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   <div class="section" id="mxnet-ndarray-ndarray-broadcast-to">
 <h1>mxnet.ndarray.NDArray.broadcast_to<a class="headerlink" href="#mxnet-ndarray-ndarray-broadcast-to" title="Permalink to this headline">¶</a></h1>
 <dl class="method">
 <dt id="mxnet.ndarray.NDArray.broadcast_to">
 <code class="sig-prename descclassname">NDArray.</code><code class="sig-name descname">broadcast_to</code><span class="sig-paren">(</span><em class="sig-param">shape</em><span class="sig-paren">)</span><a class="reference internal" href="../../../_modules/mxnet/ndarray/ndarray.html#NDArray.broadcast_to"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#mxnet.ndarray.NDArray.broadcast_to" title="Permalink to this definition">¶</a></dt>
 <dd><p>Broadcasts the input array to a new shape.</p>
 <p>Broadcasting is only allowed on axes with size 1. The new shape cannot change
 the number of dimensions.
 For example, you could broadcast from shape (2, 1) to (2, 3), but not from
 shape (2, 3) to (2, 3, 3).</p>
 <dl class="field-list simple">
 <dt class="field-odd">Parameters</dt>
 <dd class="field-odd"><p><strong>shape</strong> (<em>tuple of int</em>) – The shape of the desired array.</p>
 </dd>
 <dt class="field-even">Returns</dt>
 <dd class="field-even"><p>A NDArray with the desired shape that is not sharing data with this
 array, even if the new shape is the same as <code class="docutils literal notranslate"><span class="pre">self.shape</span></code>.</p>
 </dd>
 <dt class="field-odd">Return type</dt>
 <dd class="field-odd"><p><a class="reference internal" href="../mxnet.ndarray.NDArray.html#mxnet.ndarray.NDArray" title="mxnet.ndarray.NDArray">NDArray</a></p>
 </dd>
 </dl>
 <p class="rubric">Examples</p>
 <div class="highlight-default notranslate"><div class="highlight"><pre><span></span><span class="gp">&gt;&gt;&gt; </span><span class="n">x</span> <span class="o">=</span> <span class="n">mx</span><span class="o">.</span><span class="n">nd</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span><span class="mi">3</span><span class="p">)</span><span class="o">.</span><span class="n">reshape</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span><span class="mi">3</span><span class="p">,</span><span class="mi">1</span><span class="p">))</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">x</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
 <span class="go">array([[[ 0.],</span>
 <span class="go">        [ 1.],</span>
 <span class="go">        [ 2.]]], dtype=float32)</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y</span> <span class="o">=</span> <span class="n">x</span><span class="o">.</span><span class="n">broadcast_to</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="mi">3</span><span class="p">))</span>
 <span class="gp">&gt;&gt;&gt; </span><span class="n">y</span><span class="o">.</span><span class="n">asnumpy</span><span class="p">()</span>
 <span class="go">array([[[ 0.,  0.,  0.],</span>
 <span class="go">        [ 1.,  1.,  1.],</span>
 <span class="go">        [ 2.,  2.,  2.]],</span>

 <span class="go">       [[ 0.,  0.,  0.],</span>
 <span class="go">        [ 1.,  1.,  1.],</span>
 <span class="go">        [ 2.,  2.,  2.]]], dtype=float32)</span>
 </pre></div>
 </div>
 </dd></dl>

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