class_docstr: Node that splits the data in a Hoeffding Adaptive Tree Regression.
  
  Parameters
  ----------
  stats
      Target stats.
  depth
      The depth of the node.
  drift_detector
      The detector used to monitor concept drifts.
  attributes
      Other parameters passed to the split node.
class_docstr_tok: ["Node","that","splits","the","data","in","a","Hoeffding","Adaptive","Tree","Regression",".","Parameters","--","--","--","--","--","stats","Target","stats",".","depth","The","depth","of","the","node",".","drift_detector","The","detector","used","to","monitor","concept","drifts",".","attributes","Other","parameters","passed","to","the","split","node","."]
code: def kill_tree_children(self, tree):
      for child in self.children:
          # Delete alternate tree if it exists
          if isinstance(child, DTBranch):
              if child._alternate_tree:
                  child._alternate_tree.kill_tree_children(tree)
                  tree._n_pruned_alternate_trees += 1
                  child._alternate_tree = None
  
              # Recursive delete of SplitNodes
              child.kill_tree_children(tree)  # noqa
          else:
              if child.is_active():  # noqa
                  tree._n_active_leaves -= 1
              else:
                  tree._n_inactive_leaves -= 1
  
imports: from __future__ import annotations
  import math
  from river import stats
  from river.utils.random import poisson
  from .branch import DTBranch, NominalBinaryBranch, NominalMultiwayBranch, NumericBinaryBranch, NumericMultiwayBranch
  from .htr_nodes import LeafAdaptive, LeafMean, LeafModel
  from .leaf import HTLeaf
label_desc: Use image node_id 5 for calling the AdaBranchRegressor obj's underlying member method code with example usage: obj.kill_tree_children(tree) without return types