class='kw'>z</span> <span class='op'>=</span> <span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span>(<span class='kw'>x</span>, <span class='kw'>y</span>)
+<span class='fu'><a href='torch_max.html'>torch_max</a></span>(<span class='fu'><a href='torch_abs.html'>torch_abs</a></span>(<span class='kw'>z</span> <span class='op'>-</span> <span class='fu'><a href='torch_eye.html'>torch_eye</a></span>(<span class='fl'>4</span>)<span class='op'>$</span><span class='fu'>expand_as</span>(<span class='kw'>x</span>))) <span class='co'># Max non-zero</span>
+}
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+    <div class="page-header">
+    <h1>Irfft</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_irfft.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Irfft</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_irfft</span>(
+  <span class='kw'>self</span>,
+  <span class='kw'>signal_ndim</span>,
+  normalized = <span class='fl'>FALSE</span>,
+  onesided = <span class='fl'>TRUE</span>,
+  signal_sizes = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>()
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor of at least <code>signal_ndim</code> <code>+ 1</code>        dimensions</p></td>
+    </tr>
+    <tr>
+      <th>signal_ndim</th>
+      <td><p>(int) the number of dimensions in each signal.        <code>signal_ndim</code> can only be 1, 2 or 3</p></td>
+    </tr>
+    <tr>
+      <th>normalized</th>
+      <td><p>(bool, optional) controls whether to return normalized results.        Default: <code>FALSE</code></p></td>
+    </tr>
+    <tr>
+      <th>onesided</th>
+      <td><p>(bool, optional) controls whether <code>input</code> was halfed to avoid        redundancy, e.g., by <code><a href='torch_rfft.html'>torch_rfft()</a></code>. Default: <code>TRUE</code></p></td>
+    </tr>
+    <tr>
+      <th>signal_sizes</th>
+      <td><p>(list or <code>torch.Size</code>, optional) the size of the original        signal (without batch dimension). Default: <code>NULL</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>Due to the conjugate symmetry, `input` do not need to contain the full
+complex frequency values. Roughly half of the values will be sufficient, as
+is the case when `input` is given by [`~torch.rfft`] with
+`rfft(signal, onesided=TRUE)`. In such case, set the `onesided`
+argument of this method to `TRUE`. Moreover, the original signal shape
+information can sometimes be lost, optionally set `signal_sizes` to be
+the size of the original signal (without the batch dimensions if in batched
+mode) to recover it with correct shape.
+
+Therefore, to invert an [torch_rfft()], the `normalized` and
+`onesided` arguments should be set identically for [torch_irfft()],
+and preferably a `signal_sizes` is given to avoid size mismatch. See the
+example below for a case of size mismatch.
+
+See [torch_rfft()] for details on conjugate symmetry.
+</pre>
+
+<p>The inverse of this function is <code><a href='torch_rfft.html'>torch_rfft()</a></code>.</p>
+<pre>For CUDA tensors, an LRU cache is used for cuFFT plans to speed up
+repeatedly running FFT methods on tensors of same geometry with same
+configuration. See cufft-plan-cache for more details on how to
+monitor and control the cache.
+</pre>
+
+    <h2 class="hasAnchor" id="irfft-input-signal-ndim-normalized-false-onesided-true-signal-sizes-null-gt-tensor-"><a class="anchor" href="#irfft-input-signal-ndim-normalized-false-onesided-true-signal-sizes-null-gt-tensor-"></a>irfft(input, signal_ndim, normalized=False, onesided=TRUE, signal_sizes=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Complex-to-real Inverse Discrete Fourier Transform</p>
+<p>This method computes the complex-to-real inverse discrete Fourier transform.
+It is mathematically equivalent with <code><a href='torch_ifft.html'>torch_ifft</a></code> with differences only in
+formats of the input and output.</p>
+<p>The argument specifications are almost identical with <code><a href='torch_ifft.html'>torch_ifft</a></code>.
+Similar to <code><a href='torch_ifft.html'>torch_ifft</a></code>, if <code>normalized</code> is set to <code>TRUE</code>,
+this normalizes the result by multiplying it with
+\(\sqrt{\prod_{i=1}^K N_i}\) so that the operator is unitary, where
+\(N_i\) is the size of signal dimension \(i\).</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p>Generally speaking, input to this function should contain values
+following conjugate symmetry. Note that even if <code>onesided</code> is
+<code>TRUE</code>, often symmetry on some part is still needed. When this
+requirement is not satisfied, the behavior of <code>torch_irfft</code> is
+undefined. Since <code>torch_autograd.gradcheck</code> estimates numerical
+Jacobian with point perturbations, <code>torch_irfft</code> will almost
+certainly fail the check.</p>
+
+<p>For CPU tensors, this method is currently only available with MKL. Use
+<code>torch_backends.mkl.is_available</code> to check if MKL is installed.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='fu'><a href='torch_rfft.html'>torch_rfft</a></span>(<span class='kw'>x</span>, <span class='fl'>2</span>, onesided=<span class='fl'>TRUE</span>)
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>))
+<span class='fu'><a href='torch_rfft.html'>torch_rfft</a></span>(<span class='kw'>x</span>, <span class='fl'>2</span>, onesided=<span class='fl'>TRUE</span>)
+<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_rfft.html'>torch_rfft</a></span>(<span class='kw'>x</span>, <span class='fl'>2</span>, onesided=<span class='fl'>TRUE</span>)
+<span class='fu'>torch_irfft</span>(<span class='kw'>y</span>, <span class='fl'>2</span>, onesided=<span class='fl'>TRUE</span>, signal_sizes=<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>,<span class='fl'>5</span>))  <span class='co'># recover x</span>
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -1.2099  1.7469 -1.3247  0.0171  0.0024
+#&gt;  0.4107  0.2902 -2.0005 -0.8215 -1.4046
+#&gt; -1.2626  0.0016  2.6169 -1.2084 -0.1029
+#&gt;  0.0418  1.5131  0.9458 -0.2751 -0.5556
+#&gt; [ CPUFloatType{4,5} ]</div></pre>
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diff --git a/static/docs/reference/torch_is_complex.html b/static/docs/reference/torch_is_complex.html
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+    <div class="page-header">
+    <h1>Is_complex</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_is_complex.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Is_complex</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_is_complex</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the PyTorch tensor to test</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="is-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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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Is_floating_point</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_is_floating_point.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Is_floating_point</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_is_floating_point</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the PyTorch tensor to test</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="is-floating-point-input-gt-bool-"><a class="anchor" href="#is-floating-point-input-gt-bool-"></a>is_floating_point(input) -&gt; (bool) </h2>
+
+    
+
+
+<p>Returns TRUE if the data type of <code>input</code> is a floating point data type i.e.,
+one of <code>torch_float64</code>, <code>torch.float32</code> and <code>torch.float16</code>.</p>
+
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+    <div class="page-header">
+    <h1>Isfinite</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_isfinite.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Isfinite</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_isfinite</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) A tensor to check</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
+
+    
+
+
+<p>Returns a new tensor with boolean elements representing if each element is <code>Finite</code> or not.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_isfinite</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>Inf</span>, <span class='fl'>2</span>, <span class='op'>-</span><span class='fl'>Inf</span>, <span class='fl'>NaN</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1
+#&gt;  0
+#&gt;  1
+#&gt;  0
+#&gt;  0
+#&gt; [ CPUBoolType{5} ]</div></pre>
+  </div>
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+    <div class="page-header">
+    <h1>Isinf</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_isinf.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Isinf</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_isinf</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) A tensor to check</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
+
+    
+
+
+<p>Returns a new tensor with boolean elements representing if each element is <code>+/-INF</code> or not.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_isinf</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>Inf</span>, <span class='fl'>2</span>, <span class='op'>-</span><span class='fl'>Inf</span>, <span class='fl'>NaN</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0
+#&gt;  1
+#&gt;  0
+#&gt;  1
+#&gt;  0
+#&gt; [ CPUBoolType{5} ]</div></pre>
+  </div>
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+<div class="row">
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+    <div class="page-header">
+    <h1>Isnan</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_isnan.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Isnan</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_isnan</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) A tensor to check</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
+
+    
+
+
+<p>Returns a new tensor with boolean elements representing if each element is <code>NaN</code> or not.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_isnan</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>NaN</span>, <span class='fl'>2</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0
+#&gt;  1
+#&gt;  0
+#&gt; [ CPUBoolType{3} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
+  </div>
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+        <a class="navbar-link" href="../index.html">torch</a>
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+    <li class="dropdown-header">Beginers guide</li>
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+      <a href="../articles/getting-started/tensors.html">Tensors</a>
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+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
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+    </li>
+    <li>
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+</li>
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+
+      
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Kthvalue</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_kthvalue.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Kthvalue</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_kthvalue</span>(<span class='kw'>self</span>, <span class='kw'>k</span>, dim = <span class='op'>-</span><span class='fl'>1L</span>, keepdim = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>k</th>
+      <td><p>(int) k for the k-th smallest element</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int, optional) the dimension to find the kth value along</p></td>
+    </tr>
+    <tr>
+      <th>keepdim</th>
+      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="kthvalue-input-k-dim-null-keepdim-false-out-null-gt-tensor-longtensor-"><a class="anchor" href="#kthvalue-input-k-dim-null-keepdim-false-out-null-gt-tensor-longtensor-"></a>kthvalue(input, k, dim=NULL, keepdim=False, out=NULL) -&gt; (Tensor, LongTensor) </h2>
+
+    
+
+
+<p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the <code>k</code> th
+smallest element of each row of the <code>input</code> tensor in the given dimension
+<code>dim</code>. And <code>indices</code> is the index location of each element found.</p>
+<p>If <code>dim</code> is not given, the last dimension of the <code>input</code> is chosen.</p>
+<p>If <code>keepdim</code> is <code>TRUE</code>, both the <code>values</code> and <code>indices</code> tensors
+are the same size as <code>input</code>, except in the dimension <code>dim</code> where
+they are of size 1. Otherwise, <code>dim</code> is squeezed
+(see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in both the <code>values</code> and
+<code>indices</code> tensors having 1 fewer dimension than the <code>input</code> tensor.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>6.</span>)
+<span class='kw'>x</span>
+<span class='fu'>torch_kthvalue</span>(<span class='kw'>x</span>, <span class='fl'>4</span>)
+<span class='kw'>x</span><span class='op'>=</span><span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>,<span class='fl'>7.</span>)<span class='op'>$</span><span class='fu'>resize_</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>,<span class='fl'>3</span>))
+<span class='kw'>x</span>
+<span class='fu'>torch_kthvalue</span>(<span class='kw'>x</span>, <span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>TRUE</span>)
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt;  4  5  6
+#&gt; [ CPUFloatType{1,3} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt;  1  1  1
+#&gt; [ CPULongType{1,3} ]
+#&gt; </div></pre>
+  </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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+    </li>
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+    </li>
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+    </li>
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+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
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+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Creates the corresponding layout</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/layout.R'><code>R/layout.R</code></a></small>
+    <div class="hidden name"><code>torch_layout.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Creates the corresponding layout</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_strided</span>()
+
+<span class='fu'>torch_sparse_coo</span>()</pre>
+
+
+
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+
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+    <div class="page-header">
+    <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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_le.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Le</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_le</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the tensor to compare</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor or float) the tensor or value to compare</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="le-input-other-out-null-gt-tensor-"><a class="anchor" href="#le-input-other-out-null-gt-tensor-"></a>le(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes \(\mbox{input} \leq \mbox{other}\) element-wise.</p>
+<p>The second argument can be a number or a tensor whose shape is
+broadcastable  with the first argument.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_le</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fl'>1</span><span class='op'>:</span><span class='fl'>4</span>, ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>)), 
+         <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>4</span>,<span class='fl'>4</span>), ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1  0
+#&gt;  1  1
+#&gt; [ CPUBoolType{2,2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+    </li>
+    <li>
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+      <a href="../articles/getting-started/custom-nn.html">Custom nn modules</a>
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+    <li>
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+    </li>
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+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
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+      <a href="../articles/getting-started/neural-networks.html">Neural networks</a>
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+
+      
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Lerp</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_lerp.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Lerp</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_lerp</span>(<span class='kw'>self</span>, <span class='kw'>end</span>, <span class='kw'>weight</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the tensor with the starting points</p></td>
+    </tr>
+    <tr>
+      <th>end</th>
+      <td><p>(Tensor) the tensor with the ending points</p></td>
+    </tr>
+    <tr>
+      <th>weight</th>
+      <td><p>(float or tensor) the weight for the interpolation formula</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="lerp-input-end-weight-out-null-"><a class="anchor" href="#lerp-input-end-weight-out-null-"></a>lerp(input, end, weight, out=NULL) </h2>
+
+    
+
+
+<p>Does a linear interpolation of two tensors <code>start</code> (given by <code>input</code>) and <code>end</code> based
+on a scalar or tensor <code>weight</code> and returns the resulting <code>out</code> tensor.</p>
+<p>$$
+    \mbox{out}_i = \mbox{start}_i + \mbox{weight}_i \times (\mbox{end}_i - \mbox{start}_i)
+$$
+The shapes of <code>start</code> and <code>end</code> must be
+broadcastable . If <code>weight</code> is a tensor, then
+the shapes of <code>weight</code>, <code>start</code>, and <code>end</code> must be broadcastable .</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>start</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>5.</span>)
+<span class='kw'>end</span> <span class='op'>=</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>4</span>)<span class='op'>$</span><span class='fu'>fill_</span>(<span class='fl'>10</span>)
+<span class='kw'>start</span>
+<span class='kw'>end</span>
+<span class='fu'>torch_lerp</span>(<span class='kw'>start</span>, <span class='kw'>end</span>, <span class='fl'>0.5</span>)
+<span class='fu'>torch_lerp</span>(<span class='kw'>start</span>, <span class='kw'>end</span>, <span class='fu'><a href='torch_full_like.html'>torch_full_like</a></span>(<span class='kw'>start</span>, <span class='fl'>0.5</span>))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  5.5000
+#&gt;  6.0000
+#&gt;  6.5000
+#&gt;  7.0000
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+    <div class="page-header">
+    <h1>Lgamma</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_lgamma.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Lgamma</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_lgamma</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="lgamma-input-out-null-gt-tensor-"><a class="anchor" href="#lgamma-input-out-null-gt-tensor-"></a>lgamma(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the logarithm of the gamma function on <code>input</code>.</p>
+<p>$$
+    \mbox{out}_{i} = \log \Gamma(\mbox{input}_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0.5</span>, <span class='fl'>2</span>, <span class='fl'>0.5</span>)
+<span class='fu'>torch_lgamma</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.5724
+#&gt;  0.0000
+#&gt; -0.1208
+#&gt; [ CPUFloatType{3} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Linspace</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_linspace.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Linspace</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_linspace</span>(
+  <span class='kw'>start</span>,
+  <span class='kw'>end</span>,
+  steps = <span class='fl'>100</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>start</th>
+      <td><p>(float) the starting value for the set of points</p></td>
+    </tr>
+    <tr>
+      <th>end</th>
+      <td><p>(float) the ending value for the set of points</p></td>
+    </tr>
+    <tr>
+      <th>steps</th>
+      <td><p>(int) number of points to sample between <code>start</code>        and <code>end</code>. Default: <code>100</code>.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="linspace-start-end-steps-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#linspace-start-end-steps-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>linspace(start, end, steps=100, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a one-dimensional tensor of <code>steps</code>
+equally spaced points between <code>start</code> and <code>end</code>.</p>
+<p>The output tensor is 1-D of size <code>steps</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_linspace</span>(<span class='fl'>3</span>, <span class='fl'>10</span>, steps=<span class='fl'>5</span>)
+<span class='fu'>torch_linspace</span>(<span class='op'>-</span><span class='fl'>10</span>, <span class='fl'>10</span>, steps=<span class='fl'>5</span>)
+<span class='fu'>torch_linspace</span>(start=<span class='op'>-</span><span class='fl'>10</span>, end=<span class='fl'>10</span>, steps=<span class='fl'>5</span>)
+<span class='fu'>torch_linspace</span>(start=<span class='op'>-</span><span class='fl'>10</span>, end=<span class='fl'>10</span>, steps=<span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -10
+#&gt; [ CPUFloatType{1} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Loads a saved object</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/save.R'><code>R/save.R</code></a></small>
+    <div class="hidden name"><code>torch_load.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Loads a saved object</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_load</span>(<span class='kw'>path</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>path</th>
+      <td><p>a path to the saved object</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
+
+    <div class='dont-index'><p>Other torch_save: 
+<code><a href='torch_save.html'>torch_save</a>()</code></p></div>
+
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diff --git a/static/docs/reference/torch_log.html b/static/docs/reference/torch_log.html
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+      <a href="../articles/using-autograd.html">Using autograd</a>
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_log.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Log</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_log</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="log-input-out-null-gt-tensor-"><a class="anchor" href="#log-input-out-null-gt-tensor-"></a>log(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the natural logarithm of the elements
+of <code>input</code>.</p>
+<p>$$
+    y_{i} = \log_{e} (x_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_log</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;     nan
+#&gt;  0.8911
+#&gt;  0.0003
+#&gt;     nan
+#&gt; -1.5295
+#&gt; [ CPUFloatType{5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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+
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diff --git a/static/docs/reference/torch_log10.html b/static/docs/reference/torch_log10.html
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+    <div class="page-header">
+    <h1>Log10</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_log10.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Log10</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_log10</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="log-input-out-null-gt-tensor-"><a class="anchor" href="#log-input-out-null-gt-tensor-"></a>log10(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the logarithm to the base 10 of the elements
+of <code>input</code>.</p>
+<p>$$
+    y_{i} = \log_{10} (x_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_rand.html'>torch_rand</a></span>(<span class='fl'>5</span>)
+<span class='kw'>a</span>
+<span class='fu'>torch_log10</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0.2865
+#&gt; -0.4904
+#&gt; -0.9127
+#&gt; -0.2747
+#&gt; -0.2714
+#&gt; [ CPUFloatType{5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_log1p.html b/static/docs/reference/torch_log1p.html
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+      <a href="../articles/using-autograd.html">Using autograd</a>
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+
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+<div class="row">
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+    <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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_log1p.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Log1p</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_log1p</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>This function is more accurate than <code><a href='torch_log.html'>torch_log</a></code> for small
+values of <code>input</code></p>
+    <h2 class="hasAnchor" id="log-p-input-out-null-gt-tensor-"><a class="anchor" href="#log-p-input-out-null-gt-tensor-"></a>log1p(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the natural logarithm of (1 + <code>input</code>).</p>
+<p>$$
+    y_i = \log_{e} (x_i + 1)
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_log1p</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.6781
+#&gt;  0.2969
+#&gt;  0.6634
+#&gt;  0.5704
+#&gt; -0.1361
+#&gt; [ CPUFloatType{5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
+  </div>
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+</div>
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+
diff --git a/static/docs/reference/torch_log2.html b/static/docs/reference/torch_log2.html
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+    <div class="page-header">
+    <h1>Log2</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_log2.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Log2</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_log2</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="log-input-out-null-gt-tensor-"><a class="anchor" href="#log-input-out-null-gt-tensor-"></a>log2(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the logarithm to the base 2 of the elements
+of <code>input</code>.</p>
+<p>$$
+    y_{i} = \log_{2} (x_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_rand.html'>torch_rand</a></span>(<span class='fl'>5</span>)
+<span class='kw'>a</span>
+<span class='fu'>torch_log2</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0.6302
+#&gt; -6.7372
+#&gt; -0.0043
+#&gt; -1.2060
+#&gt; -1.4108
+#&gt; [ CPUFloatType{5} ]</div></pre>
+  </div>
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diff --git a/static/docs/reference/torch_logdet.html b/static/docs/reference/torch_logdet.html
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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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_logdet.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Logdet</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_logdet</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor of size <code>(*, n, n)</code> where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is zero or more                batch dimensions.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>Result is `-inf` if `input` has zero log determinant, and is `NaN` if
+`input` has negative determinant.
+</pre>
+
+<pre>Backward through `logdet` internally uses SVD results when `input`
+is not invertible. In this case, double backward through `logdet` will
+be unstable in when `input` doesn't have distinct singular values. See
+`~torch.svd` for details.
+</pre>
+
+    <h2 class="hasAnchor" id="logdet-input-gt-tensor-"><a class="anchor" href="#logdet-input-gt-tensor-"></a>logdet(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>Calculates log determinant of a square matrix or batches of square matrices.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='fu'><a href='torch_det.html'>torch_det</a></span>(<span class='kw'>A</span>)
+<span class='fu'>torch_logdet</span>(<span class='kw'>A</span>)
+<span class='kw'>A</span>
+<span class='kw'>A</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/det.html'>det</a></span>()
+<span class='kw'>A</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/det.html'>det</a></span>()<span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/Log.html'>log</a></span>()
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; nan
+#&gt; [ CPUFloatType{} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+    <div class="page-header">
+    <h1>Logical_and</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_logical_and.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Logical_and</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_logical_and</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor) the tensor to compute AND with</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="logical-and-input-other-out-null-gt-tensor-"><a class="anchor" href="#logical-and-input-other-out-null-gt-tensor-"></a>logical_and(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the element-wise logical AND of the given input tensors. Zeros are treated as <code>FALSE</code> and nonzeros are
+treated as <code>TRUE</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_logical_and</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>TRUE</span>, <span class='fl'>FALSE</span>, <span class='fl'>TRUE</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>TRUE</span>, <span class='fl'>FALSE</span>, <span class='fl'>FALSE</span>)))
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>10</span>, <span class='fl'>0</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int8</a></span>())
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>0</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int8</a></span>())
+<span class='fu'>torch_logical_and</span>(<span class='kw'>a</span>, <span class='kw'>b</span>)
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='fu'>torch_logical_and</span>(<span class='kw'>a</span>, <span class='kw'>b</span>, out=<span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>4</span>, dtype=<span class='fu'><a href='torch_dtype.html'>torch_bool</a></span>()))
+}
+}
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+    <div class="page-header">
+    <h1>Logical_not</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_logical_not.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Logical_not</p>
+    </div>
+
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="logical-not-input-out-null-gt-tensor-"><a class="anchor" href="#logical-not-input-out-null-gt-tensor-"></a>logical_not(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the element-wise logical NOT of the given input tensor. If not specified, the output tensor will have the bool
+dtype. If the input tensor is not a bool tensor, zeros are treated as <code>FALSE</code> and non-zeros are treated as <code>TRUE</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_logical_not</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>TRUE</span>, <span class='fl'>FALSE</span>)))
+<span class='fu'>torch_logical_not</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>1</span>, <span class='op'>-</span><span class='fl'>10</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int8</a></span>()))
+<span class='fu'>torch_logical_not</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0.</span>, <span class='fl'>1.5</span>, <span class='op'>-</span><span class='fl'>10.</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_double</a></span>()))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1
+#&gt;  0
+#&gt;  0
+#&gt; [ CPUBoolType{3} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Logical_or</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_logical_or.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Logical_or</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_logical_or</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor) the tensor to compute OR with</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="logical-or-input-other-out-null-gt-tensor-"><a class="anchor" href="#logical-or-input-other-out-null-gt-tensor-"></a>logical_or(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the element-wise logical OR of the given input tensors. Zeros are treated as <code>FALSE</code> and nonzeros are
+treated as <code>TRUE</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_logical_or</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>TRUE</span>, <span class='fl'>FALSE</span>, <span class='fl'>TRUE</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>TRUE</span>, <span class='fl'>FALSE</span>, <span class='fl'>FALSE</span>)))
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>10</span>, <span class='fl'>0</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int8</a></span>())
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>0</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int8</a></span>())
+<span class='fu'>torch_logical_or</span>(<span class='kw'>a</span>, <span class='kw'>b</span>)
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='fu'>torch_logical_or</span>(<span class='kw'>a</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/double.html'>double</a></span>(), <span class='kw'>b</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/double.html'>double</a></span>())
+<span class='fu'>torch_logical_or</span>(<span class='kw'>a</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/double.html'>double</a></span>(), <span class='kw'>b</span>)
+<span class='fu'>torch_logical_or</span>(<span class='kw'>a</span>, <span class='kw'>b</span>, out=<span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fl'>4</span>, dtype=<span class='fu'><a href='torch_dtype.html'>torch_bool</a></span>()))
+}
+}
+</div></pre>
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+
+      
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Logical_xor</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_logical_xor.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Logical_xor</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_logical_xor</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor) the tensor to compute XOR with</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="logical-xor-input-other-out-null-gt-tensor-"><a class="anchor" href="#logical-xor-input-other-out-null-gt-tensor-"></a>logical_xor(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the element-wise logical XOR of the given input tensors. Zeros are treated as <code>FALSE</code> and nonzeros are
+treated as <code>TRUE</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_logical_xor</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>TRUE</span>, <span class='fl'>FALSE</span>, <span class='fl'>TRUE</span>)), <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>TRUE</span>, <span class='fl'>FALSE</span>, <span class='fl'>FALSE</span>)))
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>10</span>, <span class='fl'>0</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int8</a></span>())
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>0</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int8</a></span>())
+<span class='fu'>torch_logical_xor</span>(<span class='kw'>a</span>, <span class='kw'>b</span>)
+<span class='fu'>torch_logical_xor</span>(<span class='kw'>a</span><span class='op'>$</span><span class='fu'>to</span>(dtype=<span class='fu'><a href='torch_dtype.html'>torch_double</a></span>()), <span class='kw'>b</span><span class='op'>$</span><span class='fu'>to</span>(dtype=<span class='fu'><a href='torch_dtype.html'>torch_double</a></span>()))
+<span class='fu'>torch_logical_xor</span>(<span class='kw'>a</span><span class='op'>$</span><span class='fu'>to</span>(dtype=<span class='fu'><a href='torch_dtype.html'>torch_double</a></span>()), <span class='kw'>b</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1
+#&gt;  1
+#&gt;  0
+#&gt;  0
+#&gt; [ CPUBoolType{4} ]</div></pre>
+  </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="row">
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+    <div class="page-header">
+    <h1>Logspace</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_logspace.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Logspace</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_logspace</span>(
+  <span class='kw'>start</span>,
+  <span class='kw'>end</span>,
+  steps = <span class='fl'>100</span>,
+  base = <span class='fl'>10</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>start</th>
+      <td><p>(float) the starting value for the set of points</p></td>
+    </tr>
+    <tr>
+      <th>end</th>
+      <td><p>(float) the ending value for the set of points</p></td>
+    </tr>
+    <tr>
+      <th>steps</th>
+      <td><p>(int) number of points to sample between <code>start</code>        and <code>end</code>. Default: <code>100</code>.</p></td>
+    </tr>
+    <tr>
+      <th>base</th>
+      <td><p>(float) base of the logarithm function. Default: <code>10.0</code>.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="logspace-start-end-steps-base-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#logspace-start-end-steps-base-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>logspace(start, end, steps=100, base=10.0, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a one-dimensional tensor of <code>steps</code> points
+logarithmically spaced with base <code>base</code> between
+\({\mbox{base}}^{\mbox{start}}\) and \({\mbox{base}}^{\mbox{end}}\).</p>
+<p>The output tensor is 1-D of size <code>steps</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_logspace</span>(start=<span class='op'>-</span><span class='fl'>10</span>, end=<span class='fl'>10</span>, steps=<span class='fl'>5</span>)
+<span class='fu'>torch_logspace</span>(start=<span class='fl'>0.1</span>, end=<span class='fl'>1.0</span>, steps=<span class='fl'>5</span>)
+<span class='fu'>torch_logspace</span>(start=<span class='fl'>0.1</span>, end=<span class='fl'>1.0</span>, steps=<span class='fl'>1</span>)
+<span class='fu'>torch_logspace</span>(start=<span class='fl'>2</span>, end=<span class='fl'>2</span>, steps=<span class='fl'>1</span>, base=<span class='fl'>2</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  4
+#&gt; [ CPUFloatType{1} ]</div></pre>
+  </div>
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diff --git a/static/docs/reference/torch_logsumexp.html b/static/docs/reference/torch_logsumexp.html
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+    <div class="page-header">
+    <h1>Logsumexp</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_logsumexp.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Logsumexp</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_logsumexp</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, keepdim = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
+    </tr>
+    <tr>
+      <th>keepdim</th>
+      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="logsumexp-input-dim-keepdim-false-out-null-"><a class="anchor" href="#logsumexp-input-dim-keepdim-false-out-null-"></a>logsumexp(input, dim, keepdim=False, out=NULL) </h2>
+
+    
+
+
+<p>Returns the log of summed exponentials of each row of the <code>input</code>
+tensor in the given dimension <code>dim</code>. The computation is numerically
+stabilized.</p>
+<p>For summation index \(j\) given by <code>dim</code> and other indices \(i\), the result is</p>
+<p>$$
+        \mbox{logsumexp}(x)_{i} = \log \sum_j \exp(x_{ij})
+$$</p>
+<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size
+as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
+Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
+output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='fu'>torch_logsumexp</span>(<span class='kw'>a</span>, <span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.7933
+#&gt;  0.4143
+#&gt;  0.9287
+#&gt; [ CPUFloatType{3} ]</div></pre>
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+    <div class="page-header">
+    <h1>Lstsq</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_lstsq.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Lstsq</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_lstsq</span>(<span class='kw'>self</span>, <span class='kw'>A</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the matrix \(B\)</p></td>
+    </tr>
+    <tr>
+      <th>A</th>
+      <td><p>(Tensor) the \(m\) by \(n\) matrix \(A\)</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>The case when \eqn{m &lt; n} is not supported on the GPU.
