class_docstr: A node in a shadow tree. Each node has left and right pointers to child nodes, if any.
  As part of tree construction process, the samples examined at each decision node or at each leaf node are
  saved into field node_samples.
class_docstr_tok: ["A","node","in","a","shadow","tree",".","Each","node","has","left","and","right","pointers","to","child","nodes",",","if","any",".","As","part","of","tree","construction","process",",","the","samples","examined","at","each","decision","node","or","at","each","leaf","node","are","saved","into","field","node_samples","."]
docstr: Returns leaf prediction.
  
  If the node is an internal node, returns None
docstr_tok: ["Returns","leaf","prediction",".","If","the","node","is","an","internal","node",",","returns","None"]
code: def prediction(self) -> (Number, None):
      """Returns leaf prediction.
  
      If the node is an internal node, returns None
      """
      if not self.isleaf():
          return None
      return self.shadow_tree.get_prediction(self.id)
  
imports: from abc import ABC, abstractmethod
  from numbers import Number
  from typing import List, Tuple, Mapping
  import numpy
  import pandas
  import sklearn
  from dtreeviz import utils
label_desc: Use image node_id 13 for calling the ShadowDecTreeNode obj's underlying member method code with example usage: obj.prediction() and returns: self, None