Relax tolerance for SVD test

[abheesht17]

1e-6 is too precise, relax tol to 1e-5. Fixes internal tests

[gemini-code-assist]

Summary of Changes

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This pull request adjusts the numerical tolerance for a Singular Value Decomposition (SVD) test within the Keras linear algebra operations. The change aims to prevent test failures due to minor floating-point discrepancies by allowing a slightly larger margin of error in the assertion.

Highlights

• SVD Test Tolerance: The assertAllClose assertion in the test_svd function for the Singular Value Decomposition (SVD) operation has been updated to include atol=1e-5 and rtol=1e-5 to relax the numerical tolerance for the comparison. This addresses potential floating-point precision issues in the test.

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[gemini-code-assist]

Code Review

This pull request relaxes the tolerance in a Singular Value Decomposition (SVD) test from 1e-6 to 1e-5. This is a common and reasonable adjustment to account for minor numerical precision differences that can occur across various backends and hardware, which helps prevent test flakiness. The new tolerance remains sufficiently strict. The change looks good. In the future, it would be helpful to include a brief description in the pull request explaining the context for the change, for instance, which backend or hardware was causing the test to fail. This helps with tracking and understanding the evolution of the codebase.

[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 82.59%. Comparing base (4e1edcf) to head (ca5c7f3).

Additional details and impacted files

@@           Coverage Diff           @@
##           master   #21731   +/-   ##
=======================================
  Coverage   82.59%   82.59%           
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  Files         572      572           
  Lines       58535    58535           
  Branches     9158     9158           
=======================================
  Hits        48345    48345           
  Misses       7853     7853           
  Partials     2337     2337           

Flag	Coverage Δ
keras	82.39% <ø> (ø)
keras-jax	63.19% <ø> (ø)
keras-numpy	57.56% <ø> (ø)
keras-openvino	34.33% <ø> (ø)
keras-tensorflow	63.94% <ø> (ø)
keras-torch	63.49% <ø> (ø)

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