Fix the vocabulary_dtype in keras.layers.IntegerLookup()

[ILCSFNO]

Fixes #21586

[gemini-code-assist]

Summary of Changes

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This pull request addresses an issue in the keras.layers.IntegerLookup() layer by clarifying the supported data types for the vocabulary_dtype parameter. The change updates the docstring to explicitly state that only int64 is currently supported, removing the previous suggestion that int32 was also an option. This ensures that the documentation accurately reflects the current implementation's capabilities, preventing potential confusion for users.

Highlights

• Documentation Update: Clarified that keras.layers.IntegerLookup() currently only supports vocabulary_dtype='int64', updating the docstring to reflect this limitation and fix issue #21586.

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[gemini-code-assist]

Code Review

This pull request updates the docstring for keras.layers.IntegerLookup to clarify that only vocabulary_dtype='int64' is currently supported. This is a helpful clarification for users. My review includes a suggestion to make this fix more robust by adding a validation check in the code to enforce this limitation, which would provide clearer and more immediate feedback to users if they attempt to use an unsupported dtype.

[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 82.73%. Comparing base (45c98ec) to head (110a380).

Additional details and impacted files

@@           Coverage Diff           @@
##           master   #21587   +/-   ##
=======================================
  Coverage   82.73%   82.73%           
=======================================
  Files         567      567           
  Lines       56678    56678           
  Branches     8839     8839           
=======================================
  Hits        46895    46895           
  Misses       7609     7609           
  Partials     2174     2174           

Flag	Coverage Δ
keras	82.54% <ø> (ø)
keras-jax	63.64% <ø> (ø)
keras-numpy	58.22% <ø> (+<0.01%)	⬆️
keras-openvino	34.61% <ø> (ø)
keras-tensorflow	64.19% <ø> (ø)
keras-torch	63.80% <ø> (ø)

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