code: def assert_feature_values_equal(
      exp_alibi: Explanation, exp_sklearn: Bunch
  ):
      """Compares feature values of `alibi` explanation and `sklearn` explanation."""
      if isinstance(exp_alibi.data["feature_names"][0], tuple):
          for i in range(len(exp_sklearn["values"])):
              assert np.allclose(
                  exp_alibi.data["feature_values"][0][i],
                  exp_sklearn["values"][i],
              )
      else:
          assert np.allclose(
              exp_alibi.data["feature_values"][0],
              exp_sklearn["values"][0],
          )
  
imports: import re
  import sys
  from copy import deepcopy
  from typing import Dict, List, Optional, Tuple, Union
  from packaging import version
  import matplotlib.pyplot
  import numpy
  import pandas
  import pytest
  from pytest_lazyfixture import lazy_fixture
  from sklearn.base import BaseEstimator
  from sklearn.datasets import make_classification
  from sklearn.ensemble import GradientBoostingClassifier
  from sklearn.exceptions import NotFittedError
  from sklearn.inspection import partial_dependence
  from sklearn.model_selection import train_test_split
  from sklearn.multioutput import MultiOutputClassifier
  from sklearn.pipeline import Pipeline
  from sklearn.utils import Bunch, shuffle
  import sklearn
  from alibi.api.defaults import DEFAULT_DATA_PD, DEFAULT_META_PD
  from alibi.api.interfaces import Explanation
  from alibi.explainers import PartialDependence, TreePartialDependence, plot_pd
  from alibi.explainers.partial_dependence import _plot_one_pd_cat, _plot_one_pd_num, _plot_two_pd_cat_cat, _plot_two_pd_num_cat, _plot_two_pd_num_num, _process_pd_ice, _sample_ice
label_desc: Use image node_id 13 for calling a global function with example usage: assert_feature_values_equal(exp_alibi, exp_sklearn) without return types