code: def parse_args():
      parser = argparse.ArgumentParser(
          description="Test with cycle gan networks."
      )
      parser.add_argument(
          "--images",
          type=str,
          required=True,
          help="Test images, use comma to split multiple.",
      )
      parser.add_argument(
          "--gpu_id",
          type=str,
          default="0",
          help="gpu id: e.g. 0. use -1 for CPU",
      )
      parser.add_argument(
          "--pretrained",
          type=str,
          required=True,
          help="Load weights from previously saved parameters.",
      )
      parser.add_argument(
          "--loadSize",
          type=int,
          default=256,
          help="scale images to this size, you can increase it when you want to test large image",
      )
      parser.add_argument(
          "--output_nc",
          type=int,
          default=3,
          help="# of output image channels",
      )
      parser.add_argument(
          "--ngf",
          type=int,
          default=64,
          help="# of gen filters in first conv layer",
      )
      parser.add_argument(
          "--which_model_netG",
          type=str,
          default="resnet_9blocks",
          help="selects model to use for netG",
      )
      parser.add_argument(
          "--no_dropout",
          action="store_false",
          help="no dropout for the generator",
      )
      args = parser.parse_args()
      return args
  
imports: import argparse
  import mxnet
  from mxnet import image
  from matplotlib import pyplot
  from mxnet.gluon.data.vision import transforms
  from .train_cgan import define_G, Resize
label_desc: Use image node_id 1 for calling a global function with example usage: parse_args() and returns: args