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  <section id="ones-like">
<h1>ones_like<a class="headerlink" href="#ones-like" 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.ones_like">
<code class="sig-prename descclassname">treetensor.torch.</code><code class="sig-name descname">ones_like</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">input</span></em>, <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#ones_like"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#treetensor.torch.ones_like" 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_like</span></code> to create a tree of tensors with all ones like another tree.</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">ones_like</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">randn</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.ones_like(torch.randn(2, 3))</span>
<span class="go">tensor([[1., 1., 1.],</span>
<span class="go">        [1., 1., 1.]])</span>

<span class="gp">&gt;&gt;&gt; </span><span class="n">ttorch</span><span class="o">.</span><span class="n">ones_like</span><span class="p">({</span>
<span class="gp">... </span>    <span class="s1">&#39;a&#39;</span><span class="p">:</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</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="gp">... </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="n">torch</span><span class="o">.</span><span class="n">randn</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="p">)},</span>
<span class="gp">... </span><span class="p">})</span>
<span class="go">&lt;Tensor 0x7ff363bbc320&gt;</span>
<span class="go">├── a --&gt; tensor([[1., 1., 1.],</span>
<span class="go">│                 [1., 1., 1.]])</span>
<span class="go">└── b --&gt; &lt;Tensor 0x7ff363bbc240&gt;</span>
<span class="go">    └── x --&gt; tensor([1., 1., 1., 1.])</span>
</pre></div>
</div>
</dd></dl>

<div class="tip admonition">
<p class="admonition-title">Torch Version Related</p>
<p>This documentation is based on
<a class="reference external" href="https://pytorch.org/docs/1.9.0+cu102/generated/torch.ones_like.html">torch.ones_like</a>
in <a class="reference external" href="https://pytorch.org/docs/1.9.0+cu102/">torch v1.9.0+cu102</a>.
<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>
<p>The arguments and keyword arguments supported in torch v1.9.0+cu102 is listed below.</p>
</div>
</section>
<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>
<dl class="py function">
<dt id="torch.ones_like">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">ones_like</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">input</span></em>, <em class="sig-param"><span class="o">*</span></em>, <em class="sig-param"><span class="n">dtype</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">layout</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">device</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">requires_grad</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">memory_format</span><span class="o">=</span><span class="default_value">torch.preserve_format</span></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.ones_like" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a tensor filled with the scalar value <cite>1</cite>, with the same size as
<code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>. <code class="docutils literal notranslate"><span class="pre">torch.ones_like(input)</span></code> is equivalent to
<code class="docutils literal notranslate"><span class="pre">torch.ones(input.size(),</span> <span class="pre">dtype=input.dtype,</span> <span class="pre">layout=input.layout,</span> <span class="pre">device=input.device)</span></code>.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>As of 0.4, this function does not support an <code class="xref py py-attr docutils literal notranslate"><span class="pre">out</span></code> keyword. As an alternative,
the old <code class="docutils literal notranslate"><span class="pre">torch.ones_like(input,</span> <span class="pre">out=output)</span></code> is equivalent to
<code class="docutils literal notranslate"><span class="pre">torch.ones(input.size(),</span> <span class="pre">out=output)</span></code>.</p>
</div>
<dl class="simple">
<dt>Args:</dt><dd><p>input (Tensor): the size of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code> will determine size of the output tensor.</p>
</dd>
<dt>Keyword arguments:</dt><dd><dl class="simple">
<dt>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.</dt><dd><p>Default: if <code class="docutils literal notranslate"><span class="pre">None</span></code>, defaults to the dtype of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</p>
</dd>
<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: if <code class="docutils literal notranslate"><span class="pre">None</span></code>, defaults to the layout of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</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>, defaults to the device of <code class="xref py py-attr docutils literal notranslate"><span class="pre">input</span></code>.</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>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.preserve_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="nb">input</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="gp">&gt;&gt;&gt; </span><span class="n">torch</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="nb">input</span><span class="p">)</span>
<span class="go">tensor([[ 1.,  1.,  1.],</span>
<span class="go">        [ 1.,  1.,  1.]])</span>
</pre></div>
</div>
</dd></dl>

</section>
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