code: def gen_content(self, metrics, loss, preds, targets, scores, context):
      batch_size = len(targets)
      assert (
          batch_size
          == len(context[DatasetFieldName.RAW_SEQUENCE])
          == len(preds)
      )
      tokens_to_sentence = (
          lambda tokens_str: ("".join(tokens_str.split()))
          .replace("▁", " ")[1:]
          .lower()
      )
      for i in range(batch_size):
          yield [
              context[BatchContext.INDEX][i],
              context[DatasetFieldName.RAW_SEQUENCE][i],
              self.tensorizers["src_seq_tokens"].stringify(
                  context[DatasetFieldName.SOURCE_SEQ_FIELD][i]
              ),
              tokens_to_sentence(
                  self.tensorizers["trg_seq_tokens"].stringify(
                      preds[i][0]
                  )
              ),
              self.tensorizers["trg_seq_tokens"].stringify(preds[i][0]),
              "|".join(
                  [
                      tokens_to_sentence(
                          self.tensorizers["trg_seq_tokens"].stringify(
                              preds[i][j]
                          )
                      )
                      for j in range(len(preds[i]))
                  ]
              ),
              tokens_to_sentence(
                  self.tensorizers["trg_seq_tokens"].stringify(
                      targets[i]
                  )
              ),
          ]
  
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 2 for calling the MaskedSeq2SeqFileChannel obj's underlying member method code with example usage: obj.gen_content(metrics, loss, preds, targets, scores, context) without return types