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<h1>torch.empty_strided<a class="headerlink" href="#torch-empty-strided" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.empty_strided">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">empty_strided</code><span class="sig-paren">(</span><em class="sig-param">size</em>, <em class="sig-param">stride</em>, <em class="sig-param">*</em>, <em class="sig-param">dtype=None</em>, <em class="sig-param">layout=None</em>, <em class="sig-param">device=None</em>, <em class="sig-param">requires_grad=False</em>, <em class="sig-param">pin_memory=False</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.empty_strided" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a tensor filled with uninitialized data. The shape and strides of the tensor is
defined by the variable argument <code class="xref py py-attr docutils literal notranslate"><span class="pre">size</span></code> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">stride</span></code> respectively.
<code class="docutils literal notranslate"><span class="pre">torch.empty_strided(size,</span> <span class="pre">stride)</span></code> is equivalent to
<code class="docutils literal notranslate"><span class="pre">torch.empty(size).as_strided(size,</span> <span class="pre">stride)</span></code>.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>More than one element of the created tensor may refer to a single memory
location. As a result, in-place operations (especially ones that are
vectorized) may result in incorrect behavior. If you need to write to
the tensors, please clone them first.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>size</strong> (<em>tuple of python:ints</em>) – the shape of the output tensor</p></li>
<li><p><strong>stride</strong> (<em>tuple of python:ints</em>) – the strides of the output tensor</p></li>
</ul>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><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 data type of returned tensor.
Default: if <code class="docutils literal notranslate"><span class="pre">None</span></code>, uses a global default (see <a class="reference internal" href="torch.set_default_tensor_type.html#torch.set_default_tensor_type" title="torch.set_default_tensor_type"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.set_default_tensor_type()</span></code></a>).</p></li>
<li><p><strong>layout</strong> (<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>, optional) – the desired layout of returned Tensor.
Default: <code class="docutils literal notranslate"><span class="pre">torch.strided</span></code>.</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 <code class="docutils literal notranslate"><span class="pre">None</span></code>, uses the current device for the default tensor type
(see <a class="reference internal" href="torch.set_default_tensor_type.html#torch.set_default_tensor_type" title="torch.set_default_tensor_type"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.set_default_tensor_type()</span></code></a>). <code class="xref py py-attr docutils literal notranslate"><span class="pre">device</span></code> will be the CPU
for CPU tensor types and the current CUDA device for CUDA tensor types.</p></li>
<li><p><strong>requires_grad</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>optional</em>) – If autograd should record operations on the
returned tensor. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
<li><p><strong>pin_memory</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>optional</em>) – If set, returned tensor would be allocated in
the pinned memory. Works only for CPU tensors. 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">a</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">empty_strided</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">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span>
<span class="gp">>>> </span><span class="n">a</span>
<span class="go">tensor([[8.9683e-44, 4.4842e-44, 5.1239e+07],</span>
<span class="go"> [0.0000e+00, 0.0000e+00, 3.0705e-41]])</span>
<span class="gp">>>> </span><span class="n">a</span><span class="o">.</span><span class="n">stride</span><span class="p">()</span>
<span class="go">(1, 2)</span>
<span class="gp">>>> </span><span class="n">a</span><span class="o">.</span><span class="n">size</span><span class="p">()</span>
<span class="go">torch.Size([2, 3])</span>
</pre></div>
</div>
</dd></dl>
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