              subbatch["prediction"] = batch["prediction"][idx]
              for key in self._additional_keys:
                  subbatch[key] = batch[key][idx]
              self.process_output(subbatch)
              landmarks.append(subbatch["landmarks"])
          batch["landmarks"] = np.average(landmarks, axis=0)
          return batch
  
      # <<< FACE NORMALIZATION METHODS >>> #
      def _normalize_faces(self, faces):
          """ Normalizes the face for feeding into model
 
</s> remove     def _normalize_faces(self, faces):
 </s> add     def _normalize_faces(self, faces: List[np.ndarray]) -> List[np.ndarray]: </s> remove             subbatch = {key: val
                        for key, val in batch.items()
                        if key not in ["feed", "prediction"] + self._additional_keys}
            subbatch["prediction"] = batch["prediction"][idx]
            for key in self._additional_keys:
                subbatch[key] = batch[key][idx]
 </s> add             # Create a pseudo object that only populates the data, feed and prediction slots with
            # the current re-feed iteration
            subbatch = AlignerBatch(image=batch.image,
                                    detected_faces=batch.detected_faces,
                                    filename=batch.filename,
                                    feed=[batch.feed[idx]],
                                    prediction=batch.prediction[idx],
                                    data=[batch.data[idx]]) </s> remove         self._normalize_method = method
 </s> add         self._normalize_method = cast(Optional[Literal["clahe", "hist", "mean"]], method) </s> remove             result = self.process_input(batch)
            if not self._additional_keys:
                self._additional_keys = [key for key in result if key not in existing_keys]
            for key in self._additional_keys:
                retval.setdefault(key, []).append(batch[key])
                del batch[key]
 </s> add             self.process_input(batch)
            landmarks.append(subbatch.landmarks)
        batch.landmarks = np.average(landmarks, axis=0)
              subbatch["prediction"] = batch["prediction"][idx]
              for key in self._additional_keys:
                  subbatch[key] = batch[key][idx]
              self.process_output(subbatch)
            landmarks.append(subbatch.landmarks)
        batch.landmarks = np.average(landmarks, axis=0)
            landmarks.append(subbatch.landmarks)
        batch.landmarks = np.average(landmarks, axis=0)
          return batch
  
      # <<< FACE NORMALIZATION METHODS >>> #
      def _normalize_faces(self, faces):
          """ Normalizes the face for feeding into model
keep keep keep keep replace replace keep keep keep keep keep
 Extract updates:
  - Default CPU detector to MTCNN
  - add basic Aligner false positive filters
  - Typing: align + plugins
  - Use specific AlignerBatch class for alignment
  - 
plugins/extract/align/_base.py