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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"><span class="pre">torch.Tensor</span></code></a> is a multi-dimensional matrix containing elements of
a single data type.</p>
<p>Torch defines eight CPU tensor types and eight GPU tensor types:</p>
<table border="1" class="docutils">
<colgroup>
<col width="19%" />
<col width="34%" />
<col width="21%" />
<col width="25%" />
</colgroup>
<thead valign="bottom">
<tr class="row-odd"><th class="head">Data type</th>
<th class="head">dtype</th>
<th class="head">CPU tensor</th>
<th class="head">GPU tensor</th>
</tr>
</thead>
<tbody valign="top">
<tr class="row-even"><td>32-bit floating point</td>
<td><code class="docutils literal"><span class="pre">torch.float32</span></code> or <code class="docutils literal"><span class="pre">torch.float</span></code></td>
<td><code class="xref py py-class docutils literal"><span class="pre">torch.FloatTensor</span></code></td>
<td><code class="xref py py-class docutils literal"><span class="pre">torch.cuda.FloatTensor</span></code></td>
</tr>
<tr class="row-odd"><td>64-bit floating point</td>
<td><code class="docutils literal"><span class="pre">torch.float64</span></code> or <code class="docutils literal"><span class="pre">torch.double</span></code></td>
<td><code class="xref py py-class docutils literal"><span class="pre">torch.DoubleTensor</span></code></td>
<td><code class="xref py py-class docutils literal"><span class="pre">torch.cuda.DoubleTensor</span></code></td>
</tr>
<tr class="row-even"><td>16-bit floating point</td>
<td><code class="docutils literal"><span class="pre">torch.float16</span></code> or <code class="docutils literal"><span class="pre">torch.half</span></code></td>
<td><code class="xref py py-class docutils literal"><span class="pre">torch.HalfTensor</span></code></td>
<td><code class="xref py py-class docutils literal"><span class="pre">torch.cuda.HalfTensor</span></code></td>
</tr>
<tr class="row-odd"><td>8-bit integer (unsigned)</td>
<td><code class="docutils literal"><span class="pre">torch.uint8</span></code></td>
<td><a class="reference internal" href="#torch.ByteTensor" title="torch.ByteTensor"><code class="xref py py-class docutils literal"><span class="pre">torch.ByteTensor</span></code></a></td>
<td><code class="xref py py-class docutils literal"><span class="pre">torch.cuda.ByteTensor</span></code></td>
</tr>
<tr class="row-even"><td>8-bit integer (signed)</td>
<td><code class="docutils literal"><span class="pre">torch.int8</span></code></td>
<td><code class="xref py py-class docutils literal"><span class="pre">torch.CharTensor</span></code></td>
<td><code class="xref py py-class docutils literal"><span class="pre">torch.cuda.CharTensor</span></code></td>
</tr>
<tr class="row-odd"><td>16-bit integer (signed)</td>
<td><code class="docutils literal"><span class="pre">torch.int16</span></code> or <code class="docutils literal"><span class="pre">torch.short</span></code></td>
<td><code class="xref py py-class docutils literal"><span class="pre">torch.ShortTensor</span></code></td>
<td><code class="xref py py-class docutils literal"><span class="pre">torch.cuda.ShortTensor</span></code></td>
</tr>
<tr class="row-even"><td>32-bit integer (signed)</td>
<td><code class="docutils literal"><span class="pre">torch.int32</span></code> or <code class="docutils literal"><span class="pre">torch.int</span></code></td>
<td><code class="xref py py-class docutils literal"><span class="pre">torch.IntTensor</span></code></td>
<td><code class="xref py py-class docutils literal"><span class="pre">torch.cuda.IntTensor</span></code></td>
</tr>
<tr class="row-odd"><td>64-bit integer (signed)</td>
<td><code class="docutils literal"><span class="pre">torch.int64</span></code> or <code class="docutils literal"><span class="pre">torch.long</span></code></td>
<td><code class="xref py py-class docutils literal"><span class="pre">torch.LongTensor</span></code></td>
<td><code class="xref py py-class docutils literal"><span class="pre">torch.cuda.LongTensor</span></code></td>
</tr>
</tbody>
</table>
<p><a class="reference internal" href="#torch.Tensor" title="torch.Tensor"><code class="xref py py-class docutils literal"><span class="pre">torch.Tensor</span></code></a> is an alias for the default tensor type (<code class="xref py py-class docutils literal"><span class="pre">torch.FloatTensor</span></code>).</p>
<p>A tensor can be constructed from a Python <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#list" title="(in Python v3.7)"><code class="xref py py-class docutils literal"><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"><span class="pre">torch.tensor()</span></code></a> constructor:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">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="first admonition-title">Warning</p>
<p class="last"><a class="reference internal" href="torch.html#torch.tensor" title="torch.tensor"><code class="xref py py-func docutils literal"><span class="pre">torch.tensor()</span></code></a> always copies <code class="xref py py-attr docutils literal"><span class="pre">data</span></code>. If you have a Tensor
<code class="xref py py-attr docutils literal"><span class="pre">data</span></code> and just want to change its <code class="docutils literal"><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"><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"><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"><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"><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"><span class="pre">torch.device</span></code></a> to a
constructor or tensor creation op:</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">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"><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"><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"><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"><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"><span class="pre">torch.autograd</span></code></a> records operations on them for automatic differentiation.</p>
<div class="highlight-default"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">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"><span class="pre">torch.Storage</span></code>, which holds its data.
