code: def test_error_messages_on_wrong_input():
      for score_func in score_funcs:
          expected = (
              r"Found input variables with inconsistent numbers "
              r"of samples: \[2, 3\]"
          )
          with pytest.raises(ValueError, match=expected):
              score_func([0, 1], [1, 1, 1])
  
          expected = r"labels_true must be 1D: shape is \(2"
          with pytest.raises(ValueError, match=expected):
              score_func([[0, 1], [1, 0]], [1, 1, 1])
  
          expected = r"labels_pred must be 1D: shape is \(2"
          with pytest.raises(ValueError, match=expected):
              score_func([0, 1, 0], [[1, 1], [0, 0]])
  
imports: import warnings
  import numpy
  import pytest
  from numpy.testing import assert_allclose, assert_array_almost_equal, assert_array_equal
  from sklearn.metrics.cluster import adjusted_mutual_info_score, adjusted_rand_score, completeness_score, contingency_matrix, entropy, expected_mutual_information, fowlkes_mallows_score, homogeneity_completeness_v_measure, homogeneity_score, mutual_info_score, normalized_mutual_info_score, pair_confusion_matrix, rand_score, v_measure_score
  from sklearn.metrics.cluster._supervised import _generalized_average, check_clusterings
  from sklearn.utils import assert_all_finite
  from sklearn.utils._testing import assert_almost_equal
label_desc: Use image node_id 1 for calling a global function with example usage: test_error_messages_on_wrong_input() without return types