code: def test_partial_dependence_feature_type(features, expected_pd_shape):
      # check all possible features type supported in PDP
      pd = pytest.importorskip("pandas")
      df = pd.DataFrame(iris.data, columns=iris.feature_names)
  
      preprocessor = make_column_transformer(
          (StandardScaler(), [iris.feature_names[i] for i in (0, 2)]),
          (RobustScaler(), [iris.feature_names[i] for i in (1, 3)]),
      )
      pipe = make_pipeline(
          preprocessor,
          LogisticRegression(max_iter=1000, random_state=0),
      )
      pipe.fit(df, iris.target)
      pdp_pipe = partial_dependence(
          pipe,
          df,
          features=features,
          grid_resolution=10,
          kind="average",
      )
      assert pdp_pipe["average"].shape == expected_pd_shape
      assert (
          len(pdp_pipe["grid_values"])
          == len(pdp_pipe["average"].shape) - 1
      )
  
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 20 for calling a global function with example usage: test_partial_dependence_feature_type(features, expected_pd_shape) without return types