The tensor class provides multi-dimensional, <a class="reference external" href="https://en.wikipedia.org/wiki/Stride_of_an_array">strided</a>
view of a storage and defines numeric operations on it.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">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"><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"><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"><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"><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="first admonition-title">Note</p>
<p class="last">Methods which mutate a tensor are marked with an underscore suffix.
For example, <code class="xref py py-func docutils literal"><span class="pre">torch.FloatTensor.abs_()</span></code> computes the absolute value
in-place and returns the modified tensor, while <code class="xref py py-func docutils literal"><span class="pre">torch.FloatTensor.abs()</span></code>
computes the result in a new tensor.</p>
</div>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">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"><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"><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"><span class="pre">to()</span></code></a> method on the tensor.</p>
</div>
<dl class="class">
<dt id="torch.Tensor">
<em class="property">class </em><code class="descclassname">torch.</code><code class="descname">Tensor</code><a class="headerlink" href="#torch.Tensor" title="Permalink to this definition">¶</a></dt>
<dd><p>There are a few main ways to create a tensor, depending on your use case.</p>
<ul class="simple">
<li>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"><span class="pre">torch.tensor()</span></code></a>.</li>
<li>To create a tensor with specific size, use <code class="docutils literal"><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>).</li>
<li>To create a tensor with the same size (and similar types) as another tensor,
use <code class="docutils literal"><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>).</li>
<li>To create a tensor with similar type but different size as another tensor,
use <code class="docutils literal"><span class="pre">tensor.new_*</span></code> creation ops.</li>
</ul>
<dl class="method">
<dt id="torch.Tensor.new_tensor">
<code class="descname">new_tensor</code><span class="sig-paren">(</span><em>data</em>, <em>dtype=None</em>, <em>device=None</em>, <em>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"><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"><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"><span class="pre">torch.device</span></code></a> as this tensor.</p>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last"><a class="reference internal" href="#torch.Tensor.new_tensor" title="torch.Tensor.new_tensor"><code class="xref py py-func docutils literal"><span class="pre">new_tensor()</span></code></a> always copies <code class="xref py py-attr docutils literal"><span class="pre">data</span></code>. If you have a Tensor
<code class="docutils literal"><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"><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"><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"><span class="pre">torch.from_numpy()</span></code></a>.</p>
</div>
<div class="admonition warning">
<p class="first admonition-title">Warning</p>
<p class="last">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"><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"><span class="pre">tensor.new_tensor(x)</span></code> is equivalent to <code class="docutils literal"><span class="pre">x.clone().detach()</span></code>
and <code class="docutils literal"><span class="pre">tensor.new_tensor(x,</span> <span class="pre">requires_grad=True)</span></code> is equivalent to <code class="docutils literal"><span class="pre">x.clone().detach().requires_grad_(True)</span></code>.
