code: def test_warm_start_equivalence():
      # warm started classifier with 5+5 estimators should be equivalent to
      # one classifier with 10 estimators
      X, y = make_hastie_10_2(n_samples=20, random_state=1)
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, random_state=43
      )
  
      clf_ws = EasyEnsembleClassifier(
          n_estimators=5, warm_start=True, random_state=3141
      )
      clf_ws.fit(X_train, y_train)
      clf_ws.set_params(n_estimators=10)
      clf_ws.fit(X_train, y_train)
      y1 = clf_ws.predict(X_test)
  
      clf = EasyEnsembleClassifier(
          n_estimators=10, warm_start=False, random_state=3141
      )
      clf.fit(X_train, y_train)
      y2 = clf.predict(X_test)
  
      assert_allclose(y1, y2)
  
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 7 for calling a global function with example usage: test_warm_start_equivalence() without return types