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","."]
docstr: Returns the entry at index of the log_softmax along the vocab dim.
docstr_tok: ["Returns","the","entry","at","index","of","the","log_softmax","along","the","vocab","dim","."]
code: def get_log_softmax_value(self, log_softmax, index):
      """Returns the entry at index of the log_softmax along the vocab dim."""
      return mtf.gather(log_softmax, index, dim=self._vocab_dim)
  
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 11 for calling the _Cluster obj's underlying member method code with example usage: obj.get_log_softmax_value(log_softmax, index) and returns: mtf