The equivalents using <code class="docutils literal"><span class="pre">clone()</span></code> and <code class="docutils literal"><span class="pre">detach()</span></code> are recommended.</p>
</div>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>data</strong> (<em>array_like</em>) – The returned Tensor copies <code class="xref py py-attr docutils literal"><span class="pre">data</span></code>.</li>
<li><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"><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"><span class="pre">torch.dtype</span></code></a> as this tensor.</li>
<li><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"><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"><span class="pre">torch.device</span></code></a> as this tensor.</li>
<li><strong>requires_grad</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If autograd should record operations on the
returned tensor. Default: <code class="docutils literal"><span class="pre">False</span></code>.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default"><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="descname">new_full</code><span class="sig-paren">(</span><em>size</em>, <em>fill_value</em>, <em>dtype=None</em>, <em>device=None</em>, <em>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"><span class="pre">size</span></code></a> filled with <code class="xref py py-attr docutils literal"><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"><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"><span class="pre">torch.device</span></code></a> as this tensor.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>fill_value</strong> (<em>scalar</em>) – the number to fill the output tensor with.</li>
<li><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"><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"><span class="pre">torch.dtype</span></code></a> as this tensor.</li>
<li><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"><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"><span class="pre">torch.device</span></code></a> as this tensor.</li>
<li><strong>requires_grad</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If autograd should record operations on the
returned tensor. Default: <code class="docutils literal"><span class="pre">False</span></code>.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default"><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="descname">new_empty</code><span class="sig-paren">(</span><em>size</em>, <em>dtype=None</em>, <em>device=None</em>, <em>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"><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"><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"><span class="pre">torch.device</span></code></a> as this tensor.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>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"><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"><span class="pre">torch.dtype</span></code></a> as this tensor.</li>
<li><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"><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"><span class="pre">torch.device</span></code></a> as this tensor.</li>
<li><strong>requires_grad</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If autograd should record operations on the
returned tensor. Default: <code class="docutils literal"><span class="pre">False</span></code>.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default"><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="descname">new_ones</code><span class="sig-paren">(</span><em>size</em>, <em>dtype=None</em>, <em>device=None</em>, <em>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"><span class="pre">size</span></code></a> filled with <code class="docutils literal"><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"><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"><span class="pre">torch.device</span></code></a> as this tensor.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>size</strong> (<em>int...</em>) – a list, tuple, or <code class="xref py py-class docutils literal"><span class="pre">torch.Size</span></code> of integers defining the
shape of the output tensor.</li>
<li><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"><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"><span class="pre">torch.dtype</span></code></a> as this tensor.</li>
<li><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"><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"><span class="pre">torch.device</span></code></a> as this tensor.</li>
<li><strong>requires_grad</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If autograd should record operations on the
returned tensor. Default: <code class="docutils literal"><span class="pre">False</span></code>.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default"><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="descname">new_zeros</code><span class="sig-paren">(</span><em>size</em>, <em>dtype=None</em>, <em>device=None</em>, <em>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"><span class="pre">size</span></code></a> filled with <code class="docutils literal"><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"><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"><span class="pre">torch.device</span></code></a> as this tensor.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>size</strong> (<em>int...</em>) – a list, tuple, or <code class="xref py py-class docutils literal"><span class="pre">torch.Size</span></code> of integers defining the
shape of the output tensor.</li>
<li><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"><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"><span class="pre">torch.dtype</span></code></a> as this tensor.</li>
<li><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"><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"><span class="pre">torch.device</span></code></a> as this tensor.</li>
<li><strong>requires_grad</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a><em>, </em><em>optional</em>) – If autograd should record operations on the
returned tensor. Default: <code class="docutils literal"><span class="pre">False</span></code>.</li>
