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","."]
code: def __str__(self):
      if self.left is None and self.right is None:
          return "<pred={value},n={n}>".format(
              value=round(self.prediction(), 1), n=self.nsamples()
          )
      else:
          return "({f}@{s} {left} {right})".format(
              f=self.feature_name(),
              s=round(self.split(), 1),
              left=self.left if self.left is not None else "",
              right=self.right if self.right is not None else "",
          )
  
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 16 for calling the ShadowDecTreeNode obj's underlying member method code with example usage: obj.__str__() and returns: str, str