code: def test_rsl_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, tree = robust_single_linkage(H, 5.5)
      n_clusters_1 = len(set(labels)) - int(-1 in labels)
      assert n_clusters_1 == n_clusters
  
      labels = (
          RobustSingleLinkage(
              cut=5.5,
              algorithm="best",
              metric="seuclidean",
              metric_params={"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 sklearn.utils.estimator_checks import check_estimator
  from sklearn.utils._testing import assert_array_equal, assert_raises
  from hdbscan import RobustSingleLinkage, robust_single_linkage
  from sklearn import datasets
  import warnings
  from sklearn.datasets import make_blobs
  from sklearn.utils import shuffle
  from sklearn.preprocessing import StandardScaler
  import pytest
label_desc: Use image node_id 9 for calling a global function with example usage: test_rsl_high_dimensional() without return types