</ul>
</td>
</tr>
</tbody>
</table>
<p>Example:</p>
<div class="highlight-default"><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="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"><span class="pre">True</span></code> if the Tensor is stored on the GPU, <code class="docutils literal"><span class="pre">False</span></code> otherwise.</p>
</dd></dl>
<dl class="attribute">
<dt id="torch.Tensor.device">
<code class="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"><span class="pre">torch.device</span></code></a> where this Tensor is.</p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.abs">
<code class="descname">abs</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.abs" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.abs" title="torch.abs"><code class="xref py py-func docutils literal"><span class="pre">torch.abs()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.abs_">
<code class="descname">abs_</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.abs_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.abs" title="torch.Tensor.abs"><code class="xref py py-meth docutils literal"><span class="pre">abs()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.acos">
<code class="descname">acos</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.acos" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.acos" title="torch.acos"><code class="xref py py-func docutils literal"><span class="pre">torch.acos()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.acos_">
<code class="descname">acos_</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.acos_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.acos" title="torch.Tensor.acos"><code class="xref py py-meth docutils literal"><span class="pre">acos()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.add">
<code class="descname">add</code><span class="sig-paren">(</span><em>value</em><span class="sig-paren">)</span> → 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"><span class="pre">torch.add()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.add_">
<code class="descname">add_</code><span class="sig-paren">(</span><em>value</em><span class="sig-paren">)</span> → 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"><span class="pre">add()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addbmm">
<code class="descname">addbmm</code><span class="sig-paren">(</span><em>beta=1</em>, <em>mat</em>, <em>alpha=1</em>, <em>batch1</em>, <em>batch2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addbmm" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.addbmm" title="torch.addbmm"><code class="xref py py-func docutils literal"><span class="pre">torch.addbmm()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addbmm_">
<code class="descname">addbmm_</code><span class="sig-paren">(</span><em>beta=1</em>, <em>mat</em>, <em>alpha=1</em>, <em>batch1</em>, <em>batch2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addbmm_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.addbmm" title="torch.Tensor.addbmm"><code class="xref py py-meth docutils literal"><span class="pre">addbmm()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addcdiv">
<code class="descname">addcdiv</code><span class="sig-paren">(</span><em>value=1</em>, <em>tensor1</em>, <em>tensor2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addcdiv" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.addcdiv" title="torch.addcdiv"><code class="xref py py-func docutils literal"><span class="pre">torch.addcdiv()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addcdiv_">
<code class="descname">addcdiv_</code><span class="sig-paren">(</span><em>value=1</em>, <em>tensor1</em>, <em>tensor2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addcdiv_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.addcdiv" title="torch.Tensor.addcdiv"><code class="xref py py-meth docutils literal"><span class="pre">addcdiv()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addcmul">
<code class="descname">addcmul</code><span class="sig-paren">(</span><em>value=1</em>, <em>tensor1</em>, <em>tensor2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addcmul" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.addcmul" title="torch.addcmul"><code class="xref py py-func docutils literal"><span class="pre">torch.addcmul()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addcmul_">
<code class="descname">addcmul_</code><span class="sig-paren">(</span><em>value=1</em>, <em>tensor1</em>, <em>tensor2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addcmul_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.addcmul" title="torch.Tensor.addcmul"><code class="xref py py-meth docutils literal"><span class="pre">addcmul()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addmm">
<code class="descname">addmm</code><span class="sig-paren">(</span><em>beta=1</em>, <em>mat</em>, <em>alpha=1</em>, <em>mat1</em>, <em>mat2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addmm" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.addmm" title="torch.addmm"><code class="xref py py-func docutils literal"><span class="pre">torch.addmm()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addmm_">
<code class="descname">addmm_</code><span class="sig-paren">(</span><em>beta=1</em>, <em>mat</em>, <em>alpha=1</em>, <em>mat1</em>, <em>mat2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addmm_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.addmm" title="torch.Tensor.addmm"><code class="xref py py-meth docutils literal"><span class="pre">addmm()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addmv">
<code class="descname">addmv</code><span class="sig-paren">(</span><em>beta=1</em>, <em>tensor</em>, <em>alpha=1</em>, <em>mat</em>, <em>vec</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addmv" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.addmv" title="torch.addmv"><code class="xref py py-func docutils literal"><span class="pre">torch.addmv()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addmv_">
