content: import torch
  import torch_tensorrt
  import torch_tensorrt as torchtrt
  import torchvision
  
  torch.hub._validate_not_a_forked_repo = lambda a, b, c: True
  
  # load model
  model = (
      torch.hub.load("pytorch/vision:v0.10.0", "resnet50", pretrained=True)
      .eval()
      .to("cuda")
  )
  
  # Compile with Torch TensorRT;
  trt_model = torch_tensorrt.compile(
      model,
      inputs=[torch_tensorrt.Input((1, 3, 224, 224))],
      enabled_precisions={torch_tensorrt.dtype.f16},
  )
  
  ts_trt_model = torch.jit.trace(trt_model, torch.rand(1, 3, 224, 224).to("cuda"))
  
  # Save the model
  torch.jit.save(ts_trt_model, "/triton_example/model_repository/resnet50/1/model.pt")
  