code: def test_missing_values_is_resilient():
      """Check that forest can deal with missing values and has decent performance."""
  
      rng = np.random.RandomState(0)
      n_samples, n_features = 1000, 10
      X, y = make_classification(
          n_samples=n_samples, n_features=n_features, random_state=rng
      )
  
      # Create dataset with missing values
      X_missing = X.copy()
      X_missing[
          rng.choice([False, True], size=X.shape, p=[0.95, 0.05])
      ] = np.nan
      assert np.isnan(X_missing).any()
  
      (
          X_missing_train,
          X_missing_test,
          y_train,
          y_test,
      ) = train_test_split(X_missing, y, random_state=0)
  
      # Train forest with missing values
      forest_with_missing = BalancedRandomForestClassifier(
          sampling_strategy="all",
          replacement=True,
          bootstrap=False,
          random_state=rng,
          n_estimators=50,
      )
      forest_with_missing.fit(X_missing_train, y_train)
      score_with_missing = forest_with_missing.score(
          X_missing_test, y_test
      )
  
      # Train forest without missing values
      X_train, X_test, y_train, y_test = train_test_split(
          X, y, random_state=0
      )
      forest = BalancedRandomForestClassifier(
          sampling_strategy="all",
          replacement=True,
          bootstrap=False,
          random_state=rng,
          n_estimators=50,
      )
      forest.fit(X_train, y_train)
      score_without_missing = forest.score(X_test, y_test)
  
      # Score is still 80 percent of the forest's score that had no missing values
      assert score_with_missing >= 0.80 * score_without_missing
  
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 14 for calling a global function with example usage: test_missing_values_is_resilient() without return types