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<p class="caption"><span class="caption-text">Notes</span></p>
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<li class="toctree-l1"><a class="reference internal" href="notes/amp_examples.html">Automatic Mixed Precision examples</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/autograd.html">Autograd mechanics</a></li>
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<li class="toctree-l1"><a class="reference internal" href="notes/cpu_threading_torchscript_inference.html">CPU threading and TorchScript inference</a></li>
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<li class="toctree-l1 current"><a class="current reference internal" href="#">torch</a></li>
<li class="toctree-l1"><a class="reference internal" href="nn.html">torch.nn</a></li>
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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 defines mathematical operations over these tensors.
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><p id="torch.is_tensor"/><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><p id="torch.is_storage"/><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><p id="torch.is_complex"/><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><p id="torch.is_floating_point"/><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>, <code class="docutils literal notranslate"><span class="pre">torch.float16</span></code>, and <code class="docutils literal notranslate"><span class="pre">torch.bfloat16</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.is_nonzero"/><a class="reference internal" href="generated/torch.is_nonzero.html#torch.is_nonzero" title="torch.is_nonzero"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_nonzero</span></code></a></p></td>
<td><p>Returns True if the <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> is a single element tensor which is not equal to zero after type conversions.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.set_default_dtype"/><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-odd"><td><p><p id="torch.get_default_dtype"/><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-even"><td><p><p id="torch.set_default_tensor_type"/><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-odd"><td><p><p id="torch.numel"/><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-even"><td><p><p id="torch.set_printoptions"/><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-odd"><td><p><p id="torch.set_flush_denormal"/><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><p id="torch.tensor"/><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><p id="torch.sparse_coo_tensor"/><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 <a class="reference internal" href="sparse.html#sparse-coo-docs"><span class="std std-ref">sparse tensor in COO(rdinate) format</span></a> with specified values at the given <code class="xref py py-attr docutils literal notranslate"><span class="pre">indices</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.as_tensor"/><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><p id="torch.as_strided"/><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><p id="torch.from_numpy"/><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 v1.21)"><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><p id="torch.zeros"/><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><p id="torch.zeros_like"/><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><p id="torch.ones"/><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><p id="torch.ones_like"/><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><p id="torch.arange"/><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><p id="torch.range"/><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><p id="torch.linspace"/><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>Creates a one-dimensional tensor of size <code class="xref py py-attr docutils literal notranslate"><span class="pre">steps</span></code> whose values are evenly spaced 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>, inclusive.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.logspace"/><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>Creates a one-dimensional tensor of size <code class="xref py py-attr docutils literal notranslate"><span class="pre">steps</span></code> whose values are evenly spaced from <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"><span class="mord text"><span class="mord"><span class="mord">base</span></span></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 mtight"><span class="mord text mtight"><span class="mord mtight"><span class="mord mtight">start</span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span> to <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"><span class="mord text"><span class="mord"><span class="mord">base</span></span></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 mtight"><span class="mord text mtight"><span class="mord mtight"><span class="mord mtight">end</span></span></span></span></span></span></span></span></span></span></span></span></span></span></span></span>, inclusive, on a logarithmic scale with base <code class="xref py py-attr docutils literal notranslate"><span class="pre">base</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.eye"/><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><p id="torch.empty"/><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><p id="torch.empty_like"/><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><p id="torch.empty_strided"/><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><p id="torch.full"/><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>Creates 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><p id="torch.full_like"/><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>Returns a tensor with the same size as <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</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-even"><td><p><p id="torch.quantize_per_tensor"/><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 a quantized tensor with given scale and zero point.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.quantize_per_channel"/><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 a per-channel quantized tensor with given scales and zero points.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.dequantize"/><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>Returns an fp32 Tensor by dequantizing a quantized Tensor</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.complex"/><a class="reference internal" href="generated/torch.complex.html#torch.complex" title="torch.complex"><code class="xref py py-obj docutils literal notranslate"><span class="pre">complex</span></code></a></p></td>
