standardize_shape normalizes the dimensions and tuple.

[hertschuh]

• standardize_shape would allow int-like objects (e.g. NumPy scalars) to be in the shape, but would not resolve them to actual ints. This could cause issues later. With this change, objects that can be cast to int will be cast to int, which includes NumPy scalars, but also TensorFlow constant or eager tensors.
• standardize_shape would have custom code to handle torch.Size. Generalized it to turn anything iterable to a plain tuple.
• Added units tests
• Removed duplicate unit tests
• Added TensorFlow specific unit test

[gemini-code-assist]

Summary of Changes

Hello @hertschuh, 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 refines the standardize_shape utility function to provide more robust and consistent handling of tensor dimensions across different backend frameworks. The primary goal is to ensure that all shape dimensions are represented as standard Python integers within a tuple, regardless of their initial type (e.g., NumPy scalars, TensorFlow tensors, or torch.Size objects). This change improves type safety and reduces potential compatibility issues by enforcing a uniform shape representation.

Highlights

• Shape Normalization: The standardize_shape function now consistently casts int-like objects (e.g., NumPy scalars, TensorFlow constant/eager tensors) to native Python integers, preventing potential issues from non-integer types in shape dimensions.
• Iterable Shape Handling: The function now generalizes the conversion of any iterable shape (including torch.Size) into a plain Python tuple, simplifying backend-specific logic.
• Improved Validation: Enhanced validation explicitly rejects string or float dimensions and ensures all dimensions are non-negative integers or None.
• Refactored Unit Tests: Unit tests have been updated, with new tests added for NumPy and TensorFlow scalar types, and redundant backend-specific tests removed for better coverage and maintainability.

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[gemini-code-assist]

Code Review

This pull request improves standardize_shape by making it more robust in handling different int-like objects and by generalizing it to convert any iterable to a plain tuple, removing backend-specific code. The accompanying test changes are also great, with better organization, removal of duplicates, and addition of new test cases for the new functionality. I've found a couple of minor issues in the test file related to naming, but overall this is a solid improvement.

[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 82.57%. Comparing base (9bcdbc7) to head (9888260).

Additional details and impacted files

@@            Coverage Diff             @@
##           master   #21867      +/-   ##
==========================================
- Coverage   82.57%   82.57%   -0.01%     
==========================================
  Files         577      577              
  Lines       59568    59572       +4     
  Branches     9345     9344       -1     
==========================================
+ Hits        49189    49191       +2     
- Misses       7974     7975       +1     
- Partials     2405     2406       +1     

Flag	Coverage Δ
keras	82.39% <100.00%> (-0.01%)	⬇️
keras-jax	62.86% <100.00%> (+<0.01%)	⬆️
keras-numpy	57.52% <100.00%> (+<0.01%)	⬆️
keras-openvino	34.33% <100.00%> (+<0.01%)	⬆️
keras-tensorflow	64.39% <100.00%> (+<0.01%)	⬆️
keras-torch	63.57% <100.00%> (+<0.01%)	⬆️

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