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<div class="section" id="torch">
<h1>torch<a class="headerlink" href="#torch" title="Permalink to this headline">¶</a></h1>
<p>The torch package contains data structures for multi-dimensional
tensors and mathematical operations over these are defined.
Additionally, it provides many utilities for efficient serializing of
Tensors and arbitrary types, and other useful utilities.</p>
<p>It has a CUDA counterpart, that enables you to run your tensor computations
on an NVIDIA GPU with compute capability >= 3.0</p>
<div class="section" id="tensors">
<h2>Tensors<a class="headerlink" href="#tensors" title="Permalink to this headline">¶</a></h2>
<table class="longtable docutils colwidths-auto align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.is_tensor.html#torch.is_tensor" title="torch.is_tensor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_tensor</span></code></a></p></td>
<td><p>Returns True if <cite>obj</cite> is a PyTorch tensor.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.is_storage.html#torch.is_storage" title="torch.is_storage"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_storage</span></code></a></p></td>
<td><p>Returns True if <cite>obj</cite> is a PyTorch storage object.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.is_complex.html#torch.is_complex" title="torch.is_complex"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_complex</span></code></a></p></td>
<td><p>Returns True if the data type of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> is a complex data type i.e., one of <code class="docutils literal notranslate"><span class="pre">torch.complex64</span></code>, and <code class="docutils literal notranslate"><span class="pre">torch.complex128</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.is_floating_point.html#torch.is_floating_point" title="torch.is_floating_point"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_floating_point</span></code></a></p></td>
<td><p>Returns True if the data type of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> is a floating point data type i.e., one of <code class="docutils literal notranslate"><span class="pre">torch.float64</span></code>, <code class="docutils literal notranslate"><span class="pre">torch.float32</span></code> and <code class="docutils literal notranslate"><span class="pre">torch.float16</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.set_default_dtype.html#torch.set_default_dtype" title="torch.set_default_dtype"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_default_dtype</span></code></a></p></td>
<td><p>Sets the default floating point dtype to <code class="xref py py-attr docutils literal notranslate"><span class="pre">d</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.get_default_dtype.html#torch.get_default_dtype" title="torch.get_default_dtype"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_default_dtype</span></code></a></p></td>
<td><p>Get the current default floating point <a class="reference internal" href="tensor_attributes.html#torch.torch.dtype" title="torch.torch.dtype"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.set_default_tensor_type.html#torch.set_default_tensor_type" title="torch.set_default_tensor_type"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_default_tensor_type</span></code></a></p></td>
<td><p>Sets the default <code class="docutils literal notranslate"><span class="pre">torch.Tensor</span></code> type to floating point tensor type <code class="docutils literal notranslate"><span class="pre">t</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.numel.html#torch.numel" title="torch.numel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numel</span></code></a></p></td>
<td><p>Returns the total number of elements in the <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.set_printoptions.html#torch.set_printoptions" title="torch.set_printoptions"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_printoptions</span></code></a></p></td>
<td><p>Set options for printing.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.set_flush_denormal.html#torch.set_flush_denormal" title="torch.set_flush_denormal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_flush_denormal</span></code></a></p></td>
<td><p>Disables denormal floating numbers on CPU.</p></td>
</tr>
</tbody>
</table>
<div class="section" id="creation-ops">
<span id="tensor-creation-ops"></span><h3>Creation Ops<a class="headerlink" href="#creation-ops" title="Permalink to this headline">¶</a></h3>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Random sampling creation ops are listed under <a class="reference internal" href="#random-sampling"><span class="std std-ref">Random sampling</span></a> and
include:
<a class="reference internal" href="generated/torch.rand.html#torch.rand" title="torch.rand"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.rand()</span></code></a>
<a class="reference internal" href="generated/torch.rand_like.html#torch.rand_like" title="torch.rand_like"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.rand_like()</span></code></a>
<a class="reference internal" href="generated/torch.randn.html#torch.randn" title="torch.randn"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.randn()</span></code></a>
<a class="reference internal" href="generated/torch.randn_like.html#torch.randn_like" title="torch.randn_like"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.randn_like()</span></code></a>
<a class="reference internal" href="generated/torch.randint.html#torch.randint" title="torch.randint"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.randint()</span></code></a>
<a class="reference internal" href="generated/torch.randint_like.html#torch.randint_like" title="torch.randint_like"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.randint_like()</span></code></a>
<a class="reference internal" href="generated/torch.randperm.html#torch.randperm" title="torch.randperm"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.randperm()</span></code></a>
You may also use <a class="reference internal" href="generated/torch.empty.html#torch.empty" title="torch.empty"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.empty()</span></code></a> with the <a class="reference internal" href="#inplace-random-sampling"><span class="std std-ref">In-place random sampling</span></a>
methods to create <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> s with values sampled from a broader
range of distributions.</p>
</div>
<table class="longtable docutils colwidths-auto align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.tensor.html#torch.tensor" title="torch.tensor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tensor</span></code></a></p></td>
<td><p>Constructs a tensor with <code class="xref py py-attr docutils literal notranslate"><span class="pre">data</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.sparse_coo_tensor.html#torch.sparse_coo_tensor" title="torch.sparse_coo_tensor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sparse_coo_tensor</span></code></a></p></td>
<td><p>Constructs a sparse tensors in COO(rdinate) format with non-zero elements at the given <code class="xref py py-attr docutils literal notranslate"><span class="pre">indices</span></code> with the given <code class="xref py py-attr docutils literal notranslate"><span class="pre">values</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.as_tensor.html#torch.as_tensor" title="torch.as_tensor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">as_tensor</span></code></a></p></td>
<td><p>Convert the data into a <cite>torch.Tensor</cite>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.as_strided.html#torch.as_strided" title="torch.as_strided"><code class="xref py py-obj docutils literal notranslate"><span class="pre">as_strided</span></code></a></p></td>
<td><p>Create a view of an existing <cite>torch.Tensor</cite> <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> with specified <code class="xref py py-attr docutils literal notranslate"><span class="pre">size</span></code>, <code class="xref py py-attr docutils literal notranslate"><span class="pre">stride</span></code> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">storage_offset</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.from_numpy.html#torch.from_numpy" title="torch.from_numpy"><code class="xref py py-obj docutils literal notranslate"><span class="pre">from_numpy</span></code></a></p></td>
<td><p>Creates a <a class="reference internal" href="tensors.html#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">Tensor</span></code></a> from a <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.18)"><code class="xref py py-class docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.zeros.html#torch.zeros" title="torch.zeros"><code class="xref py py-obj docutils literal notranslate"><span class="pre">zeros</span></code></a></p></td>
<td><p>Returns a tensor filled with the scalar value <cite>0</cite>, with the shape defined by the variable argument <code class="xref py py-attr docutils literal notranslate"><span class="pre">size</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.zeros_like.html#torch.zeros_like" title="torch.zeros_like"><code class="xref py py-obj docutils literal notranslate"><span class="pre">zeros_like</span></code></a></p></td>
