code: def test_most_frequent_and_prior_strategy_multioutput():
      X = [[0], [0], [0], [0]]  # ignored
      y = np.array([[1, 0], [2, 0], [1, 0], [1, 3]])
  
      n_samples = len(X)
  
      for strategy in ("prior", "most_frequent"):
          clf = DummyClassifier(strategy=strategy, random_state=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)
          _check_behavior_2d(clf)
  
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 7 for calling a global function with example usage: test_most_frequent_and_prior_strategy_multioutput() without return types