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<div class="section" id="torch-empty-like">
<h1>torch.empty_like<a class="headerlink" href="#torch-empty-like" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.empty_like">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">empty_like</code><span class="sig-paren">(</span><em class="sig-param">input</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">memory_format=torch.preserve_format</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.empty_like" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns an uninitialized tensor with the same size as <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.
<code class="docutils literal notranslate"><span class="pre">torch.empty_like(input)</span></code> is equivalent to
<code class="docutils literal notranslate"><span class="pre">torch.empty(input.size(),</span> <span class="pre">dtype=input.dtype,</span> <span class="pre">layout=input.layout,</span> <span class="pre">device=input.device)</span></code>.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>input</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – the size of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> will determine size of the output tensor.</p>
</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>, defaults to the dtype of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</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: if <code class="docutils literal notranslate"><span class="pre">None</span></code>, defaults to the layout of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</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>, defaults to the device of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</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>memory_format</strong> (<a class="reference internal" href="../tensor_attributes.html#torch.torch.memory_format" title="torch.torch.memory_format"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.memory_format</span></code></a>, optional) – the desired memory format of
returned Tensor. Default: <code class="docutils literal notranslate"><span class="pre">torch.preserve_format</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">torch</span><span class="o">.</span><span class="n">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="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int64</span><span class="p">)</span>
<span class="go">tensor([[ 9.4064e+13, 2.8000e+01, 9.3493e+13],</span>
<span class="go"> [ 7.5751e+18, 7.1428e+18, 7.5955e+18]])</span>
</pre></div>
</div>
</dd></dl>
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