class_docstr: Tree-based model `sklearn`  implementation of the partial dependence for tabular datasets.
  Supports multiple feature interactions. This method is faster than the general black-box implementation
  but is only supported by some tree-based estimators. The computation is based on a weighted tree traversal.
  For more details on the computation, check the `sklearn documentation page`_. The supported `sklearn`
  models are: `GradientBoostingClassifier`, `GradientBoostingRegressor`, `HistGradientBoostingClassifier`,
  `HistGradientBoostingRegressor`, `HistGradientBoostingRegressor`, `DecisionTreeRegressor`, `RandomForestRegressor`.
  
  .. _sklearn documentation page:
          https://scikit-learn.org/stable/modules/partial_dependence.html#computation-methods
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docstr: Computes the PD.
  
  Parameters
  ----------
  grid
      Cartesian product between feature values. Covers also the case of a single feature.
  features
      Feature column indices.
  **kwargs
      Other arguments. Not used.
  
  Returns
  -------
  Tuple consisting of the PD and ``None``.
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code: def _compute_pd(
      self,  # type: ignore[override]
      grid: np.ndarray,
      features: np.ndarray,
      **kwargs,
  ) -> Tuple[np.ndarray, Optional[np.ndarray]]:
      """
      Computes the PD.
  
      Parameters
      ----------
      grid
          Cartesian product between feature values. Covers also the case of a single feature.
      features
          Feature column indices.
      **kwargs
          Other arguments. Not used.
  
      Returns
      -------
      Tuple consisting of the PD and ``None``.
      """
      avg_preds = self.predictor._compute_partial_dependence_recursion(grid, features)  # type: ignore[union-attr]
      return avg_preds, None
  
imports: import copy
  import logging
  import math
  import numbers
  import sys
  from abc import ABC, abstractmethod
  from enum import Enum
  from typing import Callable, Dict, Iterable, List, Optional, Tuple, Union, no_type_check
  import matplotlib.pyplot
  import numpy
  from scipy.stats.mstats import mquantiles
  from sklearn.base import BaseEstimator, is_classifier, is_regressor
  from sklearn.ensemble import RandomForestRegressor
  from sklearn.ensemble._gb import BaseGradientBoosting
  from sklearn.ensemble._hist_gradient_boosting.gradient_boosting import BaseHistGradientBoosting
  from sklearn.tree import DecisionTreeRegressor
  from sklearn.utils.extmath import cartesian
  from sklearn.utils.validation import check_is_fitted
  from tqdm import tqdm
  from alibi.api.defaults import DEFAULT_DATA_PD, DEFAULT_META_PD
  from alibi.api.interfaces import Explainer, Explanation
  from alibi.explainers.ale import get_quantiles
  from alibi.utils import _get_options_string
label_desc: Use image node_id 4 for calling the TreePartialDependence obj's underlying member method code with example usage: obj._compute_pd(grid, features) and returns: avg_preds,  None