<td><p>Returns a tensor filled with the scalar value <cite>0</cite>, with the same size as <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.ones.html#torch.ones" title="torch.ones"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ones</span></code></a></p></td>
<td><p>Returns a tensor filled with the scalar value <cite>1</cite>, with the shape defined by the variable argument <code class="xref py py-attr docutils literal notranslate"><span class="pre">size</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.ones_like.html#torch.ones_like" title="torch.ones_like"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ones_like</span></code></a></p></td>
<td><p>Returns a tensor filled with the scalar value <cite>1</cite>, with the same size as <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.arange.html#torch.arange" title="torch.arange"><code class="xref py py-obj docutils literal notranslate"><span class="pre">arange</span></code></a></p></td>
<td><p>Returns a 1-D tensor of size <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo fence="true">⌈</mo><mfrac><mrow><mtext>end</mtext><mo>−</mo><mtext>start</mtext></mrow><mtext>step</mtext></mfrac><mo fence="true">⌉</mo></mrow><annotation encoding="application/x-tex">\left\lceil \frac{\text{end} - \text{start}}{\text{step}} \right\rceil</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.80002em;vertical-align:-0.65002em;"></span><span class="minner"><span class="mopen delimcenter" style="top:0em;"><span class="delimsizing size2">⌈</span></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.8801079999999999em;"><span style="top:-2.6550000000000002em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">step</span></span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.394em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">end</span></span><span class="mbin mtight">−</span><span class="mord text mtight"><span class="mord mtight">start</span></span></span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.481108em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span><span class="mclose delimcenter" style="top:0em;"><span class="delimsizing size2">⌉</span></span></span></span></span></span>
</span> with values from the interval <code class="docutils literal notranslate"><span class="pre">[start,</span> <span class="pre">end)</span></code> taken with common difference <code class="xref py py-attr docutils literal notranslate"><span class="pre">step</span></code> beginning from <cite>start</cite>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.range.html#torch.range" title="torch.range"><code class="xref py py-obj docutils literal notranslate"><span class="pre">range</span></code></a></p></td>
<td><p>Returns a 1-D tensor of size <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mrow><mo fence="true">⌊</mo><mfrac><mrow><mtext>end</mtext><mo>−</mo><mtext>start</mtext></mrow><mtext>step</mtext></mfrac><mo fence="true">⌋</mo></mrow><mo>+</mo><mn>1</mn></mrow><annotation encoding="application/x-tex">\left\lfloor \frac{\text{end} - \text{start}}{\text{step}} \right\rfloor + 1</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.80002em;vertical-align:-0.65002em;"></span><span class="minner"><span class="mopen delimcenter" style="top:0em;"><span class="delimsizing size2">⌊</span></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.8801079999999999em;"><span style="top:-2.6550000000000002em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">step</span></span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.394em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">end</span></span><span class="mbin mtight">−</span><span class="mord text mtight"><span class="mord mtight">start</span></span></span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.481108em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span><span class="mclose delimcenter" style="top:0em;"><span class="delimsizing size2">⌋</span></span></span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span></span><span class="base"><span class="strut" style="height:0.64444em;vertical-align:0em;"></span><span class="mord">1</span></span></span></span>
</span> with values from <code class="xref py py-attr docutils literal notranslate"><span class="pre">start</span></code> to <code class="xref py py-attr docutils literal notranslate"><span class="pre">end</span></code> with step <code class="xref py py-attr docutils literal notranslate"><span class="pre">step</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.linspace.html#torch.linspace" title="torch.linspace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">linspace</span></code></a></p></td>
<td><p>Returns a one-dimensional tensor of <code class="xref py py-attr docutils literal notranslate"><span class="pre">steps</span></code> equally spaced points between <code class="xref py py-attr docutils literal notranslate"><span class="pre">start</span></code> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">end</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.logspace.html#torch.logspace" title="torch.logspace"><code class="xref py py-obj docutils literal notranslate"><span class="pre">logspace</span></code></a></p></td>
<td><p>Returns a one-dimensional tensor of <code class="xref py py-attr docutils literal notranslate"><span class="pre">steps</span></code> points logarithmically spaced with base <code class="xref py py-attr docutils literal notranslate"><span class="pre">base</span></code> between <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msup><mtext>base</mtext><mtext>start</mtext></msup></mrow><annotation encoding="application/x-tex">{\text{base}}^{\text{start}}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.8778959999999999em;vertical-align:0em;"></span><span class="mord"><span class="mord"><span class="mord text"><span class="mord">base</span></span></span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.8778959999999999em;"><span style="top:-3.1473400000000002em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">start</span></span></span></span></span></span></span></span></span></span></span></span></span>
</span> and <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msup><mtext>base</mtext><mtext>end</mtext></msup></mrow><annotation encoding="application/x-tex">{\text{base}}^{\text{end}}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.9334479999999998em;vertical-align:0em;"></span><span class="mord"><span class="mord"><span class="mord text"><span class="mord">base</span></span></span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.9334479999999998em;"><span style="top:-3.1473400000000002em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">end</span></span></span></span></span></span></span></span></span></span></span></span></span>
</span>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.eye.html#torch.eye" title="torch.eye"><code class="xref py py-obj docutils literal notranslate"><span class="pre">eye</span></code></a></p></td>
<td><p>Returns a 2-D tensor with ones on the diagonal and zeros elsewhere.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.empty.html#torch.empty" title="torch.empty"><code class="xref py py-obj docutils literal notranslate"><span class="pre">empty</span></code></a></p></td>
<td><p>Returns a tensor filled with uninitialized data.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.empty_like.html#torch.empty_like" title="torch.empty_like"><code class="xref py py-obj docutils literal notranslate"><span class="pre">empty_like</span></code></a></p></td>
<td><p>Returns an uninitialized tensor with the same size as <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.empty_strided.html#torch.empty_strided" title="torch.empty_strided"><code class="xref py py-obj docutils literal notranslate"><span class="pre">empty_strided</span></code></a></p></td>
<td><p>Returns a tensor filled with uninitialized data.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.full.html#torch.full" title="torch.full"><code class="xref py py-obj docutils literal notranslate"><span class="pre">full</span></code></a></p></td>
<td><p>Returns a tensor of size <code class="xref py py-attr docutils literal notranslate"><span class="pre">size</span></code> filled with <code class="xref py py-attr docutils literal notranslate"><span class="pre">fill_value</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.full_like.html#torch.full_like" title="torch.full_like"><code class="xref py py-obj docutils literal notranslate"><span class="pre">full_like</span></code></a></p></td>
<td><p>full_like(input, fill_value, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.quantize_per_tensor.html#torch.quantize_per_tensor" title="torch.quantize_per_tensor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">quantize_per_tensor</span></code></a></p></td>
<td><p>Converts a float tensor to quantized tensor with given scale and zero point.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.quantize_per_channel.html#torch.quantize_per_channel" title="torch.quantize_per_channel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">quantize_per_channel</span></code></a></p></td>
