class_docstr: Tree structure representing the hierarchical clusters of BisectingKMeans.
docstr: Create a new cluster node in the tree.
  
  The node holds the center of this cluster and the indices of the data points
  that belong to it.
docstr_tok: ["Create","a","new","cluster","node","in","the","tree",".","The","node","holds","the","center","of","this","cluster","and","the","indices","of","the","data","points","that","belong","to","it","."]
code: def __init__(self, center, indices, score):
      """Create a new cluster node in the tree.
  
      The node holds the center of this cluster and the indices of the data points
      that belong to it.
      """
      self.center = center
      self.indices = indices
      self.score = score
  
      self.left = None
      self.right = 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 1 to create a new _BisectingTree object  with example: obj = _BisectingTree(center, indices, score)