class_docstr: A Keras callback to save the model weights at the best epoch.
  
  Args:
      objective: An `Objective` instance.
      filepath: String. The file path to save the model weights.
class_docstr_tok: ["A","Keras","callback","to","save","the","model","weights","at","the","best","epoch",".","Args",":","objective",":","An","`","Objective","`","instance",".","filepath",":","String",".","The","file","path","to","save","the","model","weights","."]
code: def __init__(self, objective, filepath):
      super().__init__()
      self.objective = objective
      self.filepath = filepath
      if self.objective.direction == "max":
          self.best_value = float("-inf")
      else:
          self.best_value = float("inf")
  
imports: import collections
  import statistics
  import numpy
  from keras_tuner import backend
  from keras_tuner import errors
  from keras_tuner.backend import config
  from keras_tuner.backend import keras
  from keras_tuner.engine import hyperparameters
  from keras_tuner.engine import objective
label_desc: Use image node_id 1 to create a new SaveBestEpoch object from inherited base classes: keras.callbacks with example: obj = SaveBestEpoch(objective, filepath)