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: Encode a python string as a list of integers.
  
  Args:
    s: a string
  Returns:
    a list of integers (not terminated by EOS)
docstr_tok: ["Encode","a","python","string","as","a","list","of","integers",".","Args",":","s",":","a","string","Returns",":","a","list","of","integers","(","not","terminated","by","EOS",")"]
code: def encode(self, s):
      """Encode a python string as a list of integers.
  
      Args:
        s: a string
      Returns:
        a list of integers (not terminated by EOS)
      """
      return self._subword_text_encoder.encode(s)
  
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 3 for calling the T2tVocabulary obj's underlying member method code with example usage: obj.encode(s) and returns: self