                                 self._args).optimizer
          if self._settings.use_mixed_precision:
              optimizer = self._settings.LossScaleOptimizer(optimizer, loss_scale="dynamic")
  
          loss = _Loss(self._model.inputs, self._model.outputs)
          self._model.compile(optimizer=optimizer, loss=loss.functions)
          if not self._is_predict:
              self._state.add_session_loss_names(loss.names)
          logger.debug("Compiled Model: %s", self._model)
  
      def _legacy_mapping(self):  # pylint:disable=no-self-use
 
</s> remove             self._state.add_session_loss_names(loss.names)
 </s> add             self._state.add_session_loss_names(self._loss.names) </s> remove         self.set_globals()
 </s> add         self._set_globals()
        self._set_loss() </s> remove         for sect in ("global", self.section):
 </s> add         sections = [sect for sect in self.config.sections() if sect.startswith("global")]
        sections.append(self.section)
        for sect in sections: </s> remove     @property
    def _selected_mask_loss(self):
        """ :func:`keras.losses.Loss`: The selected mask loss function. Currently returns mean
        standard error as the default function. """
        loss_func = self._loss_dict["mse"]
        logger.debug("loss_func: %s", loss_func)
        return loss_func

 </s> add  </s> remove         selected_loss = self._loss_dict[self._config.get("loss_function", "mae")]
 </s> add         selected_loss = self._loss_dict[self._config["loss_function"]]
        mask_loss = self._loss_dict[self._config["mask_loss_function"]]
        self._loss.configure(self._model.inputs, self._model.outputs)
        self._model.compile(optimizer=optimizer, loss=self._loss.functions)
                                 self._args).optimizer
          if self._settings.use_mixed_precision:
              optimizer = self._settings.LossScaleOptimizer(optimizer, loss_scale="dynamic")
  
        self._loss.configure(self._model.inputs, self._model.outputs)
        self._model.compile(optimizer=optimizer, loss=self._loss.functions)
        self._loss.configure(self._model.inputs, self._model.outputs)
        self._model.compile(optimizer=optimizer, loss=self._loss.functions)
          if not self._is_predict:
              self._state.add_session_loss_names(loss.names)
          logger.debug("Compiled Model: %s", self._model)
  
      def _legacy_mapping(self):  # pylint:disable=no-self-use
keep keep keep keep replace replace keep keep keep keep keep
 Training Config - Split Loss to it's own section 
plugins/train/model/_base.py