code: def test_constant_strategy_multioutput():
      X = [[0], [0], [0], [0]]  # ignored
      y = np.array([[2, 3], [1, 3], [2, 3], [2, 0]])
  
      n_samples = len(X)
  
      clf = DummyClassifier(
          strategy="constant", random_state=0, constant=[1, 0]
      )
      clf.fit(X, y)
      assert_array_equal(
          clf.predict(X),
          np.hstack(
              [np.ones((n_samples, 1)), np.zeros((n_samples, 1))]
          ),
      )
      _check_predict_proba(clf, X, y)
  
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 30 for calling a global function with example usage: test_constant_strategy_multioutput() without return types