code: def _is_best_so_far(
      self, this_epoch_val_metric, validation_metric_per_epoch
  ):
      if not validation_metric_per_epoch:
          return True
      elif self._validation_metric_decreases:
          return this_epoch_val_metric < min(
              validation_metric_per_epoch
          )
      else:
          return this_epoch_val_metric > max(
              validation_metric_per_epoch
          )
  
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 16 for calling the Trainer obj's underlying member method code with example usage: obj._is_best_so_far(this_epoch_val_metric, validation_metric_per_epoch) and returns: True, unknown, unknown