class_docstr: Implementation of the partial dependence(PD) variance feature importance and feature interaction for
  tabular datasets. The method measure the importance feature importance as the variance within the PD function.
  Similar, the potential feature interaction is measured by computing the variance within the two-way PD function
  by holding one variable constant and letting the other vary. Supports black-box models and the following `sklearn`
  tree-based models: `GradientBoostingClassifier`, `GradientBoostingRegressor`, `HistGradientBoostingClassifier`,
  `HistGradientBoostingRegressor`, `HistGradientBoostingRegressor`, `DecisionTreeRegressor`,
  `RandomForestRegressor`.
  
  For details of the method see the original paper: https://arxiv.org/abs/1805.04755 .
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docstr: Computes the feature importance.
  
  Parameters
  ----------
  features
      List of features to compute the importance for.
  pd_explanation
      Partial dependence explanation object.
  
  Returns
  -------
  Dictionary with all the keys necessary to build the explanation.
docstr_tok: ["Computes","the","feature","importance",".","Parameters","--","--","--","--","--","features","List","of","features","to","compute","the","importance","for",".","pd_explanation","Partial","dependence","explanation","object",".","Returns","--","--","--","-","Dictionary","with","all","the","keys","necessary","to","build","the","explanation","."]
code: def _compute_feature_importance(
      self, features: List[int], pd_explanation: Explanation
  ) -> Dict[str, Any]:
      """
      Computes the feature importance.
  
      Parameters
      ----------
      features
          List of features to compute the importance for.
      pd_explanation
          Partial dependence explanation object.
  
      Returns
      -------
      Dictionary with all the keys necessary to build the explanation.
      """
      return {
          "feature_deciles": pd_explanation.data["feature_deciles"],
          "pd_values": pd_explanation.data["pd_values"],
          "feature_values": pd_explanation.data["feature_values"],
          "feature_names": pd_explanation.data["feature_names"],
          "feature_importance": self._compute_pd_variance(
              features=features,
              pd_values=pd_explanation.data["pd_values"],
          ).T,
      }
  
imports: import copy
  import itertools
  import logging
  import math
  import numbers
  import sys
  from enum import Enum
  from typing import Any, Callable, Dict, List, Optional, Tuple, Union
  import matplotlib.pyplot
  import numpy
  from sklearn.base import BaseEstimator
  from alibi.api.defaults import DEFAULT_DATA_PD, DEFAULT_DATA_PDVARIANCE, DEFAULT_META_PDVARIANCE
  from alibi.api.interfaces import Explainer, Explanation
  from alibi.explainers import plot_pd
  from alibi.explainers.partial_dependence import Kind, PartialDependence, TreePartialDependence
  from alibi.utils import _get_options_string
label_desc: Use image node_id 6 for calling the PartialDependenceVariance obj's underlying member method code with example usage: obj._compute_feature_importance(features, pd_explanation) and returns: dict