code: def test_hdbscan_centroids_medoids():
      centers = [(0.0, 0.0), (3.0, 3.0)]
      H, y = make_blobs(
          n_samples=1000,
          random_state=0,
          centers=centers,
          cluster_std=0.5,
      )
      clusterer = HDBSCAN().fit(H)
  
      for idx, center in enumerate(centers):
          centroid = clusterer.weighted_cluster_centroid(idx)
          assert_array_almost_equal(centroid, center, decimal=1)
  
          medoid = clusterer.weighted_cluster_medoid(idx)
          assert_array_almost_equal(medoid, center, decimal=1)
  
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 37 for calling a global function with example usage: test_hdbscan_centroids_medoids() without return types