code: def test_kind_average_and_average_of_individual(Estimator, data):
      est = Estimator()
      (X, y), n_targets = data
      est.fit(X, y)
  
      pdp_avg = partial_dependence(
          est, X=X, features=[1, 2], kind="average"
      )
      pdp_ind = partial_dependence(
          est, X=X, features=[1, 2], kind="individual"
      )
      avg_ind = np.mean(pdp_ind["individual"], axis=1)
      assert_allclose(avg_ind, pdp_avg["average"])
  
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 22 for calling a global function with example usage: test_kind_average_and_average_of_individual(Estimator, data) without return types