<td><p>Converts a float tensor to per-channel quantized tensor with given scales and zero points.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.dequantize.html#torch.dequantize" title="torch.dequantize"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dequantize</span></code></a></p></td>
<td><p>Given a quantized Tensor, dequantize it and return an fp32 Tensor</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="indexing-slicing-joining-mutating-ops">
<h3>Indexing, Slicing, Joining, Mutating Ops<a class="headerlink" href="#indexing-slicing-joining-mutating-ops" title="Permalink to this headline">¶</a></h3>
<table class="longtable docutils colwidths-auto align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.cat.html#torch.cat" title="torch.cat"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cat</span></code></a></p></td>
<td><p>Concatenates the given sequence of <code class="xref py py-attr docutils literal notranslate"><span class="pre">seq</span></code> tensors in the given dimension.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.chunk.html#torch.chunk" title="torch.chunk"><code class="xref py py-obj docutils literal notranslate"><span class="pre">chunk</span></code></a></p></td>
<td><p>Splits a tensor into a specific number of chunks.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.gather.html#torch.gather" title="torch.gather"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gather</span></code></a></p></td>
<td><p>Gathers values along an axis specified by <cite>dim</cite>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.index_select.html#torch.index_select" title="torch.index_select"><code class="xref py py-obj docutils literal notranslate"><span class="pre">index_select</span></code></a></p></td>
<td><p>Returns a new tensor which indexes the <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor along dimension <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code> using the entries in <code class="xref py py-attr docutils literal notranslate"><span class="pre">index</span></code> which is a <cite>LongTensor</cite>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.masked_select.html#torch.masked_select" title="torch.masked_select"><code class="xref py py-obj docutils literal notranslate"><span class="pre">masked_select</span></code></a></p></td>
<td><p>Returns a new 1-D tensor which indexes the <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor according to the boolean mask <code class="xref py py-attr docutils literal notranslate"><span class="pre">mask</span></code> which is a <cite>BoolTensor</cite>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.narrow.html#torch.narrow" title="torch.narrow"><code class="xref py py-obj docutils literal notranslate"><span class="pre">narrow</span></code></a></p></td>
<td><p>Returns a new tensor that is a narrowed version of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.nonzero.html#torch.nonzero" title="torch.nonzero"><code class="xref py py-obj docutils literal notranslate"><span class="pre">nonzero</span></code></a></p></td>
<td><p></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.reshape.html#torch.reshape" title="torch.reshape"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reshape</span></code></a></p></td>
<td><p>Returns a tensor with the same data and number of elements as <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>, but with the specified shape.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.split.html#torch.split" title="torch.split"><code class="xref py py-obj docutils literal notranslate"><span class="pre">split</span></code></a></p></td>
<td><p>Splits the tensor into chunks.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.squeeze.html#torch.squeeze" title="torch.squeeze"><code class="xref py py-obj docutils literal notranslate"><span class="pre">squeeze</span></code></a></p></td>
<td><p>Returns a tensor with all the dimensions of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> of size <cite>1</cite> removed.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.stack.html#torch.stack" title="torch.stack"><code class="xref py py-obj docutils literal notranslate"><span class="pre">stack</span></code></a></p></td>
<td><p>Concatenates sequence of tensors along a new dimension.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.t.html#torch.t" title="torch.t"><code class="xref py py-obj docutils literal notranslate"><span class="pre">t</span></code></a></p></td>
<td><p>Expects <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> to be <= 2-D tensor and transposes dimensions 0 and 1.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.take.html#torch.take" title="torch.take"><code class="xref py py-obj docutils literal notranslate"><span class="pre">take</span></code></a></p></td>
<td><p>Returns a new tensor with the elements of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> at the given indices.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.transpose.html#torch.transpose" title="torch.transpose"><code class="xref py py-obj docutils literal notranslate"><span class="pre">transpose</span></code></a></p></td>
<td><p>Returns a tensor that is a transposed version of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.unbind.html#torch.unbind" title="torch.unbind"><code class="xref py py-obj docutils literal notranslate"><span class="pre">unbind</span></code></a></p></td>
<td><p>Removes a tensor dimension.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.unsqueeze.html#torch.unsqueeze" title="torch.unsqueeze"><code class="xref py py-obj docutils literal notranslate"><span class="pre">unsqueeze</span></code></a></p></td>
<td><p>Returns a new tensor with a dimension of size one inserted at the specified position.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.where.html#torch.where" title="torch.where"><code class="xref py py-obj docutils literal notranslate"><span class="pre">where</span></code></a></p></td>
<td><p>Return a tensor of elements selected from either <code class="xref py py-attr docutils literal notranslate"><span class="pre">x</span></code> or <code class="xref py py-attr docutils literal notranslate"><span class="pre">y</span></code>, depending on <code class="xref py py-attr docutils literal notranslate"><span class="pre">condition</span></code>.</p></td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="section" id="generators">
<span id="id1"></span><h2>Generators<a class="headerlink" href="#generators" title="Permalink to this headline">¶</a></h2>
<table class="longtable docutils colwidths-auto align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch._C.Generator.html#torch._C.Generator" title="torch._C.Generator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">_C.Generator</span></code></a></p></td>
<td><p>Creates and returns a generator object which manages the state of the algorithm that produces pseudo random numbers.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="random-sampling">
<span id="id2"></span><h2>Random sampling<a class="headerlink" href="#random-sampling" title="Permalink to this headline">¶</a></h2>
<table class="longtable docutils colwidths-auto align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.seed.html#torch.seed" title="torch.seed"><code class="xref py py-obj docutils literal notranslate"><span class="pre">seed</span></code></a></p></td>
<td><p>Sets the seed for generating random numbers to a non-deterministic random number.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.manual_seed.html#torch.manual_seed" title="torch.manual_seed"><code class="xref py py-obj docutils literal notranslate"><span class="pre">manual_seed</span></code></a></p></td>
<td><p>Sets the seed for generating random numbers.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.initial_seed.html#torch.initial_seed" title="torch.initial_seed"><code class="xref py py-obj docutils literal notranslate"><span class="pre">initial_seed</span></code></a></p></td>
<td><p>Returns the initial seed for generating random numbers as a Python <cite>long</cite>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.get_rng_state.html#torch.get_rng_state" title="torch.get_rng_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_rng_state</span></code></a></p></td>
<td><p>Returns the random number generator state as a <cite>torch.ByteTensor</cite>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.set_rng_state.html#torch.set_rng_state" title="torch.set_rng_state"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_rng_state</span></code></a></p></td>
<td><p>Sets the random number generator state.</p></td>
</tr>
</tbody>
</table>
<dl class="attribute">
<dt id="torch.torch.default_generator">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">default_generator</code><em class="property"> Returns the default CPU torch.Generator</em><a class="headerlink" href="#torch.torch.default_generator" title="Permalink to this definition">¶</a></dt>
<dd></dd></dl>
