                                        dict(beta_1=0.5, beta_2=0.99, epsilon=epsilon)),
                              "rms-prop": (optimizers.RMSprop, dict(epsilon=epsilon))}
          self._optimizer, self._kwargs = valid_optimizers[optimizer]
  
          self._configure(learning_rate, clipnorm, mixed_precision, arguments)
          logger.verbose("Using %s optimizer", optimizer.title())  # type:ignore
          logger.debug("Initialized: %s", self.__class__.__name__)
  
      @property
      def optimizer(self) -> keras.optimizers.Optimizer:
 
</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         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:
        self._configure(learning_rate, autoclip)
                                        dict(beta_1=0.5, beta_2=0.99, epsilon=epsilon)),
                              "rms-prop": (optimizers.RMSprop, dict(epsilon=epsilon))}
          self._optimizer, self._kwargs = valid_optimizers[optimizer]
  
        self._configure(learning_rate, autoclip)
          logger.verbose("Using %s optimizer", optimizer.title())  # type:ignore
          logger.debug("Initialized: %s", self.__class__.__name__)
  
      @property
      def optimizer(self) -> keras.optimizers.Optimizer:
keep keep keep keep replace keep keep keep keep keep
 Nvidia: Add AutoClip training option 
plugins/train/model/_base/settings.py