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: import os
  import torch
  import pandas
  import numpy
  from pathlib import Path
  import collections
  import math
  from io import open
  import json
  from .data_qa import BertQADataBunch
  from .learner_util import Learner
  from fastprogress.fastprogress import master_bar, progress_bar
  from tensorboardX import SummaryWriter
  from transformers.tokenization_bert import BasicTokenizer, whitespace_tokenize
  from transformers import BertConfig, BertForQuestionAnswering, XLNetConfig, XLNetForQuestionAnswering, XLMConfig, XLMForQuestionAnswering, DistilBertConfig, DistilBertForQuestionAnswering
  from .utils_squad_evaluate import EVAL_OPTS, main
label_desc: Use image node_id 4 for calling a global function with example usage: _get_best_indexes(logits, n_best_size) and returns: best_indexes