                          self._rollover = ExtractMedia(
                              item.filename,
                              item.image,
                              detected_faces=item.detected_faces[f_idx + 1:])
                          logger.trace("Rolled over %s faces of %s to next batch for '%s'",
                                       len(self._rollover.detected_faces), frame_faces,
                                       item.filename)
                      break
          if batch:
              logger.trace("Returning batch: %s", {k: v.shape if isinstance(v, np.ndarray) else v
                                                   for k, v in batch.items()})
 
</s> remove             logger.trace("Returning batch: %s", {k: v.shape if isinstance(v, np.ndarray) else v
                                                 for k, v in batch.items()})
 </s> add             logger.trace("Returning batch: %s", {k: v.shape  # type:ignore
                                                 if isinstance(v, np.ndarray) else v
                                                 for k, v in batch.__dict__.items()}) </s> remove             logger.trace(item)
 </s> add             logger.trace(item)  # type:ignore </s> remove         logger.trace("Item out: %s", {key: val.shape if isinstance(val, np.ndarray) else val
                                      for key, val in batch.items()})
 </s> add         logger.trace("Item out: %s", {key: val.shape  # type:ignore
                                      if isinstance(val, np.ndarray) else val
                                      for key, val in batch.__dict__.items()}) </s> remove         for filename, face in zip(batch["filename"], batch["detected_faces"]):
 </s> add         for frame, filename, face in zip(batch.image, batch.filename, batch.detected_faces): </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)
                        logger.trace("Rolled over %s faces of %s to next batch "  # type:ignore
                                     "for '%s'", len(self._rollover.detected_faces), frame_faces,
                          self._rollover = ExtractMedia(
                              item.filename,
                              item.image,
                              detected_faces=item.detected_faces[f_idx + 1:])
                        logger.trace("Rolled over %s faces of %s to next batch "  # type:ignore
                                     "for '%s'", len(self._rollover.detected_faces), frame_faces,
                        logger.trace("Rolled over %s faces of %s to next batch "  # type:ignore
                                     "for '%s'", len(self._rollover.detected_faces), frame_faces,
                                       item.filename)
                      break
          if batch:
              logger.trace("Returning batch: %s", {k: v.shape if isinstance(v, np.ndarray) else v
                                                   for k, v in batch.items()})
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