fix: mutable default arg and bool comparison

[LincolnBurrows2017]

Fixed 3 issues:

1. Mutable default argument: list[str] = [] -> Optional[list[str]] = None
2. Bool comparison: is False -> not
3. Typo: state_b -> state_dict_b

[gemini-code-assist]

Summary of Changes

Hello, 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 focuses on enhancing the robustness and readability of the compare_model utility function. It addresses a common Python pitfall by correctly handling mutable default arguments and refactors a boolean comparison for improved clarity, ensuring more predictable and maintainable code.

Highlights

• Mutable Default Argument: The diff_keys parameter in the compare_model function was updated from a mutable default list (list[str] = []) to Optional[list[str]] = None, with an explicit check to initialize an empty list if None is passed. This prevents unintended shared state across function calls.
• Boolean Comparison: The boolean comparison is False was replaced with the more idiomatic and robust not operator in the compare_model function, improving code readability and adherence to best practices.

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[gemini-code-assist]

Code Review

This pull request correctly refactors the compare_model function to address a mutable default argument and improve boolean comparisons, aligning with Python best practices. I've added one suggestion to further improve the test utility by adding descriptive messages to assertions, which will enhance debuggability.

[gemini-code-assist]

It's good practice to include a message with assert statements. This can be very helpful for debugging when a test fails, as it provides immediate context on what condition was not met. Consider adding descriptive messages to your assertions to improve debuggability.

Suggested change

	if any(key in name for key in diff_keys):
	assert torch.allclose(state_dict_a[name], state_dict_b[name], rtol=1e-4, atol=1e-5) is False
	assert not torch.allclose(state_dict_a[name], state_dict_b[name], rtol=1e-4, atol=1e-5)
	else:
	assert torch.allclose(state_dict_a[name], state_dict_b[name], rtol=1e-4, atol=1e-5) is True
	assert torch.allclose(state_dict_a[name], state_dict_b[name], rtol=1e-4, atol=1e-5)
	if any(key in name for key in diff_keys):
	assert not torch.allclose(state_dict_a[name], state_dict_b[name], rtol=1e-4, atol=1e-5), f"Tensors for key '{name}' should be different but are close."
	else:
	assert torch.allclose(state_dict_a[name], state_dict_b[name], rtol=1e-4, atol=1e-5), f"Tensors for key '{name}' should be close but are different."