code: def calculate_metric(self):
      total_exact_match = 0
      pred_exact_match = 0
      num_samples = len(self.all_target_trees)
  
      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_pred, target in zip(
          self.all_beam_preds, self.all_target_trees
      ):
          for index, pred in enumerate(beam_pred):
              if self._compare_target_prediction_tokens(pred, target):
                  total_exact_match += 1
                  if index == 0:
                      pred_exact_match += 1
                  break
  
      for beam_preds, target in zip(self.all_preds, self.all_targets):
          pred = beam_preds[0]
          # 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()
          )
  
      bleu_score = round(
          0.0 if len(self.all_beam_preds) == 0 else bleu_scorer.score(),
          2,
      )
      exact_match = round(
          safe_division(pred_exact_match, num_samples) * 100.0, 2
      )
      exact_match_top_k = round(
          safe_division(total_exact_match, num_samples) * 100.0, 2
      )
      k = 0 if len(self.all_preds) == 0 else len(self.all_beam_preds[0])
      length_metrics, length_reports = compute_length_metrics(
          self.all_target_lens,
          self.all_target_length_preds,
          self.select_length_beam,
      )
      return MaskedSeq2SeqTopKMetrics(
          loss=self.calculate_loss(),
          exact_match=exact_match,
          f1=-1,
          bleu=bleu_score,
          k=k,
          exact_match_top_k=exact_match_top_k,
          f1_top_k=-1,
          bleu_top_k=-1,
          length_metrics=length_metrics,
          length_reports=length_reports,
      )
  
imports: from collections import defaultdict
  from typing import Dict, List
  import numpy
  import torch
  from pytext.common.constants import BatchContext, DatasetFieldName, Stage
  from pytext.data.tensorizers import Tensorizer
  from pytext.metric_reporters.channel import ConsoleChannel
  from pytext.metric_reporters.seq2seq_compositional import CompositionalSeq2SeqFileChannel, Seq2SeqCompositionalMetricReporter
  from pytext.metric_reporters.seq2seq_metric_reporter import Seq2SeqFileChannel, Seq2SeqMetricReporter
  from pytext.metric_reporters.seq2seq_utils import stringify
  from pytext.metrics import safe_division
  from pytext.metrics.mask_metrics import compute_length_metrics
  from pytext.metrics.seq2seq_metrics import MaskedSeq2SeqTopKMetrics
label_desc: Use image node_id 6 for calling the MaskedSeq2SeqTopKMetricReporter obj's underlying member method code with example usage: obj.calculate_metric() and returns: MaskedSeq2SeqTopKMetrics