code: def calculate_metric(self):
      total_exact_match = 0
      total_f1 = 0.0
      num_samples = len(self.all_targets)
  
      trg_vocab = self.tensorizers["trg_seq_tokens"].vocab
      bleu_scorer = bleu.Scorer(
          bleu.BleuConfig(
              pad=trg_vocab.get_pad_index(),
              eos=trg_vocab.get_eos_index(),
              unk=trg_vocab.get_unk_index(),
          )
      )
  
      for beam_preds, target in zip(self.all_preds, self.all_targets):
          pred = beam_preds[0]
          if self._compare_target_prediction_tokens(pred, target):
              total_exact_match += 1
          total_f1 += compute_f1(pred, target)
          # Bleu Metric calculation is always done with tensors on CPU or
          # type checks in fairseq/bleu.py:add() will fail
          bleu_scorer.add(
              torch.IntTensor(target).cpu(), torch.IntTensor(pred).cpu()
          )
  
      loss = self.calculate_loss()
      exact_match = round(
          safe_division(total_exact_match, num_samples) * 100.0, 2
      )
      f1 = round(safe_division(total_f1, num_samples) * 100.0, 2)
      bleu_score = round(
          0.0 if len(self.all_preds) == 0 else bleu_scorer.score(), 2
      )
  
      return Seq2SeqMetrics(loss, exact_match, f1, bleu_score)
  
imports: from typing import Dict, List
  import torch
  from pytext.common.constants import BatchContext, DatasetFieldName, RawExampleFieldName, Stage
  from pytext.data.tensorizers import Tensorizer
  from pytext.metric_reporters.channel import Channel, ConsoleChannel, FileChannel
  from pytext.metric_reporters.metric_reporter import MetricReporter
  from pytext.metrics import safe_division
  from pytext.metrics.seq2seq_metrics import compute_f1, Seq2SeqMetrics
label_desc: Use image node_id 5 for calling the Seq2SeqMetricReporter obj's underlying member method code with example usage: obj.calculate_metric() and returns: Seq2SeqMetrics