code: def _compute_softmax(scores):
      """Compute softmax probability over raw logits."""
      if not scores:
          return []
  
      max_score = None
      for score in scores:
          if max_score is None or score > max_score:
              max_score = score
  
      exp_scores = []
      total_sum = 0.0
      for score in scores:
          x = math.exp(score - max_score)
          exp_scores.append(x)
          total_sum += x
  
      probs = []
      for score in exp_scores:
          probs.append(score / total_sum)
      return probs
  
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 5 for calling a global function with example usage: _compute_softmax(scores) and returns: probs, list