code: def stylize(args):
      device = torch.device("cuda" if args.cuda else "cpu")
  
      content_transform = transforms.Compose(
          [
              transforms.ToTensor(),
              transforms.Lambda(lambda x: x.mul(255)),
          ]
      )
  
      content_image = utils.load_image(
          args.content_image, scale=args.content_scale
      )
      content_image = content_transform(content_image)
      content_image = content_image.unsqueeze(0).to(device)
  
      with torch.no_grad():
          style_model = torch.load(args.model)
          style_model.to(device)
          output = style_model(content_image).cpu()
          utils.save_image(args.output_image, output[0])
  
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 5 for calling a global function with example usage: stylize(args) without return types