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<div class="section" id="torch-tensor">
<h1>torch.tensor<a class="headerlink" href="#torch-tensor" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.tensor">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">tensor</code><span class="sig-paren">(</span><em class="sig-param">data</em>, <em class="sig-param">*</em>, <em class="sig-param">dtype=None</em>, <em class="sig-param">device=None</em>, <em class="sig-param">requires_grad=False</em>, <em class="sig-param">pin_memory=False</em><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.tensor" title="Permalink to this definition">¶</a></dt>
<dd><p>Constructs a tensor with <code class="xref py py-attr docutils literal notranslate"><span class="pre">data</span></code>.</p>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p><a class="reference internal" href="#torch.tensor" title="torch.tensor"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.tensor()</span></code></a> always copies <code class="xref py py-attr docutils literal notranslate"><span class="pre">data</span></code>. If you have a Tensor
<code class="docutils literal notranslate"><span class="pre">data</span></code> and want to avoid a copy, use <a class="reference internal" href="../tensors.html#torch.Tensor.requires_grad_" title="torch.Tensor.requires_grad_"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.Tensor.requires_grad_()</span></code></a>
or <a class="reference internal" href="../autograd.html#torch.Tensor.detach" title="torch.Tensor.detach"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.Tensor.detach()</span></code></a>.
If you have a NumPy <code class="docutils literal notranslate"><span class="pre">ndarray</span></code> and want to avoid a copy, use
<a class="reference internal" href="torch.as_tensor.html#torch.as_tensor" title="torch.as_tensor"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.as_tensor()</span></code></a>.</p>
</div>
<div class="admonition warning">
<p class="admonition-title">Warning</p>
<p>When data is a tensor <cite>x</cite>, <a class="reference internal" href="#torch.tensor" title="torch.tensor"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.tensor()</span></code></a> reads out ‘the data’ from whatever it is passed,
and constructs a leaf variable. Therefore <code class="docutils literal notranslate"><span class="pre">torch.tensor(x)</span></code> is equivalent to <code class="docutils literal notranslate"><span class="pre">x.clone().detach()</span></code>
and <code class="docutils literal notranslate"><span class="pre">torch.tensor(x,</span> <span class="pre">requires_grad=True)</span></code> is equivalent to <code class="docutils literal notranslate"><span class="pre">x.clone().detach().requires_grad_(True)</span></code>.
The equivalents using <code class="docutils literal notranslate"><span class="pre">clone()</span></code> and <code class="docutils literal notranslate"><span class="pre">detach()</span></code> are recommended.</p>
</div>
<dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><p><strong>data</strong> (<em>array_like</em>) – Initial data for the tensor. Can be a list, tuple,
NumPy <code class="docutils literal notranslate"><span class="pre">ndarray</span></code>, scalar, and other types.</p>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><ul class="simple">
<li><p><strong>dtype</strong> (<a class="reference internal" href="../tensor_attributes.html#torch.torch.dtype" title="torch.torch.dtype"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.dtype</span></code></a>, optional) – the desired data type of returned tensor.
Default: if <code class="docutils literal notranslate"><span class="pre">None</span></code>, infers data type from <code class="xref py py-attr docutils literal notranslate"><span class="pre">data</span></code>.</p></li>
<li><p><strong>device</strong> (<a class="reference internal" href="../tensor_attributes.html#torch.torch.device" title="torch.torch.device"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.device</span></code></a>, optional) – the desired device of returned tensor.
Default: if <code class="docutils literal notranslate"><span class="pre">None</span></code>, uses the current device for the default tensor type
(see <a class="reference internal" href="torch.set_default_tensor_type.html#torch.set_default_tensor_type" title="torch.set_default_tensor_type"><code class="xref py py-func docutils literal notranslate"><span class="pre">torch.set_default_tensor_type()</span></code></a>). <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></li>
<li><p><strong>requires_grad</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>optional</em>) – If autograd should record operations on the
returned tensor. Default: <code class="docutils literal notranslate"><span class="pre">False</span></code>.</p></li>
<li><p><strong>pin_memory</strong> (<a class="reference external" href="https://docs.python.org/3/library/functions.html#bool" title="(in Python v3.9)"><em>bool</em></a><em>, </em><em>optional</em>) – If set, returned tensor would be allocated in
the pinned memory. Works only for CPU tensors. Default: <code class="docutils literal notranslate"><span class="pre">False</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">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([[</span><span class="mf">0.1</span><span class="p">,</span> <span class="mf">1.2</span><span class="p">],</span> <span class="p">[</span><span class="mf">2.2</span><span class="p">,</span> <span class="mf">3.1</span><span class="p">],</span> <span class="p">[</span><span class="mf">4.9</span><span class="p">,</span> <span class="mf">5.2</span><span class="p">]])</span>
<span class="go">tensor([[ 0.1000, 1.2000],</span>
<span class="go"> [ 2.2000, 3.1000],</span>
<span class="go"> [ 4.9000, 5.2000]])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span> <span class="c1"># Type inference on data</span>
<span class="go">tensor([ 0, 1])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([[</span><span class="mf">0.11111</span><span class="p">,</span> <span class="mf">0.222222</span><span class="p">,</span> <span class="mf">0.3333333</span><span class="p">]],</span>
<span class="go"> dtype=torch.float64,</span>
<span class="go"> device=torch.device('cuda:0')) # creates a torch.cuda.DoubleTensor</span>
<span class="go">tensor([[ 0.1111, 0.2222, 0.3333]], dtype=torch.float64, device='cuda:0')</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">(</span><span class="mf">3.14159</span><span class="p">)</span> <span class="c1"># Create a scalar (zero-dimensional tensor)</span>
<span class="go">tensor(3.1416)</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">tensor</span><span class="p">([])</span> <span class="c1"># Create an empty tensor (of size (0,))</span>
<span class="go">tensor([])</span>
</pre></div>
</div>
</dd></dl>
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