Ensure keras.ops.eye behavior is consistent across backends.

[danielenricocahall]

This PR is addressing #20616 by adding a check to the eye function to ensure that N and M are either integers or Tensors with an integer dtype, which is consistent with the current numpy behavior.

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[gemini-code-assist]

Summary of Changes

Hello @danielenricocahall, 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 addresses an inconsistency in the keras.ops.eye function by enforcing strict type checking for its N and M arguments. By ensuring these parameters are always integers or integer tensors, the change standardizes the function's behavior to match numpy's expectations, thereby preventing potential runtime errors and improving the overall robustness and predictability of Keras operations across different backends.

Highlights

• Input Validation for keras.ops.eye: Introduced validation checks within the keras.ops.eye function to ensure that the N and M parameters are strictly integers or integer tensors, aligning its behavior with numpy.
• Error Handling for Float Inputs: Added ValueError exceptions that are raised when N or M are provided as float values, preventing incorrect usage and promoting type consistency.
• Enhanced Test Coverage: Included new test cases in numpy_test.py specifically designed to verify that keras.ops.eye correctly raises ValueError when float inputs are passed for N or M.

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[gemini-code-assist]

Code Review

This pull request enhances the keras.ops.eye function by adding checks to ensure its arguments are integers or integer tensors, aligning its behavior with NumPy and improving consistency across backends. The changes are well-implemented and include corresponding tests. I've provided a couple of suggestions to further improve the code's robustness and test coverage.

[gemini-code-assist]

The implementation of is_float can be simplified to be more Pythonic. Also, it's missing bfloat16 from the check. Using the dtypes.FLOAT_TYPES constant would make this more robust and readable.

    def is_float(v):
        return isinstance(v, float) or getattr(v, "dtype", None) in dtypes.FLOAT_TYPES

[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 82.59%. Comparing base (3137cb0) to head (392d05c).

Additional details and impacted files

@@           Coverage Diff           @@
##           master   #21738   +/-   ##
=======================================
  Coverage   82.59%   82.59%           
=======================================
  Files         572      572           
  Lines       58535    58541    +6     
  Branches     9158     9160    +2     
=======================================
+ Hits        48345    48351    +6     
  Misses       7853     7853           
  Partials     2337     2337           

Flag	Coverage Δ
keras	82.39% <100.00%> (+<0.01%)	⬆️
keras-jax	63.20% <100.00%> (+<0.01%)	⬆️
keras-numpy	57.56% <100.00%> (+<0.01%)	⬆️
keras-openvino	34.33% <0.00%> (-0.01%)	⬇️
keras-tensorflow	63.95% <100.00%> (+<0.01%)	⬆️
keras-torch	63.49% <100.00%> (+<0.01%)	⬆️

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[hertschuh]

Thanks for the PR!