          logger.debug("Initializing %s: (model_input_size: %s, model_output_shape: %s, "
                       "training_opts: %s, landmarks: %s)",
                       self.__class__.__name__, model_input_size, model_output_size,
                       {key: val for key, val in training_opts.items() if key != "landmarks"},
                       bool(training_opts.get("landmarks", None)))
          self.batchsize = 0
          self.model_input_size = model_input_size
          self.model_output_size = model_output_size
          self.training_opts = training_opts
          self.mask_class = self.set_mask_class()
 
</s> remove                      "training_opts: %s, landmarks: %s)",
 </s> add                      "training_opts: %s, landmarks: %s, config: %s)", </s> remove     def __init__(self, model_input_size, model_output_size, training_opts):
 </s> add     def __init__(self, model_input_size, model_output_size, training_opts, config): </s> remove                      "batchers: '%s')", self.__class__.__name__, model, use_mask,
                     coverage_ratio, batchers)
 </s> add                      "preview_images: %s, batchers: '%s')", self.__class__.__name__, model,
                     use_mask, coverage_ratio, preview_images, batchers)
        self.preview_images = preview_images </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)
                     bool(training_opts.get("landmarks", None)), config)
          logger.debug("Initializing %s: (model_input_size: %s, model_output_shape: %s, "
                       "training_opts: %s, landmarks: %s)",
                       self.__class__.__name__, model_input_size, model_output_size,
                       {key: val for key, val in training_opts.items() if key != "landmarks"},
                     bool(training_opts.get("landmarks", None)), config)
          self.batchsize = 0
          self.model_input_size = model_input_size
          self.model_output_size = model_output_size
          self.training_opts = training_opts
          self.mask_class = self.set_mask_class()
keep keep keep keep replace keep keep keep keep keep
 Expose Augmentation Options to config 
lib/training_data.py