code: def _get_best_indexes(logits, n_best_size):
      """Get the n-best logits from a list."""
      index_and_score = sorted(
          enumerate(logits), key=lambda x: x[1], reverse=True
      )
  
      best_indexes = []
      for i in range(len(index_and_score)):
          if i >= n_best_size:
              break
          best_indexes.append(index_and_score[i][0])
      return best_indexes
  
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import collections
  import json
  import math
  import os
  import random
  import mesh_tensorflow
  import mesh_tensorflow.bert.bert
  import mesh_tensorflow.bert.optimization
  import mesh_tensorflow.bert.tokenization
  import six
  from six.moves import range
  import tensorflow.compat.v1
  from tensorflow.compat.v1 import estimator
label_desc: Use image node_id 10 for calling a global function with example usage: _get_best_indexes(logits, n_best_size) and returns: best_indexes