<td><p>Constructs a complex tensor with its real part equal to <a class="reference internal" href="generated/torch.real.html#torch.real" title="torch.real"><code class="xref py py-attr docutils literal notranslate"><span class="pre">real</span></code></a> and its imaginary part equal to <a class="reference internal" href="generated/torch.imag.html#torch.imag" title="torch.imag"><code class="xref py py-attr docutils literal notranslate"><span class="pre">imag</span></code></a>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.polar"/><a class="reference internal" href="generated/torch.polar.html#torch.polar" title="torch.polar"><code class="xref py py-obj docutils literal notranslate"><span class="pre">polar</span></code></a></p></td>
<td><p>Constructs a complex tensor whose elements are Cartesian coordinates corresponding to the polar coordinates with absolute value <a class="reference internal" href="generated/torch.abs.html#torch.abs" title="torch.abs"><code class="xref py py-attr docutils literal notranslate"><span class="pre">abs</span></code></a> and angle <a class="reference internal" href="generated/torch.angle.html#torch.angle" title="torch.angle"><code class="xref py py-attr docutils literal notranslate"><span class="pre">angle</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.heaviside"/><a class="reference internal" href="generated/torch.heaviside.html#torch.heaviside" title="torch.heaviside"><code class="xref py py-obj docutils literal notranslate"><span class="pre">heaviside</span></code></a></p></td>
<td><p>Computes the Heaviside step function for each element in <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="indexing-slicing-joining-mutating-ops">
<span id="indexing-slicing-joining"></span><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><p id="torch.cat"/><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><p id="torch.chunk"/><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><p id="torch.dsplit"/><a class="reference internal" href="generated/torch.dsplit.html#torch.dsplit" title="torch.dsplit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dsplit</span></code></a></p></td>
<td><p>Splits <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>, a tensor with three or more dimensions, into multiple tensors depthwise according to <code class="xref py py-attr docutils literal notranslate"><span class="pre">indices_or_sections</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.column_stack"/><a class="reference internal" href="generated/torch.column_stack.html#torch.column_stack" title="torch.column_stack"><code class="xref py py-obj docutils literal notranslate"><span class="pre">column_stack</span></code></a></p></td>
<td><p>Creates a new tensor by horizontally stacking the tensors in <code class="xref py py-attr docutils literal notranslate"><span class="pre">tensors</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.dstack"/><a class="reference internal" href="generated/torch.dstack.html#torch.dstack" title="torch.dstack"><code class="xref py py-obj docutils literal notranslate"><span class="pre">dstack</span></code></a></p></td>
<td><p>Stack tensors in sequence depthwise (along third axis).</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.gather"/><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-odd"><td><p><p id="torch.hsplit"/><a class="reference internal" href="generated/torch.hsplit.html#torch.hsplit" title="torch.hsplit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hsplit</span></code></a></p></td>
<td><p>Splits <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>, a tensor with one or more dimensions, into multiple tensors horizontally according to <code class="xref py py-attr docutils literal notranslate"><span class="pre">indices_or_sections</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.hstack"/><a class="reference internal" href="generated/torch.hstack.html#torch.hstack" title="torch.hstack"><code class="xref py py-obj docutils literal notranslate"><span class="pre">hstack</span></code></a></p></td>
<td><p>Stack tensors in sequence horizontally (column wise).</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.index_select"/><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-even"><td><p><p id="torch.masked_select"/><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-odd"><td><p><p id="torch.movedim"/><a class="reference internal" href="generated/torch.movedim.html#torch.movedim" title="torch.movedim"><code class="xref py py-obj docutils literal notranslate"><span class="pre">movedim</span></code></a></p></td>
<td><p>Moves the dimension(s) of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> at the position(s) in <code class="xref py py-attr docutils literal notranslate"><span class="pre">source</span></code> to the position(s) in <code class="xref py py-attr docutils literal notranslate"><span class="pre">destination</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.moveaxis"/><a class="reference internal" href="generated/torch.moveaxis.html#torch.moveaxis" title="torch.moveaxis"><code class="xref py py-obj docutils literal notranslate"><span class="pre">moveaxis</span></code></a></p></td>
<td><p>Alias for <a class="reference internal" href="generated/torch.movedim.html#torch.movedim" title="torch.movedim"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.movedim()</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.narrow"/><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-even"><td><p><p id="torch.nonzero"/><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-odd"><td><p><p id="torch.reshape"/><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-even"><td><p><p id="torch.row_stack"/><a class="reference internal" href="generated/torch.row_stack.html#torch.row_stack" title="torch.row_stack"><code class="xref py py-obj docutils literal notranslate"><span class="pre">row_stack</span></code></a></p></td>
