code: def _bfs_from_cluster_tree(tree, bfs_root):
      """
      Perform a breadth first search on a tree in condensed tree format
      """
  
      result = []
      to_process = [bfs_root]
  
      while to_process:
          result.extend(to_process)
          to_process = tree["child"][
              np.in1d(tree["parent"], to_process)
          ].tolist()
  
      return result
  
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 1 for calling a global function with example usage: _bfs_from_cluster_tree(tree, bfs_root) and returns: result