Update finetuned_regressor.py

[jonaslandsgesell]

Pass through kwargs,

Motivation and Context

e.g. needed for finetuned_reg.predict(X_test, output_type='full')

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Public API Changes

• No Public API changes
• [x ] Yes, Public API changes (Details below)

Checklist

• [x ] The changes have been tested locally.
• Documentation has been updated (if the public API or usage changes).
• A changelog entry has been added (see changelog/README.md), or "no changelog needed" label requested.
• The code follows the project's style guidelines.
• I have considered the impact of these changes on the public API.

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❌ Jonas Landsgesell

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[gemini-code-assist]

Code Review

This pull request adds support for passing keyword arguments to the predict method of FinetunedTabPFNRegressor, which is a good enhancement for flexibility. My review focuses on ensuring the type hints are updated to reflect the new capabilities. I've identified that the return type of predict is now incorrect given the new arguments and have suggested a fix to make it accurate.

[adrian-prior]

@jonaslandsgesell Thanks for the PR! The changes look correct to me, but you can see in the failing tests that there are a few things missing (changelog, formatting, etc.). Please add these and them I happy to give it a proper review :)

[adrian-prior]

@jonaslandsgesell Also, have you already signed the CLA? See the bot commenting above. In addition, I think you also might have incorrectly formatted the changelog (see the changelog test on how to correctly add that)

[adrian-prior]

@jonaslandsgesell Have you already signed the CLA? The test still indicates that it hasn't been signed.

[jonaslandsgesell]

@jonaslandsgesell Have you already signed the CLA? The test still indicates that it hasn't been signed.

Yes I signed with this GitHub account.

[adrian-prior]

Generally, the changes look good to me. Please also modify predict and predict_prob in the FinetunedClassifier accordingly. Regarding the CLA:
I think some of the commits aren't associated with your GitHub Account. Hopefully, I can merge the PR without the CLA test passing. Otherwise, there seems to be an option to add the correct email in retrospect.

[jonaslandsgesell]

As far as I see, the predict and predict_proba method in the TabPFNClassifier do not seem to currently take additional keyword arguments. Do you want me to still add the kwargs parameters?

[adrian-prior]

@jonaslandsgesell Good spot. I would still go for it. Could very well be the case that this will change soon :)