code: def test_multioutput_estimator(
      multioutput_classifier, multioutput_dataset
  ):
      """Check if raises error for multi-output model"""
      X_train, Y_train = (
          multioutput_dataset["X_train"],
          multioutput_dataset["Y_train"],
      )
      multioutput_classifier.fit(X_train, Y_train)
      with pytest.raises(ValueError) as err:
          TreePartialDependence(predictor=multioutput_classifier)
      assert re.search(
          "multiclass-multioutput", err.value.args[0].lower()
      )
  
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 5 for calling a global function with example usage: test_multioutput_estimator(multioutput_classifier, multioutput_dataset) without return types