<table class="longtable docutils colwidths-auto align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.bernoulli.html#torch.bernoulli" title="torch.bernoulli"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bernoulli</span></code></a></p></td>
<td><p>Draws binary random numbers (0 or 1) from a Bernoulli distribution.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.multinomial.html#torch.multinomial" title="torch.multinomial"><code class="xref py py-obj docutils literal notranslate"><span class="pre">multinomial</span></code></a></p></td>
<td><p>Returns a tensor where each row contains <code class="xref py py-attr docutils literal notranslate"><span class="pre">num_samples</span></code> indices sampled from the multinomial probability distribution located in the corresponding row of tensor <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.normal.html#torch.normal" title="torch.normal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">normal</span></code></a></p></td>
<td><p>Returns a tensor of random numbers drawn from separate normal distributions whose mean and standard deviation are given.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.poisson.html#torch.poisson" title="torch.poisson"><code class="xref py py-obj docutils literal notranslate"><span class="pre">poisson</span></code></a></p></td>
<td><p>Returns a tensor of the same size as <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> with each element sampled from a Poisson distribution with rate parameter given by the corresponding element in <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> i.e.,</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.rand.html#torch.rand" title="torch.rand"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rand</span></code></a></p></td>
<td><p>Returns a tensor filled with random numbers from a uniform distribution on the interval <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">[</mo><mn>0</mn><mo separator="true">,</mo><mn>1</mn><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">[0, 1)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">[</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord">1</span><span class="mclose">)</span></span></span></span>
</span></p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.rand_like.html#torch.rand_like" title="torch.rand_like"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rand_like</span></code></a></p></td>
<td><p>Returns a tensor with the same size as <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> that is filled with random numbers from a uniform distribution on the interval <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo stretchy="false">[</mo><mn>0</mn><mo separator="true">,</mo><mn>1</mn><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">[0, 1)</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mopen">[</span><span class="mord">0</span><span class="mpunct">,</span><span class="mspace" style="margin-right:0.16666666666666666em;"></span><span class="mord">1</span><span class="mclose">)</span></span></span></span>
</span>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.randint.html#torch.randint" title="torch.randint"><code class="xref py py-obj docutils literal notranslate"><span class="pre">randint</span></code></a></p></td>
<td><p>randint(low=0, high, size, *, generator=None, out=None, </p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.randint_like.html#torch.randint_like" title="torch.randint_like"><code class="xref py py-obj docutils literal notranslate"><span class="pre">randint_like</span></code></a></p></td>
<td><p>randint_like(input, low=0, high, dtype=None, layout=torch.strided, device=None, requires_grad=False, memory_format=torch.preserve_format) -> Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.randn.html#torch.randn" title="torch.randn"><code class="xref py py-obj docutils literal notranslate"><span class="pre">randn</span></code></a></p></td>
<td><p>Returns a tensor filled with random numbers from a normal distribution with mean <cite>0</cite> and variance <cite>1</cite> (also called the standard normal distribution).</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.randn_like.html#torch.randn_like" title="torch.randn_like"><code class="xref py py-obj docutils literal notranslate"><span class="pre">randn_like</span></code></a></p></td>
<td><p>Returns a tensor with the same size as <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> that is filled with random numbers from a normal distribution with mean 0 and variance 1.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.randperm.html#torch.randperm" title="torch.randperm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">randperm</span></code></a></p></td>
<td><p>Returns a random permutation of integers from <code class="docutils literal notranslate"><span class="pre">0</span></code> to <code class="docutils literal notranslate"><span class="pre">n</span> <span class="pre">-</span> <span class="pre">1</span></code>.</p></td>
</tr>
</tbody>
</table>
<div class="section" id="in-place-random-sampling">
<span id="inplace-random-sampling"></span><h3>In-place random sampling<a class="headerlink" href="#in-place-random-sampling" title="Permalink to this headline">¶</a></h3>
<p>There are a few more in-place random sampling functions defined on Tensors as well. Click through to refer to their documentation:</p>
<ul class="simple">
<li><p><a class="reference internal" href="tensors.html#torch.Tensor.bernoulli_" title="torch.Tensor.bernoulli_"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.Tensor.bernoulli_()</span></code></a> - in-place version of <a class="reference internal" href="generated/torch.bernoulli.html#torch.bernoulli" title="torch.bernoulli"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.bernoulli()</span></code></a></p></li>
<li><p><a class="reference internal" href="tensors.html#torch.Tensor.cauchy_" title="torch.Tensor.cauchy_"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.Tensor.cauchy_()</span></code></a> - numbers drawn from the Cauchy distribution</p></li>
<li><p><a class="reference internal" href="tensors.html#torch.Tensor.exponential_" title="torch.Tensor.exponential_"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.Tensor.exponential_()</span></code></a> - numbers drawn from the exponential distribution</p></li>
<li><p><a class="reference internal" href="tensors.html#torch.Tensor.geometric_" title="torch.Tensor.geometric_"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.Tensor.geometric_()</span></code></a> - elements drawn from the geometric distribution</p></li>
<li><p><a class="reference internal" href="tensors.html#torch.Tensor.log_normal_" title="torch.Tensor.log_normal_"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.Tensor.log_normal_()</span></code></a> - samples from the log-normal distribution</p></li>
<li><p><a class="reference internal" href="tensors.html#torch.Tensor.normal_" title="torch.Tensor.normal_"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.Tensor.normal_()</span></code></a> - in-place version of <a class="reference internal" href="generated/torch.normal.html#torch.normal" title="torch.normal"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.normal()</span></code></a></p></li>
<li><p><a class="reference internal" href="tensors.html#torch.Tensor.random_" title="torch.Tensor.random_"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.Tensor.random_()</span></code></a> - numbers sampled from the discrete uniform distribution</p></li>
<li><p><a class="reference internal" href="tensors.html#torch.Tensor.uniform_" title="torch.Tensor.uniform_"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.Tensor.uniform_()</span></code></a> - numbers sampled from the continuous uniform distribution</p></li>
</ul>
</div>
<div class="section" id="quasi-random-sampling">
<h3>Quasi-random sampling<a class="headerlink" href="#quasi-random-sampling" title="Permalink to this headline">¶</a></h3>
<table class="longtable docutils colwidths-auto align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.quasirandom.SobolEngine.html#torch.quasirandom.SobolEngine" title="torch.quasirandom.SobolEngine"><code class="xref py py-obj docutils literal notranslate"><span class="pre">quasirandom.SobolEngine</span></code></a></p></td>
<td><p>The <a class="reference internal" href="generated/torch.quasirandom.SobolEngine.html#torch.quasirandom.SobolEngine" title="torch.quasirandom.SobolEngine"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.quasirandom.SobolEngine</span></code></a> is an engine for generating (scrambled) Sobol sequences.</p></td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="section" id="serialization">
<h2>Serialization<a class="headerlink" href="#serialization" title="Permalink to this headline">¶</a></h2>
<table class="longtable docutils colwidths-auto align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.save.html#torch.save" title="torch.save"><code class="xref py py-obj docutils literal notranslate"><span class="pre">save</span></code></a></p></td>
<td><p>Saves an object to a disk file.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.load.html#torch.load" title="torch.load"><code class="xref py py-obj docutils literal notranslate"><span class="pre">load</span></code></a></p></td>
