docstr: Computes SQuAD scores (F1 and EM).
  :param list predictions:
  List of question-answers dictionaries with the following key-values:
      - 'id': id of the question-answer pair as given in the references
      - 'prediction_text': the text of the answer
  :param list references:
  List of question-answers dictionaries with the following key-values:
      - 'id': id of the question-answer pair (see above),
      - 'answers': a Dict in the SQuAD dataset format
          {
              'text': list of possible texts for the answer
              'answer_start': list of start positions for the answer
          }
  :return: dict obj to save metrics result
code: def __call__(self, predictions, references):
      r"""
      Computes SQuAD scores (F1 and EM).
      :param list predictions:
      List of question-answers dictionaries with the following key-values:
          - 'id': id of the question-answer pair as given in the references
          - 'prediction_text': the text of the answer
      :param list references:
      List of question-answers dictionaries with the following key-values:
          - 'id': id of the question-answer pair (see above),
          - 'answers': a Dict in the SQuAD dataset format
              {
                  'text': list of possible texts for the answer
                  'answer_start': list of start positions for the answer
              }
      :return: dict obj to save metrics result
      """
      return self.metric.compute(
          predictions=predictions, references=references
      )
  
file_path: textflint/textflint/input/model/metrics/metrics.py
imports: import torch
  import datasets
  from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
label_desc: Use image node_id 2 for calling the SQuADMetric obj's underlying member method code with example usage: obj.__call__(predictions, references) and returns: self