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 in TensorFlow.
  
  I don't know when we will use this, but it seems logical to
  have if we can.
  
  Args:
    ids: a 1d tf.Tensor with dtype tf.int32
  Returns:
    a tf Scalar with dtype tf.string
docstr_tok: ["Decode","in","TensorFlow",".","I","do","n't","know","when","we","will","use","this",",","but","it","seems","logical","to","have","if","we","can",".","Args",":","ids",":","a","1d","tf.Tensor","with","dtype","tf.int32","Returns",":","a","tf","Scalar","with","dtype","tf.string"]
code: def decode_tf(self, ids):
      """Decode in TensorFlow.
  
      I don't know when we will use this, but it seems logical to
      have if we can.
  
      Args:
        ids: a 1d tf.Tensor with dtype tf.int32
      Returns:
        a tf Scalar with dtype tf.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 5 for calling the Vocabulary obj's underlying member method code with example usage: obj.decode_tf(ids) without return types