  
      def train_one_batch(self, do_preview):
          """ Train a batch """
          logger.trace("Training one step: (side: %s)", self.side)
          batch = self.get_next(do_preview)
          try:
              loss = self.model.predictors[self.side].train_on_batch(*batch)
          except tf_errors.ResourceExhaustedError as err:
              msg = ("You do not have enough GPU memory available to train the selected model at "
                     "the selected settings. You can try a number of things:"
 
</s> remove             loss = self.model.predictors[self.side].train_on_batch(*batch)
 </s> add             loss = self.model.predictors[self.side].train_on_batch(x=model_inputs, y=model_targets) </s> remove     def train_one_batch(self, do_preview):
 </s> add     def train_one_batch(self): </s> remove                    "\nIf you are training with a mask or using 'warp to landmarks' then every "
 </s> add                    "\nIf you are using 'warp to landmarks' then every " </s> remove     def _set__mask_class(self):
        """ Returns the correct mask class from :mod:`lib`.model.masks` as defined in the
        :attr:`mask_type` parameter. """
        mask_type = self._training_opts.get("mask_type", None)
        if mask_type:
            logger.debug("Mask type: '%s'", mask_type)
            _mask_class = getattr(masks, mask_type)
        else:
            _mask_class = None
        logger.debug("Mask class: %s", _mask_class)
        return _mask_class

 </s> add  </s> remove     def get_next(self, do_preview):
 </s> add     def get_next(self):
        model_inputs, model_targets = self.get_next()
  
      def train_one_batch(self, do_preview):
          """ Train a batch """
          logger.trace("Training one step: (side: %s)", self.side)
        model_inputs, model_targets = self.get_next()
          try:
              loss = self.model.predictors[self.side].train_on_batch(*batch)
          except tf_errors.ResourceExhaustedError as err:
              msg = ("You do not have enough GPU memory available to train the selected model at "
                     "the selected settings. You can try a number of things:"
keep keep keep keep replace keep keep keep keep keep
 Smart Mask Exposure for Extraction & Training (#831)

Smart Masks - Initial Commit 
plugins/train/trainer/_base.py