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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>
<li class="toctree-l1"><a class="reference internal" href="notes/broadcasting.html">Broadcasting semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/cpu_threading_torchscript_inference.html">CPU threading and TorchScript inference</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/cuda.html">CUDA semantics</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/ddp.html">Distributed Data Parallel</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/extending.html">Extending PyTorch</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/faq.html">Frequently Asked Questions</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/large_scale_deployments.html">Features for large-scale deployments</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/multiprocessing.html">Multiprocessing best practices</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/randomness.html">Reproducibility</a></li>
<li class="toctree-l1"><a class="reference internal" href="notes/serialization.html">Serialization semantics</a></li>
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<li class="toctree-l1"><a class="reference internal" href="tensor_attributes.html">Tensor Attributes</a></li>
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<li class="toctree-l1"><a class="reference internal" href="autograd.html">torch.autograd</a></li>
<li class="toctree-l1"><a class="reference internal" href="cuda.html">torch.cuda</a></li>
<li class="toctree-l1"><a class="reference internal" href="amp.html">torch.cuda.amp</a></li>
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<li class="toctree-l1"><a class="reference internal" href="type_info.html">Type Info</a></li>
<li class="toctree-l1"><a class="reference internal" href="named_tensor.html">Named Tensors</a></li>
<li class="toctree-l1"><a class="reference internal" href="name_inference.html">Named Tensors operator coverage</a></li>
<li class="toctree-l1"><a class="reference internal" href="__config__.html">torch.__config__</a></li>
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<p class="caption"><span class="caption-text">Libraries</span></p>
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<li class="toctree-l1"><a class="reference external" href="https://pytorch.org/audio">torchaudio</a></li>
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<div class="section" id="torch-tensor">
<span id="tensor-doc"></span><h1>torch.Tensor<a class="headerlink" href="#torch-tensor" title="Permalink to this headline">¶</a></h1>
<p>A <a class="reference internal" href="#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> is a multi-dimensional matrix containing elements of
a single data type.</p>
<p>Torch defines 10 tensor types with CPU and GPU variants which are as follows:</p>
<table class="docutils colwidths-auto align-default">
<thead>
<tr class="row-odd"><th class="head"><p>Data type</p></th>
<th class="head"><p>dtype</p></th>
<th class="head"><p>CPU tensor</p></th>
<th class="head"><p>GPU tensor</p></th>
</tr>
</thead>
<tbody>
<tr class="row-even"><td><p>32-bit floating point</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.float32</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.float</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.FloatTensor</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.cuda.FloatTensor</span></code></p></td>
</tr>
<tr class="row-odd"><td><p>64-bit floating point</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.float64</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.double</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.DoubleTensor</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.cuda.DoubleTensor</span></code></p></td>
</tr>
<tr class="row-even"><td><p>16-bit floating point <a class="footnote-reference brackets" href="#id3" id="id1">1</a></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.float16</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.half</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.HalfTensor</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.cuda.HalfTensor</span></code></p></td>
</tr>
<tr class="row-odd"><td><p>16-bit floating point <a class="footnote-reference brackets" href="#id4" id="id2">2</a></p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.bfloat16</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.BFloat16Tensor</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.cuda.BFloat16Tensor</span></code></p></td>
</tr>
<tr class="row-even"><td><p>32-bit complex</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.complex32</span></code></p></td>
<td></td>
<td></td>
</tr>
<tr class="row-odd"><td><p>64-bit complex</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.complex64</span></code></p></td>
<td></td>
<td></td>
</tr>
<tr class="row-even"><td><p>128-bit complex</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.complex128</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.cdouble</span></code></p></td>
<td></td>
<td></td>
</tr>
<tr class="row-odd"><td><p>8-bit integer (unsigned)</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.uint8</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.ByteTensor</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.cuda.ByteTensor</span></code></p></td>
</tr>
<tr class="row-even"><td><p>8-bit integer (signed)</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.int8</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.CharTensor</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.cuda.CharTensor</span></code></p></td>
</tr>
<tr class="row-odd"><td><p>16-bit integer (signed)</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.int16</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.short</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.ShortTensor</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.cuda.ShortTensor</span></code></p></td>
</tr>
<tr class="row-even"><td><p>32-bit integer (signed)</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.int32</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.int</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.IntTensor</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.cuda.IntTensor</span></code></p></td>
</tr>
<tr class="row-odd"><td><p>64-bit integer (signed)</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.int64</span></code> or <code class="docutils literal notranslate"><span class="pre">torch.long</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.LongTensor</span></code></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.cuda.LongTensor</span></code></p></td>
</tr>
<tr class="row-even"><td><p>Boolean</p></td>
<td><p><code class="docutils literal notranslate"><span class="pre">torch.bool</span></code></p></td>
<td><p><a class="reference internal" href="#torch.BoolTensor" title="torch.BoolTensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.BoolTensor</span></code></a></p></td>
<td><p><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.cuda.BoolTensor</span></code></p></td>
