          logger.debug("Initializing: %s: (loss_functions: %s)",
                       self.__class__.__name__, loss_functions)
          self._loss_functions = []
          self._loss_weights = []
          self._compile_losses(loss_functions)
          logger.debug("Initialized: %s", self.__class__.__name__)
  
      def _compile_losses(self, loss_functions):
          """ Splits the given loss_functions into the corresponding :attr:`_loss_functions' and
          :attr:`_loss_weights' lists.
 
</s> remove         self._compile_losses(loss_functions)
 </s> add         self._mask_channels = [] </s> remove     def _compile_losses(self, loss_functions):
        """ Splits the given loss_functions into the corresponding :attr:`_loss_functions' and
        :attr:`_loss_weights' lists.

        Loss functions are compiled into :class:`keras.compile_utils.LossesContainer` objects
 </s> add     def add_loss(self, function, weight=1.0, mask_channel=-1):
        """ Add the given loss function with the given weight to the loss function chain. </s> remove     def _compile_losses(self, loss_functions):
        """ Splits the given loss_functions into the corresponding :attr:`_loss_functions' and
        :attr:`_loss_weights' lists.

        Loss functions are compiled into :class:`keras.compile_utils.LossesContainer` objects
 </s> add     def add_loss(self, function, weight=1.0, mask_channel=-1):
        """ Add the given loss function with the given weight to the loss function chain. </s> remove 
    Parameters
    ----------
    loss_functions: list
        A list of either a tuple of (:class:`keras.losses.Loss`, scalar weight) or just a
        :class:`keras.losses.Loss` function. If just the loss function is passed, then the weight
        is assumed to be 1.0 """
    def __init__(self, loss_functions):
        logger.debug("Initializing: %s: (loss_functions: %s)",
                     self.__class__.__name__, loss_functions)
 </s> add     """
    def __init__(self):
        logger.debug("Initializing: %s", self.__class__.__name__) </s> remove     single output.

    Parameters
    ----------
    loss_functions: list
        A list of either a tuple of (:class:`keras.losses.Loss`, scalar weight) or just a
        :class:`keras.losses.Loss` function. If just the loss function is passed, then the weight
        is assumed to be 1.0 """
    def __init__(self, loss_functions):
        logger.debug("Initializing: %s: (loss_functions: %s)",
                     self.__class__.__name__, loss_functions)
 </s> add     single output and masking.
    """
    def __init__(self):
        logger.debug("Initializing: %s", self.__class__.__name__)
        self._mask_channels = []
          logger.debug("Initializing: %s: (loss_functions: %s)",
                       self.__class__.__name__, loss_functions)
          self._loss_functions = []
          self._loss_weights = []
        self._mask_channels = []
          logger.debug("Initialized: %s", self.__class__.__name__)
  
      def _compile_losses(self, loss_functions):
          """ Splits the given loss_functions into the corresponding :attr:`_loss_functions' and
          :attr:`_loss_weights' lists.
keep keep keep keep replace keep keep keep keep keep
 Add Mouth and Eye Priority to Loss options (#1054)

* Priority Training for Mouth and Eyes - Tensorflow

* Use chosen loss function for area multipliers

* loss multipliers for AMD

* Fix mask multipliers for plaid and roll PenalizedMaskLoss into LossWrapper

* losses_tf: roll PenalizedMaskLoss into LossWrapper 
lib/model/losses_plaid.py