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<h1>to<a class="headerlink" href="#to" 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 class">
<dt id="treetensor.torch.Tensor">
<em class="property">class </em><code class="sig-prename descclassname">treetensor.torch.</code><code class="sig-name descname">Tensor</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</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/tensor.html#Tensor"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#treetensor.torch.Tensor" title="Permalink to this definition"></a></dt>
<dd><dl class="py method">
<dt id="treetensor.torch.Tensor.to">
<code class="sig-name descname">to</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/tensor.html#Tensor.to"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#treetensor.torch.Tensor.to" title="Permalink to this definition"></a></dt>
<dd><p>Turn the original tree tensor to another format.</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">tensor</span><span class="p">({</span>
<span class="gp">... </span>    <span class="s1">&#39;a&#39;</span><span class="p">:</span> <span class="p">[[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">11</span><span class="p">],</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">22</span><span class="p">],</span> <span class="p">[</span><span class="mi">3</span><span class="p">,</span> <span class="mi">33</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="p">[[</span><span class="mi">4</span><span class="p">,</span> <span class="mi">5</span><span class="p">],</span> <span class="p">[</span><span class="mi">6</span><span class="p">,</span> <span class="mi">7</span><span class="p">]]},</span>
<span class="gp">... </span><span class="p">})</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">pytorch</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
<span class="go">&lt;Tensor 0x7ff363bb6518&gt;</span>
<span class="go">├── a --&gt; tensor([[ 1., 11.],</span>
<span class="go">│                 [ 2., 22.],</span>
<span class="go">│                 [ 3., 33.]], dtype=torch.float64)</span>
<span class="go">└── b --&gt; &lt;Tensor 0x7ff363bb6ef0&gt;</span>
<span class="go">    └── x --&gt; tensor([[4., 5.],</span>
<span class="go">                      [6., 7.]], dtype=torch.float64)</span>
</pre></div>
</div>
</dd></dl>

</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/1.12.1+cu102/tensors.html#torch.Tensor.to">torch.Tensor.to</a>
in <a class="reference external" href="https://pytorch.org/docs/1.12.1+cu102/">torch v1.12.1+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 v1.12.1+cu102 is listed below.</p>
</div>
</section>
<section id="description-from-torch-v1-12-1-cu102">
<h2>Description From Torch v1.12.1+cu102<a class="headerlink" href="#description-from-torch-v1-12-1-cu102" title="Permalink to this headline"></a></h2>
<dl class="py class">
<dt id="torch.Tensor">
<em class="property">class </em><code class="sig-prename descclassname">torch.</code><code class="sig-name descname">Tensor</code><a class="headerlink" href="#torch.Tensor" title="Permalink to this definition"></a></dt>
<dd><dl class="py method">
<dt id="torch.Tensor.to">
<code class="sig-name descname">to</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> &#x2192; <a class="reference internal" href="trees.Tensor.where.auto.html#torch.Tensor" title="torch.Tensor">Tensor</a><a class="headerlink" href="#torch.Tensor.to" title="Permalink to this definition"></a></dt>
<dd><p>Performs Tensor dtype and/or device conversion. A <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code> and <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code> are
inferred from the arguments of <code class="docutils literal notranslate"><span class="pre">self.to(*args,</span> <span class="pre">**kwargs)</span></code>.</p>
<div class="admonition note">
<p class="admonition-title">Note</p>
<p>If the <code class="docutils literal notranslate"><span class="pre">self</span></code> Tensor already
has the correct <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code> and <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code>, then <code class="docutils literal notranslate"><span class="pre">self</span></code> is returned.
Otherwise, the returned tensor is a copy of <code class="docutils literal notranslate"><span class="pre">self</span></code> with the desired
<code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code> and <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code>.</p>
</div>
<p>Here are the ways to call <code class="docutils literal notranslate"><span class="pre">to</span></code>:</p>
<dl class="py function">
<dt id="id0">
<code class="sig-name descname">to</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dtype</span></em>, <em class="sig-param"><span class="n">non_blocking</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">copy</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="#id0" title="Permalink to this definition"></a></dt>
<dd><p>Returns a Tensor with the specified <code class="xref py py-attr docutils literal notranslate"><span class="pre">dtype</span></code></p>
<dl class="simple">
<dt>Args:</dt><dd><p>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
returned Tensor. Default: <code class="docutils literal notranslate"><span class="pre">torch.preserve_format</span></code>.</p>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt id="id1">
<code class="sig-name descname">to</code><span class="sig-paren">(</span><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">dtype</span><span class="o">=</span><span class="default_value">None</span></em>, <em class="sig-param"><span class="n">non_blocking</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">copy</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="#id1" title="Permalink to this definition"></a></dt>
<dd><p>Returns a Tensor with the specified <code class="xref py py-attr docutils literal notranslate"><span class="pre">device</span></code> and (optional)
<code class="xref py py-attr docutils literal notranslate"><span class="pre">dtype</span></code>. If <code class="xref py py-attr docutils literal notranslate"><span class="pre">dtype</span></code> is <code class="docutils literal notranslate"><span class="pre">None</span></code> it is inferred to be <code class="docutils literal notranslate"><span class="pre">self.dtype</span></code>.
When <code class="xref py py-attr docutils literal notranslate"><span class="pre">non_blocking</span></code>, tries to convert asynchronously with respect to
the host if possible, e.g., converting a CPU Tensor with pinned memory to a
CUDA Tensor.
When <code class="xref py py-attr docutils literal notranslate"><span class="pre">copy</span></code> is set, a new Tensor is created even when the Tensor
already matches the desired conversion.</p>
<dl class="simple">
<dt>Args:</dt><dd><p>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
returned Tensor. Default: <code class="docutils literal notranslate"><span class="pre">torch.preserve_format</span></code>.</p>
</dd>
</dl>
</dd></dl>

