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<div class="section" id="torch-arange">
<h1>torch.arange<a class="headerlink" href="#torch-arange" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="torch.arange">
<code class="sig-prename descclassname">torch.</code><code class="sig-name descname">arange</code><span class="sig-paren">(</span><em class="sig-param">start=0</em>, <em class="sig-param">end</em>, <em class="sig-param">step=1</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><span class="sig-paren">)</span> → Tensor<a class="headerlink" href="#torch.arange" title="Permalink to this definition">¶</a></dt>
<dd><p>Returns a 1-D tensor of size <span class="math"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mo fence="true">⌈</mo><mfrac><mrow><mtext>end</mtext><mo>−</mo><mtext>start</mtext></mrow><mtext>step</mtext></mfrac><mo fence="true">⌉</mo></mrow><annotation encoding="application/x-tex">\left\lceil \frac{\text{end} - \text{start}}{\text{step}} \right\rceil</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:1.80002em;vertical-align:-0.65002em;"></span><span class="minner"><span class="mopen delimcenter" style="top:0em;"><span class="delimsizing size2">⌈</span></span><span class="mord"><span class="mopen nulldelimiter"></span><span class="mfrac"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.8801079999999999em;"><span style="top:-2.6550000000000002em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">step</span></span></span></span></span><span style="top:-3.23em;"><span class="pstrut" style="height:3em;"></span><span class="frac-line" style="border-bottom-width:0.04em;"></span></span><span style="top:-3.394em;"><span class="pstrut" style="height:3em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord text mtight"><span class="mord mtight">end</span></span><span class="mbin mtight">−</span><span class="mord text mtight"><span class="mord mtight">start</span></span></span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.481108em;"><span></span></span></span></span></span><span class="mclose nulldelimiter"></span></span><span class="mclose delimcenter" style="top:0em;"><span class="delimsizing size2">⌉</span></span></span></span></span></span>
</span>
with values from the interval <code class="docutils literal notranslate"><span class="pre">[start,</span> <span class="pre">end)</span></code> taken with common difference
<code class="xref py py-attr docutils literal notranslate"><span class="pre">step</span></code> beginning from <cite>start</cite>.</p>
<p>Note that non-integer <code class="xref py py-attr docutils literal notranslate"><span class="pre">step</span></code> is subject to floating point rounding errors when
comparing against <code class="xref py py-attr docutils literal notranslate"><span class="pre">end</span></code>; to avoid inconsistency, we advise adding a small epsilon to <code class="xref py py-attr docutils literal notranslate"><span class="pre">end</span></code>
in such cases.</p>
<div class="math">
<span class="katex-display"><span class="katex"><span class="katex-mathml"><math xmlns="http://www.w3.org/1998/Math/MathML" display="block"><semantics><mrow><msub><mtext>out</mtext><mrow><mi>i</mi><mo>+</mo><mn>1</mn></mrow></msub><mo>=</mo><msub><mtext>out</mtext><mi>i</mi></msub><mo>+</mo><mtext>step</mtext></mrow><annotation encoding="application/x-tex">\text{out}_{{i+1}} = \text{out}_{i} + \text{step}
</annotation></semantics></math></span><span class="katex-html" aria-hidden="true"><span class="base"><span class="strut" style="height:0.8234109999999999em;vertical-align:-0.208331em;"></span><span class="mord"><span class="mord text"><span class="mord">out</span></span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.311664em;"><span style="top:-2.5500000000000003em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mtight"><span class="mord mathnormal mtight">i</span><span class="mbin mtight">+</span><span class="mord mtight">1</span></span></span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.208331em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2777777777777778em;"></span><span class="mrel">=</span><span class="mspace" style="margin-right:0.2777777777777778em;"></span></span><span class="base"><span class="strut" style="height:0.76508em;vertical-align:-0.15em;"></span><span class="mord"><span class="mord text"><span class="mord">out</span></span><span class="msupsub"><span class="vlist-t vlist-t2"><span class="vlist-r"><span class="vlist" style="height:0.31166399999999994em;"><span style="top:-2.5500000000000003em;margin-right:0.05em;"><span class="pstrut" style="height:2.7em;"></span><span class="sizing reset-size6 size3 mtight"><span class="mord mtight"><span class="mord mathnormal mtight">i</span></span></span></span></span><span class="vlist-s"></span></span><span class="vlist-r"><span class="vlist" style="height:0.15em;"><span></span></span></span></span></span></span><span class="mspace" style="margin-right:0.2222222222222222em;"></span><span class="mbin">+</span><span class="mspace" style="margin-right:0.2222222222222222em;"></span></span><span class="base"><span class="strut" style="height:0.80952em;vertical-align:-0.19444em;"></span><span class="mord text"><span class="mord">step</span></span></span></span></span></span>
</div><dl class="field-list simple">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><ul class="simple">
<li><p><strong>start</strong> (<em>Number</em>) – the starting value for the set of points. Default: <code class="docutils literal notranslate"><span class="pre">0</span></code>.</p></li>
<li><p><strong>end</strong> (<em>Number</em>) – the ending value for the set of points</p></li>
<li><p><strong>step</strong> (<em>Number</em>) – the gap between each pair of adjacent points. Default: <code class="docutils literal notranslate"><span class="pre">1</span></code>.</p></li>
</ul>
</dd>
<dt class="field-even">Keyword Arguments</dt>
<dd class="field-even"><ul class="simple">
<li><p><strong>out</strong> (<a class="reference internal" href="../tensors.html#torch.Tensor" title="torch.Tensor"><em>Tensor</em></a><em>, </em><em>optional</em>) – the output tensor.</p></li>
<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>, uses a global default (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>). If <cite>dtype</cite> is not given, infer the data type from the other input
arguments. If any of <cite>start</cite>, <cite>end</cite>, or <cite>stop</cite> are floating-point, the
<cite>dtype</cite> is inferred to be the default dtype, see
<a class="reference internal" href="torch.get_default_dtype.html#torch.get_default_dtype" title="torch.get_default_dtype"><code class="xref py py-meth docutils literal notranslate"><span class="pre">get_default_dtype()</span></code></a>. Otherwise, the <cite>dtype</cite> is inferred to
be <cite>torch.int64</cite>.</p></li>
<li><p><strong>layout</strong> (<a class="reference internal" href="../tensor_attributes.html#torch.torch.layout" title="torch.torch.layout"><code class="xref py py-class docutils literal notranslate"><span class="pre">torch.layout</span></code></a>, optional) – the desired layout of returned Tensor.
Default: <code class="docutils literal notranslate"><span class="pre">torch.strided</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>
</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">arange</span><span class="p">(</span><span class="mi">5</span><span class="p">)</span>
<span class="go">tensor([ 0, 1, 2, 3, 4])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">)</span>
<span class="go">tensor([ 1, 2, 3])</span>
<span class="gp">>>> </span><span class="n">torch</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mf">2.5</span><span class="p">,</span> <span class="mf">0.5</span><span class="p">)</span>
<span class="go">tensor([ 1.0000, 1.5000, 2.0000])</span>
</pre></div>
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