feat: Add __repr__ generation support for @c_class dataclasses

[Dayuxiaoshui]

• Add repr parameter to c_class() decorator (default: True)
• Add repr parameter to field() function (default: True)
• Implement method_repr() to generate repr methods
• Generated repr format: ClassName(field1=value1, field2=value2, ...)
• Fields with repr=False are excluded from the representation

This implements part of #356 dataclass feature parity.

[gemini-code-assist]

Summary of Changes

Hello @Dayuxiaoshui, 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 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 How far are we from @dataclass #356.

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[gemini-code-assist]

Code Review

This pull request introduces support for automatic __repr__ generation for @c_class dataclasses, which is a great step towards feature parity with standard Python dataclasses. The implementation is clean, adding repr parameters to the @c_class decorator and field() function to control the behavior. The dynamic generation of the __repr__ method is well-handled. I've included a couple of suggestions in python/tvm_ffi/dataclasses/_utils.py to improve code maintainability by reducing duplication and to enhance code style for better readability.

[Dayuxiaoshui]

@tqchen cc

[junrushao]

Please add some unittests

[Dayuxiaoshui]

@junrushao I am ready, please review it again.