code: def test_warm_start_smaller_n_estimators():
      # Test if warm start'ed second fit with smaller n_estimators raises error.
      X, y = make_hastie_10_2(n_samples=20, random_state=1)
      clf = BalancedBaggingClassifier(n_estimators=5, warm_start=True)
      clf.fit(X, y)
      clf.set_params(n_estimators=4)
      with pytest.raises(ValueError):
          clf.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 11 for calling a global function with example usage: test_warm_start_smaller_n_estimators() without return types