class_docstr: Wrapper for tensorflow_datasets encoders.
  
  In the TFDS encoders, ID=0 is reserved for padding.
  We want to also reserve ID=1 for EOS, so we shift all IDs up by 1.
class_docstr_tok: ["Wrapper","for","tensorflow_datasets","encoders",".","In","the","TFDS","encoders",",","ID=0","is","reserved","for","padding",".","We","want","to","also","reserve","ID=1","for","EOS",",","so","we","shift","all","IDs","up","by","1","."]
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._tfds_encoder.decode([i - 1 for i in ids])
  
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 4 for calling the TFDSVocabulary obj's underlying member method code with example usage: obj.decode(ids) and returns: self