+</pre>
+
+    <h2 class="hasAnchor" id="lstsq-input-a-out-null-gt-tensor-"><a class="anchor" href="#lstsq-input-a-out-null-gt-tensor-"></a>lstsq(input, A, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the solution to the least squares and least norm problems for a full
+rank matrix \(A\) of size \((m \times n)\) and a matrix \(B\) of
+size \((m \times k)\).</p>
+<p>If \(m \geq n\), <code>torch_lstsq()</code> solves the least-squares problem:</p>
+<p>$$
+   \begin{array}{ll}
+   \min_X &amp; \|AX-B\|_2.
+   \end{array}
+$$
+If \(m &lt; n\), <code>torch_lstsq()</code> solves the least-norm problem:</p>
+<p>$$
+   \begin{array}{llll}
+   \min_X &amp; \|X\|_2 &amp; \mbox{subject to} &amp; AX = B.
+   \end{array}
+$$
+Returned tensor \(X\) has shape \((\mbox{max}(m, n) \times k)\). The first \(n\)
+rows of \(X\) contains the solution. If \(m \geq n\), the residual sum of squares
+for the solution in each column is given by the sum of squares of elements in the
+remaining \(m - n\) rows of that column.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span>(
+ <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>1</span>),
+ <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>,<span class='fl'>3</span>,<span class='fl'>4</span>),
+ <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>,<span class='fl'>5</span>,<span class='fl'>2</span>),
+ <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>,<span class='fl'>2</span>,<span class='fl'>5</span>),
+ <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>,<span class='fl'>4</span>,<span class='fl'>3</span>)
+))
+<span class='kw'>B</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span>(
+ <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>10</span>, <span class='op'>-</span><span class='fl'>3</span>),
+ <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>12</span>, <span class='fl'>14</span>),
+ <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>14</span>, <span class='fl'>12</span>),
+ <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>16</span>, <span class='fl'>16</span>),
+ <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>18</span>, <span class='fl'>16</span>)
+))
+<span class='kw'>out</span> <span class='op'>=</span> <span class='fu'>torch_lstsq</span>(<span class='kw'>B</span>, <span class='kw'>A</span>)
+<span class='kw'>out</span>[[<span class='fl'>1</span>]]
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;   2.0000   1.0000
+#&gt;   1.0000   1.0000
+#&gt;   1.0000   2.0000
+#&gt;  10.9635   4.8501
+#&gt;   8.9332   5.2418
+#&gt; [ CPUFloatType{5,2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/torch_lt.html b/static/docs/reference/torch_lt.html
new file mode 100644
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@@ -0,0 +1,259 @@
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+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_lt.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Lt</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_lt</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the tensor to compare</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor or float) the tensor or value to compare</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="lt-input-other-out-null-gt-tensor-"><a class="anchor" href="#lt-input-other-out-null-gt-tensor-"></a>lt(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes \(\mbox{input} &lt; \mbox{other}\) element-wise.</p>
+<p>The second argument can be a number or a tensor whose shape is
+broadcastable  with the first argument.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_lt</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fl'>1</span><span class='op'>:</span><span class='fl'>4</span>, ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>)), 
+         <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>1</span>,<span class='fl'>4</span>,<span class='fl'>4</span>), ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0  0
+#&gt;  1  0
+#&gt; [ CPUBoolType{2,2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
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diff --git a/static/docs/reference/torch_lu.html b/static/docs/reference/torch_lu.html
new file mode 100644
index 000000000..c8eb4f82a
--- /dev/null
+++ b/static/docs/reference/torch_lu.html
@@ -0,0 +1,281 @@
+<!-- Generated by pkgdown: do not edit by hand -->
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+    <li class="dropdown-header">Beginers guide</li>
+    <li>
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+      <a href="../articles/getting-started/tensors.html">Tensors</a>
+    </li>
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+      <a href="../articles/getting-started/tensors-and-autograd.html">Tensors and autograd</a>
+    </li>
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+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/optim.html">optim: optimizers in torch</a>
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+    <li>
+      <a href="../articles/getting-started/control-flow-and-weight-sharing.html">Control flow &amp; Weight sharing</a>
+    </li>
+    <li class="dropdown-header">Torch Mechanics</li>
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+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
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+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>LU</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
+    <div class="hidden name"><code>torch_lu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Computes the LU factorization of a matrix or batches of matrices A. Returns a
+tuple containing the LU factorization and pivots of A. Pivoting is done if pivot
+is set to True.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_lu</span>(<span class='kw'>A</span>, pivot = <span class='fl'>TRUE</span>, get_infos = <span class='fl'>FALSE</span>, out = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>A</th>
+      <td><p>(Tensor) the tensor to factor of size (<em>, m, n)(</em>,m,n)</p></td>
+    </tr>
+    <tr>
+      <th>pivot</th>
+      <td><p>(bool, optional) – controls whether pivoting is done. Default: TRUE</p></td>
+    </tr>
+    <tr>
+      <th>get_infos</th>
+      <td><p>(bool, optional) – if set to True, returns an info IntTensor. Default: FALSE</p></td>
+    </tr>
+    <tr>
+      <th>out</th>
+      <td><p>(tuple, optional) – optional output tuple. If get_infos is True, then the elements
+in the tuple are Tensor, IntTensor, and IntTensor. If get_infos is False, then the
+elements in the tuple are Tensor, IntTensor. Default: NULL</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='fu'>torch_lu</span>(<span class='kw'>A</span>)
+
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;   0.3564  0.0937 -0.2445
+#&gt;   0.3677  1.8428 -0.7129
+#&gt;  -0.8647  0.0045  0.4332
+#&gt; 
+#&gt; (2,.,.) = 
+#&gt;   1.5740 -0.5029 -0.3207
+#&gt;   0.2765 -0.6417  0.4304
+#&gt;   0.1308  0.1404  0.1848
+#&gt; [ CPUFloatType{2,3,3} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt;  3  3  3
+#&gt;  3  2  3
+#&gt; [ CPUIntType{2,3} ]
+#&gt; </div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+<div class="row">
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+    <div class="page-header">
+    <h1>Lu_solve</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_lu_solve.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Lu_solve</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_lu_solve</span>(<span class='kw'>self</span>, <span class='kw'>LU_data</span>, <span class='kw'>LU_pivots</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the RHS tensor of size \((*, m, k)\), where \(*\)                is zero or more batch dimensions.</p></td>
+    </tr>
+    <tr>
+      <th>LU_data</th>
+      <td><p>(Tensor) the pivoted LU factorization of A from <code>torch_lu</code> of size \((*, m, m)\),                       where \(*\) is zero or more batch dimensions.</p></td>
+    </tr>
+    <tr>
+      <th>LU_pivots</th>
+      <td><p>(IntTensor) the pivots of the LU factorization from <code>torch_lu</code> of size \((*, m)\),                           where \(*\) is zero or more batch dimensions.                           The batch dimensions of <code>LU_pivots</code> must be equal to the batch dimensions of                           <code>LU_data</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="lu-solve-input-lu-data-lu-pivots-out-null-gt-tensor-"><a class="anchor" href="#lu-solve-input-lu-data-lu-pivots-out-null-gt-tensor-"></a>lu_solve(input, LU_data, LU_pivots, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the LU solve of the linear system \(Ax = b\) using the partially pivoted
+LU factorization of A from <code>torch_lu</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>1</span>))
+<span class='kw'>out</span> <span class='op'>=</span> <span class='fu'><a href='torch_lu.html'>torch_lu</a></span>(<span class='kw'>A</span>)
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'>torch_lu_solve</span>(<span class='kw'>b</span>, <span class='kw'>out</span>[[<span class='fl'>1</span>]], <span class='kw'>out</span>[[<span class='fl'>2</span>]])
+<span class='fu'><a href='torch_norm.html'>torch_norm</a></span>(<span class='fu'><a href='torch_bmm.html'>torch_bmm</a></span>(<span class='kw'>A</span>, <span class='kw'>x</span>) <span class='op'>-</span> <span class='kw'>b</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; 1.58402e-07
+#&gt; [ CPUFloatType{} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+
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+
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+      <th>seed</th>
+      <td><p>integer seed.</p></td>
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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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_masked_select.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Masked_select</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_masked_select</span>(<span class='kw'>self</span>, <span class='kw'>mask</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>mask</th>
+      <td><p>(BoolTensor) the tensor containing the binary mask to index with</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>The returned tensor does <strong>not</strong> use the same storage
+as the original tensor</p>
+    <h2 class="hasAnchor" id="masked-select-input-mask-out-null-gt-tensor-"><a class="anchor" href="#masked-select-input-mask-out-null-gt-tensor-"></a>masked_select(input, mask, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new 1-D tensor which indexes the <code>input</code> tensor according to
+the boolean mask <code>mask</code> which is a <code>BoolTensor</code>.</p>
+<p>The shapes of the <code>mask</code> tensor and the <code>input</code> tensor don't need
+to match, but they must be broadcastable .</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>4</span>))
+<span class='kw'>x</span>
+<span class='kw'>mask</span> <span class='op'>=</span> <span class='kw'>x</span><span class='op'>$</span><span class='fu'>ge</span>(<span class='fl'>0.5</span>)
+<span class='kw'>mask</span>
+<span class='fu'>torch_masked_select</span>(<span class='kw'>x</span>, <span class='kw'>mask</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.2190
+#&gt;  1.2591
+#&gt;  2.0310
+#&gt;  0.7883
+#&gt; [ CPUFloatType{4} ]</div></pre>
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+    <h1>Matmul</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_matmul.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Matmul</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_matmul</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the first tensor to be multiplied</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor) the second tensor to be multiplied</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>The 1-dimensional dot product version of this function does not support an `out` parameter.
+</pre>
+
+    <h2 class="hasAnchor" id="matmul-input-other-out-null-gt-tensor-"><a class="anchor" href="#matmul-input-other-out-null-gt-tensor-"></a>matmul(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Matrix product of two tensors.</p>
+<p>The behavior depends on the dimensionality of the tensors as follows:</p><ul>
+<li><p>If both tensors are 1-dimensional, the dot product (scalar) is returned.</p></li>
+<li><p>If both arguments are 2-dimensional, the matrix-matrix product is returned.</p></li>
+<li><p>If the first argument is 1-dimensional and the second argument is 2-dimensional,
+a 1 is prepended to its dimension for the purpose of the matrix multiply.
+After the matrix multiply, the prepended dimension is removed.</p></li>
+<li><p>If the first argument is 2-dimensional and the second argument is 1-dimensional,
+the matrix-vector product is returned.</p></li>
+<li><p>If both arguments are at least 1-dimensional and at least one argument is
+N-dimensional (where N &gt; 2), then a batched matrix multiply is returned.  If the first
+argument is 1-dimensional, a 1 is prepended to its dimension for the purpose of the
+batched matrix multiply and removed after.  If the second argument is 1-dimensional, a
+1 is appended to its dimension for the purpose of the batched matrix multiple and removed after.
+The non-matrix (i.e. batch) dimensions are broadcasted  (and thus
+must be broadcastable).  For example, if <code>input</code> is a
+\((j \times 1 \times n \times m)\) tensor and <code>other</code> is a \((k \times m \times p)\)
+tensor, <code>out</code> will be an \((j \times k \times n \times p)\) tensor.</p></li>
+</ul>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='co'># vector x vector</span>
+<span class='kw'>tensor1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>))
+<span class='kw'>tensor2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>))
+<span class='fu'>torch_matmul</span>(<span class='kw'>tensor1</span>, <span class='kw'>tensor2</span>)
+<span class='co'># matrix x vector</span>
+<span class='kw'>tensor1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>4</span>))
+<span class='kw'>tensor2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='fu'>torch_matmul</span>(<span class='kw'>tensor1</span>, <span class='kw'>tensor2</span>)
+<span class='co'># batched matrix x broadcasted vector</span>
+<span class='kw'>tensor1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>4</span>))
+<span class='kw'>tensor2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='fu'>torch_matmul</span>(<span class='kw'>tensor1</span>, <span class='kw'>tensor2</span>)
+<span class='co'># batched matrix x batched matrix</span>
+<span class='kw'>tensor1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>4</span>))
+<span class='kw'>tensor2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>4</span>, <span class='fl'>5</span>))
+<span class='fu'>torch_matmul</span>(<span class='kw'>tensor1</span>, <span class='kw'>tensor2</span>)
+<span class='co'># batched matrix x broadcasted matrix</span>
+<span class='kw'>tensor1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>4</span>))
+<span class='kw'>tensor2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>))
+<span class='fu'>torch_matmul</span>(<span class='kw'>tensor1</span>, <span class='kw'>tensor2</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;  -0.2185  0.1251  0.5783 -0.7380  1.9290
+#&gt;  -0.1489  0.9409  0.2506  0.7444 -0.3201
+#&gt;   0.2165  2.5157  0.8195 -1.2013  2.7602
+#&gt; 
+#&gt; (2,.,.) = 
+#&gt;  -0.6251  1.0054  1.5308  1.0346  1.7585
+#&gt;   0.7581 -0.5255 -1.6358 -0.3028 -2.8103
+#&gt;   1.9208  0.9674 -2.9281  1.9148 -7.9955
+#&gt; 
+#&gt; (3,.,.) = 
+#&gt;  -0.1272  1.0480  0.5100 -0.5049  1.5373
+#&gt;  -0.6784 -2.4129  0.4735  0.3082  0.6775
+#&gt;  -0.9108 -1.5962  1.0820 -0.0427  2.2469
+#&gt; 
+#&gt; (4,.,.) = 
+#&gt;   0.1000  1.6520  0.6610  1.2012 -0.2146
+#&gt;  -0.9429 -1.2208  0.9296 -1.5397  3.7074
+#&gt;   0.6559 -0.7159 -1.7710 -0.6774 -2.5811
+#&gt; 
+#&gt; (5,.,.) = 
+#&gt;  -0.1291  1.6498  1.3190  0.7676  1.5068
+#&gt;   0.1177 -0.1660 -0.6345  1.6803 -3.0527
+#&gt;   0.5094 -2.0094 -1.7119  2.0194 -5.5159
+#&gt; 
+#&gt; (6,.,.) = 
+#&gt;   0.0387  1.2627  0.4658 -0.2528  1.1118
+#&gt;  -0.0531 -0.8584 -0.3116 -1.4711  1.1272
+#&gt;   0.7294 -0.9226 -1.9783  1.3631 -5.2894
+#&gt; 
+#&gt; (7,.,.) = 
+#&gt;  -0.7140 -1.2579  0.6561 -1.1249  2.6681
+#&gt;   0.8946  1.1603 -1.5402  0.5578 -3.6267
+#&gt;  -0.8431  0.8911  1.9896  0.9616  2.7199
+#&gt; 
+#&gt; (8,.,.) = 
+#&gt;   0.7925  0.9144 -1.5220 -0.5466 -2.3076
+#&gt;  -0.6783  0.0011  1.2162 -0.9383  3.4450
+#&gt;   0.2356 -0.6907 -0.6703 -0.1563 -1.0982
+#&gt; 
+#&gt; (9,.,.) = 
+#&gt;  -0.7889  0.4032  1.3563 -0.5223  3.2740
+#&gt;   0.3636 -0.9006 -1.1470 -1.1717 -0.8283
+#&gt;  -0.4663 -0.2173  0.5950  4.1339 -3.4726
+#&gt; 
+#&gt; (10,.,.) = 
+#&gt;  -0.7729  1.7140  2.1228  2.9509  0.6895
+#&gt;   0.4515 -0.0429 -1.0629 -2.8544  1.2142
+#&gt;  -1.5890 -0.3097  2.8757  1.6369  3.7579
+#&gt; [ CPUFloatType{10,3,5} ]</div></pre>
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+    <div class="page-header">
+    <h1>Matrix_power</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_matrix_power.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Matrix_power</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_matrix_power</span>(<span class='kw'>self</span>, <span class='kw'>n</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>n</th>
+      <td><p>(int) the power to raise the matrix to</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="matrix-power-input-n-gt-tensor-"><a class="anchor" href="#matrix-power-input-n-gt-tensor-"></a>matrix_power(input, n) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the matrix raised to the power <code>n</code> for square matrices.
+For batch of matrices, each individual matrix is raised to the power <code>n</code>.</p>
+<p>If <code>n</code> is negative, then the inverse of the matrix (if invertible) is
+raised to the power <code>n</code>.  For a batch of matrices, the batched inverse
+(if invertible) is raised to the power <code>n</code>. If <code>n</code> is 0, then an identity matrix
+is returned.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>, <span class='fl'>2</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_matrix_power</span>(<span class='kw'>a</span>, <span class='fl'>3</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;  -0.4133 -0.7320
+#&gt;  -0.0927  0.0367
+#&gt; 
+#&gt; (2,.,.) = 
+#&gt;  -1.2180  2.0019
+#&gt;  -4.7379 -8.9109
+#&gt; [ CPUFloatType{2,2,2} ]</div></pre>
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+<div class="row">
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+    <div class="page-header">
+    <h1>Matrix_rank</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_matrix_rank.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Matrix_rank</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_matrix_rank</span>(<span class='kw'>self</span>, <span class='kw'>tol</span>, symmetric = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input 2-D tensor</p></td>
+    </tr>
+    <tr>
+      <th>tol</th>
+      <td><p>(float, optional) the tolerance value. Default: <code>NULL</code></p></td>
+    </tr>
+    <tr>
+      <th>symmetric</th>
+      <td><p>(bool, optional) indicates whether <code>input</code> is symmetric.                               Default: <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="matrix-rank-input-tol-null-symmetric-false-gt-tensor-"><a class="anchor" href="#matrix-rank-input-tol-null-symmetric-false-gt-tensor-"></a>matrix_rank(input, tol=NULL, symmetric=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the numerical rank of a 2-D tensor. The method to compute the
+matrix rank is done using SVD by default. If <code>symmetric</code> is <code>TRUE</code>,
+then <code>input</code> is assumed to be symmetric, and the computation of the
+rank is done by obtaining the eigenvalues.</p>
+<p><code>tol</code> is the threshold below which the singular values (or the eigenvalues
+when <code>symmetric</code> is <code>TRUE</code>) are considered to be 0. If <code>tol</code> is not
+specified, <code>tol</code> is set to <code>S.max() * max(S.size()) * eps</code> where <code>S</code> is the
+singular values (or the eigenvalues when <code>symmetric</code> is <code>TRUE</code>), and <code>eps</code>
+is the epsilon value for the datatype of <code>input</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_eye.html'>torch_eye</a></span>(<span class='fl'>10</span>)
+<span class='fu'>torch_matrix_rank</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; 10
+#&gt; [ CPULongType{} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Max</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_max.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Max</p>
+    </div>
+
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the dimension to reduce.</p></td>
+    </tr>
+    <tr>
+      <th>keepdim</th>
+      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>out</th>
+      <td><p>(tuple, optional) the result tuple of two output tensors (max, max_indices)</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor) the second input tensor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>When the shapes do not match, the shape of the returned output tensor
+follows the broadcasting rules .</p>
+    <h2 class="hasAnchor" id="max-input-gt-tensor-"><a class="anchor" href="#max-input-gt-tensor-"></a>max(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the maximum value of all elements in the <code>input</code> tensor.</p>
+    <h2 class="hasAnchor" id="max-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"><a class="anchor" href="#max-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"></a>max(input, dim, keepdim=False, out=NULL) -&gt; (Tensor, LongTensor) </h2>
+
+    
+
+
+<p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the maximum
+value of each row of the <code>input</code> tensor in the given dimension
+<code>dim</code>. And <code>indices</code> is the index location of each maximum value found
+(argmax).</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p><code>indices</code> does not necessarily contain the first occurrence of each
+maximal value found, unless it is unique.
+The exact implementation details are device-specific.
+Do not expect the same result when run on CPU and GPU in general.</p>
+<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensors are of the same size
+as <code>input</code> except in the dimension <code>dim</code> where they are of size 1.
+Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting
+in the output tensors having 1 fewer dimension than <code>input</code>.</p>
+    <h2 class="hasAnchor" id="max-input-other-out-null-gt-tensor-"><a class="anchor" href="#max-input-other-out-null-gt-tensor-"></a>max(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Each element of the tensor <code>input</code> is compared with the corresponding
+element of the tensor <code>other</code> and an element-wise maximum is taken.</p>
+<p>The shapes of <code>input</code> and <code>other</code> don't need to match,
+but they must be broadcastable .</p>
+<p>$$
+    \mbox{out}_i = \max(\mbox{tensor}_i, \mbox{other}_i)
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_max</span>(<span class='kw'>a</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_max</span>(<span class='kw'>a</span>, dim = <span class='fl'>1</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>b</span>
+<span class='fu'>torch_max</span>(<span class='kw'>a</span>, other = <span class='kw'>b</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.4118
+#&gt; -0.1116
+#&gt;  0.7360
+#&gt;  0.9171
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
+  </div>
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+
+
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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> 1.6.1.</p>
+</div>
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diff --git a/static/docs/reference/torch_mean.html b/static/docs/reference/torch_mean.html
new file mode 100644
index 000000000..00cd66b50
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+        <a class="navbar-link" href="../index.html">torch</a>
+        <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">0.0.3</span>
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+  </a>
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+    Getting started
+     
+    <span class="caret"></span>
+  </a>
+  <ul class="dropdown-menu" role="menu">
+    <li class="dropdown-header">Beginers guide</li>
+    <li>
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+    </li>
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+    </li>
+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
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+    </li>
+    <li class="dropdown-header">Torch Mechanics</li>
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+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
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+      <a href="../articles/getting-started/neural-networks.html">Neural networks</a>
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+    <li class="dropdown-header">Tensors</li>
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+    </div><!--/.nav-collapse -->
+  </div><!--/.container -->
+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Mean</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_mean.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Mean</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_mean</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, keepdim = <span class='fl'>FALSE</span>, dtype = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
+    </tr>
+    <tr>
+      <th>keepdim</th>
+      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>the resulting data type.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="mean-input-gt-tensor-"><a class="anchor" href="#mean-input-gt-tensor-"></a>mean(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the mean value of all elements in the <code>input</code> tensor.</p>
+    <h2 class="hasAnchor" id="mean-input-dim-keepdim-false-out-null-gt-tensor-"><a class="anchor" href="#mean-input-dim-keepdim-false-out-null-gt-tensor-"></a>mean(input, dim, keepdim=False, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the mean value of each row of the <code>input</code> tensor in the given
+dimension <code>dim</code>. If <code>dim</code> is a list of dimensions,
+reduce over all of them.</p>
+<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size
+as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
+Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
+output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_mean</span>(<span class='kw'>a</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_mean</span>(<span class='kw'>a</span>, <span class='fl'>1</span>)
+<span class='fu'>torch_mean</span>(<span class='kw'>a</span>, <span class='fl'>1</span>, <span class='fl'>TRUE</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.1962  0.3079 -0.5549  0.3102
+#&gt; [ CPUFloatType{1,4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_median.html b/static/docs/reference/torch_median.html
new file mode 100644
index 000000000..d79258ad2
--- /dev/null
+++ b/static/docs/reference/torch_median.html
@@ -0,0 +1,294 @@
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+
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+    <div class="page-header">
+    <h1>Median</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_median.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Median</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_median</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, keepdim = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the dimension to reduce.</p></td>
+    </tr>
+    <tr>
+      <th>keepdim</th>
+      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="median-input-gt-tensor-"><a class="anchor" href="#median-input-gt-tensor-"></a>median(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the median value of all elements in the <code>input</code> tensor.</p>
+    <h2 class="hasAnchor" id="median-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"><a class="anchor" href="#median-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"></a>median(input, dim=-1, keepdim=False, out=NULL) -&gt; (Tensor, LongTensor) </h2>
+
+    
+
+
+<p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the median
+value of each row of the <code>input</code> tensor in the given dimension
+<code>dim</code>. And <code>indices</code> is the index location of each median value found.</p>
+<p>By default, <code>dim</code> is the last dimension of the <code>input</code> tensor.</p>
+<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensors are of the same size
+as <code>input</code> except in the dimension <code>dim</code> where they are of size 1.
+Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in
+the outputs tensor having 1 fewer dimension than <code>input</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_median</span>(<span class='kw'>a</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_median</span>(<span class='kw'>a</span>, <span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt;  0.1832
+#&gt; -0.8122
+#&gt; -1.1052
+#&gt; -1.2011
+#&gt; -0.0244
+#&gt; [ CPUFloatType{5} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt;  3
+#&gt;  0
+#&gt;  3
+#&gt;  0
+#&gt;  1
+#&gt; [ CPULongType{5} ]
+#&gt; </div></pre>
+  </div>
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diff --git a/static/docs/reference/torch_memory_format.html b/static/docs/reference/torch_memory_format.html
new file mode 100644
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+    <div class="page-header">
+    <h1>Memory format</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/memory_format.R'><code>R/memory_format.R</code></a></small>
+    <div class="hidden name"><code>torch_memory_format.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <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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diff --git a/static/docs/reference/torch_meshgrid.html b/static/docs/reference/torch_meshgrid.html
new file mode 100644
index 000000000..5a4691b7e
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+++ b/static/docs/reference/torch_meshgrid.html
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+     
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+  <ul class="dropdown-menu" role="menu">
+    <li class="dropdown-header">Beginers guide</li>
+    <li>
+      <a href="../articles/getting-started/warmup.html">Warm-up</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/tensors.html">Tensors</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/tensors-and-autograd.html">Tensors and autograd</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/optim.html">optim: optimizers in torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/custom-nn.html">Custom nn modules</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/control-flow-and-weight-sharing.html">Control flow &amp; Weight sharing</a>
+    </li>
+    <li class="dropdown-header">Torch Mechanics</li>
+    <li>
+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/neural-networks.html">Neural networks</a>
+    </li>
+  </ul>
+</li>
+<li class="dropdown">
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
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+      <a href="../articles/tensor-creation.html">Creating tensors</a>
+    </li>
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+      <a href="../articles/indexing.html">Indexing</a>
+    </li>
+    <li>
+      <a href="../articles/tensor/index.html">Tensor class</a>
+    </li>
+    <li class="dropdown-header">Datasets</li>
+    <li>
+      <a href="../articles/loading-data.html">Loading Data</a>
+    </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-header">Examples</li>
+<li>
+  <a href="../reference/index.html">Reference</a>
+</li>
+<li>
+  <a href="../news/index.html">Changelog</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>Meshgrid</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_meshgrid.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Meshgrid</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_meshgrid</span>(<span class='kw'>tensors</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensors</th>
+      <td><p>(list of Tensor) list of scalars or 1 dimensional tensors. Scalars will be
+treated (1,).</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
+
+    
+
+
+<p>Take \(N\) tensors, each of which can be either scalar or 1-dimensional
+vector, and create \(N\) N-dimensional grids, where the \(i\) <code>th</code> grid is defined by
+expanding the \(i\) <code>th</code> input over dimensions defined by other inputs.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>))
+<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>, <span class='fl'>6</span>))
+<span class='kw'>out</span> <span class='op'>=</span> <span class='fu'>torch_meshgrid</span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>x</span>, <span class='kw'>y</span>))
+<span class='kw'>out</span>
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt;  1  1  1
+#&gt;  2  2  2
+#&gt;  3  3  3
+#&gt; [ CPUFloatType{3,3} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt;  4  5  6
+#&gt;  4  5  6
+#&gt;  4  5  6
+#&gt; [ CPUFloatType{3,3} ]
+#&gt; </div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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+    Getting started
+     
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+  </a>
+  <ul class="dropdown-menu" role="menu">
+    <li class="dropdown-header">Beginers guide</li>
+    <li>
+      <a href="../articles/getting-started/warmup.html">Warm-up</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/tensors.html">Tensors</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/tensors-and-autograd.html">Tensors and autograd</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/optim.html">optim: optimizers in torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/custom-nn.html">Custom nn modules</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/control-flow-and-weight-sharing.html">Control flow &amp; Weight sharing</a>
+    </li>
+    <li class="dropdown-header">Torch Mechanics</li>
+    <li>
+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/neural-networks.html">Neural networks</a>
+    </li>
+  </ul>
+</li>
+<li class="dropdown">
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" aria-expanded="false">
+    Articles
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+  </a>
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+    <li class="dropdown-header">Tensors</li>
+    <li>
+      <a href="../articles/tensor-creation.html">Creating tensors</a>
+    </li>
+    <li>
+      <a href="../articles/indexing.html">Indexing</a>
+    </li>
+    <li>
+      <a href="../articles/tensor/index.html">Tensor class</a>
+    </li>
+    <li class="dropdown-header">Datasets</li>
+    <li>
+      <a href="../articles/loading-data.html">Loading Data</a>
+    </li>
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+      <a href="../articles/using-autograd.html">Using autograd</a>
+    </li>
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+      <a href="../articles/extending-autograd.html">Extending autograd</a>
+    </li>
+  </ul>
+</li>
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+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Min</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_min.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Min</p>
+    </div>
+
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the dimension to reduce.</p></td>
+    </tr>
+    <tr>
+      <th>keepdim</th>
+      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
+    </tr>
+    <tr>
+      <th>out</th>
+      <td><p>(tuple, optional) the tuple of two output tensors (min, min_indices)</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor) the second input tensor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>When the shapes do not match, the shape of the returned output tensor
+follows the broadcasting rules .</p>
+    <h2 class="hasAnchor" id="min-input-gt-tensor-"><a class="anchor" href="#min-input-gt-tensor-"></a>min(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the minimum value of all elements in the <code>input</code> tensor.</p>
+    <h2 class="hasAnchor" id="min-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"><a class="anchor" href="#min-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"></a>min(input, dim, keepdim=False, out=NULL) -&gt; (Tensor, LongTensor) </h2>
+
+    
+
+
+<p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the minimum
+value of each row of the <code>input</code> tensor in the given dimension
+<code>dim</code>. And <code>indices</code> is the index location of each minimum value found
+(argmin).</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p><code>indices</code> does not necessarily contain the first occurrence of each
+minimal value found, unless it is unique.