</tr>
</tbody>
</table>
<dl class="footnote brackets">
<dt class="label" id="id3"><span class="brackets"><a class="fn-backref" href="#id1">1</a></span></dt>
<dd><p>Sometimes referred to as binary16: uses 1 sign, 5 exponent, and 10
significand bits. Useful when precision is important at the expense of range.</p>
</dd>
<dt class="label" id="id4"><span class="brackets"><a class="fn-backref" href="#id2">2</a></span></dt>
<dd><p>Sometimes referred to as Brain Floating Point: uses 1 sign, 8 exponent, and 7
significand bits. Useful when range is important, since it has the same
number of exponent bits as <code class="docutils literal notranslate"><span class="pre">float32</span></code></p>
</dd>
</dl>
<p><a class="reference internal" href="#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> is an alias for the default tensor type (<code class="xref py py-class docutils literal notranslate"><span class="pre">torch.FloatTensor</span></code>).</p>
<p>A tensor can be constructed from a Python <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.9)"><code class="xref py py-class docutils literal notranslate"><span class="pre">list</span></code></a> or sequence using the
<a class="reference internal" href="generated/torch.tensor.html#torch.tensor" title="torch.tensor"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.tensor()</span></code></a> constructor:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([[</span><span class="mf">1.</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.</span><span class="p">]])</span>
<span class="go">tensor([[ 1.0000, -1.0000],</span>
<span class="go"> [ 1.0000, -1.0000]])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]]))</span>
<span class="go">tensor([[ 1, 2, 3],</span>
<span class="go"> [ 4, 5, 6]])</span>
</pre></div>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p><a class="reference internal" href="generated/torch.tensor.html#torch.tensor" title="torch.tensor"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.tensor()</span></code></a> always copies <code class="xref py py-attr docutils literal notranslate"><span class="pre">data</span></code>. If you have a Tensor
<code class="xref py py-attr docutils literal notranslate"><span class="pre">data</span></code> and just want to change its <code class="docutils literal notranslate"><span class="pre">requires_grad</span></code> flag, use
<a class="reference internal" href="#torch.Tensor.requires_grad_" title="torch.Tensor.requires_grad_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">requires_grad_()</span></code></a> or
<a class="reference internal" href="autograd.html#torch.Tensor.detach" title="torch.Tensor.detach"><code class="xref py py-meth docutils literal notranslate"><span class="pre">detach()</span></code></a> to avoid a copy.
If you have a numpy array and want to avoid a copy, use
<a class="reference internal" href="generated/torch.as_tensor.html#torch.as_tensor" title="torch.as_tensor"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.as_tensor()</span></code></a>.</p>
</div>
<p>A tensor of specific data type can be constructed by passing a
<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> and/or a <a class="reference internal" href="tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a> to a
constructor or tensor creation op:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">zeros</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
<span class="go">tensor([[ 0, 0, 0, 0],</span>
<span class="go"> [ 0, 0, 0, 0]], dtype=torch.int32)</span>
<span class="gp">>>> </span><span class="n">cuda0</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">'cuda:0'</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">([</span><span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float64</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">cuda0</span><span class="p">)</span>
<span class="go">tensor([[ 1.0000, 1.0000, 1.0000, 1.0000],</span>
<span class="go"> [ 1.0000, 1.0000, 1.0000, 1.0000]], dtype=torch.float64, device='cuda:0')</span>
</pre></div>
</div>
<p>The contents of a tensor can be accessed and modified using Python’s indexing
and slicing notation:</p>
<div class="highlight-default 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">tensor</span><span class="p">([[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">],</span> <span class="p">[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">][</span><span class="mi">2</span><span class="p">])</span>
<span class="go">tensor(6)</span>
<span class="gp">>>> </span><span class="n">x</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="mi">8</span>
<span class="gp">>>> </span><span class="nb">print</span><span class="p">(</span><span class="n">x</span><span class="p">)</span>
<span class="go">tensor([[ 1, 8, 3],</span>
<span class="go"> [ 4, 5, 6]])</span>
</pre></div>
</div>
<p>Use <a class="reference internal" href="#torch.Tensor.item" title="torch.Tensor.item"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.item()</span></code></a> to get a Python number from a tensor containing a
single value:</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">tensor</span><span class="p">([[</span><span class="mi">1</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="n">x</span>
<span class="go">tensor([[ 1]])</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
<span class="go">1</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">tensor</span><span class="p">(</span><span class="mf">2.5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">x</span>
<span class="go">tensor(2.5000)</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">item</span><span class="p">()</span>
<span class="go">2.5</span>
</pre></div>
</div>
<p>A tensor can be created with <code class="xref py py-attr docutils literal notranslate"><span class="pre">requires_grad=True</span></code> so that
<a class="reference internal" href="autograd.html#module-torch.autograd" title="torch.autograd"><code class="xref py py-mod docutils literal notranslate"><span class="pre">torch.autograd</span></code></a> records operations on them for automatic differentiation.</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">tensor</span><span class="p">([[</span><span class="mf">1.</span><span class="p">,</span> <span class="o">-</span><span class="mf">1.</span><span class="p">],</span> <span class="p">[</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">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="n">out</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">pow</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span><span class="o">.</span><span class="n">sum</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">out</span><span class="o">.</span><span class="n">backward</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">grad</span>
<span class="go">tensor([[ 2.0000, -2.0000],</span>
<span class="go"> [ 2.0000, 2.0000]])</span>
</pre></div>
</div>
<p>Each tensor has an associated <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Storage</span></code>, which holds its data.
