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<!DOCTYPE html>
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<title>torch.Tensor — PyTorch 0.1.12_2 documentation</title>
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<p class="caption"><span class="caption-text">Notes</span></p>
<ul>
<li class="toctree-l1"><a class="reference internal" href="notes/autograd.html">Autograd mechanics</a><ul>
<li class="toctree-l2"><a class="reference internal" href="notes/autograd.html#excluding-subgraphs-from-backward">Excluding subgraphs from backward</a><ul>
<li class="toctree-l3"><a class="reference internal" href="notes/autograd.html#requires-grad"><code class="docutils literal"><span class="pre">requires_grad</span></code></a></li>
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<li class="toctree-l2"><a class="reference internal" href="notes/autograd.html#how-autograd-encodes-the-history">How autograd encodes the history</a></li>
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<li class="toctree-l3"><a class="reference internal" href="notes/cuda.html#use-pinned-memory-buffers">Use pinned memory buffers</a></li>
<li class="toctree-l3"><a class="reference internal" href="notes/cuda.html#use-nn-dataparallel-instead-of-multiprocessing">Use nn.DataParallel instead of multiprocessing</a></li>
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<li class="toctree-l3"><a class="reference internal" href="notes/serialization.html#recommended-approach-for-saving-a-model">Recommended approach for saving a model</a></li>
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<p class="caption"><span class="caption-text">Package Reference</span></p>
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<li class="toctree-l1"><a class="reference internal" href="torch.html">torch</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="nn.html#logsoftmax"><span class="hidden-section">LogSoftmax</span></a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn.html#normalization-layers">Normalization layers</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="nn.html#batchnorm3d"><span class="hidden-section">BatchNorm3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#instancenorm1d"><span class="hidden-section">InstanceNorm1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#instancenorm2d"><span class="hidden-section">InstanceNorm2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#instancenorm3d"><span class="hidden-section">InstanceNorm3d</span></a></li>
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<li class="toctree-l3"><a class="reference internal" href="nn.html#gru"><span class="hidden-section">GRU</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#rnncell"><span class="hidden-section">RNNCell</span></a></li>
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<li class="toctree-l3"><a class="reference internal" href="nn.html#linear"><span class="hidden-section">Linear</span></a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="nn.html#dropout-layers">Dropout layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#dropout"><span class="hidden-section">Dropout</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#dropout2d"><span class="hidden-section">Dropout2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#dropout3d"><span class="hidden-section">Dropout3d</span></a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="nn.html#sparse-layers">Sparse layers</a><ul>
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<li class="toctree-l2"><a class="reference internal" href="nn.html#distance-functions">Distance functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#pairwisedistance"><span class="hidden-section">PairwiseDistance</span></a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn.html#loss-functions">Loss functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#l1loss"><span class="hidden-section">L1Loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#mseloss"><span class="hidden-section">MSELoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#crossentropyloss"><span class="hidden-section">CrossEntropyLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#nllloss"><span class="hidden-section">NLLLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#nllloss2d"><span class="hidden-section">NLLLoss2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#kldivloss"><span class="hidden-section">KLDivLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#bceloss"><span class="hidden-section">BCELoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#marginrankingloss"><span class="hidden-section">MarginRankingLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#hingeembeddingloss"><span class="hidden-section">HingeEmbeddingLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#multilabelmarginloss"><span class="hidden-section">MultiLabelMarginLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#smoothl1loss"><span class="hidden-section">SmoothL1Loss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#softmarginloss"><span class="hidden-section">SoftMarginLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#multilabelsoftmarginloss"><span class="hidden-section">MultiLabelSoftMarginLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#cosineembeddingloss"><span class="hidden-section">CosineEmbeddingLoss</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#multimarginloss"><span class="hidden-section">MultiMarginLoss</span></a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn.html#vision-layers">Vision layers</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#pixelshuffle"><span class="hidden-section">PixelShuffle</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#upsamplingnearest2d"><span class="hidden-section">UpsamplingNearest2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#upsamplingbilinear2d"><span class="hidden-section">UpsamplingBilinear2d</span></a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn.html#multi-gpu-layers">Multi-GPU layers</a><ul>
