                          self._num_images)
          self._feeder.set_timelapse_feed(images, batchsize)
          logger.debug("Set up time-lapse")
  
      def output_timelapse(self, timelapse_kwargs):
          """ Generate the time-lapse samples and output the created time-lapse to the specified
          output folder.
  
          Parameters
          ----------
 
</s> remove         if output is None:
 </s> add         if not output: </s> remove         self._output_file = str(output)
 </s> add         self._output_file = output </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() </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 clear_tensorboard(self):
 </s> add     def _update_viewers(self,
                        viewer: Optional[Callable[[np.ndarray, str], None]],
                        timelapse_kwargs: Optional[Dict[str, str]]) -> None:
        """ Update the preview viewer and timelapse output

        Parameters
        ----------
        viewer: :func:`scripts.train.Train._show` or ``None``
            The function that will display the preview image
        timelapse_kwargs: dict
            The keyword arguments for generating time-lapse previews. If a time-lapse preview is
            not required then this should be ``None``. Otherwise all values should be full paths
            the keys being `input_a`, `input_b`, `output`.
        """
        if viewer is not None:
            self._samples.images = self._feeder.generate_preview()
            samples = self._samples.show_sample()
            if samples is not None:
                viewer(samples,
                       "Training - 'S': Save Now. 'R': Refresh Preview. 'M': Toggle Mask. 'F': "
                       "Toggle Screen Fit-Actual Size. 'ENTER': Save and Quit")

        if timelapse_kwargs:
            self._timelapse.output_timelapse(timelapse_kwargs)

    def clear_tensorboard(self) -> None:
    def output_timelapse(self, timelapse_kwargs: Dict[str, str]) -> None:
                          self._num_images)
          self._feeder.set_timelapse_feed(images, batchsize)
          logger.debug("Set up time-lapse")
  
    def output_timelapse(self, timelapse_kwargs: Dict[str, str]) -> None:
          """ Generate the time-lapse samples and output the created time-lapse to the specified
          output folder.
  
          Parameters
          ----------
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