class_docstr: Performs validation while training a KerasModel.
  
  This is a callback that can be passed to fit().  It periodically computes a
  set of metrics over a validation set, writes them to a file, and keeps track
  of the best score. In addition, it can save the best model parameters found
  so far to a directory on disk, updating them every time it finds a new best
  validation score.
  
  If Tensorboard logging is enabled on the KerasModel, the metrics are also
  logged to Tensorboard.  This only happens when validation coincides with a
  step on which the model writes to the log.  You should therefore make sure
  that this callback's reporting interval is an even fraction or multiple of
  the model's logging interval.
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docstr: This getter returns the best score evaluated on the validation set.
  
  Returns
  -------
  float
      The best score.
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code: def get_best_score(self):
      """This getter returns the best score evaluated on the validation set.
  
      Returns
      -------
      float
          The best score.
      """
      if self.save_on_minimum:
          return self._best_score
      else:
          return -self._best_score
  
imports: import sys
label_desc: Use image node_id 3 for calling the ValidationCallback obj's underlying member method code with example usage: obj.get_best_score() and returns: self, unknown