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: 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._tfds_encoder.vocab_size + 1
  
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 2 for calling the TFDSVocabulary obj's underlying member method code with example usage: obj.vocab_size() and returns: unknown