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<div class="section" id="torch-matrix-power">
<h1>torch.matrix_power<a class="headerlink" href="#torch-matrix-power" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.matrix_power">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">matrix_power</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">n</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.matrix_power" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns the matrix raised to the power <code class="xref py py-attr docutils literal notranslate"><span class="pre">n</span></code> for square matrices.
For batch of matrices, each individual matrix is raised to the power <code class="xref py py-attr docutils literal notranslate"><span class="pre">n</span></code>.</p>
<p>If <code class="xref py py-attr docutils literal notranslate"><span class="pre">n</span></code> is negative, then the inverse of the matrix (if invertible) is
raised to the power <code class="xref py py-attr docutils literal notranslate"><span class="pre">n</span></code>. For a batch of matrices, the batched inverse
(if invertible) is raised to the power <code class="xref py py-attr docutils literal notranslate"><span class="pre">n</span></code>. If <code class="xref py py-attr docutils literal notranslate"><span class="pre">n</span></code> is 0, then an identity matrix
is returned.</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 input tensor.</p></li>
<li><p><strong>n</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 power to raise the matrix to</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">a</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">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">2</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">a</span>
<span class="go">tensor([[[-1.9975, -1.9610],</span>
<span class="go"> [ 0.9592, -2.3364]],</span>
<span class="go"> [[-1.2534, -1.3429],</span>
<span class="go"> [ 0.4153, -1.4664]]])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">matrix_power</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="go">tensor([[[ 3.9392, -23.9916],</span>
<span class="go"> [ 11.7357, -0.2070]],</span>
<span class="go"> [[ 0.2468, -6.7168],</span>
<span class="go"> [ 2.0774, -0.8187]]])</span>
</pre></div>
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