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<h1>torch.mode<a class="headerlink" href="#torch-mode" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.mode">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">mode</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">dim=-1</em>, <em class="sig-param">keepdim=False</em>, <em class="sig-param">*</em>, <em class="sig-param">out=None) -> (Tensor</em>, <em class="sig-param">LongTensor</em><span class="sig-paren">)</span><a class="headerlink" href="#torch.mode" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a namedtuple <code class="docutils literal notranslate"><span class="pre">(values,</span> <span class="pre">indices)</span></code> where <code class="docutils literal notranslate"><span class="pre">values</span></code> is the mode
value of each row of the <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor in the given dimension
<code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code>, i.e. a value which appears most often
in that row, and <code class="docutils literal notranslate"><span class="pre">indices</span></code> is the index location of each mode value found.</p>
<p>By default, <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code> is the last dimension of the <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> tensor.</p>
<p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">keepdim</span></code> is <code class="docutils literal notranslate"><span class="pre">True</span></code>, the output tensors are of the same size as
<code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> except in the dimension <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code> where they are of size 1.
Otherwise, <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code> is squeezed (see <a class="reference internal" href="torch.squeeze.html#torch.squeeze" title="torch.squeeze"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.squeeze()</span></code></a>), resulting
in the output tensors having 1 fewer dimension than <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 not defined for <code class="docutils literal notranslate"><span class="pre">torch.cuda.Tensor</span></code> yet.</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>dim</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a>) – the dimension to reduce.</p></li>
<li><p><strong>keepdim</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a>) – whether the output tensor has <code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code> retained or not.</p></li>
</ul>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><p><strong>out</strong> (<a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#tuple" title="(in Python v3.9)"><em>tuple</em></a><em>, </em><em>optional</em>) – the result tuple of two output tensors (values, indices)</p>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">>>> </span><span class="n">a</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randint</span><span class="p">(</span><span class="mi">10</span><span class="p">,</span> <span class="p">(</span><span class="mi">5</span><span class="p">,))</span>
<span class="gp">>>> </span><span class="n">a</span>
<span class="go">tensor([6, 5, 1, 0, 2])</span>
<span class="gp">>>> </span><span class="n">b</span> <span class="o">=</span> <span class="n">a</span> <span class="o">+</span> <span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">50</span><span class="p">,</span> <span class="mi">1</span><span class="p">)</span> <span class="o">*</span> <span class="mi">5</span><span class="p">)</span><span class="o">.</span><span class="n">long</span><span class="p">()</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">mode</span><span class="p">(</span><span class="n">b</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
<span class="go">torch.return_types.mode(values=tensor([6, 5, 1, 0, 2]), indices=tensor([2, 2, 2, 2, 2]))</span>
</pre></div>
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