code: def test_constant_strategy_multioutput_regressor(global_random_seed):
      random_state = np.random.RandomState(seed=global_random_seed)
  
      X_learn = random_state.randn(10, 10)
      y_learn = random_state.randn(10, 5)
  
      # test with 2d array
      constants = random_state.randn(5)
  
      X_test = random_state.randn(20, 10)
      y_test = random_state.randn(20, 5)
  
      # Correctness oracle
      est = DummyRegressor(strategy="constant", constant=constants)
      est.fit(X_learn, y_learn)
      y_pred_learn = est.predict(X_learn)
      y_pred_test = est.predict(X_test)
  
      _check_equality_regressor(
          constants, y_learn, y_pred_learn, y_test, y_pred_test
      )
      _check_behavior_2d_for_constant(est)
  
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 25 for calling a global function with example usage: test_constant_strategy_multioutput_regressor(global_random_seed) without return types