class_docstr: Creates scores on epoch level based on batch level scores
  
  This callback doesn't calculate any new scores but instead passes
  through a score that was created on the batch level. Based on that
  score, an average across the batch is created (honoring the batch
  size) and recorded in the history for the given epoch.
  
  Use this callback when there already is a score calculated on the
  batch level. If that score has yet to be calculated, use
  :class:`.BatchScoring` instead.
  
  Parameters
  ----------
  name : str
    Name of the score recorded on a batch level in the history.
  
  lower_is_better : bool (default=True)
    Whether lower (e.g. log loss) or higher (e.g. accuracy) scores
    are better.
  
  on_train : bool (default=False)
    Whether this should be called during train or validation.
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code: def _is_best_score(self, current_score):
      if self.lower_is_better is None:
          return None
      if self.lower_is_better:
          return current_score < self.best_score_
      return current_score > self.best_score_
  
imports: from contextlib import contextmanager
  from contextlib import suppress
  from functools import partial
  import warnings
  import numpy
  import sklearn
  from sklearn.metrics import make_scorer, check_scoring
  from skorch.callbacks import Callback
  from skorch.dataset import unpack_data
  from sklearn.metrics._scorer import _BaseScorer
  from skorch.utils import data_from_dataset
  from skorch.utils import is_skorch_dataset
  from skorch.utils import to_numpy
  from skorch.utils import check_indexing
  from skorch.utils import to_device
label_desc: Use image node_id 3 for calling the PassthroughScoring obj's underlying member method code with example usage: obj._is_best_score(current_score) and returns: unknown, None, unknown