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<div class="section" id="torch-masked-select">
<h1>torch.masked_select<a class="headerlink" href="#torch-masked-select" title="Permalink to this headline">¶</a></h1>
<dl class="py function">
<dt id="torch.masked_select">
<code class="sig-prename descclassname"><span class="pre">torch.</span></code><code class="sig-name descname"><span class="pre">masked_select</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">mask</span></span></em>, <em class="sig-param"><span class="o"><span class="pre">*</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">out</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">None</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.masked_select" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a new 1-D tensor which indexes the <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor according to
the boolean mask <code class="xref py py-attr docutils literal notranslate"><span class="pre">mask</span></code> which is a <cite>BoolTensor</cite>.</p>
<p>The shapes of the <code class="xref py py-attr docutils literal notranslate"><span class="pre">mask</span></code> tensor and the <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor don’t need
to match, but they must be <a class="reference internal" href="../notes/broadcasting.html#broadcasting-semantics"><span class="std std-ref">broadcastable</span></a>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>The returned tensor does <strong>not</strong> use the same storage
as the original tensor</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>mask</strong> (<em>BoolTensor</em>) – the tensor containing the binary mask to index with</p></li>
</ul>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><p><strong>out</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>, </em><em>optional</em>) – the output tensor.</p>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><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">randn</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="n">x</span>
<span class="go">tensor([[ 0.3552, -2.3825, -0.8297, 0.3477],</span>
<span class="go"> [-1.2035, 1.2252, 0.5002, 0.6248],</span>
<span class="go"> [ 0.1307, -2.0608, 0.1244, 2.0139]])</span>
<span class="gp">>>> </span><span class="n">mask</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">ge</span><span class="p">(</span><span class="mf">0.5</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">mask</span>
<span class="go">tensor([[False, False, False, False],</span>
<span class="go"> [False, True, True, True],</span>
<span class="go"> [False, False, False, True]])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">masked_select</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">mask</span><span class="p">)</span>
<span class="go">tensor([ 1.2252, 0.5002, 0.6248, 2.0139])</span>
</pre></div>
</div>
</dd></dl>
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