The tensor class also provides multi-dimensional, <a class="reference external" href="https://en.wikipedia.org/wiki/Stride_of_an_array">strided</a>
view of a storage and defines numeric operations on it.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>For more information on tensor views, see <a class="reference internal" href="tensor_view.html#tensor-view-doc"><span class="std std-ref">Tensor Views</span></a>.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>For more information on the <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>, <a class="reference internal" href="tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>, and
<a class="reference internal" href="tensor_attributes.html#torch.torch.layout" title="torch.torch.layout"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.layout</span></code></a> attributes of a <a class="reference internal" href="#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a>, see
<a class="reference internal" href="tensor_attributes.html#tensor-attributes-doc"><span class="std std-ref">Tensor Attributes</span></a>.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Methods which mutate a tensor are marked with an underscore suffix.
For example, <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.FloatTensor.abs_()</span></code> computes the absolute value
in-place and returns the modified tensor, while <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.FloatTensor.abs()</span></code>
computes the result in a new tensor.</p>
</div>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>To change an existing tensor’s <a class="reference internal" href="tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a> and/or <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>, consider using
<a class="reference internal" href="#torch.Tensor.to" title="torch.Tensor.to"><code class="xref py py-meth docutils literal notranslate"><span class="pre">to()</span></code></a> method on the tensor.</p>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>Current implementation of <a class="reference internal" href="#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Tensor</span></code></a> introduces memory overhead,
thus it might lead to unexpectedly high memory usage in the applications with many tiny tensors.
If this is your case, consider using one large structure.</p>
</div>
<dl class="class">
<dt id="torch.Tensor">
<em class="property">class </em><code class="sig-prename descclassname">torch.</code><code class="sig-name descname">Tensor</code><a class="headerlink" href="#torch.Tensor" title="Permalink to this definition">¶</a></dt>
<dd><p>There are a few main ways to create a tensor, depending on your use case.</p>
<ul class="simple">
<li><p>To create a tensor with pre-existing data, use <a class="reference internal" href="generated/torch.tensor.html#torch.tensor" title="torch.tensor"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.tensor()</span></code></a>.</p></li>
<li><p>To create a tensor with specific size, use <code class="docutils literal notranslate"><span class="pre">torch.*</span></code> tensor creation
ops (see <a class="reference internal" href="torch.html#tensor-creation-ops"><span class="std std-ref">Creation Ops</span></a>).</p></li>
<li><p>To create a tensor with the same size (and similar types) as another tensor,
use <code class="docutils literal notranslate"><span class="pre">torch.*_like</span></code> tensor creation ops
(see <a class="reference internal" href="torch.html#tensor-creation-ops"><span class="std std-ref">Creation Ops</span></a>).</p></li>
<li><p>To create a tensor with similar type but different size as another tensor,
use <code class="docutils literal notranslate"><span class="pre">tensor.new_*</span></code> creation ops.</p></li>
</ul>
<dl class="method">
<dt id="torch.Tensor.new_tensor">
<code class="sig-name descname">new_tensor</code><span class="sig-paren">(</span><em class="sig-param">data</em>, <em class="sig-param">dtype=None</em>, <em class="sig-param">device=None</em>, <em class="sig-param">requires_grad=False</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.new_tensor" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a new Tensor with <code class="xref py py-attr docutils literal notranslate"><span class="pre">data</span></code> as the tensor data.
By default, the returned Tensor has the same <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> and
<a class="reference internal" href="tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a> as this tensor.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p><a class="reference internal" href="#torch.Tensor.new_tensor" title="torch.Tensor.new_tensor"><code class="xref py py-func docutils literal notranslate"><span class="pre">new_tensor()</span></code></a> always copies <code class="xref py py-attr docutils literal notranslate"><span class="pre">data</span></code>. If you have a Tensor
<code class="docutils literal notranslate"><span class="pre">data</span></code> and want to avoid a copy, use <a class="reference internal" href="#torch.Tensor.requires_grad_" title="torch.Tensor.requires_grad_"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.Tensor.requires_grad_()</span></code></a>
or <a class="reference internal" href="autograd.html#torch.Tensor.detach" title="torch.Tensor.detach"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.Tensor.detach()</span></code></a>.
