code: def test_torchvision_models(model_fn):
      model = model_fn()
      model.eval()
      dummy_inputs = (torch.rand(2, 3, 224, 224),)
      traced = concrete_trace(model, dummy_inputs, dce=False)
      traced_dce = concrete_trace(model, dummy_inputs, dce=True)
      out_orig = model.forward(*dummy_inputs)
      out_traced = traced.forward(*dummy_inputs)
      out_traced_dce = traced_dce.forward(*dummy_inputs)
      assert check_equal(out_orig, out_traced), f"{traced.code}"
      assert check_equal(out_orig, out_traced_dce), f"{traced_dce.code}"
      del out_orig, out_traced, out_traced_dce
  
imports: import pytest
  import torch
  import torchvision.models
  from nni.common.concrete_trace_utils import concrete_trace
label_desc: Use image node_id 2 for calling a global function with example usage: test_torchvision_models(model_fn) without return types