code: def test_mutual_info_score_positive_constant_label(
      labels_true, labels_pred
  ):
      # Check that MI = 0 when one or both labelling are constant
      # non-regression test for #16355
      assert mutual_info_score(labels_true, labels_pred) == 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 21 for calling a global function with example usage: test_mutual_info_score_positive_constant_label(labels_true, labels_pred) without return types