code: def test_bootstrap_features():
      # Test that bootstrapping features may generate duplicate features.
      X, y = make_imbalance(
          iris.data,
          iris.target,
          sampling_strategy={0: 20, 1: 25, 2: 50},
          random_state=0,
      )
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, random_state=0
      )
  
      ensemble = BalancedBaggingClassifier(
          estimator=DecisionTreeClassifier(),
          max_features=1.0,
          bootstrap_features=False,
          random_state=0,
      ).fit(X_train, y_train)
  
      for features in ensemble.estimators_features_:
          assert np.unique(features).shape[0] == X.shape[1]
  
      ensemble = BalancedBaggingClassifier(
          estimator=DecisionTreeClassifier(),
          max_features=1.0,
          bootstrap_features=True,
          random_state=0,
      ).fit(X_train, y_train)
  
      unique_features = [
          np.unique(features).shape[0]
          for features in ensemble.estimators_features_
      ]
      assert np.median(unique_features) < X.shape[1]
  
imports: from collections import Counter
  import numpy
  import pytest
  import sklearn
  from sklearn.cluster import KMeans
  from sklearn.datasets import load_iris, make_classification, make_hastie_10_2
  from sklearn.dummy import DummyClassifier
  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
  from sklearn.svm import SVC
  from sklearn.tree import DecisionTreeClassifier
  from sklearn.utils._testing import assert_allclose, assert_array_almost_equal, assert_array_equal
  from sklearn.utils.fixes import parse_version
  from imblearn import FunctionSampler
  from imblearn.datasets import make_imbalance
  from imblearn.ensemble import BalancedBaggingClassifier
  from imblearn.over_sampling import SMOTE, RandomOverSampler
  from imblearn.pipeline import make_pipeline
  from imblearn.under_sampling import ClusterCentroids, RandomUnderSampler
label_desc: Use image node_id 3 for calling a global function with example usage: test_bootstrap_features() without return types