If you have a numpy array and want to avoid a copy, use
<a class="reference internal" href="generated/torch.from_numpy.html#torch.from_numpy" title="torch.from_numpy"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.from_numpy()</span></code></a>.</p>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>When data is a tensor <cite>x</cite>, <a class="reference internal" href="#torch.Tensor.new_tensor" title="torch.Tensor.new_tensor"><code class="xref py py-func docutils literal notranslate"><span class="pre">new_tensor()</span></code></a> reads out ‘the data’ from whatever it is passed,
and constructs a leaf variable. Therefore <code class="docutils literal notranslate"><span class="pre">tensor.new_tensor(x)</span></code> is equivalent to <code class="docutils literal notranslate"><span class="pre">x.clone().detach()</span></code>
and <code class="docutils literal notranslate"><span class="pre">tensor.new_tensor(x,</span> <span class="pre">requires_grad=True)</span></code> is equivalent to <code class="docutils literal notranslate"><span class="pre">x.clone().detach().requires_grad_(True)</span></code>.
The equivalents using <code class="docutils literal notranslate"><span class="pre">clone()</span></code> and <code class="docutils literal notranslate"><span class="pre">detach()</span></code> are recommended.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>data</strong> (<em>array_like</em>) – The returned Tensor copies <code class="xref py py-attr docutils literal notranslate"><span class="pre">data</span></code>.</p></li>
<li><p><strong>dtype</strong> (<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>, optional) – the desired type of returned tensor.
Default: if None, same <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> as this tensor.</p></li>
<li><p><strong>device</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>, optional) – the desired device of returned tensor.
Default: if None, same <a class="reference internal" href="tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a> as this tensor.</p></li>
<li><p><strong>requires_grad</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>optional</em>) – If autograd should record operations on the
returned tensor. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
</ul>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int8</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">data</span> <span class="o">=</span> <span class="p">[[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">]]</span>
<span class="gp">>>> </span><span class="n">tensor</span><span class="o">.</span><span class="n">new_tensor</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
<span class="go">tensor([[ 0, 1],</span>
<span class="go"> [ 2, 3]], dtype=torch.int8)</span>
</pre></div>
</div>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.new_full">
<code class="sig-name descname">new_full</code><span class="sig-paren">(</span><em class="sig-param">size</em>, <em class="sig-param">fill_value</em>, <em class="sig-param">dtype=None</em>, <em class="sig-param">device=None</em>, <em class="sig-param">requires_grad=False</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.new_full" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a Tensor of size <a class="reference internal" href="#torch.Tensor.size" title="torch.Tensor.size"><code class="xref py py-attr docutils literal notranslate"><span class="pre">size</span></code></a> filled with <code class="xref py py-attr docutils literal notranslate"><span class="pre">fill_value</span></code>.
By default, the returned Tensor has the same <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> and
<a class="reference internal" href="tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a> as this tensor.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>fill_value</strong> (<em>scalar</em>) – the number to fill the output tensor with.</p></li>
<li><p><strong>dtype</strong> (<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>, optional) – the desired type of returned tensor.
Default: if None, same <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> as this tensor.</p></li>
<li><p><strong>device</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>, optional) – the desired device of returned tensor.
Default: if None, same <a class="reference internal" href="tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a> as this tensor.</p></li>
<li><p><strong>requires_grad</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>optional</em>) – If autograd should record operations on the
returned tensor. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
</ul>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">tensor</span><span class="o">.</span><span class="n">new_full</span><span class="p">((</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">),</span> <span class="mf">3.141592</span><span class="p">)</span>
<span class="go">tensor([[ 3.1416, 3.1416, 3.1416, 3.1416],</span>
<span class="go"> [ 3.1416, 3.1416, 3.1416, 3.1416],</span>
<span class="go"> [ 3.1416, 3.1416, 3.1416, 3.1416]], dtype=torch.float64)</span>
</pre></div>
</div>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.new_empty">
<code class="sig-name descname">new_empty</code><span class="sig-paren">(</span><em class="sig-param">size</em>, <em class="sig-param">dtype=None</em>, <em class="sig-param">device=None</em>, <em class="sig-param">requires_grad=False</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.new_empty" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a Tensor of size <a class="reference internal" href="#torch.Tensor.size" title="torch.Tensor.size"><code class="xref py py-attr docutils literal notranslate"><span class="pre">size</span></code></a> filled with uninitialized data.
By default, the returned Tensor has the same <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> and
<a class="reference internal" href="tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a> as this tensor.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>dtype</strong> (<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>, optional) – the desired type of returned tensor.
Default: if None, same <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> as this tensor.</p></li>
<li><p><strong>device</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>, optional) – the desired device of returned tensor.
