ponder-lab / Hybridize-Functions-Refactoring / #3266

87%

main: 88%

Build Type

Pull #1006

github

Committed by khatchad

Commit Message

Decline hybridizing a custom-gradient method with a bound argument

Under `tf.function`, `tf.custom_gradient` runs in graph mode and
converts every argument to a tensor, so an instance method's `self` or a
classmethod's `cls` raises. With `tf.function` beneath
`tf.custom_gradient` instead, the gradient function's return is
rejected. No decorator order works, so CONVERT_TO_HYBRID is declined
with the new IS_BOUND_CUSTOM_GRADIENT_METHOD (32), and an already-hybrid
such method is reported rather than reconfigured. A staticmethod and a
plain function have no bound argument and are still hybridized above
`tf.custom_gradient` (#996).

The test is structural, so it holds whether or not the analysis reaches
the method.

Fixes #1000.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01NTnpjPjJj2aZy1ZkUD1eAn

Pull Request Pull Request #1006: Decline hybridizing a custom-gradient method with a bound argument

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22 of 22 new or added lines in 2 files covered. (100.0%)

108 existing lines in 1 file now uncovered.

4305 of 4941 relevant lines covered (87.13%)

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