class_docstr: Embedding that uses Mixtape in computing logits.
  
  Expressing the token distributions in this way improves expressiveness and
  enables the matrix of token probabilities given all contexts to be high rank.
  
  Mixtape has the advantage of added efficiency over other methods such as
  mixture of softmax.
  
  The vocab embedding is the same as the default, which just a simple embedding.
  
  See
  https://papers.nips.cc/paper/9723-mixtape-breaking-the-softmax-bottleneck-efficiently.pdf
  for more details.
docstr: Reorder along the vocab dim so the last few tokens don't share gates.
docstr_tok: ["Reorder","along","the","vocab","dim","so","the","last","few","tokens","do","n't","share","gates","."]
code: def _rearrange_sentinels(self, logits):
      """Reorder along the vocab dim so the last few tokens don't share gates."""
      if not self._extra_ids:
          return logits
      sentinels, nonsentinels = mtf.split(
          logits,
          self._vocab_dim,
          [self._extra_ids, self._vocab_dim.size - self._extra_ids],
      )
      return mtf.concat([nonsentinels, sentinels], self._vocab_dim.name)
  
imports: import gin
  import mesh_tensorflow
  from mesh_tensorflow.transformer import transformer
  import tensorflow.compat.v1
label_desc: Use image node_id 5 for calling the Mixtape obj's underlying member method code with example usage: obj._rearrange_sentinels(logits) and returns: mtf, logits