+The exact implementation details are device-specific.
+Do not expect the same result when run on CPU and GPU in general.</p>
+<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensors are of the same size as
+<code>input</code> except in the dimension <code>dim</code> where they are of size 1.
+Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in
+the output tensors having 1 fewer dimension than <code>input</code>.</p>
+    <h2 class="hasAnchor" id="min-input-other-out-null-gt-tensor-"><a class="anchor" href="#min-input-other-out-null-gt-tensor-"></a>min(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Each element of the tensor <code>input</code> is compared with the corresponding
+element of the tensor <code>other</code> and an element-wise minimum is taken.
+The resulting tensor is returned.</p>
+<p>The shapes of <code>input</code> and <code>other</code> don't need to match,
+but they must be broadcastable .</p>
+<p>$$
+    \mbox{out}_i = \min(\mbox{tensor}_i, \mbox{other}_i)
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_min</span>(<span class='kw'>a</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_min</span>(<span class='kw'>a</span>, dim = <span class='fl'>1</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>b</span>
+<span class='fu'>torch_min</span>(<span class='kw'>a</span>, other = <span class='kw'>b</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0.5633
+#&gt; -0.5903
+#&gt; -2.0560
+#&gt; -1.0671
+#&gt; [ CPUFloatType{4} ]</div></pre>
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+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_mm.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Mm</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_mm</span>(<span class='kw'>self</span>, <span class='kw'>mat2</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the first matrix to be multiplied</p></td>
+    </tr>
+    <tr>
+      <th>mat2</th>
+      <td><p>(Tensor) the second matrix to be multiplied</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>This function does not broadcast .
+For broadcasting matrix products, see <code><a href='torch_matmul.html'>torch_matmul</a></code>.</p>
+    <h2 class="hasAnchor" id="mm-input-mat-out-null-gt-tensor-"><a class="anchor" href="#mm-input-mat-out-null-gt-tensor-"></a>mm(input, mat2, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Performs a matrix multiplication of the matrices <code>input</code> and <code>mat2</code>.</p>
+<p>If <code>input</code> is a \((n \times m)\) tensor, <code>mat2</code> is a
+\((m \times p)\) tensor, <code>out</code> will be a \((n \times p)\) tensor.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>mat1</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+<span class='kw'>mat2</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='fu'>torch_mm</span>(<span class='kw'>mat1</span>, <span class='kw'>mat2</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.1067 -2.8593  1.3572
+#&gt; -2.8347  0.9182  1.1475
+#&gt; [ CPUFloatType{2,3} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Mode</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_mode.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Mode</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_mode</span>(<span class='kw'>self</span>, dim = <span class='op'>-</span><span class='fl'>1L</span>, keepdim = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the dimension to reduce.</p></td>
+    </tr>
+    <tr>
+      <th>keepdim</th>
+      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>This function is not defined for <code>torch_cuda.Tensor</code> yet.</p>
+    <h2 class="hasAnchor" id="mode-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"><a class="anchor" href="#mode-input-dim-keepdim-false-out-null-gt-tensor-longtensor-"></a>mode(input, dim=-1, keepdim=False, out=NULL) -&gt; (Tensor, LongTensor) </h2>
+
+    
+
+
+<p>Returns a namedtuple <code>(values, indices)</code> where <code>values</code> is the mode
+value of each row of the <code>input</code> tensor in the given dimension
+<code>dim</code>, i.e. a value which appears most often
+in that row, and <code>indices</code> is the index location of each mode value found.</p>
+<p>By default, <code>dim</code> is the last dimension of the <code>input</code> tensor.</p>
+<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensors are of the same size as
+<code>input</code> except in the dimension <code>dim</code> where they are of size 1.
+Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting
+in the output tensors having 1 fewer dimension than <code>input</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randint.html'>torch_randint</a></span>(<span class='fl'>0</span>, <span class='fl'>50</span>, size = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>5</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_mode</span>(<span class='kw'>a</span>, <span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt; 34
+#&gt; [ CPUFloatType{} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt; 2
+#&gt; [ CPULongType{} ]
+#&gt; </div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Mul</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_mul.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Mul</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_mul</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the first multiplicand tensor</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor) the second multiplicand tensor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="mul-input-other-out-null-"><a class="anchor" href="#mul-input-other-out-null-"></a>mul(input, other, out=NULL) </h2>
+
+    
+
+
+<p>Multiplies each element of the input <code>input</code> with the scalar
+<code>other</code> and returns a new resulting tensor.</p>
+<p>$$
+    \mbox{out}_i = \mbox{other} \times \mbox{input}_i
+$$
+If <code>input</code> is of type <code>FloatTensor</code> or <code>DoubleTensor</code>, <code>other</code>
+should be a real number, otherwise it should be an integer</p>
+
+
+<p>Each element of the tensor <code>input</code> is multiplied by the corresponding
+element of the Tensor <code>other</code>. The resulting tensor is returned.</p>
+<p>The shapes of <code>input</code> and <code>other</code> must be
+broadcastable .</p>
+<p>$$
+    \mbox{out}_i = \mbox{input}_i \times \mbox{other}_i
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_mul</span>(<span class='kw'>a</span>, <span class='fl'>100</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>1</span>))
+<span class='kw'>a</span>
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>4</span>))
+<span class='kw'>b</span>
+<span class='fu'>torch_mul</span>(<span class='kw'>a</span>, <span class='kw'>b</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.9656  0.2508 -0.9725  0.1089
+#&gt; -0.4285 -0.1113  0.4315 -0.0483
+#&gt; -0.5136 -0.1334  0.5173 -0.0579
+#&gt;  0.3787  0.0984 -0.3814  0.0427
+#&gt; [ CPUFloatType{4,4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Multinomial</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_multinomial.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Multinomial</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_multinomial</span>(<span class='kw'>self</span>, <span class='kw'>num_samples</span>, replacement = <span class='fl'>FALSE</span>, generator = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor containing probabilities</p></td>
+    </tr>
+    <tr>
+      <th>num_samples</th>
+      <td><p>(int) number of samples to draw</p></td>
+    </tr>
+    <tr>
+      <th>replacement</th>
+      <td><p>(bool, optional) whether to draw with replacement or not</p></td>
+    </tr>
+    <tr>
+      <th>generator</th>
+      <td><p>(<code>torch.Generator</code>, optional) a pseudorandom number generator for sampling</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>The rows of `input` do not need to sum to one (in which case we use
+the values as weights), but must be non-negative, finite and have
+a non-zero sum.
+</pre>
+
+<p>Indices are ordered from left to right according to when each was sampled
+(first samples are placed in first column).</p>
+<p>If <code>input</code> is a vector, <code>out</code> is a vector of size <code>num_samples</code>.</p>
+<p>If <code>input</code> is a matrix with <code>m</code> rows, <code>out</code> is an matrix of shape
+\((m \times \mbox{num\_samples})\).</p>
+<p>If replacement is <code>TRUE</code>, samples are drawn with replacement.</p>
+<p>If not, they are drawn without replacement, which means that when a
+sample index is drawn for a row, it cannot be drawn again for that row.</p>
+<pre>When drawn without replacement, `num_samples` must be lower than
+number of non-zero elements in `input` (or the min number of non-zero
+elements in each row of `input` if it is a matrix).
+</pre>
+
+    <h2 class="hasAnchor" id="multinomial-input-num-samples-replacement-false-generator-null-out-null-gt-longtensor-"><a class="anchor" href="#multinomial-input-num-samples-replacement-false-generator-null-out-null-gt-longtensor-"></a>multinomial(input, num_samples, replacement=False, *, generator=NULL, out=NULL) -&gt; LongTensor </h2>
+
+    
+
+
+<p>Returns a tensor where each row contains <code>num_samples</code> indices sampled
+from the multinomial probability distribution located in the corresponding row
+of tensor <code>input</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>weights</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>10</span>, <span class='fl'>3</span>, <span class='fl'>0</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_float</a></span>()) <span class='co'># create a tensor of weights</span>
+<span class='fu'>torch_multinomial</span>(<span class='kw'>weights</span>, <span class='fl'>2</span>)
+<span class='fu'>torch_multinomial</span>(<span class='kw'>weights</span>, <span class='fl'>4</span>, replacement=<span class='fl'>TRUE</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1
+#&gt;  1
+#&gt;  2
+#&gt;  1
+#&gt; [ CPULongType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Mv</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_mv.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Mv</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_mv</span>(<span class='kw'>self</span>, <span class='kw'>vec</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) matrix to be multiplied</p></td>
+    </tr>
+    <tr>
+      <th>vec</th>
+      <td><p>(Tensor) vector to be multiplied</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>This function does not broadcast .</p>
+    <h2 class="hasAnchor" id="mv-input-vec-out-null-gt-tensor-"><a class="anchor" href="#mv-input-vec-out-null-gt-tensor-"></a>mv(input, vec, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Performs a matrix-vector product of the matrix <code>input</code> and the vector
+<code>vec</code>.</p>
+<p>If <code>input</code> is a \((n \times m)\) tensor, <code>vec</code> is a 1-D tensor of
+size \(m\), <code>out</code> will be 1-D of size \(n\).</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>mat</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+<span class='kw'>vec</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>))
+<span class='fu'>torch_mv</span>(<span class='kw'>mat</span>, <span class='kw'>vec</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -5.7310
+#&gt;  1.3023
+#&gt; [ CPUFloatType{2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Mvlgamma</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_mvlgamma.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Mvlgamma</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_mvlgamma</span>(<span class='kw'>self</span>, <span class='kw'>p</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the tensor to compute the multivariate log-gamma function</p></td>
+    </tr>
+    <tr>
+      <th>p</th>
+      <td><p>(int) the number of dimensions</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="mvlgamma-input-p-gt-tensor-"><a class="anchor" href="#mvlgamma-input-p-gt-tensor-"></a>mvlgamma(input, p) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the <code>multivariate log-gamma function &lt;https://en.wikipedia.org/wiki/Multivariate_gamma_function&gt;</code>_) with dimension
+\(p\) element-wise, given by</p>
+<p>$$
+    \log(\Gamma_{p}(a)) = C + \displaystyle \sum_{i=1}^{p} \log\left(\Gamma\left(a - \frac{i - 1}{2}\right)\right)
+$$
+where \(C = \log(\pi) \times \frac{p (p - 1)}{4}\) and \(\Gamma(\cdot)\) is the Gamma function.</p>
+<p>All elements must be greater than \(\frac{p - 1}{2}\), otherwise an error would be thrown.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))<span class='op'>$</span><span class='fu'>uniform_</span>(<span class='fl'>1</span>, <span class='fl'>2</span>)
+<span class='kw'>a</span>
+<span class='fu'>torch_mvlgamma</span>(<span class='kw'>a</span>, <span class='fl'>2</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.4040  0.4059  0.7450
+#&gt;  0.3997  0.8720  0.4162
+#&gt; [ CPUFloatType{2,3} ]</div></pre>
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+
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+    <div class="page-header">
+    <h1>Narrow</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_narrow.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Narrow</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_narrow</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, <span class='kw'>start</span>, <span class='kw'>length</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the tensor to narrow</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the dimension along which to narrow</p></td>
+    </tr>
+    <tr>
+      <th>start</th>
+      <td><p>(int) the starting dimension</p></td>
+    </tr>
+    <tr>
+      <th>length</th>
+      <td><p>(int) the distance to the ending dimension</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="narrow-input-dim-start-length-gt-tensor-"><a class="anchor" href="#narrow-input-dim-start-length-gt-tensor-"></a>narrow(input, dim, start, length) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor that is a narrowed version of <code>input</code> tensor. The
+dimension <code>dim</code> is input from <code>start</code> to <code>start + length</code>. The
+returned tensor and <code>input</code> tensor share the same underlying storage.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span><span class='op'>:</span><span class='fl'>9</span>), ncol = <span class='fl'>3</span>, byrow= <span class='fl'>TRUE</span>))
+<span class='fu'>torch_narrow</span>(<span class='kw'>x</span>, <span class='fl'>1</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fl'>0L</span>)<span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/sum.html'>sum</a></span>(dim = <span class='fl'>1</span>), <span class='fl'>2</span>)
+<span class='fu'>torch_narrow</span>(<span class='kw'>x</span>, <span class='fl'>2</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fl'>1L</span>)<span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/sum.html'>sum</a></span>(dim = <span class='fl'>1</span>), <span class='fl'>2</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  2  3
+#&gt;  5  6
+#&gt;  8  9
+#&gt; [ CPULongType{3,2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    </li>
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+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
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+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
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+    <li>
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+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
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+
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+    <div class="page-header">
+    <h1>Ne</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_ne.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Ne</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_ne</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the tensor to compare</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor or float) the tensor or value to compare</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="ne-input-other-out-null-gt-tensor-"><a class="anchor" href="#ne-input-other-out-null-gt-tensor-"></a>ne(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes \(input \neq other\) element-wise.</p>
+<p>The second argument can be a number or a tensor whose shape is
+broadcastable  with the first argument.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_ne</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fl'>1</span><span class='op'>:</span><span class='fl'>4</span>, ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>)), 
+         <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/rep.html'>rep</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>4</span>), each = <span class='fl'>2</span>), ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0  1
+#&gt;  1  0
+#&gt; [ CPUBoolType{2,2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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+    </li>
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+    </li>
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+
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+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Neg</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_neg.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Neg</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_neg</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="neg-input-out-null-gt-tensor-"><a class="anchor" href="#neg-input-out-null-gt-tensor-"></a>neg(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the negative of the elements of <code>input</code>.</p>
+<p>$$
+    \mbox{out} = -1 \times \mbox{input}
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_neg</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.0573
+#&gt;  0.4788
+#&gt;  0.7503
+#&gt; -0.4747
+#&gt; -0.4677
+#&gt; [ CPUFloatType{5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <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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+</div>
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+    <div class="page-header">
+    <h1>Nonzero</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_nonzero.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Nonzero</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_nonzero</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>[`torch_nonzero(..., as_tuple=False) &lt;torch.nonzero&gt;`] (default) returns a
+2-D tensor where each row is the index for a nonzero value.
+
+[`torch_nonzero(..., as_tuple=TRUE) &lt;torch.nonzero&gt;`] returns a tuple of 1-D
+index tensors, allowing for advanced indexing, so `x[x.nonzero(as_tuple=TRUE)]`
+gives all nonzero values of tensor `x`. Of the returned tuple, each index tensor
+contains nonzero indices for a certain dimension.
+
+See below for more details on the two behaviors.
+</pre>
+
+    <h2 class="hasAnchor" id="nonzero-input-out-null-as-tuple-false-gt-longtensor-or-tuple-of-longtensors-"><a class="anchor" href="#nonzero-input-out-null-as-tuple-false-gt-longtensor-or-tuple-of-longtensors-"></a>nonzero(input, *, out=NULL, as_tuple=False) -&gt; LongTensor or tuple of LongTensors </h2>
+
+    
+
+
+<p><strong>When</strong> <code>as_tuple</code> <strong>is <code>FALSE</code> (default)</strong>:</p>
+<p>Returns a tensor containing the indices of all non-zero elements of
+<code>input</code>.  Each row in the result contains the indices of a non-zero
+element in <code>input</code>. The result is sorted lexicographically, with
+the last index changing the fastest (C-style).</p>
+<p>If <code>input</code> has \(n\) dimensions, then the resulting indices tensor
+<code>out</code> is of size \((z \times n)\), where \(z\) is the total number of
+non-zero elements in the <code>input</code> tensor.</p>
+<p><strong>When</strong> <code>as_tuple</code> <strong>is <code>TRUE</code></strong>:</p>
+<p>Returns a tuple of 1-D tensors, one for each dimension in <code>input</code>,
+each containing the indices (in that dimension) of all non-zero elements of
+<code>input</code> .</p>
+<p>If <code>input</code> has \(n\) dimensions, then the resulting tuple contains \(n\)
+tensors of size \(z\), where \(z\) is the total number of
+non-zero elements in the <code>input</code> tensor.</p>
+<p>As a special case, when <code>input</code> has zero dimensions and a nonzero scalar
+value, it is treated as a one-dimensional tensor with one element.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_nonzero</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>0</span>, <span class='fl'>1</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0
+#&gt;  1
+#&gt;  2
+#&gt;  4
+#&gt; [ CPULongType{4,1} ]</div></pre>
+  </div>
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+    <h1>Norm</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_norm.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Norm</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_norm</span>(<span class='kw'>self</span>, p = <span class='fl'>2L</span>, <span class='kw'>dim</span>, keepdim = <span class='fl'>FALSE</span>, <span class='kw'>dtype</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>p</th>
+      <td><p>(int, float, inf, -inf, 'fro', 'nuc', optional) the order of norm. Default: <code>'fro'</code>        The following norms can be calculated:        =====  ============================  ==========================        ord    matrix norm                   vector norm        =====  ============================  ==========================        NULL   Frobenius norm                2-norm        'fro'  Frobenius norm                --        'nuc'  nuclear norm                  --        Other  as vec norm when dim is NULL  sum(abs(x)<strong>ord)</strong>(1./ord)        =====  ============================  ==========================</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int, 2-tuple of ints, 2-list of ints, optional) If it is an int,        vector norm will be calculated, if it is 2-tuple of ints, matrix norm        will be calculated. If the value is NULL, matrix norm will be calculated        when the input tensor only has two dimensions, vector norm will be        calculated when the input tensor only has one dimension. If the input        tensor has more than two dimensions, the vector norm will be applied to        last dimension.</p></td>
+    </tr>
+    <tr>
+      <th>keepdim</th>
+      <td><p>(bool, optional) whether the output tensors have <code>dim</code>        retained or not. Ignored if <code>dim</code> = <code>NULL</code> and        <code>out</code> = <code>NULL</code>. Default: <code>FALSE</code>
+Ignored if        <code>dim</code> = <code>NULL</code> and <code>out</code> = <code>NULL</code>.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of        returned tensor. If specified, the input tensor is casted to        'dtype' while performing the operation. Default: NULL.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
+
+    
+
+
+<p>Returns the matrix norm or vector norm of a given tensor.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0</span>, <span class='fl'>9</span>, dtype = <span class='fu'><a href='torch_dtype.html'>torch_float</a></span>())
+<span class='kw'>b</span> <span class='op'>=</span> <span class='kw'>a</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/stats/reshape.html'>reshape</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='fu'>torch_norm</span>(<span class='kw'>a</span>)
+<span class='fu'>torch_norm</span>(<span class='kw'>b</span>)
+<span class='fu'>torch_norm</span>(<span class='kw'>a</span>, <span class='fl'>Inf</span>)
+<span class='fu'>torch_norm</span>(<span class='kw'>b</span>, <span class='fl'>Inf</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; 8
+#&gt; [ CPUFloatType{} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+<div class="row">
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+    <div class="page-header">
+    <h1>Normal</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
+    <div class="hidden name"><code>torch_normal.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Normal</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_normal</span>(<span class='kw'>mean</span>, std = <span class='fl'>1L</span>, <span class='kw'>size</span>, generator = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>mean</th>
+      <td><p>(Tensor) the tensor of per-element means</p></td>
+    </tr>
+    <tr>
+      <th>std</th>
+      <td><p>(Tensor) the tensor of per-element standard deviations</p></td>
+    </tr>
+    <tr>
+      <th>size</th>
+      <td><p>(int...) a sequence of integers defining the shape of the output tensor.</p></td>
+    </tr>
+    <tr>
+      <th>generator</th>
+      <td><p>(<code>torch.Generator</code>, optional) a pseudorandom number generator for sampling</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>When the shapes do not match, the shape of <code>mean</code>
+is used as the shape for the returned output tensor</p>
+    <h2 class="hasAnchor" id="normal-mean-std-generator-null-out-null-gt-tensor-"><a class="anchor" href="#normal-mean-std-generator-null-out-null-gt-tensor-"></a>normal(mean, std, *, generator=NULL, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a tensor of random numbers drawn from separate normal distributions
+whose mean and standard deviation are given.</p>
+<p>The <code>mean</code> is a tensor with the mean of
+each output element's normal distribution</p>
+<p>The <code>std</code> is a tensor with the standard deviation of
+each output element's normal distribution</p>
+<p>The shapes of <code>mean</code> and <code>std</code> don't need to match, but the
+total number of elements in each tensor need to be the same.</p>
+    <h2 class="hasAnchor" id="normal-mean-std-out-null-gt-tensor-"><a class="anchor" href="#normal-mean-std-out-null-gt-tensor-"></a>normal(mean=0.0, std, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Similar to the function above, but the means are shared among all drawn
+elements.</p>
+    <h2 class="hasAnchor" id="normal-mean-std-out-null-gt-tensor-"><a class="anchor" href="#normal-mean-std-out-null-gt-tensor-"></a>normal(mean, std=1.0, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Similar to the function above, but the standard-deviations are shared among
+all drawn elements.</p>
+    <h2 class="hasAnchor" id="normal-mean-std-size-out-null-gt-tensor-"><a class="anchor" href="#normal-mean-std-size-out-null-gt-tensor-"></a>normal(mean, std, size, *, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Similar to the function above, but the means and standard deviations are shared
+among all drawn elements. The resulting tensor has size given by <code>size</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='fu'>torch_normal</span>(mean=<span class='fl'>0</span>, std=<span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1</span>, <span class='fl'>0</span>, <span class='op'>-</span><span class='fl'>0.1</span>))
+
+
+<span class='fu'>torch_normal</span>(mean=<span class='fl'>0.5</span>, std=<span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>6.</span>))
+
+
+<span class='fu'>torch_normal</span>(mean=<span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>6.</span>))
+
+
+<span class='fu'>torch_normal</span>(<span class='fl'>2</span>, <span class='fl'>3</span>, size=<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>1</span>, <span class='fl'>4</span>))
+}
+}
+</div></pre>
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+</li>
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+
+      
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Ones</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_ones.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Ones</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_ones</span>(
+  <span class='kw'>...</span>,
+  names = <span class='kw'>NULL</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>...</th>
+      <td><p>(int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.</p></td>
+    </tr>
+    <tr>
+      <th>names</th>
+      <td><p>optional names for the dimensions</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="ones-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#ones-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>ones(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a tensor filled with the scalar value <code>1</code>, with the shape defined
+by the variable argument <code>size</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_ones</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+<span class='fu'>torch_ones</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1
+#&gt;  1
+#&gt;  1
+#&gt;  1
+#&gt;  1
+#&gt; [ CPUFloatType{5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Ones_like</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_ones_like.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Ones_like</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_ones_like</span>(
+  <span class='kw'>input</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>,
+  memory_format = <span class='fu'><a href='torch_memory_format.html'>torch_preserve_format</a></span>()
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>memory_format</th>
+      <td><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="ones-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"><a class="anchor" href="#ones-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a>ones_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a tensor filled with the scalar value <code>1</code>, with the same size as
+<code>input</code>. <code>torch_ones_like(input)</code> is equivalent to
+<code><a href='torch_ones.html'>torch_ones(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)</a></code>.</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p>As of 0.4, this function does not support an <code>out</code> keyword. As an alternative,
+the old <code>torch_ones_like(input, out=output)</code> is equivalent to
+<code><a href='torch_ones.html'>torch_ones(input.size(), out=output)</a></code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+<span class='fu'>torch_ones_like</span>(<span class='kw'>input</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1  1  1
+#&gt;  1  1  1
+#&gt; [ CPUFloatType{2,3} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Orgqr</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_orgqr.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Orgqr</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_orgqr</span>(<span class='kw'>self</span>, <span class='kw'>input2</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the <code>a</code> from <code><a href='torch_geqrf.html'>torch_geqrf</a></code>.</p></td>
+    </tr>
+    <tr>
+      <th>input2</th>
+      <td><p>(Tensor) the <code>tau</code> from <code><a href='torch_geqrf.html'>torch_geqrf</a></code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="orgqr-input-input-gt-tensor-"><a class="anchor" href="#orgqr-input-input-gt-tensor-"></a>orgqr(input, input2) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the orthogonal matrix <code>Q</code> of a QR factorization, from the <code>(input, input2)</code>
+tuple returned by <code><a href='torch_geqrf.html'>torch_geqrf</a></code>.</p>
+<p>This directly calls the underlying LAPACK function <code>?orgqr</code>.
+See <code>LAPACK documentation for orgqr</code>_ for further details.</p>
+
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diff --git a/static/docs/reference/torch_ormqr.html b/static/docs/reference/torch_ormqr.html
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+    <div class="page-header">
+    <h1>Ormqr</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_ormqr.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Ormqr</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_ormqr</span>(<span class='kw'>self</span>, <span class='kw'>input2</span>, <span class='kw'>input3</span>, left = <span class='fl'>TRUE</span>, transpose = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the <code>a</code> from <code><a href='torch_geqrf.html'>torch_geqrf</a></code>.</p></td>
+    </tr>
+    <tr>
+      <th>input2</th>
+      <td><p>(Tensor) the <code>tau</code> from <code><a href='torch_geqrf.html'>torch_geqrf</a></code>.</p></td>
+    </tr>
+    <tr>
+      <th>input3</th>
+      <td><p>(Tensor) the matrix to be multiplied.</p></td>
+    </tr>
+    <tr>
+      <th>left</th>
+      <td><p>see LAPACK documentation</p></td>
+    </tr>
+    <tr>
+      <th>transpose</th>
+      <td><p>see LAPACK documentation</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="ormqr-input-input-input-left-true-transpose-false-gt-tensor-"><a class="anchor" href="#ormqr-input-input-input-left-true-transpose-false-gt-tensor-"></a>ormqr(input, input2, input3, left=TRUE, transpose=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Multiplies <code>mat</code> (given by <code>input3</code>) by the orthogonal <code>Q</code> matrix of the QR factorization
+formed by <code><a href='torch_geqrf.html'>torch_geqrf()</a></code> that is represented by <code>(a, tau)</code> (given by (<code>input</code>, <code>input2</code>)).</p>
+<p>This directly calls the underlying LAPACK function <code>?ormqr</code>.
