code: def test_multiclass_multioutput(Estimator):
      # Make sure error is raised for multiclass-multioutput classifiers
  
      # make multiclass-multioutput dataset
      X, y = make_classification(
          n_classes=3, n_clusters_per_class=1, random_state=0
      )
      y = np.array([y, y]).T
  
      est = Estimator()
      est.fit(X, y)
  
      with pytest.raises(
          ValueError,
          match="Multiclass-multioutput estimators are not supported",
      ):
          partial_dependence(est, X, [0])
  
imports: import warnings
  import numpy
  import pytest
  import sklearn
  from sklearn.base import BaseEstimator, ClassifierMixin, clone, is_regressor
  from sklearn.cluster import KMeans
  from sklearn.compose import make_column_transformer
  from sklearn.datasets import load_iris, make_classification, make_regression
  from sklearn.dummy import DummyClassifier
  from sklearn.ensemble import GradientBoostingClassifier, GradientBoostingRegressor, HistGradientBoostingClassifier, HistGradientBoostingRegressor, RandomForestRegressor
  from sklearn.exceptions import NotFittedError
  from sklearn.inspection import partial_dependence
  from sklearn.inspection._partial_dependence import _grid_from_X, _partial_dependence_brute, _partial_dependence_recursion
  from sklearn.linear_model import LinearRegression, LogisticRegression, MultiTaskLasso
  from sklearn.metrics import r2_score
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import PolynomialFeatures, RobustScaler, StandardScaler, scale
  from sklearn.tree import DecisionTreeRegressor
  from sklearn.tree.tests.test_tree import assert_is_subtree
  from sklearn.utils import _IS_32BIT
  from sklearn.utils._testing import assert_allclose, assert_array_equal
  from sklearn.utils.validation import check_random_state
label_desc: Use image node_id 10 for calling a global function with example usage: test_multiclass_multioutput(Estimator) without return types