Default: if None, same <a class="reference internal" href="tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a> as this tensor.</p></li>
<li><p><strong>requires_grad</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>optional</em>) – If autograd should record operations on the
returned tensor. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
</ul>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">ones</span><span class="p">(())</span>
<span class="gp">>>> </span><span class="n">tensor</span><span class="o">.</span><span class="n">new_empty</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="go">tensor([[ 5.8182e-18, 4.5765e-41, -1.0545e+30],</span>
<span class="go"> [ 3.0949e-41, 4.4842e-44, 0.0000e+00]])</span>
</pre></div>
</div>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.new_ones">
<code class="sig-name descname">new_ones</code><span class="sig-paren">(</span><em class="sig-param">size</em>, <em class="sig-param">dtype=None</em>, <em class="sig-param">device=None</em>, <em class="sig-param">requires_grad=False</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.new_ones" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a Tensor of size <a class="reference internal" href="#torch.Tensor.size" title="torch.Tensor.size"><code class="xref py py-attr docutils literal notranslate"><span class="pre">size</span></code></a> filled with <code class="docutils literal notranslate"><span class="pre">1</span></code>.
By default, the returned Tensor has the same <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> and
<a class="reference internal" href="tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a> as this tensor.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>size</strong> (<em>int...</em>) – a list, tuple, or <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Size</span></code> of integers defining the
shape of the output tensor.</p></li>
<li><p><strong>dtype</strong> (<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>, optional) – the desired type of returned tensor.
Default: if None, same <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> as this tensor.</p></li>
<li><p><strong>device</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>, optional) – the desired device of returned tensor.
Default: if None, same <a class="reference internal" href="tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a> as this tensor.</p></li>
<li><p><strong>requires_grad</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>optional</em>) – If autograd should record operations on the
returned tensor. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
</ul>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">((),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">tensor</span><span class="o">.</span><span class="n">new_ones</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="go">tensor([[ 1, 1, 1],</span>
<span class="go"> [ 1, 1, 1]], dtype=torch.int32)</span>
</pre></div>
</div>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.new_zeros">
<code class="sig-name descname">new_zeros</code><span class="sig-paren">(</span><em class="sig-param">size</em>, <em class="sig-param">dtype=None</em>, <em class="sig-param">device=None</em>, <em class="sig-param">requires_grad=False</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.new_zeros" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a Tensor of size <a class="reference internal" href="#torch.Tensor.size" title="torch.Tensor.size"><code class="xref py py-attr docutils literal notranslate"><span class="pre">size</span></code></a> filled with <code class="docutils literal notranslate"><span class="pre">0</span></code>.
By default, the returned Tensor has the same <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> and
<a class="reference internal" href="tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a> as this tensor.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>size</strong> (<em>int...</em>) – a list, tuple, or <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.Size</span></code> of integers defining the
shape of the output tensor.</p></li>
<li><p><strong>dtype</strong> (<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>, optional) – the desired type of returned tensor.
Default: if None, same <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> as this tensor.</p></li>
<li><p><strong>device</strong> (<a class="reference internal" href="tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>, optional) – the desired device of returned tensor.
Default: if None, same <a class="reference internal" href="tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a> as this tensor.</p></li>
<li><p><strong>requires_grad</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>optional</em>) – If autograd should record operations on the
returned tensor. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
</ul>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">tensor</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">((),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">tensor</span><span class="o">.</span><span class="n">new_zeros</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
<span class="go">tensor([[ 0., 0., 0.],</span>
<span class="go"> [ 0., 0., 0.]], dtype=torch.float64)</span>
</pre></div>
</div>
</dd></dl>
<dl class="attribute">
<dt id="torch.Tensor.is_cuda">
<code class="sig-name descname">is_cuda</code><a class="headerlink" href="#torch.Tensor.is_cuda" title="Permalink to this definition">¶</a></dt>
<dd><p>Is <code class="docutils literal notranslate"><span class="pre">True</span></code> if the Tensor is stored on the GPU, <code class="docutils literal notranslate"><span class="pre">False</span></code> otherwise.</p>
</dd></dl>
<dl class="attribute">
<dt id="torch.Tensor.is_quantized">
<code class="sig-name descname">is_quantized</code><a class="headerlink" href="#torch.Tensor.is_quantized" title="Permalink to this definition">¶</a></dt>
<dd><p>Is <code class="docutils literal notranslate"><span class="pre">True</span></code> if the Tensor is quantized, <code class="docutils literal notranslate"><span class="pre">False</span></code> otherwise.</p>
</dd></dl>
<dl class="attribute">
<dt id="torch.Tensor.is_meta">
<code class="sig-name descname">is_meta</code><a class="headerlink" href="#torch.Tensor.is_meta" title="Permalink to this definition">¶</a></dt>
<dd><p>Is <code class="docutils literal notranslate"><span class="pre">True</span></code> if the Tensor is a meta tensor, <code class="docutils literal notranslate"><span class="pre">False</span></code> otherwise. Meta tensors
are like normal tensors, but they carry no data.</p>
</dd></dl>
<dl class="attribute">
<dt id="torch.Tensor.device">
<code class="sig-name descname">device</code><a class="headerlink" href="#torch.Tensor.device" title="Permalink to this definition">¶</a></dt>
<dd><p>Is the <a class="reference internal" href="tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a> where this Tensor is.</p>
</dd></dl>
<dl class="attribute">
<dt>
<code class="sig-name descname">grad</code></dt>
<dd><p>This attribute is <code class="docutils literal notranslate"><span class="pre">None</span></code> by default and becomes a Tensor the first time a call to
<a class="reference internal" href="autograd.html#torch.Tensor.backward" title="torch.Tensor.backward"><code class="xref py py-func docutils literal notranslate"><span class="pre">backward()</span></code></a> computes gradients for <code class="docutils literal notranslate"><span class="pre">self</span></code>.
