code: def test_hdbscan_high_dimensional():
      H, y = make_blobs(n_samples=50, random_state=0, n_features=64)
      # H, y = shuffle(X, y, random_state=7)
      H = StandardScaler().fit_transform(H)
      labels, p, persist, ctree, ltree, mtree = hdbscan(H)
      n_clusters_1 = len(set(labels)) - int(-1 in labels)
      assert n_clusters_1 == n_clusters
  
      labels = (
          HDBSCAN(
              algorithm="best",
              metric="seuclidean",
              V=np.ones(H.shape[1]),
          )
          .fit(H)
          .labels_
      )
      n_clusters_2 = len(set(labels)) - int(-1 in labels)
      assert n_clusters_2 == n_clusters
  
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 16 for calling a global function with example usage: test_hdbscan_high_dimensional() without return types