<code class="descname">addmv_</code><span class="sig-paren">(</span><em>beta=1</em>, <em>tensor</em>, <em>alpha=1</em>, <em>mat</em>, <em>vec</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addmv_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.addmv" title="torch.Tensor.addmv"><code class="xref py py-meth docutils literal"><span class="pre">addmv()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addr">
<code class="descname">addr</code><span class="sig-paren">(</span><em>beta=1</em>, <em>alpha=1</em>, <em>vec1</em>, <em>vec2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addr" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.addr" title="torch.addr"><code class="xref py py-func docutils literal"><span class="pre">torch.addr()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.addr_">
<code class="descname">addr_</code><span class="sig-paren">(</span><em>beta=1</em>, <em>alpha=1</em>, <em>vec1</em>, <em>vec2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.addr_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.addr" title="torch.Tensor.addr"><code class="xref py py-meth docutils literal"><span class="pre">addr()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.allclose">
<code class="descname">allclose</code><span class="sig-paren">(</span><em>other</em>, <em>rtol=1e-05</em>, <em>atol=1e-08</em>, <em>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"><span class="pre">torch.allclose()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.apply_">
<code class="descname">apply_</code><span class="sig-paren">(</span><em>callable</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.apply_" title="Permalink to this definition">¶</a></dt>
<dd><p>Applies the function <code class="xref py py-attr docutils literal"><span class="pre">callable</span></code> to each element in the tensor, replacing
each element with the value returned by <code class="xref py py-attr docutils literal"><span class="pre">callable</span></code>.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">This function only works with CPU tensors and should not be used in code
sections that require high performance.</p>
</div>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.argmax">
<code class="descname">argmax</code><span class="sig-paren">(</span><em>dim=None</em>, <em>keepdim=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/tensor.html#Tensor.argmax"><span class="viewcode-link">[source]</span></a><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"><span class="pre">torch.argmax()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.argmin">
<code class="descname">argmin</code><span class="sig-paren">(</span><em>dim=None</em>, <em>keepdim=False</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/tensor.html#Tensor.argmin"><span class="viewcode-link">[source]</span></a><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"><span class="pre">torch.argmin()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.asin">
<code class="descname">asin</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.asin" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.asin" title="torch.asin"><code class="xref py py-func docutils literal"><span class="pre">torch.asin()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.asin_">
<code class="descname">asin_</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.asin_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.asin" title="torch.Tensor.asin"><code class="xref py py-meth docutils literal"><span class="pre">asin()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.atan">
<code class="descname">atan</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.atan" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.atan" title="torch.atan"><code class="xref py py-func docutils literal"><span class="pre">torch.atan()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.atan2">
<code class="descname">atan2</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.atan2" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.atan2" title="torch.atan2"><code class="xref py py-func docutils literal"><span class="pre">torch.atan2()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.atan2_">
<code class="descname">atan2_</code><span class="sig-paren">(</span><em>other</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.atan2_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.atan2" title="torch.Tensor.atan2"><code class="xref py py-meth docutils literal"><span class="pre">atan2()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.atan_">
<code class="descname">atan_</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.atan_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.atan" title="torch.Tensor.atan"><code class="xref py py-meth docutils literal"><span class="pre">atan()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.baddbmm">
<code class="descname">baddbmm</code><span class="sig-paren">(</span><em>beta=1</em>, <em>alpha=1</em>, <em>batch1</em>, <em>batch2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.baddbmm" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.baddbmm" title="torch.baddbmm"><code class="xref py py-func docutils literal"><span class="pre">torch.baddbmm()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.baddbmm_">
<code class="descname">baddbmm_</code><span class="sig-paren">(</span><em>beta=1</em>, <em>alpha=1</em>, <em>batch1</em>, <em>batch2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.baddbmm_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.baddbmm" title="torch.Tensor.baddbmm"><code class="xref py py-meth docutils literal"><span class="pre">baddbmm()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.bernoulli">
<code class="descname">bernoulli</code><span class="sig-paren">(</span><em>*</em>, <em>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">\(\texttt{result[i]}\)</span> is independently
sampled from <span class="math">\(\text{Bernoulli}(\texttt{self[i]})\)</span>. <code class="xref py py-attr docutils literal"><span class="pre">self</span></code> must have
floating point <code class="docutils literal"><span class="pre">dtype</span></code>, and the result will have the same <code class="docutils literal"><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"><span class="pre">torch.bernoulli()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.bernoulli_">
