code: def test_iforest(global_random_seed):
      """Check Isolation Forest for various parameter settings."""
      X_train = np.array([[0, 1], [1, 2]])
      X_test = np.array([[2, 1], [1, 1]])
  
      grid = ParameterGrid(
          {
              "n_estimators": [3],
              "max_samples": [0.5, 1.0, 3],
              "bootstrap": [True, False],
          }
      )
  
      with ignore_warnings():
          for params in grid:
              IsolationForest(
                  random_state=global_random_seed, **params
              ).fit(X_train).predict(X_test)
  
imports: import warnings
  from unittest.mock import Mock, patch
  import numpy
  import pytest
  from sklearn.datasets import load_diabetes, load_iris, make_classification
  from sklearn.ensemble import IsolationForest
  from sklearn.ensemble._iforest import _average_path_length
  from sklearn.metrics import roc_auc_score
  from sklearn.model_selection import ParameterGrid, train_test_split
  from sklearn.utils import check_random_state
  from sklearn.utils._testing import assert_allclose, assert_array_almost_equal, assert_array_equal, ignore_warnings
  from sklearn.utils.fixes import CSC_CONTAINERS, CSR_CONTAINERS
label_desc: Use image node_id 1 for calling a global function with example usage: test_iforest(global_random_seed) without return types