The attribute will then contain the gradients computed and future calls to
<a class="reference internal" href="autograd.html#torch.Tensor.backward" title="torch.Tensor.backward"><code class="xref py py-func docutils literal notranslate"><span class="pre">backward()</span></code></a> will accumulate (add) gradients into it.</p>
</dd></dl>
<dl class="attribute">
<dt id="torch.Tensor.ndim">
<code class="sig-name descname">ndim</code><a class="headerlink" href="#torch.Tensor.ndim" title="Permalink to this definition">¶</a></dt>
<dd><p>Alias for <a class="reference internal" href="#torch.Tensor.dim" title="torch.Tensor.dim"><code class="xref py py-meth docutils literal notranslate"><span class="pre">dim()</span></code></a></p>
</dd></dl>
<dl class="attribute">
<dt id="torch.Tensor.T">
<code class="sig-name descname">T</code><a class="headerlink" href="#torch.Tensor.T" title="Permalink to this definition">¶</a></dt>
<dd><p>Is this Tensor with its dimensions reversed.</p>
<p>If <code class="docutils literal notranslate"><span class="pre">n</span></code> is the number of dimensions in <code class="docutils literal notranslate"><span class="pre">x</span></code>,
<code class="docutils literal notranslate"><span class="pre">x.T</span></code> is equivalent to <code class="docutils literal notranslate"><span class="pre">x.permute(n-1,</span> <span class="pre">n-2,</span> <span class="pre">...,</span> <span class="pre">0)</span></code>.</p>
</dd></dl>
<dl class="attribute">
<dt id="torch.Tensor.real">
<code class="sig-name descname">real</code><a class="headerlink" href="#torch.Tensor.real" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a new tensor containing real values of the <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> tensor.
The returned tensor and <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> share the same underlying storage.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p><a class="reference internal" href="generated/torch.real.html#torch.real" title="torch.real"><code class="xref py py-func docutils literal notranslate"><span class="pre">real()</span></code></a> is only supported for tensors with complex dtypes.</p>
</div>
<dl>
<dt>Example::</dt><dd><div class="doctest 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">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cfloat</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">x</span>
<span class="go">tensor([(0.3100+0.3553j), (-0.5445-0.7896j), (-1.6492-0.0633j), (-0.0638-0.8119j)])</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">real</span>
<span class="go">tensor([ 0.3100, -0.5445, -1.6492, -0.0638])</span>
</pre></div>
</div>
</dd>
</dl>
</dd></dl>
<dl class="attribute">
<dt id="torch.Tensor.imag">
<code class="sig-name descname">imag</code><a class="headerlink" href="#torch.Tensor.imag" title="Permalink to this definition">¶</a></dt>
<dd><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.
The returned tensor and <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> share the same underlying storage.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p><a class="reference internal" href="generated/torch.imag.html#torch.imag" title="torch.imag"><code class="xref py py-func docutils literal notranslate"><span class="pre">imag()</span></code></a> is only supported for tensors with complex dtypes.</p>
</div>
<dl>
<dt>Example::</dt><dd><div class="doctest 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">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">cfloat</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">x</span>
<span class="go">tensor([(0.3100+0.3553j), (-0.5445-0.7896j), (-1.6492-0.0633j), (-0.0638-0.8119j)])</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">imag</span>
<span class="go">tensor([ 0.3553, -0.7896, -0.0633, -0.8119])</span>
</pre></div>
</div>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.abs">
<code class="sig-name descname">abs</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.abs" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="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>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.abs_">
<code class="sig-name descname">abs_</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.abs_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.abs" title="torch.Tensor.abs"><code class="xref py py-meth docutils literal notranslate"><span class="pre">abs()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.absolute">
<code class="sig-name descname">absolute</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.absolute" title="Permalink to this definition">¶</a></dt>
<dd><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">abs()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.absolute_">
<code class="sig-name descname">absolute_</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.absolute_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.absolute" title="torch.Tensor.absolute"><code class="xref py py-meth docutils literal notranslate"><span class="pre">absolute()</span></code></a>
Alias for <a class="reference internal" href="#torch.Tensor.abs_" title="torch.Tensor.abs_"><code class="xref py py-func docutils literal notranslate"><span class="pre">abs_()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.acos">
<code class="sig-name descname">acos</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.acos" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="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>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.acos_">
<code class="sig-name descname">acos_</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.acos_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.acos" title="torch.Tensor.acos"><code class="xref py py-meth docutils literal notranslate"><span class="pre">acos()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.arccos">
<code class="sig-name descname">arccos</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.arccos" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="generated/torch.arccos.html#torch.arccos" title="torch.arccos"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.arccos()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.arccos_">
<code class="sig-name descname">arccos_</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.arccos_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.arccos" title="torch.Tensor.arccos"><code class="xref py py-meth docutils literal notranslate"><span class="pre">arccos()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.add">
