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<div class="section" id="torch-clone">
<h1>torch.clone<a class="headerlink" href="#torch-clone" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.clone">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">clone</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">*</em>, <em class="sig-param">memory_format=torch.preserve_format</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.clone" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a copy of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>This function is differentiable, so gradients will flow back from the
result of this operation to <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>. To create a tensor without an
autograd relationship to <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> see <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>.</p>
</div>
<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 input tensor.</p>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><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>
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