class_docstr: Wrapper around tensor2tensor SubwordTextEncoder.
  
  1 is already reserved for EOS - no need to shift.
class_docstr_tok: ["Wrapper","around","tensor2tensor","SubwordTextEncoder",".","1","is","already","reserved","for","EOS","-","no","need","to","shift","."]
docstr: Number of ids (including 0=PAD and 1=EOS).
  
  Returns:
    an integer
docstr_tok: ["Number","of","ids","(","including","0=PAD","and","1=EOS",")",".","Returns",":","an","integer"]
code: def vocab_size(self):
      """Number of ids (including 0=PAD and 1=EOS).
  
      Returns:
        an integer
      """
      return self._subword_text_encoder.vocab_size
  
file_path: mesh/mesh_tensorflow/transformer/t2t_vocabulary.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import os
  import gin
  from mesh_tensorflow.transformer import vocabulary
label_desc: Use image node_id 2 for calling the T2tVocabulary obj's underlying member method code with example usage: obj.vocab_size() and returns: self