code: def test_dummy_regressor_return_std():
      X = [[0]] * 3  # ignored
      y = np.array([2, 2, 2])
      y_std_expected = np.array([0, 0, 0])
      cls = DummyRegressor()
      cls.fit(X, y)
      y_pred_list = cls.predict(X, return_std=True)
      # there should be two elements when return_std is True
      assert len(y_pred_list) == 2
      # the second element should be all zeros
      assert_array_equal(y_pred_list[1], y_std_expected)
  
imports: import numpy
  import pytest
  import scipy.sparse
  from sklearn.base import clone
  from sklearn.dummy import DummyClassifier, DummyRegressor
  from sklearn.exceptions import NotFittedError
  from sklearn.utils._testing import assert_almost_equal, assert_array_almost_equal, assert_array_equal, ignore_warnings
  from sklearn.utils.fixes import CSC_CONTAINERS
  from sklearn.utils.stats import _weighted_percentile
label_desc: Use image node_id 40 for calling a global function with example usage: test_dummy_regressor_return_std() without return types