  
          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
          """ The mapping of separate model files to single model layers for transferring of legacy
          weights.
 
</s> remove         loss = _Loss(self._model.inputs, self._model.outputs)
        self._model.compile(optimizer=optimizer, loss=loss.functions)
 </s> add         self._loss.configure(self._model.inputs, self._model.outputs)
        self._model.compile(optimizer=optimizer, loss=self._loss.functions) </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     """ Holds loss names and functions for an Autoencoder.

    Parameters
    ----------
    inputs: list
        A list of input tensors to the model in the order ("a", "b")
    outputs: list
        A list of output tensors to the model in the order ("a", "b")
    """
    def __init__(self, inputs, outputs):
        logger.debug("Initializing %s: (inputs: %s, outputs: %s)",
                     self.__class__.__name__, inputs, outputs)
 </s> add     """ Holds loss names and functions for an Autoencoder. """
    def __init__(self):
        logger.debug("Initializing %s", self.__class__.__name__) </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._state.add_session_loss_names(self._loss.names)
  
          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(self._loss.names)
          logger.debug("Compiled Model: %s", self._model)
  
      def _legacy_mapping(self):  # pylint:disable=no-self-use
          """ The mapping of separate model files to single model layers for transferring of legacy
          weights.
keep keep keep keep replace keep keep keep keep keep
 Training Config - Split Loss to it's own section 
plugins/train/model/_base.py