code: def _compute(self, predictions, references, no_answer_threshold=1.0):
      no_answer_probabilities = {
          p["id"]: p["no_answer_probability"] for p in predictions
      }
      dataset = [{"paragraphs": [{"qas": references}]}]
      predictions = {p["id"]: p["prediction_text"] for p in predictions}
  
      qid_to_has_ans = make_qid_to_has_ans(
          dataset
      )  # maps qid to True/False
      has_ans_qids = [k for k, v in qid_to_has_ans.items() if v]
      no_ans_qids = [k for k, v in qid_to_has_ans.items() if not v]
  
      exact_raw, f1_raw = get_raw_scores(dataset, predictions)
      exact_thresh = apply_no_ans_threshold(
          exact_raw,
          no_answer_probabilities,
          qid_to_has_ans,
          no_answer_threshold,
      )
      f1_thresh = apply_no_ans_threshold(
          f1_raw,
          no_answer_probabilities,
          qid_to_has_ans,
          no_answer_threshold,
      )
      out_eval = make_eval_dict(exact_thresh, f1_thresh)
  
      if has_ans_qids:
          has_ans_eval = make_eval_dict(
              exact_thresh, f1_thresh, qid_list=has_ans_qids
          )
          merge_eval(out_eval, has_ans_eval, "HasAns")
      if no_ans_qids:
          no_ans_eval = make_eval_dict(
              exact_thresh, f1_thresh, qid_list=no_ans_qids
          )
          merge_eval(out_eval, no_ans_eval, "NoAns")
      find_all_best_thresh(
          out_eval,
          predictions,
          exact_raw,
          f1_raw,
          no_answer_probabilities,
          qid_to_has_ans,
      )
      return dict(out_eval)
  
imports: import datasets
  from .evaluate import apply_no_ans_threshold, find_all_best_thresh, get_raw_scores, make_eval_dict, make_qid_to_has_ans, merge_eval
label_desc: Use image node_id 2 for calling the SquadV2 obj's underlying member method code with example usage: obj._compute(predictions, references, no_answer_threshold) and returns: dict