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docs/build/html/usage/lazy_evaluation.html vendored
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@@ -952,7 +952,7 @@ stochastic gradient descent). A natural and usually efficient place to use
</div>
<p>An important behavior to be aware of is when the graph will be implicitly
evaluated. Anytime you <code class="docutils literal notranslate"><span class="pre">print</span></code> an array, convert it to an
<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.1)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>, or otherwise access it’s memory via <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#memoryview" title="(in Python v3.13)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">memoryview</span></code></a>,
<a class="reference external" href="https://numpy.org/doc/stable/reference/generated/numpy.ndarray.html#numpy.ndarray" title="(in NumPy v2.1)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">numpy.ndarray</span></code></a>, or otherwise access its memory via <a class="reference external" href="https://docs.python.org/3/library/stdtypes.html#memoryview" title="(in Python v3.13)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">memoryview</span></code></a>,
the graph will be evaluated. Saving arrays via <a class="reference internal" href="../python/_autosummary/mlx.core.save.html#mlx.core.save" title="mlx.core.save"><code class="xref py py-func docutils literal notranslate"><span class="pre">save()</span></code></a> (or any other MLX
saving functions) will also evaluate the array.</p>
<p>Calling <a class="reference internal" href="../python/_autosummary/mlx.core.array.item.html#mlx.core.array.item" title="mlx.core.array.item"><code class="xref py py-func docutils literal notranslate"><span class="pre">array.item()</span></code></a> on a scalar array will also evaluate it. In the