                             self.config["allow_growth"],
                             self._exclude_gpus,
                             **self.kwargs)
  
      def process_input(self, batch):
          """ Compile the detection image(s) for prediction """
          batch["feed"] = (batch["image"] - 127.5) / 127.5
          return batch
  
      def predict(self, batch):
          """ Run model to get predictions """
 
</s> remove     def predict(self, batch):
        """ Run model to get predictions """
 </s> add     def predict(self, batch: dict) -> dict:
        """ Run model to get predictions

        Parameters
        ----------
        batch: dict
            Contains the batch to pass through the MTCNN model

        Returns
        -------
        dict
            The batch with the predictions added to the dictionary
        """ </s> remove         logger.trace("filename: %s, prediction: %s, mtcnn_points: %s",
 </s> add         logger.trace("filename: %s, prediction: %s, mtcnn_points: %s",  # type:ignore </s> remove     def process_output(self, batch):
        """ Post process the detected faces """
 </s> add     def process_output(self, batch: dict) -> dict:
        """ MTCNN performs no post processing so the original batch is returned

        Parameters
        ----------
        batch: dict
            Contains the batch to apply postprocessing to

        Returns
        -------
        dict
            The originally received batch
        """ </s> remove     def init_model(self):
        """ Initialize S3FD Model"""
 </s> add     def init_model(self) -> None:
        """ Initialize MTCNN Model. """
    def process_input(self, batch: dict) -> dict:
        """ Compile the detection image(s) for prediction

        Parameters
        ----------
        batch: dict
            Contains the batch that is currently being passed through the plugin process

        Returns
        -------
        dict
            The batch with input processed

        """
                             self.config["allow_growth"],
                             self._exclude_gpus,
                             **self.kwargs)
  
    def process_input(self, batch: dict) -> dict:
        """ Compile the detection image(s) for prediction

        Parameters
        ----------
        batch: dict
            Contains the batch that is currently being passed through the plugin process

        Returns
        -------
        dict
            The batch with input processed

        """
    def process_input(self, batch: dict) -> dict:
        """ Compile the detection image(s) for prediction

        Parameters
        ----------
        batch: dict
            Contains the batch that is currently being passed through the plugin process

        Returns
        -------
        dict
            The batch with input processed

        """
          batch["feed"] = (batch["image"] - 127.5) / 127.5
          return batch
  
      def predict(self, batch):
          """ Run model to get predictions """
keep keep keep keep replace replace keep keep keep keep keep
 Add CPU option to BiSeNet and MTCNN
  - Add CPU option to KSession
  - MTCNN optimizations
  - Update docs for bisenet + mtcnn 
plugins/extract/detect/mtcnn.py