<td><p>Alias of <a class="reference internal" href="generated/torch.vstack.html#torch.vstack" title="torch.vstack"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.vstack()</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.scatter"/><a class="reference internal" href="generated/torch.scatter.html#torch.scatter" title="torch.scatter"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scatter</span></code></a></p></td>
<td><p>Out-of-place version of <a class="reference internal" href="generated/torch.Tensor.scatter_.html#torch.Tensor.scatter_" title="torch.Tensor.scatter_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.scatter_()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.scatter_add"/><a class="reference internal" href="generated/torch.scatter_add.html#torch.scatter_add" title="torch.scatter_add"><code class="xref py py-obj docutils literal notranslate"><span class="pre">scatter_add</span></code></a></p></td>
<td><p>Out-of-place version of <a class="reference internal" href="generated/torch.Tensor.scatter_add_.html#torch.Tensor.scatter_add_" title="torch.Tensor.scatter_add_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.scatter_add_()</span></code></a></p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.split"/><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><p id="torch.squeeze"/><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><p id="torch.stack"/><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 a sequence of tensors along a new dimension.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.swapaxes"/><a class="reference internal" href="generated/torch.swapaxes.html#torch.swapaxes" title="torch.swapaxes"><code class="xref py py-obj docutils literal notranslate"><span class="pre">swapaxes</span></code></a></p></td>
<td><p>Alias for <a class="reference internal" href="generated/torch.transpose.html#torch.transpose" title="torch.transpose"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.transpose()</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.swapdims"/><a class="reference internal" href="generated/torch.swapdims.html#torch.swapdims" title="torch.swapdims"><code class="xref py py-obj docutils literal notranslate"><span class="pre">swapdims</span></code></a></p></td>
<td><p>Alias for <a class="reference internal" href="generated/torch.transpose.html#torch.transpose" title="torch.transpose"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.transpose()</span></code></a>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.t"/><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><p id="torch.take"/><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><p id="torch.take_along_dim"/><a class="reference internal" href="generated/torch.take_along_dim.html#torch.take_along_dim" title="torch.take_along_dim"><code class="xref py py-obj docutils literal notranslate"><span class="pre">take_along_dim</span></code></a></p></td>
<td><p>Selects values from <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> at the 1-dimensional indices from <code class="xref py py-attr docutils literal notranslate"><span class="pre">indices</span></code> along the given <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.tensor_split"/><a class="reference internal" href="generated/torch.tensor_split.html#torch.tensor_split" title="torch.tensor_split"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tensor_split</span></code></a></p></td>
<td><p>Splits a tensor into multiple sub-tensors, all of which are views of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>, along dimension <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code> according to the indices or number of sections specified by <code class="xref py py-attr docutils literal notranslate"><span class="pre">indices_or_sections</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.tile"/><a class="reference internal" href="generated/torch.tile.html#torch.tile" title="torch.tile"><code class="xref py py-obj docutils literal notranslate"><span class="pre">tile</span></code></a></p></td>
<td><p>Constructs a tensor by repeating 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><p id="torch.transpose"/><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-even"><td><p><p id="torch.unbind"/><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-odd"><td><p><p id="torch.unsqueeze"/><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-even"><td><p><p id="torch.vsplit"/><a class="reference internal" href="generated/torch.vsplit.html#torch.vsplit" title="torch.vsplit"><code class="xref py py-obj docutils literal notranslate"><span class="pre">vsplit</span></code></a></p></td>
<td><p>Splits <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>, a tensor with two or more dimensions, into multiple tensors vertically according to <code class="xref py py-attr docutils literal notranslate"><span class="pre">indices_or_sections</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.vstack"/><a class="reference internal" href="generated/torch.vstack.html#torch.vstack" title="torch.vstack"><code class="xref py py-obj docutils literal notranslate"><span class="pre">vstack</span></code></a></p></td>
<td><p>Stack tensors in sequence vertically (row wise).</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.where"/><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><p id="torch.Generator"/><a class="reference internal" href="generated/torch.Generator.html#torch.Generator" title="torch.Generator"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Generator</span></code></a></p></td>
