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@shashaka shashaka commented Sep 7, 2025

Adds keras.ops.lcm, which computes the element-wise least common multiple of two tensor inputs (x1 and x2).
Equivalent to applying the integer lcm operation with broadcasting support.

Supported across NumPy, TensorFlow, PyTorch, and JAX backends.
Not supported on OpenVINO.

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Summary of Changes

Hello @shashaka, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request expands the Keras operations library by adding a new lcm (least common multiple) function. This enhancement provides a fundamental mathematical operation for integer tensors, making Keras more versatile for numerical computations that require LCM, while ensuring broad compatibility across its supported backends.

Highlights

  • New lcm operation: Introduces keras.ops.lcm to compute the element-wise least common multiple of two tensor inputs, supporting broadcasting.
  • Backend Support: The lcm operation is implemented and supported across NumPy, TensorFlow, PyTorch, and JAX backends.
  • OpenVINO Exclusion: The lcm operation is explicitly marked as not supported on the OpenVINO backend, with corresponding tests excluded.
  • Comprehensive Testing: New tests have been added to keras/src/ops/numpy_test.py to cover symbolic shapes, static shapes, correctness across various inputs (including broadcasting), and dtype handling for the lcm operation.
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Code Review

This pull request introduces keras.ops.lcm to compute the element-wise least common multiple, with support for NumPy, TensorFlow, PyTorch, and JAX backends. The implementation is well-structured, following the existing patterns for Keras ops, and includes comprehensive tests for correctness, shape inference, and data types. The backend-specific implementations correctly leverage the respective libraries' functionalities. My main feedback is to add a usage example to the lcm function's docstring to align with the repository's style guide.

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codecov-commenter commented Sep 7, 2025

Codecov Report

❌ Patch coverage is 92.00000% with 4 lines in your changes missing coverage. Please review.
✅ Project coverage is 82.55%. Comparing base (1bdf25b) to head (5c9b10d).

Files with missing lines Patch % Lines
keras/src/backend/tensorflow/numpy.py 87.50% 1 Missing and 1 partial ⚠️
keras/api/_tf_keras/keras/ops/__init__.py 0.00% 1 Missing ⚠️
keras/api/_tf_keras/keras/ops/numpy/__init__.py 0.00% 1 Missing ⚠️
Additional details and impacted files
@@           Coverage Diff           @@
##           master   #21636   +/-   ##
=======================================
  Coverage   82.54%   82.55%           
=======================================
  Files         571      571           
  Lines       57584    57634   +50     
  Branches     8994     8997    +3     
=======================================
+ Hits        47531    47577   +46     
- Misses       7760     7763    +3     
- Partials     2293     2294    +1     
Flag Coverage Δ
keras 82.35% <92.00%> (+<0.01%) ⬆️
keras-jax 63.54% <50.00%> (-0.02%) ⬇️
keras-numpy 57.89% <52.00%> (-0.01%) ⬇️
keras-openvino 34.33% <20.00%> (-0.02%) ⬇️
keras-tensorflow 64.26% <70.00%> (+<0.01%) ⬆️
keras-torch 63.75% <50.00%> (-0.02%) ⬇️

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