<td><p>Loads an object saved with <a class="reference internal" href="generated/torch.save.html#torch.save" title="torch.save"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.save()</span></code></a> from a file.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="parallelism">
<h2>Parallelism<a class="headerlink" href="#parallelism" title="Permalink to this headline">¶</a></h2>
<table class="longtable docutils colwidths-auto align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.get_num_threads.html#torch.get_num_threads" title="torch.get_num_threads"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_num_threads</span></code></a></p></td>
<td><p>Returns the number of threads used for parallelizing CPU operations</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.set_num_threads.html#torch.set_num_threads" title="torch.set_num_threads"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_num_threads</span></code></a></p></td>
<td><p>Sets the number of threads used for intraop parallelism on CPU.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.get_num_interop_threads.html#torch.get_num_interop_threads" title="torch.get_num_interop_threads"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_num_interop_threads</span></code></a></p></td>
<td><p>Returns the number of threads used for inter-op parallelism on CPU (e.g.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.set_num_interop_threads.html#torch.set_num_interop_threads" title="torch.set_num_interop_threads"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_num_interop_threads</span></code></a></p></td>
<td><p>Sets the number of threads used for interop parallelism (e.g.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="locally-disabling-gradient-computation">
<h2>Locally disabling gradient computation<a class="headerlink" href="#locally-disabling-gradient-computation" title="Permalink to this headline">¶</a></h2>
<p>The context managers <a class="reference internal" href="generated/torch.no_grad.html#torch.no_grad" title="torch.no_grad"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.no_grad()</span></code></a>, <a class="reference internal" href="generated/torch.enable_grad.html#torch.enable_grad" title="torch.enable_grad"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.enable_grad()</span></code></a>, and
<a class="reference internal" href="generated/torch.set_grad_enabled.html#torch.set_grad_enabled" title="torch.set_grad_enabled"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.set_grad_enabled()</span></code></a> are helpful for locally disabling and enabling
gradient computation. See <a class="reference internal" href="autograd.html#locally-disable-grad"><span class="std std-ref">Locally disabling gradient computation</span></a> for more details on
their usage. These context managers are thread local, so they won’t
work if you send work to another thread using the <code class="docutils literal notranslate"><span class="pre">threading</span></code> module, etc.</p>
<p>Examples:</p>
<div class="highlight-default notranslate"><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">zeros</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">requires_grad</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="gp">>>> </span><span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">no_grad</span><span class="p">():</span>
<span class="gp">... </span> <span class="n">y</span> <span class="o">=</span> <span class="n">x</span> <span class="o">*</span> <span class="mi">2</span>
<span class="gp">>>> </span><span class="n">y</span><span class="o">.</span><span class="n">requires_grad</span>
<span class="go">False</span>
<span class="gp">>>> </span><span class="n">is_train</span> <span class="o">=</span> <span class="kc">False</span>
<span class="gp">>>> </span><span class="k">with</span> <span class="n">torch</span><span class="o">.</span><span class="n">set_grad_enabled</span><span class="p">(</span><span class="n">is_train</span><span class="p">):</span>
<span class="gp">... </span> <span class="n">y</span> <span class="o">=</span> <span class="n">x</span> <span class="o">*</span> <span class="mi">2</span>
<span class="gp">>>> </span><span class="n">y</span><span class="o">.</span><span class="n">requires_grad</span>
<span class="go">False</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">set_grad_enabled</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span> <span class="c1"># this can also be used as a function</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">x</span> <span class="o">*</span> <span class="mi">2</span>
<span class="gp">>>> </span><span class="n">y</span><span class="o">.</span><span class="n">requires_grad</span>
<span class="go">True</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">set_grad_enabled</span><span class="p">(</span><span class="kc">False</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">y</span> <span class="o">=</span> <span class="n">x</span> <span class="o">*</span> <span class="mi">2</span>
<span class="gp">>>> </span><span class="n">y</span><span class="o">.</span><span class="n">requires_grad</span>
<span class="go">False</span>
</pre></div>
</div>
<table class="longtable docutils colwidths-auto align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.no_grad.html#torch.no_grad" title="torch.no_grad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">no_grad</span></code></a></p></td>
<td><p>Context-manager that disabled gradient calculation.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.enable_grad.html#torch.enable_grad" title="torch.enable_grad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">enable_grad</span></code></a></p></td>
<td><p>Context-manager that enables gradient calculation.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.set_grad_enabled.html#torch.set_grad_enabled" title="torch.set_grad_enabled"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_grad_enabled</span></code></a></p></td>
<td><p>Context-manager that sets gradient calculation to on or off.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="math-operations">
<h2>Math operations<a class="headerlink" href="#math-operations" title="Permalink to this headline">¶</a></h2>
<div class="section" id="pointwise-ops">
<h3>Pointwise Ops<a class="headerlink" href="#pointwise-ops" title="Permalink to this headline">¶</a></h3>
<table class="longtable docutils colwidths-auto align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.abs.html#torch.abs" title="torch.abs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">abs</span></code></a></p></td>
<td><p>Computes the element-wise absolute value of the given <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.absolute.html#torch.absolute" title="torch.absolute"><code class="xref py py-obj docutils literal notranslate"><span class="pre">absolute</span></code></a></p></td>
<td><p>Alias for <a class="reference internal" href="generated/torch.abs.html#torch.abs" title="torch.abs"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.abs()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.acos.html#torch.acos" title="torch.acos"><code class="xref py py-obj docutils literal notranslate"><span class="pre">acos</span></code></a></p></td>
<td><p>Returns a new tensor with the arccosine of the elements of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.add.html#torch.add" title="torch.add"><code class="xref py py-obj docutils literal notranslate"><span class="pre">add</span></code></a></p></td>
<td><p>Adds the scalar <code class="xref py py-attr docutils literal notranslate"><span class="pre">other</span></code> to each element of the input <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> and returns a new resulting tensor.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.addcdiv.html#torch.addcdiv" title="torch.addcdiv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">addcdiv</span></code></a></p></td>
<td><p>Performs the element-wise division of <code class="xref py py-attr docutils literal notranslate"><span class="pre">tensor1</span></code> by <code class="xref py py-attr docutils literal notranslate"><span class="pre">tensor2</span></code>, multiply the result by the scalar <code class="xref py py-attr docutils literal notranslate"><span class="pre">value</span></code> and add it to <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.addcmul.html#torch.addcmul" title="torch.addcmul"><code class="xref py py-obj docutils literal notranslate"><span class="pre">addcmul</span></code></a></p></td>
<td><p>Performs the element-wise multiplication of <code class="xref py py-attr docutils literal notranslate"><span class="pre">tensor1</span></code> by <code class="xref py py-attr docutils literal notranslate"><span class="pre">tensor2</span></code>, multiply the result by the scalar <code class="xref py py-attr docutils literal notranslate"><span class="pre">value</span></code> and add it to <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.angle.html#torch.angle" title="torch.angle"><code class="xref py py-obj docutils literal notranslate"><span class="pre">angle</span></code></a></p></td>
