Replace np.reshape(x, newshape=y) with np.reshape(x, y).

[hertschuh]

The newshape argument to reshape is removed in NumPy 2.4.

[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 addresses a compatibility concern with the upcoming NumPy 2.4 release by modifying all instances of np.reshape to no longer use the newshape keyword argument. This change is a proactive measure to prevent breakage when the newshape argument is officially removed from NumPy, ensuring the codebase remains functional and up-to-date with its dependencies.

Highlights

• NumPy Compatibility: Updated np.reshape calls to remove the newshape keyword argument, ensuring compatibility with the upcoming NumPy 2.4 release where this argument will be removed.
• Code Modernization: Refactored np.reshape usage to align with modern NumPy API practices, making the code more robust against future library updates.

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[gemini-code-assist]

Code Review

This pull request correctly updates the usage of np.reshape in a test file to be compatible with the upcoming NumPy 2.4 release by removing the deprecated newshape keyword argument. The change is straightforward, well-contained, and necessary for future compatibility. I find no issues with this modification.

[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 76.27%. Comparing base (7631c1a) to head (592558f).

Additional details and impacted files

@@           Coverage Diff           @@
##           master   #21899   +/-   ##
=======================================
  Coverage   76.27%   76.27%           
=======================================
  Files         579      579           
  Lines       59917    59917           
  Branches     9403     9403           
=======================================
  Hits        45703    45703           
  Misses      11748    11748           
  Partials     2466     2466           

Flag	Coverage Δ
keras	76.14% <ø> (ø)
keras-jax	62.70% <ø> (ø)
keras-numpy	57.36% <ø> (ø)
keras-openvino	34.29% <ø> (ø)
keras-torch	63.29% <ø> (ø)

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