class_docstr: Tree structure representing the hierarchical clusters of BisectingKMeans.
docstr: Split the cluster node into two subclusters.
docstr_tok: ["Split","the","cluster","node","into","two","subclusters","."]
code: def split(self, labels, centers, scores):
      """Split the cluster node into two subclusters."""
      self.left = _BisectingTree(
          indices=self.indices[labels == 0],
          center=centers[0],
          score=scores[0],
      )
      self.right = _BisectingTree(
          indices=self.indices[labels == 1],
          center=centers[1],
          score=scores[1],
      )
  
      # reset the indices attribute to save memory
      self.indices = None
  
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 2 for calling the _BisectingTree obj's underlying member method code with example usage: obj.split(labels, centers, scores) without return types