          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.
  
          Loss functions are compiled into :class:`keras.compile_utils.LossesContainer` objects
  
          Parameters
          ----------
          loss_functions: list
              A list of either a tuple of (:class:`keras.losses.Loss`, scalar weight) or just a
 
</s>
    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.
          self._loss_weights = []
          self._compile_losses(loss_functions)
          logger.debug("Initialized: %s", self.__class__.__name__)
  
    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.
    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.
    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.
    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.
    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.
  
          Parameters
          ----------
          loss_functions: list
              A list of either a tuple of (:class:`keras.losses.Loss`, scalar weight) or just a
keep keep keep keep replace replace replace replace 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_tf.py