              return "max"
  
          return None
  
      def filtered_mask(self, faces: List[DetectedFace], minimum_dimension: int) -> List[bool]:
          """ Obtain a list of boolean values for the given faces indicating whether they pass the
          filter test.
  
          Parameters
          ----------
 
</s> remove         minimum_dimension: int
            The minimum (height, width) of the original frame
 </s> add         minimum_dimension: list
            The minimum (height, width) of the original frames that the faces come from </s> remove             landmarks.append(subbatch.landmarks)
        batch.landmarks = np.average(landmarks, axis=0)
 </s> add             landmark_list.append(subbatch.landmarks)

            if self.config["filter_refeed"]:
                fcs = [DetectedFace(landmarks_xy=lm) for lm in subbatch.landmarks.copy()]
                min_sizes = [min(img.shape[:2]) for img in batch.image]
                masks.append(self._filter.filtered_mask(fcs, min_sizes))

        batch.landmarks = self._get_mean_landmarks(np.array(landmark_list), masks) </s> remove         for idx, face in enumerate(faces):
 </s> add         for idx, (face, dim) in enumerate(zip(faces, minimum_dimension)):
    def filtered_mask(self, faces: List[DetectedFace], minimum_dimension: List[int]) -> List[bool]:
              return "max"
  
          return None
  
    def filtered_mask(self, faces: List[DetectedFace], minimum_dimension: List[int]) -> List[bool]:
          """ Obtain a list of boolean values for the given faces indicating whether they pass the
          filter test.
  
          Parameters
          ----------
keep keep keep keep replace keep keep keep keep keep
 Aligner updates
  - Add filter re-feeds option
  - bugfix roll calculation 
plugins/extract/align/_base.py