code: def test_bad_extract():
      # Test an extraction of eps too close to original eps
      msg = "Specify an epsilon smaller than 0.15. Got 0.3."
      centers = [[1, 1], [-1, -1], [1, -1]]
      X, labels_true = make_blobs(
          n_samples=750,
          centers=centers,
          cluster_std=0.4,
          random_state=0,
      )
  
      # Compute OPTICS
      clust = OPTICS(
          max_eps=5.0 * 0.03,
          cluster_method="dbscan",
          eps=0.3,
          min_samples=10,
      )
      with pytest.raises(ValueError, match=msg):
          clust.fit(X)
  
imports: import warnings
  import numpy
  import pytest
  from sklearn.cluster import DBSCAN, OPTICS
  from sklearn.cluster._optics import _extend_region, _extract_xi_labels
  from sklearn.cluster.tests.common import generate_clustered_data
  from sklearn.datasets import make_blobs
  from sklearn.exceptions import DataConversionWarning, EfficiencyWarning
  from sklearn.metrics.cluster import contingency_matrix
  from sklearn.metrics.pairwise import pairwise_distances
  from sklearn.utils import shuffle
  from sklearn.utils._testing import assert_allclose, assert_array_equal
  from sklearn.utils.fixes import CSR_CONTAINERS
label_desc: Use image node_id 8 for calling a global function with example usage: test_bad_extract() without return types