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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 nine CPU tensor types and nine GPU tensor types:</p>
<table class="docutils align-default">
<colgroup>
<col style="width: 19%" />
<col style="width: 34%" />
<col style="width: 21%" />
<col style="width: 25%" />
</colgroup>
<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</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>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>
<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 v3.8)"><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="torch.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="torch.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="torch.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 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 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="torch.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="torch.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 v3.8)"><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 v3.8)"><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 v3.8)"><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 v3.8)"><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 v3.8)"><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.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="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="torch.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.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="torch.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.add">
<code class="sig-name descname">add</code><span class="sig-paren">(</span><em class="sig-param">value</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.add" title="Permalink to this definition">¶</a></dt>
<dd><p>add(value=1, other) -> Tensor</p>
<p>See <a class="reference internal" href="torch.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">value</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.add_" title="Permalink to this definition">¶</a></dt>
<dd><p>add_(value=1, other) -> Tensor</p>
<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">beta=1</em>, <em class="sig-param">alpha=1</em>, <em class="sig-param">batch1</em>, <em class="sig-param">batch2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addbmm" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.addbmm" title="torch.addbmm"><code class="xref py py-func docutils literal 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">beta=1</em>, <em class="sig-param">alpha=1</em>, <em class="sig-param">batch1</em>, <em class="sig-param">batch2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addbmm_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.addbmm" title="torch.Tensor.addbmm"><code class="xref py py-meth docutils literal 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">value=1</em>, <em class="sig-param">tensor1</em>, <em class="sig-param">tensor2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addcdiv" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.addcdiv" title="torch.addcdiv"><code class="xref py py-func docutils literal 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">value=1</em>, <em class="sig-param">tensor1</em>, <em class="sig-param">tensor2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addcdiv_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.addcdiv" title="torch.Tensor.addcdiv"><code class="xref py py-meth docutils literal 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">value=1</em>, <em class="sig-param">tensor1</em>, <em class="sig-param">tensor2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addcmul" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.addcmul" title="torch.addcmul"><code class="xref py py-func docutils literal 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">value=1</em>, <em class="sig-param">tensor1</em>, <em class="sig-param">tensor2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addcmul_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.addcmul" title="torch.Tensor.addcmul"><code class="xref py py-meth docutils literal 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">beta=1</em>, <em class="sig-param">alpha=1</em>, <em class="sig-param">mat1</em>, <em class="sig-param">mat2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addmm" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.addmm" title="torch.addmm"><code class="xref py py-func docutils literal 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">beta=1</em>, <em class="sig-param">alpha=1</em>, <em class="sig-param">mat1</em>, <em class="sig-param">mat2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addmm_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.addmm" title="torch.Tensor.addmm"><code class="xref py py-meth docutils literal 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">beta=1</em>, <em class="sig-param">alpha=1</em>, <em class="sig-param">mat</em>, <em class="sig-param">vec</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addmv" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.addmv" title="torch.addmv"><code class="xref py py-func docutils literal 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">beta=1</em>, <em class="sig-param">alpha=1</em>, <em class="sig-param">mat</em>, <em class="sig-param">vec</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addmv_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.addmv" title="torch.Tensor.addmv"><code class="xref py py-meth docutils literal 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">beta=1</em>, <em class="sig-param">alpha=1</em>, <em class="sig-param">vec1</em>, <em class="sig-param">vec2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addr" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.addr" title="torch.addr"><code class="xref py py-func docutils literal 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">beta=1</em>, <em class="sig-param">alpha=1</em>, <em class="sig-param">vec1</em>, <em class="sig-param">vec2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addr_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.addr" title="torch.Tensor.addr"><code class="xref py py-meth docutils literal 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="torch.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.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="torch.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="torch.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="torch.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="torch.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="torch.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.as_strided">
<code class="sig-name descname">as_strided</code><span class="sig-paren">(</span><em class="sig-param">size</em>, <em class="sig-param">stride</em>, <em class="sig-param">storage_offset=0</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.as_strided" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.as_strided" title="torch.as_strided"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.as_strided()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.atan">
<code class="sig-name descname">atan</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.atan" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.atan" title="torch.atan"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.atan()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.atan2">
<code class="sig-name descname">atan2</code><span class="sig-paren">(</span><em class="sig-param">other</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.atan2" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.atan2" title="torch.atan2"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.atan2()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.atan2_">
<code class="sig-name descname">atan2_</code><span class="sig-paren">(</span><em class="sig-param">other</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.atan2_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.atan2" title="torch.Tensor.atan2"><code class="xref py py-meth docutils literal notranslate"><span class="pre">atan2()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.atan_">
<code class="sig-name descname">atan_</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.atan_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.atan" title="torch.Tensor.atan"><code class="xref py py-meth docutils literal notranslate"><span class="pre">atan()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt>
<code class="sig-name descname">backward</code><span class="sig-paren">(</span><em class="sig-param">gradient=None</em>, <em class="sig-param">retain_graph=None</em>, <em class="sig-param">create_graph=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/tensor.html#Tensor.backward"><span class="viewcode-link">[source]</span></a></dt>
<dd><p>Computes the gradient of current tensor w.r.t. graph leaves.</p>
<p>The graph is differentiated using the chain rule. If the tensor is
non-scalar (i.e. its data has more than one element) and requires
gradient, the function additionally requires specifying <code class="docutils literal notranslate"><span class="pre">gradient</span></code>.
