code: def init():
      global output_path, args
      global style_model, device
      output_path = os.environ["AZUREML_BI_OUTPUT_PATH"]
      print(f"output path: {output_path}")
      print(f"Cuda available? {torch.cuda.is_available()}")
  
      arg_parser = argparse.ArgumentParser(
          description="parser for fast-neural-style"
      )
      arg_parser.add_argument("--style", type=str, help="style name")
      args, unknown_args = arg_parser.parse_known_args()
      device = torch.device(
          "cuda" if torch.cuda.is_available() else "cpu"
      )
      with torch.no_grad():
          style_model = TransformerNet()
          model_path = Model.get_model_path(args.style)
          state_dict = torch.load(os.path.join(model_path))
          # remove saved deprecated running_* keys in InstanceNorm from the checkpoint
          for k in list(state_dict.keys()):
              if re.search(r"in\d+\.running_(mean|var)$", k):
                  del state_dict[k]
          style_model.load_state_dict(state_dict)
          style_model.to(device)
      print(f"Model loaded successfully. Path: {model_path}")
  
imports: import argparse
  import os
  import sys
  import re
  import json
  import traceback
  from PIL import Image
  import torch
  from torchvision import transforms
  from azureml.core.model import Model
label_desc: Use image node_id 3 for calling a global function with example usage: init() without return types