      scaling: float, optional
          The amount to scale the final preview image by. Default: `1.0`
      image_count: int
          The number of preview images to be displayed in the time-lapse
      feeder: dict
          The :class:`_Feeder` for generating the time-lapse images.
      image_paths: dict
          The full paths to the training images for each side of the model
      """
      def __init__(self, model, coverage_ratio, image_count, feeder, image_paths):
          logger.debug("Initializing %s: model: %s, coverage_ratio: %s, image_count: %s, "
 
</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         images: list
            The list of full paths to the images for creating the time-lapse for this
            :class:`_Feeder`
 </s> add         images: dict
            The list of full paths to the images for creating the time-lapse for each side </s> remove     def __init__(self, model, coverage_ratio):
 </s> add     def __init__(self, model: "ModelBase", coverage_ratio: float) -> None: </s> remove         batch_size: int
            The requested batch size for each training iterations
        samples: dict, optional
            Dictionary for side "a", "b" of :class:`numpy.ndarray`. The sample images that should
            be used for creating the preview. If ``None`` then the samples will be generated from
            the internal random image generator. Default: ``None``
        images: dict, optional
            Dictionary for side "a", "b" of :class:`numpy.ndarray`. The target images that should
            be used for creating the preview. If ``None`` then the targets will be generated from
            the internal random image generator. Default: ``None``
        masks: dict, optional
 </s> add         image_count: int
            The number of images to limit the sample output to.
        feed: dict
            Dictionary for side "a", "b" of :class:`numpy.ndarray`. The images that should be fed
            into the model for obtaining a prediction
        samples: dict
            Dictionary for side "a", "b" of :class:`numpy.ndarray`. The 100% coverage target images
            that should be used for creating the preview.
        masks: dict </s> remove 
    Attributes
    ----------
    initialized: bool
        Flag to indicate whether :class:`ImageAugmentation` has been initialized with the training
        image size in order to cache certain augmentation operations (see :func:`initialize`)
    is_display: bool
        Flag to indicate whether these augmentations are for time-lapses/preview images (``True``)
        or standard training data (``False``)
 </s> add 
    feeder: :class:`_Feeder`
        The feeder for generating the time-lapse images.
      scaling: float, optional
          The amount to scale the final preview image by. Default: `1.0`
      image_count: int
          The number of preview images to be displayed in the time-lapse
    feeder: :class:`_Feeder`
        The feeder for generating the time-lapse images.
    feeder: :class:`_Feeder`
        The feeder for generating the time-lapse images.
      image_paths: dict
          The full paths to the training images for each side of the model
      """
      def __init__(self, model, coverage_ratio, image_count, feeder, image_paths):
          logger.debug("Initializing %s: model: %s, coverage_ratio: %s, image_count: %s, "
keep keep keep keep replace 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