It should be a tensor of matching type and location, that contains
the gradient of the differentiated function w.r.t. <code class="docutils literal notranslate"><span class="pre">self</span></code>.</p>
<p>This function accumulates gradients in the leaves - you might need to
zero them before calling it.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>gradient</strong> (<a class="reference internal" href="#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/constants.html#None" title="(in Python v3.8)"><em>None</em></a>) – Gradient w.r.t. the
tensor. If it is a tensor, it will be automatically converted
to a Tensor that does not require grad unless <code class="docutils literal notranslate"><span class="pre">create_graph</span></code> is True.
None values can be specified for scalar Tensors or ones that
don’t require grad. If a None value would be acceptable then
this argument is optional.</p></li>
<li><p><strong>retain_graph</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.8)"><em>bool</em></a><em>, </em><em>optional</em>) – If <code class="docutils literal notranslate"><span class="pre">False</span></code>, the graph used to compute
the grads will be freed. Note that in nearly all cases setting
this option to True is not needed and often can be worked around
in a much more efficient way. Defaults to the value of
<code class="docutils literal notranslate"><span class="pre">create_graph</span></code>.</p></li>
<li><p><strong>create_graph</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.8)"><em>bool</em></a><em>, </em><em>optional</em>) – If <code class="docutils literal notranslate"><span class="pre">True</span></code>, graph of the derivative will
be constructed, allowing to compute higher order derivative
products. Defaults to <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
</ul>
</dd>
</dl>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.baddbmm">
<code class="sig-name descname">baddbmm</code><span class="sig-paren">(</span><em class="sig-param">beta=1</em>, <em class="sig-param">alpha=1</em>, <em class="sig-param">batch1</em>, <em class="sig-param">batch2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.baddbmm" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.baddbmm" title="torch.baddbmm"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.baddbmm()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.baddbmm_">
<code class="sig-name descname">baddbmm_</code><span class="sig-paren">(</span><em class="sig-param">beta=1</em>, <em class="sig-param">alpha=1</em>, <em class="sig-param">batch1</em>, <em class="sig-param">batch2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.baddbmm_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.baddbmm" title="torch.Tensor.baddbmm"><code class="xref py py-meth docutils literal notranslate"><span class="pre">baddbmm()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.bernoulli">
<code class="sig-name descname">bernoulli</code><span class="sig-paren">(</span><em class="sig-param">*</em>, <em class="sig-param">generator=None</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.bernoulli" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a result tensor where each <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext mathvariant="monospace">result[i]</mtext></mrow><annotation encoding="application/x-tex">\texttt{result[i]}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.77777em;vertical-align:-0.08333em;"></span><span class="mord text"><span class="mord texttt">result[i]</span></span></span></span></span>
</span> is independently
sampled from <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>Bernoulli</mtext><mo stretchy="false">(</mo><mtext mathvariant="monospace">self[i]</mtext><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{Bernoulli}(\texttt{self[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 text"><span class="mord">Bernoulli</span></span><span class="mopen">(</span><span class="mord text"><span class="mord texttt">self[i]</span></span><span class="mclose">)</span></span></span></span>
</span>. <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> must have
floating point <code class="docutils literal notranslate"><span class="pre">dtype</span></code>, and the result will have the same <code class="docutils literal notranslate"><span class="pre">dtype</span></code>.</p>
<p>See <a class="reference internal" href="torch.html#torch.bernoulli" title="torch.bernoulli"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.bernoulli()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.bernoulli_">
<code class="sig-name descname">bernoulli_</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.Tensor.bernoulli_" title="Permalink to this definition">¶</a></dt>
