          """ Process the output from the model """
          self.get_pts_from_predict(batch)
          return batch
  
      def get_pts_from_predict(self, batch):
          """ Get points from predictor """
          logger.debug("Obtain points from prediction")
          num_images, num_landmarks, height, width = batch["prediction"].shape
          image_slice = np.repeat(np.arange(num_images)[:, None], num_landmarks, axis=1)
          landmark_slice = np.repeat(np.arange(num_landmarks)[None, :], num_images, axis=0)
          resolution = np.full((num_images, num_landmarks), 64, dtype='int32')
 
</s> remove     def process_output(self, batch):
        """ Process the output from the model """
 </s> add     def process_output(self, batch: AlignerBatch) -> AlignerBatch:
        """ Process the output from the model

        Parameters
        ----------
        batch: :class:`AlignerBatch`
            The current batch from the model with :attr:`predictions` populated

        Returns
        -------
        :class:`AlignerBatch`
            The current batch with the :attr:`landmarks` populated
        """ </s> remove     @staticmethod
    def transform(points, center_scales, resolutions):
        """ Transform Image """
 </s> add     @classmethod
    def transform(cls,
                  points: np.ndarray,
                  center_scales: np.ndarray,
                  resolutions: np.ndarray) -> np.ndarray:
        """ Transform Image

        Parameters
        ----------
        points: :class:`numpy.ndarray`
            The points to transform
        center_scales: :class:`numpy.ndarray`
            The calculated centers and scales for the batch
        resolutions: :class:`numpy.ndarray`
            The resolutions
        """ </s> remove     @staticmethod
    def get_pts_from_predict(batch):
        """ Get points from predictor """
        for prediction, roi, offset in zip(batch["prediction"], batch["roi"], batch["offsets"]):
 </s> add     @classmethod
    def get_pts_from_predict(cls, batch: AlignerBatch):
        """ Get points from predictor and populates the :attr:`landmarks` property

        Parameters
        ----------
        batch: :class:`AlignerBatch`
            The current batch from the model with :attr:`predictions` populated
        """
        landmarks = []
        for prediction, roi, offset in zip(batch.prediction,
                                           batch.data[0]["roi"],
                                           batch.data[0]["offsets"]): </s> remove         logger.trace(retval.shape)
 </s> add         logger.trace(retval.shape)  # type:ignore </s> remove     def process_output(self, batch):
        """ Process the output from the model """
 </s> add     def process_output(self, batch: AlignerBatch) -> AlignerBatch:
        """ Process the output from the model

        Parameters
        ----------
        batch: :class:`AlignerBatch`
            The current batch from the model with :attr:`predictions` populated

        Returns
        -------
        :class:`AlignerBatch`
            The current batch with the :attr:`landmarks` populated
        """
    def get_pts_from_predict(self, batch: AlignerBatch):
        """ Get points from predictor and populate the :attr:`landmarks` property of the
        :class:`AlignerBatch`

        Parameters
        ----------
        batch: :class:`AlignerBatch`
            The current batch from the model with :attr:`predictions` populated
        """
          """ Process the output from the model """
          self.get_pts_from_predict(batch)
          return batch
  
    def get_pts_from_predict(self, batch: AlignerBatch):
        """ Get points from predictor and populate the :attr:`landmarks` property of the
        :class:`AlignerBatch`

        Parameters
        ----------
        batch: :class:`AlignerBatch`
            The current batch from the model with :attr:`predictions` populated
        """
    def get_pts_from_predict(self, batch: AlignerBatch):
        """ Get points from predictor and populate the :attr:`landmarks` property of the
        :class:`AlignerBatch`

        Parameters
        ----------
        batch: :class:`AlignerBatch`
            The current batch from the model with :attr:`predictions` populated
        """
          logger.debug("Obtain points from prediction")
          num_images, num_landmarks, height, width = batch["prediction"].shape
          image_slice = np.repeat(np.arange(num_images)[:, None], num_landmarks, axis=1)
          landmark_slice = np.repeat(np.arange(num_landmarks)[None, :], num_images, axis=0)
          resolution = np.full((num_images, num_landmarks), 64, dtype='int32')
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/fan.py