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)
      )
      for i in range(batch_size):
          yield [
              context[BatchContext.INDEX][i],
              context[DatasetFieldName.RAW_SEQUENCE][i],
              self.tensorizers["trg_seq_tokens"].stringify(preds[i][0]),
              self.tensorizers["trg_seq_tokens"].stringify(targets[i]),
          ]
  
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 3 for calling the Seq2SeqFileChannel obj's underlying member method code with example usage: obj.gen_content(metrics, loss, preds, targets, scores, context) without return types