<td><p>Computes the element-wise angle (in radians) of the given <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.asin.html#torch.asin" title="torch.asin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">asin</span></code></a></p></td>
<td><p>Returns a new tensor with the arcsine of the elements of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.atan.html#torch.atan" title="torch.atan"><code class="xref py py-obj docutils literal notranslate"><span class="pre">atan</span></code></a></p></td>
<td><p>Returns a new tensor with the arctangent of the elements of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.atan2.html#torch.atan2" title="torch.atan2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">atan2</span></code></a></p></td>
<td><p>Element-wise arctangent of <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msub><mtext>input</mtext><mi>i</mi></msub><mi mathvariant="normal">/</mi><msub><mtext>other</mtext><mi>i</mi></msub></mrow><annotation encoding="application/x-tex">\text{input}_{i} / \text{other}_{i}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1em;vertical-align:-0.25em;"></span><span class="mord"><span class="mord text"><span class="mord">input</span></span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.21752399999999997em;"><span style="top:-2.4558600000000004em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathdefault mtight">i</span></span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.24414em;"><span></span></span></span></span></span></span><span class="mord">/</span><span class="mord"><span class="mord text"><span class="mord">other</span></span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.31166399999999994em;"><span style="top:-2.5500000000000003em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathdefault mtight">i</span></span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span></span></span></span>
</span> with consideration of the quadrant.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.bitwise_not.html#torch.bitwise_not" title="torch.bitwise_not"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bitwise_not</span></code></a></p></td>
<td><p>Computes the bitwise NOT of the given input tensor.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.bitwise_and.html#torch.bitwise_and" title="torch.bitwise_and"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bitwise_and</span></code></a></p></td>
<td><p>Computes the bitwise AND of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">other</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.bitwise_or.html#torch.bitwise_or" title="torch.bitwise_or"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bitwise_or</span></code></a></p></td>
<td><p>Computes the bitwise OR of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">other</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.bitwise_xor.html#torch.bitwise_xor" title="torch.bitwise_xor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">bitwise_xor</span></code></a></p></td>
<td><p>Computes the bitwise XOR of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">other</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.ceil.html#torch.ceil" title="torch.ceil"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ceil</span></code></a></p></td>
<td><p>Returns a new tensor with the ceil of the elements of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>, the smallest integer greater than or equal to each element.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.clamp.html#torch.clamp" title="torch.clamp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">clamp</span></code></a></p></td>
<td><p>Clamp all elements in <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> into the range <cite>[</cite> <a class="reference internal" href="generated/torch.min.html#torch.min" title="torch.min"><code class="xref py py-attr docutils literal notranslate"><span class="pre">min</span></code></a>, <a class="reference internal" href="generated/torch.max.html#torch.max" title="torch.max"><code class="xref py py-attr docutils literal notranslate"><span class="pre">max</span></code></a> <cite>]</cite> and return a resulting tensor:</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.conj.html#torch.conj" title="torch.conj"><code class="xref py py-obj docutils literal notranslate"><span class="pre">conj</span></code></a></p></td>
<td><p>Computes the element-wise conjugate of the given <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.cos.html#torch.cos" title="torch.cos"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cos</span></code></a></p></td>
<td><p>Returns a new tensor with the cosine of the elements of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.cosh.html#torch.cosh" title="torch.cosh"><code class="xref py py-obj docutils literal notranslate"><span class="pre">cosh</span></code></a></p></td>
<td><p>Returns a new tensor with the hyperbolic cosine of the elements of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.div.html#torch.div" title="torch.div"><code class="xref py py-obj docutils literal notranslate"><span class="pre">div</span></code></a></p></td>
<td><p>Divides each element of the input <code class="docutils literal notranslate"><span class="pre">input</span></code> with the scalar <code class="docutils literal notranslate"><span class="pre">other</span></code> and returns a new resulting tensor.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.digamma.html#torch.digamma" title="torch.digamma"><code class="xref py py-obj docutils literal notranslate"><span class="pre">digamma</span></code></a></p></td>
<td><p>Computes the logarithmic derivative of the gamma function on <cite>input</cite>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.erf.html#torch.erf" title="torch.erf"><code class="xref py py-obj docutils literal notranslate"><span class="pre">erf</span></code></a></p></td>
<td><p>Computes the error function of each element.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.erfc.html#torch.erfc" title="torch.erfc"><code class="xref py py-obj docutils literal notranslate"><span class="pre">erfc</span></code></a></p></td>
<td><p>Computes the complementary error function of each element of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.erfinv.html#torch.erfinv" title="torch.erfinv"><code class="xref py py-obj docutils literal notranslate"><span class="pre">erfinv</span></code></a></p></td>
<td><p>Computes the inverse error function of each element of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.exp.html#torch.exp" title="torch.exp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">exp</span></code></a></p></td>
<td><p>Returns a new tensor with the exponential of the elements of the input tensor <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.expm1.html#torch.expm1" title="torch.expm1"><code class="xref py py-obj docutils literal notranslate"><span class="pre">expm1</span></code></a></p></td>
<td><p>Returns a new tensor with the exponential of the elements minus 1 of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.floor.html#torch.floor" title="torch.floor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">floor</span></code></a></p></td>
<td><p>Returns a new tensor with the floor of the elements of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>, the largest integer less than or equal to each element.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.floor_divide.html#torch.floor_divide" title="torch.floor_divide"><code class="xref py py-obj docutils literal notranslate"><span class="pre">floor_divide</span></code></a></p></td>
<td><p>Return the division of the inputs rounded down to the nearest integer.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.fmod.html#torch.fmod" title="torch.fmod"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fmod</span></code></a></p></td>
<td><p>Computes the element-wise remainder of division.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.frac.html#torch.frac" title="torch.frac"><code class="xref py py-obj docutils literal notranslate"><span class="pre">frac</span></code></a></p></td>
<td><p>Computes the fractional portion of each element in <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.imag.html#torch.imag" title="torch.imag"><code class="xref py py-obj docutils literal notranslate"><span class="pre">imag</span></code></a></p></td>
<td><p>Returns the imaginary part of the <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.lerp.html#torch.lerp" title="torch.lerp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lerp</span></code></a></p></td>
<td><p>Does a linear interpolation of two tensors <code class="xref py py-attr docutils literal notranslate"><span class="pre">start</span></code> (given by <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>) and <code class="xref py py-attr docutils literal notranslate"><span class="pre">end</span></code> based on a scalar or tensor <code class="xref py py-attr docutils literal notranslate"><span class="pre">weight</span></code> and returns the resulting <code class="xref py py-attr docutils literal notranslate"><span class="pre">out</span></code> tensor.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.lgamma.html#torch.lgamma" title="torch.lgamma"><code class="xref py py-obj docutils literal notranslate"><span class="pre">lgamma</span></code></a></p></td>
