code: def test_hdbscan_empty_cluster_tree():
      raw_tree = np.recarray(
          shape=(5,),
          formats=[np.intp, np.intp, float, np.intp],
          names=("parent", "child", "lambda_val", "child_size"),
      )
  
      raw_tree["parent"] = np.asarray([5, 5, 5, 5, 5])
      raw_tree["child"] = [0, 1, 2, 3, 4]
      raw_tree["lambda_val"] = [1.0, 1.0, 1.0, 1.0, 1.0]
      raw_tree["child_size"] = [1, 1, 1, 1, 1]
  
      condensed_tree = CondensedTree(raw_tree, 0.0, True)
  
      cuml_agg = HDBSCAN(
          allow_single_cluster=True, cluster_selection_method="eom"
      )
      cuml_agg._extract_clusters(condensed_tree)
  
      # We just care that all points are assigned to the root cluster
      assert np.sum(cuml_agg.labels_test.to_output("numpy")) == 0
  
imports: from cuml.internals.safe_imports import gpu_only_import
  from sklearn.model_selection import train_test_split
  from sklearn import datasets
  from hdbscan.plots import CondensedTree
  import hdbscan
  from cuml.internals import logger
  import pytest
  from cuml.cluster.hdbscan import HDBSCAN, condense_hierarchy
  from cuml.cluster.hdbscan.prediction import all_points_membership_vectors, approximate_predict, membership_vector
  from sklearn.datasets import make_blobs
  from cuml.metrics import adjusted_rand_score
  from cuml.testing.utils import get_pattern, array_equal
  from cuml.internals.safe_imports import cpu_only_import
label_desc: Use image node_id 13 for calling a global function with example usage: test_hdbscan_empty_cluster_tree() without return types