<td><p>Creates and returns a generator object that manages the state of the algorithm which 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><p id="torch.seed"/><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><p id="torch.manual_seed"/><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><p id="torch.initial_seed"/><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><p id="torch.get_rng_state"/><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><p id="torch.set_rng_state"/><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><p id="torch.bernoulli"/><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><p id="torch.multinomial"/><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><p id="torch.normal"/><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><p id="torch.poisson"/><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><p id="torch.rand"/><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><p id="torch.rand_like"/><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><p id="torch.randint"/><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>Returns a tensor filled with random integers generated uniformly between <code class="xref py py-attr docutils literal notranslate"><span class="pre">low</span></code> (inclusive) and <code class="xref py py-attr docutils literal notranslate"><span class="pre">high</span></code> (exclusive).</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.randint_like"/><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>Returns a tensor with the same shape as Tensor <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> filled with random integers generated uniformly between <code class="xref py py-attr docutils literal notranslate"><span class="pre">low</span></code> (inclusive) and <code class="xref py py-attr docutils literal notranslate"><span class="pre">high</span></code> (exclusive).</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.randn"/><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><p id="torch.randn_like"/><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><p id="torch.randperm"/><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="generated/torch.Tensor.bernoulli_.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="generated/torch.Tensor.cauchy_.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="generated/torch.Tensor.exponential_.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="generated/torch.Tensor.geometric_.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="generated/torch.Tensor.log_normal_.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="generated/torch.Tensor.normal_.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="generated/torch.Tensor.random_.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="generated/torch.Tensor.uniform_.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><p id="torch.save"/><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><p id="torch.load"/><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><p id="torch.get_num_threads"/><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><p id="torch.set_num_threads"/><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><p id="torch.get_num_interop_threads"/><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><p id="torch.set_num_interop_threads"/><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><p id="torch.no_grad"/><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><p id="torch.enable_grad"/><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><p id="torch.set_grad_enabled"/><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>
<tr class="row-even"><td><p><p id="torch.is_grad_enabled"/><a class="reference internal" href="generated/torch.is_grad_enabled.html#torch.is_grad_enabled" title="torch.is_grad_enabled"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_grad_enabled</span></code></a></p></td>
<td><p>Returns True if grad mode is currently enabled.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.inference_mode"/><a class="reference internal" href="generated/torch.inference_mode.html#torch.inference_mode" title="torch.inference_mode"><code class="xref py py-obj docutils literal notranslate"><span class="pre">inference_mode</span></code></a></p></td>
<td><p>Context-manager that enables or disables inference mode</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.is_inference_mode_enabled"/><a class="reference internal" href="generated/torch.is_inference_mode_enabled.html#torch.is_inference_mode_enabled" title="torch.is_inference_mode_enabled"><code class="xref py py-obj docutils literal notranslate"><span class="pre">is_inference_mode_enabled</span></code></a></p></td>
<td><p>Returns True if inference mode is currently enabled.</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><p id="torch.abs"/><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 absolute value 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-even"><td><p><p id="torch.absolute"/><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><p id="torch.acos"/><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>Computes the inverse cosine 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-even"><td><p><p id="torch.arccos"/><a class="reference internal" href="generated/torch.arccos.html#torch.arccos" title="torch.arccos"><code class="xref py py-obj docutils literal notranslate"><span class="pre">arccos</span></code></a></p></td>
<td><p>Alias for <a class="reference internal" href="generated/torch.acos.html#torch.acos" title="torch.acos"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.acos()</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.acosh"/><a class="reference internal" href="generated/torch.acosh.html#torch.acosh" title="torch.acosh"><code class="xref py py-obj docutils literal notranslate"><span class="pre">acosh</span></code></a></p></td>
<td><p>Returns a new tensor with the inverse 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><p id="torch.arccosh"/><a class="reference internal" href="generated/torch.arccosh.html#torch.arccosh" title="torch.arccosh"><code class="xref py py-obj docutils literal notranslate"><span class="pre">arccosh</span></code></a></p></td>
<td><p>Alias for <a class="reference internal" href="generated/torch.acosh.html#torch.acosh" title="torch.acosh"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.acosh()</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.add"/><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-even"><td><p><p id="torch.addcdiv"/><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-odd"><td><p><p id="torch.addcmul"/><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-even"><td><p><p id="torch.angle"/><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-odd"><td><p><p id="torch.asin"/><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-even"><td><p><p id="torch.arcsin"/><a class="reference internal" href="generated/torch.arcsin.html#torch.arcsin" title="torch.arcsin"><code class="xref py py-obj docutils literal notranslate"><span class="pre">arcsin</span></code></a></p></td>