<td><p>Computes the logarithm of the gamma function on <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.log.html#torch.log" title="torch.log"><code class="xref py py-obj docutils literal notranslate"><span class="pre">log</span></code></a></p></td>
<td><p>Returns a new tensor with the natural logarithm of the elements of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.log10.html#torch.log10" title="torch.log10"><code class="xref py py-obj docutils literal notranslate"><span class="pre">log10</span></code></a></p></td>
<td><p>Returns a new tensor with the logarithm to the base 10 of the elements of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.log1p.html#torch.log1p" title="torch.log1p"><code class="xref py py-obj docutils literal notranslate"><span class="pre">log1p</span></code></a></p></td>
<td><p>Returns a new tensor with the natural logarithm of (1 + <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>).</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.log2.html#torch.log2" title="torch.log2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">log2</span></code></a></p></td>
<td><p>Returns a new tensor with the logarithm to the base 2 of the elements of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.logical_and.html#torch.logical_and" title="torch.logical_and"><code class="xref py py-obj docutils literal notranslate"><span class="pre">logical_and</span></code></a></p></td>
<td><p>Computes the element-wise logical AND of the given input tensors.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.logical_not.html#torch.logical_not" title="torch.logical_not"><code class="xref py py-obj docutils literal notranslate"><span class="pre">logical_not</span></code></a></p></td>
<td><p>Computes the element-wise logical NOT of the given input tensor.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.logical_or.html#torch.logical_or" title="torch.logical_or"><code class="xref py py-obj docutils literal notranslate"><span class="pre">logical_or</span></code></a></p></td>
<td><p>Computes the element-wise logical OR of the given input tensors.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.logical_xor.html#torch.logical_xor" title="torch.logical_xor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">logical_xor</span></code></a></p></td>
<td><p>Computes the element-wise logical XOR of the given input tensors.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.mul.html#torch.mul" title="torch.mul"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mul</span></code></a></p></td>
<td><p>Multiplies each element of the input <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> with the scalar <code class="xref py py-attr docutils literal notranslate"><span class="pre">other</span></code> and returns a new resulting tensor.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.mvlgamma.html#torch.mvlgamma" title="torch.mvlgamma"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mvlgamma</span></code></a></p></td>
<td><p>Computes the <a class="reference external" href="https://en.wikipedia.org/wiki/Multivariate_gamma_function">multivariate log-gamma function</a>) with dimension <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>p</mi></mrow><annotation encoding="application/x-tex">p</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.625em;vertical-align:-0.19444em;"></span><span class="mord mathdefault">p</span></span></span></span>
</span> element-wise, given by</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.neg.html#torch.neg" title="torch.neg"><code class="xref py py-obj docutils literal notranslate"><span class="pre">neg</span></code></a></p></td>
<td><p>Returns a new tensor with the negative of the elements of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.polygamma.html#torch.polygamma" title="torch.polygamma"><code class="xref py py-obj docutils literal notranslate"><span class="pre">polygamma</span></code></a></p></td>
<td><p>Computes the <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msup><mi>n</mi><mrow><mi>t</mi><mi>h</mi></mrow></msup></mrow><annotation encoding="application/x-tex">n^{th}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.849108em;vertical-align:0em;"></span><span class="mord"><span class="mord mathdefault">n</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.849108em;"><span style="top:-3.063em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathdefault mtight">t</span><span class="mord mathdefault mtight">h</span></span></span></span></span></span></span></span></span></span></span></span>
</span> derivative of the digamma function on <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.pow.html#torch.pow" title="torch.pow"><code class="xref py py-obj docutils literal notranslate"><span class="pre">pow</span></code></a></p></td>
<td><p>Takes the power of each element in <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> with <code class="xref py py-attr docutils literal notranslate"><span class="pre">exponent</span></code> and returns a tensor with the result.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.real.html#torch.real" title="torch.real"><code class="xref py py-obj docutils literal notranslate"><span class="pre">real</span></code></a></p></td>
<td><p>Returns the real part of the <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.reciprocal.html#torch.reciprocal" title="torch.reciprocal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">reciprocal</span></code></a></p></td>
<td><p>Returns a new tensor with the reciprocal of the elements of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code></p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.remainder.html#torch.remainder" title="torch.remainder"><code class="xref py py-obj docutils literal notranslate"><span class="pre">remainder</span></code></a></p></td>
<td><p>Computes the element-wise remainder of division.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.round.html#torch.round" title="torch.round"><code class="xref py py-obj docutils literal notranslate"><span class="pre">round</span></code></a></p></td>
<td><p>Returns a new tensor with each of the elements of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> rounded to the closest integer.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.rsqrt.html#torch.rsqrt" title="torch.rsqrt"><code class="xref py py-obj docutils literal notranslate"><span class="pre">rsqrt</span></code></a></p></td>
<td><p>Returns a new tensor with the reciprocal of the square-root of each of the elements of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.sigmoid.html#torch.sigmoid" title="torch.sigmoid"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sigmoid</span></code></a></p></td>
<td><p>Returns a new tensor with the sigmoid of the elements of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.sign.html#torch.sign" title="torch.sign"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sign</span></code></a></p></td>
<td><p>Returns a new tensor with the signs of the elements of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.sin.html#torch.sin" title="torch.sin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sin</span></code></a></p></td>
<td><p>Returns a new tensor with the sine of the elements of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.sinh.html#torch.sinh" title="torch.sinh"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sinh</span></code></a></p></td>
<td><p>Returns a new tensor with the hyperbolic sine of the elements of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.sqrt.html#torch.sqrt" title="torch.sqrt"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sqrt</span></code></a></p></td>
<td><p>Returns a new tensor with the square-root of the elements of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.square.html#torch.square" title="torch.square"><code class="xref py py-obj docutils literal notranslate"><span class="pre">square</span></code></a></p></td>
<td><p>Returns a new tensor with the square of the elements of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.tan.html#torch.tan" title="torch.tan"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tan</span></code></a></p></td>
<td><p>Returns a new tensor with the tangent of the elements of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.tanh.html#torch.tanh" title="torch.tanh"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tanh</span></code></a></p></td>
<td><p>Returns a new tensor with the hyperbolic tangent of the elements of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.true_divide.html#torch.true_divide" title="torch.true_divide"><code class="xref py py-obj docutils literal notranslate"><span class="pre">true_divide</span></code></a></p></td>
<td><p>Performs “true division” that always computes the division in floating point.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.trunc.html#torch.trunc" title="torch.trunc"><code class="xref py py-obj docutils literal notranslate"><span class="pre">trunc</span></code></a></p></td>
