Add support for model hyperparameter tuning

[fcogidi]

The model wrappers have a method that is intended for tuning the model hyperparameters and returning the best model.
The method has the following signature:

    find_best(
        self,
        X: ArrayLike,
        y: ArrayLike,
        parameters: Union[Dict, List[Dict]],
        metric: Union[str, Callable, Sequence, Dict] = None,
        method: Literal["grid", "random"] = "grid",
        **kwargs,
    )

Currently, only the scikit-learn model wrapper SKModel implements this method, and that implementation would benefit from the following improvements:

• Support using metrics from cyclops.evaluate.metrics in the hyperparameter search, potentially using the sklearn.metrics.make_scorer method.
• Handle data splits e.g. predefined split, split by percentage, cross-validation split etc.
• Support passing group and fit_params arguments when calling clf.fit.

The PyTorch model wrapper (PTModel) should implement this method as well, with the same behaviour as the sklearn model wrapper.