<dl class="py function">
<dt id="id2">
<code class="sig-name descname">to</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">other</span></em>, <em class="sig-param"><span class="n">non_blocking</span><span class="o">=</span><span class="default_value">False</span></em>, <em class="sig-param"><span class="n">copy</span><span class="o">=</span><span class="default_value">False</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="#id2" title="Permalink to this definition"></a></dt>
<dd><p>Returns a Tensor with same <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code> and <code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code> as
the Tensor <code class="xref py py-attr docutils literal notranslate"><span class="pre">other</span></code>. When <code class="xref py py-attr docutils literal notranslate"><span class="pre">non_blocking</span></code>, tries to convert
asynchronously with respect to the host if possible, e.g., converting a CPU
Tensor with pinned memory to a CUDA Tensor.
When <code class="xref py py-attr docutils literal notranslate"><span class="pre">copy</span></code> is set, a new Tensor is created even when the Tensor
already matches the desired conversion.</p>
</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">tensor</span> <span class="o">=</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">2</span><span class="p">)</span>  <span class="c1"># Initially dtype=float32, device=cpu</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">tensor</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">torch</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span>
<span class="go">tensor([[-0.5044,  0.0005],</span>
<span class="go">        [ 0.3310, -0.0584]], dtype=torch.float64)</span>

<span class="gp">&gt;&gt;&gt; </span><span class="n">cuda0</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="s1">&#39;cuda:0&#39;</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">tensor</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">cuda0</span><span class="p">)</span>
<span class="go">tensor([[-0.5044,  0.0005],</span>
<span class="go">        [ 0.3310, -0.0584]], device=&#39;cuda:0&#39;)</span>

<span class="gp">&gt;&gt;&gt; </span><span class="n">tensor</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">cuda0</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">float64</span><span class="p">)</span>
<span class="go">tensor([[-0.5044,  0.0005],</span>
<span class="go">        [ 0.3310, -0.0584]], dtype=torch.float64, device=&#39;cuda:0&#39;)</span>

<span class="gp">&gt;&gt;&gt; </span><span class="n">other</span> <span class="o">=</span> <span class="n">torch</span><span class="o">.</span><span class="n">randn</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">float64</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">cuda0</span><span class="p">)</span>
<span class="gp">&gt;&gt;&gt; </span><span class="n">tensor</span><span class="o">.</span><span class="n">to</span><span class="p">(</span><span class="n">other</span><span class="p">,</span> <span class="n">non_blocking</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="go">tensor([[-0.5044,  0.0005],</span>
<span class="go">        [ 0.3310, -0.0584]], dtype=torch.float64, device=&#39;cuda:0&#39;)</span>
</pre></div>
</div>
</dd></dl>

</dd></dl>

</section>
</section>



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