                                            pad_r,
                                            cv2.BORDER_CONSTANT,
                                            value=(0, 0, 0))
          offsets = (pad_l - pad_r, pad_t - pad_b)
          logger.trace("image_shape: %s, Padded shape: %s, box: %s, offsets: %s",
                       image.shape, padded_image.shape, box, offsets)
          return padded_image, offsets
  
      def predict(self, batch):
          """ Predict the 68 point landmarks """
 
</s> remove     def predict(self, batch):
        """ Predict the 68 point landmarks """
        logger.trace("Predicting Landmarks")
 </s> add     def predict(self, batch: AlignerBatch) -> np.ndarray:
        """ Predict the 68 point landmarks

        Parameters
        ----------
        batch: :class:`numpy.ndarray`
            The batch to feed into the aligner

        Returns
        -------
        :class:`numpy.ndarray`
            The predictions from the aligner
        """
        logger.trace("Predicting Landmarks")  # type:ignore </s> remove         logger.trace("Padding: (l: %s, t: %s, r: %s, b: %s)", pad_l, pad_t, pad_r, pad_b)
 </s> add         logger.trace("Padding: (l: %s, t: %s, r: %s, b: %s)",  # type:ignore
                     pad_l, pad_t, pad_r, pad_b) </s> remove     def predict(self, batch):
        """ Predict the 68 point landmarks """
 </s> add     def predict(self, batch: np.ndarray) -> np.ndarray:
        """ Predict the 68 point landmarks

        Parameters
        ----------
        batch: :class:`numpy.ndarray`
            The batch to feed into the aligner

        Returns
        -------
        :class:`numpy.ndarray`
            The predictions from the aligner
        """ </s> remove         logger.trace("Transformed Points: %s", retval)
 </s> add         logger.trace("Transformed Points: %s", retval)  # type:ignore </s> remove                  min_size: int = 20,
                 normalize_method: Optional[str] = None,
 </s> add                  min_size: int = 0,
                 normalize_method:  Optional[Literal["none", "clahe", "hist", "mean"]] = None,
        logger.trace("image_shape: %s, Padded shape: %s, box: %s, offsets: %s",  # type:ignore
                                            pad_r,
                                            cv2.BORDER_CONSTANT,
                                            value=(0, 0, 0))
          offsets = (pad_l - pad_r, pad_t - pad_b)
        logger.trace("image_shape: %s, Padded shape: %s, box: %s, offsets: %s",  # type:ignore
                       image.shape, padded_image.shape, box, offsets)
          return padded_image, offsets
  
      def predict(self, batch):
          """ Predict the 68 point landmarks """
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/cv2_dnn.py