code: def __init__(
      self,
      channels: List[Channel],
      log_gradient: bool,
      tensorizers: Dict[str, Tensorizer],
  ) -> None:
      super().__init__(channels, log_gradient)
      self.tensorizers = tensorizers
  
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 1 to create a new Seq2SeqMetricReporter object from inherited base classes: MetricReporter with example: obj = Seq2SeqMetricReporter(channels, log_gradient, tensorizers)