          load_process.start()
          logger.debug("Batching to queue: (side: '%s', is_timelapse: %s)", side, is_timelapse)
          return self.minibatch(side, is_timelapse, load_process)
  
      def load_batches(self, mem_gen, images, side, is_timelapse,
                       do_shuffle=True, batchsize=0):
          """ Load the warped images and target images to queue """
          logger.debug("Loading batch: (image_count: %s, side: '%s', is_timelapse: %s, "
 
</s> remove             self.load_batches,
            batch_shape,
 </s> add             method=self.load_batches,
            shapes=batch_shape,
            in_queue=queue_in,
            out_queue=queue_out, </s> remove     EVENT = mp.Event
    QUEUE = mp.Queue
 </s> add     CTX = mp.get_context("spawn")
    EVENT = CTX.Event </s> remove     def __init__(self, method, shapes,
 </s> add     def __init__(self, method, shapes, in_queue, out_queue, </s> remove     def train_one_batch(self, is_preview_iteration):
 </s> add     def train_one_batch(self, do_preview):
    @staticmethod
    def make_queues(side, is_timelapse):
        """ Create the buffer token queues for Fixed Producer Dispatcher """
        q_name = "timelapse_{}".format(side) if is_timelapse else "train_{}".format(side)
        q_names = ["{}_{}".format(q_name, direction) for direction in ("in", "out")]
        logger.debug(q_names)
        queues = [queue_manager.get_queue(queue) for queue in q_names]
        return queues

          load_process.start()
          logger.debug("Batching to queue: (side: '%s', is_timelapse: %s)", side, is_timelapse)
          return self.minibatch(side, is_timelapse, load_process)
  
    @staticmethod
    def make_queues(side, is_timelapse):
        """ Create the buffer token queues for Fixed Producer Dispatcher """
        q_name = "timelapse_{}".format(side) if is_timelapse else "train_{}".format(side)
        q_names = ["{}_{}".format(q_name, direction) for direction in ("in", "out")]
        logger.debug(q_names)
        queues = [queue_manager.get_queue(queue) for queue in q_names]
        return queues

      def load_batches(self, mem_gen, images, side, is_timelapse,
                       do_shuffle=True, batchsize=0):
          """ Load the warped images and target images to queue """
          logger.debug("Loading batch: (image_count: %s, side: '%s', is_timelapse: %s, "
keep keep keep add keep keep keep keep
 Minor FixedProducerDispatcher changes. Fix timelapse bug 
lib/training_data.py