<td><p>Alias for <a class="reference internal" href="generated/torch.asin.html#torch.asin" title="torch.asin"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.asin()</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.asinh"/><a class="reference internal" href="generated/torch.asinh.html#torch.asinh" title="torch.asinh"><code class="xref py py-obj docutils literal notranslate"><span class="pre">asinh</span></code></a></p></td>
<td><p>Returns a new tensor with the inverse 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><p id="torch.arcsinh"/><a class="reference internal" href="generated/torch.arcsinh.html#torch.arcsinh" title="torch.arcsinh"><code class="xref py py-obj docutils literal notranslate"><span class="pre">arcsinh</span></code></a></p></td>
<td><p>Alias for <a class="reference internal" href="generated/torch.asinh.html#torch.asinh" title="torch.asinh"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.asinh()</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.atan"/><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><p id="torch.arctan"/><a class="reference internal" href="generated/torch.arctan.html#torch.arctan" title="torch.arctan"><code class="xref py py-obj docutils literal notranslate"><span class="pre">arctan</span></code></a></p></td>
<td><p>Alias for <a class="reference internal" href="generated/torch.atan.html#torch.atan" title="torch.atan"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.atan()</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.atanh"/><a class="reference internal" href="generated/torch.atanh.html#torch.atanh" title="torch.atanh"><code class="xref py py-obj docutils literal notranslate"><span class="pre">atanh</span></code></a></p></td>
<td><p>Returns a new tensor with the inverse 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><p id="torch.arctanh"/><a class="reference internal" href="generated/torch.arctanh.html#torch.arctanh" title="torch.arctanh"><code class="xref py py-obj docutils literal notranslate"><span class="pre">arctanh</span></code></a></p></td>
<td><p>Alias for <a class="reference internal" href="generated/torch.atanh.html#torch.atanh" title="torch.atanh"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.atanh()</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.atan2"/><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 mathnormal 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 mathnormal 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-even"><td><p><p id="torch.bitwise_not"/><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-odd"><td><p><p id="torch.bitwise_and"/><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-even"><td><p><p id="torch.bitwise_or"/><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-odd"><td><p><p id="torch.bitwise_xor"/><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-even"><td><p><p id="torch.ceil"/><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-odd"><td><p><p id="torch.clamp"/><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>Clamps 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>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.clip"/><a class="reference internal" href="generated/torch.clip.html#torch.clip" title="torch.clip"><code class="xref py py-obj docutils literal notranslate"><span class="pre">clip</span></code></a></p></td>
<td><p>Alias for <a class="reference internal" href="generated/torch.clamp.html#torch.clamp" title="torch.clamp"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.clamp()</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.conj"/><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><p id="torch.copysign"/><a class="reference internal" href="generated/torch.copysign.html#torch.copysign" title="torch.copysign"><code class="xref py py-obj docutils literal notranslate"><span class="pre">copysign</span></code></a></p></td>
<td><p>Create a new floating-point tensor with the magnitude of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> and the sign of <code class="xref py py-attr docutils literal notranslate"><span class="pre">other</span></code>, elementwise.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.cos"/><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-even"><td><p><p id="torch.cosh"/><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-odd"><td><p><p id="torch.deg2rad"/><a class="reference internal" href="generated/torch.deg2rad.html#torch.deg2rad" title="torch.deg2rad"><code class="xref py py-obj docutils literal notranslate"><span class="pre">deg2rad</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> converted from angles in degrees to radians.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.div"/><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> by 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-odd"><td><p><p id="torch.divide"/><a class="reference internal" href="generated/torch.divide.html#torch.divide" title="torch.divide"><code class="xref py py-obj docutils literal notranslate"><span class="pre">divide</span></code></a></p></td>
<td><p>Alias for <a class="reference internal" href="generated/torch.div.html#torch.div" title="torch.div"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.div()</span></code></a>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.digamma"/><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-odd"><td><p><p id="torch.erf"/><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>Alias for <a class="reference internal" href="special.html#torch.special.erf" title="torch.special.erf"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.special.erf()</span></code></a>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.erfc"/><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>Alias for <a class="reference internal" href="special.html#torch.special.erfc" title="torch.special.erfc"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.special.erfc()</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.erfinv"/><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>Alias for <a class="reference internal" href="special.html#torch.special.erfinv" title="torch.special.erfinv"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.special.erfinv()</span></code></a>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.exp"/><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-odd"><td><p><p id="torch.exp2"/><a class="reference internal" href="generated/torch.exp2.html#torch.exp2" title="torch.exp2"><code class="xref py py-obj docutils literal notranslate"><span class="pre">exp2</span></code></a></p></td>
