code: def test_pair_confusion_matrix_single_cluster():
      # edge case: only one cluster
      N = 100
      clustering1 = np.zeros((N,))
      clustering2 = clustering1
      expected = np.array([[0, 0], [0, N * (N - 1)]])
      assert_array_equal(
          pair_confusion_matrix(clustering1, clustering2), expected
      )
  
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 24 for calling a global function with example usage: test_pair_confusion_matrix_single_cluster() without return types