code: def test_gridsearch():
      # Check that bagging ensembles can be grid-searched.
      # Transform iris into a binary classification task
      X, y = iris.data, iris.target.copy()
      y[y == 2] = 1
  
      # Grid search with scoring based on decision_function
      parameters = {"n_estimators": (1, 2), "estimator__C": (1, 2)}
  
      GridSearchCV(
          BalancedBaggingClassifier(SVC(gamma="scale")),
          parameters,
          cv=3,
          scoring="roc_auc",
      ).fit(X, y)
  
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 7 for calling a global function with example usage: test_gridsearch() without return types