<td><p>Alias for <a class="reference internal" href="special.html#torch.special.exp2" title="torch.special.exp2"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.special.exp2()</span></code></a>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.expm1"/><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>Alias for <a class="reference internal" href="special.html#torch.special.expm1" title="torch.special.expm1"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.special.expm1()</span></code></a>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.fake_quantize_per_channel_affine"/><a class="reference internal" href="generated/torch.fake_quantize_per_channel_affine.html#torch.fake_quantize_per_channel_affine" title="torch.fake_quantize_per_channel_affine"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fake_quantize_per_channel_affine</span></code></a></p></td>
<td><p>Returns a new tensor with the data in <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> fake quantized per channel using <code class="xref py py-attr docutils literal notranslate"><span class="pre">scale</span></code>, <code class="xref py py-attr docutils literal notranslate"><span class="pre">zero_point</span></code>, <code class="xref py py-attr docutils literal notranslate"><span class="pre">quant_min</span></code> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">quant_max</span></code>, across the channel specified by <code class="xref py py-attr docutils literal notranslate"><span class="pre">axis</span></code>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.fake_quantize_per_tensor_affine"/><a class="reference internal" href="generated/torch.fake_quantize_per_tensor_affine.html#torch.fake_quantize_per_tensor_affine" title="torch.fake_quantize_per_tensor_affine"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fake_quantize_per_tensor_affine</span></code></a></p></td>
<td><p>Returns a new tensor with the data in <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> fake quantized using <code class="xref py py-attr docutils literal notranslate"><span class="pre">scale</span></code>, <code class="xref py py-attr docutils literal notranslate"><span class="pre">zero_point</span></code>, <code class="xref py py-attr docutils literal notranslate"><span class="pre">quant_min</span></code> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">quant_max</span></code>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.fix"/><a class="reference internal" href="generated/torch.fix.html#torch.fix" title="torch.fix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">fix</span></code></a></p></td>
<td><p>Alias for <a class="reference internal" href="generated/torch.trunc.html#torch.trunc" title="torch.trunc"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.trunc()</span></code></a></p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.float_power"/><a class="reference internal" href="generated/torch.float_power.html#torch.float_power" title="torch.float_power"><code class="xref py py-obj docutils literal notranslate"><span class="pre">float_power</span></code></a></p></td>
<td><p>Raises <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> to the power of <code class="xref py py-attr docutils literal notranslate"><span class="pre">exponent</span></code>, elementwise, in double precision.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.floor"/><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><p id="torch.floor_divide"/><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></p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.fmod"/><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><p id="torch.frac"/><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><p id="torch.frexp"/><a class="reference internal" href="generated/torch.frexp.html#torch.frexp" title="torch.frexp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">frexp</span></code></a></p></td>
<td><p>Decomposes <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> into mantissa and exponent tensors such that <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>mantissa</mtext><mo>×</mo><msup><mn>2</mn><mtext>exponent</mtext></msup></mrow><annotation encoding="application/x-tex">\text{input} = \text{mantissa} \times 2^{\text{exponent}}</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:0.75119em;vertical-align:-0.08333em;"></span><span class="mord text"><span class="mord">mantissa</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:0.7935559999999999em;vertical-align:0em;"></span><span class="mord"><span class="mord">2</span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.7935559999999999em;"><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 text mtight"><span class="mord mtight">exponent</span></span></span></span></span></span></span></span></span></span></span></span></span></span>.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.gradient"/><a class="reference internal" href="generated/torch.gradient.html#torch.gradient" title="torch.gradient"><code class="xref py py-obj docutils literal notranslate"><span class="pre">gradient</span></code></a></p></td>
<td><p>This function is analogous to NumPy’s gradient function.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.imag"/><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 a new tensor containing imaginary values of the <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> tensor.</p></td>
</tr>
<tr class="row-even"><td><p><p id="torch.ldexp"/><a class="reference internal" href="generated/torch.ldexp.html#torch.ldexp" title="torch.ldexp"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ldexp</span></code></a></p></td>
<td><p>Multiplies <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> by 2**:attr:<cite>other</cite>.</p></td>
</tr>
<tr class="row-odd"><td><p><p id="torch.lerp"/><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-even"><td><p><p id="torch.lgamma"/><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 natural logarithm of the absolute value 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-odd"><td><p><p id="torch.log"/><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-even"><td><p><p id="torch.log10"/><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>