code: def test_warm_start(random_state=42):
      # Test if fitting incrementally with warm start gives a forest of the
      # right size and the same results as a normal fit.
      X, y = make_hastie_10_2(n_samples=20, random_state=1)
  
      clf_ws = None
      for n_estimators in [5, 10]:
          if clf_ws is None:
              clf_ws = BalancedBaggingClassifier(
                  n_estimators=n_estimators,
                  random_state=random_state,
                  warm_start=True,
              )
          else:
              clf_ws.set_params(n_estimators=n_estimators)
          clf_ws.fit(X, y)
          assert len(clf_ws) == n_estimators
  
      clf_no_ws = BalancedBaggingClassifier(
          n_estimators=10, random_state=random_state, warm_start=False
      )
      clf_no_ws.fit(X, y)
  
      assert {pipe.steps[-1][1].random_state for pipe in clf_ws} == {
          pipe.steps[-1][1].random_state for pipe in clf_no_ws
      }
  
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 10 for calling a global function with example usage: test_warm_start(random_state) without return types