+See <a href='https://software.intel.com/content/www/us/en/develop/documentation/mkl-developer-reference-c/top/scalapack-routines/scalapack-computational-routines/orthogonal-factorizations-scalapack-computational-routines/p-ormqr.html'>LAPACK documentation for ormqr</a> for further details.</p>
+
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diff --git a/static/docs/reference/torch_pdist.html b/static/docs/reference/torch_pdist.html
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+
+      
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Pdist</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_pdist.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Pdist</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_pdist</span>(<span class='kw'>self</span>, p = <span class='fl'>2L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>NA input tensor of shape \(N \times M\).</p></td>
+    </tr>
+    <tr>
+      <th>p</th>
+      <td><p>NA p value for the p-norm distance to calculate between each vector pair        \(\in [0, \infty]\).</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="pdist-input-p-gt-tensor-"><a class="anchor" href="#pdist-input-p-gt-tensor-"></a>pdist(input, p=2) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the p-norm distance between every pair of row vectors in the input.
+This is identical to the upper triangular portion, excluding the diagonal, of
+<code>torch_norm(input[:, NULL] - input, dim=2, p=p)</code>. This function will be faster
+if the rows are contiguous.</p>
+<p>If input has shape \(N \times M\) then the output will have shape
+\(\frac{1}{2} N (N - 1)\).</p>
+<p>This function is equivalent to <code>scipy.spatial.distance.pdist(input, 'minkowski', p=p)</code> if \(p \in (0, \infty)\). When \(p = 0\) it is
+equivalent to <code>scipy.spatial.distance.pdist(input, 'hamming') * M</code>.
+When \(p = \infty\), the closest scipy function is
+<code>scipy.spatial.distance.pdist(xn, lambda x, y: np.abs(x - y).max())</code>.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    </li>
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+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/optim.html">optim: optimizers in torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/custom-nn.html">Custom nn modules</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/control-flow-and-weight-sharing.html">Control flow &amp; Weight sharing</a>
+    </li>
+    <li class="dropdown-header">Torch Mechanics</li>
+    <li>
+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/neural-networks.html">Neural networks</a>
+    </li>
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+    <li>
+      <a href="../articles/tensor/index.html">Tensor class</a>
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+      <a href="../articles/using-autograd.html">Using autograd</a>
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+    <div class="page-header">
+    <h1>Pinverse</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_pinverse.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Pinverse</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_pinverse</span>(<span class='kw'>self</span>, rcond = <span class='fl'>0</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) The input tensor of size \((*, m, n)\) where \(*\) is zero or more batch dimensions</p></td>
+    </tr>
+    <tr>
+      <th>rcond</th>
+      <td><p>(float) A floating point value to determine the cutoff for small singular values.                   Default: 1e-15</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>This method is implemented using the Singular Value Decomposition.
+</pre>
+
+<pre>The pseudo-inverse is not necessarily a continuous function in the elements of the matrix `[1]`_.
+Therefore, derivatives are not always existent, and exist for a constant rank only `[2]`_.
+However, this method is backprop-able due to the implementation by using SVD results, and
+could be unstable. Double-backward will also be unstable due to the usage of SVD internally.
+See `~torch.svd` for more details.
+</pre>
+
+    <h2 class="hasAnchor" id="pinverse-input-rcond-e-gt-tensor-"><a class="anchor" href="#pinverse-input-rcond-e-gt-tensor-"></a>pinverse(input, rcond=1e-15) -&gt; Tensor </h2>
+
+    
+
+
+<p>Calculates the pseudo-inverse (also known as the Moore-Penrose inverse) of a 2D tensor.
+Please look at <code>Moore-Penrose inverse</code>_ for more details</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>5</span>))
+<span class='kw'>input</span>
+<span class='fu'>torch_pinverse</span>(<span class='kw'>input</span>)
+<span class='co'># Batched pinverse example</span>
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>,<span class='fl'>6</span>,<span class='fl'>3</span>))
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'>torch_pinverse</span>(<span class='kw'>a</span>)
+<span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span>(<span class='kw'>b</span>, <span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;   1.0000e+00 -5.9605e-08  1.0431e-07
+#&gt;   2.9802e-08  1.0000e+00  2.2352e-08
+#&gt;   2.9802e-08  8.9407e-08  1.0000e+00
+#&gt; 
+#&gt; (2,.,.) = 
+#&gt;   1.0000e+00 -5.9605e-08 -1.7136e-07
+#&gt;  -7.4506e-09  1.0000e+00 -1.2666e-07
+#&gt;  -8.1956e-08 -2.1607e-07  1.0000e+00
+#&gt; [ CPUFloatType{2,3,3} ]</div></pre>
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diff --git a/static/docs/reference/torch_pixel_shuffle.html b/static/docs/reference/torch_pixel_shuffle.html
new file mode 100644
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+
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+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Pixel_shuffle</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_pixel_shuffle.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Pixel_shuffle</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_pixel_shuffle</span>(<span class='kw'>self</span>, <span class='kw'>upscale_factor</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>upscale_factor</th>
+      <td><p>(int) factor to increase spatial resolution by</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="rearranges-elements-in-a-tensor-of-shape-"><a class="anchor" href="#rearranges-elements-in-a-tensor-of-shape-"></a>Rearranges elements in a tensor of shape </h2>
+
+    <p>math:<code>(*, C \times r^2, H, W)</code> to a :</p>
+<p>Rearranges elements in a tensor of shape \((*, C \times r^2, H, W)\) to a
+tensor of shape \((*, C, H \times r, W \times r)\).</p>
+<p>See <code>~torch.nn.PixelShuffle</code> for details.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>9</span>, <span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>output</span> <span class='op'>=</span> <span class='fu'><a href='nnf_pixel_shuffle.html'>nnf_pixel_shuffle</a></span>(<span class='kw'>input</span>, <span class='fl'>3</span>)
+<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='kw'>output</span><span class='op'>$</span><span class='fu'>size</span>())
+}
+</div><div class='output co'>#&gt; [1]  1  1 12 12</div></pre>
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+    <div class="page-header">
+    <h1>Poisson</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_poisson.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Poisson</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_poisson</span>(<span class='kw'>self</span>, generator = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor containing the rates of the Poisson distribution</p></td>
+    </tr>
+    <tr>
+      <th>generator</th>
+      <td><p>(<code>torch.Generator</code>, optional) a pseudorandom number generator for sampling</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="poisson-input-generator-null-gt-tensor-"><a class="anchor" href="#poisson-input-generator-null-gt-tensor-"></a>poisson(input *, generator=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a tensor of the same size as <code>input</code> with each element
+sampled from a Poisson distribution with rate parameter given by the corresponding
+element in <code>input</code> i.e.,</p>
+<p>$$
+    \mbox{out}_i \sim \mbox{Poisson}(\mbox{input}_i)
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>rates</span> <span class='op'>=</span> <span class='fu'><a href='torch_rand.html'>torch_rand</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>)) <span class='op'>*</span> <span class='fl'>5</span>  <span class='co'># rate parameter between 0 and 5</span>
+<span class='fu'>torch_poisson</span>(<span class='kw'>rates</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1  0  5  2
+#&gt;  8  2  0  4
+#&gt;  3  0  3  0
+#&gt;  3  6  6  1
+#&gt; [ CPUFloatType{4,4} ]</div></pre>
+  </div>
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Polygamma</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_polygamma.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Polygamma</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_polygamma</span>(<span class='kw'>n</span>, <span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>n</th>
+      <td><p>(int) the order of the polygamma function</p></td>
+    </tr>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>This function is not implemented for \eqn{n \geq 2}.
+</pre>
+
+    <h2 class="hasAnchor" id="polygamma-n-input-out-null-gt-tensor-"><a class="anchor" href="#polygamma-n-input-out-null-gt-tensor-"></a>polygamma(n, input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the \(n^{th}\) derivative of the digamma function on <code>input</code>.
+\(n \geq 0\) is called the order of the polygamma function.</p>
+<p>$$
+    \psi^{(n)}(x) = \frac{d^{(n)}}{dx^{(n)}} \psi(x)
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>0.5</span>))
+<span class='fu'>torch_polygamma</span>(<span class='fl'>1</span>, <span class='kw'>a</span>)
+}
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_pow.html b/static/docs/reference/torch_pow.html
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+    <div class="page-header">
+    <h1>Pow</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_pow.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Pow</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_pow</span>(<span class='kw'>self</span>, <span class='kw'>exponent</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(float) the scalar base value for the power operation</p></td>
+    </tr>
+    <tr>
+      <th>exponent</th>
+      <td><p>(float or tensor) the exponent value</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="pow-input-exponent-out-null-gt-tensor-"><a class="anchor" href="#pow-input-exponent-out-null-gt-tensor-"></a>pow(input, exponent, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Takes the power of each element in <code>input</code> with <code>exponent</code> and
+returns a tensor with the result.</p>
+<p><code>exponent</code> can be either a single <code>float</code> number or a <code>Tensor</code>
+with the same number of elements as <code>input</code>.</p>
+<p>When <code>exponent</code> is a scalar value, the operation applied is:</p>
+<p>$$
+    \mbox{out}_i = x_i^{\mbox{exponent}}
+$$
+When <code>exponent</code> is a tensor, the operation applied is:</p>
+<p>$$
+    \mbox{out}_i = x_i^{\mbox{exponent}_i}
+$$
+When <code>exponent</code> is a tensor, the shapes of <code>input</code>
+and <code>exponent</code> must be broadcastable .</p>
+    <h2 class="hasAnchor" id="pow-self-exponent-out-null-gt-tensor-"><a class="anchor" href="#pow-self-exponent-out-null-gt-tensor-"></a>pow(self, exponent, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p><code>self</code> is a scalar <code>float</code> value, and <code>exponent</code> is a tensor.
+The returned tensor <code>out</code> is of the same shape as <code>exponent</code></p>
+<p>The operation applied is:</p>
+<p>$$
+    \mbox{out}_i = \mbox{self} ^ {\mbox{exponent}_i}
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_pow</span>(<span class='kw'>a</span>, <span class='fl'>2</span>)
+<span class='kw'>exp</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>5.</span>)
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>5.</span>)
+<span class='kw'>a</span>
+<span class='kw'>exp</span>
+<span class='fu'>torch_pow</span>(<span class='kw'>a</span>, <span class='kw'>exp</span>)
+
+
+<span class='kw'>exp</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>5.</span>)
+<span class='kw'>base</span> <span class='op'>=</span> <span class='fl'>2</span>
+<span class='fu'>torch_pow</span>(<span class='kw'>base</span>, <span class='kw'>exp</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;   2
+#&gt;   4
+#&gt;   8
+#&gt;  16
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </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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+     
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+    <li class="dropdown-header">Beginers guide</li>
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Prod</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_prod.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Prod</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_prod</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, keepdim = <span class='fl'>FALSE</span>, dtype = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the dimension to reduce.</p></td>
+    </tr>
+    <tr>
+      <th>keepdim</th>
+      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to <code>dtype</code> before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="prod-input-dtype-null-gt-tensor-"><a class="anchor" href="#prod-input-dtype-null-gt-tensor-"></a>prod(input, dtype=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the product of all elements in the <code>input</code> tensor.</p>
+    <h2 class="hasAnchor" id="prod-input-dim-keepdim-false-dtype-null-gt-tensor-"><a class="anchor" href="#prod-input-dim-keepdim-false-dtype-null-gt-tensor-"></a>prod(input, dim, keepdim=False, dtype=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the product of each row of the <code>input</code> tensor in the given
+dimension <code>dim</code>.</p>
+<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size
+as <code>input</code> except in the dimension <code>dim</code> where it is of size 1.
+Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in
+the output tensor having 1 fewer dimension than <code>input</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_prod</span>(<span class='kw'>a</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>2</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_prod</span>(<span class='kw'>a</span>, <span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; 0.001 *
+#&gt; -1.8251
+#&gt; -120.0931
+#&gt; [ CPUFloatType{2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_promote_types.html b/static/docs/reference/torch_promote_types.html
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--- /dev/null
+++ b/static/docs/reference/torch_promote_types.html
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+
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+<div class="row">
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+    <div class="page-header">
+    <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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_promote_types.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Promote_types</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_promote_types</span>(<span class='kw'>type1</span>, <span class='kw'>type2</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>type1</th>
+      <td><p>(<code>torch.dtype</code>)</p></td>
+    </tr>
+    <tr>
+      <th>type2</th>
+      <td><p>(<code>torch.dtype</code>)</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="promote-types-type-type-gt-dtype-"><a class="anchor" href="#promote-types-type-type-gt-dtype-"></a>promote_types(type1, type2) -&gt; dtype </h2>
+
+    
+
+
+<p>Returns the <code>torch_dtype</code> with the smallest size and scalar kind that is
+not smaller nor of lower kind than either <code>type1</code> or <code>type2</code>. See type promotion
+documentation  for more information on the type
+promotion logic.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_promote_types</span>(<span class='fu'><a href='torch_dtype.html'>torch_int32</a></span>(), <span class='fu'><a href='torch_dtype.html'>torch_float32</a></span>())
+<span class='fu'>torch_promote_types</span>(<span class='fu'><a href='torch_dtype.html'>torch_uint8</a></span>(), <span class='fu'><a href='torch_dtype.html'>torch_long</a></span>())
+}
+</div><div class='output co'>#&gt; torch_Long</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+    <div class="page-header">
+    <h1>Qr</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_qr.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Qr</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_qr</span>(<span class='kw'>self</span>, some = <span class='fl'>TRUE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor of size \((*, m, n)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is zero or more                batch dimensions consisting of matrices of dimension \(m \times n\).</p></td>
+    </tr>
+    <tr>
+      <th>some</th>
+      <td><p>(bool, optional) Set to <code>TRUE</code> for reduced QR decomposition and <code>FALSE</code> for                complete QR decomposition.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>precision may be lost if the magnitudes of the elements of <code>input</code>
+are large</p>
+<p>While it should always give you a valid decomposition, it may not
+give you the same one across platforms - it will depend on your
+LAPACK implementation.</p>
+    <h2 class="hasAnchor" id="qr-input-some-true-out-null-gt-tensor-tensor-"><a class="anchor" href="#qr-input-some-true-out-null-gt-tensor-tensor-"></a>qr(input, some=TRUE, out=NULL) -&gt; (Tensor, Tensor) </h2>
+
+    
+
+
+<p>Computes the QR decomposition of a matrix or a batch of matrices <code>input</code>,
+and returns a namedtuple (Q, R) of tensors such that \(\mbox{input} = Q R\)
+with \(Q\) being an orthogonal matrix or batch of orthogonal matrices and
+\(R\) being an upper triangular matrix or batch of upper triangular matrices.</p>
+<p>If <code>some</code> is <code>TRUE</code>, then this function returns the thin (reduced) QR factorization.
+Otherwise, if <code>some</code> is <code>FALSE</code>, this function returns the complete QR factorization.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>12.</span>, <span class='op'>-</span><span class='fl'>51</span>, <span class='fl'>4</span>, <span class='fl'>6</span>, <span class='fl'>167</span>, <span class='op'>-</span><span class='fl'>68</span>, <span class='op'>-</span><span class='fl'>4</span>, <span class='fl'>24</span>, <span class='op'>-</span><span class='fl'>41</span>), ncol = <span class='fl'>3</span>, byrow = <span class='fl'>TRUE</span>))
+<span class='kw'>out</span> <span class='op'>=</span> <span class='fu'>torch_qr</span>(<span class='kw'>a</span>)
+<span class='kw'>q</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>1</span>]]
+<span class='kw'>r</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>2</span>]]
+<span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='kw'>q</span>, <span class='kw'>r</span>)<span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/Round.html'>round</a></span>()
+<span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='kw'>q</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>(), <span class='kw'>q</span>)<span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/Round.html'>round</a></span>()
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1  0  0
+#&gt;  0  1  0
+#&gt;  0  0  1
+#&gt; [ CPUFloatType{3,3} ]</div></pre>
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diff --git a/static/docs/reference/torch_qscheme.html b/static/docs/reference/torch_qscheme.html
new file mode 100644
index 000000000..557b34642
--- /dev/null
+++ b/static/docs/reference/torch_qscheme.html
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+
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+    <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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diff --git a/static/docs/reference/torch_quantize_per_channel.html b/static/docs/reference/torch_quantize_per_channel.html
new file mode 100644
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+    <div class="page-header">
+    <h1>Quantize_per_channel</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_quantize_per_channel.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Quantize_per_channel</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_quantize_per_channel</span>(<span class='kw'>self</span>, <span class='kw'>scales</span>, <span class='kw'>zero_points</span>, <span class='kw'>axis</span>, <span class='kw'>dtype</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) float tensor to quantize</p></td>
+    </tr>
+    <tr>
+      <th>scales</th>
+      <td><p>(Tensor) float 1D tensor of scales to use, size should match <code>input.size(axis)</code></p></td>
+    </tr>
+    <tr>
+      <th>zero_points</th>
+      <td><p>(int) integer 1D tensor of offset to use, size should match <code>input.size(axis)</code></p></td>
+    </tr>
+    <tr>
+      <th>axis</th>
+      <td><p>(int) dimension on which apply per-channel quantization</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>) the desired data type of returned tensor.        Has to be one of the quantized dtypes: <code>torch_quint8</code>, <code>torch.qint8</code>, <code>torch.qint32</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="quantize-per-channel-input-scales-zero-points-axis-dtype-gt-tensor-"><a class="anchor" href="#quantize-per-channel-input-scales-zero-points-axis-dtype-gt-tensor-"></a>quantize_per_channel(input, scales, zero_points, axis, dtype) -&gt; Tensor </h2>
+
+    
+
+
+<p>Converts a float tensor to per-channel quantized tensor with given scales and zero points.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>1.0</span>, <span class='fl'>0.0</span>, <span class='fl'>1.0</span>, <span class='fl'>2.0</span>), ncol = <span class='fl'>2</span>, byrow = <span class='fl'>TRUE</span>))
+<span class='fu'>torch_quantize_per_channel</span>(<span class='kw'>x</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0.1</span>, <span class='fl'>0.01</span>)), 
+                           <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10L</span>, <span class='fl'>0L</span>)), <span class='fl'>0</span>, <span class='fu'><a href='torch_dtype.html'>torch_quint8</a></span>())
+<span class='fu'>torch_quantize_per_channel</span>(<span class='kw'>x</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0.1</span>, <span class='fl'>0.01</span>)), 
+                           <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>10L</span>, <span class='fl'>0L</span>)), <span class='fl'>0</span>, <span class='fu'><a href='torch_dtype.html'>torch_quint8</a></span>())<span class='op'>$</span><span class='fu'>int_repr</span>()
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;    0   10
+#&gt;  100  200
+#&gt; [ CPUByteType{2,2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_quantize_per_tensor.html b/static/docs/reference/torch_quantize_per_tensor.html
new file mode 100644
index 000000000..499139caa
--- /dev/null
+++ b/static/docs/reference/torch_quantize_per_tensor.html
@@ -0,0 +1,266 @@
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+    <div class="page-header">
+    <h1>Quantize_per_tensor</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_quantize_per_tensor.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Quantize_per_tensor</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_quantize_per_tensor</span>(<span class='kw'>self</span>, <span class='kw'>scale</span>, <span class='kw'>zero_point</span>, <span class='kw'>dtype</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) float tensor to quantize</p></td>
+    </tr>
+    <tr>
+      <th>scale</th>
+      <td><p>(float) scale to apply in quantization formula</p></td>
+    </tr>
+    <tr>
+      <th>zero_point</th>
+      <td><p>(int) offset in integer value that maps to float zero</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>) the desired data type of returned tensor.        Has to be one of the quantized dtypes: <code>torch_quint8</code>, <code>torch.qint8</code>, <code>torch.qint32</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="quantize-per-tensor-input-scale-zero-point-dtype-gt-tensor-"><a class="anchor" href="#quantize-per-tensor-input-scale-zero-point-dtype-gt-tensor-"></a>quantize_per_tensor(input, scale, zero_point, dtype) -&gt; Tensor </h2>
+
+    
+
+
+<p>Converts a float tensor to quantized tensor with given scale and zero point.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='fu'>torch_quantize_per_tensor</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>1.0</span>, <span class='fl'>0.0</span>, <span class='fl'>1.0</span>, <span class='fl'>2.0</span>)), <span class='fl'>0.1</span>, <span class='fl'>10</span>, <span class='fu'><a href='torch_dtype.html'>torch_quint8</a></span>())
+<span class='fu'>torch_quantize_per_tensor</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>1.0</span>, <span class='fl'>0.0</span>, <span class='fl'>1.0</span>, <span class='fl'>2.0</span>)), <span class='fl'>0.1</span>, <span class='fl'>10</span>, <span class='fu'><a href='torch_dtype.html'>torch_quint8</a></span>())<span class='op'>$</span><span class='fu'>int_repr</span>()
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;   0
+#&gt;  10
+#&gt;  20
+#&gt;  30
+#&gt; [ CPUByteType{4} ]</div></pre>
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+    <h1>Rand</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_rand.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Rand</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_rand</span>(
+  <span class='kw'>...</span>,
+  names = <span class='kw'>NULL</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>...</th>
+      <td><p>(int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.</p></td>
+    </tr>
+    <tr>
+      <th>names</th>
+      <td><p>optional dimension names</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="rand-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#rand-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>rand(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a tensor filled with random numbers from a uniform distribution
+on the interval \([0, 1)\)</p>
+<p>The shape of the tensor is defined by the variable argument <code>size</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_rand</span>(<span class='fl'>4</span>)
+<span class='fu'>torch_rand</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.4034  0.1802  0.2925
+#&gt;  0.6916  0.6648  0.9430
+#&gt; [ CPUFloatType{2,3} ]</div></pre>
+  </div>
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+    <div class="page-header">
+    <h1>Rand_like</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_rand_like.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Rand_like</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_rand_like</span>(
+  <span class='kw'>input</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>,
+  memory_format = <span class='fu'><a href='torch_memory_format.html'>torch_preserve_format</a></span>()
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>memory_format</th>
+      <td><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="rand-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"><a class="anchor" href="#rand-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a>rand_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a tensor with the same size as <code>input</code> that is filled with
+random numbers from a uniform distribution on the interval \([0, 1)\).
+<code>torch_rand_like(input)</code> is equivalent to
+<code><a href='torch_rand.html'>torch_rand(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)</a></code>.</p>
+
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+    <div class="page-header">
+    <h1>Randint</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_randint.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Randint</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_randint</span>(
+  <span class='kw'>low</span>,
+  <span class='kw'>high</span>,
+  <span class='kw'>size</span>,
+  generator = <span class='kw'>NULL</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>,
+  memory_format = <span class='fu'><a href='torch_memory_format.html'>torch_preserve_format</a></span>()
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>low</th>
+      <td><p>(int, optional) Lowest integer to be drawn from the distribution. Default: 0.</p></td>
+    </tr>
+    <tr>
+      <th>high</th>
+      <td><p>(int) One above the highest integer to be drawn from the distribution.</p></td>
+    </tr>
+    <tr>
+      <th>size</th>
+      <td><p>(tuple) a tuple defining the shape of the output tensor.</p></td>
+    </tr>
+    <tr>
+      <th>generator</th>
+      <td><p>(<code>torch.Generator</code>, optional) a pseudorandom number generator for sampling</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>memory_format</th>
+      <td><p>memory format for the resulting tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="randint-low-high-size-generator-null-out-null-"><a class="anchor" href="#randint-low-high-size-generator-null-out-null-"></a>randint(low=0, high, size, *, generator=NULL, out=NULL, \ </h2>
+
+    
+
+
+<p>dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor</p>
+<p>Returns a tensor filled with random integers generated uniformly
+between <code>low</code> (inclusive) and <code>high</code> (exclusive).</p>
+<p>The shape of the tensor is defined by the variable argument <code>size</code>.</p>
+<p>.. note:
+With the global dtype default (<code>torch_float32</code>), this function returns
+a tensor with dtype <code>torch_int64</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_randint</span>(<span class='fl'>3</span>, <span class='fl'>5</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>3</span>))
+<span class='fu'>torch_randint</span>(<span class='fl'>0</span>, <span class='fl'>10</span>, size = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>))
+<span class='fu'>torch_randint</span>(<span class='fl'>3</span>, <span class='fl'>10</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  7  8
+#&gt;  8  9
+#&gt; [ CPUFloatType{2,2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+<div class="row">
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+    <div class="page-header">
+    <h1>Randint_like</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_randint_like.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Randint_like</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_randint_like</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>low</span>,
+  <span class='kw'>high</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
+    </tr>
+    <tr>
+      <th>low</th>
+      <td><p>(int, optional) Lowest integer to be drawn from the distribution. Default: 0.</p></td>
+    </tr>
+    <tr>
+      <th>high</th>
+      <td><p>(int) One above the highest integer to be drawn from the distribution.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="randint-like-input-low-high-dtype-null-layout-torch-strided-device-null-requires-grad-false-"><a class="anchor" href="#randint-like-input-low-high-dtype-null-layout-torch-strided-device-null-requires-grad-false-"></a>randint_like(input, low=0, high, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False, </h2>
+
+    
+
+
+<p>memory_format=torch.preserve_format) -&gt; Tensor</p>
+<p>Returns a tensor with the same shape as Tensor <code>input</code> filled with
+random integers generated uniformly between <code>low</code> (inclusive) and
+<code>high</code> (exclusive).</p>
+<p>.. note:
+With the global dtype default (<code>torch_float32</code>), this function returns
+a tensor with dtype <code>torch_int64</code>.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+    <h1>Randn</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_randn.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Randn</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_randn</span>(
+  <span class='kw'>...</span>,
+  names = <span class='kw'>NULL</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>...</th>
+      <td><p>(int...) a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.</p></td>
+    </tr>
+    <tr>
+      <th>names</th>
+      <td><p>optional names for the dimensions</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="randn-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#randn-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>randn(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a tensor filled with random numbers from a normal distribution
+with mean <code>0</code> and variance <code>1</code> (also called the standard normal
+distribution).</p>
+<p>$$
+    \mbox{out}_{i} \sim \mathcal{N}(0, 1)
+$$
+The shape of the tensor is defined by the variable argument <code>size</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_randn</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='fu'>torch_randn</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.7302  1.3721 -0.0691
+#&gt;  0.4933 -0.7643 -0.5334
+#&gt; [ CPUFloatType{2,3} ]</div></pre>
+  </div>
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+    <div class="page-header">
+    <h1>Randn_like</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_randn_like.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Randn_like</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_randn_like</span>(
+  <span class='kw'>input</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>,
+  memory_format = <span class='fu'><a href='torch_memory_format.html'>torch_preserve_format</a></span>()
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>memory_format</th>
+      <td><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="randn-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"><a class="anchor" href="#randn-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a>randn_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a tensor with the same size as <code>input</code> that is filled with
+random numbers from a normal distribution with mean 0 and variance 1.
+<code>torch_randn_like(input)</code> is equivalent to
+<code><a href='torch_randn.html'>torch_randn(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)</a></code>.</p>
+
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diff --git a/static/docs/reference/torch_randperm.html b/static/docs/reference/torch_randperm.html
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+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Randperm</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_randperm.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Randperm</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_randperm</span>(
+  <span class='kw'>n</span>,
+  dtype = <span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>(),
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>n</th>
+      <td><p>(int) the upper bound (exclusive)</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: <code>torch_int64</code>.</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="randperm-n-out-null-dtype-torch-int-layout-torch-strided-device-null-requires-grad-false-gt-longtensor-"><a class="anchor" href="#randperm-n-out-null-dtype-torch-int-layout-torch-strided-device-null-requires-grad-false-gt-longtensor-"></a>randperm(n, out=NULL, dtype=torch.int64, layout=torch.strided, device=NULL, requires_grad=False) -&gt; LongTensor </h2>
+
+    
+
+
+<p>Returns a random permutation of integers from <code>0</code> to <code>n - 1</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_randperm</span>(<span class='fl'>4</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  3
+#&gt;  2
+#&gt;  0
+#&gt;  1
+#&gt; [ CPULongType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Range</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_range.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Range</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_range</span>(
+  <span class='kw'>start</span>,
+  <span class='kw'>end</span>,
+  step = <span class='fl'>1</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>start</th>
+      <td><p>(float) the starting value for the set of points. Default: <code>0</code>.</p></td>
+    </tr>
+    <tr>
+      <th>end</th>
+      <td><p>(float) the ending value for the set of points</p></td>
+    </tr>
+    <tr>
+      <th>step</th>
+      <td><p>(float) the gap between each pair of adjacent points. Default: <code>1</code>.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>). If <code>dtype</code> is not given, infer the data type from the other input        arguments. If any of <code>start</code>, <code>end</code>, or <code>stop</code> are floating-point, the        <code>dtype</code> is inferred to be the default dtype, see        <code>~torch.get_default_dtype</code>. Otherwise, the <code>dtype</code> is inferred to        be <code>torch.int64</code>.</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="range-start-end-step-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#range-start-end-step-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>range(start=0, end, step=1, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a 1-D tensor of size \(\left\lfloor \frac{\mbox{end} - \mbox{start}}{\mbox{step}} \right\rfloor + 1\)
+with values from <code>start</code> to <code>end</code> with step <code>step</code>. Step is
+the gap between two values in the tensor.</p>
+<p>$$
+    \mbox{out}_{i+1} = \mbox{out}_i + \mbox{step}.