<td><p>Returns a new tensor with the truncated integer values of the elements of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="reduction-ops">
<h3>Reduction Ops<a class="headerlink" href="#reduction-ops" title="Permalink to this headline">¶</a></h3>
<table class="longtable docutils colwidths-auto align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.argmax.html#torch.argmax" title="torch.argmax"><code class="xref py py-obj docutils literal notranslate"><span class="pre">argmax</span></code></a></p></td>
<td><p>Returns the indices of the maximum value of all elements in the <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.argmin.html#torch.argmin" title="torch.argmin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">argmin</span></code></a></p></td>
<td><p>Returns the indices of the minimum value of all elements in the <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.dist.html#torch.dist" title="torch.dist"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dist</span></code></a></p></td>
<td><p>Returns the p-norm of (<code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> - <code class="xref py py-attr docutils literal notranslate"><span class="pre">other</span></code>)</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.logsumexp.html#torch.logsumexp" title="torch.logsumexp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">logsumexp</span></code></a></p></td>
<td><p>Returns the log of summed exponentials of each row of the <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor in the given dimension <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.mean.html#torch.mean" title="torch.mean"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mean</span></code></a></p></td>
<td><p>Returns the mean value of all elements in the <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.median.html#torch.median" title="torch.median"><code class="xref py py-obj docutils literal notranslate"><span class="pre">median</span></code></a></p></td>
<td><p>Returns the median value of all elements in the <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.mode.html#torch.mode" title="torch.mode"><code class="xref py py-obj docutils literal notranslate"><span class="pre">mode</span></code></a></p></td>
<td><p>Returns a namedtuple <code class="docutils literal notranslate"><span class="pre">(values,</span> <span class="pre">indices)</span></code> where <code class="docutils literal notranslate"><span class="pre">values</span></code> is the mode value of each row of the <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor in the given dimension <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code>, i.e.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.norm.html#torch.norm" title="torch.norm"><code class="xref py py-obj docutils literal notranslate"><span class="pre">norm</span></code></a></p></td>
<td><p>Returns the matrix norm or vector norm of a given tensor.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.prod.html#torch.prod" title="torch.prod"><code class="xref py py-obj docutils literal notranslate"><span class="pre">prod</span></code></a></p></td>
<td><p>Returns the product of all elements in the <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.std.html#torch.std" title="torch.std"><code class="xref py py-obj docutils literal notranslate"><span class="pre">std</span></code></a></p></td>
<td><p>Returns the standard-deviation of all elements in the <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.std_mean.html#torch.std_mean" title="torch.std_mean"><code class="xref py py-obj docutils literal notranslate"><span class="pre">std_mean</span></code></a></p></td>
<td><p>Returns the standard-deviation and mean of all elements in the <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.sum.html#torch.sum" title="torch.sum"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sum</span></code></a></p></td>
<td><p>Returns the sum of all elements in the <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.unique.html#torch.unique" title="torch.unique"><code class="xref py py-obj docutils literal notranslate"><span class="pre">unique</span></code></a></p></td>
<td><p>Returns the unique elements of the input tensor.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.unique_consecutive.html#torch.unique_consecutive" title="torch.unique_consecutive"><code class="xref py py-obj docutils literal notranslate"><span class="pre">unique_consecutive</span></code></a></p></td>
<td><p>Eliminates all but the first element from every consecutive group of equivalent elements.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.var.html#torch.var" title="torch.var"><code class="xref py py-obj docutils literal notranslate"><span class="pre">var</span></code></a></p></td>
<td><p>Returns the variance of all elements in the <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.var_mean.html#torch.var_mean" title="torch.var_mean"><code class="xref py py-obj docutils literal notranslate"><span class="pre">var_mean</span></code></a></p></td>
<td><p>Returns the variance and mean of all elements in the <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor.</p></td>
</tr>
</tbody>
</table>
</div>
<div class="section" id="comparison-ops">
<h3>Comparison Ops<a class="headerlink" href="#comparison-ops" title="Permalink to this headline">¶</a></h3>
<table class="longtable docutils colwidths-auto align-default">
<tbody>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.allclose.html#torch.allclose" title="torch.allclose"><code class="xref py py-obj docutils literal notranslate"><span class="pre">allclose</span></code></a></p></td>
<td><p>This function checks if all <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">other</span></code> satisfy the condition:</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.argsort.html#torch.argsort" title="torch.argsort"><code class="xref py py-obj docutils literal notranslate"><span class="pre">argsort</span></code></a></p></td>
<td><p>Returns the indices that sort a tensor along a given dimension in ascending order by value.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.eq.html#torch.eq" title="torch.eq"><code class="xref py py-obj docutils literal notranslate"><span class="pre">eq</span></code></a></p></td>
<td><p>Computes element-wise equality</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.equal.html#torch.equal" title="torch.equal"><code class="xref py py-obj docutils literal notranslate"><span class="pre">equal</span></code></a></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">True</span></code> if two tensors have the same size and elements, <code class="docutils literal notranslate"><span class="pre">False</span></code> otherwise.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.ge.html#torch.ge" title="torch.ge"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ge</span></code></a></p></td>
<td><p>Computes <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>input</mtext><mo>≥</mo><mtext>other</mtext></mrow><annotation encoding="application/x-tex">\text{input} \geq \text{other}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.8623000000000001em;vertical-align:-0.19444em;"></span><span class="mord text"><span class="mord">input</span></span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">≥</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:0.69444em;vertical-align:0em;"></span><span class="mord text"><span class="mord">other</span></span></span></span></span>
</span> element-wise.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.gt.html#torch.gt" title="torch.gt"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gt</span></code></a></p></td>
<td><p>Computes <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>input</mtext><mo>></mo><mtext>other</mtext></mrow><annotation encoding="application/x-tex">\text{input} > \text{other}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.8623000000000001em;vertical-align:-0.19444em;"></span><span class="mord text"><span class="mord">input</span></span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">></span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:0.69444em;vertical-align:0em;"></span><span class="mord text"><span class="mord">other</span></span></span></span></span>
</span> element-wise.</p></td>
</tr>
<tr class="row-odd"><td><p><a class="reference internal" href="generated/torch.isclose.html#torch.isclose" title="torch.isclose"><code class="xref py py-obj docutils literal notranslate"><span class="pre">isclose</span></code></a></p></td>
<td><p>Returns a new tensor with boolean elements representing if each element of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> is “close” to the corresponding element of <code class="xref py py-attr docutils literal notranslate"><span class="pre">other</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><a class="reference internal" href="generated/torch.isfinite.html#torch.isfinite" title="torch.isfinite"><code class="xref py py-obj docutils literal notranslate"><span class="pre">isfinite</span></code></a></p></td>
<td><p>Returns a new tensor with boolean elements representing if each element is <cite>finite</cite> or not.</p></td>