code: def test_partial_dependence_equivalence_equal_sample_weight(
      Estimator, data
  ):
      """Check that `sample_weight=None` is equivalent to having equal weights."""
  
      est = Estimator()
      (X, y), n_targets = data
      est.fit(X, y)
  
      sample_weight, params = None, {
          "X": X,
          "features": [1, 2],
          "kind": "average",
      }
      pdp_sw_none = partial_dependence(
          est, **params, sample_weight=sample_weight
      )
      sample_weight = np.ones(len(y))
      pdp_sw_unit = partial_dependence(
          est, **params, sample_weight=sample_weight
      )
      assert_allclose(pdp_sw_none["average"], pdp_sw_unit["average"])
      sample_weight = 2 * np.ones(len(y))
      pdp_sw_doubling = partial_dependence(
          est, **params, sample_weight=sample_weight
      )
      assert_allclose(
          pdp_sw_none["average"], pdp_sw_doubling["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 25 for calling a global function with example usage: test_partial_dependence_equivalence_equal_sample_weight(Estimator, data) without return types