code: def test_warm_start_equal_n_estimators():
      # Test that nothing happens when fitting without increasing n_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 = EasyEnsembleClassifier(
          n_estimators=5, warm_start=True, random_state=83
      )
      clf.fit(X_train, y_train)
  
      y_pred = clf.predict(X_test)
      # modify X to nonsense values, this should not change anything
      X_train += 1.0
  
      warn_msg = "Warm-start fitting without increasing n_estimators"
      with pytest.warns(UserWarning, match=warn_msg):
          clf.fit(X_train, y_train)
      assert_array_equal(y_pred, clf.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 6 for calling a global function with example usage: test_warm_start_equal_n_estimators() without return types