code: def test_blackbox_classification(
      lr_classifier, iris_data, kind, features
  ):
      """Test the black-box predictor for a classification function."""
      X_train, _ = iris_data["X_train"], iris_data["y_train"]
      lr, _ = lr_classifier
  
      # define explainer and compute explanation
      explainer = PartialDependence(predictor=lr.predict_proba)
      explainer.explain(
          X=X_train, features=features, kind=kind, grid_resolution=10
      )
  
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 10 for calling a global function with example usage: test_blackbox_classification(lr_classifier, iris_data, kind, features) without return types