code: def _get_leaves(condensed_tree):
      cluster_tree = condensed_tree[condensed_tree["child_size"] > 1]
      if cluster_tree.shape[0] == 0:
          # Return the only cluster, the root
          return [condensed_tree["parent"].min()]
  
      root = cluster_tree["parent"].min()
      return _recurse_leaf_dfs(cluster_tree, root)
  
imports: import numpy
  from scipy.cluster.hierarchy import dendrogram
  from sklearn.manifold import TSNE
  from sklearn.decomposition import PCA
  from sklearn.metrics import pairwise_distances
  from warnings import warn
  from ._hdbscan_tree import compute_stability, labelling_at_cut, recurse_leaf_dfs
label_desc: Use image node_id 3 for calling a global function with example usage: _get_leaves(condensed_tree) and returns: _recurse_leaf_dfs, list