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<li class="toctree-l1"><a class="reference internal" href="../notes/amp_examples.html">Automatic Mixed Precision examples</a></li>
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<div class="section" id="torch-flatten">
<h1>torch.flatten<a class="headerlink" href="#torch-flatten" title="Permalink to this headline">¶</a></h1>
<dl class="py function">
<dt id="torch.flatten">
<code class="sig-prename descclassname"><span class="pre">torch.</span></code><code class="sig-name descname"><span class="pre">flatten</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="n"><span class="pre">input</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">start_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">0</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">end_dim</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">-</span> <span class="pre">1</span></span></em><span class="sig-paren">)</span> → <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a><a class="headerlink" href="#torch.flatten" title="Permalink to this definition">¶</a></dt>
<dd><p>Flattens <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> by reshaping it into a one-dimensional tensor. If <code class="xref py py-attr docutils literal notranslate"><span class="pre">start_dim</span></code> or <code class="xref py py-attr docutils literal notranslate"><span class="pre">end_dim</span></code>
are passed, only dimensions starting with <code class="xref py py-attr docutils literal notranslate"><span class="pre">start_dim</span></code> and ending with <code class="xref py py-attr docutils literal notranslate"><span class="pre">end_dim</span></code> are flattened.
The order of elements in <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> is unchanged.</p>
<p>Unlike NumPy’s flatten, which always copies input’s data, this function may return the original object, a view,
or copy. If no dimensions are flattened, then the original object <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> is returned. Otherwise, if input can
be viewed as the flattened shape, then that view is returned. Finally, only if the input cannot be viewed as the
flattened shape is input’s data copied. See <a class="reference internal" href="torch.Tensor.view.html#torch.Tensor.view" title="torch.Tensor.view"><code class="xref py py-meth docutils literal notranslate"><span class="pre">torch.Tensor.view()</span></code></a> for details on when a view will be returned.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>Flattening a zero-dimensional tensor will return a one-dimensional view.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – the input tensor.</p></li>
<li><p><strong>start_dim</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – the first dim to flatten</p></li>
<li><p><strong>end_dim</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.10)"><em>int</em></a>) – the last dim to flatten</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">t</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="gp">... </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="gp">... </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="p">[</span><span class="mi">7</span><span class="p">,</span> <span class="mi">8</span><span class="p">]]])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">t</span><span class="p">)</span>
<span class="go">tensor([1, 2, 3, 4, 5, 6, 7, 8])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">flatten</span><span class="p">(</span><span class="n">t</span><span class="p">,</span> <span class="n">start_dim</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="go">tensor([[1, 2, 3, 4],</span>
<span class="go"> [5, 6, 7, 8]])</span>
</pre></div>
</div>
</dd></dl>
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