code: def __init__(
      self,
      channels,
      log_gradient,
      tensorizers,
      model_select_metric_key,
      select_length_beam,
      print_length_metrics,
  ):
      super().__init__(channels, log_gradient, tensorizers)
      self.model_select_metric_key = model_select_metric_key
      if model_select_metric_key == "em":
          self.lower_is_better = False
      else:
          self.lower_is_better = True
      self.select_length_beam = select_length_beam
      self.print_length_metrics = print_length_metrics
  
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 1 to create a new MaskedSeq2SeqTopKMetricReporter object from inherited base classes: Seq2SeqMetricReporter with example: obj = MaskedSeq2SeqTopKMetricReporter(channels, log_gradient, tensorizers, model_select_metric_key, select_length_beam, print_length_metrics)