code: def __init__(self):
      # For mutiple process scheduled in same node, torch.hub.load doesn't
      # handle the multi process to dowloand module well. So this is to make sure
      # there is only one replica to download the package
      if os.path.exists("/home/ray/.cache/torch/") is False:
          self.utils = torch.hub.load(
              "NVIDIA/DeepLearningExamples:torchhub",
              "nvidia_convnets_processing_utils",
          )
          with open("/home/ray/.cache/torch/success", "w") as _:
              pass
  
      else:
          counter = 3
          while counter:
              print("waiting for torch hub NVIDIA package download...")
              time.sleep(20)
              if os.path.exists("/home/ray/.cache/torch/success"):
                  self.utils = torch.hub.load(
                      "NVIDIA/DeepLearningExamples:torchhub",
                      "nvidia_convnets_processing_utils",
                  )
                  break
              counter -= 1
          if counter == 0:
              raise Exception(
                  "Failed to load module nvidia_convnets_processing_utils"
              )
  
file_path: ray/release/serve_tests/workloads/serve_resnet_benchmark.py
imports: import os
  from typing import List, Optional
  import asyncio
  import time
  import aiohttp
  import click
  import numpy
  import starlette.requests
  import torch
  from torchvision import models
  from ray import serve
  from ray.serve.handle import DeploymentHandle
  from serve_test_utils import save_test_results
label_desc: Use image node_id 1 to create a new DataDownloader object  with example: obj = DataDownloader()