def METHOD_NAME(model: "torchbenchmark.util.model.BenchmarkModel", backend_args: List[str]):
    FP16 = True if model.dargs.precision == "fp16" else False
    HF_MODEL = True if is_hf_model(model) else False
    assert (
        model.device == "cuda" and model.test == "eval"
    ), f"fx2trt only works on CUDA inference tests."
    def _fx2trt():
        from torch_tensorrt.fx import compile
        from torch_tensorrt.fx.utils import LowerPrecision
        module, example_inputs = model.get_module()
        precision = LowerPrecision.FP16 if FP16 else LowerPrecision.FP32
        if HF_MODEL:
            from transformers.utils.fx import symbolic_trace as hf_symbolic_trace
            traced_model = hf_symbolic_trace(
                module, batch_size=model.batch_size, sequence_lenghth=model.max_length
            )
            trt_model = compile(
                traced_model,
                example_inputs,
                max_batch_size=model.batch_size,
                lower_precision=precision,
                explicit_batch_dimension=True,
                max_workspace_size=20 << 30,
            )
        else:
            trt_model = compile(
                module=module,
                input=example_inputs,
                max_batch_size=model.batch_size,
                lower_precision=precision,
            )
        model.set_module(trt_model)
    return _fx2trt, backend_args