RMSNormalization
================

This is RMS normalization defined in ONNX as function as described in the paper https://arxiv.org/pdf/1910.07467. The overall computation can be split into two stages. The root mean squared norm is taken over the last D dimensions, where D is the dimension of normalized_shape. For example, if normal...

**Abstract Signature:**

``RMSNormalization(X: Tensor, scale, axis: int, epsilon: float, stash_type: int)``

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        <div id="Keras_0" class="op-tab-pane active"><h4>Keras</h4><div class="op-detail-row"><span class="label">API:</span> <code>keras.layers.RMSNormalization</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.RMSNormalization" target="_blank" class="op-doc-link">Official Docs ↗</a></div></div><div id="TensorFlow_1" class="op-tab-pane "><h4>TensorFlow</h4><div class="op-detail-row"><span class="label">API:</span> <code>keras.layers.RMSNormalization</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://www.tensorflow.org/api_docs/python/keras/layers/RMSNormalization" target="_blank" class="op-doc-link">Official Docs ↗</a></div></div> 
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