          logger.debug("Set preview feed. Batchsize: %s", batchsize)
  
      def compile_sample(self, batch_size, samples=None, images=None):
          """ Training samples to display in the viewer """
          num_images = self.model.training_opts.get("preview_images", 14)
          num_images = min(batch_size, num_images) if batch_size is not None else num_images
          logger.debug("Compiling samples: (side: '%s', samples: %s)", self.side, num_images)
          images = images if images is not None else self.target
          samples = [samples[0:num_images]] if samples is not None else [self.samples[0:num_images]]
          if self.use_mask:
 
</s> remove     def __init__(self, side, images, model, use_mask, batch_size):
        logger.debug("Initializing %s: side: '%s', num_images: %s, batch_size: %s)",
                     self.__class__.__name__, side, len(images), batch_size)
 </s> add     def __init__(self, side, images, model, use_mask, batch_size, config):
        logger.debug("Initializing %s: side: '%s', num_images: %s, batch_size: %s, config: %s)",
                     self.__class__.__name__, side, len(images), batch_size, config) </s> remove         batchsize = min(len(self.images), self.model.training_opts.get("preview_images", 14))
 </s> add         preview_images = self.config.get("preview_images", 14)
        preview_images = min(max(preview_images, 2), 16)
        batchsize = min(len(self.images), preview_images) </s> remove                         self.model.training_opts.get("preview_images", 14))
 </s> add                         self.preview_images) </s> remove                                             training_opts.get("coverage_ratio", 0.625))
 </s> add                                             training_opts.get("coverage_ratio", 0.625),
                                            config)
        num_images = self.config.get("preview_images", 14)
          logger.debug("Set preview feed. Batchsize: %s", batchsize)
  
      def compile_sample(self, batch_size, samples=None, images=None):
          """ Training samples to display in the viewer """
        num_images = self.config.get("preview_images", 14)
          num_images = min(batch_size, num_images) if batch_size is not None else num_images
          logger.debug("Compiling samples: (side: '%s', samples: %s)", self.side, num_images)
          images = images if images is not None else self.target
          samples = [samples[0:num_images]] if samples is not None else [self.samples[0:num_images]]
          if self.use_mask:
keep keep keep keep replace keep keep keep keep keep
 Expose Augmentation Options to config 
plugins/train/trainer/_base.py