def METHOD_NAME(in_dtype, quant_args, in_data, verify_output_data):
    shape = in_data.shape
    input_data = relay.var("input_data", shape=shape, dtype=in_dtype)
    min_range = quant_args["min_range"]
    max_range = quant_args["max_range"]
    dequantized_output = dequantize_mxnet_min_max(
        input_data, min_range=min_range, max_range=max_range, in_dtype=in_dtype
    )
    mod = relay.Function(relay.analysis.free_vars(dequantized_output), dequantized_output)
    mod = tvm.IRModule.from_expr(mod)
    with tvm.transform.PassContext(opt_level=3):
        graph, lib, params = relay.build(mod, "llvm", params=None)
        rt_mod = graph_executor.create(graph, lib, device=tvm.cpu(0))
        rt_mod.set_input(input_data=in_data)
        rt_mod.set_input(**params)
        rt_mod.run()
        res = rt_mod.get_output(0).numpy()
        assert np.allclose(res, verify_output_data)
        assert res.dtype == np.float32