          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])]
  
          logger.trace("Processed batch: (filenames: %s, side: '%s', processed: %s)",
                       filenames,
                       side,
                       {k: v.shape if isinstance(v, np.ndarray) else[i.shape for i in v]
 
</s> remove         if not self._config["disable_warp"]:
 </s> add         if self._no_warp:
            processed["feed"] = [self._processing.skip_warp(batch[..., :3])]
        else: </s> remove                      "coverage_ratio: %s, augment_color: %s, no_flip: %s, warp_to_landmarks: %s, "
                     "alignments: %s, config: %s)",
 </s> add                      "coverage_ratio: %s, augment_color: %s, no_flip: %s, no_warp: %s, "
                     "warp_to_landmarks: %s, alignments: %s, config: %s)", </s> remove                      coverage_ratio, augment_color, no_flip, warp_to_landmarks,
 </s> add                      coverage_ratio, augment_color, no_flip, no_warp, warp_to_landmarks,
          if not self._config["disable_warp"]:
              processed["feed"] = [self._processing.warp(batch[..., :3],
                                                         self._warp_to_landmarks,
                                                         **warp_kwargs)]


  
          logger.trace("Processed batch: (filenames: %s, side: '%s', processed: %s)",
                       filenames,
                       side,
                       {k: v.shape if isinstance(v, np.ndarray) else[i.shape for i in v]
keep keep keep keep replace replace keep keep keep keep keep
 Training - Move disable warp option out of config 
lib/training_data.py