code: def main():
      main_arg_parser = argparse.ArgumentParser(
          description="parser for fast-neural-style"
      )
      subparsers = main_arg_parser.add_subparsers(
          title="subcommands", dest="subcommand"
      )
  
      train_arg_parser = subparsers.add_parser(
          "train", help="parser for training arguments"
      )
      train_arg_parser.add_argument(
          "--epochs",
          type=int,
          default=2,
          help="number of training epochs, default is 2",
      )
      train_arg_parser.add_argument(
          "--batch_size",
          type=int,
          default=8,
          help="batch size for training, default is 8",
      )
      train_arg_parser.add_argument(
          "--dataset",
          type=str,
          required=True,
          choices={"test", "folder", "mscoco"},
          help="type of dataset to be used.",
      )
      train_arg_parser.add_argument(
          "--dataroot",
          type=str,
          required=True,
          help="path to training dataset, the path should point to a folder "
          "containing another folder with all the training images",
      )
      train_arg_parser.add_argument(
          "--style_image",
          type=str,
          default="test",
          help="path to style-image",
      )
      train_arg_parser.add_argument(
          "--test_image",
          type=str,
          default="test",
          help="path to test-image",
      )
      train_arg_parser.add_argument(
          "--checkpoint_model_dir",
          type=str,
          default="/tmp/checkpoints",
          help="path to folder where checkpoints of trained models will be saved",
      )
      train_arg_parser.add_argument(
          "--checkpoint_interval",
          type=int,
          default=1,
          help="number of batches after which a checkpoint of trained model will be created",
      )
      train_arg_parser.add_argument(
          "--image_size",
          type=int,
          default=256,
          help="size of training images, default is 256 X 256",
      )
      train_arg_parser.add_argument(
          "--style_size",
          type=int,
          default=None,
          help="size of style-image, default is the original size of style image",
      )
      train_arg_parser.add_argument(
          "--cuda",
          type=int,
          default=1,
          help="set it to 1 for running on GPU, 0 for CPU",
      )
      train_arg_parser.add_argument(
          "--seed",
          type=int,
          default=42,
          help="random seed for training",
      )
      train_arg_parser.add_argument(
          "--content_weight",
          type=float,
          default=1e5,
          help="weight for content-loss, default is 1e5",
      )
      train_arg_parser.add_argument(
          "--style_weight",
          type=float,
          default=1e10,
          help="weight for style-loss, default is 1e10",
      )
      train_arg_parser.add_argument(
          "--lr",
          type=float,
          default=1e-3,
          help="learning rate, default is 1e-3",
      )
  
      eval_arg_parser = subparsers.add_parser(
          "eval", help="parser for evaluation/stylizing arguments"
      )
      eval_arg_parser.add_argument(
          "--content_image",
          type=str,
          required=True,
          help="path to content image you want to stylize",
      )
      eval_arg_parser.add_argument(
          "--content_scale",
          type=float,
          default=None,
          help="factor for scaling down the content image",
      )
      eval_arg_parser.add_argument(
          "--output_image",
          type=str,
          required=True,
          help="path for saving the output image",
      )
      eval_arg_parser.add_argument(
          "--model",
          type=str,
          required=True,
          help="saved model to be used for stylizing the image.",
      )
      eval_arg_parser.add_argument(
          "--cuda",
          type=int,
          required=True,
          help="set it to 1 for running on GPU, 0 for CPU",
      )
  
      args = main_arg_parser.parse_args()
  
      if args.subcommand is None:
          raise ValueError("ERROR: specify either train or eval")
      if args.cuda and not torch.cuda.is_available():
          raise ValueError(
              "ERROR: cuda is not available, try running on CPU"
          )
  
      if args.subcommand == "train":
          check_manual_seed(args)
          check_paths(args)
          train(args)
      else:
          stylize(args)
  
imports: import argparse
  import random
  from collections import OrderedDict
  from pathlib import Path
  import numpy
  import torch
  import utils
  from handlers import Progbar
  from torch.optim import Adam
  from torch.utils.data import DataLoader
  from torchvision import datasets, transforms
  from transformer_net import TransformerNet
  from vgg import Vgg16
  from ignite.engine import Engine, Events
  from ignite.handlers import ModelCheckpoint
label_desc: Use image node_id 6 for calling a global function with example usage: main() without return types