          self._image_paths = image_paths
          self._output_file = None
          logger.debug("Initialized %s", self.__class__.__name__)
  
      def _setup(self, input_a=None, input_b=None, output=None):
          """ Setup the time-lapse folder locations and the time-lapse feed.
  
          Parameters
          ----------
          input_a: str
 
</s> remove         self._output_file = None
 </s> add         self._output_file = "" </s> remove         if output is None:
 </s> add         if not output: </s> remove     def minibatch_ab(self, images, batchsize, side,
                     do_shuffle=True, is_preview=False, is_timelapse=False):
        """ A Background iterator to return augmented images, samples and targets.
 </s> add         if self._warp_to_landmarks:
            self._face_cache.pre_fill(images, side)
        self._processing = ImageAugmentation(batch_size,
                                             self._process_size,
                                             self._config)
        self._nearest_landmarks: Dict[str, Tuple[str, ...]] = {}
        logger.debug("Initialized %s", self.__class__.__name__) </s> remove     def compile_timelapse_sample(self):
        """ Compile the sample images for creating a time-lapse frame.

        Returns
        -------
        dict
            For sides "a" and "b"; The list of samples, targets and masks as
            :class:`numpy.ndarrays` for creating a time-lapse frame
        """
        batchsizes = []
        samples = {}
        images = {}
        masks = {}
        for side in ("a", "b"):
            batch = next(self._display_feeds["timelapse"][side])
            batchsizes.append(len(batch["samples"]))
            samples[side] = batch["samples"]
            images[side] = batch["targets"][-1]
            masks[side] = batch["masks"]
        batchsize = min(batchsizes)
        sample = self.compile_sample(batchsize, samples=samples, images=images, masks=masks)
        return sample

    def set_timelapse_feed(self, images, batch_size):
 </s> add     def set_timelapse_feed(self,
                           images: Dict[Literal["a", "b"], List[str]],
                           batch_size: int) -> None: </s> remove         for idx, side in enumerate(("a", "b")):
            self._display_feeds["timelapse"][side] = self._load_generator(idx).minibatch_ab(
                images[side][:batch_size],
                batch_size,
                side,
                do_shuffle=False,
                is_timelapse=True)
 </s> add         # MyPy can't recurse into nested dicts to get the type :(
        iterator = cast(Dict[Literal["a", "b"], Generator[BatchType, None, None]],
                        self._display_feeds["timelapse"])

        for side in get_args(Literal["a", "b"]):
            imgs = images[side]
            logger.debug("Setting preview feed: (side: '%s', images: %s)", side, len(imgs))

            iterator[side] = self._load_generator(side,
                                                  True,
                                                  batch_size=batch_size,
                                                  images=imgs).minibatch_ab()
    def _setup(self, input_a: str, input_b: str, output: str) -> None:
          self._image_paths = image_paths
          self._output_file = None
          logger.debug("Initialized %s", self.__class__.__name__)
  
    def _setup(self, input_a: str, input_b: str, output: str) -> None:
          """ Setup the time-lapse folder locations and the time-lapse feed.
  
          Parameters
          ----------
          input_a: str
keep keep keep keep replace keep keep keep keep keep
 Data Augmentation update (#1263)

- lib.detected_face
    - Subclass Masks for Landmark based masks
    - Add training mask propery + methods to DetectedFace
  - lib.training_training
    - subclass TrainingDataGenerator for training and preview data
    - Split cache into own module
    - Reduce thread count to 1 to prevent image corruption + data re-use
    - Process on largest model input/output size rather than stored image size
    - Size and crop masks during caching stage
    - Implement ring buffer for data flow
    - Fix preview reload bug
  - augmentation
    - typing
    - switch color aug order
    - better initialization
    - Fix warp + landmark warp to correctly apply at different image scales
    - Slightly improved warp caching
    - Don't store whether image is_preview. Handle all data as training images implicitly
  - plugins.trainer: Typing and fixes to work with trainingdata refactor 
plugins/train/trainer/_base.py