Improved analytics for tracking usage of different fit modes

[safaricd]

Issue

Previously, we had no knowledge of the different fit_mode params that were used, meaning it was difficult to decide on the exact focus area for improving any of the fit modes.

Public API Changes

• No Public API changes

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How Has This Been Tested?

Unit tests and manual testing.

Checklist

• The changes have been tested locally.
• Documentation has been updated (if the public API or usage changes).
• A entry has been added to CHANGELOG.md (if relevant for users).
• The code follows the project's style guidelines.
• I have considered the impact of these changes on the public API.

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[gemini-code-assist]

Code Review

This pull request adds telemetry to track the usage of different fit_mode options in TabPFNClassifier and TabPFNRegressor. This is achieved by bumping the tabpfn-common-utils dependency and using the new set_init_params function.

The implementation is straightforward and correct. I have one suggestion to refactor the duplicated telemetry initialization logic into a helper function to improve maintainability.

Additionally, the documentation in TELEMETRY.md should be updated to reflect that fit_mode is now being collected as part of the anonymous usage data. This is important for transparency with users.

[gemini-code-assist]

This telemetry initialization logic, including the call to set_init_params, is also present in TabPFNRegressor.__init__. To improve maintainability and reduce code duplication, consider creating a new helper function in src/tabpfn/base.py that encapsulates this logic.

For example, you could create a function in base.py:

from tabpfn_common_utils.telemetry import set_init_params

def initialize_telemetry_with_params(**params: Any) -> None:
    """Initializes telemetry and sets additional anonymous parameters."""
    initialize_telemetry()
    if params:
        set_init_params(params)

Then you could replace these lines in both TabPFNClassifier and TabPFNRegressor with:

        initialize_telemetry_with_params(fit_mode=self.fit_mode)

This would centralize the telemetry setup and make it easier to add more parameters in the future.

[oscarkey]

Should we do the same thing as for model_path and validate that it's a known fit mode? To avoid accidentally collecting PII.
We could define FitMode = Literal["low_memory","fit_preprocessors","fit_with_cache","batched"] in inference.py, import it here and in the regressor interface, and then use typing.get_args() to check the provided one is valid?