code: def test_correct_labelsize():
      # Assert 1 < n_labels < n_samples
      dataset = datasets.load_iris()
      X = dataset.data
  
      # n_labels = n_samples
      y = np.arange(X.shape[0])
      err_msg = (
          r"Number of labels is %d\. Valid values are 2 "
          r"to n_samples - 1 \(inclusive\)" % len(np.unique(y))
      )
      with pytest.raises(ValueError, match=err_msg):
          silhouette_score(X, y)
  
      # n_labels = 1
      y = np.zeros(X.shape[0])
      err_msg = (
          r"Number of labels is %d\. Valid values are 2 "
          r"to n_samples - 1 \(inclusive\)" % len(np.unique(y))
      )
      with pytest.raises(ValueError, match=err_msg):
          silhouette_score(X, y)
  
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 4 for calling a global function with example usage: test_correct_labelsize() without return types