class_docstr: The callback that saves average model weights.
  
  The callback that should be used with optimizers that extend
  `tfa.optimizers.AveragedOptimizerWrapper`, i.e.,
  `tfa.optimizers.MovingAverage` and
  `tfa.optimizers.StochasticAverage` optimizers.
  It saves and, optionally, assigns the averaged weights.
  
  Args:
      update_weights: If `True`, assign the moving average weights
          to the model, and save them. If False, keep the old
          non-averaged weights, but the saved model uses the
          average weights.
  
      See `tf.keras.callbacks.ModelCheckpoint` for the other args.
code: def __init__(
      self,
      update_weights: bool,
      filepath: str,
      monitor: str = "val_loss",
      verbose: int = 0,
      save_best_only: bool = False,
      save_weights_only: bool = False,
      mode: str = "auto",
      save_freq: str = "epoch",
      **kwargs,
  ):
      self.update_weights = update_weights
      super().__init__(
          filepath,
          monitor,
          verbose,
          save_best_only,
          save_weights_only,
          mode,
          save_freq,
          **kwargs,
      )
  
file_path: addons/tensorflow_addons/callbacks/average_model_checkpoint.py
imports: import tensorflow
  from typeguard import typechecked
  from tensorflow_addons.optimizers.average_wrapper import AveragedOptimizerWrapper
label_desc: Use image node_id 1 to create a new AverageModelCheckpoint object from inherited base classes: tf.keras.callbacks with example: obj = AverageModelCheckpoint(update_weights, filepath, monitor, verbose, save_best_only, save_weights_only, mode, save_freq)