code: def _description_from_metrics(self, metrics):
      if not self._warned_tqdm_ignores_underscores and any(
          metric_name.startswith("_") for metric_name in metrics
      ):
          logger.warning(
              'Metrics with names beginning with "_" will '
              "not be logged to the tqdm progress bar."
          )
          self._warned_tqdm_ignores_underscores = True
      return (
          ", ".join(
              [
                  "%s: %.4f" % (name, value)
                  for name, value in list(metrics.items())
                  if not name.startswith("_")
              ]
          )
          + " ||"
      )
  
imports: from __future__ import with_statement
  from __future__ import division
  from __future__ import absolute_import
  import logging
  import os
  import shutil
  import time
  import re
  import datetime
  import traceback
  import torch
  import torch.optim.lr_scheduler
  from torch.nn.parallel import replicate, parallel_apply
  from torch.nn.parallel.scatter_gather import scatter_kwargs, gather
  from allennlp.common import Params
  from allennlp.common.checks import ConfigurationError
  from allennlp.common.util import peak_memory_mb, gpu_memory_mb
  from allennlp.common.tqdm import Tqdm
  from allennlp.data.instance import Instance
  from allennlp.data.iterators.data_iterator import DataIterator
  from allennlp.models.model import Model
  from allennlp.nn import util
  from allennlp.training.learning_rate_schedulers import LearningRateScheduler
  from allennlp.training.optimizers import Optimizer
label_desc: Use image node_id 17 for calling the Trainer obj's underlying member method code with example usage: obj._description_from_metrics(metrics) and returns: unknown