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  <section id="empty">
<h1>empty<a class="headerlink" href="#empty" title="Permalink to this headline"></a></h1>
<section id="documentation">
<h2>Documentation<a class="headerlink" href="#documentation" title="Permalink to this headline"></a></h2>
<dl class="py function">
<dt id="treetensor.torch.empty">
<code class="sig-prename descclassname">treetensor.torch.</code><code class="sig-name descname">empty</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">*</span><span class="n">args</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="../../_modules/treetensor/torch/funcs/construct.html#empty"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#treetensor.torch.empty" title="Permalink to this definition"></a></dt>
<dd><p>In <code class="docutils literal notranslate"><span class="pre">treetensor</span></code>, you can use <code class="docutils literal notranslate"><span class="pre">ones</span></code> to create a tree of tensors with
the uninitialized values.</p>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">torch</span>
<span class="gp">&gt;&gt;&gt; </span><span class="kn">import</span> <span class="nn">treetensor.torch</span> <span class="k">as</span> <span class="nn">ttorch</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">ttorch</span><span class="o">.</span><span class="n">empty</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">)</span>  <span class="c1"># the same as torch.empty(2, 3)</span>
<span class="go">tensor([[-1.3267e-36,  3.0802e-41,  2.3000e+00],</span>
<span class="go">        [ 2.3000e+00,  2.3000e+00,  2.3000e+00]])</span>

<span class="gp">&gt;&gt;&gt; </span><span class="n">ttorch</span><span class="o">.</span><span class="n">empty</span><span class="p">({</span><span class="s1">&#39;a&#39;</span><span class="p">:</span> <span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">),</span> <span class="s1">&#39;b&#39;</span><span class="p">:</span> <span class="p">{</span><span class="s1">&#39;x&#39;</span><span class="p">:</span> <span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="p">)}})</span>
<span class="go">&lt;Tensor 0x7ff363bb6080&gt;</span>
<span class="go">├── a --&gt; tensor([[-3.6515e+14,  4.5900e-41, -1.3253e-36],</span>
<span class="go">│                 [ 3.0802e-41,  2.3000e+00,  2.3000e+00]])</span>
<span class="go">└── b --&gt; &lt;Tensor 0x7ff363bb66d8&gt;</span>
<span class="go">    └── x --&gt; tensor([-3.6515e+14,  4.5900e-41, -3.8091e-38,  3.0802e-41])</span>
</pre></div>
</div>
</dd></dl>

<div class="tip admonition">
<p class="admonition-title">Torch Version Related</p>
<p>This documentation is based on
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<a class="reference external" href="https://pytorch.org/docs/1.9.0+cu102/generated/torch.empty.html">torch.empty</a>
in <a class="reference external" href="https://pytorch.org/docs/1.9.0+cu102/">torch v1.9.0+cu102</a>.
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<strong>Its arguments’ arrangements depend on the version of pytorch you installed</strong>.</p>
<p>If some arguments listed here are not working properly, please check your pytorch’s version
with the following command and find its documentation.</p>
<div class="highlight-shell notranslate"><div class="highlight"><table class="highlighttable"><tr><td class="linenos"><div class="linenodiv"><pre><span class="normal">1</span></pre></div></td><td class="code"><div><pre><span></span>python -c <span class="s1">&#39;import torch;print(torch.__version__)&#39;</span>
</pre></div></td></tr></table></div>
</div>
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<p>The arguments and keyword arguments supported in torch v1.9.0+cu102 is listed below.</p>
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</div>
</section>
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<section id="description-from-torch-v1-9-0-cu102">
<h2>Description From Torch v1.9.0+cu102<a class="headerlink" href="#description-from-torch-v1-9-0-cu102" title="Permalink to this headline"></a></h2>
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<dl class="py function">
<dt id="torch.empty">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">empty</code><span class="sig-paren">(</span><em class="sig-param">*size</em>, <em class="sig-param">*</em>, <em class="sig-param">out=None</em>, <em class="sig-param">dtype=None</em>, <em class="sig-param">layout=torch.strided</em>, <em class="sig-param">device=None</em>, <em class="sig-param">requires_grad=False</em>, <em class="sig-param">pin_memory=False</em>, <em class="sig-param">memory_format=torch.contiguous_format</em><span class="sig-paren">)</span> &#x2192; <a class="reference internal" href="trees.Tensor.where.auto.html#torch.Tensor" title="torch.Tensor">Tensor</a><a class="headerlink" href="#torch.empty" title="Permalink to this definition"></a></dt>
<dd><p>Returns a tensor filled with uninitialized data. The shape of the tensor is
defined by the variable argument <code class="xref py py-attr docutils literal notranslate"><span class="pre">size</span></code>.</p>
<dl>
<dt>Args:</dt><dd><dl class="simple">
<dt>size (int…): a sequence of integers defining the shape of the output tensor.</dt><dd><p>Can be a variable number of arguments or a collection like a list or tuple.</p>
</dd>
</dl>
</dd>
<dt>Keyword args:</dt><dd><p>out (Tensor, optional): the output tensor.
dtype (<code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code>, optional): the desired data type of returned tensor.</p>
<blockquote>
<div><p>Default: if <code class="docutils literal notranslate"><span class="pre">None</span></code>, uses a global default (see <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.set_default_tensor_type()</span></code>).</p>
</div></blockquote>
<dl class="simple">
<dt>layout (<code class="xref py py-class docutils literal notranslate"><span class="pre">torch.layout</span></code>, optional): the desired layout of returned Tensor.</dt><dd><p>Default: <code class="docutils literal notranslate"><span class="pre">torch.strided</span></code>.</p>
</dd>
<dt>device (<code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code>, optional): the desired device of returned tensor.</dt><dd><p>Default: if <code class="docutils literal notranslate"><span class="pre">None</span></code>, uses the current device for the default tensor type
(see <code class="xref py py-func docutils literal notranslate"><span class="pre">torch.set_default_tensor_type()</span></code>). <code class="xref py py-attr docutils literal notranslate"><span class="pre">device</span></code> will be the CPU
for CPU tensor types and the current CUDA device for CUDA tensor types.</p>
</dd>
<dt>requires_grad (bool, optional): If autograd should record operations on the</dt><dd><p>returned tensor. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
</dd>
<dt>pin_memory (bool, optional): If set, returned tensor would be allocated in</dt><dd><p>the pinned memory. Works only for CPU tensors. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p>
</dd>
<dt>memory_format (<code class="xref py py-class docutils literal notranslate"><span class="pre">torch.memory_format</span></code>, optional): the desired memory format of</dt><dd><p>returned Tensor. Default: <code class="docutils literal notranslate"><span class="pre">torch.contiguous_format</span></code>.</p>
</dd>
</dl>
</dd>
</dl>
<p>Example:</p>
<div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">a</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">empty</span><span class="p">((</span><span class="mi">2</span><span class="p">,</span><span class="mi">3</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">torch</span><span class="o">.</span><span class="n">int32</span><span class="p">,</span> <span class="n">device</span> <span class="o">=</span> <span class="s1">&#39;cuda&#39;</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">torch</span><span class="o">.</span><span class="n">empty_like</span><span class="p">(</span><span class="n">a</span><span class="p">)</span>
<span class="go">tensor([[0, 0, 0],</span>
<span class="go">        [0, 0, 0]], device=&#39;cuda:0&#39;, dtype=torch.int32)</span>
</pre></div>
</div>
</dd></dl>

</section>
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