code: def test_get_response_values_multilabel_indicator(response_method):
      X, Y = make_multilabel_classification(random_state=0)
      estimator = ClassifierChain(LogisticRegression()).fit(X, Y)
  
      y_pred, pos_label = _get_response_values(
          estimator, X, response_method=response_method
      )
      assert pos_label is None
      assert y_pred.shape == Y.shape
  
      if response_method == "predict_proba":
          assert np.logical_and(y_pred >= 0, y_pred <= 1).all()
      elif response_method == "decision_function":
          # values returned by `decision_function` are not bounded in [0, 1]
          assert (y_pred < 0).sum() > 0
          assert (y_pred > 1).sum() > 0
      else:  # response_method == "predict"
          assert np.logical_or(y_pred == 0, y_pred == 1).all()
  
imports: import numpy
  import pytest
  from sklearn.datasets import load_iris, make_classification, make_multilabel_classification, make_regression
  from sklearn.ensemble import IsolationForest
  from sklearn.linear_model import LinearRegression, LogisticRegression
  from sklearn.multioutput import ClassifierChain
  from sklearn.preprocessing import scale
  from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
  from sklearn.utils._mocking import _MockEstimatorOnOffPrediction
  from sklearn.utils._response import _get_response_values, _get_response_values_binary
  from sklearn.utils._testing import assert_allclose, assert_array_equal
label_desc: Use image node_id 13 for calling a global function with example usage: test_get_response_values_multilabel_indicator(response_method) without return types