LogCumSumExp
============

Logarithm of the cumulative summation of exponentiation.

**Abstract Signature:**

``LogCumSumExp(input: Tensor, dim: int)``

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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, 'NumPy_2')">NumPy</button><button class="op-tab-btn " onclick="openOpTab(event, 'Keras_3')">Keras</button><button class="op-tab-btn " onclick="openOpTab(event, 'TensorFlow_4')">TensorFlow</button><button class="op-tab-btn " onclick="openOpTab(event, 'Apple MLX_5')">Apple MLX</button><button class="op-tab-btn " onclick="openOpTab(event, 'Flax NNX_6')">Flax NNX</button><button class="op-tab-btn " onclick="openOpTab(event, 'PaxML / Praxis_7')">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.logcumsumexp</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.logcumsumexp.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.numpy.logaddexp.accumulate</code></div><div class="op-detail-row"><span class="label">Strategy:</span> <span>Macro 'jax.numpy.log(jax.numpy.cumsum(jax.numpy.exp({input}), axis={dim}))'</span></div></div><div id="NumPy_2" class="op-tab-pane "><h4>NumPy</h4><div class="op-detail-row"><span class="label">API:</span> <code>numpy.logaddexp.accumulate</code></div><div class="op-detail-row"><span class="label">Strategy:</span> <span>Macro 'numpy.log(numpy.cumsum(numpy.exp({input}), axis={dim}))'</span></div></div><div id="Keras_3" class="op-tab-pane "><h4>Keras</h4><div class="op-detail-row"><span class="label">API:</span> <code>keras.ops.logcumsumexp</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.ops.logcumsumexp" target="_blank" class="op-doc-link">Official Docs ↗</a></div></div><div id="TensorFlow_4" class="op-tab-pane "><h4>TensorFlow</h4><div class="op-detail-row"><span class="label">API:</span> <code>tf.math.cumulative_logsumexp</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/cumulative_logsumexp" target="_blank" class="op-doc-link">Official Docs ↗</a></div></div><div id="Apple MLX_5" class="op-tab-pane "><h4>Apple MLX</h4><div class="op-detail-row"><span class="label">API:</span> <code>mlx.core.logcumsumexp</code></div><div class="op-detail-row"><span class="label">Strategy:</span> <span>Macro 'mlx.core.log(mlx.core.cumsum(mlx.core.exp({input}), axis={dim}))'</span></div></div><div id="Flax NNX_6" class="op-tab-pane "><h4>Flax NNX</h4><div class="op-detail-row"><span class="label">API:</span> <code>jax.numpy.logaddexp.accumulate</code></div><div class="op-detail-row"><span class="label">Strategy:</span> <span>Macro 'jax.numpy.log(jax.numpy.cumsum(jax.numpy.exp({input}), axis={dim}))'</span></div></div><div id="PaxML / Praxis_7" class="op-tab-pane "><h4>PaxML / Praxis</h4><div class="op-detail-row"><span class="label">API:</span> <code>jax.numpy.logaddexp.accumulate</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://github.com/search?q=repo%3Agoogle%2Fpaxml+jax.numpy.logaddexp.accumulate&type=code" target="_blank" class="op-doc-link">Official Docs ↗</a></div></div> 
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