+$$</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p>This function is deprecated in favor of <code><a href='torch_arange.html'>torch_arange</a></code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_range</span>(<span class='fl'>1</span>, <span class='fl'>4</span>)
+<span class='fu'>torch_range</span>(<span class='fl'>1</span>, <span class='fl'>4</span>, <span class='fl'>0.5</span>)
+}
+</div><div class='output co'>#&gt; <span class='warning'>Warning: This function is deprecated in favor of torch_arange.</span></div><div class='output co'>#&gt; <span class='warning'>Warning: This function is deprecated in favor of torch_arange.</span></div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.0000
+#&gt;  1.5000
+#&gt;  2.0000
+#&gt;  2.5000
+#&gt;  3.0000
+#&gt;  3.5000
+#&gt; [ CPUFloatType{6} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Real</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_real.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Real</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_real</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="real-input-gt-tensor-"><a class="anchor" href="#real-input-gt-tensor-"></a>real(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the real part of the <code>input</code> tensor. If
+<code>input</code> is a real (non-complex) tensor, this function just
+returns it.</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p>Not yet implemented for complex tensors.</p>
+<p>$$
+    \mbox{out}_{i} = real(\mbox{input}_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='fu'>torch_real</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>1</span> <span class='op'>+</span> <span class='fl'>1i</span>, <span class='op'>-</span><span class='fl'>2</span> <span class='op'>+</span> <span class='fl'>2i</span>, <span class='fl'>3</span> <span class='op'>-</span> <span class='fl'>3i</span>)))
+}
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Reciprocal</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_reciprocal.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Reciprocal</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_reciprocal</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="reciprocal-input-out-null-gt-tensor-"><a class="anchor" href="#reciprocal-input-out-null-gt-tensor-"></a>reciprocal(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the reciprocal of the elements of <code>input</code></p>
+<p>$$
+    \mbox{out}_{i} = \frac{1}{\mbox{input}_{i}}
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_reciprocal</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  5.6343
+#&gt; -0.4645
+#&gt;  4.8170
+#&gt;  2.1907
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
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+    <h1>Creates the reduction objet</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/reduction.R'><code>R/reduction.R</code></a></small>
+    <div class="hidden name"><code>torch_reduction.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Creates the reduction objet</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_reduction_sum</span>()
+
+<span class='fu'>torch_reduction_mean</span>()
+
+<span class='fu'>torch_reduction_none</span>()</pre>
+
+
+
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diff --git a/static/docs/reference/torch_relu.html b/static/docs/reference/torch_relu.html
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+    <div class="page-header">
+    <h1>Relu</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_relu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Relu</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_relu</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="relu-input-gt-tensor-"><a class="anchor" href="#relu-input-gt-tensor-"></a>relu(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the relu tranformation.</p>
+
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+    <h1>Relu_</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_relu_.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Relu_</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_relu_</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="relu-input-gt-tensor-"><a class="anchor" href="#relu-input-gt-tensor-"></a>relu_(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>In-place version of <code><a href='torch_relu.html'>torch_relu()</a></code>.</p>
+
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+    <div class="page-header">
+    <h1>Remainder</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_remainder.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Remainder</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_remainder</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the dividend</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor or float) the divisor that may be either a number or a                               Tensor of the same shape as the dividend</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="remainder-input-other-out-null-gt-tensor-"><a class="anchor" href="#remainder-input-other-out-null-gt-tensor-"></a>remainder(input, other, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the element-wise remainder of division.</p>
+<p>The divisor and dividend may contain both for integer and floating point
+numbers. The remainder has the same sign as the divisor.</p>
+<p>When <code>other</code> is a tensor, the shapes of <code>input</code> and
+<code>other</code> must be broadcastable .</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_remainder</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>3.</span>, <span class='op'>-</span><span class='fl'>2</span>, <span class='op'>-</span><span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>)), <span class='fl'>2</span>)
+<span class='fu'>torch_remainder</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1.</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span>)), <span class='fl'>1.5</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.0000
+#&gt;  0.5000
+#&gt;  0.0000
+#&gt;  1.0000
+#&gt;  0.5000
+#&gt; [ CPUFloatType{5} ]</div></pre>
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+    <h1>Renorm</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_renorm.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Renorm</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_renorm</span>(<span class='kw'>self</span>, <span class='kw'>p</span>, <span class='kw'>dim</span>, <span class='kw'>maxnorm</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>p</th>
+      <td><p>(float) the power for the norm computation</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the dimension to slice over to get the sub-tensors</p></td>
+    </tr>
+    <tr>
+      <th>maxnorm</th>
+      <td><p>(float) the maximum norm to keep each sub-tensor under</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>If the norm of a row is lower than <code>maxnorm</code>, the row is unchanged</p>
+    <h2 class="hasAnchor" id="renorm-input-p-dim-maxnorm-out-null-gt-tensor-"><a class="anchor" href="#renorm-input-p-dim-maxnorm-out-null-gt-tensor-"></a>renorm(input, p, dim, maxnorm, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a tensor where each sub-tensor of <code>input</code> along dimension
+<code>dim</code> is normalized such that the <code>p</code>-norm of the sub-tensor is lower
+than the value <code>maxnorm</code></p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_ones.html'>torch_ones</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='kw'>x</span>[<span class='fl'>2</span>,]<span class='op'>$</span><span class='fu'>fill_</span>(<span class='fl'>2</span>)
+<span class='kw'>x</span>[<span class='fl'>3</span>,]<span class='op'>$</span><span class='fu'>fill_</span>(<span class='fl'>3</span>)
+<span class='kw'>x</span>
+<span class='fu'>torch_renorm</span>(<span class='kw'>x</span>, <span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>5</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.0000  1.0000  1.0000
+#&gt;  1.6667  1.6667  1.6667
+#&gt;  1.6667  1.6667  1.6667
+#&gt; [ CPUFloatType{3,3} ]</div></pre>
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+
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+    <div class="page-header">
+    <h1>Repeat_interleave</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_repeat_interleave.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Repeat_interleave</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_repeat_interleave</span>(<span class='kw'>self</span>, <span class='kw'>repeats</span>, dim = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>repeats</th>
+      <td><p>(Tensor or int) The number of repetitions for each element.        repeats is broadcasted to fit the shape of the given axis.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int, optional) The dimension along which to repeat values.        By default, use the flattened input array, and return a flat output        array.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="repeat-interleave-input-repeats-dim-null-gt-tensor-"><a class="anchor" href="#repeat-interleave-input-repeats-dim-null-gt-tensor-"></a>repeat_interleave(input, repeats, dim=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Repeat elements of a tensor.</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+<pre>This is different from `torch_Tensor.repeat` but similar to `numpy.repeat`.
+</pre>
+
+    <h2 class="hasAnchor" id="repeat-interleave-repeats-gt-tensor-"><a class="anchor" href="#repeat-interleave-repeats-gt-tensor-"></a>repeat_interleave(repeats) -&gt; Tensor </h2>
+
+    
+
+
+<p>If the <code>repeats</code> is <code>tensor([n1, n2, n3, ...])</code>, then the output will be
+<code>tensor([0, 0, ..., 1, 1, ..., 2, 2, ..., ...])</code> where <code>0</code> appears <code>n1</code> times,
+<code>1</code> appears <code>n2</code> times, <code>2</code> appears <code>n3</code> times, etc.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>))
+<span class='kw'>x</span><span class='op'>$</span><span class='fu'>repeat_interleave</span>(<span class='fl'>2</span>)
+<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span>), ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>))
+<span class='fu'>torch_repeat_interleave</span>(<span class='kw'>y</span>, <span class='fl'>2</span>)
+<span class='fu'>torch_repeat_interleave</span>(<span class='kw'>y</span>, <span class='fl'>3</span>, dim=<span class='fl'>1</span>)
+<span class='fu'>torch_repeat_interleave</span>(<span class='kw'>y</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>)), dim=<span class='fl'>1</span>)
+}
+}
+</div></pre>
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+    <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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_reshape.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Reshape</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_reshape</span>(<span class='kw'>self</span>, <span class='kw'>shape</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the tensor to be reshaped</p></td>
+    </tr>
+    <tr>
+      <th>shape</th>
+      <td><p>(tuple of ints) the new shape</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="reshape-input-shape-gt-tensor-"><a class="anchor" href="#reshape-input-shape-gt-tensor-"></a>reshape(input, shape) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a tensor with the same data and number of elements as <code>input</code>,
+but with the specified shape. When possible, the returned tensor will be a view
+of <code>input</code>. Otherwise, it will be a copy. Contiguous inputs and inputs
+with compatible strides can be reshaped without copying, but you should not
+depend on the copying vs. viewing behavior.</p>
+<p>See <code>torch_Tensor.view</code> on when it is possible to return a view.</p>
+<p>A single dimension may be -1, in which case it's inferred from the remaining
+dimensions and the number of elements in <code>input</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0</span>, <span class='fl'>4</span>)
+<span class='fu'>torch_reshape</span>(<span class='kw'>a</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>))
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>), ncol = <span class='fl'>2</span>, byrow=<span class='fl'>TRUE</span>))
+<span class='fu'>torch_reshape</span>(<span class='kw'>b</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='op'>-</span><span class='fl'>1</span>))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0
+#&gt;  1
+#&gt;  2
+#&gt;  3
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
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+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Result_type</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
+    <div class="hidden name"><code>torch_result_type.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Result_type</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_result_type</span>(<span class='kw'>tensor1</span>, <span class='kw'>tensor2</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensor1</th>
+      <td><p>(Tensor or Number) an input tensor or number</p></td>
+    </tr>
+    <tr>
+      <th>tensor2</th>
+      <td><p>(Tensor or Number) an input tensor or number</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="result-type-tensor-tensor-gt-dtype-"><a class="anchor" href="#result-type-tensor-tensor-gt-dtype-"></a>result_type(tensor1, tensor2) -&gt; dtype </h2>
+
+    
+
+
+<p>Returns the <code>torch_dtype</code> that would result from performing an arithmetic
+operation on the provided input tensors. See type promotion documentation
+for more information on the type promotion logic.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_result_type</span>(tensor1 = <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int</a></span>()), tensor2 = <span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; torch_Float</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Rfft</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_rfft.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Rfft</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_rfft</span>(<span class='kw'>self</span>, <span class='kw'>signal_ndim</span>, normalized = <span class='fl'>FALSE</span>, onesided = <span class='fl'>TRUE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor of at least <code>signal_ndim</code> dimensions</p></td>
+    </tr>
+    <tr>
+      <th>signal_ndim</th>
+      <td><p>(int) the number of dimensions in each signal.        <code>signal_ndim</code> can only be 1, 2 or 3</p></td>
+    </tr>
+    <tr>
+      <th>normalized</th>
+      <td><p>(bool, optional) controls whether to return normalized results.        Default: <code>FALSE</code></p></td>
+    </tr>
+    <tr>
+      <th>onesided</th>
+      <td><p>(bool, optional) controls whether to return half of results to        avoid redundancy. Default: <code>TRUE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>For CUDA tensors, an LRU cache is used for cuFFT plans to speed up
+repeatedly running FFT methods on tensors of same geometry with same
+configuration. See cufft-plan-cache for more details on how to
+monitor and control the cache.
+</pre>
+
+    <h2 class="hasAnchor" id="rfft-input-signal-ndim-normalized-false-onesided-true-gt-tensor-"><a class="anchor" href="#rfft-input-signal-ndim-normalized-false-onesided-true-gt-tensor-"></a>rfft(input, signal_ndim, normalized=False, onesided=TRUE) -&gt; Tensor </h2>
+
+    
+
+
+<p>Real-to-complex Discrete Fourier Transform</p>
+<p>This method computes the real-to-complex discrete Fourier transform. It is
+mathematically equivalent with <code><a href='torch_fft.html'>torch_fft</a></code> with differences only in
+formats of the input and output.</p>
+<p>This method supports 1D, 2D and 3D real-to-complex transforms, indicated
+by <code>signal_ndim</code>. <code>input</code> must be a tensor with at least
+<code>signal_ndim</code> dimensions with optionally arbitrary number of leading batch
+dimensions. If <code>normalized</code> is set to <code>TRUE</code>, this normalizes the result
+by dividing it with \(\sqrt{\prod_{i=1}^K N_i}\) so that the operator is
+unitary, where \(N_i\) is the size of signal dimension \(i\).</p>
+<p>The real-to-complex Fourier transform results follow conjugate symmetry:</p>
+<p>$$
+    X[\omega_1, \dots, \omega_d] = X^*[N_1 - \omega_1, \dots, N_d - \omega_d],
+$$
+where the index arithmetic is computed modulus the size of the corresponding
+dimension, \(\ ^*\) is the conjugate operator, and
+\(d\) = <code>signal_ndim</code>. <code>onesided</code> flag controls whether to avoid
+redundancy in the output results. If set to <code>TRUE</code> (default), the output will
+not be full complex result of shape \((*, 2)\), where \(*\) is the shape
+of <code>input</code>, but instead the last dimension will be halfed as of size
+\(\lfloor \frac{N_d}{2} \rfloor + 1\).</p>
+<p>The inverse of this function is <code><a href='torch_irfft.html'>torch_irfft</a></code>.</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p>For CPU tensors, this method is currently only available with MKL. Use
+<code>torch_backends.mkl.is_available</code> to check if MKL is installed.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>, <span class='fl'>5</span>))
+<span class='fu'>torch_rfft</span>(<span class='kw'>x</span>, <span class='fl'>2</span>)
+<span class='fu'>torch_rfft</span>(<span class='kw'>x</span>, <span class='fl'>2</span>, onesided=<span class='fl'>FALSE</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;  -0.4625  0.0000
+#&gt;  -5.1624  3.2586
+#&gt;  -6.0212  1.9752
+#&gt;  -6.0212 -1.9752
+#&gt;  -5.1624 -3.2586
+#&gt; 
+#&gt; (2,.,.) = 
+#&gt;   1.6002 -2.9581
+#&gt;   2.1200  0.1343
+#&gt;   3.0293 -7.1277
+#&gt;   3.5287  3.5903
+#&gt;  -0.8434 -0.1112
+#&gt; 
+#&gt; (3,.,.) = 
+#&gt;  -3.0107 -2.3239
+#&gt;   0.9071  0.0358
+#&gt;  -0.0607 -0.0974
+#&gt;  -2.2262  2.3303
+#&gt;   1.6252  1.9184
+#&gt; 
+#&gt; (4,.,.) = 
+#&gt;  -3.0107  2.3239
+#&gt;   1.6252 -1.9184
+#&gt;  -2.2262 -2.3303
+#&gt;  -0.0607  0.0974
+#&gt;   0.9071 -0.0358
+#&gt; 
+#&gt; (5,.,.) = 
+#&gt;   1.6002  2.9581
+#&gt;  -0.8434  0.1112
+#&gt;   3.5287 -3.5903
+#&gt;   3.0293  7.1277
+#&gt;   2.1200 -0.1343
+#&gt; [ CPUFloatType{5,5,2} ]</div></pre>
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+    <h1>Roll</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_roll.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Roll</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_roll</span>(<span class='kw'>self</span>, <span class='kw'>shifts</span>, dims = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>())</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>shifts</th>
+      <td><p>(int or tuple of ints) The number of places by which the elements        of the tensor are shifted. If shifts is a tuple, dims must be a tuple of        the same size, and each dimension will be rolled by the corresponding        value</p></td>
+    </tr>
+    <tr>
+      <th>dims</th>
+      <td><p>(int or tuple of ints) Axis along which to roll</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="roll-input-shifts-dims-null-gt-tensor-"><a class="anchor" href="#roll-input-shifts-dims-null-gt-tensor-"></a>roll(input, shifts, dims=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Roll the tensor along the given dimension(s). Elements that are shifted beyond the
+last position are re-introduced at the first position. If a dimension is not
+specified, the tensor will be flattened before rolling and then restored
+to the original shape.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span>, <span class='fl'>6</span>, <span class='fl'>7</span>, <span class='fl'>8</span>))<span class='op'>$</span><span class='fu'>view</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>2</span>))
+<span class='kw'>x</span>
+<span class='fu'>torch_roll</span>(<span class='kw'>x</span>, <span class='fl'>1</span>, <span class='fl'>1</span>)
+<span class='fu'>torch_roll</span>(<span class='kw'>x</span>, <span class='op'>-</span><span class='fl'>1</span>, <span class='fl'>1</span>)
+<span class='fu'>torch_roll</span>(<span class='kw'>x</span>, shifts=<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>), dims=<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  6  5
+#&gt;  8  7
+#&gt;  2  1
+#&gt;  4  3
+#&gt; [ CPUFloatType{4,2} ]</div></pre>
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diff --git a/static/docs/reference/torch_rot90.html b/static/docs/reference/torch_rot90.html
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@@ -0,0 +1,271 @@
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+    <div class="page-header">
+    <h1>Rot90</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_rot90.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Rot90</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_rot90</span>(<span class='kw'>self</span>, k = <span class='fl'>1L</span>, dims = <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>1</span>))</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>k</th>
+      <td><p>(int) number of times to rotate</p></td>
+    </tr>
+    <tr>
+      <th>dims</th>
+      <td><p>(a list or tuple) axis to rotate</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="rot-input-k-dims-gt-tensor-"><a class="anchor" href="#rot-input-k-dims-gt-tensor-"></a>rot90(input, k, dims) -&gt; Tensor </h2>
+
+    
+
+
+<p>Rotate a n-D tensor by 90 degrees in the plane specified by dims axis.
+Rotation direction is from the first towards the second axis if k &gt; 0, and from the second towards the first for k &lt; 0.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0</span>, <span class='fl'>4</span>)<span class='op'>$</span><span class='fu'>view</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>))
+<span class='kw'>x</span>
+<span class='fu'>torch_rot90</span>(<span class='kw'>x</span>, <span class='fl'>1</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>))
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0</span>, <span class='fl'>8</span>)<span class='op'>$</span><span class='fu'>view</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>, <span class='fl'>2</span>))
+<span class='kw'>x</span>
+<span class='fu'>torch_rot90</span>(<span class='kw'>x</span>, <span class='fl'>1</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,.,.) = 
+#&gt;   2  3
+#&gt;   6  7
+#&gt; 
+#&gt; (2,.,.) = 
+#&gt;   0  1
+#&gt;   4  5
+#&gt; [ CPUFloatType{2,2,2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/torch_round.html b/static/docs/reference/torch_round.html
new file mode 100644
index 000000000..24caf7992
--- /dev/null
+++ b/static/docs/reference/torch_round.html
@@ -0,0 +1,257 @@
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+      
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+  </div><!--/.container -->
+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Round</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_round.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Round</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_round</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="round-input-out-null-gt-tensor-"><a class="anchor" href="#round-input-out-null-gt-tensor-"></a>round(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with each of the elements of <code>input</code> rounded
+to the closest integer.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_round</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1
+#&gt;  1
+#&gt;  0
+#&gt;  1
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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+
diff --git a/static/docs/reference/torch_rrelu_.html b/static/docs/reference/torch_rrelu_.html
new file mode 100644
index 000000000..276681d77
--- /dev/null
+++ b/static/docs/reference/torch_rrelu_.html
@@ -0,0 +1,265 @@
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+      <a href="../articles/using-autograd.html">Using autograd</a>
+    </li>
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Rrelu_</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_rrelu_.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Rrelu_</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_rrelu_</span>(
+  <span class='kw'>self</span>,
+  lower = <span class='fl'>0.125</span>,
+  upper = <span class='fl'>0.333333</span>,
+  training = <span class='fl'>FALSE</span>,
+  generator = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>lower</th>
+      <td><p>lower bound of the uniform distribution. Default: 1/8</p></td>
+    </tr>
+    <tr>
+      <th>upper</th>
+      <td><p>upper bound of the uniform distribution. Default: 1/3</p></td>
+    </tr>
+    <tr>
+      <th>training</th>
+      <td><p>bool wether it's a training pass. DEfault: FALSE</p></td>
+    </tr>
+    <tr>
+      <th>generator</th>
+      <td><p>random number generator</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="rrelu-input-lower-upper-training-false-gt-tensor-"><a class="anchor" href="#rrelu-input-lower-upper-training-false-gt-tensor-"></a>rrelu_(input, lower=1./8, upper=1./3, training=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>In-place version of <code>torch_rrelu</code>.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Rsqrt</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_rsqrt.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Rsqrt</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_rsqrt</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="rsqrt-input-out-null-gt-tensor-"><a class="anchor" href="#rsqrt-input-out-null-gt-tensor-"></a>rsqrt(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the reciprocal of the square-root of each of
+the elements of <code>input</code>.</p>
+<p>$$
+    \mbox{out}_{i} = \frac{1}{\sqrt{\mbox{input}_{i}}}
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_rsqrt</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;     nan
+#&gt;  1.0875
+#&gt;  1.7124
+#&gt;  1.1438
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+    <h1>Saves an object to a disk file.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/save.R'><code>R/save.R</code></a></small>
+    <div class="hidden name"><code>torch_save.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>This function is experimental, don't use for long
+term storage.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_save</span>(<span class='kw'>obj</span>, <span class='kw'>path</span>, <span class='kw'>...</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>obj</th>
+      <td><p>the saved object</p></td>
+    </tr>
+    <tr>
+      <th>path</th>
+      <td><p>a connection or the name of the file to save.</p></td>
+    </tr>
+    <tr>
+      <th>...</th>
+      <td><p>not currently used.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
+
+    <div class='dont-index'><p>Other torch_save: 
+<code><a href='torch_load.html'>torch_load</a>()</code></p></div>
+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Selu</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_selu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Selu</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_selu</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="selu-input-gt-tensor-"><a class="anchor" href="#selu-input-gt-tensor-"></a>selu(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>Computes the selu transformation.</p>
+
+  </div>
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+<div class="row">
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+    <div class="page-header">
+    <h1>Selu_</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_selu_.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Selu_</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_selu_</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="selu-input-gt-tensor-"><a class="anchor" href="#selu-input-gt-tensor-"></a>selu_(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>In-place version of <code><a href='torch_selu.html'>torch_selu()</a></code>.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
+  </div>
+</div>
+
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+</div>
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+
+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Sigmoid</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_sigmoid.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Sigmoid</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_sigmoid</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="sigmoid-input-out-null-gt-tensor-"><a class="anchor" href="#sigmoid-input-out-null-gt-tensor-"></a>sigmoid(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the sigmoid of the elements of <code>input</code>.</p>
+<p>$$
+    \mbox{out}_{i} = \frac{1}{1 + e^{-\mbox{input}_{i}}}
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_sigmoid</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.4169
+#&gt;  0.4884
+#&gt;  0.4421
+#&gt;  0.6942
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+    <div class="page-header">
+    <h1>Sign</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_sign.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Sign</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_sign</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="sign-input-out-null-gt-tensor-"><a class="anchor" href="#sign-input-out-null-gt-tensor-"></a>sign(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the signs of the elements of <code>input</code>.</p>
+<p>$$
+    \mbox{out}_{i} = \mbox{sgn}(\mbox{input}_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0.7</span>, <span class='op'>-</span><span class='fl'>1.2</span>, <span class='fl'>0.</span>, <span class='fl'>2.3</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_sign</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1
+#&gt; -1
+#&gt;  0
+#&gt;  1
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/torch_sin.html b/static/docs/reference/torch_sin.html
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@@ -0,0 +1,259 @@
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+    <div class="page-header">
+    <h1>Sin</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_sin.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Sin</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_sin</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="sin-input-out-null-gt-tensor-"><a class="anchor" href="#sin-input-out-null-gt-tensor-"></a>sin(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the sine of the elements of <code>input</code>.</p>
+<p>$$
+    \mbox{out}_{i} = \sin(\mbox{input}_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_sin</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0.2805
+#&gt;  0.5482
+#&gt;  0.8525
+#&gt; -0.6915
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+
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+<div class="row">
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+    <div class="page-header">
+    <h1>Sinh</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_sinh.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Sinh</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_sinh</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="sinh-input-out-null-gt-tensor-"><a class="anchor" href="#sinh-input-out-null-gt-tensor-"></a>sinh(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the hyperbolic sine of the elements of
+<code>input</code>.</p>
+<p>$$
+    \mbox{out}_{i} = \sinh(\mbox{input}_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_sinh</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0.7905
+#&gt;  1.1799
+#&gt;  1.2017
+#&gt; -2.5987
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
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+    </li>
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+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
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+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
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+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
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+      <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-header">Examples</li>
+<li>
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+</div><!--/.navbar -->
+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_slogdet.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Slogdet</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_slogdet</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor of size <code>(*, n, n)</code> where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is zero or more                batch dimensions.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>If `input` has zero determinant, this returns `(0, -inf)`.
+</pre>
+
+<pre>Backward through `slogdet` internally uses SVD results when `input`
+is not invertible. In this case, double backward through `slogdet`
+will be unstable in when `input` doesn't have distinct singular values.
+See `~torch.svd` for details.
+</pre>
+
+    <h2 class="hasAnchor" id="slogdet-input-gt-tensor-tensor-"><a class="anchor" href="#slogdet-input-gt-tensor-tensor-"></a>slogdet(input) -&gt; (Tensor, Tensor) </h2>
+
+    
+
+
+<p>Calculates the sign and log absolute value of the determinant(s) of a square matrix or batches of square matrices.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='kw'>A</span>
+<span class='fu'><a href='torch_det.html'>torch_det</a></span>(<span class='kw'>A</span>)
+<span class='fu'><a href='torch_logdet.html'>torch_logdet</a></span>(<span class='kw'>A</span>)
+<span class='fu'>torch_slogdet</span>(<span class='kw'>A</span>)
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt; 1
+#&gt; [ CPUFloatType{} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt; -0.447307
+#&gt; [ CPUFloatType{} ]
+#&gt; </div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <li class="dropdown-header">Torch Mechanics</li>
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+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
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+
+      
+
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+
+<div class="row">
+  <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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_solve.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Solve</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_solve</span>(<span class='kw'>self</span>, <span class='kw'>A</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) input matrix \(B\) of size \((*, m, k)\) , where \(*\)                is zero or more batch dimensions.</p></td>
+    </tr>
+    <tr>
+      <th>A</th>
+      <td><p>(Tensor) input square matrix of size \((*, m, m)\), where                \(*\) is zero or more batch dimensions.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>Irrespective of the original strides, the returned matrices
+`solution` and `LU` will be transposed, i.e. with strides like
+`B$contiguous()$transpose(-1, -2)$stride()` and
+`A$contiguous()$transpose(-1, -2)$stride()` respectively.