<dd><dl class="function">
<dt>
<code class="sig-name descname">bernoulli_</code><span class="sig-paren">(</span><em class="sig-param">p=0.5</em>, <em class="sig-param">*</em>, <em class="sig-param">generator=None</em><span class="sig-paren">)</span> → Tensor</dt>
<dd><p>Fills each location of <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> with an independent sample from
<span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>Bernoulli</mtext><mo stretchy="false">(</mo><mtext mathvariant="monospace">p</mtext><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{Bernoulli}(\texttt{p})</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 text"><span class="mord">Bernoulli</span></span><span class="mopen">(</span><span class="mord text"><span class="mord texttt">p</span></span><span class="mclose">)</span></span></span></span>
</span>. <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> can have integral
<code class="docutils literal notranslate"><span class="pre">dtype</span></code>.</p>
</dd></dl>
<dl class="function">
<dt>
<code class="sig-name descname">bernoulli_</code><span class="sig-paren">(</span><em class="sig-param">p_tensor</em>, <em class="sig-param">*</em>, <em class="sig-param">generator=None</em><span class="sig-paren">)</span> → Tensor</dt>
<dd><p><code class="xref py py-attr docutils literal notranslate"><span class="pre">p_tensor</span></code> should be a tensor containing probabilities to be used for
drawing the binary random number.</p>
<p>The <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><msup><mtext>i</mtext><mrow><mi>t</mi><mi>h</mi></mrow></msup></mrow><annotation encoding="application/x-tex">\text{i}^{th}</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.906868em;vertical-align:0em;"></span><span class="mord"><span class="mord text"><span class="mord">i</span></span><span class="msupsub"><span class="vlist-t"><span class="vlist-r"><span class="vlist" style="height:0.906868em;"><span style="top:-3.12076em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathdefault mtight">t</span><span class="mord mathdefault mtight">h</span></span></span></span></span></span></span></span></span></span></span></span>
</span> element of <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> tensor will be set to a
value sampled from <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mtext>Bernoulli</mtext><mo stretchy="false">(</mo><mtext mathvariant="monospace">p_tensor[i]</mtext><mo stretchy="false">)</mo></mrow><annotation encoding="application/x-tex">\text{Bernoulli}(\texttt{p\_tensor[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 text"><span class="mord">Bernoulli</span></span><span class="mopen">(</span><span class="mord text"><span class="mord texttt">p_tensor[i]</span></span><span class="mclose">)</span></span></span></span>
</span>.</p>
<p><code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> can have integral <code class="docutils literal notranslate"><span class="pre">dtype</span></code>, but <code class="xref py py-attr docutils literal notranslate"><span class="pre">p_tensor</span></code> must have
floating point <code class="docutils literal notranslate"><span class="pre">dtype</span></code>.</p>
</dd></dl>
<p>See also <a class="reference internal" href="#torch.Tensor.bernoulli" title="torch.Tensor.bernoulli"><code class="xref py py-meth docutils literal notranslate"><span class="pre">bernoulli()</span></code></a> and <a class="reference internal" href="torch.html#torch.bernoulli" title="torch.bernoulli"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.bernoulli()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.bfloat16">
<code class="sig-name descname">bfloat16</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.bfloat16" title="Permalink to this definition">¶</a></dt>
<dd><p><code class="docutils literal notranslate"><span class="pre">self.bfloat16()</span></code> is equivalent to <code class="docutils literal notranslate"><span class="pre">self.to(torch.bfloat16)</span></code>. See <a class="reference internal" href="#torch.Tensor.to" title="torch.Tensor.to"><code class="xref py py-func docutils literal notranslate"><span class="pre">to()</span></code></a>.</p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.bincount">
<code class="sig-name descname">bincount</code><span class="sig-paren">(</span><em class="sig-param">weights=None</em>, <em class="sig-param">minlength=0</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.bincount" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.bincount" title="torch.bincount"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.bincount()</span></code></a></p>
</dd></dl>