          A list of ``str`` full paths to the images to be loaded.
  
      Returns
      -------
      numpy.ndarray
          The batch of images in `BGR` channel order.
  
      Notes
 
</s> remove         training, preview and timelapses.
 </s> add         training, preview and time-lapses. </s> remove         """ Timelapse samples """
        batch = next(self.timelapse_feed)
 </s> add         """ Compile the sample images for creating a time-lapse frame.

        Returns
        -------
        list
            The list of samples, targets and masks as :class:`numpy.ndarrays` for creating a
            time-lapse frame
        """
        batch = next(self._timelapse_feed) </s> remove     """ Batch images from a single side """
 </s> add     """ Handles the processing of a Batch for a single side.

    Parameters
    ----------
    side: {"a" or "b"}
        The side that this :class:`Batcher` belongs to
    images: list
        The list of full paths to the training images for this :class:`Batcher`
    model: plugin from :mod:`plugins.train.model`
        The selected model that will be running this trainer
    use_mask: bool
        ``True`` if a mask is required for training otherwise ``False``
    batch_size: int
        The size of the batch to be processed at each iteration
    config: :class:`lib.config.FaceswapConfig`
        The configuration for this trainer
    """ </s> remove     def overlay_foreground(backgrounds, foregrounds):
        """ Overlay the training images into the center of the background """
 </s> add     def _overlay_foreground(backgrounds, foregrounds):
        """ Overlay the preview images into the center of the background images

        Parameters
        ----------
        backgrounds: list
            List of :class:`numpy.ndarray` background images for placing the preview images onto
        backgrounds: list
            List of :class:`numpy.ndarray` preview images for placing onto the background images

        Returns
        -------
        :class:`numpy.ndarray`
            The preview images compiled into the full frame size for each preview
        """
    list
        Filenames in the correct order as they are returned
          A list of ``str`` full paths to the images to be loaded.
  
      Returns
      -------
    list
        Filenames in the correct order as they are returned
      numpy.ndarray
          The batch of images in `BGR` channel order.
  
      Notes
keep keep keep add keep keep keep keep
 Smart Masks - Training Implementation (#914)

* Smart Masks - Training

- Reinstate smart mask training code
- Reinstate mask_type back to model.config
- change 'replicate_input_mask to 'learn_mask'
- Add learn mask option
- Add mask loading from alignments to plugins.train.trainer
- Add mask_blur and mask threshold options
- _base.py - Pass mask options through training_opts dict
- plugins.train.model - check for mask_type not None for learn_mask and penalized_mask_loss
- Limit alignments loading to just those faces that appear in the training folder
- Raise error if not all training images have an alignment, and alignment file is required
- lib.training_data - Mask generation code
- lib.faces_detect - cv2 dimension stripping bugfix
- Remove cv2 linting code

* Update mask helptext in cli.py

* Fix Warp to Landmarks
Remove SHA1 hashing from training data

* Update mask training config

* Capture missing masks at training init

* lib.image.read_image_batch - Return filenames with batch for ordering

* scripts.train - Documentation

* plugins.train.trainer - documentation

* Ensure backward compatibility.
Fix convert for new predicted masks

* Update removed masks to components for legacy models. 
lib/image.py