[WIP] Add improved goodness of fit implementation

[stes]

This adds a better goodness of fit measure. Instead of the old variant which simply matched the InfoNCE and depends on the batch size, the proposed measure

• is at 0 for "chance level" (vs. log batch size)
• does not need an adjustment for single session vs. multi-session solvers
• increases as the model gets better, which might be more intuitive

The conversion is quite simply done via

GoF(model) = log (batch_size_per_session * num_sessions) - InfoNCE(model)

This measure is also used in DeWolf et al., 2024, Eq. (43)

Application example (GoF improves from 0 to a larger value during training):

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Close https://github.com/AdaptiveMotorControlLab/CEBRA-dev/pull/669

[stes]

TODO: Fix case where batch_size is None