code: def calculate_metric(self):
      all_rows = zip(
          self.all_context[self.ROW_INDEX],
          self.all_context[self.ANSWERS_COLUMN],
          self.all_context[self.QUES_COLUMN],
          self.all_context[self.DOC_COLUMN],
          self.all_pred_answers,
          self.all_start_pos_preds,
          self.all_end_pos_preds,
          self.all_has_answer_preds,
          self.all_start_pos_targets,
          self.all_end_pos_targets,
          self.all_has_answer_targets,
          self.all_start_pos_scores,
          self.all_end_pos_scores,
          self.all_has_answer_scores,
      )
  
      all_rows_dict = {}
      for row in all_rows:
          try:
              all_rows_dict[row[0]].append(row)
          except KeyError:
              all_rows_dict[row[0]] = [row]
  
      all_rows = []
      for rows in all_rows_dict.values():
          argmax = np.argmax([row[11] + row[12] for row in rows])
          all_rows.append(rows[argmax])
  
      sorted(all_rows, key=lambda x: int(x[0]))
  
      (
          self.all_context[self.ROW_INDEX],
          self.all_context[self.ANSWERS_COLUMN],
          self.all_context[self.QUES_COLUMN],
          self.all_context[self.DOC_COLUMN],
          self.all_pred_answers,
          self.all_start_pos_preds,
          self.all_end_pos_preds,
          self.all_has_answer_preds,
          self.all_start_pos_targets,
          self.all_end_pos_targets,
          self.all_has_answer_targets,
          self.all_start_pos_scores,
          self.all_end_pos_scores,
          self.all_has_answer_scores,
      ) = zip(*all_rows)
  
      exact_matches = self._compute_exact_matches(
          self.all_pred_answers,
          self.all_context[self.ANSWERS_COLUMN],
          self.all_has_answer_preds,
          self.all_has_answer_targets,
      )
      f1_score, f1_score_pos_only = self._compute_f1_score(
          self.all_pred_answers,
          self.all_context[self.ANSWERS_COLUMN],
          self.all_has_answer_preds,
          self.all_has_answer_targets,
      )
      count = len(self.all_has_answer_preds)
      count_pos = sum(self.all_has_answer_targets)
      self.all_preds = (
          self.all_pred_answers,
          self.all_start_pos_preds,
          self.all_end_pos_preds,
          self.all_has_answer_preds,
      )
      self.all_targets = (
          self.all_context[self.ANSWERS_COLUMN],
          self.all_start_pos_targets,
          self.all_end_pos_targets,
          self.all_has_answer_targets,
      )
      self.all_scores = (
          self.all_start_pos_scores,
          self.all_end_pos_scores,
          self.all_has_answer_scores,
      )
      label_predictions = None
      if not self.ignore_impossible:
          label_predictions = [
              LabelPrediction(scores, pred, expect)
              for scores, pred, expect in zip_longest(
                  self.all_has_answer_scores,
                  self.all_has_answer_preds,
                  self.all_has_answer_targets,
                  fillvalue=[],
              )
          ]
  
      metrics = SquadMetrics(
          exact_matches=100.0 * exact_matches / count,
          f1_score=100.0 * f1_score / count,
          f1_score_pos_only=100.0 * f1_score_pos_only / count_pos,
          num_examples=count,
          classification_metrics=compute_classification_metrics(
              label_predictions,
              self.has_answer_labels,
              self.calculate_loss(),
          )
          if label_predictions
          else None,
      )
      return metrics
  
imports: import re
  import string
  from collections import Counter
  from itertools import zip_longest
  from typing import Dict, List
  import numpy
  from pytext.common.constants import Stage
  from pytext.metric_reporters.channel import Channel, ConsoleChannel, FileChannel
  from pytext.metric_reporters.metric_reporter import MetricReporter
  from pytext.metrics import compute_classification_metrics, LabelPrediction
  from pytext.metrics.squad_metrics import SquadMetrics
label_desc: Use image node_id 11 for calling the SquadMetricReporter obj's underlying member method code with example usage: obj.calculate_metric() and returns: metrics