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<li class="toctree-l2"><a class="reference internal" href="nn.html#utilities">Utilities</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#clip-grad-norm"><span class="hidden-section">clip_grad_norm</span></a></li>
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<li class="toctree-l3"><a class="reference internal" href="nn.html#pack-padded-sequence"><span class="hidden-section">pack_padded_sequence</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#pad-packed-sequence"><span class="hidden-section">pad_packed_sequence</span></a></li>
</ul>
</li>
</ul>
</li>
<li class="toctree-l1"><a class="reference internal" href="nn.html#torch-nn-functional">torch.nn.functional</a><ul>
<li class="toctree-l2"><a class="reference internal" href="nn.html#convolution-functions">Convolution functions</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="nn.html#id14"><span class="hidden-section">conv2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id15"><span class="hidden-section">conv3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#conv-transpose1d"><span class="hidden-section">conv_transpose1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#conv-transpose2d"><span class="hidden-section">conv_transpose2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#conv-transpose3d"><span class="hidden-section">conv_transpose3d</span></a></li>
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</li>
<li class="toctree-l2"><a class="reference internal" href="nn.html#pooling-functions">Pooling functions</a><ul>
<li class="toctree-l3"><a class="reference internal" href="nn.html#avg-pool1d"><span class="hidden-section">avg_pool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#avg-pool2d"><span class="hidden-section">avg_pool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#avg-pool3d"><span class="hidden-section">avg_pool3d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#max-pool1d"><span class="hidden-section">max_pool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#max-pool2d"><span class="hidden-section">max_pool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#max-pool3d"><span class="hidden-section">max_pool3d</span></a></li>
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<li class="toctree-l3"><a class="reference internal" href="nn.html#max-unpool2d"><span class="hidden-section">max_unpool2d</span></a></li>
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<li class="toctree-l3"><a class="reference internal" href="nn.html#lp-pool2d"><span class="hidden-section">lp_pool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#adaptive-max-pool1d"><span class="hidden-section">adaptive_max_pool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#adaptive-max-pool2d"><span class="hidden-section">adaptive_max_pool2d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#adaptive-avg-pool1d"><span class="hidden-section">adaptive_avg_pool1d</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#adaptive-avg-pool2d"><span class="hidden-section">adaptive_avg_pool2d</span></a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn.html#non-linear-activation-functions">Non-linear activation functions</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="nn.html#id17"><span class="hidden-section">relu</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id18"><span class="hidden-section">hardtanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id19"><span class="hidden-section">relu6</span></a></li>
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<li class="toctree-l3"><a class="reference internal" href="nn.html#id22"><span class="hidden-section">logsigmoid</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#hardshrink"><span class="hidden-section">hardshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id23"><span class="hidden-section">tanhshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id24"><span class="hidden-section">softsign</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id25"><span class="hidden-section">softplus</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id26"><span class="hidden-section">softmin</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id27"><span class="hidden-section">softmax</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id28"><span class="hidden-section">softshrink</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#log-softmax"><span class="hidden-section">log_softmax</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id29"><span class="hidden-section">tanh</span></a></li>
<li class="toctree-l3"><a class="reference internal" href="nn.html#id30"><span class="hidden-section">sigmoid</span></a></li>
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<li class="toctree-l2"><a class="reference internal" href="nn.html#normalization-functions">Normalization functions</a><ul>
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<li class="toctree-l2"><a class="reference internal" href="nn.html#id33">Distance functions</a><ul>
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<li class="toctree-l2"><a class="reference internal" href="nn.html#id34">Loss functions</a><ul>
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<li class="toctree-l3"><a class="reference internal" href="nn.html#pad"><span class="hidden-section">pad</span></a></li>
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<li class="toctree-l4"><a class="reference internal" href="optim.html#optimizer-step-closure"><code class="docutils literal"><span class="pre">optimizer.step(closure)</span></code></a></li>
