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: Update dictionary from a collection of documents. Each document is a list
  of tokens.
  
  Args:
      docs (list): documents to add.
docstr_tok: ["Update","dictionary","from","a","collection","of","documents",".","Each","document","is","a","list","of","tokens",".","Args",":","docs","(","list",")",":","documents","to","add","."]
code: def add_documents(self, docs):
      """Update dictionary from a collection of documents. Each document is a list
      of tokens.
  
      Args:
          docs (list): documents to add.
      """
      for sent in docs:
          sent = map(self.process_token, sent)
          self._token_count.update(sent)
  
imports: import math
  import os
  from collections import Counter
  import numpy
  from keras.utils import Sequence, get_file
label_desc: Use image node_id 4 for calling the Vocabulary obj's underlying member method code with example usage: obj.add_documents(docs) without return types