code: def get_raw_scores(examples, preds):
      """
      Computes the exact and f1 scores from the examples and the model predictions
      """
      exact_scores = {}
      f1_scores = {}
  
      for example in examples:
          qas_id = example.qas_id
          gold_answers = [
              answer["text"]
              for answer in example.answers
              if normalize_answer(answer["text"])
          ]
  
          if not gold_answers:
              # For unanswerable questions, only correct answer is empty string
              gold_answers = [""]
  
          if qas_id not in preds:
              print(f"Missing prediction for {qas_id}")
              continue
  
          prediction = preds[qas_id]
          exact_scores[qas_id] = max(
              compute_exact(a, prediction) for a in gold_answers
          )
          f1_scores[qas_id] = max(
              compute_f1(a, prediction) for a in gold_answers
          )
  
      return exact_scores, f1_scores
  
imports: import collections
  import json
  import math
  import re
  import string
  from ...models.bert import BasicTokenizer
  from ...utils import logging
label_desc: Use image node_id 5 for calling a global function with example usage: get_raw_scores(examples, preds) and returns: exact_scores,  f1_scores