class_docstr: Tree structure representing the hierarchical clusters of BisectingKMeans.
docstr: Return the cluster node to bisect next.
  
  It's based on the score of the cluster, which can be either the number of
  data points assigned to that cluster or the inertia of that cluster
  (see `bisecting_strategy` for details).
docstr_tok: ["Return","the","cluster","node","to","bisect","next",".","It","'s","based","on","the","score","of","the","cluster",",","which","can","be","either","the","number","of","data","points","assigned","to","that","cluster","or","the","inertia","of","that","cluster","(","see","`","bisecting_strategy","`","for","details",")","."]
code: def get_cluster_to_bisect(self):
      """Return the cluster node to bisect next.
  
      It's based on the score of the cluster, which can be either the number of
      data points assigned to that cluster or the inertia of that cluster
      (see `bisecting_strategy` for details).
      """
      max_score = None
  
      for cluster_leaf in self.iter_leaves():
          if max_score is None or cluster_leaf.score > max_score:
              max_score = cluster_leaf.score
              best_cluster_leaf = cluster_leaf
  
      return best_cluster_leaf
  
imports: import warnings
  import numpy
  import scipy.sparse
  from ..base import _fit_context
  from ..utils._openmp_helpers import _openmp_effective_n_threads
  from ..utils._param_validation import StrOptions
  from ..utils.extmath import row_norms
  from ..utils.validation import _check_sample_weight, check_is_fitted, check_random_state
  from ._k_means_common import _inertia_dense, _inertia_sparse
  from ._kmeans import _BaseKMeans, _kmeans_single_elkan, _kmeans_single_lloyd, _labels_inertia_threadpool_limit
label_desc: Use image node_id 3 for calling the _BisectingTree obj's underlying member method code with example usage: obj.get_cluster_to_bisect() and returns: best_cluster_leaf