code: def pd_pdv_explainers(request, adult_data):
      """Initialize `PartialDependence` and `PartialDependenceVariance` explainers."""
      clf, preprocessor = request.param
      feature_names = adult_data["metadata"]["feature_names"]
      categorical_names = adult_data["metadata"]["category_map"]
  
      # define predictor function with a single output to be used
      def predictor(x):
          return clf.predict_proba(preprocessor.transform(x))[:, 1:]
  
      # compute manually the feature importance using the `PartialDependence` explainer
      pd_explainer = PartialDependence(
          predictor=predictor,
          feature_names=feature_names,
          categorical_names=categorical_names,
      )
  
      # compute feature importance using the `PartialDependenceVariance` explainer.
      pdv_explainer = PartialDependenceVariance(
          predictor=predictor,
          feature_names=feature_names,
          categorical_names=categorical_names,
      )
      return pd_explainer, pdv_explainer
  
imports: import re
  from copy import deepcopy
  import matplotlib.pyplot
  import numpy
  import pandas
  import pytest
  from pytest_lazyfixture import lazy_fixture
  from sklearn.ensemble import GradientBoostingRegressor
  from sklearn.linear_model import LinearRegression
  from alibi.api.defaults import DEFAULT_DATA_PDVARIANCE, DEFAULT_META_PDVARIANCE
  from alibi.api.interfaces import Explanation
  from alibi.explainers import PartialDependence, PartialDependenceVariance, TreePartialDependence
  from alibi.explainers.pd_variance import _plot_feature_importance, _plot_feature_interaction, _plot_hbar, plot_pd_variance
label_desc: Use image node_id 9 for calling a global function with example usage: pd_pdv_explainers(request, adult_data) and returns: pd_explainer,  pdv_explainer, clf