          output.
          """
          logger.trace("target_batch shapes: %s", [tgt.shape for tgt in target_batch])
          retval = dict(targets=[batch[..., :3] for batch in target_batch],
                        masks=[target_batch[-1][..., 3:]])
          logger.trace("returning: %s", {k: [tgt.shape for tgt in v] for k, v in retval.items()})
          return retval
  
      # <<< COLOR AUGMENTATION >>> #
      def color_adjust(self, batch):
          """ Perform color augmentation on the passed in batch.
 
</s> remove         The target masks are returned as its own item and is the 4th channel of the final target
        output.
 </s> add         Returns
        -------
        dict:
            The targets and the masks separated into their own items. The targets are a list of
            3 channel, 4 dimensional :class:`numpy.ndarray` objects sized for each output from the
            model. The masks are a :class:`numpy.ndarray` of the final output size. Any additional
            masks(e.g. eye and mouth masks) will be collated together into a :class:`numpy.ndarray`
            of the final output size. The number of channels will be the number of additional
            masks available </s> remove                      [tgt_images.shape[1:] for tgt_images in target_batch])
 </s> add                      [tgt_images.shape for tgt_images in target_batch]) </s> remove         logger.debug(retval)
 </s> add         if self._model.config["eye_multiplier"] > 1:
            retval["masks_eye"] = alignments.masks_eye

        if self._model.config["mouth_multiplier"] > 1:
            retval["masks_mouth"] = alignments.masks_mouth

        logger.debug({key: {k: len(v) for k, v in val.items()} for key, val in retval.items()}) </s> remove         Returns the targets as a list of 4-dimensional :class:`numpy.ndarray` s of shape
        (`batchsize`, `height`, `width`, `3`).
 </s> add         Parameters
        ----------
        target_batch: list
            List of 4 dimension :class:`numpy.ndarray` objects resized the model outputs.
            The 4th channel of the array contains the face mask, any additional channels after
            this are additional masks (e.g. eye mask and mouth mask)
                      masks=target_batch[-1][..., 3][..., None])
        if target_batch[-1].shape[-1] > 4:
            retval["additional_masks"] = target_batch[-1][..., 4:]
        logger.trace("returning: %s", {k: v.shape if isinstance(v, np.ndarray) else [tgt.shape
                                                                                     for tgt in v]
                                       for k, v in retval.items()})
          output.
          """
          logger.trace("target_batch shapes: %s", [tgt.shape for tgt in target_batch])
          retval = dict(targets=[batch[..., :3] for batch in target_batch],
                      masks=target_batch[-1][..., 3][..., None])
        if target_batch[-1].shape[-1] > 4:
            retval["additional_masks"] = target_batch[-1][..., 4:]
        logger.trace("returning: %s", {k: v.shape if isinstance(v, np.ndarray) else [tgt.shape
                                                                                     for tgt in v]
                                       for k, v in retval.items()})
                      masks=target_batch[-1][..., 3][..., None])
        if target_batch[-1].shape[-1] > 4:
            retval["additional_masks"] = target_batch[-1][..., 4:]
        logger.trace("returning: %s", {k: v.shape if isinstance(v, np.ndarray) else [tgt.shape
                                                                                     for tgt in v]
                                       for k, v in retval.items()})
          return retval
  
      # <<< COLOR AUGMENTATION >>> #
      def color_adjust(self, batch):
          """ Perform color augmentation on the passed in batch.
keep keep keep keep 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/training_data.py