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<div class="section" id="torch-renorm">
<h1>torch.renorm<a class="headerlink" href="#torch-renorm" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.renorm">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">renorm</code><span class="sig-paren">(</span><em class="sig-param">input</em>, <em class="sig-param">p</em>, <em class="sig-param">dim</em>, <em class="sig-param">maxnorm</em>, <em class="sig-param">*</em>, <em class="sig-param">out=None</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.renorm" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a tensor where each sub-tensor of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> along dimension
<code class="xref py py-attr docutils literal notranslate"><span class="pre">dim</span></code> is normalized such that the <cite>p</cite>-norm of the sub-tensor is lower
than the value <code class="xref py py-attr docutils literal notranslate"><span class="pre">maxnorm</span></code></p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>If the norm of a row is lower than <cite>maxnorm</cite>, the row is unchanged</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>p</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a>) – the power for the norm computation</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 slice over to get the sub-tensors</p></li>
<li><p><strong>maxnorm</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a>) – the maximum norm to keep each sub-tensor under</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">ones</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>
<span class="gp">>>> </span><span class="n">x</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">fill_</span><span class="p">(</span><span class="mi">2</span><span class="p">)</span>
<span class="go">tensor([ 2., 2., 2.])</span>
<span class="gp">>>> </span><span class="n">x</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span><span class="o">.</span><span class="n">fill_</span><span class="p">(</span><span class="mi">3</span><span class="p">)</span>
<span class="go">tensor([ 3., 3., 3.])</span>
<span class="gp">>>> </span><span class="n">x</span>
<span class="go">tensor([[ 1., 1., 1.],</span>
<span class="go"> [ 2., 2., 2.],</span>
<span class="go"> [ 3., 3., 3.]])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">renorm</span><span class="p">(</span><span class="n">x</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">5</span><span class="p">)</span>
<span class="go">tensor([[ 1.0000, 1.0000, 1.0000],</span>
<span class="go"> [ 1.6667, 1.6667, 1.6667],</span>
<span class="go"> [ 1.6667, 1.6667, 1.6667]])</span>
</pre></div>
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