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<div class="section" id="torch-fmod">
<h1>torch.fmod<a class="headerlink" href="#torch-fmod" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.fmod">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">fmod</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">other</em>, <em class="sig-param">out=None</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.fmod" title="Permalink to this definition">¶</a></dt>
<dd><p>Computes the element-wise remainder of division.</p>
<p>The dividend and divisor may contain both for integer and floating point
numbers. The remainder has the same sign as the dividend <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p>
<p>When <code class="xref py py-attr docutils literal notranslate"><span class="pre">other</span></code> is a tensor, the shapes 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> must be <a class="reference internal" href="../notes/broadcasting.html#broadcasting-semantics"><span class="std std-ref">broadcastable</span></a>.</p>
<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 dividend</p></li>
<li><p><strong>other</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.8)"><em>float</em></a>) – the divisor, which may be either a number or a tensor of the same shape as the dividend</p></li>
<li><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></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">torch</span><span class="o">.</span><span class="n">fmod</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="mf">3.</span><span class="p">,</span> <span class="o">-</span><span class="mi">2</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</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="mi">3</span><span class="p">]),</span> <span class="mi">2</span><span class="p">)</span>
<span class="go">tensor([-1., -0., -1., 1., 0., 1.])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">fmod</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="mf">1.</span><span class="p">,</span> <span class="mi">2</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="mi">5</span><span class="p">]),</span> <span class="mf">1.5</span><span class="p">)</span>
<span class="go">tensor([ 1.0000, 0.5000, 0.0000, 1.0000, 0.5000])</span>
</pre></div>
</div>
</dd></dl>
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