<code class="descname">bernoulli_</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="headerlink" href="#torch.Tensor.bernoulli_" title="Permalink to this definition">¶</a></dt>
<dd><dl class="function">
<dt>
<code class="descname">bernoulli_</code><span class="sig-paren">(</span><em>p=0.5</em>, <em>*</em>, <em>generator=None</em><span class="sig-paren">)</span> → Tensor</dt>
<dd><p>Fills each location of <code class="xref py py-attr docutils literal"><span class="pre">self</span></code> with an independent sample from
<span class="math">\(\text{Bernoulli}(\texttt{p})\)</span>. <code class="xref py py-attr docutils literal"><span class="pre">self</span></code> can have integral
<code class="docutils literal"><span class="pre">dtype</span></code>.</p>
</dd></dl>
<dl class="function">
<dt>
<code class="descname">bernoulli_</code><span class="sig-paren">(</span><em>p_tensor</em>, <em>*</em>, <em>generator=None</em><span class="sig-paren">)</span> → Tensor</dt>
<dd><p><code class="xref py py-attr docutils literal"><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">\(\text{i}^{th}\)</span> element of <code class="xref py py-attr docutils literal"><span class="pre">self</span></code> tensor will be set to a
value sampled from <span class="math">\(\text{Bernoulli}(\texttt{p\_tensor[i]})\)</span>.</p>
<p><code class="xref py py-attr docutils literal"><span class="pre">self</span></code> can have integral <code class="docutils literal"><span class="pre">dtype</span></code>, but :attr`p_tensor` must have
floating point <code class="docutils literal"><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"><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"><span class="pre">torch.bernoulli()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.bmm">
<code class="descname">bmm</code><span class="sig-paren">(</span><em>batch2</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.bmm" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.bmm" title="torch.bmm"><code class="xref py py-func docutils literal"><span class="pre">torch.bmm()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.byte">
<code class="descname">byte</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.byte" title="Permalink to this definition">¶</a></dt>
<dd><p><code class="docutils literal"><span class="pre">self.byte()</span></code> is equivalent to <code class="docutils literal"><span class="pre">self.to(torch.uint8)</span></code>. See <a class="reference internal" href="#torch.Tensor.to" title="torch.Tensor.to"><code class="xref py py-func docutils literal"><span class="pre">to()</span></code></a>.</p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.btrifact">
<code class="descname">btrifact</code><span class="sig-paren">(</span><em>info=None</em>, <em>pivot=True</em><span class="sig-paren">)</span><a class="reference internal" href="_modules/torch/tensor.html#Tensor.btrifact"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.Tensor.btrifact" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.btrifact" title="torch.btrifact"><code class="xref py py-func docutils literal"><span class="pre">torch.btrifact()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.btrifact_with_info">
<code class="descname">btrifact_with_info</code><span class="sig-paren">(</span><em>pivot=True) -> (Tensor</em>, <em>Tensor</em>, <em>Tensor</em><span class="sig-paren">)</span><a class="headerlink" href="#torch.Tensor.btrifact_with_info" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.btrifact_with_info" title="torch.btrifact_with_info"><code class="xref py py-func docutils literal"><span class="pre">torch.btrifact_with_info()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.btrisolve">
<code class="descname">btrisolve</code><span class="sig-paren">(</span><em>LU_data</em>, <em>LU_pivots</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.btrisolve" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.btrisolve" title="torch.btrisolve"><code class="xref py py-func docutils literal"><span class="pre">torch.btrisolve()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.cauchy_">
<code class="descname">cauchy_</code><span class="sig-paren">(</span><em>median=0</em>, <em>sigma=1</em>, <em>*</em>, <em>generator=None</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.cauchy_" title="Permalink to this definition">¶</a></dt>
<dd><p>Fills the tensor with numbers drawn from the Cauchy distribution:</p>
<div class="math">
\[f(x) = \dfrac{1}{\pi} \dfrac{\sigma}{(x - \text{median})^2 + \sigma^2}\]</div>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.ceil">
<code class="descname">ceil</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.ceil" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.ceil" title="torch.ceil"><code class="xref py py-func docutils literal"><span class="pre">torch.ceil()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.ceil_">
<code class="descname">ceil_</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.ceil_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.ceil" title="torch.Tensor.ceil"><code class="xref py py-meth docutils literal"><span class="pre">ceil()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.char">
<code class="descname">char</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.char" title="Permalink to this definition">¶</a></dt>
<dd><p><code class="docutils literal"><span class="pre">self.char()</span></code> is equivalent to <code class="docutils literal"><span class="pre">self.to(torch.int8)</span></code>. See <a class="reference internal" href="#torch.Tensor.to" title="torch.Tensor.to"><code class="xref py py-func docutils literal"><span class="pre">to()</span></code></a>.</p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.cholesky">
<code class="descname">cholesky</code><span class="sig-paren">(</span><em>upper=False</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.cholesky" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.cholesky" title="torch.cholesky"><code class="xref py py-func docutils literal"><span class="pre">torch.cholesky()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.chunk">
