code: def test_balanced_random_forest_error_warning_warm_start(
      imbalanced_dataset,
  ):
      brf = BalancedRandomForestClassifier(
          n_estimators=5,
          sampling_strategy="all",
          replacement=True,
          bootstrap=False,
      )
      brf.fit(*imbalanced_dataset)
  
      with pytest.raises(
          ValueError, match="must be larger or equal to"
      ):
          brf.set_params(warm_start=True, n_estimators=2)
          brf.fit(*imbalanced_dataset)
  
      brf.set_params(n_estimators=10)
      brf.fit(*imbalanced_dataset)
  
      with pytest.warns(
          UserWarning, match="Warm-start fitting without"
      ):
          brf.fit(*imbalanced_dataset)
  
imports: import numpy
  import pytest
  import sklearn
  from sklearn.datasets import load_iris, make_classification
  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.ensemble import BalancedRandomForestClassifier
label_desc: Use image node_id 2 for calling a global function with example usage: test_balanced_random_forest_error_warning_warm_start(imbalanced_dataset) without return types