class_docstr: Helper class for adaptive embeddings specifying a cluster of tokens.
  
  Essentially a wrapper around a vocab embedding for the cluster with additional
  metadata so that we can apply the embedding to the actual ids and hidden
  states.
class_docstr_tok: ["Helper","class","for","adaptive","embeddings","specifying","a","cluster","of","tokens",".","Essentially","a","wrapper","around","a","vocab","embedding","for","the","cluster","with","additional","metadata","so","that","we","can","apply","the","embedding","to","the","actual","ids","and","hidden","states","."]
code: def end_token_id(self):
      return self._end_token_id
  
imports: import math
  from typing import Dict, Sequence, Union
  import gin
  import mesh_tensorflow
  from mesh_tensorflow.transformer import transformer
  from mesh_tensorflow.transformer import vocab_embeddings
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 2 for calling the _Cluster obj's underlying member method code with example usage: obj.end_token_id() and returns: self