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Summary

Clarifies the backend_module parameter documentation in smart_resize() to prevent user confusion.

Changes

Updated the docstring for the backend_module parameter to clarify:

  • It's primarily for internal use
  • Should be left as None for typical usage
  • Does not allow switching between TensorFlow/JAX/PyTorch backends

Motivation

Fixes issue #21711 where users attempt to pass keras.backend or other modules, resulting in AttributeError. The current documentation suggests this parameter offers more flexibility than it actually provides.

Current documentation & proposed change:

backend_module: Backend module to use (if different from the default
    backend).

Proposed change:

backend_module: Backend module to use (if different from the default
    backend). This parameter is primarily for internal use and should
    typically be left as None (default: `keras.src.backend`).
    Note: This does not allow switching between TensorFlow/JAX/PyTorch
    backends. Use `keras.config.set_backend()` to change the global backend.

Testing

  • Documentation builds successfully
  • Existing tests pass
  • No functional changes, only documentation update

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Summary of Changes

Hello @Flakes342, 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 enhances the clarity of the smart_resize function's backend_module parameter documentation. The update aims to prevent common user errors and confusion by explicitly detailing the parameter's internal nature, its typical None usage, and its inability to switch Keras backends. This ensures users understand its intended purpose and avoid incorrect usage, ultimately improving the developer experience without altering any core functionality.

Highlights

  • Documentation Clarity: The documentation for the backend_module parameter in the smart_resize function has been updated to prevent user confusion.
  • Internal Use Clarification: The docstring now explicitly states that backend_module is primarily for internal use and should typically be left as None.
  • Backend Switching Guidance: It clarifies that this parameter does not allow switching between TensorFlow/JAX/PyTorch backends, directing users to keras.config.set_backend() for that purpose.
  • Issue Resolution: This change addresses issue #21711, which arose from users attempting to pass keras.backend or other modules, leading to AttributeError.
  • No Functional Change: This pull request is a documentation-only update with no functional changes to the codebase.
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Code Review

This pull request clarifies the documentation for the backend_module parameter in smart_resize to indicate it's for internal use and shouldn't be used to switch backends. This is a helpful change to prevent user confusion. However, the implementation has a small but critical issue where the argument name backend_module: is removed from the docstring, which will likely break documentation generation. I've added a comment with a suggestion to fix this.

Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>
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codecov-commenter commented Oct 5, 2025

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 82.59%. Comparing base (94ca6ef) to head (7abb45d).

Additional details and impacted files
@@           Coverage Diff           @@
##           master   #21722   +/-   ##
=======================================
  Coverage   82.59%   82.59%           
=======================================
  Files         572      572           
  Lines       58401    58401           
  Branches     9146     9146           
=======================================
  Hits        48238    48238           
  Misses       7828     7828           
  Partials     2335     2335           
Flag Coverage Δ
keras 82.40% <ø> (ø)
keras-jax 63.24% <ø> (ø)
keras-numpy 57.58% <ø> (ø)
keras-openvino 34.37% <ø> (ø)
keras-tensorflow 63.97% <ø> (ø)
keras-torch 63.56% <ø> (ø)

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3 participants