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<li class="toctree-l2"><a class="reference internal" href="multiprocessing.html#strategy-management">Strategy management</a></li>
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<li class="toctree-l3"><a class="reference internal" href="multiprocessing.html#file-system-file-system">File system - <code class="docutils literal"><span class="pre">file_system</span></code></a></li>
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<p class="caption"><span class="caption-text">torchvision Reference</span></p>
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<li class="toctree-l3"><a class="reference internal" href="torchvision/datasets.html#captions">Captions:</a></li>
<li class="toctree-l3"><a class="reference internal" href="torchvision/datasets.html#detection">Detection:</a></li>
</ul>
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<li class="toctree-l2"><a class="reference internal" href="torchvision/datasets.html#lsun">LSUN</a></li>
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<div class="section" id="torch-tensor">
<h1>torch.Tensor<a class="headerlink" href="#torch-tensor" title="Permalink to this headline">¶</a></h1>
<p>A <a class="reference internal" href="#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal"><span class="pre">torch.Tensor</span></code></a> is a multi-dimensional matrix containing elements of
a single data type.</p>
<p>Torch defines seven CPU tensor types and eight GPU tensor types:</p>
<table border="1" class="docutils">
<colgroup>
<col width="29%" />
<col width="33%" />
<col width="39%" />
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">Data type</th>
<th class="head">CPU tensor</th>
<th class="head">GPU tensor</th>
</tr>
</thead>
<tbody valign="top">
<tr class="row-even"><td>32-bit floating point</td>
<td><code class="xref py py-class docutils literal"><span class="pre">torch.FloatTensor</span></code></td>
<td><code class="xref py py-class docutils literal"><span class="pre">torch.cuda.FloatTensor</span></code></td>
</tr>
<tr class="row-odd"><td>64-bit floating point</td>
<td><code class="xref py py-class docutils literal"><span class="pre">torch.DoubleTensor</span></code></td>
<td><code class="xref py py-class docutils literal"><span class="pre">torch.cuda.DoubleTensor</span></code></td>
</tr>
<tr class="row-even"><td>16-bit floating point</td>
<td>N/A</td>
<td><code class="xref py py-class docutils literal"><span class="pre">torch.cuda.HalfTensor</span></code></td>
</tr>
<tr class="row-odd"><td>8-bit integer (unsigned)</td>
<td><code class="xref py py-class docutils literal"><span class="pre">torch.ByteTensor</span></code></td>
<td><code class="xref py py-class docutils literal"><span class="pre">torch.cuda.ByteTensor</span></code></td>
</tr>
<tr class="row-even"><td>8-bit integer (signed)</td>
<td><code class="xref py py-class docutils literal"><span class="pre">torch.CharTensor</span></code></td>
<td><code class="xref py py-class docutils literal"><span class="pre">torch.cuda.CharTensor</span></code></td>
</tr>
<tr class="row-odd"><td>16-bit integer (signed)</td>
<td><code class="xref py py-class docutils literal"><span class="pre">torch.ShortTensor</span></code></td>
<td><code class="xref py py-class docutils literal"><span class="pre">torch.cuda.ShortTensor</span></code></td>
</tr>
<tr class="row-even"><td>32-bit integer (signed)</td>
<td><code class="xref py py-class docutils literal"><span class="pre">torch.IntTensor</span></code></td>
<td><code class="xref py py-class docutils literal"><span class="pre">torch.cuda.IntTensor</span></code></td>
</tr>
<tr class="row-odd"><td>64-bit integer (signed)</td>
<td><code class="xref py py-class docutils literal"><span class="pre">torch.LongTensor</span></code></td>
<td><code class="xref py py-class docutils literal"><span class="pre">torch.cuda.LongTensor</span></code></td>
</tr>
</tbody>
</table>
<p>The <a class="reference internal" href="#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal"><span class="pre">torch.Tensor</span></code></a> constructor is an alias for the default tensor type
(<code class="xref py py-class docutils literal"><span class="pre">torch.FloatTensor</span></code>).</p>
<p>A tensor can be constructed from a Python <code class="xref py py-class docutils literal"><span class="pre">list</span></code> or sequence:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]])</span>
<span class="go">1 2 3</span>
<span class="go">4 5 6</span>
<span class="go">[torch.FloatTensor of size 2x3]</span>
</pre></div>
</div>
<p>An empty tensor can be constructed by specifying its size:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">IntTensor</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span><span class="o">.</span><span class="n">zero_</span><span class="p">()</span>
<span class="go">0 0 0 0</span>
<span class="go">0 0 0 0</span>
<span class="go">[torch.IntTensor of size 2x4]</span>
</pre></div>
</div>
<p>The contents of a tensor can be accessed and modified using Python’s indexing
and slicing notation:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">FloatTensor</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">2</span><span class="p">])</span>
<span class="go">6.0</span>
<span class="gp">>>> </span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">8</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="go"> 1 8 3</span>
<span class="go"> 4 5 6</span>
<span class="go">[torch.FloatTensor of size 2x3]</span>
</pre></div>
</div>
<p>Each tensor has an associated <code class="xref py py-class docutils literal"><span class="pre">torch.Storage</span></code>, which holds its data.
