          self._samples = _Samples(model, coverage_ratio)
          self._model = model
          self._feeder = feeder
          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.
  
 
</s> remove     def _setup(self, input_a=None, input_b=None, output=None):
 </s> add     def _setup(self, input_a: str, input_b: str, output: str) -> None: </s> remove         self.images = {}
 </s> add         self.images: Dict[Literal["a", "b"], List[np.ndarray]] = {} </s> remove     def __init__(self, model, coverage_ratio, image_count, feeder, image_paths):
 </s> add     def __init__(self,
                 model: "ModelBase",
                 coverage_ratio: float,
                 image_count: int,
                 feeder: _Feeder,
                 image_paths: Dict[Literal["a", "b"], List[str]]) -> None: </s> remove         if output is None:
 </s> add         if not output: </s> remove         self._target = {}
        self._samples = {}
        self._masks = {}

        self._feeds = {side: self._load_generator(idx).minibatch_ab(images[side], batch_size, side)
                       for idx, side in enumerate(("a", "b"))}
 </s> add         self._feeds = {side: self._load_generator(side, False).minibatch_ab()
                       for side in get_args(Literal["a", "b"])}
        self._output_file = ""
          self._samples = _Samples(model, coverage_ratio)
          self._model = model
          self._feeder = feeder
          self._image_paths = image_paths
        self._output_file = ""
          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.
  
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