          batch, dict
              A dictionary of lists of :attr:`~plugins.extract._base.Extractor.batchsize`:
          """
          exhausted = False
          batch = {}
          idx = 0
          while idx < self.batchsize:
              item = self._collect_item(queue)
              if item == "EOF":
                  logger.trace("EOF received")
 
</s> remove                 logger.trace("EOF received")
 </s> add                 logger.trace("EOF received")  # type:ignore
                self._filter.output_counts() </s> remove                 batch.setdefault("image", []).append(converted_image)
                batch.setdefault("detected_faces", []).append(face)
                batch.setdefault("filename", []).append(item.filename)
 </s> add                 batch.image.append(converted_image)
                batch.detected_faces.append(face)
                batch.filename.append(item.filename) </s> remove     @staticmethod
    def pad_image(box, image):
        """Pad image if face-box falls outside of boundaries """
 </s> add     @classmethod
    def pad_image(cls, box: List[int], image: np.ndarray) -> Tuple[np.ndarray, Tuple[int, int]]:
        """Pad image if face-box falls outside of boundaries

        Parameters
        ----------
        box: list
            The (`left`, `top`, `right`, `bottom`) roi box positions
        image: :class:`numpy.ndarray`
            The image to be padded

        Returns
        -------
        :class:`numpy.ndarray`
            The padded image
        """ </s> remove                 logger.trace("Getting from queue: (filename: %s, faces: %s)",
 </s> add                 logger.trace("Getting from queue: (filename: %s, faces: %s)",  # type:ignore </s> remove             logger.trace("Getting from _rollover: (filename: `%s`, faces: %s)",
 </s> add             logger.trace("Getting from _rollover: (filename: `%s`, faces: %s)",  # type:ignore
        batch = AlignerBatch()
          batch, dict
              A dictionary of lists of :attr:`~plugins.extract._base.Extractor.batchsize`:
          """
          exhausted = False
        batch = AlignerBatch()
          idx = 0
          while idx < self.batchsize:
              item = self._collect_item(queue)
              if item == "EOF":
                  logger.trace("EOF received")
keep keep keep keep 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