<code class="sig-name descname">add</code><span class="sig-paren">(</span><em class="sig-param">other</em>, <em class="sig-param">*</em>, <em class="sig-param">alpha=1</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.add" title="Permalink to this definition">¶</a></dt>
<dd><p>Add a scalar or tensor to <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> tensor. If both <code class="xref py py-attr docutils literal notranslate"><span class="pre">alpha</span></code>
and <code class="xref py py-attr docutils literal notranslate"><span class="pre">other</span></code> are specified, each element of <code class="xref py py-attr docutils literal notranslate"><span class="pre">other</span></code> is scaled by
<code class="xref py py-attr docutils literal notranslate"><span class="pre">alpha</span></code> before being used.</p>
<p>When <code class="xref py py-attr docutils literal notranslate"><span class="pre">other</span></code> is a tensor, the shape of <code class="xref py py-attr docutils literal notranslate"><span class="pre">other</span></code> must be
<a class="reference internal" href="notes/broadcasting.html#broadcasting-semantics"><span class="std std-ref">broadcastable</span></a> with the shape of the underlying
tensor</p>
<p>See <a class="reference internal" href="generated/torch.add.html#torch.add" title="torch.add"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.add()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.add_">
<code class="sig-name descname">add_</code><span class="sig-paren">(</span><em class="sig-param">other</em>, <em class="sig-param">*</em>, <em class="sig-param">alpha=1</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.add_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.add" title="torch.Tensor.add"><code class="xref py py-meth docutils literal notranslate"><span class="pre">add()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addbmm">
<code class="sig-name descname">addbmm</code><span class="sig-paren">(</span><em class="sig-param">batch1</em>, <em class="sig-param">batch2</em>, <em class="sig-param">*</em>, <em class="sig-param">beta=1</em>, <em class="sig-param">alpha=1</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addbmm" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="generated/torch.addbmm.html#torch.addbmm" title="torch.addbmm"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.addbmm()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addbmm_">
<code class="sig-name descname">addbmm_</code><span class="sig-paren">(</span><em class="sig-param">batch1</em>, <em class="sig-param">batch2</em>, <em class="sig-param">*</em>, <em class="sig-param">beta=1</em>, <em class="sig-param">alpha=1</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addbmm_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.addbmm" title="torch.Tensor.addbmm"><code class="xref py py-meth docutils literal notranslate"><span class="pre">addbmm()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addcdiv">
<code class="sig-name descname">addcdiv</code><span class="sig-paren">(</span><em class="sig-param">tensor1</em>, <em class="sig-param">tensor2</em>, <em class="sig-param">*</em>, <em class="sig-param">value=1</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addcdiv" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="generated/torch.addcdiv.html#torch.addcdiv" title="torch.addcdiv"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.addcdiv()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addcdiv_">
<code class="sig-name descname">addcdiv_</code><span class="sig-paren">(</span><em class="sig-param">tensor1</em>, <em class="sig-param">tensor2</em>, <em class="sig-param">*</em>, <em class="sig-param">value=1</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addcdiv_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.addcdiv" title="torch.Tensor.addcdiv"><code class="xref py py-meth docutils literal notranslate"><span class="pre">addcdiv()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addcmul">
<code class="sig-name descname">addcmul</code><span class="sig-paren">(</span><em class="sig-param">tensor1</em>, <em class="sig-param">tensor2</em>, <em class="sig-param">*</em>, <em class="sig-param">value=1</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addcmul" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="generated/torch.addcmul.html#torch.addcmul" title="torch.addcmul"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.addcmul()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addcmul_">
<code class="sig-name descname">addcmul_</code><span class="sig-paren">(</span><em class="sig-param">tensor1</em>, <em class="sig-param">tensor2</em>, <em class="sig-param">*</em>, <em class="sig-param">value=1</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addcmul_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.addcmul" title="torch.Tensor.addcmul"><code class="xref py py-meth docutils literal notranslate"><span class="pre">addcmul()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addmm">
<code class="sig-name descname">addmm</code><span class="sig-paren">(</span><em class="sig-param">mat1</em>, <em class="sig-param">mat2</em>, <em class="sig-param">*</em>, <em class="sig-param">beta=1</em>, <em class="sig-param">alpha=1</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addmm" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="generated/torch.addmm.html#torch.addmm" title="torch.addmm"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.addmm()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addmm_">
<code class="sig-name descname">addmm_</code><span class="sig-paren">(</span><em class="sig-param">mat1</em>, <em class="sig-param">mat2</em>, <em class="sig-param">*</em>, <em class="sig-param">beta=1</em>, <em class="sig-param">alpha=1</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addmm_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.addmm" title="torch.Tensor.addmm"><code class="xref py py-meth docutils literal notranslate"><span class="pre">addmm()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addmv">