The tensor class provides multi-dimensional, <a class="reference external" href="https://en.wikipedia.org/wiki/Stride_of_an_array">strided</a>
view of a storage and defines numeric operations on it.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Methods which mutate a tensor are marked with an underscore suffix.
For example, <code class="xref py py-func docutils literal"><span class="pre">torch.FloatTensor.abs_()</span></code> computes the absolute value
in-place and returns the modified tensor, while <code class="xref py py-func docutils literal"><span class="pre">torch.FloatTensor.abs()</span></code>
computes the result in a new tensor.</p>
</div>
<dl class="class">
<dt id="torch.Tensor">
<em class="property">class </em><code class="descclassname">torch.</code><code class="descname">Tensor</code><a class="headerlink" href="#torch.Tensor" title="Permalink to this definition">¶</a></dt>
<dt>
<em class="property">class </em><code class="descclassname">torch.</code><code class="descname">Tensor</code><span class="sig-paren">(</span><em>*sizes</em><span class="sig-paren">)</span></dt>
<dt>
<em class="property">class </em><code class="descclassname">torch.</code><code class="descname">Tensor</code><span class="sig-paren">(</span><em>size</em><span class="sig-paren">)</span></dt>
<dt>
<em class="property">class </em><code class="descclassname">torch.</code><code class="descname">Tensor</code><span class="sig-paren">(</span><em>sequence</em><span class="sig-paren">)</span></dt>
<dt>
<em class="property">class </em><code class="descclassname">torch.</code><code class="descname">Tensor</code><span class="sig-paren">(</span><em>ndarray</em><span class="sig-paren">)</span></dt>
<dt>
<em class="property">class </em><code class="descclassname">torch.</code><code class="descname">Tensor</code><span class="sig-paren">(</span><em>tensor</em><span class="sig-paren">)</span></dt>
<dt>
<em class="property">class </em><code class="descclassname">torch.</code><code class="descname">Tensor</code><span class="sig-paren">(</span><em>storage</em><span class="sig-paren">)</span></dt>
<dd><p>Creates a new tensor from an optional size or data.</p>
<p>If no arguments are given, an empty zero-dimensional tensor is returned.
If a <a class="reference external" href="https://docs.scipy.org/doc/numpy/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.13)"><code class="xref py py-class docutils literal"><span class="pre">numpy.ndarray</span></code></a>, <a class="reference internal" href="#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal"><span class="pre">torch.Tensor</span></code></a>, or <code class="xref py py-class docutils literal"><span class="pre">torch.Storage</span></code>
is given, a new tensor that shares the same data is returned. If a Python
sequence is given, a new tensor is created from a copy of the sequence.</p>
<dl class="method">
<dt id="torch.Tensor.abs">
<code class="descname">abs</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.abs" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.abs" title="torch.abs"><code class="xref py py-func docutils literal"><span class="pre">torch.abs()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.abs_">
<code class="descname">abs_</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.abs_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.abs" title="torch.Tensor.abs"><code class="xref py py-meth docutils literal"><span class="pre">abs()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.acos">
<code class="descname">acos</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.acos" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.acos" title="torch.acos"><code class="xref py py-func docutils literal"><span class="pre">torch.acos()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.acos_">
<code class="descname">acos_</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.acos_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.acos" title="torch.Tensor.acos"><code class="xref py py-meth docutils literal"><span class="pre">acos()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.add">
<code class="descname">add</code><span class="sig-paren">(</span><em>value</em><span class="sig-paren">)</span><a class="headerlink" href="#torch.Tensor.add" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.add" title="torch.add"><code class="xref py py-func docutils literal"><span class="pre">torch.add()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.add_">
<code class="descname">add_</code><span class="sig-paren">(</span><em>value</em><span class="sig-paren">)</span><a class="headerlink" href="#torch.Tensor.add_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.add" title="torch.Tensor.add"><code class="xref py py-meth docutils literal"><span class="pre">add()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addbmm">
<code class="descname">addbmm</code><span class="sig-paren">(</span><em>beta=1</em>, <em>mat</em>, <em>alpha=1</em>, <em>batch1</em>, <em>batch2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addbmm" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.addbmm" title="torch.addbmm"><code class="xref py py-func docutils literal"><span class="pre">torch.addbmm()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addbmm_">
<code class="descname">addbmm_</code><span class="sig-paren">(</span><em>beta=1</em>, <em>mat</em>, <em>alpha=1</em>, <em>batch1</em>, <em>batch2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addbmm_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.addbmm" title="torch.Tensor.addbmm"><code class="xref py py-meth docutils literal"><span class="pre">addbmm()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addcdiv">
<code class="descname">addcdiv</code><span class="sig-paren">(</span><em>value=1</em>, <em>tensor1</em>, <em>tensor2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addcdiv" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.addcdiv" title="torch.addcdiv"><code class="xref py py-func docutils literal"><span class="pre">torch.addcdiv()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addcdiv_">
