code: def test_easy_ensemble_classifier_single_estimator():
      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
      )
  
      clf1 = EasyEnsembleClassifier(n_estimators=1, random_state=0).fit(
          X_train, y_train
      )
      clf2 = make_pipeline(
          RandomUnderSampler(random_state=0),
          AdaBoostClassifier(random_state=0),
      ).fit(X_train, y_train)
  
      assert_array_equal(clf1.predict(X_test), clf2.predict(X_test))
  
imports: import numpy
  import pytest
  import sklearn
  from sklearn.datasets import load_iris, make_hastie_10_2
  from sklearn.ensemble import AdaBoostClassifier
  from sklearn.feature_selection import SelectKBest
  from sklearn.model_selection import GridSearchCV, train_test_split
  from sklearn.utils._testing import assert_allclose, assert_array_equal
  from sklearn.utils.fixes import parse_version
  from imblearn.datasets import make_imbalance
  from imblearn.ensemble import EasyEnsembleClassifier
  from imblearn.pipeline import make_pipeline
  from imblearn.under_sampling import RandomUnderSampler
label_desc: Use image node_id 8 for calling a global function with example usage: test_easy_ensemble_classifier_single_estimator() without return types