+</pre>
+
+    <h2 class="hasAnchor" id="solve-input-a-gt-tensor-tensor-"><a class="anchor" href="#solve-input-a-gt-tensor-tensor-"></a>solve(input, A) -&gt; (Tensor, Tensor) </h2>
+
+    
+
+
+<p>This function returns the solution to the system of linear
+equations represented by \(AX = B\) and the LU factorization of
+A, in order as a namedtuple <code>solution, LU</code>.</p>
+<p><code>LU</code> contains <code>L</code> and <code>U</code> factors for LU factorization of <code>A</code>.</p>
+<p><code>torch_solve(B, A)</code> can take in 2D inputs <code>B, A</code> or inputs that are
+batches of 2D matrices. If the inputs are batches, then returns
+batched outputs <code>solution, LU</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>6.80</span>, <span class='op'>-</span><span class='fl'>2.11</span>,  <span class='fl'>5.66</span>,  <span class='fl'>5.97</span>,  <span class='fl'>8.23</span>),
+                      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>6.05</span>, <span class='op'>-</span><span class='fl'>3.30</span>,  <span class='fl'>5.36</span>, <span class='op'>-</span><span class='fl'>4.44</span>,  <span class='fl'>1.08</span>),
+                      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>0.45</span>,  <span class='fl'>2.58</span>, <span class='op'>-</span><span class='fl'>2.70</span>,  <span class='fl'>0.27</span>,  <span class='fl'>9.04</span>),
+                      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>8.32</span>,  <span class='fl'>2.71</span>,  <span class='fl'>4.35</span>,  <span class='op'>-</span><span class='fl'>7.17</span>,  <span class='fl'>2.14</span>),
+                      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>9.67</span>, <span class='op'>-</span><span class='fl'>5.14</span>, <span class='op'>-</span><span class='fl'>7.26</span>,  <span class='fl'>6.08</span>, <span class='op'>-</span><span class='fl'>6.87</span>)))<span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>()
+<span class='kw'>B</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/cbind.html'>rbind</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4.02</span>,  <span class='fl'>6.19</span>, <span class='op'>-</span><span class='fl'>8.22</span>, <span class='op'>-</span><span class='fl'>7.57</span>, <span class='op'>-</span><span class='fl'>3.03</span>),
+                      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='op'>-</span><span class='fl'>1.56</span>,  <span class='fl'>4.00</span>, <span class='op'>-</span><span class='fl'>8.67</span>,  <span class='fl'>1.75</span>,  <span class='fl'>2.86</span>),
+                      <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>9.81</span>, <span class='op'>-</span><span class='fl'>4.09</span>, <span class='op'>-</span><span class='fl'>4.57</span>, <span class='op'>-</span><span class='fl'>8.61</span>,  <span class='fl'>8.99</span>)))<span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>()
+<span class='kw'>out</span> <span class='op'>=</span> <span class='fu'>torch_solve</span>(<span class='kw'>B</span>, <span class='kw'>A</span>)
+<span class='kw'>X</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>1</span>]]
+<span class='kw'>LU</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>2</span>]]
+<span class='fu'><a href='torch_dist.html'>torch_dist</a></span>(<span class='kw'>B</span>, <span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='kw'>A</span>, <span class='kw'>X</span>))
+<span class='co'># Batched solver example</span>
+<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>1</span>, <span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>B</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>1</span>, <span class='fl'>4</span>, <span class='fl'>6</span>))
+<span class='kw'>out</span> <span class='op'>=</span> <span class='fu'>torch_solve</span>(<span class='kw'>B</span>, <span class='kw'>A</span>)
+<span class='kw'>X</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>1</span>]]
+<span class='kw'>LU</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>2</span>]]
+<span class='fu'><a href='torch_dist.html'>torch_dist</a></span>(<span class='kw'>B</span>, <span class='kw'>A</span><span class='op'>$</span><span class='fu'>matmul</span>(<span class='kw'>X</span>))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; 1.29687e-05
+#&gt; [ CPUFloatType{} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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+</div>
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+</div>
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diff --git a/static/docs/reference/torch_sort.html b/static/docs/reference/torch_sort.html
new file mode 100644
index 000000000..bb1dd9dcc
--- /dev/null
+++ b/static/docs/reference/torch_sort.html
@@ -0,0 +1,281 @@
+<!-- Generated by pkgdown: do not edit by hand -->
+<!DOCTYPE html>
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+    <li class="dropdown-header">Beginers guide</li>
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+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
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+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
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+      <a href="../articles/getting-started/control-flow-and-weight-sharing.html">Control flow &amp; Weight sharing</a>
+    </li>
+    <li class="dropdown-header">Torch Mechanics</li>
+    <li>
+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
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+      <a href="../articles/tensor-creation.html">Creating tensors</a>
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+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Sort</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_sort.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Sort</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_sort</span>(<span class='kw'>self</span>, dim = <span class='op'>-</span><span class='fl'>1L</span>, descending = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int, optional) the dimension to sort along</p></td>
+    </tr>
+    <tr>
+      <th>descending</th>
+      <td><p>(bool, optional) controls the sorting order (ascending or descending)</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="sort-input-dim-descending-false-gt-tensor-longtensor-"><a class="anchor" href="#sort-input-dim-descending-false-gt-tensor-longtensor-"></a>sort(input, dim=-1, descending=FALSE) -&gt; (Tensor, LongTensor) </h2>
+
+    
+
+
+<p>Sorts the elements of the <code>input</code> tensor along a given dimension
+in ascending order by value.</p>
+<p>If <code>dim</code> is not given, the last dimension of the <code>input</code> is chosen.</p>
+<p>If <code>descending</code> is <code>TRUE</code> then the elements are sorted in descending
+order by value.</p>
+<p>A namedtuple of (values, indices) is returned, where the <code>values</code> are the
+sorted values and <code>indices</code> are the indices of the elements in the original
+<code>input</code> tensor.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>4</span>))
+<span class='kw'>out</span> <span class='op'>=</span> <span class='fu'>torch_sort</span>(<span class='kw'>x</span>)
+<span class='kw'>out</span>
+<span class='kw'>out</span> <span class='op'>=</span> <span class='fu'>torch_sort</span>(<span class='kw'>x</span>, <span class='fl'>1</span>)
+<span class='kw'>out</span>
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt; -0.7961  0.1753 -0.2432 -1.1334
+#&gt; -0.2084  0.5509  0.3876 -0.9865
+#&gt;  0.1948  0.9346  0.5226 -0.1232
+#&gt; [ CPUFloatType{3,4} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt;  0  1  0  2
+#&gt;  2  2  1  0
+#&gt;  1  0  2  1
+#&gt; [ CPULongType{3,4} ]
+#&gt; </div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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diff --git a/static/docs/reference/torch_sparse_coo_tensor.html b/static/docs/reference/torch_sparse_coo_tensor.html
new file mode 100644
index 000000000..99aa3f3df
--- /dev/null
+++ b/static/docs/reference/torch_sparse_coo_tensor.html
@@ -0,0 +1,294 @@
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+
+      
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Sparse_coo_tensor</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
+    <div class="hidden name"><code>torch_sparse_coo_tensor.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Sparse_coo_tensor</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_sparse_coo_tensor</span>(
+  <span class='kw'>indices</span>,
+  <span class='kw'>values</span>,
+  size = <span class='kw'>NULL</span>,
+  dtype = <span class='kw'>NULL</span>,
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>indices</th>
+      <td><p>(array_like) Initial data for the tensor. Can be a list, tuple,        NumPy <code>ndarray</code>, scalar, and other types. Will be cast to a <code>torch_LongTensor</code>        internally. The indices are the coordinates of the non-zero values in the matrix, and thus        should be two-dimensional where the first dimension is the number of tensor dimensions and        the second dimension is the number of non-zero values.</p></td>
+    </tr>
+    <tr>
+      <th>values</th>
+      <td><p>(array_like) Initial values for the tensor. Can be a list, tuple,        NumPy <code>ndarray</code>, scalar, and other types.</p></td>
+    </tr>
+    <tr>
+      <th>size</th>
+      <td><p>(list, tuple, or <code>torch.Size</code>, optional) Size of the sparse tensor. If not        provided the size will be inferred as the minimum size big enough to hold all non-zero        elements.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if NULL, infers data type from <code>values</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if NULL, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="sparse-coo-tensor-indices-values-size-null-dtype-null-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#sparse-coo-tensor-indices-values-size-null-dtype-null-device-null-requires-grad-false-gt-tensor-"></a>sparse_coo_tensor(indices, values, size=NULL, dtype=NULL, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Constructs a sparse tensors in COO(rdinate) format with non-zero elements at the given <code>indices</code>
+with the given <code>values</code>. A sparse tensor can be <code>uncoalesced</code>, in that case, there are duplicate
+coordinates in the indices, and the value at that index is the sum of all duplicate value entries:
+<code>torch_sparse</code>_.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>i</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>1</span>, <span class='fl'>3</span>), ncol = <span class='fl'>3</span>, byrow = <span class='fl'>TRUE</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>())
+<span class='kw'>v</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_float32</a></span>())
+<span class='fu'>torch_sparse_coo_tensor</span>(<span class='kw'>i</span>, <span class='kw'>v</span>)
+<span class='fu'>torch_sparse_coo_tensor</span>(<span class='kw'>i</span>, <span class='kw'>v</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>4</span>))
+
+<span class='co'># create empty sparse tensors</span>
+<span class='kw'>S</span> <span class='op'>=</span> <span class='fu'>torch_sparse_coo_tensor</span>(
+  <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>0</span>), dtype = <span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>()), 
+  <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/numeric.html'>numeric</a></span>(), dtype = <span class='fu'><a href='torch_dtype.html'>torch_float32</a></span>()), 
+  <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>)
+)
+<span class='kw'>S</span> <span class='op'>=</span> <span class='fu'>torch_sparse_coo_tensor</span>(
+  <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>0</span>), dtype = <span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>()), 
+  <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>0</span>, <span class='fl'>2</span>)), 
+  <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>)
+)
+}
+</div></pre>
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diff --git a/static/docs/reference/torch_split.html b/static/docs/reference/torch_split.html
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+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
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+    </li>
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Split</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_split.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Split</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_split</span>(<span class='kw'>self</span>, <span class='kw'>split_size</span>, dim = <span class='fl'>1L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) tensor to split.</p></td>
+    </tr>
+    <tr>
+      <th>split_size</th>
+      <td><p>(int) size of a single chunk or        list of sizes for each chunk</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) dimension along which to split the tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
+
+    
+
+
+<p>Splits the tensor into chunks. Each chunk is a view of the original tensor.</p><pre>If `split_size_or_sections` is an integer type, then `tensor` will
+be split into equally sized chunks (if possible). Last chunk will be smaller if
+the tensor size along the given dimension `dim` is not divisible by
+`split_size`.
+
+If `split_size_or_sections` is a list, then `tensor` will be split
+into `len(split_size_or_sections)` chunks with sizes in `dim` according
+to `split_size_or_sections`.
+</pre>
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+    <div class="page-header">
+    <h1>Sqrt</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_sqrt.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Sqrt</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_sqrt</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="sqrt-input-out-null-gt-tensor-"><a class="anchor" href="#sqrt-input-out-null-gt-tensor-"></a>sqrt(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the square-root of the elements of <code>input</code>.</p>
+<p>$$
+    \mbox{out}_{i} = \sqrt{\mbox{input}_{i}}
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_sqrt</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;     nan
+#&gt;  0.2255
+#&gt;  0.5333
+#&gt;  0.3449
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
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+
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+    <div class="page-header">
+    <h1>Square</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_square.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Square</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_square</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="square-input-out-null-gt-tensor-"><a class="anchor" href="#square-input-out-null-gt-tensor-"></a>square(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the square of the elements of <code>input</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_square</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1.5194
+#&gt;  0.2682
+#&gt;  1.6344
+#&gt;  0.8489
+#&gt; [ CPUFloatType{4} ]</div></pre>
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+    <div class="page-header">
+    <h1>Squeeze</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_squeeze.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Squeeze</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_squeeze</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int, optional) if given, the input will be squeezed only in           this dimension</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>The returned tensor shares the storage with the input tensor,
+so changing the contents of one will change the contents of the other.</p>
+    <h2 class="hasAnchor" id="squeeze-input-dim-null-out-null-gt-tensor-"><a class="anchor" href="#squeeze-input-dim-null-out-null-gt-tensor-"></a>squeeze(input, dim=NULL, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a tensor with all the dimensions of <code>input</code> of size <code>1</code> removed.</p>
+<p>For example, if <code>input</code> is of shape:
+\((A \times 1 \times B \times C \times 1 \times D)\) then the <code>out</code> tensor
+will be of shape: \((A \times B \times C \times D)\).</p>
+<p>When <code>dim</code> is given, a squeeze operation is done only in the given
+dimension. If <code>input</code> is of shape: \((A \times 1 \times B)\),
+<code>squeeze(input, 0)</code> leaves the tensor unchanged, but <code>squeeze(input, 1)</code>
+will squeeze the tensor to the shape \((A \times B)\).</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_zeros.html'>torch_zeros</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>1</span>, <span class='fl'>2</span>))
+<span class='kw'>x</span>
+<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'>torch_squeeze</span>(<span class='kw'>x</span>)
+<span class='kw'>y</span>
+<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'>torch_squeeze</span>(<span class='kw'>x</span>, <span class='fl'>1</span>)
+<span class='kw'>y</span>
+<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'>torch_squeeze</span>(<span class='kw'>x</span>, <span class='fl'>2</span>)
+<span class='kw'>y</span>
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; (1,1,.,.) = 
+#&gt;   0  0
+#&gt; 
+#&gt; (2,1,.,.) = 
+#&gt;   0  0
+#&gt; 
+#&gt; (1,2,.,.) = 
+#&gt;   0  0
+#&gt; 
+#&gt; (2,2,.,.) = 
+#&gt;   0  0
+#&gt; [ CPUFloatType{2,2,1,2} ]</div></pre>
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+    </div>
+
+    <pre class="usage"><span class='fu'>torch_stack</span>(<span class='kw'>tensors</span>, dim = <span class='fl'>1L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>tensors</th>
+      <td><p>(sequence of Tensors) sequence of tensors to concatenate</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) dimension to insert. Has to be between 0 and the number        of dimensions of concatenated tensors (inclusive)</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="stack-tensors-dim-out-null-gt-tensor-"><a class="anchor" href="#stack-tensors-dim-out-null-gt-tensor-"></a>stack(tensors, dim=0, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Concatenates sequence of tensors along a new dimension.</p>
+<p>All tensors need to be of the same size.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Std</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_std.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Std</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_std</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, unbiased = <span class='fl'>TRUE</span>, keepdim = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
+    </tr>
+    <tr>
+      <th>unbiased</th>
+      <td><p>(bool) whether to use the unbiased estimation or not</p></td>
+    </tr>
+    <tr>
+      <th>keepdim</th>
+      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="std-input-unbiased-true-gt-tensor-"><a class="anchor" href="#std-input-unbiased-true-gt-tensor-"></a>std(input, unbiased=TRUE) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the standard-deviation of all elements in the <code>input</code> tensor.</p>
+<p>If <code>unbiased</code> is <code>FALSE</code>, then the standard-deviation will be calculated
+via the biased estimator. Otherwise, Bessel's correction will be used.</p>
+    <h2 class="hasAnchor" id="std-input-dim-unbiased-true-keepdim-false-out-null-gt-tensor-"><a class="anchor" href="#std-input-dim-unbiased-true-keepdim-false-out-null-gt-tensor-"></a>std(input, dim, unbiased=TRUE, keepdim=False, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the standard-deviation of each row of the <code>input</code> tensor in the
+dimension <code>dim</code>. If <code>dim</code> is a list of dimensions,
+reduce over all of them.</p>
+<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size
+as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
+Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
+output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
+<p>If <code>unbiased</code> is <code>FALSE</code>, then the standard-deviation will be calculated
+via the biased estimator. Otherwise, Bessel's correction will be used.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_std</span>(<span class='kw'>a</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_std</span>(<span class='kw'>a</span>, dim=<span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.5958
+#&gt;  0.5692
+#&gt;  1.5368
+#&gt;  0.7848
+#&gt; [ CPUFloatType{4} ]</div></pre>
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+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Std_mean</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_std_mean.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Std_mean</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_std_mean</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, unbiased = <span class='fl'>TRUE</span>, keepdim = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
+    </tr>
+    <tr>
+      <th>unbiased</th>
+      <td><p>(bool) whether to use the unbiased estimation or not</p></td>
+    </tr>
+    <tr>
+      <th>keepdim</th>
+      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="std-mean-input-unbiased-true-gt-tensor-tensor-"><a class="anchor" href="#std-mean-input-unbiased-true-gt-tensor-tensor-"></a>std_mean(input, unbiased=TRUE) -&gt; (Tensor, Tensor) </h2>
+
+    
+
+
+<p>Returns the standard-deviation and mean of all elements in the <code>input</code> tensor.</p>
+<p>If <code>unbiased</code> is <code>FALSE</code>, then the standard-deviation will be calculated
+via the biased estimator. Otherwise, Bessel's correction will be used.</p>
+    <h2 class="hasAnchor" id="std-mean-input-dim-unbiased-true-keepdim-false-gt-tensor-tensor-"><a class="anchor" href="#std-mean-input-dim-unbiased-true-keepdim-false-gt-tensor-tensor-"></a>std_mean(input, dim, unbiased=TRUE, keepdim=False) -&gt; (Tensor, Tensor) </h2>
+
+    
+
+
+<p>Returns the standard-deviation and mean of each row of the <code>input</code> tensor in the
+dimension <code>dim</code>. If <code>dim</code> is a list of dimensions,
+reduce over all of them.</p>
+<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size
+as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
+Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
+output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
+<p>If <code>unbiased</code> is <code>FALSE</code>, then the standard-deviation will be calculated
+via the biased estimator. Otherwise, Bessel's correction will be used.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_std_mean</span>(<span class='kw'>a</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_std_mean</span>(<span class='kw'>a</span>, <span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt;  1.4613
+#&gt;  0.7194
+#&gt;  0.5630
+#&gt;  1.2705
+#&gt; [ CPUFloatType{4} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt;  0.2608
+#&gt; -0.1669
+#&gt; -0.7519
+#&gt; -1.0647
+#&gt; [ CPUFloatType{4} ]
+#&gt; </div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
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+    <div class="page-header">
+    <h1>Stft</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
+    <div class="hidden name"><code>torch_stft.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Stft</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_stft</span>(
+  <span class='kw'>input</span>,
+  <span class='kw'>n_fft</span>,
+  hop_length = <span class='kw'>NULL</span>,
+  win_length = <span class='kw'>NULL</span>,
+  window = <span class='kw'>NULL</span>,
+  center = <span class='fl'>TRUE</span>,
+  pad_mode = <span class='st'>"reflect"</span>,
+  normalized = <span class='fl'>FALSE</span>,
+  onesided = <span class='fl'>TRUE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(Tensor) the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>n_fft</th>
+      <td><p>(int) size of Fourier transform</p></td>
+    </tr>
+    <tr>
+      <th>hop_length</th>
+      <td><p>(int, optional) the distance between neighboring sliding window        frames. Default: <code>NULL</code> (treated as equal to <code><a href='https://rdrr.io/r/base/Round.html'>floor(n_fft / 4)</a></code>)</p></td>
+    </tr>
+    <tr>
+      <th>win_length</th>
+      <td><p>(int, optional) the size of window frame and STFT filter.        Default: <code>NULL</code>  (treated as equal to <code>n_fft</code>)</p></td>
+    </tr>
+    <tr>
+      <th>window</th>
+      <td><p>(Tensor, optional) the optional window function.        Default: <code>NULL</code> (treated as window of all \(1\) s)</p></td>
+    </tr>
+    <tr>
+      <th>center</th>
+      <td><p>(bool, optional) whether to pad <code>input</code> on both sides so        that the \(t\)-th frame is centered at time \(t \times \mbox{hop\_length}\).        Default: <code>TRUE</code></p></td>
+    </tr>
+    <tr>
+      <th>pad_mode</th>
+      <td><p>(string, optional) controls the padding method used when        <code>center</code> is <code>TRUE</code>. Default: <code>"reflect"</code></p></td>
+    </tr>
+    <tr>
+      <th>normalized</th>
+      <td><p>(bool, optional) controls whether to return the normalized STFT results         Default: <code>FALSE</code></p></td>
+    </tr>
+    <tr>
+      <th>onesided</th>
+      <td><p>(bool, optional) controls whether to return half of results to        avoid redundancy Default: <code>TRUE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="short-time-fourier-transform-stft-"><a class="anchor" href="#short-time-fourier-transform-stft-"></a>Short-time Fourier transform (STFT). </h2>
+
+    
+
+
+<p>Short-time Fourier transform (STFT).</p><pre>Ignoring the optional batch dimension, this method computes the following
+expression:
+</pre>
+
+<p>$$
+        X[m, \omega] = \sum_{k = 0}^{\mbox{win\_length-1}}%
+                            \mbox{window}[k]\ \mbox{input}[m \times \mbox{hop\_length} + k]\ %
+                            \exp\left(- j \frac{2 \pi \cdot \omega k}{\mbox{win\_length}}\right),
+$$
+where \(m\) is the index of the sliding window, and \(\omega\) is
+the frequency that \(0 \leq \omega &lt; \mbox{n\_fft}\). When
+<code>onesided</code> is the default value <code>TRUE</code>,</p><pre>* `input` must be either a 1-D time sequence or a 2-D batch of time
+  sequences.
+
+* If `hop_length` is `NULL` (default), it is treated as equal to
+  `floor(n_fft / 4)`.
+
+* If `win_length` is `NULL` (default), it is treated as equal to
+  `n_fft`.
+
+* `window` can be a 1-D tensor of size `win_length`, e.g., from
+  `torch_hann_window`. If `window` is `NULL` (default), it is
+  treated as if having \eqn{1} everywhere in the window. If
+  \eqn{\mbox{win\_length} &lt; \mbox{n\_fft}}, `window` will be padded on
+  both sides to length `n_fft` before being applied.
+
+* If `center` is `TRUE` (default), `input` will be padded on
+  both sides so that the \eqn{t}-th frame is centered at time
+  \eqn{t \times \mbox{hop\_length}}. Otherwise, the \eqn{t}-th frame
+  begins at time  \eqn{t \times \mbox{hop\_length}}.
+
+* `pad_mode` determines the padding method used on `input` when
+  `center` is `TRUE`. See `torch_nn.functional.pad` for
+  all available options. Default is `"reflect"`.
+
+* If `onesided` is `TRUE` (default), only values for \eqn{\omega}
+  in \eqn{\left[0, 1, 2, \dots, \left\lfloor \frac{\mbox{n\_fft}}{2} \right\rfloor + 1\right]}
+  are returned because the real-to-complex Fourier transform satisfies the
+  conjugate symmetry, i.e., \eqn{X[m, \omega] = X[m, \mbox{n\_fft} - \omega]^*}.
+
+* If `normalized` is `TRUE` (default is `FALSE`), the function
+  returns the normalized STFT results, i.e., multiplied by \eqn{(\mbox{frame\_length})^{-0.5}}.
+
+Returns the real and the imaginary parts together as one tensor of size
+\eqn{(* \times N \times T \times 2)}, where \eqn{*} is the optional
+batch size of `input`, \eqn{N} is the number of frequencies where
+STFT is applied, \eqn{T} is the total number of frames used, and each pair
+in the last dimension represents a complex number as the real part and the
+imaginary part.
+</pre>
+
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p>This function changed signature at version 0.4.1. Calling with the
+previous signature may cause error or return incorrect result.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_sum.html b/static/docs/reference/torch_sum.html
new file mode 100644
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+    <div class="page-header">
+    <h1>Sum</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_sum.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Sum</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_sum</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, keepdim = <span class='fl'>FALSE</span>, dtype = <span class='kw'>NULL</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
+    </tr>
+    <tr>
+      <th>keepdim</th>
+      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        If specified, the input tensor is casted to <code>dtype</code> before the operation        is performed. This is useful for preventing data type overflows. Default: NULL.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="sum-input-dtype-null-gt-tensor-"><a class="anchor" href="#sum-input-dtype-null-gt-tensor-"></a>sum(input, dtype=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the sum of all elements in the <code>input</code> tensor.</p>
+    <h2 class="hasAnchor" id="sum-input-dim-keepdim-false-dtype-null-gt-tensor-"><a class="anchor" href="#sum-input-dim-keepdim-false-dtype-null-gt-tensor-"></a>sum(input, dim, keepdim=False, dtype=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the sum of each row of the <code>input</code> tensor in the given
+dimension <code>dim</code>. If <code>dim</code> is a list of dimensions,
+reduce over all of them.</p>
+<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size
+as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
+Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
+output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_sum</span>(<span class='kw'>a</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_sum</span>(<span class='kw'>a</span>, <span class='fl'>1</span>)
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>0</span>, <span class='fl'>4</span> <span class='op'>*</span> <span class='fl'>5</span> <span class='op'>*</span> <span class='fl'>6</span>)<span class='op'>$</span><span class='fu'>view</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>, <span class='fl'>6</span>))
+<span class='fu'>torch_sum</span>(<span class='kw'>b</span>, <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;   435
+#&gt;  1335
+#&gt;  2235
+#&gt;  3135
+#&gt; [ CPUFloatType{4} ]</div></pre>
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diff --git a/static/docs/reference/torch_svd.html b/static/docs/reference/torch_svd.html
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Svd</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_svd.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Svd</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_svd</span>(<span class='kw'>self</span>, some = <span class='fl'>TRUE</span>, compute_uv = <span class='fl'>TRUE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor of size \((*, m, n)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is zero or more                    batch dimensions consisting of \(m \times n\) matrices.</p></td>
+    </tr>
+    <tr>
+      <th>some</th>
+      <td><p>(bool, optional) controls the shape of returned <code>U</code> and <code>V</code></p></td>
+    </tr>
+    <tr>
+      <th>compute_uv</th>
+      <td><p>(bool, optional) option whether to compute <code>U</code> and <code>V</code> or not</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>The singular values are returned in descending order. If <code>input</code> is a batch of matrices,
+then the singular values of each matrix in the batch is returned in descending order.</p>
+<p>The implementation of SVD on CPU uses the LAPACK routine <code>?gesdd</code> (a divide-and-conquer
+algorithm) instead of <code>?gesvd</code> for speed. Analogously, the SVD on GPU uses the MAGMA routine
+<code>gesdd</code> as well.</p>
+<p>Irrespective of the original strides, the returned matrix <code>U</code>
+will be transposed, i.e. with strides <code>U.contiguous().transpose(-2, -1).stride()</code></p>
+<p>Extra care needs to be taken when backward through <code>U</code> and <code>V</code>
+outputs. Such operation is really only stable when <code>input</code> is
+full rank with all distinct singular values. Otherwise, <code>NaN</code> can
+appear as the gradients are not properly defined. Also, notice that
+double backward will usually do an additional backward through <code>U</code> and
+<code>V</code> even if the original backward is only on <code>S</code>.</p>
+<p>When <code>some</code> = <code>FALSE</code>, the gradients on <code>U[..., :, min(m, n):]</code>
+and <code>V[..., :, min(m, n):]</code> will be ignored in backward as those vectors
+can be arbitrary bases of the subspaces.</p>
+<p>When <code>compute_uv</code> = <code>FALSE</code>, backward cannot be performed since <code>U</code> and <code>V</code>
+from the forward pass is required for the backward operation.</p>
+    <h2 class="hasAnchor" id="svd-input-some-true-compute-uv-true-gt-tensor-tensor-tensor-"><a class="anchor" href="#svd-input-some-true-compute-uv-true-gt-tensor-tensor-tensor-"></a>svd(input, some=TRUE, compute_uv=TRUE) -&gt; (Tensor, Tensor, Tensor) </h2>
+
+    
+
+
+<p>This function returns a namedtuple <code>(U, S, V)</code> which is the singular value
+decomposition of a input real matrix or batches of real matrices <code>input</code> such that
+\(input = U \times diag(S) \times V^T\).</p>
+<p>If <code>some</code> is <code>TRUE</code> (default), the method returns the reduced singular value decomposition
+i.e., if the last two dimensions of <code>input</code> are <code>m</code> and <code>n</code>, then the returned
+<code>U</code> and <code>V</code> matrices will contain only \(min(n, m)\) orthonormal columns.</p>
+<p>If <code>compute_uv</code> is <code>FALSE</code>, the returned <code>U</code> and <code>V</code> matrices will be zero matrices
+of shape \((m \times m)\) and \((n \times n)\) respectively. <code>some</code> will be ignored here.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span>
+<span class='kw'>out</span> <span class='op'>=</span> <span class='fu'>torch_svd</span>(<span class='kw'>a</span>)
+<span class='kw'>u</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>1</span>]]
+<span class='kw'>s</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>2</span>]]
+<span class='kw'>v</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>3</span>]]
+<span class='fu'><a href='torch_dist.html'>torch_dist</a></span>(<span class='kw'>a</span>, <span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='fu'><a href='torch_mm.html'>torch_mm</a></span>(<span class='kw'>u</span>, <span class='fu'><a href='torch_diag.html'>torch_diag</a></span>(<span class='kw'>s</span>)), <span class='kw'>v</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>()))
+<span class='kw'>a_big</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>7</span>, <span class='fl'>5</span>, <span class='fl'>3</span>))
+<span class='kw'>out</span> <span class='op'>=</span> <span class='fu'>torch_svd</span>(<span class='kw'>a_big</span>)
+<span class='kw'>u</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>1</span>]]
+<span class='kw'>s</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>2</span>]]
+<span class='kw'>v</span> <span class='op'>=</span> <span class='kw'>out</span>[[<span class='fl'>3</span>]]
+<span class='fu'><a href='torch_dist.html'>torch_dist</a></span>(<span class='kw'>a_big</span>, <span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span>(<span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span>(<span class='kw'>u</span>, <span class='fu'><a href='torch_diag_embed.html'>torch_diag_embed</a></span>(<span class='kw'>s</span>)), <span class='kw'>v</span><span class='op'>$</span><span class='fu'>transpose</span>(<span class='op'>-</span><span class='fl'>2</span>, <span class='op'>-</span><span class='fl'>1</span>)))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; 2.94448e-06
+#&gt; [ CPUFloatType{} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Symeig</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_symeig.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Symeig</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_symeig</span>(<span class='kw'>self</span>, eigenvectors = <span class='fl'>FALSE</span>, upper = <span class='fl'>TRUE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor of size \((*, n, n)\) where <code><a href='https://rdrr.io/r/base/Arithmetic.html'>*</a></code> is zero or more                    batch dimensions consisting of symmetric matrices.</p></td>
+    </tr>
+    <tr>
+      <th>eigenvectors</th>
+      <td><p>(boolean, optional) controls whether eigenvectors have to be computed</p></td>
+    </tr>
+    <tr>
+      <th>upper</th>
+      <td><p>(boolean, optional) controls whether to consider upper-triangular or lower-triangular region</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    <p>The eigenvalues are returned in ascending order. If <code>input</code> is a batch of matrices,
+then the eigenvalues of each matrix in the batch is returned in ascending order.</p>
+<p>Irrespective of the original strides, the returned matrix <code>V</code> will
+be transposed, i.e. with strides <code>V.contiguous().transpose(-1, -2).stride()</code>.</p>
+<p>Extra care needs to be taken when backward through outputs. Such
+operation is really only stable when all eigenvalues are distinct.
