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