MkldnnAdaptiveAvgPool2d
=======================

Applies adaptive average pooling (MKLDNN backend optimization).

**Abstract Signature:**

``MkldnnAdaptiveAvgPool2d(input: Tensor, output_size: int)``

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        <div id="PyTorch_0" class="op-tab-pane active"><h4>PyTorch</h4><div class="op-detail-row"><span class="label">API:</span> <code>torch.mkldnn_adaptive_avg_pool2d</code></div><div class="op-detail-row"><span class="label">Strategy:</span> <span>Direct Mapping</span></div><div class="op-detail-row"><a href="https://pytorch.org/docs/stable/generated/torch.mkldnn_adaptive_avg_pool2d.html" target="_blank" class="op-doc-link">Official Docs ↗</a></div></div><div id="JAX (Core)_1" class="op-tab-pane "><h4>JAX (Core)</h4><div class="op-detail-row"><span class="label">API:</span> <code>jax.image.resize</code></div><div class="op-detail-row"><span class="label">Strategy:</span> <span>Plugin (adaptive_pool)</span></div></div><div id="Keras_2" class="op-tab-pane "><h4>Keras</h4><div class="op-detail-row"><span class="label">API:</span> <code>keras.layers.GlobalAveragePooling2D</code></div><div class="op-detail-row"><span class="label">Strategy:</span> <span>Direct Mapping</span></div><div class="op-detail-row"><a href="https://keras.io/search.html?q=keras.layers.GlobalAveragePooling2D" target="_blank" class="op-doc-link">Official Docs ↗</a></div></div><div id="Apple MLX_3" class="op-tab-pane "><h4>Apple MLX</h4><div class="op-detail-row"><span class="label">API:</span> <code>mlx.nn.AvgPool2d</code></div><div class="op-detail-row"><span class="label">Strategy:</span> <span>Direct Mapping</span></div><div class="op-detail-row"><a href="https://ml-explore.github.io/mlx/build/html/python/_autosummary/mlx.nn.AvgPool2d.html" target="_blank" class="op-doc-link">Official Docs ↗</a></div></div> 
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