<code class="descname">chunk</code><span class="sig-paren">(</span><em>chunks</em>, <em>dim=0</em><span class="sig-paren">)</span> → List of Tensors<a class="headerlink" href="#torch.Tensor.chunk" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.chunk" title="torch.chunk"><code class="xref py py-func docutils literal"><span class="pre">torch.chunk()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.clamp">
<code class="descname">clamp</code><span class="sig-paren">(</span><em>min</em>, <em>max</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.clamp" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.clamp" title="torch.clamp"><code class="xref py py-func docutils literal"><span class="pre">torch.clamp()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.clamp_">
<code class="descname">clamp_</code><span class="sig-paren">(</span><em>min</em>, <em>max</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.clamp_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.clamp" title="torch.Tensor.clamp"><code class="xref py py-meth docutils literal"><span class="pre">clamp()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.clone">
<code class="descname">clone</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.clone" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a copy of the <code class="xref py py-attr docutils literal"><span class="pre">self</span></code> tensor. The copy has the same size and data
type as <code class="xref py py-attr docutils literal"><span class="pre">self</span></code>.</p>
<div class="admonition note">
<p class="first admonition-title">Note</p>
<p class="last">Unlike <cite>copy_()</cite>, this function is recorded in the computation graph. Gradients
propagating to the cloned tensor will propagate to the original tensor.</p>
</div>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.contiguous">
<code class="descname">contiguous</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.contiguous" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a contiguous tensor containing the same data as <code class="xref py py-attr docutils literal"><span class="pre">self</span></code> tensor. If
<code class="xref py py-attr docutils literal"><span class="pre">self</span></code> tensor is contiguous, this function returns the <code class="xref py py-attr docutils literal"><span class="pre">self</span></code>
tensor.</p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.copy_">
<code class="descname">copy_</code><span class="sig-paren">(</span><em>src</em>, <em>non_blocking=False</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.copy_" title="Permalink to this definition">¶</a></dt>
<dd><p>Copies the elements from <code class="xref py py-attr docutils literal"><span class="pre">src</span></code> into <code class="xref py py-attr docutils literal"><span class="pre">self</span></code> tensor and returns
<code class="xref py py-attr docutils literal"><span class="pre">self</span></code>.</p>
<p>The <code class="xref py py-attr docutils literal"><span class="pre">src</span></code> tensor must be <a class="reference internal" href="notes/broadcasting.html#broadcasting-semantics"><span class="std std-ref">broadcastable</span></a>
with the <code class="xref py py-attr docutils literal"><span class="pre">self</span></code> tensor. It may be of a different data type or reside on a
different device.</p>
<table class="docutils field-list" frame="void" rules="none">
<col class="field-name" />
<col class="field-body" />
<tbody valign="top">
<tr class="field-odd field"><th class="field-name">Parameters:</th><td class="field-body"><ul class="first last simple">
<li><strong>src</strong> (<a class="reference internal" href="#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – the source tensor to copy from</li>
<li><strong>non_blocking</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.7)"><em>bool</em></a>) – if <code class="docutils literal"><span class="pre">True</span></code> and this copy is between CPU and GPU,
the copy may occur asynchronously with respect to the host. For other
cases, this argument has no effect.</li>
</ul>
</td>
</tr>
</tbody>
</table>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.cos">
<code class="descname">cos</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.cos" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.cos" title="torch.cos"><code class="xref py py-func docutils literal"><span class="pre">torch.cos()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.cos_">
<code class="descname">cos_</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.cos_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.cos" title="torch.Tensor.cos"><code class="xref py py-meth docutils literal"><span class="pre">cos()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.cosh">
<code class="descname">cosh</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.cosh" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.cosh" title="torch.cosh"><code class="xref py py-func docutils literal"><span class="pre">torch.cosh()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.cosh_">
<code class="descname">cosh_</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.cosh_" title="Permalink to this definition">¶</a></dt>
<dd><p>In-place version of <a class="reference internal" href="#torch.Tensor.cosh" title="torch.Tensor.cosh"><code class="xref py py-meth docutils literal"><span class="pre">cosh()</span></code></a></p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.cpu">
<code class="descname">cpu</code><span class="sig-paren">(</span><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.cpu" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a copy of this object in CPU memory.</p>
<p>If this object is already in CPU memory and on the correct device,
then no copy is performed and the original object is returned.</p>
</dd></dl>
<dl class="method">
<dt id="torch.Tensor.cross">
<code class="descname">cross</code><span class="sig-paren">(</span><em>other</em>, <em>dim=-1</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.Tensor.cross" title="Permalink to this definition">¶</a></dt>
<dd><p>See <a class="reference internal" href="torch.html#torch.cross" title="torch.cross"><code class="xref py py-func docutils literal"><span class="pre">torch.cross()</span></code></a></p>
</dd></dl>
<dl class="method">