+Otherwise, <code>NaN</code> can appear as the gradients are not properly defined.</p>
+    <h2 class="hasAnchor" id="symeig-input-eigenvectors-false-upper-true-gt-tensor-tensor-"><a class="anchor" href="#symeig-input-eigenvectors-false-upper-true-gt-tensor-tensor-"></a>symeig(input, eigenvectors=False, upper=TRUE) -&gt; (Tensor, Tensor) </h2>
+
+    
+
+
+<p>This function returns eigenvalues and eigenvectors
+of a real symmetric matrix <code>input</code> or a batch of real symmetric matrices,
+represented by a namedtuple (eigenvalues, eigenvectors).</p>
+<p>This function calculates all eigenvalues (and vectors) of <code>input</code>
+such that \(\mbox{input} = V \mbox{diag}(e) V^T\).</p>
+<p>The boolean argument <code>eigenvectors</code> defines computation of
+both eigenvectors and eigenvalues or eigenvalues only.</p>
+<p>If it is <code>FALSE</code>, only eigenvalues are computed. If it is <code>TRUE</code>,
+both eigenvalues and eigenvectors are computed.</p>
+<p>Since the input matrix <code>input</code> is supposed to be symmetric,
+only the upper triangular portion is used by default.</p>
+<p>If <code>upper</code> is <code>FALSE</code>, then lower triangular portion is used.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>, <span class='fl'>5</span>))
+<span class='kw'>a</span> <span class='op'>=</span> <span class='kw'>a</span> <span class='op'>+</span> <span class='kw'>a</span><span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/base/t.html'>t</a></span>()  <span class='co'># To make a symmetric</span>
+<span class='kw'>a</span>
+<span class='kw'>o</span> <span class='op'>=</span> <span class='fu'>torch_symeig</span>(<span class='kw'>a</span>, eigenvectors=<span class='fl'>TRUE</span>)
+<span class='kw'>e</span> <span class='op'>=</span> <span class='kw'>o</span>[[<span class='fl'>1</span>]]
+<span class='kw'>v</span> <span class='op'>=</span> <span class='kw'>o</span>[[<span class='fl'>2</span>]]
+<span class='kw'>e</span>
+<span class='kw'>v</span>
+<span class='kw'>a_big</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>, <span class='fl'>2</span>, <span class='fl'>2</span>))
+<span class='kw'>a_big</span> <span class='op'>=</span> <span class='kw'>a_big</span> <span class='op'>+</span> <span class='kw'>a_big</span><span class='op'>$</span><span class='fu'>transpose</span>(<span class='op'>-</span><span class='fl'>2</span>, <span class='op'>-</span><span class='fl'>1</span>)  <span class='co'># To make a_big symmetric</span>
+<span class='kw'>o</span> <span class='op'>=</span> <span class='kw'>a_big</span><span class='op'>$</span><span class='fu'>symeig</span>(eigenvectors=<span class='fl'>TRUE</span>)
+<span class='kw'>e</span> <span class='op'>=</span> <span class='kw'>o</span>[[<span class='fl'>1</span>]]
+<span class='kw'>v</span> <span class='op'>=</span> <span class='kw'>o</span>[[<span class='fl'>2</span>]]
+<span class='fu'><a href='torch_allclose.html'>torch_allclose</a></span>(<span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span>(<span class='kw'>v</span>, <span class='fu'><a href='torch_matmul.html'>torch_matmul</a></span>(<span class='kw'>e</span><span class='op'>$</span><span class='fu'>diag_embed</span>(), <span class='kw'>v</span><span class='op'>$</span><span class='fu'>transpose</span>(<span class='op'>-</span><span class='fl'>2</span>, <span class='op'>-</span><span class='fl'>1</span>))), <span class='kw'>a_big</span>)
+}
+</div><div class='output co'>#&gt; [1] TRUE</div></pre>
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+    </li>
+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
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+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>T</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_t.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>T</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_t</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="t-input-gt-tensor-"><a class="anchor" href="#t-input-gt-tensor-"></a>t(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>Expects <code>input</code> to be &lt;= 2-D tensor and transposes dimensions 0
+and 1.</p>
+<p>0-D and 1-D tensors are returned as is. When input is a 2-D tensor this
+is equivalent to <code>transpose(input, 0, 1)</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>,<span class='fl'>3</span>))
+<span class='kw'>x</span>
+<span class='fu'>torch_t</span>(<span class='kw'>x</span>)
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>))
+<span class='kw'>x</span>
+<span class='fu'>torch_t</span>(<span class='kw'>x</span>)
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+<span class='kw'>x</span>
+<span class='fu'>torch_t</span>(<span class='kw'>x</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.9294 -0.8336
+#&gt;  1.1379  0.4580
+#&gt; -2.2674  0.2512
+#&gt; [ CPUFloatType{3,2} ]</div></pre>
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+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_take.html b/static/docs/reference/torch_take.html
new file mode 100644
index 000000000..fc13a3a8e
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@@ -0,0 +1,260 @@
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+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/optim.html">optim: optimizers in torch</a>
+    </li>
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+      <a href="../articles/getting-started/custom-nn.html">Custom nn modules</a>
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+    <li>
+      <a href="../articles/getting-started/control-flow-and-weight-sharing.html">Control flow &amp; Weight sharing</a>
+    </li>
+    <li class="dropdown-header">Torch Mechanics</li>
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+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
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+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Take</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_take.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Take</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_take</span>(<span class='kw'>self</span>, <span class='kw'>index</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>index</th>
+      <td><p>(LongTensor) the indices into tensor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="take-input-index-gt-tensor-"><a class="anchor" href="#take-input-index-gt-tensor-"></a>take(input, index) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the elements of <code>input</code> at the given indices.
+The input tensor is treated as if it were viewed as a 1-D tensor. The result
+takes the same shape as the indices.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>src</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>,<span class='fl'>3</span>,<span class='fl'>5</span>,<span class='fl'>6</span>,<span class='fl'>7</span>,<span class='fl'>8</span>), ncol = <span class='fl'>3</span>, byrow = <span class='fl'>TRUE</span>))
+<span class='fu'>torch_take</span>(<span class='kw'>src</span>, <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>5</span>), dtype = <span class='fu'><a href='torch_dtype.html'>torch_int64</a></span>()))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  4
+#&gt;  3
+#&gt;  7
+#&gt; [ CPUFloatType{3} ]</div></pre>
+  </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="row">
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+    <div class="page-header">
+    <h1>Tan</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_tan.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Tan</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_tan</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="tan-input-out-null-gt-tensor-"><a class="anchor" href="#tan-input-out-null-gt-tensor-"></a>tan(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the tangent of the elements of <code>input</code>.</p>
+<p>$$
+    \mbox{out}_{i} = \tan(\mbox{input}_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_tan</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0.2036
+#&gt; -0.6868
+#&gt;  0.0959
+#&gt;  1.0523
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+
+      
+
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_tanh.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Tanh</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_tanh</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="tanh-input-out-null-gt-tensor-"><a class="anchor" href="#tanh-input-out-null-gt-tensor-"></a>tanh(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the hyperbolic tangent of the elements
+of <code>input</code>.</p>
+<p>$$
+    \mbox{out}_{i} = \tanh(\mbox{input}_{i})
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_tanh</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.4501
+#&gt; -0.2993
+#&gt;  0.4767
+#&gt;  0.4199
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
+      <h2 data-toc-skip>Contents</h2>
+    </nav>
+  </div>
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+
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+    <h1>Converts R objects to a torch tensor</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/tensor.R'><code>R/tensor.R</code></a></small>
+    <div class="hidden name"><code>torch_tensor.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Converts R objects to a torch tensor</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_tensor</span>(
+  <span class='kw'>data</span>,
+  dtype = <span class='kw'>NULL</span>,
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>,
+  pin_memory = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>data</th>
+      <td><p>an R atomic vector, matrix or array</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>a <a href='torch_dtype.html'>torch_dtype</a> instance</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>a device creted with <code><a href='torch_device.html'>torch_device()</a></code></p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>if autograd should record operations on the returned tensor.</p></td>
+    </tr>
+    <tr>
+      <th>pin_memory</th>
+      <td><p>If set, returned tensor would be allocated in the pinned memory.</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='fu'>torch_tensor</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>3</span>,<span class='fl'>4</span>))
+<span class='fu'>torch_tensor</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>,<span class='fl'>2</span>,<span class='fl'>3</span>,<span class='fl'>4</span>), dtype = <span class='fu'><a href='torch_dtype.html'>torch_int</a></span>())
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1
+#&gt;  2
+#&gt;  3
+#&gt;  4
+#&gt; [ CPUIntType{4} ]</div></pre>
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+<div class="row">
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+    <div class="page-header">
+    <h1>Tensordot</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
+    <div class="hidden name"><code>torch_tensordot.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Returns a contraction of a and b over multiple dimensions.
+<code>tensordot</code> implements a generalized matrix product.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_tensordot</span>(<span class='kw'>a</span>, <span class='kw'>b</span>, dims = <span class='fl'>2</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>a</th>
+      <td><p>(Tensor) Left tensor to contract</p></td>
+    </tr>
+    <tr>
+      <th>b</th>
+      <td><p>(Tensor) Right tensor to contract</p></td>
+    </tr>
+    <tr>
+      <th>dims</th>
+      <td><p>(int or tuple of two lists of integers) number of dimensions to     contract or explicit lists of dimensions for <code>a</code> and     <code>b</code> respectively</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(start = <span class='fl'>0</span>, end = <span class='fl'>60.</span>)<span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/stats/reshape.html'>reshape</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span>))
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(start = <span class='fl'>0</span>, end = <span class='fl'>24.</span>)<span class='op'>$</span><span class='fu'><a href='https://rdrr.io/r/stats/reshape.html'>reshape</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>3</span>, <span class='fl'>2</span>))
+<span class='fu'>torch_tensordot</span>(<span class='kw'>a</span>, <span class='kw'>b</span>, dims = <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>1</span>), <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>)))
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>5</span>, device=<span class='st'>'cuda'</span>)
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fl'>4</span>, <span class='fl'>5</span>, <span class='fl'>6</span>, device=<span class='st'>'cuda'</span>)
+<span class='kw'>c</span> <span class='op'>=</span> <span class='fu'>torch_tensordot</span>(<span class='kw'>a</span>, <span class='kw'>b</span>, dims=<span class='fl'>2</span>)<span class='op'>$</span><span class='fu'>cpu</span>()
+}
+}
+</div></pre>
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diff --git a/static/docs/reference/torch_threshold_.html b/static/docs/reference/torch_threshold_.html
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+    <div class="page-header">
+    <h1>Threshold_</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_threshold_.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Threshold_</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_threshold_</span>(<span class='kw'>self</span>, <span class='kw'>threshold</span>, <span class='kw'>value</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>input tensor</p></td>
+    </tr>
+    <tr>
+      <th>threshold</th>
+      <td><p>The value to threshold at</p></td>
+    </tr>
+    <tr>
+      <th>value</th>
+      <td><p>The value to replace with</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="threshold-input-threshold-value-gt-tensor-"><a class="anchor" href="#threshold-input-threshold-value-gt-tensor-"></a>threshold_(input, threshold, value) -&gt; Tensor </h2>
+
+    
+
+
+<p>In-place version of <code>torch_threshold</code>.</p>
+
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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diff --git a/static/docs/reference/torch_topk.html b/static/docs/reference/torch_topk.html
new file mode 100644
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+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_topk.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Topk</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_topk</span>(<span class='kw'>self</span>, <span class='kw'>k</span>, dim = <span class='op'>-</span><span class='fl'>1L</span>, largest = <span class='fl'>TRUE</span>, sorted = <span class='fl'>TRUE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>k</th>
+      <td><p>(int) the k in "top-k"</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int, optional) the dimension to sort along</p></td>
+    </tr>
+    <tr>
+      <th>largest</th>
+      <td><p>(bool, optional) controls whether to return largest or           smallest elements</p></td>
+    </tr>
+    <tr>
+      <th>sorted</th>
+      <td><p>(bool, optional) controls whether to return the elements           in sorted order</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="topk-input-k-dim-null-largest-true-sorted-true-gt-tensor-longtensor-"><a class="anchor" href="#topk-input-k-dim-null-largest-true-sorted-true-gt-tensor-longtensor-"></a>topk(input, k, dim=NULL, largest=TRUE, sorted=TRUE) -&gt; (Tensor, LongTensor) </h2>
+
+    
+
+
+<p>Returns the <code>k</code> largest elements of the given <code>input</code> tensor along
+a given dimension.</p>
+<p>If <code>dim</code> is not given, the last dimension of the <code>input</code> is chosen.</p>
+<p>If <code>largest</code> is <code>FALSE</code> then the <code>k</code> smallest elements are returned.</p>
+<p>A namedtuple of <code>(values, indices)</code> is returned, where the <code>indices</code> are the indices
+of the elements in the original <code>input</code> tensor.</p>
+<p>The boolean option <code>sorted</code> if <code>TRUE</code>, will make sure that the returned
+<code>k</code> elements are themselves sorted</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>6.</span>)
+<span class='kw'>x</span>
+<span class='fu'>torch_topk</span>(<span class='kw'>x</span>, <span class='fl'>3</span>)
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt;  5
+#&gt;  4
+#&gt;  3
+#&gt; [ CPUFloatType{3} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt;  4
+#&gt;  3
+#&gt;  2
+#&gt; [ CPULongType{3} ]
+#&gt; </div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+      <a href="../articles/using-autograd.html">Using autograd</a>
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+    <span class="fab fa fab fa-github fa-lg"></span>
+     
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+      </ul>
+      
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+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Trace</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_trace.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Trace</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_trace</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>the input tensor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="trace-input-gt-tensor-"><a class="anchor" href="#trace-input-gt-tensor-"></a>trace(input) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the sum of the elements of the diagonal of the input 2-D matrix.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_arange.html'>torch_arange</a></span>(<span class='fl'>1.</span>, <span class='fl'>10.</span>)<span class='op'>$</span><span class='fu'>view</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='kw'>x</span>
+<span class='fu'>torch_trace</span>(<span class='kw'>x</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; 15
+#&gt; [ CPUFloatType{} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+    <div class="page-header">
+    <h1>Transpose</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_transpose.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Transpose</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_transpose</span>(<span class='kw'>self</span>, <span class='kw'>dim0</span>, <span class='kw'>dim1</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim0</th>
+      <td><p>(int) the first dimension to be transposed</p></td>
+    </tr>
+    <tr>
+      <th>dim1</th>
+      <td><p>(int) the second dimension to be transposed</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="transpose-input-dim-dim-gt-tensor-"><a class="anchor" href="#transpose-input-dim-dim-gt-tensor-"></a>transpose(input, dim0, dim1) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a tensor that is a transposed version of <code>input</code>.
+The given dimensions <code>dim0</code> and <code>dim1</code> are swapped.</p>
+<p>The resulting <code>out</code> tensor shares it's underlying storage with the
+<code>input</code> tensor, so changing the content of one would change the content
+of the other.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+<span class='kw'>x</span>
+<span class='fu'>torch_transpose</span>(<span class='kw'>x</span>, <span class='fl'>1</span>, <span class='fl'>2</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.4633 -0.6867
+#&gt; -1.8115  0.4476
+#&gt; -1.5475  1.2365
+#&gt; [ CPUFloatType{3,2} ]</div></pre>
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+    <div class="page-header">
+    <h1>Trapz</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_trapz.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Trapz</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_trapz</span>(<span class='kw'>y</span>, dx = <span class='fl'>1L</span>, <span class='kw'>x</span>, dim = <span class='op'>-</span><span class='fl'>1L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>y</th>
+      <td><p>(Tensor) The values of the function to integrate</p></td>
+    </tr>
+    <tr>
+      <th>dx</th>
+      <td><p>(float) The distance between points at which <code>y</code> is sampled.</p></td>
+    </tr>
+    <tr>
+      <th>x</th>
+      <td><p>(Tensor) The points at which the function <code>y</code> is sampled.        If <code>x</code> is not in ascending order, intervals on which it is decreasing        contribute negatively to the estimated integral (i.e., the convention        \(\int_a^b f = -\int_b^a f\) is followed).</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) The dimension along which to integrate.        By default, use the last dimension.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="trapz-y-x-dim-gt-tensor-"><a class="anchor" href="#trapz-y-x-dim-gt-tensor-"></a>trapz(y, x, *, dim=-1) -&gt; Tensor </h2>
+
+    
+
+
+<p>Estimate \(\int y\,dx\) along <code>dim</code>, using the trapezoid rule.</p>
+    <h2 class="hasAnchor" id="trapz-y-dx-dim-gt-tensor-"><a class="anchor" href="#trapz-y-dx-dim-gt-tensor-"></a>trapz(y, *, dx=1, dim=-1) -&gt; Tensor </h2>
+
+    
+
+
+<p>As above, but the sample points are spaced uniformly at a distance of <code>dx</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+<span class='kw'>y</span>
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>, <span class='fl'>4</span>, <span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>), ncol = <span class='fl'>3</span>, byrow=<span class='fl'>TRUE</span>))
+<span class='fu'>torch_trapz</span>(<span class='kw'>y</span>, x = <span class='kw'>x</span>)
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -1.7562
+#&gt; -0.8343
+#&gt; [ CPUFloatType{2} ]</div></pre>
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+    <div class="page-header">
+    <h1>Triangular_solve</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_triangular_solve.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Triangular_solve</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_triangular_solve</span>(
+  <span class='kw'>self</span>,
+  <span class='kw'>A</span>,
+  upper = <span class='fl'>TRUE</span>,
+  transpose = <span class='fl'>FALSE</span>,
+  unitriangular = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) multiple right-hand sides of size \((*, m, k)\) where                \(*\) is zero of more batch dimensions (\(b\))</p></td>
+    </tr>
+    <tr>
+      <th>A</th>
+      <td><p>(Tensor) the input triangular coefficient matrix of size \((*, m, m)\)                where \(*\) is zero or more batch dimensions</p></td>
+    </tr>
+    <tr>
+      <th>upper</th>
+      <td><p>(bool, optional) whether to solve the upper-triangular system        of equations (default) or the lower-triangular system of equations. Default: <code>TRUE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>transpose</th>
+      <td><p>(bool, optional) whether \(A\) should be transposed before        being sent into the solver. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>unitriangular</th>
+      <td><p>(bool, optional) whether \(A\) is unit triangular.        If TRUE, the diagonal elements of \(A\) are assumed to be        1 and not referenced from \(A\). Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="triangular-solve-input-a-upper-true-transpose-false-unitriangular-false-gt-tensor-tensor-"><a class="anchor" href="#triangular-solve-input-a-upper-true-transpose-false-unitriangular-false-gt-tensor-tensor-"></a>triangular_solve(input, A, upper=TRUE, transpose=False, unitriangular=False) -&gt; (Tensor, Tensor) </h2>
+
+    
+
+
+<p>Solves a system of equations with a triangular coefficient matrix \(A\)
+and multiple right-hand sides \(b\).</p>
+<p>In particular, solves \(AX = b\) and assumes \(A\) is upper-triangular
+with the default keyword arguments.</p>
+<p><code>torch_triangular_solve(b, A)</code> can take in 2D inputs <code>b, A</code> or inputs that are
+batches of 2D matrices. If the inputs are batches, then returns
+batched outputs <code>X</code></p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>A</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>2</span>))<span class='op'>$</span><span class='fu'>triu</span>()
+<span class='kw'>A</span>
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+<span class='kw'>b</span>
+<span class='fu'>torch_triangular_solve</span>(<span class='kw'>b</span>, <span class='kw'>A</span>)
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt;  5.9921  7.3633  6.5760
+#&gt;  0.6631  3.9283  2.7029
+#&gt; [ CPUFloatType{2,3} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt;  0.1910  0.0582
+#&gt;  0.0000  0.2591
+#&gt; [ CPUFloatType{2,2} ]
+#&gt; </div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Tril</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_tril.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Tril</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_tril</span>(<span class='kw'>self</span>, diagonal = <span class='fl'>0L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>diagonal</th>
+      <td><p>(int, optional) the diagonal to consider</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="tril-input-diagonal-out-null-gt-tensor-"><a class="anchor" href="#tril-input-diagonal-out-null-gt-tensor-"></a>tril(input, diagonal=0, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the lower triangular part of the matrix (2-D tensor) or batch of matrices
+<code>input</code>, the other elements of the result tensor <code>out</code> are set to 0.</p>
+<p>The lower triangular part of the matrix is defined as the elements on and
+below the diagonal.</p>
+<p>The argument <code>diagonal</code> controls which diagonal to consider. If
+<code>diagonal</code> = 0, all elements on and below the main diagonal are
+retained. A positive value includes just as many diagonals above the main
+diagonal, and similarly a negative value excludes just as many diagonals below
+the main diagonal. The main diagonal are the set of indices
+\(\lbrace (i, i) \rbrace\) for \(i \in [0, \min\{d_{1}, d_{2}\} - 1]\) where
+\(d_{1}, d_{2}\) are the dimensions of the matrix.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_tril</span>(<span class='kw'>a</span>)
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>6</span>))
+<span class='kw'>b</span>
+<span class='fu'>torch_tril</span>(<span class='kw'>b</span>, diagonal=<span class='fl'>1</span>)
+<span class='fu'>torch_tril</span>(<span class='kw'>b</span>, diagonal=<span class='op'>-</span><span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.0000  0.0000  0.0000  0.0000  0.0000  0.0000
+#&gt; -0.0705  0.0000  0.0000  0.0000  0.0000  0.0000
+#&gt;  1.4173  0.3856  0.0000  0.0000  0.0000  0.0000
+#&gt;  1.3653 -1.3079 -1.1473  0.0000  0.0000  0.0000
+#&gt; [ CPUFloatType{4,6} ]</div></pre>
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diff --git a/static/docs/reference/torch_tril_indices.html b/static/docs/reference/torch_tril_indices.html
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index 000000000..3d08e21ec
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+    <div class="page-header">
+    <h1>Tril_indices</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
+    <div class="hidden name"><code>torch_tril_indices.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Tril_indices</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_tril_indices</span>(
+  <span class='kw'>row</span>,
+  <span class='kw'>col</span>,
+  offset = <span class='fl'>0</span>,
+  dtype = <span class='fu'><a href='torch_dtype.html'>torch_long</a></span>(),
+  device = <span class='st'>"cpu"</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>()
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>row</th>
+      <td><p>(<code>int</code>) number of rows in the 2-D matrix.</p></td>
+    </tr>
+    <tr>
+      <th>col</th>
+      <td><p>(<code>int</code>) number of columns in the 2-D matrix.</p></td>
+    </tr>
+    <tr>
+      <th>offset</th>
+      <td><p>(<code>int</code>) diagonal offset from the main diagonal.        Default: if not provided, 0.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, <code>torch_long</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) currently only support <code>torch_strided</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>When running on CUDA, `row * col` must be less than \eqn{2^{59}} to
+prevent overflow during calculation.
+</pre>
+
+    <h2 class="hasAnchor" id="tril-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-strided-gt-tensor-"><a class="anchor" href="#tril-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-strided-gt-tensor-"></a>tril_indices(row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the indices of the lower triangular part of a <code>row</code>-by-
+<code>col</code> matrix in a 2-by-N Tensor, where the first row contains row
+coordinates of all indices and the second row contains column coordinates.
+Indices are ordered based on rows and then columns.</p>
+<p>The lower triangular part of the matrix is defined as the elements on and
+below the diagonal.</p>
+<p>The argument <code>offset</code> controls which diagonal to consider. If
+<code>offset</code> = 0, all elements on and below the main diagonal are
+retained. A positive value includes just as many diagonals above the main
+diagonal, and similarly a negative value excludes just as many diagonals below
+the main diagonal. The main diagonal are the set of indices
+\(\lbrace (i, i) \rbrace\) for \(i \in [0, \min\{d_{1}, d_{2}\} - 1]\)
+where \(d_{1}, d_{2}\) are the dimensions of the matrix.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'>torch_tril_indices</span>(<span class='fl'>3</span>, <span class='fl'>3</span>)
+<span class='kw'>a</span>
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'>torch_tril_indices</span>(<span class='fl'>4</span>, <span class='fl'>3</span>, <span class='op'>-</span><span class='fl'>1</span>)
+<span class='kw'>a</span>
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'>torch_tril_indices</span>(<span class='fl'>4</span>, <span class='fl'>3</span>, <span class='fl'>1</span>)
+<span class='kw'>a</span>
+}
+}
+</div></pre>
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+    <h1>Triu</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_triu.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Triu</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_triu</span>(<span class='kw'>self</span>, diagonal = <span class='fl'>0L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>diagonal</th>
+      <td><p>(int, optional) the diagonal to consider</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="triu-input-diagonal-out-null-gt-tensor-"><a class="anchor" href="#triu-input-diagonal-out-null-gt-tensor-"></a>triu(input, diagonal=0, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the upper triangular part of a matrix (2-D tensor) or batch of matrices
+<code>input</code>, the other elements of the result tensor <code>out</code> are set to 0.</p>
+<p>The upper triangular part of the matrix is defined as the elements on and
+above the diagonal.</p>
+<p>The argument <code>diagonal</code> controls which diagonal to consider. If
+<code>diagonal</code> = 0, all elements on and above the main diagonal are
+retained. A positive value excludes just as many diagonals above the main
+diagonal, and similarly a negative value includes just as many diagonals below
+the main diagonal. The main diagonal are the set of indices
+\(\lbrace (i, i) \rbrace\) for \(i \in [0, \min\{d_{1}, d_{2}\} - 1]\) where
+\(d_{1}, d_{2}\) are the dimensions of the matrix.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_triu</span>(<span class='kw'>a</span>)
+<span class='fu'>torch_triu</span>(<span class='kw'>a</span>, diagonal=<span class='fl'>1</span>)
+<span class='fu'>torch_triu</span>(<span class='kw'>a</span>, diagonal=<span class='op'>-</span><span class='fl'>1</span>)
+<span class='kw'>b</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>6</span>))
+<span class='kw'>b</span>
+<span class='fu'>torch_triu</span>(<span class='kw'>b</span>, diagonal=<span class='fl'>1</span>)
+<span class='fu'>torch_triu</span>(<span class='kw'>b</span>, diagonal=<span class='op'>-</span><span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.8511  0.3136 -0.8565 -0.3131  0.8333 -1.6256
+#&gt;  0.6987 -0.8917  2.5117  1.6975 -0.5125  1.6937
+#&gt;  0.0000  0.6684  1.6408  0.0282  0.3932 -1.6401
+#&gt;  0.0000  0.0000 -0.9555  0.2990  0.3913 -0.5259
+#&gt; [ CPUFloatType{4,6} ]</div></pre>
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+    <div class="page-header">
+    <h1>Triu_indices</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/wrapers.R'><code>R/wrapers.R</code></a></small>
+    <div class="hidden name"><code>torch_triu_indices.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Triu_indices</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_triu_indices</span>(
+  <span class='kw'>row</span>,
+  <span class='kw'>col</span>,
+  offset = <span class='fl'>0</span>,
+  dtype = <span class='fu'><a href='torch_dtype.html'>torch_long</a></span>(),
+  device = <span class='st'>"cpu"</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>()
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>row</th>
+      <td><p>(<code>int</code>) number of rows in the 2-D matrix.</p></td>
+    </tr>
+    <tr>
+      <th>col</th>
+      <td><p>(<code>int</code>) number of columns in the 2-D matrix.</p></td>
+    </tr>
+    <tr>
+      <th>offset</th>
+      <td><p>(<code>int</code>) diagonal offset from the main diagonal.        Default: if not provided, 0.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, <code>torch_long</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) currently only support <code>torch_strided</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>When running on CUDA, `row * col` must be less than \eqn{2^{59}} to
+prevent overflow during calculation.
