          return self._optimizer(**self._kwargs)
  
      def _configure(self,
                     learning_rate: float,
                     clipnorm: bool,
                     mixed_precision: bool,
                     arguments: "Namespace") -> None:
          """ Configure the optimizer based on user settings.
  
          Parameters
          ----------
          learning_rate: float
 
</s> remove                  clipnorm: bool,
                 epsilon: float,
                 mixed_precision: bool,
                 arguments: "Namespace") -> None:
        logger.debug("Initializing %s: (optimizer: %s, learning_rate: %s, clipnorm: %s, "
                     "epsilon: %s, mixed_precision: %s, arguments: %s)", self.__class__.__name__,
                     optimizer, learning_rate, clipnorm, epsilon, mixed_precision, arguments)
 </s> add                  autoclip: bool,
                 epsilon: float) -> None:
        logger.debug("Initializing %s: (optimizer: %s, learning_rate: %s, autoclip: %s, "
                     ", epsilon: %s)", self.__class__.__name__, optimizer, learning_rate,
                     autoclip, epsilon) </s> remove     mixed_precision: bool
        ``True`` if mixed precision training is to be enabled otherwise ``False``
    arguments: :class:`argparse.Namespace`
        The arguments that were passed to the train or convert process as generated from
        Faceswap's command line arguments
 </s> add  </s> remove         clipnorm: bool
            Whether to clip gradients to avoid exploding/vanishing gradients
        mixed_precision: bool
            ``True`` if mixed precision training is to be enabled otherwise ``False``
        arguments: :class:`argparse.Namespace`
            The arguments that were passed to the train or convert process as generated from
            Faceswap's command line arguments
 </s> add         autoclip: bool
            ``True`` if AutoClip should be enabled otherwise ``False`` </s> remove     clipnorm: bool
        Whether to clip gradients to avoid exploding/vanishing gradients
 </s> add     autoclip: bool
        ``True`` if AutoClip should be enabled otherwise ``False``
                   autoclip: bool) -> None:
          return self._optimizer(**self._kwargs)
  
      def _configure(self,
                     learning_rate: float,
                   autoclip: bool) -> None:
                   autoclip: bool) -> None:
                   autoclip: bool) -> None:
          """ Configure the optimizer based on user settings.
  
          Parameters
          ----------
          learning_rate: float
keep keep keep keep replace replace replace keep keep keep keep keep
 Nvidia: Add AutoClip training option 
plugins/train/model/_base/settings.py