class_docstr: Use random functions while keeping track of the random state to make it
  reproducible and deterministic.
  taken from the torchtext Library
class_docstr_tok: ["Use","random","functions","while","keeping","track","of","the","random","state","to","make","it","reproducible","and","deterministic",".","taken","from","the","torchtext","Library"]
code: def random_state(self):
      return deepcopy(self._random_state)
  
imports: import torch
  import random
  from contextlib import contextmanager
  import inspect
  import numpy
  import os
  from copy import deepcopy
label_desc: Use image node_id 3 for calling the RandomShuffler obj's underlying member method code with example usage: obj.random_state() and returns: deepcopy