code: def test_bagging_classifier_with_missing_inputs():
      # Check that BaggingClassifier 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 = np.array([3, 6, 6, 6, 6])
      classifier = DecisionTreeClassifier()
      pipeline = make_pipeline(FunctionTransformer(replace), classifier)
      pipeline.fit(X, y).predict(X)
      bagging_classifier = BaggingClassifier(pipeline)
      bagging_classifier.fit(X, y)
      y_hat = bagging_classifier.predict(X)
      assert y.shape == y_hat.shape
      bagging_classifier.predict_log_proba(X)
      bagging_classifier.predict_proba(X)
  
      # Verify that exceptions can be raised by wrapper classifier
      classifier = DecisionTreeClassifier()
      pipeline = make_pipeline(classifier)
      with pytest.raises(ValueError):
          pipeline.fit(X, y)
      bagging_classifier = BaggingClassifier(pipeline)
      with pytest.raises(ValueError):
          bagging_classifier.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 31 for calling a global function with example usage: test_bagging_classifier_with_missing_inputs() without return types