Add eye support for OpenVINO backend

[danielenricocahall]

Per #20910, add eye support for OpenVINO backend.

[gemini-code-assist]

Summary of Changes

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This pull request introduces support for the eye function within the OpenVINO backend, directly addressing a reported issue. By implementing this core NumPy array creation operation, the OpenVINO backend's capabilities are enhanced, allowing for the proper generation of identity matrices. This change improves the overall compatibility and robustness of Keras models when utilizing the OpenVINO optimization framework.

Highlights

• OpenVINO eye function support: Implemented the eye function for the OpenVINO backend, resolving the NotImplementedError previously raised for this operation.
• Test exclusion removal: Removed NumpyArrayCreateOpsCorrectnessTest::test_eye from the list of excluded tests, indicating that the eye function now passes its correctness test for the OpenVINO backend.

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[gemini-code-assist]

Code Review

This pull request adds support for numpy.eye in the OpenVINO backend. The change is good, but the implementation of eye has some issues with the default data type and inconsistent argument handling. I've provided a suggestion to refactor the function for correctness and clarity.

[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 82.59%. Comparing base (3137cb0) to head (b3e7162).

Additional details and impacted files

@@           Coverage Diff           @@
##           master   #21739   +/-   ##
=======================================
  Coverage   82.59%   82.59%           
=======================================
  Files         572      572           
  Lines       58535    58540    +5     
  Branches     9158     9159    +1     
=======================================
+ Hits        48345    48350    +5     
  Misses       7853     7853           
  Partials     2337     2337           

Flag	Coverage Δ
keras	82.39% <100.00%> (+<0.01%)	⬆️
keras-jax	63.19% <0.00%> (-0.01%)	⬇️
keras-numpy	57.56% <0.00%> (-0.01%)	⬇️
keras-openvino	34.34% <100.00%> (+<0.01%)	⬆️
keras-tensorflow	63.94% <0.00%> (-0.01%)	⬇️
keras-torch	63.48% <0.00%> (-0.01%)	⬇️

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