code: def test_little_tree_with_small_max_samples():
      rng = np.random.RandomState(1)
  
      X = rng.randn(10000, 2)
      y = rng.randn(10000) > 0
  
      # First fit with no restriction on max samples
      est1 = BalancedRandomForestClassifier(
          n_estimators=1,
          random_state=rng,
          max_samples=None,
          sampling_strategy="all",
          replacement=True,
          bootstrap=True,
      )
  
      # Second fit with max samples restricted to just 2
      est2 = BalancedRandomForestClassifier(
          n_estimators=1,
          random_state=rng,
          max_samples=2,
          sampling_strategy="all",
          replacement=True,
          bootstrap=True,
      )
  
      est1.fit(X, y)
      est2.fit(X, y)
  
      tree1 = est1.estimators_[0].tree_
      tree2 = est2.estimators_[0].tree_
  
      msg = "Tree without `max_samples` restriction should have more nodes"
      assert tree1.node_count > tree2.node_count, msg
  
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 8 for calling a global function with example usage: test_little_tree_with_small_max_samples() without return types