      """
      def __init__(self,
                   optimizer: str,
                   learning_rate: float,
                   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)
          valid_optimizers = {"adabelief": (optimizers.AdaBelief,
                                            dict(beta_1=0.5, beta_2=0.99, epsilon=epsilon)),
                              "adam": (optimizers.Adam,
                                       dict(beta_1=0.5, beta_2=0.99, epsilon=epsilon)),
                              "nadam": (optimizers.Nadam,
 
</s> remove         self._configure(learning_rate, clipnorm, mixed_precision, arguments)
 </s> add         self._configure(learning_rate, autoclip) </s> remove                    clipnorm: bool,
                   mixed_precision: bool,
                   arguments: "Namespace") -> None:
 </s> add                    autoclip: bool) -> None: </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
 </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
        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``
                 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)
      """
      def __init__(self,
                   optimizer: str,
                   learning_rate: float,
                 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)
                 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)
                 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)
                 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)
                 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)
                 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)
                 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)
          valid_optimizers = {"adabelief": (optimizers.AdaBelief,
                                            dict(beta_1=0.5, beta_2=0.99, epsilon=epsilon)),
                              "adam": (optimizers.Adam,
                                       dict(beta_1=0.5, beta_2=0.99, epsilon=epsilon)),
                              "nadam": (optimizers.Nadam,
keep keep keep keep replace replace replace replace replace replace replace keep keep keep keep keep
 Nvidia: Add AutoClip training option 
plugins/train/model/_base/settings.py