code: def test_hdbscan_min_cluster_size():
      for min_cluster_size in range(2, len(X) + 1, 1):
          labels, p, persist, ctree, ltree, mtree = hdbscan(
              X, min_cluster_size=min_cluster_size
          )
          true_labels = [label for label in labels if label != -1]
          if len(true_labels) != 0:
              assert (
                  np.min(np.bincount(true_labels)) >= min_cluster_size
              )
  
          labels = (
              HDBSCAN(min_cluster_size=min_cluster_size).fit(X).labels_
          )
          true_labels = [label for label in labels if label != -1]
          if len(true_labels) != 0:
              assert (
                  np.min(np.bincount(true_labels)) >= min_cluster_size
              )
  
imports: import numpy
  from scipy.spatial import distance
  from scipy import sparse
  from scipy import stats
  from sklearn.utils.estimator_checks import check_estimator
  from sklearn.utils._testing import assert_array_equal, assert_array_almost_equal, assert_raises
  from hdbscan import HDBSCAN, hdbscan, validity_index, approximate_predict, approximate_predict_scores, membership_vector, all_points_membership_vectors
  from sklearn.datasets import make_blobs
  from sklearn.utils import shuffle
  from sklearn.preprocessing import StandardScaler
  from scipy.stats import mode
  from tempfile import mkdtemp
  from functools import wraps
  import pytest
  from sklearn import datasets
  import warnings
label_desc: Use image node_id 19 for calling a global function with example usage: test_hdbscan_min_cluster_size() without return types