code: def test_constant_strategy_sparse_target(csc_container):
      X = [[0]] * 5  # ignored
      y = csc_container(
          np.array([[0, 1], [4, 0], [1, 1], [1, 4], [1, 1]])
      )
  
      n_samples = len(X)
  
      clf = DummyClassifier(
          strategy="constant", random_state=0, constant=[1, 0]
      )
      clf.fit(X, y)
      y_pred = clf.predict(X)
      assert sp.issparse(y_pred)
      assert_array_equal(
          y_pred.toarray(),
          np.hstack(
              [np.ones((n_samples, 1)), np.zeros((n_samples, 1))]
          ),
      )
  
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 33 for calling a global function with example usage: test_constant_strategy_sparse_target(csc_container) without return types