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.
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docstr: Iterate over leaves from the left-most one to the right-most one.
  
  Overrides the base implementation by also including alternate subtrees.
docstr_tok: ["Iterate","over","leaves","from","the","left-most","one","to","the","right-most","one",".","Overrides","the","base","implementation","by","also","including","alternate","subtrees","."]
code: def iter_leaves(self):
      """Iterate over leaves from the left-most one to the right-most one.
  
      Overrides the base implementation by also including alternate subtrees.
      """
      for child in self.children:
          yield from child.iter_leaves()
  
          if (
              isinstance(child, AdaBranchRegressor)
              and child._alternate_tree
          ):
              yield from child._alternate_tree.iter_leaves()
  
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 3 for calling the AdaBranchRegressor obj's underlying member method code with example usage: obj.iter_leaves() without return types