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<h1>torch.Tensor.numpy<a class="headerlink" href="#torch-tensor-numpy" title="Permalink to this headline">¶</a></h1>
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<dt id="torch.Tensor.numpy">
<code class="sig-prename descclassname"><span class="pre">Tensor.</span></code><code class="sig-name descname"><span class="pre">numpy</span></code><span class="sig-paren">(</span><span class="sig-paren">)</span> → <a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v1.22)"><span class="pre">numpy.ndarray</span></a><a class="headerlink" href="#torch.Tensor.numpy" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> tensor as a NumPy <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code>. This tensor and the
returned <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code> share the same underlying storage. Changes to
<code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> tensor will be reflected in the <code class="xref py py-class docutils literal notranslate"><span class="pre">ndarray</span></code> and vice versa.</p>
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