class_docstr: Abstract class for encoding strings as lists of integers.
  
  We will subclass this and wrap multiple implementations of text encoders.
  We follow the convention that ids 0=PAD and 1=EOS are reserved.
class_docstr_tok: ["Abstract","class","for","encoding","strings","as","lists","of","integers",".","We","will","subclass","this","and","wrap","multiple","implementations","of","text","encoders",".","We","follow","the","convention","that","ids","0=PAD","and","1=EOS","are","reserved","."]
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
      """
      raise NotImplementedError("Not implemented.")
  
file_path: mesh/mesh_tensorflow/transformer/vocabulary.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import gin
  import tensorflow_datasets
label_desc: Use image node_id 3 for calling the Vocabulary obj's underlying member method code with example usage: obj.decode(ids) without return types