          Parameters
          ----------
          learning_rate: float
              The selected learning rate to use
          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
  
          Notes
          -----
          Clip-norm is ballooning VRAM usage, which is not expected behavior and may be a bug in
          Keras/Tensorflow.
 
</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,
                   mixed_precision: bool,
                   arguments: "Namespace") -> None:
 </s> add                    autoclip: bool) -> None: </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
        autoclip: bool
            ``True`` if AutoClip should be enabled otherwise ``False``
          Parameters
          ----------
          learning_rate: float
              The selected learning rate to use
        autoclip: bool
            ``True`` if AutoClip should be enabled otherwise ``False``
        autoclip: bool
            ``True`` if AutoClip should be enabled otherwise ``False``
        autoclip: bool
            ``True`` if AutoClip should be enabled otherwise ``False``
        autoclip: bool
            ``True`` if AutoClip should be enabled otherwise ``False``
        autoclip: bool
            ``True`` if AutoClip should be enabled otherwise ``False``
        autoclip: bool
            ``True`` if AutoClip should be enabled otherwise ``False``
        autoclip: bool
            ``True`` if AutoClip should be enabled otherwise ``False``
  
          Notes
          -----
          Clip-norm is ballooning VRAM usage, which is not expected behavior and may be a bug in
          Keras/Tensorflow.
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