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: Return the number of samples associated with this node. If this is a leaf node, it indicates the samples
  used to compute the predicted value or class . If this is an internal node, it is the number of samples used
  to compute the split point.
docstr_tok: ["Return","the","number","of","samples","associated","with","this","node",".","If","this","is","a","leaf","node",",","it","indicates","the","samples","used","to","compute","the","predicted","value","or","class",".","If","this","is","an","internal","node",",","it","is","the","number","of","samples","used","to","compute","the","split","point","."]
code: def nsamples(self) -> int:
      """
      Return the number of samples associated with this node. If this is a leaf node, it indicates the samples
      used to compute the predicted value or class . If this is an internal node, it is the number of samples used
      to compute the split point.
      """
      return self.shadow_tree.get_node_nsamples(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 6 for calling the ShadowDecTreeNode obj's underlying member method code with example usage: obj.nsamples() and returns: self