class_docstr: Learning node  for the Hoeffding Anytime Tree that uses Naive Bayes
  models.
  
  Parameters
  ----------
  stats
      Initial class observations
  depth
      The depth of the node.
  splitter
      The numeric attribute observer algorithm used to monitor target statistics
      and perform split attempts.
  kwargs
      Other parameters passed to the learning nodes.
class_docstr_tok: ["Learning","node","for","the","Hoeffding","Anytime","Tree","that","uses","Naive","Bayes","models",".","Parameters","--","--","--","--","--","stats","Initial","class","observations","depth","The","depth","of","the","node",".","splitter","The","numeric","attribute","observer","algorithm","used","to","monitor","target","statistics","and","perform","split","attempts",".","kwargs","Other","parameters","passed","to","the","learning","nodes","."]
code: def __init__(self, stats, depth, splitter, **kwargs):
      super().__init__(stats, depth, splitter, **kwargs)
  
imports: from __future__ import annotations
  import copy
  import numbers
  from river.tree.utils import BranchFactory
  from river.utils.norm import normalize_values_in_dict
  from ..splitter.nominal_splitter_classif import NominalSplitterClassif
  from .branch import DTBranch, NominalBinaryBranch, NominalMultiwayBranch, NumericBinaryBranch, NumericMultiwayBranch
  from .htc_nodes import LeafMajorityClass, LeafNaiveBayes, LeafNaiveBayesAdaptive
  from .leaf import HTLeaf
label_desc: Use image node_id 1 to create a new EFDTLeafNaiveBayes object from inherited base classes: BaseEFDTLeaf, LeafNaiveBayes with example: obj = EFDTLeafNaiveBayes(stats, depth, splitter)