class_docstr: A vocabulary that maps tokens to ints (storing a vocabulary).
  
  Attributes:
      _token_count: A collections.Counter object holding the frequencies of tokens
          in the data used to build the Vocabulary.
      _token2id: A collections.defaultdict instance mapping token strings to
          numerical identifiers.
      _id2token: A list of token strings indexed by their numerical identifiers.
class_docstr_tok: ["A","vocabulary","that","maps","tokens","to","ints","(","storing","a","vocabulary",")",".","Attributes",":","_token_count",":","A","collections.Counter","object","holding","the","frequencies","of","tokens","in","the","data","used","to","build","the","Vocabulary",".","_token2id",":","A","collections.defaultdict","instance","mapping","token","strings","to","numerical","identifiers",".","_id2token",":","A","list","of","token","strings","indexed","by","their","numerical","identifiers","."]
docstr: Return the vocabulary as a reversed dict object.
  
  Returns:
      dict: reversed vocabulary object.
docstr_tok: ["Return","the","vocabulary","as","a","reversed","dict","object",".","Returns",":","dict",":","reversed","vocabulary","object","."]
code: def reverse_vocab(self):
      """Return the vocabulary as a reversed dict object.
  
      Returns:
          dict: reversed vocabulary object.
      """
      return self._id2token
  
imports: import math
  import os
  from collections import Counter
  import numpy
  from keras.utils import Sequence, get_file
label_desc: Use image node_id 12 for calling the Vocabulary obj's underlying member method code with example usage: obj.reverse_vocab() and returns: self