Document complex dtype limitation in ops.correlate #21984
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Summary of ChangesHello @PES2UG23CS205, 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 primarily intends to improve the clarity of the Highlights
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This pull request appears to combine two unrelated changes. The main purpose, as described, is to document a limitation in keras.ops.correlate. However, the provided file changes only show the removal of NumPyTestRot90 from the excluded tests for the OpenVINO backend, which seems related to enabling rot90 tests. It is generally recommended to keep pull requests focused on a single logical change. I am unable to review the documentation change for correlate as the relevant file is not included in the provided patch. My review is therefore limited to the changes in keras/src/backend/openvino/excluded_concrete_tests.txt. In that file, I've noted that a blank line has been added at the beginning, which should be removed.
| NNOpsDynamicShapeTest::test_glu | ||
| NNOpsBehaviorTest::test_invalid_strategy_ctc_decode | ||
| NNOpsBehaviorTest::test_logit_recovery_binary_crossentropy | ||
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This change introduces a blank line at the beginning of the file. While this might not cause issues, it's generally better to avoid leading blank lines in configuration files like this to prevent potential parsing errors. Please remove the blank line.
hertschuh
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hertschuh
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This change appears to change all carriage returns from Unix style to Windows style, thus showing every line changed:
https://github.com/keras-team/keras/pull/21984/changes
Can you revert the carriage returns?
| @@ -1,264 +1,271 @@ | |||
| NumPyTestRot90 | |||
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Thanks for the review! You’re right , the previous version of this PR unintentionally included OpenVINO files due to CRLF line endings. |
3 participants
This PR documents the current limitation of
keras.ops.correlatewith complexdtypes on the TensorFlow and PyTorch backends, where complex inputs are cast to
floating-point types and imaginary components are dropped.
This clarification aligns with the discussion in issue #21617.