[RFC] Add interpretability API as xgboost.interpret module functions

## Summary

We have new work underway on shapley values and other related interpretability concepts. This will add new functionality, however the current feature importance/shapley features are included in the predict API. I propose extending the python api with a module (`xgboost.interpret`) for interpretability, containing stateless functions exposing upcoming features.

These functions accept either a `Booster` or an sklearn-style `XGB*` model, plus `DMatrix`/array-like inputs, and return well-typed results (arrays and or light-weight result objects).

## Motivation
- Minimize disruption to existing `Booster` / sklearn APIs while adding interpretability features.
- Improve discoverability and documentation (module-level functions are easy to document and test).
- Allow incremental implementation: start as wrappers over existing `predict(pred_contribs=..., pred_interactions=...)`, then evolve internals (esp. top-k) without changing the public API.

## Proposed public API
Add a new module:
- `xgboost/interpret.py`

Functions (accept `Booster | XGBModel` and `DMatrix | array-like | pandas`):
- `shap_values(model, X,*, X_background=None, output_margin=False, iteration_range=None, approx=False, validate_features=True, feature_names=None, return_bias=False)`
- `shap_interactions(model, X, *, X_background=None, output_margin=False, iteration_range=None, approx=False, validate_features=True, feature_names=None)`
- `topk_interactions(model, X, *, X_background=None, k=50, metric="mean_abs", output_margin=False, iteration_range=None, validate_features=True, feature_names=None)` - Note: possibly just fold this into shap_interactions
- `partial_dependence(model, X, *, features, grid_resolution=50, percentiles=(0.05,0.95), grid=None, sample_weights=None, random_state=0, output="prediction", iteration_range=None)`
- Possibly adding shap_values, shap_values methods to booster/sklearn class for convenience

## Dispatch/behavior notes
- Internally normalize `model` to a `Booster` via `model` being `Booster` or having `get_booster()`.
- Normalize `X` to `DMatrix` if needed; respect feature names where possible.
- Initial SHAP implementations can wrap existing `Booster.predict(..., pred_contribs=True/pred_interactions=True)` for compatibility.
- `topk_interactions` should ideally avoid materializing full (n, p, p) tensors; target a C++ implementation to compute aggregated top-k pairs efficiently.

## Return types
Prefer lightweight result objects to keep outputs consistent and extensible:
- `ShapValues(values, base_values, feature_names, model_output, ...)`
- `ShapInteractions(values, feature_names, ...)` with helpers for main effects / pair extraction
- `TopKInteractions(pairs, scores, pair_names=None, per_row=None, ...)`
- `PDP(features, grid_values, averages, ...)`

## Documentation plan (Sphinx)
- Add `docs/python/interpretability.rst`
- narrative examples + API reference using `.. autofunction::` for each function
- `.. autoclass::` for result types

@ron-wettenstein