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 .
class_docstr_tok: ["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","."]
docstr: Computes the PD variance along the final axis for a numerical feature.
  
  Parameters
  ----------
  pd_values
      Array of partial dependence values for a numerical features of size `T x N1 x ... x Nk`, where `T` is the
      number of targets and `Ni` is the number of feature values along the axis `i`.
  
  Returns
  -------
  PD variance along the final axis for a numerical feature.
docstr_tok: ["Computes","the","PD","variance","along","the","final","axis","for","a","numerical","feature",".","Parameters","--","--","--","--","--","pd_values","Array","of","partial","dependence","values","for","a","numerical","features","of","size","`","T","x","N1","x","...","x","Nk","`",",","where","`","T","`","is","the","number","of","targets","and","`","Ni","`","is","the","number","of","feature","values","along","the","axis","`","i","`",".","Returns","--","--","--","-","PD","variance","along","the","final","axis","for","a","numerical","feature","."]
code: def _compute_pd_variance_num(pd_values: np.ndarray) -> np.ndarray:
      """
      Computes the PD variance along the final axis for a numerical feature.
  
      Parameters
      ----------
      pd_values
          Array of partial dependence values for a numerical features of size `T x N1 x ... x Nk`, where `T` is the
          number of targets and `Ni` is the number of feature values along the axis `i`.
  
      Returns
      -------
      PD variance along the final axis for a numerical feature.
      """
      return np.std(pd_values, axis=-1, ddof=1, keepdims=False)
  
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 4 for calling the PartialDependenceVariance obj's underlying member method code with example usage: obj._compute_pd_variance_num(pd_values) and returns: np