          if not autoclip:
              return
  
          logger.debug("optimizer kwargs: %s", self._kwargs)
  
  
  class Settings():
 
</s> remove         if clipnorm and (arguments.distributed or mixed_precision):
            logger.warning("Clipnorm has been selected, but is unsupported when using distributed "
                           "or mixed_precision training, so has been disabled. If you wish to "
                           "enable clipnorm, then you must disable these other options.")
            clipnorm = False
        if clipnorm and get_backend() == "amd":
            # TODO add clipnorm in for plaidML when it is fixed upstream. Still not fixed in
            # release 0.7.0.
            logger.warning("Due to a bug in plaidML, clipnorm cannot be used on AMD backends so "
                           "has been disabled")
            clipnorm = False
        if clipnorm:
            self._kwargs["clipnorm"] = 1.0
 </s> add         if not autoclip:
            return </s> remove                    clipnorm: bool,
                   mixed_precision: bool,
                   arguments: "Namespace") -> None:
 </s> add                    autoclip: bool) -> None: </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         self._configure(learning_rate, clipnorm, mixed_precision, arguments)
 </s> add         self._configure(learning_rate, autoclip)
        logger.info("Enabling AutoClip")
        self._kwargs["gradient_transformers"] = [AutoClipper(10, history_size=10000)]
          if not autoclip:
              return
  
        logger.info("Enabling AutoClip")
        self._kwargs["gradient_transformers"] = [AutoClipper(10, history_size=10000)]
          logger.debug("optimizer kwargs: %s", self._kwargs)
  
  
  class Settings():
keep keep add keep keep keep keep
 Nvidia: Add AutoClip training option 
plugins/train/model/_base/settings.py