<code class="descname">addcdiv_</code><span class="sig-paren">(</span><em>value=1</em>, <em>tensor1</em>, <em>tensor2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addcdiv_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.addcdiv" title="torch.Tensor.addcdiv"><code class="xref py py-meth docutils literal"><span class="pre">addcdiv()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addcmul">
<code class="descname">addcmul</code><span class="sig-paren">(</span><em>value=1</em>, <em>tensor1</em>, <em>tensor2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addcmul" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.addcmul" title="torch.addcmul"><code class="xref py py-func docutils literal"><span class="pre">torch.addcmul()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addcmul_">
<code class="descname">addcmul_</code><span class="sig-paren">(</span><em>value=1</em>, <em>tensor1</em>, <em>tensor2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addcmul_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.addcmul" title="torch.Tensor.addcmul"><code class="xref py py-meth docutils literal"><span class="pre">addcmul()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addmm">
<code class="descname">addmm</code><span class="sig-paren">(</span><em>beta=1</em>, <em>mat</em>, <em>alpha=1</em>, <em>mat1</em>, <em>mat2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addmm" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.addmm" title="torch.addmm"><code class="xref py py-func docutils literal"><span class="pre">torch.addmm()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addmm_">
<code class="descname">addmm_</code><span class="sig-paren">(</span><em>beta=1</em>, <em>mat</em>, <em>alpha=1</em>, <em>mat1</em>, <em>mat2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addmm_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.addmm" title="torch.Tensor.addmm"><code class="xref py py-meth docutils literal"><span class="pre">addmm()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addmv">
<code class="descname">addmv</code><span class="sig-paren">(</span><em>beta=1</em>, <em>tensor</em>, <em>alpha=1</em>, <em>mat</em>, <em>vec</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addmv" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.addmv" title="torch.addmv"><code class="xref py py-func docutils literal"><span class="pre">torch.addmv()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addmv_">
<code class="descname">addmv_</code><span class="sig-paren">(</span><em>beta=1</em>, <em>tensor</em>, <em>alpha=1</em>, <em>mat</em>, <em>vec</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addmv_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.addmv" title="torch.Tensor.addmv"><code class="xref py py-meth docutils literal"><span class="pre">addmv()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addr">
<code class="descname">addr</code><span class="sig-paren">(</span><em>beta=1</em>, <em>alpha=1</em>, <em>vec1</em>, <em>vec2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addr" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.addr" title="torch.addr"><code class="xref py py-func docutils literal"><span class="pre">torch.addr()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addr_">
<code class="descname">addr_</code><span class="sig-paren">(</span><em>beta=1</em>, <em>alpha=1</em>, <em>vec1</em>, <em>vec2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addr_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.addr" title="torch.Tensor.addr"><code class="xref py py-meth docutils literal"><span class="pre">addr()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.apply_">
<code class="descname">apply_</code><span class="sig-paren">(</span><em>callable</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.apply_" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies the function <code class="xref py py-attr docutils literal"><span class="pre">callable</span></code> to each element in the tensor, replacing
each element with the value returned by <code class="xref py py-attr docutils literal"><span class="pre">callable</span></code>.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">This function only works with CPU tensors and should not be used in code
sections that require high performance.</p>
</div>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.asin">
<code class="descname">asin</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.asin" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.asin" title="torch.asin"><code class="xref py py-func docutils literal"><span class="pre">torch.asin()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.asin_">
<code class="descname">asin_</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.asin_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.asin" title="torch.Tensor.asin"><code class="xref py py-meth docutils literal"><span class="pre">asin()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.atan">
<code class="descname">atan</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.atan" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.atan" title="torch.atan"><code class="xref py py-func docutils literal"><span class="pre">torch.atan()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.atan2">
<code class="descname">atan2</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.atan2" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.atan2" title="torch.atan2"><code class="xref py py-func docutils literal"><span class="pre">torch.atan2()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.atan2_">
<code class="descname">atan2_</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.atan2_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.atan2" title="torch.Tensor.atan2"><code class="xref py py-meth docutils literal"><span class="pre">atan2()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.atan_">
