code: def test_unwrap_model_fn():
      """Test for unwrap_model_fn"""
  
      dummy_model = torch.nn.Sequential(
          torch.nn.Linear(10, 20),
          torch.nn.Linear(20, 40),
      )
  
      # test DataParallel wrapping
      wrapped_model_dp = torch.nn.DataParallel(dummy_model)
      unwrapped_model_dp = unwrap_model_fn(wrapped_model_dp)
      check_models(dummy_model, unwrapped_model_dp)
  
      # Initialize the distributed environment
      os.environ["MASTER_ADDR"] = "localhost"
      os.environ["MASTER_PORT"] = "1234"
      dist.init_process_group(backend="gloo", rank=0, world_size=1)
  
      # test DDP wrapping
      wrapped_model_ddp = torch.nn.parallel.DistributedDataParallel(
          dummy_model
      )
      unwrapped_model_ddp = unwrap_model_fn(wrapped_model_ddp)
  
      check_models(dummy_model, unwrapped_model_ddp)
      # clean up DDP environment
      dist.destroy_process_group()
  
imports: import os
  import torch
  import torch.distributed
  from utils.common_utils import unwrap_model_fn
label_desc: Use image node_id 2 for calling a global function with example usage: test_unwrap_model_fn() without return types