code: def test_partial_dependence_unfitted(estimator):
      X = iris.data
      preprocessor = make_column_transformer(
          (StandardScaler(), [0, 2]), (RobustScaler(), [1, 3])
      )
      pipe = make_pipeline(preprocessor, estimator)
      with pytest.raises(NotFittedError, match="is not fitted yet"):
          partial_dependence(
              pipe, X, features=[0, 2], grid_resolution=10
          )
      with pytest.raises(NotFittedError, match="is not fitted yet"):
          partial_dependence(
              estimator, X, features=[0, 2], grid_resolution=10
          )
  
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 21 for calling a global function with example usage: test_partial_dependence_unfitted(estimator) without return types