<code class="descname">atan_</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.atan_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.atan" title="torch.Tensor.atan"><code class="xref py py-meth docutils literal"><span class="pre">atan()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.baddbmm">
<code class="descname">baddbmm</code><span class="sig-paren">(</span><em>beta=1</em>, <em>alpha=1</em>, <em>batch1</em>, <em>batch2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.baddbmm" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.baddbmm" title="torch.baddbmm"><code class="xref py py-func docutils literal"><span class="pre">torch.baddbmm()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.baddbmm_">
<code class="descname">baddbmm_</code><span class="sig-paren">(</span><em>beta=1</em>, <em>alpha=1</em>, <em>batch1</em>, <em>batch2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.baddbmm_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.baddbmm" title="torch.Tensor.baddbmm"><code class="xref py py-meth docutils literal"><span class="pre">baddbmm()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.bernoulli">
<code class="descname">bernoulli</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.bernoulli" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.bernoulli" title="torch.bernoulli"><code class="xref py py-func docutils literal"><span class="pre">torch.bernoulli()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.bernoulli_">
<code class="descname">bernoulli_</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.bernoulli_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.bernoulli" title="torch.Tensor.bernoulli"><code class="xref py py-meth docutils literal"><span class="pre">bernoulli()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.bmm">
<code class="descname">bmm</code><span class="sig-paren">(</span><em>batch2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.bmm" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.bmm" title="torch.bmm"><code class="xref py py-func docutils literal"><span class="pre">torch.bmm()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.byte">
<code class="descname">byte</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.Tensor.byte" title="Permalink to this definition">¶</a></dt>
<dd><p>Casts this tensor to byte type</p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.cauchy_">
<code class="descname">cauchy_</code><span class="sig-paren">(</span><em>median=0</em>, <em>sigma=1</em>, <em>*</em>, <em>generator=None</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.cauchy_" title="Permalink to this definition">¶</a></dt>
<dd><p>Fills the tensor with numbers drawn from the Cauchy distribution:</p>
<div class="math">
\[P(x) = \dfrac{1}{\pi} \dfrac{\sigma}{(x - median)^2 + \sigma^2}\]</div>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.ceil">
<code class="descname">ceil</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.ceil" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.ceil" title="torch.ceil"><code class="xref py py-func docutils literal"><span class="pre">torch.ceil()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.ceil_">
<code class="descname">ceil_</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.ceil_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.ceil" title="torch.Tensor.ceil"><code class="xref py py-meth docutils literal"><span class="pre">ceil()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.char">
<code class="descname">char</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.Tensor.char" title="Permalink to this definition">¶</a></dt>
<dd><p>Casts this tensor to char type</p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.chunk">
<code class="descname">chunk</code><span class="sig-paren">(</span><em>n_chunks</em>, <em>dim=0</em><span class="sig-paren">)</span><a class="headerlink" href="#torch.Tensor.chunk" title="Permalink to this definition">¶</a></dt>
<dd><p>Splits this tensor into a tuple of tensors.</p>
<p>See <a class="reference internal" href="torch.html#torch.chunk" title="torch.chunk"><code class="xref py py-func docutils literal"><span class="pre">torch.chunk()</span></code></a>.</p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.clamp">
<code class="descname">clamp</code><span class="sig-paren">(</span><em>min</em>, <em>max</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.clamp" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.clamp" title="torch.clamp"><code class="xref py py-func docutils literal"><span class="pre">torch.clamp()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.clamp_">
<code class="descname">clamp_</code><span class="sig-paren">(</span><em>min</em>, <em>max</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.clamp_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.clamp" title="torch.Tensor.clamp"><code class="xref py py-meth docutils literal"><span class="pre">clamp()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.clone">
<code class="descname">clone</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.clone" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a copy of the tensor. The copy has the same size and data type as the
original tensor.</p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.contiguous">
<code class="descname">contiguous</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.contiguous" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a contiguous Tensor containing the same data as this tensor. If this
tensor is contiguous, this function returns the original tensor.</p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.copy_">
<code class="descname">copy_</code><span class="sig-paren">(</span><em>src</em>, <em>async=False</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.copy_" title="Permalink to this definition">¶</a></dt>
