code: def get_args(default_batch=128, default_z_dim=100):
      parser = argparse.ArgumentParser()
      parser.add_argument(
          "--gpu", help="comma separated list of GPU(s) to use."
      )
      parser.add_argument("--load", help="load model")
      parser.add_argument(
          "--sample",
          action="store_true",
          help="view generated examples",
      )
      parser.add_argument("--data", help="a jpeg directory")
      parser.add_argument(
          "--load-size",
          help="size to load the original images",
          type=int,
      )
      parser.add_argument(
          "--crop-size", help="crop the original images", type=int
      )
      parser.add_argument(
          "--final-size",
          default=64,
          type=int,
          help="resize to this shape as inputs to network",
      )
      parser.add_argument(
          "--z-dim",
          help="hidden dimension",
          type=int,
          default=default_z_dim,
      )
      parser.add_argument(
          "--batch", help="batch size", type=int, default=default_batch
      )
      global args
      args = parser.parse_args()
      if args.gpu:
          os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu
      return args
  
imports: import argparse
  import glob
  import numpy
  import os
  import tensorflow
  from tensorpack import *
  from tensorpack.tfutils.scope_utils import auto_reuse_variable_scope
  from tensorpack.utils.viz import stack_patches
  from GAN import GANModelDesc, GANTrainer, RandomZData
label_desc: Use image node_id 4 for calling a global function with example usage: get_args(default_batch, default_z_dim) and returns: args