code: def test_hdbscan_min_cluster_size():
      """
      Test that the smallest non-noise cluster has at least `min_cluster_size`
      many points
      """
      for min_cluster_size in range(2, len(X), 1):
          labels = HDBSCAN(
              min_cluster_size=min_cluster_size
          ).fit_predict(X)
          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
  import pytest
  from scipy import stats
  from scipy.spatial import distance
  from sklearn.cluster import HDBSCAN
  from sklearn.cluster._hdbscan._tree import CONDENSED_dtype, _condense_tree, _do_labelling
  from sklearn.cluster._hdbscan.hdbscan import _OUTLIER_ENCODING
  from sklearn.datasets import make_blobs
  from sklearn.metrics import fowlkes_mallows_score
  from sklearn.metrics.pairwise import _VALID_METRICS, euclidean_distances
  from sklearn.neighbors import BallTree, KDTree
  from sklearn.preprocessing import StandardScaler
  from sklearn.utils import shuffle
  from sklearn.utils._testing import assert_allclose, assert_array_equal
  from sklearn.utils.fixes import CSC_CONTAINERS, CSR_CONTAINERS
label_desc: Use image node_id 11 for calling a global function with example usage: test_hdbscan_min_cluster_size() without return types