<code class="sig-name descname">addmv</code><span class="sig-paren">(</span><em class="sig-param">mat</em>, <em class="sig-param">vec</em>, <em class="sig-param">*</em>, <em class="sig-param">beta=1</em>, <em class="sig-param">alpha=1</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addmv" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="generated/torch.addmv.html#torch.addmv" title="torch.addmv"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.addmv()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addmv_">
<code class="sig-name descname">addmv_</code><span class="sig-paren">(</span><em class="sig-param">mat</em>, <em class="sig-param">vec</em>, <em class="sig-param">*</em>, <em class="sig-param">beta=1</em>, <em class="sig-param">alpha=1</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addmv_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.addmv" title="torch.Tensor.addmv"><code class="xref py py-meth docutils literal notranslate"><span class="pre">addmv()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addr">
<code class="sig-name descname">addr</code><span class="sig-paren">(</span><em class="sig-param">vec1</em>, <em class="sig-param">vec2</em>, <em class="sig-param">*</em>, <em class="sig-param">beta=1</em>, <em class="sig-param">alpha=1</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addr" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="generated/torch.addr.html#torch.addr" title="torch.addr"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.addr()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addr_">
<code class="sig-name descname">addr_</code><span class="sig-paren">(</span><em class="sig-param">vec1</em>, <em class="sig-param">vec2</em>, <em class="sig-param">*</em>, <em class="sig-param">beta=1</em>, <em class="sig-param">alpha=1</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addr_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.addr" title="torch.Tensor.addr"><code class="xref py py-meth docutils literal notranslate"><span class="pre">addr()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.allclose">
<code class="sig-name descname">allclose</code><span class="sig-paren">(</span><em class="sig-param">other</em>, <em class="sig-param">rtol=1e-05</em>, <em class="sig-param">atol=1e-08</em>, <em class="sig-param">equal_nan=False</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.allclose" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="generated/torch.allclose.html#torch.allclose" title="torch.allclose"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.allclose()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.amax">
<code class="sig-name descname">amax</code><span class="sig-paren">(</span><em class="sig-param">dim=None</em>, <em class="sig-param">keepdim=False</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.amax" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="generated/torch.amax.html#torch.amax" title="torch.amax"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.amax()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.amin">
<code class="sig-name descname">amin</code><span class="sig-paren">(</span><em class="sig-param">dim=None</em>, <em class="sig-param">keepdim=False</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.amin" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="generated/torch.amin.html#torch.amin" title="torch.amin"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.amin()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.angle">
<code class="sig-name descname">angle</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.angle" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="generated/torch.angle.html#torch.angle" title="torch.angle"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.angle()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.apply_">
<code class="sig-name descname">apply_</code><span class="sig-paren">(</span><em class="sig-param">callable</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.apply_" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies the function <code class="xref py py-attr docutils literal notranslate"><span class="pre">callable</span></code> to each element in the tensor, replacing
each element with the value returned by <code class="xref py py-attr docutils literal notranslate"><span class="pre">callable</span></code>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This function only works with CPU tensors and should not be used in code
sections that require high performance.</p>
</div>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.argmax">
<code class="sig-name descname">argmax</code><span class="sig-paren">(</span><em class="sig-param">dim=None</em>, <em class="sig-param">keepdim=False</em><span class="sig-paren">)</span> → LongTensor<a class="headerlink" href="#torch.Tensor.argmax" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="generated/torch.argmax.html#torch.argmax" title="torch.argmax"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.argmax()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.argmin">
<code class="sig-name descname">argmin</code><span class="sig-paren">(</span><em class="sig-param">dim=None</em>, <em class="sig-param">keepdim=False</em><span class="sig-paren">)</span> → LongTensor<a class="headerlink" href="#torch.Tensor.argmin" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="generated/torch.argmin.html#torch.argmin" title="torch.argmin"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.argmin()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.argsort">
<code class="sig-name descname">argsort</code><span class="sig-paren">(</span><em class="sig-param">dim=-1</em>, <em class="sig-param">descending=False</em><span class="sig-paren">)</span> → LongTensor<a class="headerlink" href="#torch.Tensor.argsort" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="generated/torch.argsort.html#torch.argsort" title="torch.argsort"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.argsort()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.asin">
<code class="sig-name descname">asin</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.asin" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="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>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.asin_">
<code class="sig-name descname">asin_</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.asin_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.asin" title="torch.Tensor.asin"><code class="xref py py-meth docutils literal notranslate"><span class="pre">asin()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.arcsin">