code: def test_bagging_regressor_with_missing_inputs():
      # Check that BaggingRegressor can accept X with missing/infinite data
      X = np.array(
          [
              [1, 3, 5],
              [2, None, 6],
              [2, np.nan, 6],
              [2, np.inf, 6],
              [2, -np.inf, 6],
          ]
      )
      y_values = [
          np.array([2, 3, 3, 3, 3]),
          np.array(
              [
                  [2, 1, 9],
                  [3, 6, 8],
                  [3, 6, 8],
                  [3, 6, 8],
                  [3, 6, 8],
              ]
          ),
      ]
      for y in y_values:
          regressor = DecisionTreeRegressor()
          pipeline = make_pipeline(
              FunctionTransformer(replace), regressor
          )
          pipeline.fit(X, y).predict(X)
          bagging_regressor = BaggingRegressor(pipeline)
          y_hat = bagging_regressor.fit(X, y).predict(X)
          assert y.shape == y_hat.shape
  
          # Verify that exceptions can be raised by wrapper regressor
          regressor = DecisionTreeRegressor()
          pipeline = make_pipeline(regressor)
          with pytest.raises(ValueError):
              pipeline.fit(X, y)
          bagging_regressor = BaggingRegressor(pipeline)
          with pytest.raises(ValueError):
              bagging_regressor.fit(X, y)
  
imports: from itertools import cycle, product
  import joblib
  import numpy
  import pytest
  from sklearn.base import BaseEstimator
  from sklearn.datasets import load_diabetes, load_iris, make_hastie_10_2
  from sklearn.dummy import DummyClassifier, DummyRegressor
  from sklearn.ensemble import BaggingClassifier, BaggingRegressor, HistGradientBoostingClassifier, HistGradientBoostingRegressor
  from sklearn.feature_selection import SelectKBest
  from sklearn.linear_model import LogisticRegression, Perceptron
  from sklearn.model_selection import GridSearchCV, ParameterGrid, train_test_split
  from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor
  from sklearn.pipeline import make_pipeline
  from sklearn.preprocessing import FunctionTransformer, scale
  from sklearn.random_projection import SparseRandomProjection
  from sklearn.svm import SVC, SVR
  from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
  from sklearn.utils import check_random_state
  from sklearn.utils._testing import assert_array_almost_equal, assert_array_equal
  from sklearn.utils.fixes import CSC_CONTAINERS, CSR_CONTAINERS
label_desc: Use image node_id 30 for calling a global function with example usage: test_bagging_regressor_with_missing_inputs() without return types