code: def test_cluster_size_1():
      # Assert Silhouette Coefficient == 0 when there is 1 sample in a cluster
      # (cluster 0). We also test the case where there are identical samples
      # as the only members of a cluster (cluster 2). To our knowledge, this case
      # is not discussed in reference material, and we choose for it a sample
      # score of 1.
      X = [[0.0], [1.0], [1.0], [2.0], [3.0], [3.0]]
      labels = np.array([0, 1, 1, 1, 2, 2])
  
      # Cluster 0: 1 sample -> score of 0 by Rousseeuw's convention
      # Cluster 1: intra-cluster = [.5, .5, 1]
      #            inter-cluster = [1, 1, 1]
      #            silhouette    = [.5, .5, 0]
      # Cluster 2: intra-cluster = [0, 0]
      #            inter-cluster = [arbitrary, arbitrary]
      #            silhouette    = [1., 1.]
  
      silhouette = silhouette_score(X, labels)
      assert not np.isnan(silhouette)
      ss = silhouette_samples(X, labels)
      assert_array_equal(ss, [0, 0.5, 0.5, 0, 1, 1])
  
imports: import warnings
  import numpy
  import pytest
  from numpy.testing import assert_allclose
  from scipy.sparse import issparse
  from sklearn import datasets
  from sklearn.metrics import pairwise_distances
  from sklearn.metrics.cluster import calinski_harabasz_score, davies_bouldin_score, silhouette_samples, silhouette_score
  from sklearn.metrics.cluster._unsupervised import _silhouette_reduce
  from sklearn.utils._testing import assert_array_equal
  from sklearn.utils.fixes import CSC_CONTAINERS, CSR_CONTAINERS, DOK_CONTAINERS, LIL_CONTAINERS
label_desc: Use image node_id 2 for calling a global function with example usage: test_cluster_size_1() without return types