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
      y[y == 2] = 1
  
      # Grid search with scoring based on decision_function
      parameters = {"n_estimators": (1, 2), "estimator__C": (1, 2)}
  
      GridSearchCV(
          BaggingClassifier(SVC()), parameters, scoring="roc_auc"
      ).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 14 for calling a global function with example usage: test_gridsearch() without return types