class_docstr: Each node in a CFTree is called a CFNode.
  
  The CFNode can have a maximum of branching_factor
  number of CFSubclusters.
  
  Parameters
  ----------
  threshold : float
      Threshold needed for a new subcluster to enter a CFSubcluster.
  
  branching_factor : int
      Maximum number of CF subclusters in each node.
  
  is_leaf : bool
      We need to know if the CFNode is a leaf or not, in order to
      retrieve the final subclusters.
  
  n_features : int
      The number of features.
  
  Attributes
  ----------
  subclusters_ : list
      List of subclusters for a particular CFNode.
  
  prev_leaf_ : _CFNode
      Useful only if is_leaf is True.
  
  next_leaf_ : _CFNode
      next_leaf. Useful only if is_leaf is True.
      the final subclusters.
  
  init_centroids_ : ndarray of shape (branching_factor + 1, n_features)
      Manipulate ``init_centroids_`` throughout rather than centroids_ since
      the centroids are just a view of the ``init_centroids_`` .
  
  init_sq_norm_ : ndarray of shape (branching_factor + 1,)
      manipulate init_sq_norm_ throughout. similar to ``init_centroids_``.
  
  centroids_ : ndarray of shape (branching_factor + 1, n_features)
      View of ``init_centroids_``.
  
  squared_norm_ : ndarray of shape (branching_factor + 1,)
      View of ``init_sq_norm_``.
docstr: Insert a new subcluster into the node.
docstr_tok: ["Insert","a","new","subcluster","into","the","node","."]
code: def insert_cf_subcluster(self, subcluster):
      """Insert a new subcluster into the node."""
      if not self.subclusters_:
          self.append_subcluster(subcluster)
          return False
  
      threshold = self.threshold
      branching_factor = self.branching_factor
      # We need to find the closest subcluster among all the
      # subclusters so that we can insert our new subcluster.
      dist_matrix = np.dot(self.centroids_, subcluster.centroid_)
      dist_matrix *= -2.0
      dist_matrix += self.squared_norm_
      closest_index = np.argmin(dist_matrix)
      closest_subcluster = self.subclusters_[closest_index]
  
      # If the subcluster has a child, we need a recursive strategy.
      if closest_subcluster.child_ is not None:
          split_child = closest_subcluster.child_.insert_cf_subcluster(
              subcluster
          )
  
          if not split_child:
              # If it is determined that the child need not be split, we
              # can just update the closest_subcluster
              closest_subcluster.update(subcluster)
              self.init_centroids_[closest_index] = self.subclusters_[
                  closest_index
              ].centroid_
              self.init_sq_norm_[closest_index] = self.subclusters_[
                  closest_index
              ].sq_norm_
              return False
  
          # things not too good. we need to redistribute the subclusters in
          # our child node, and add a new subcluster in the parent
          # subcluster to accommodate the new child.
          else:
              new_subcluster1, new_subcluster2 = _split_node(
                  closest_subcluster.child_,
                  threshold,
                  branching_factor,
              )
              self.update_split_subclusters(
                  closest_subcluster, new_subcluster1, new_subcluster2
              )
  
              if len(self.subclusters_) > self.branching_factor:
                  return True
              return False
  
      # good to go!
      else:
          merged = closest_subcluster.merge_subcluster(
              subcluster, self.threshold
          )
          if merged:
              self.init_centroids_[
                  closest_index
              ] = closest_subcluster.centroid_
              self.init_sq_norm_[
                  closest_index
              ] = closest_subcluster.sq_norm_
              return False
  
          # not close to any other subclusters, and we still
          # have space, so add.
          elif len(self.subclusters_) < self.branching_factor:
              self.append_subcluster(subcluster)
              return False
  
          # We do not have enough space nor is it closer to an
          # other subcluster. We need to split.
          else:
              self.append_subcluster(subcluster)
              return True
  
imports: import warnings
  from math import sqrt
  from numbers import Integral, Real
  import numpy
  from scipy import sparse
  from .._config import config_context
  from ..base import BaseEstimator, ClassNamePrefixFeaturesOutMixin, ClusterMixin, TransformerMixin, _fit_context
  from ..exceptions import ConvergenceWarning
  from ..metrics import pairwise_distances_argmin
  from ..metrics.pairwise import euclidean_distances
  from ..utils._param_validation import Interval
  from ..utils.extmath import row_norms
  from ..utils.validation import check_is_fitted
  from .None import AgglomerativeClustering
label_desc: Use image node_id 4 for calling the _CFNode obj's underlying member method code with example usage: obj.insert_cf_subcluster(subcluster) and returns: False, False, False, False, True, False, True