          retval = self.model.predict(batch)[-1].transpose(0, 3, 1, 2)
          logger.trace(retval.shape)
          return retval
  
      def process_output(self, batch):
          """ 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 """
 
</s> remove         logger.trace(retval.shape)
 </s> add         logger.trace(retval.shape)  # type:ignore </s> remove     def get_pts_from_predict(self, batch):
        """ Get points from predictor """
 </s> add     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
        """ </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     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     @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"]):
    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
        """
          retval = self.model.predict(batch)[-1].transpose(0, 3, 1, 2)
          logger.trace(retval.shape)
          return retval
  
    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 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
        """
          self.get_pts_from_predict(batch)
          return batch
  
      def get_pts_from_predict(self, batch):
          """ Get points from predictor """
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