code: def test_balanced_random_forest(imbalanced_dataset):
      n_estimators = 10
      brf = BalancedRandomForestClassifier(
          n_estimators=n_estimators,
          random_state=0,
          sampling_strategy="all",
          replacement=True,
          bootstrap=False,
      )
      brf.fit(*imbalanced_dataset)
  
      assert len(brf.samplers_) == n_estimators
      assert len(brf.estimators_) == n_estimators
      assert len(brf.pipelines_) == n_estimators
      assert (
          len(brf.feature_importances_)
          == imbalanced_dataset[0].shape[1]
      )
  
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 3 for calling a global function with example usage: test_balanced_random_forest(imbalanced_dataset) without return types