          # Get Targets
          processed.update(self._processing.get_targets(batch))
  
          # Random Warp # TODO change masks to have a input mask and a warped target mask
          if not self._config["disable_warp"]:
              processed["feed"] = [self._processing.warp(batch[..., :3],
                                                         self._warp_to_landmarks,
                                                         **warp_kwargs)]
          else:
              processed["feed"] = [self._processing.skip_warp(batch[..., :3])]
 
</s> remove         else:
            processed["feed"] = [self._processing.skip_warp(batch[..., :3])]
 </s> add  </s> remove     disable_warp=dict(
        default=False,
        info="Disable warp augmentation. Warping is integral to the Neural Network training. If "
             "you decide to disable warping, you should only do so towards the end of a model's "
             "training session.",
        datatype=bool,
        group="image augmentation",
        fixed=False),
 </s> add 
        if self._no_warp:
            processed["feed"] = [self._processing.skip_warp(batch[..., :3])]
        else:
          # Get Targets
          processed.update(self._processing.get_targets(batch))
  
          # Random Warp # TODO change masks to have a input mask and a warped target mask
        if self._no_warp:
            processed["feed"] = [self._processing.skip_warp(batch[..., :3])]
        else:
              processed["feed"] = [self._processing.warp(batch[..., :3],
                                                         self._warp_to_landmarks,
                                                         **warp_kwargs)]
          else:
              processed["feed"] = [self._processing.skip_warp(batch[..., :3])]
keep keep keep keep replace keep keep keep keep keep
 Training - Move disable warp option out of config 
lib/training_data.py