gemini-code-assist[bot] commented on PR #411: URL: https://github.com/apache/tvm-ffi/pull/411#issuecomment-3749458508
## Summary of Changes Hello @Dayuxiaoshui, I'm Gemini Code Assist[^1]! 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 significantly enhances the `@c_class` decorator by introducing automatic `__repr__` generation, similar to Python's built-in dataclasses. It provides new `repr` parameters at both the class and field levels, allowing developers to customize the string representation of their C++-backed Python objects for improved debugging and readability. This moves the `c_class` functionality closer to full dataclass feature parity. ### Highlights * **New 'repr' parameter in @c_class decorator**: A new `repr` parameter (defaulting to `True`) has been added to the `c_class()` decorator, allowing control over automatic `__repr__` generation for the decorated class. * **New 'repr' parameter in field() function**: The `field()` function now includes a `repr` parameter (defaulting to `True`), enabling fine-grained control over whether individual fields are included in the generated `__repr__` output. * **Dynamic __repr__ method generation**: A new utility function, `method_repr()`, has been implemented to dynamically generate `__repr__` methods for `c_class` instances. This method collects fields from the class hierarchy and formats them as `ClassName(field1=value1, field2=value2, ...)`. * **Exclusion of fields from __repr__**: Fields marked with `repr=False` in their `field()` definition will be excluded from the automatically generated `__repr__` string. * **Dataclass feature parity**: These changes contribute to achieving greater feature parity with standard Python dataclasses, specifically addressing issue #356. 🧠**New Feature in Public Preview:** You can now enable **Memory** to help **Gemini Code Assist** learn from your team's feedback. 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