code: def test_iforest_subsampled_features():
      # It tests non-regression for #5732 which failed at predict.
      rng = check_random_state(0)
      X_train, X_test, y_train, y_test = train_test_split(
          diabetes.data[:50], diabetes.target[:50], random_state=rng
      )
      clf = IsolationForest(max_features=0.8)
      clf.fit(X_train, y_train)
      clf.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 10 for calling a global function with example usage: test_iforest_subsampled_features() without return types