code: def get_raw_scores(dataset, preds):
      exact_scores = {}
      f1_scores = {}
      for article in dataset:
          for p in article["paragraphs"]:
              for qa in p["qas"]:
                  qid = qa["id"]
                  gold_answers = [
                      a["text"]
                      for a in qa["answers"]
                      if normalize_answer(a["text"])
                  ]
                  if not gold_answers:
                      # For unanswerable questions, only correct answer is empty string
                      gold_answers = [""]
                  if qid not in preds:
                      print("Missing prediction for %s" % qid)
                      continue
                  a_pred = preds[qid]
                  # Take max over all gold answers
                  exact_scores[qid] = max(
                      compute_exact(a, a_pred) for a in gold_answers
                  )
                  f1_scores[qid] = max(
                      compute_f1(a, a_pred) for a in gold_answers
                  )
      return exact_scores, f1_scores
  
imports: import argparse
  import collections
  import json
  import numpy
  import os
  import re
  import string
  import sys
label_desc: Use image node_id 8 for calling a global function with example usage: get_raw_scores(dataset, preds) and returns: exact_scores,  f1_scores