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<div class="section" id="torch-nn-utils-clip-grad-norm">
<h1>torch.nn.utils.clip_grad_norm_<a class="headerlink" href="#torch-nn-utils-clip-grad-norm" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.nn.utils.clip_grad_norm_">
<code class="sig-prename descclassname">torch.nn.utils.</code><code class="sig-name descname">clip_grad_norm_</code><span class="sig-paren">(</span><em class="sig-param">parameters: Union[torch.Tensor, Iterable[torch.Tensor]], max_norm: float, norm_type: float = 2.0</em><span class="sig-paren">)</span> → torch.Tensor<a class="reference internal" href="../_modules/torch/nn/utils/clip_grad.html#clip_grad_norm_"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#torch.nn.utils.clip_grad_norm_" title="Permalink to this definition">¶</a></dt>
<dd><p>Clips gradient norm of an iterable of parameters.</p>
<p>The norm is computed over all gradients together, as if they were
concatenated into a single vector. Gradients are modified in-place.</p>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>parameters</strong> (<em>Iterable</em><em>[</em><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>] or </em><a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a>) – an iterable of Tensors or a
single Tensor that will have gradients normalized</p></li>
<li><p><strong>max_norm</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a>) – max norm of the gradients</p></li>
<li><p><strong>norm_type</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#float" title="(in Python v3.9)"><em>float</em></a><em> or </em><a class="reference external" href="https://docs.python.org/3/library/functions.html#int" title="(in Python v3.9)"><em>int</em></a>) – type of the used p-norm. Can be <code class="docutils literal notranslate"><span class="pre">'inf'</span></code> for
infinity norm.</p></li>
</ul>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><p>Total norm of the parameters (viewed as a single vector).</p>
</dd>
</dl>
</dd></dl>
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