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<div class="section" id="torch-bitwise-and">
<h1>torch.bitwise_and<a class="headerlink" href="#torch-bitwise-and" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.bitwise_and">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">bitwise_and</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">other</em>, <em class="sig-param">*</em>, <em class="sig-param">out=None</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.bitwise_and" title="Permalink to this definition">¶</a></dt>
<dd><p>Computes the bitwise AND of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> and <code class="xref py py-attr docutils literal notranslate"><span class="pre">other</span></code>. The input tensor must be of
integral or Boolean types. For bool tensors, it computes the logical AND.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>input</strong> – the first input tensor</p></li>
<li><p><strong>other</strong> – the second input tensor</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 class="rubric">Example</p>
<div class="doctest 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">bitwise_and</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="o">-</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">int8</span><span class="p">),</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">0</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">int8</span><span class="p">))</span>
<span class="go">tensor([1, 0, 3], dtype=torch.int8)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">bitwise_and</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="kc">True</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">]),</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="kc">False</span><span class="p">,</span> <span class="kc">True</span><span class="p">,</span> <span class="kc">False</span><span class="p">]))</span>
<span class="go">tensor([ False, True, False])</span>
</pre></div>
</div>
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