+</pre>
+
+    <h2 class="hasAnchor" id="triu-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-strided-gt-tensor-"><a class="anchor" href="#triu-indices-row-col-offset-dtype-torch-long-device-cpu-layout-torch-strided-gt-tensor-"></a>triu_indices(row, col, offset=0, dtype=torch.long, device='cpu', layout=torch.strided) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the indices of the upper triangular part of a <code>row</code> by
+<code>col</code> matrix in a 2-by-N Tensor, where the first row contains row
+coordinates of all indices and the second row contains column coordinates.
+Indices are ordered based on rows and then columns.</p>
+<p>The upper triangular part of the matrix is defined as the elements on and
+above the diagonal.</p>
+<p>The argument <code>offset</code> controls which diagonal to consider. If
+<code>offset</code> = 0, all elements on and above the main diagonal are
+retained. A positive value excludes just as many diagonals above the main
+diagonal, and similarly a negative value includes just as many diagonals below
+the main diagonal. The main diagonal are the set of indices
+\(\lbrace (i, i) \rbrace\) for \(i \in [0, \min\{d_{1}, d_{2}\} - 1]\)
+where \(d_{1}, d_{2}\) are the dimensions of the matrix.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'>torch_triu_indices</span>(<span class='fl'>3</span>, <span class='fl'>3</span>)
+<span class='kw'>a</span>
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'>torch_triu_indices</span>(<span class='fl'>4</span>, <span class='fl'>3</span>, <span class='op'>-</span><span class='fl'>1</span>)
+<span class='kw'>a</span>
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'>torch_triu_indices</span>(<span class='fl'>4</span>, <span class='fl'>3</span>, <span class='fl'>1</span>)
+<span class='kw'>a</span>
+}
+}
+</div></pre>
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+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
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+    </li>
+    <li>
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+
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+<div class="row">
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+    <div class="page-header">
+    <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>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_true_divide.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>TRUE_divide</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_true_divide</span>(<span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the dividend</p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor or Scalar) the divisor</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="true-divide-dividend-divisor-gt-tensor-"><a class="anchor" href="#true-divide-dividend-divisor-gt-tensor-"></a>true_divide(dividend, divisor) -&gt; Tensor </h2>
+
+    
+
+
+<p>Performs "true division" that always computes the division
+in floating point. Analogous to division in Python 3 and equivalent to
+<code><a href='torch_div.html'>torch_div</a></code> except when both inputs have bool or integer scalar types,
+in which case they are cast to the default (floating) scalar type before the division.</p>
+<p>$$
+    \mbox{out}_i = \frac{\mbox{dividend}_i}{\mbox{divisor}}
+$$</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>dividend</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>, <span class='fl'>3</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int</a></span>())
+<span class='kw'>divisor</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>), dtype=<span class='fu'><a href='torch_dtype.html'>torch_int</a></span>())
+<span class='fu'>torch_true_divide</span>(<span class='kw'>dividend</span>, <span class='kw'>divisor</span>)
+<span class='fu'>torch_true_divide</span>(<span class='kw'>dividend</span>, <span class='fl'>2</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  2.5000
+#&gt;  1.5000
+#&gt; [ CPUFloatType{2} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+
+      
+
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Trunc</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_trunc.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Trunc</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_trunc</span>(<span class='kw'>self</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="trunc-input-out-null-gt-tensor-"><a class="anchor" href="#trunc-input-out-null-gt-tensor-"></a>trunc(input, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with the truncated integer values of
+the elements of <code>input</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_trunc</span>(<span class='kw'>a</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; -0
+#&gt; -0
+#&gt; -1
+#&gt;  1
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+    </li>
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+
+      
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+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Unbind</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_unbind.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Unbind</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_unbind</span>(<span class='kw'>self</span>, dim = <span class='fl'>1L</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the tensor to 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'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_unbind</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/matrix.html'>matrix</a></span>(<span class='fl'>1</span><span class='op'>:</span><span class='fl'>9</span>, ncol = <span class='fl'>3</span>, byrow=<span class='fl'>TRUE</span>)))
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt;  1
+#&gt;  2
+#&gt;  3
+#&gt; [ CPULongType{3} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt;  4
+#&gt;  5
+#&gt;  6
+#&gt; [ CPULongType{3} ]
+#&gt; 
+#&gt; [[3]]
+#&gt; torch_tensor 
+#&gt;  7
+#&gt;  8
+#&gt;  9
+#&gt; [ CPULongType{3} ]
+#&gt; </div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
+    <nav id="toc" data-toggle="toc" class="sticky-top">
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+<div class="row">
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+    <div class="page-header">
+    <h1>Unique_consecutive</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_unique_consecutive.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Unique_consecutive</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_unique_consecutive</span>(
+  <span class='kw'>self</span>,
+  return_inverse = <span class='fl'>FALSE</span>,
+  return_counts = <span class='fl'>FALSE</span>,
+  dim = <span class='kw'>NULL</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor</p></td>
+    </tr>
+    <tr>
+      <th>return_inverse</th>
+      <td><p>(bool) Whether to also return the indices for where        elements in the original input ended up in the returned unique list.</p></td>
+    </tr>
+    <tr>
+      <th>return_counts</th>
+      <td><p>(bool) Whether to also return the counts for each unique        element.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the dimension to apply unique. If <code>NULL</code>, the unique of the        flattened input is returned. default: <code>NULL</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="test-"><a class="anchor" href="#test-"></a>TEST </h2>
+
+    
+
+
+<p>Eliminates all but the first element from every consecutive group of equivalent elements.</p><pre>.. note:: This function is different from [`torch_unique`] in the sense that this function
+    only eliminates consecutive duplicate values. This semantics is similar to `std::unique`
+    in C++.
+</pre>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>1</span>, <span class='fl'>1</span>, <span class='fl'>2</span>))
+<span class='kw'>output</span> <span class='op'>=</span> <span class='fu'>torch_unique_consecutive</span>(<span class='kw'>x</span>)
+<span class='kw'>output</span>
+<span class='fu'>torch_unique_consecutive</span>(<span class='kw'>x</span>, return_inverse=<span class='fl'>TRUE</span>)
+<span class='fu'>torch_unique_consecutive</span>(<span class='kw'>x</span>, return_counts=<span class='fl'>TRUE</span>)
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt;  1
+#&gt;  2
+#&gt;  3
+#&gt;  1
+#&gt;  2
+#&gt; [ CPUFloatType{5} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt; [ CPULongType{0} ]
+#&gt; 
+#&gt; [[3]]
+#&gt; torch_tensor 
+#&gt;  2
+#&gt;  2
+#&gt;  1
+#&gt;  2
+#&gt;  1
+#&gt; [ CPULongType{5} ]
+#&gt; </div></pre>
+  </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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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+    <h1>Unsqueeze</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_unsqueeze.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Unsqueeze</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_unsqueeze</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int) the index at which to insert the singleton dimension</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="unsqueeze-input-dim-gt-tensor-"><a class="anchor" href="#unsqueeze-input-dim-gt-tensor-"></a>unsqueeze(input, dim) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a new tensor with a dimension of size one inserted at the
+specified position.</p>
+<p>The returned tensor shares the same underlying data with this tensor.</p>
+<p>A <code>dim</code> value within the range <code>[-input.dim() - 1, input.dim() + 1)</code>
+can be used. Negative <code>dim</code> will correspond to <code>unsqueeze</code>
+applied at <code>dim</code> = <code>dim + input.dim() + 1</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>2</span>, <span class='fl'>3</span>, <span class='fl'>4</span>))
+<span class='fu'>torch_unsqueeze</span>(<span class='kw'>x</span>, <span class='fl'>1</span>)
+<span class='fu'>torch_unsqueeze</span>(<span class='kw'>x</span>, <span class='fl'>2</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  1
+#&gt;  2
+#&gt;  3
+#&gt;  4
+#&gt; [ CPUFloatType{4,1} ]</div></pre>
+  </div>
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+    </li>
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+    <div class="page-header">
+    <h1>Var</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_var.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Var</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_var</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, unbiased = <span class='fl'>TRUE</span>, keepdim = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
+    </tr>
+    <tr>
+      <th>unbiased</th>
+      <td><p>(bool) whether to use the unbiased estimation or not</p></td>
+    </tr>
+    <tr>
+      <th>keepdim</th>
+      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="var-input-unbiased-true-gt-tensor-"><a class="anchor" href="#var-input-unbiased-true-gt-tensor-"></a>var(input, unbiased=TRUE) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the variance of all elements in the <code>input</code> tensor.</p>
+<p>If <code>unbiased</code> is <code>FALSE</code>, then the variance will be calculated via the
+biased estimator. Otherwise, Bessel's correction will be used.</p>
+    <h2 class="hasAnchor" id="var-input-dim-keepdim-false-unbiased-true-out-null-gt-tensor-"><a class="anchor" href="#var-input-dim-keepdim-false-unbiased-true-out-null-gt-tensor-"></a>var(input, dim, keepdim=False, unbiased=TRUE, out=NULL) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns the variance of each row of the <code>input</code> tensor in the given
+dimension <code>dim</code>.</p>
+<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size
+as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
+Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
+output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
+<p>If <code>unbiased</code> is <code>FALSE</code>, then the variance will be calculated via the
+biased estimator. Otherwise, Bessel's correction will be used.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_var</span>(<span class='kw'>a</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_var</span>(<span class='kw'>a</span>, <span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0.3456
+#&gt;  0.0521
+#&gt;  1.0536
+#&gt;  0.3194
+#&gt; [ CPUFloatType{4} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+    <div class="page-header">
+    <h1>Var_mean</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_var_mean.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Var_mean</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_var_mean</span>(<span class='kw'>self</span>, <span class='kw'>dim</span>, unbiased = <span class='fl'>TRUE</span>, keepdim = <span class='fl'>FALSE</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) the input tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dim</th>
+      <td><p>(int or tuple of ints) the dimension or dimensions to reduce.</p></td>
+    </tr>
+    <tr>
+      <th>unbiased</th>
+      <td><p>(bool) whether to use the unbiased estimation or not</p></td>
+    </tr>
+    <tr>
+      <th>keepdim</th>
+      <td><p>(bool) whether the output tensor has <code>dim</code> retained or not.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="var-mean-input-unbiased-true-gt-tensor-tensor-"><a class="anchor" href="#var-mean-input-unbiased-true-gt-tensor-tensor-"></a>var_mean(input, unbiased=TRUE) -&gt; (Tensor, Tensor) </h2>
+
+    
+
+
+<p>Returns the variance and mean of all elements in the <code>input</code> tensor.</p>
+<p>If <code>unbiased</code> is <code>FALSE</code>, then the variance will be calculated via the
+biased estimator. Otherwise, Bessel's correction will be used.</p>
+    <h2 class="hasAnchor" id="var-mean-input-dim-keepdim-false-unbiased-true-gt-tensor-tensor-"><a class="anchor" href="#var-mean-input-dim-keepdim-false-unbiased-true-gt-tensor-tensor-"></a>var_mean(input, dim, keepdim=False, unbiased=TRUE) -&gt; (Tensor, Tensor) </h2>
+
+    
+
+
+<p>Returns the variance and mean of each row of the <code>input</code> tensor in the given
+dimension <code>dim</code>.</p>
+<p>If <code>keepdim</code> is <code>TRUE</code>, the output tensor is of the same size
+as <code>input</code> except in the dimension(s) <code>dim</code> where it is of size 1.
+Otherwise, <code>dim</code> is squeezed (see <code><a href='torch_squeeze.html'>torch_squeeze</a></code>), resulting in the
+output tensor having 1 (or <code>len(dim)</code>) fewer dimension(s).</p>
+<p>If <code>unbiased</code> is <code>FALSE</code>, then the variance will be calculated via the
+biased estimator. Otherwise, Bessel's correction will be used.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>1</span>, <span class='fl'>3</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_var_mean</span>(<span class='kw'>a</span>)
+
+
+<span class='kw'>a</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>4</span>, <span class='fl'>4</span>))
+<span class='kw'>a</span>
+<span class='fu'>torch_var_mean</span>(<span class='kw'>a</span>, <span class='fl'>1</span>)
+}
+</div><div class='output co'>#&gt; [[1]]
+#&gt; torch_tensor 
+#&gt;  0.2877
+#&gt;  1.0334
+#&gt;  0.7203
+#&gt;  1.7788
+#&gt; [ CPUFloatType{4} ]
+#&gt; 
+#&gt; [[2]]
+#&gt; torch_tensor 
+#&gt;  0.1609
+#&gt; -0.4014
+#&gt;  0.6819
+#&gt;  0.3604
+#&gt; [ CPUFloatType{4} ]
+#&gt; </div></pre>
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+    <div class="page-header">
+    <h1>Where</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace.R'><code>R/gen-namespace.R</code></a></small>
+    <div class="hidden name"><code>torch_where.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Where</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_where</span>(<span class='kw'>condition</span>, <span class='kw'>self</span>, <span class='kw'>other</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>condition</th>
+      <td><p>(BoolTensor) When TRUE (nonzero), yield x, otherwise yield y</p></td>
+    </tr>
+    <tr>
+      <th>self</th>
+      <td><p>(Tensor) values selected at indices where <code>condition</code> is <code>TRUE</code></p></td>
+    </tr>
+    <tr>
+      <th>other</th>
+      <td><p>(Tensor) values selected at indices where <code>condition</code> is <code>FALSE</code></p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="note"><a class="anchor" href="#note"></a>Note</h2>
+
+    
+<pre>The tensors `condition`, `x`, `y` must be broadcastable .
+</pre>
+
+<p>See also <code><a href='torch_nonzero.html'>torch_nonzero()</a></code>.</p>
+    <h2 class="hasAnchor" id="where-condition-x-y-gt-tensor-"><a class="anchor" href="#where-condition-x-y-gt-tensor-"></a>where(condition, x, y) -&gt; Tensor </h2>
+
+    
+
+
+<p>Return a tensor of elements selected from either <code>x</code> or <code>y</code>, depending on <code>condition</code>.</p>
+<p>The operation is defined as:</p>
+<p>$$
+    \mbox{out}_i = \left\{ \begin{array}{ll}
+        \mbox{x}_i &amp; \mbox{if } \mbox{condition}_i \\
+        \mbox{y}_i &amp; \mbox{otherwise} \\
+    \end{array}
+    \right.
+$$</p>
+    <h2 class="hasAnchor" id="where-condition-gt-tuple-of-longtensor-"><a class="anchor" href="#where-condition-gt-tuple-of-longtensor-"></a>where(condition) -&gt; tuple of LongTensor </h2>
+
+    
+
+
+<p><code>torch_where(condition)</code> is identical to
+<code><a href='torch_nonzero.html'>torch_nonzero(condition, as_tuple=TRUE)</a></code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='co'>if</span> (<span class='fl'>FALSE</span>) {
+<span class='kw'>x</span> <span class='op'>=</span> <span class='fu'><a href='torch_randn.html'>torch_randn</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>))
+<span class='kw'>y</span> <span class='op'>=</span> <span class='fu'><a href='torch_ones.html'>torch_ones</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>2</span>))
+<span class='kw'>x</span>
+<span class='fu'>torch_where</span>(<span class='kw'>x</span> <span class='op'>&gt;</span> <span class='fl'>0</span>, <span class='kw'>x</span>, <span class='kw'>y</span>)
+}
+
+
+
+}
+</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Zeros</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_zeros.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Zeros</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_zeros</span>(
+  <span class='kw'>...</span>,
+  names = <span class='kw'>NULL</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>...</th>
+      <td><p>a sequence of integers defining the shape of the output tensor.        Can be a variable number of arguments or a collection like a list or tuple.</p></td>
+    </tr>
+    <tr>
+      <th>names</th>
+      <td><p>optional dimension names</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned tensor.        Default: if <code>NULL</code>, uses a global default (see <code>torch_set_default_tensor_type</code>).</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned Tensor.        Default: <code>torch_strided</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, uses the current device for the default tensor type        (see <code>torch_set_default_tensor_type</code>). <code>device</code> will be the CPU        for CPU tensor types and the current CUDA device for CUDA tensor types.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="zeros-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"><a class="anchor" href="#zeros-size-out-null-dtype-null-layout-torch-strided-device-null-requires-grad-false-gt-tensor-"></a>zeros(*size, out=NULL, dtype=NULL, layout=torch.strided, device=NULL, requires_grad=False) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a tensor filled with the scalar value <code>0</code>, with the shape defined
+by the variable argument <code>size</code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='fu'>torch_zeros</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+<span class='fu'>torch_zeros</span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>5</span>))
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0
+#&gt;  0
+#&gt;  0
+#&gt;  0
+#&gt;  0
+#&gt; [ CPUFloatType{5} ]</div></pre>
+  </div>
+  <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar">
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+  <ul class="dropdown-menu" role="menu">
+    <li class="dropdown-header">Beginers guide</li>
+    <li>
+      <a href="../articles/getting-started/warmup.html">Warm-up</a>
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+      <a href="../articles/getting-started/tensors.html">Tensors</a>
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+      <a href="../articles/getting-started/tensors-and-autograd.html">Tensors and autograd</a>
+    </li>
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+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/optim.html">optim: optimizers in torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/custom-nn.html">Custom nn modules</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/control-flow-and-weight-sharing.html">Control flow &amp; Weight sharing</a>
+    </li>
+    <li class="dropdown-header">Torch Mechanics</li>
+    <li>
+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
+    <li>
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+    </li>
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+      <a href="../articles/tensor/index.html">Tensor class</a>
+    </li>
+    <li class="dropdown-header">Datasets</li>
+    <li>
+      <a href="../articles/loading-data.html">Loading Data</a>
+    </li>
+    <li class="dropdown-header">Autograd</li>
+    <li>
+      <a href="../articles/using-autograd.html">Using autograd</a>
+    </li>
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+      <a href="../articles/extending-autograd.html">Extending autograd</a>
+    </li>
+  </ul>
+</li>
+<li class="dropdown-header">Examples</li>
+<li>
+  <a href="../reference/index.html">Reference</a>
+</li>
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+  <a href="../news/index.html">Changelog</a>
+</li>
+      </ul>
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+        <li>
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+     
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+      
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+  </div><!--/.container -->
+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Zeros_like</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/creation-ops.R'><code>R/creation-ops.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-docs.R'><code>R/gen-namespace-docs.R</code></a>, <a href='https://github.com/mlverse/torch/blob/master/R/gen-namespace-examples.R'><code>R/gen-namespace-examples.R</code></a></small>
+    <div class="hidden name"><code>torch_zeros_like.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Zeros_like</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>torch_zeros_like</span>(
+  <span class='kw'>input</span>,
+  dtype = <span class='kw'>NULL</span>,
+  layout = <span class='fu'><a href='torch_layout.html'>torch_strided</a></span>(),
+  device = <span class='kw'>NULL</span>,
+  requires_grad = <span class='fl'>FALSE</span>,
+  memory_format = <span class='fu'><a href='torch_memory_format.html'>torch_preserve_format</a></span>()
+)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>input</th>
+      <td><p>(Tensor) the size of <code>input</code> will determine size of the output tensor.</p></td>
+    </tr>
+    <tr>
+      <th>dtype</th>
+      <td><p>(<code>torch.dtype</code>, optional) the desired data type of returned Tensor.        Default: if <code>NULL</code>, defaults to the dtype of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>layout</th>
+      <td><p>(<code>torch.layout</code>, optional) the desired layout of returned tensor.        Default: if <code>NULL</code>, defaults to the layout of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>device</th>
+      <td><p>(<code>torch.device</code>, optional) the desired device of returned tensor.        Default: if <code>NULL</code>, defaults to the device of <code>input</code>.</p></td>
+    </tr>
+    <tr>
+      <th>requires_grad</th>
+      <td><p>(bool, optional) If autograd should record operations on the        returned tensor. Default: <code>FALSE</code>.</p></td>
+    </tr>
+    <tr>
+      <th>memory_format</th>
+      <td><p>(<code>torch.memory_format</code>, optional) the desired memory format of        returned Tensor. Default: <code>torch_preserve_format</code>.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="zeros-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"><a class="anchor" href="#zeros-like-input-dtype-null-layout-null-device-null-requires-grad-false-memory-format-torch-preserve-format-gt-tensor-"></a>zeros_like(input, dtype=NULL, layout=NULL, device=NULL, requires_grad=False, memory_format=torch.preserve_format) -&gt; Tensor </h2>
+
+    
+
+
+<p>Returns a tensor filled with the scalar value <code>0</code>, with the same size as
+<code>input</code>. <code>torch_zeros_like(input)</code> is equivalent to
+<code><a href='torch_zeros.html'>torch_zeros(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)</a></code>.</p>
+    <h2 class="hasAnchor" id="warning"><a class="anchor" href="#warning"></a>Warning</h2>
+
+    
+
+<p>As of 0.4, this function does not support an <code>out</code> keyword. As an alternative,
+the old <code>torch_zeros_like(input, out=output)</code> is equivalent to
+<code><a href='torch_zeros.html'>torch_zeros(input.size(), out=output)</a></code>.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>input</span> <span class='op'>=</span> <span class='fu'><a href='torch_empty.html'>torch_empty</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>2</span>, <span class='fl'>3</span>))
+<span class='fu'>torch_zeros_like</span>(<span class='kw'>input</span>)
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  0  0  0
+#&gt;  0  0  0
+#&gt; [ CPUFloatType{2,3} ]</div></pre>
+  </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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+  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+</div>
+
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diff --git a/static/docs/reference/with_enable_grad.html b/static/docs/reference/with_enable_grad.html
new file mode 100644
index 000000000..04143facb
--- /dev/null
+++ b/static/docs/reference/with_enable_grad.html
@@ -0,0 +1,259 @@
+<!-- Generated by pkgdown: do not edit by hand -->
+<!DOCTYPE html>
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+<meta property="og:title" content="Enable grad — with_enable_grad" />
+<meta property="og:description" content="Context-manager that enables gradient calculation.
+Enables gradient calculation, if it has been disabled via with_no_grad." />
+
+
+
+
+<!-- mathjax -->
+<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script>
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+      <a href="../articles/getting-started/tensors.html">Tensors</a>
+    </li>
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+      <a href="../articles/getting-started/tensors-and-autograd.html">Tensors and autograd</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/new-autograd-functions.html">Defining new autograd functions</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/nn.html">nn: neural networks with torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/optim.html">optim: optimizers in torch</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/custom-nn.html">Custom nn modules</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/control-flow-and-weight-sharing.html">Control flow &amp; Weight sharing</a>
+    </li>
+    <li class="dropdown-header">Torch Mechanics</li>
+    <li>
+      <a href="../articles/getting-started/what-is-torch.html">What's torch?</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/autograd.html">Autograd: automatic differentiation</a>
+    </li>
+    <li>
+      <a href="../articles/getting-started/neural-networks.html">Neural networks</a>
+    </li>
+  </ul>
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+     
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+    <li class="dropdown-header">Tensors</li>
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+      <a href="../articles/tensor-creation.html">Creating tensors</a>
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+    <li>
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+    <li>
+      <a href="../articles/loading-data.html">Loading Data</a>
+    </li>
+    <li class="dropdown-header">Autograd</li>
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+      <a href="../articles/using-autograd.html">Using autograd</a>
+    </li>
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+      <a href="../articles/extending-autograd.html">Extending autograd</a>
+    </li>
+  </ul>
+</li>
+<li class="dropdown-header">Examples</li>
+<li>
+  <a href="../reference/index.html">Reference</a>
+</li>
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+  <a href="../news/index.html">Changelog</a>
+</li>
+      </ul>
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+    <span class="fab fa fab fa-github fa-lg"></span>
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+      
+    </div><!--/.nav-collapse -->
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+</div><!--/.navbar -->
+
+      
+
+      </header>
+
+<div class="row">
+  <div class="col-md-9 contents">
+    <div class="page-header">
+    <h1>Enable grad</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/autograd.R'><code>R/autograd.R</code></a></small>
+    <div class="hidden name"><code>with_enable_grad.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Context-manager that enables gradient calculation.
+Enables gradient calculation, if it has been disabled via <a href='with_no_grad.html'>with_no_grad</a>.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>with_enable_grad</span>(<span class='kw'>code</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>code</th>
+      <td><p>code to be executed with gradient recording.</p></td>
+    </tr>
+    </table>
+
+    <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2>
+
+    <p>This context manager is thread local; it will not affect computation in
+other threads.</p>
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+
+<span class='kw'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fl'>1</span>, requires_grad=<span class='fl'>TRUE</span>)
+<span class='fu'><a href='with_no_grad.html'>with_no_grad</a></span>({
+  <span class='fu'>with_enable_grad</span>({
+    <span class='kw'>y</span> <span class='op'>=</span> <span class='kw'>x</span> <span class='op'>*</span> <span class='fl'>2</span>
+  })
+})
+<span class='kw'>y</span><span class='op'>$</span><span class='fu'>backward</span>()
+<span class='kw'>x</span><span class='op'>$</span><span class='kw'>grad</span>
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt;  2
+#&gt; [ CPUFloatType{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">
+      <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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+  <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p>
+</div>
+
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+</html>
+
+
diff --git a/static/docs/reference/with_no_grad.html b/static/docs/reference/with_no_grad.html
new file mode 100644
index 000000000..85d16d558
--- /dev/null
+++ b/static/docs/reference/with_no_grad.html
@@ -0,0 +1,249 @@
+<!-- Generated by pkgdown: do not edit by hand -->
+<!DOCTYPE html>
+<html lang="en">
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+<meta http-equiv="X-UA-Compatible" content="IE=edge">
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+
+<title>Temporarily modify gradient recording. — with_no_grad • torch</title>
+
+
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+    <h1>Temporarily modify gradient recording.</h1>
+    <small class="dont-index">Source: <a href='https://github.com/mlverse/torch/blob/master/R/autograd.R'><code>R/autograd.R</code></a></small>
+    <div class="hidden name"><code>with_no_grad.Rd</code></div>
+    </div>
+
+    <div class="ref-description">
+    <p>Temporarily modify gradient recording.</p>
+    </div>
+
+    <pre class="usage"><span class='fu'>with_no_grad</span>(<span class='kw'>code</span>)</pre>
+
+    <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2>
+    <table class="ref-arguments">
+    <colgroup><col class="name" /><col class="desc" /></colgroup>
+    <tr>
+      <th>code</th>
+      <td><p>code to be executed with no gradient recording.</p></td>
+    </tr>
+    </table>
+
+
+    <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2>
+    <pre class="examples"><div class='input'><span class='co'>if</span> (<span class='fu'><a href='torch_is_installed.html'>torch_is_installed</a></span>()) {
+<span class='kw'>x</span> <span class='op'>&lt;-</span> <span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/stats/Uniform.html'>runif</a></span>(<span class='fl'>5</span>), requires_grad = <span class='fl'>TRUE</span>)
+<span class='fu'>with_no_grad</span>({
+  <span class='kw'>x</span><span class='op'>$</span><span class='fu'>sub_</span>(<span class='fu'><a href='torch_tensor.html'>torch_tensor</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/numeric.html'>as.numeric</a></span>(<span class='fl'>1</span><span class='op'>:</span><span class='fl'>5</span>)))
+})
+<span class='kw'>x</span>
+<span class='kw'>x</span><span class='op'>$</span><span class='kw'>grad</span>
+
+}
+</div><div class='output co'>#&gt; torch_tensor 
+#&gt; [ Tensor (undefined) ]</div></pre>
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