code: def from_config(
      cls, config: Config, tensorizers: Dict[str, Tensorizer]
  ):
      channels = [ConsoleChannel()]
      if config.TEMP_DUMP_PREDICTIONS:
          channels.append(
              MaskedSeq2SeqFileChannel(
                  [Stage.TEST], config.output_path, tensorizers
              ),
          )
      return cls(
          channels,
          config.log_gradient,
          tensorizers,
          config.model_select_metric_key,
          config.select_length_beam,
          config.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 2 for calling the MaskedSeq2SeqTopKMetricReporter obj's underlying member method code with example usage: obj.from_config(cls, config, tensorizers) and returns: cls