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<div class="section" id="torch-tensor-resize">
<h1>torch.Tensor.resize_<a class="headerlink" href="#torch-tensor-resize" title="Permalink to this headline">¶</a></h1>
<dl class="py method">
<dt id="torch.Tensor.resize_">
<code class="sig-prename descclassname"><span class="pre">Tensor.</span></code><code class="sig-name descname"><span class="pre">resize_</span></code><span class="sig-paren">(</span><em class="sig-param"><span class="o"><span class="pre">*</span></span><span class="n"><span class="pre">sizes</span></span></em>, <em class="sig-param"><span class="n"><span class="pre">memory_format</span></span><span class="o"><span class="pre">=</span></span><span class="default_value"><span class="pre">torch.contiguous_format</span></span></em><span class="sig-paren">)</span> → <a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><span class="pre">Tensor</span></a><a class="headerlink" href="#torch.Tensor.resize_" title="Permalink to this definition">¶</a></dt>
<dd><p>Resizes <code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> tensor to the specified size. If the number of elements is
larger than the current storage size, then the underlying storage is resized
to fit the new number of elements. If the number of elements is smaller, the
underlying storage is not changed. Existing elements are preserved but any new
memory is uninitialized.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>This is a low-level method. The storage is reinterpreted as C-contiguous,
ignoring the current strides (unless the target size equals the current
size, in which case the tensor is left unchanged). For most purposes, you
will instead want to use <a class="reference internal" href="torch.Tensor.view.html#torch.Tensor.view" title="torch.Tensor.view"><code class="xref py py-meth docutils literal notranslate"><span class="pre">view()</span></code></a>, which checks for
contiguity, or <a class="reference internal" href="torch.Tensor.reshape.html#torch.Tensor.reshape" title="torch.Tensor.reshape"><code class="xref py py-meth docutils literal notranslate"><span class="pre">reshape()</span></code></a>, which copies data if needed. To
change the size in-place with custom strides, see <a class="reference internal" href="torch.Tensor.set_.html#torch.Tensor.set_" title="torch.Tensor.set_"><code class="xref py py-meth docutils literal notranslate"><span class="pre">set_()</span></code></a>.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>sizes</strong> (<em>torch.Size</em><em> or </em><em>int...</em>) – the desired size</p></li>
<li><p><strong>memory_format</strong> (<a class="reference internal" href="../tensor_attributes.html#torch.torch.memory_format" title="torch.torch.memory_format"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.memory_format</span></code></a>, optional) – the desired memory format of
Tensor. Default: <code class="docutils literal notranslate"><span class="pre">torch.contiguous_format</span></code>. Note that memory format of
<code class="xref py py-attr docutils literal notranslate"><span class="pre">self</span></code> is going to be unaffected if <code class="docutils literal notranslate"><span class="pre">self.size()</span></code> matches <code class="docutils literal notranslate"><span class="pre">sizes</span></code>.</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">x</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">tensor</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="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">4</span><span class="p">],</span> <span class="p">[</span><span class="mi">5</span><span class="p">,</span> <span class="mi">6</span><span class="p">]])</span>
<span class="gp">>>> </span><span class="n">x</span><span class="o">.</span><span class="n">resize_</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="go">tensor([[ 1, 2],</span>
<span class="go"> [ 3, 4]])</span>
</pre></div>
</div>
</dd></dl>
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