L2Normalize
===========

Normalizes along dimension `axis` using an L2 norm.

**Abstract Signature:**

``L2Normalize(x: Tensor, axis: int, epsilon: float = 1e-12)``

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        <button class="op-tab-btn active" onclick="openOpTab(event, 'PyTorch_0')">PyTorch</button><button class="op-tab-btn " onclick="openOpTab(event, 'JAX (Core)_1')">JAX (Core)</button><button class="op-tab-btn " onclick="openOpTab(event, 'Keras_2')">Keras</button><button class="op-tab-btn " onclick="openOpTab(event, 'TensorFlow_3')">TensorFlow</button><button class="op-tab-btn " onclick="openOpTab(event, 'Apple MLX_4')">Apple MLX</button><button class="op-tab-btn " onclick="openOpTab(event, 'Flax NNX_5')">Flax NNX</button><button class="op-tab-btn " onclick="openOpTab(event, 'PaxML / Praxis_6')">PaxML / Praxis</button> 
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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.nn.functional.normalize</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.nn.functional.normalize.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>—</code></div><div class="op-detail-row"><span class="label">Strategy:</span> <span>Macro '{x} / jnp.sqrt(jnp.maximum(jnp.sum({x}**2, axis={axis}, keepdims=True), {epsilon}))'</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.utils.normalize</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.utils.normalize" target="_blank" class="op-doc-link">Official Docs ↗</a></div></div><div id="TensorFlow_3" class="op-tab-pane "><h4>TensorFlow</h4><div class="op-detail-row"><span class="label">API:</span> <code>tf.math.l2_normalize</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/tf/math/l2_normalize" target="_blank" class="op-doc-link">Official Docs ↗</a></div></div><div id="Apple MLX_4" class="op-tab-pane "><h4>Apple MLX</h4><div class="op-detail-row"><span class="label">API:</span> <code>—</code></div><div class="op-detail-row"><span class="label">Strategy:</span> <span>Macro '{x} / mx.sqrt(mx.maximum(mx.sum({x}**2, axis={axis}, keepdims=True), {epsilon}))'</span></div></div><div id="Flax NNX_5" class="op-tab-pane "><h4>Flax NNX</h4><div class="op-detail-row"><span class="label">API:</span> <code>—</code></div><div class="op-detail-row"><span class="label">Strategy:</span> <span>Macro '{x} / jnp.sqrt(jnp.maximum(jnp.sum({x}**2, axis={axis}, keepdims=True), {epsilon}))'</span></div></div><div id="PaxML / Praxis_6" class="op-tab-pane "><h4>PaxML / Praxis</h4><div class="op-detail-row"><span class="label">API:</span> <code>—</code></div><div class="op-detail-row"><span class="label">Strategy:</span> <span>Macro '{x} / jnp.sqrt(jnp.maximum(jnp.sum({x}**2, axis={axis}, keepdims=True), {epsilon}))'</span></div></div> 
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