<dd><p>Copies the elements from <code class="xref py py-attr docutils literal"><span class="pre">src</span></code> into this tensor and returns this tensor.</p>
<p>The source tensor should have the same number of elements as this tensor. It
may be of a different data type or reside on a different device.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>src</strong> (<a class="reference internal" href="#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – Source tensor to copy</li>
<li><strong>async</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#bool" title="(in Python v2.7)"><em>bool</em></a>) – If True and this copy is between CPU and GPU, then the copy
may occur asynchronously with respect to the host. For other
copies, this argument has no effect.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.cos">
<code class="descname">cos</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.cos" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.cos" title="torch.cos"><code class="xref py py-func docutils literal"><span class="pre">torch.cos()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.cos_">
<code class="descname">cos_</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.cos_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.cos" title="torch.Tensor.cos"><code class="xref py py-meth docutils literal"><span class="pre">cos()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.cosh">
<code class="descname">cosh</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.cosh" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.cosh" title="torch.cosh"><code class="xref py py-func docutils literal"><span class="pre">torch.cosh()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.cosh_">
<code class="descname">cosh_</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.cosh_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.cosh" title="torch.Tensor.cosh"><code class="xref py py-meth docutils literal"><span class="pre">cosh()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.cpu">
<code class="descname">cpu</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.Tensor.cpu" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a CPU copy of this tensor if it’s not already on the CPU</p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.cross">
<code class="descname">cross</code><span class="sig-paren">(</span><em>other</em>, <em>dim=-1</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.cross" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.cross" title="torch.cross"><code class="xref py py-func docutils literal"><span class="pre">torch.cross()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.cuda">
<code class="descname">cuda</code><span class="sig-paren">(</span><em>device=None</em>, <em>async=False</em><span class="sig-paren">)</span><a class="headerlink" href="#torch.Tensor.cuda" title="Permalink to this definition">¶</a></dt>
<dd><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>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>device</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#int" title="(in Python v2.7)"><em>int</em></a>) – The destination GPU id. Defaults to the current device.</li>
<li><strong>async</strong> (<a class="reference external" href="https://docs.python.org/2/library/functions.html#bool" title="(in Python v2.7)"><em>bool</em></a>) – If True and the source is in pinned memory, the copy will
be asynchronous with respect to the host. Otherwise, the
argument has no effect.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.cumprod">
<code class="descname">cumprod</code><span class="sig-paren">(</span><em>dim</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.cumprod" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.cumprod" title="torch.cumprod"><code class="xref py py-func docutils literal"><span class="pre">torch.cumprod()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.cumsum">
<code class="descname">cumsum</code><span class="sig-paren">(</span><em>dim</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.cumsum" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.cumsum" title="torch.cumsum"><code class="xref py py-func docutils literal"><span class="pre">torch.cumsum()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.data_ptr">
<code class="descname">data_ptr</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → int<a class="headerlink" href="#torch.Tensor.data_ptr" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the address of the first element of this tensor.</p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.diag">
<code class="descname">diag</code><span class="sig-paren">(</span><em>diagonal=0</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.diag" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.diag" title="torch.diag"><code class="xref py py-func docutils literal"><span class="pre">torch.diag()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.dim">
<code class="descname">dim</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → int<a class="headerlink" href="#torch.Tensor.dim" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the number of dimensions of this tensor.</p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.dist">
<code class="descname">dist</code><span class="sig-paren">(</span><em>other</em>, <em>p=2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.dist" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.dist" title="torch.dist"><code class="xref py py-func docutils literal"><span class="pre">torch.dist()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.div">
<code class="descname">div</code><span class="sig-paren">(</span><em>value</em><span class="sig-paren">)</span><a class="headerlink" href="#torch.Tensor.div" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.div" title="torch.div"><code class="xref py py-func docutils literal"><span class="pre">torch.div()</span></code></a></p>
</dd></dl>