          """
          lr_key = "lr" if get_backend() == "amd" else "learning_rate"
          self._kwargs[lr_key] = learning_rate
  
          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
  
          logger.debug("optimizer kwargs: %s", self._kwargs)
  
  
  class Settings():
 
</s> remove         priors = ["dssim_loss", "mask_type", "mask_type", "l2_reg_term"]
        new_items = ["loss_function", "learn_mask", "mask_type", "loss_function_2"]
 </s> add         priors = ["dssim_loss", "mask_type", "mask_type", "l2_reg_term", "clipnorm"]
        new_items = ["loss_function", "learn_mask", "mask_type", "loss_function_2",
                     "autoclip"] </s> remove                               self.config.get("clipnorm", False),
                              10 ** int(self.config["epsilon_exponent"]),
                              self._mixed_precision,
                              self._args).optimizer
 </s> add                               autoclip,
                              10 ** int(self.config["epsilon_exponent"])).optimizer
        if not autoclip:
            return
          """
          lr_key = "lr" if get_backend() == "amd" else "learning_rate"
          self._kwargs[lr_key] = learning_rate
  
        if not autoclip:
            return
        if not autoclip:
            return
        if not autoclip:
            return
        if not autoclip:
            return
        if not autoclip:
            return
        if not autoclip:
            return
        if not autoclip:
            return
        if not autoclip:
            return
        if not autoclip:
            return
        if not autoclip:
            return
        if not autoclip:
            return
        if not autoclip:
            return
        if not autoclip:
            return
  
          logger.debug("optimizer kwargs: %s", self._kwargs)
  
  
  class Settings():
keep keep keep keep replace replace replace replace replace replace replace replace replace replace replace replace replace keep keep keep keep keep
 Nvidia: Add AutoClip training option 
plugins/train/model/_base/settings.py