code: def test_min_cluster_size(min_cluster_size, global_dtype):
      redX = X[::2].astype(global_dtype, copy=False)  # reduce for speed
      clust = OPTICS(
          min_samples=9, min_cluster_size=min_cluster_size
      ).fit(redX)
      cluster_sizes = np.bincount(clust.labels_[clust.labels_ != -1])
      if cluster_sizes.size:
          assert min(cluster_sizes) >= min_cluster_size
      # check behaviour is the same when min_cluster_size is a fraction
      clust_frac = OPTICS(
          min_samples=9,
          min_cluster_size=min_cluster_size / redX.shape[0],
      )
      clust_frac.fit(redX)
      assert_array_equal(clust.labels_, clust_frac.labels_)
  
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 16 for calling a global function with example usage: test_min_cluster_size(min_cluster_size, global_dtype) without return types