code: def test_partial_dependence_non_null_weight_idx(
      estimator, non_null_weight_idx
  ):
      """Check that if we pass a `sample_weight` of zeros with only one index with
      sample weight equals one, then the average `partial_dependence` with this
      `sample_weight` is equal to the individual `partial_dependence` of the
      corresponding index.
      """
      X, y = iris.data, iris.target
      preprocessor = make_column_transformer(
          (StandardScaler(), [0, 2]), (RobustScaler(), [1, 3])
      )
      pipe = make_pipeline(preprocessor, estimator).fit(X, y)
  
      sample_weight = np.zeros_like(y)
      sample_weight[non_null_weight_idx] = 1
      pdp_sw = partial_dependence(
          pipe,
          X,
          [2, 3],
          kind="average",
          sample_weight=sample_weight,
          grid_resolution=10,
      )
      pdp_ind = partial_dependence(
          pipe, X, [2, 3], kind="individual", grid_resolution=10
      )
      output_dim = 1 if is_regressor(pipe) else len(np.unique(y))
      for i in range(output_dim):
          assert_allclose(
              pdp_ind["individual"][i][non_null_weight_idx],
              pdp_sw["average"][i],
          )
  
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 24 for calling a global function with example usage: test_partial_dependence_non_null_weight_idx(estimator, non_null_weight_idx) without return types