def METHOD_NAME():
    """
    End-to-end test of TF-Lite file using UMA
    """
    tflite_file = "/tmp/model.tflite"
    if os.path.exists(tflite_file):
        os.remove(tflite_file)
    generate_tflite_file(tflite_file)
    pytest.importorskip("tflite")
    interpreter = tf.lite.Interpreter(model_path=tflite_file)
    tf_model_details = interpreter.get_input_details()
    mod, _, params = create_relay_module_and_inputs_from_tflite_file(
        tflite_file, bind_params_by_name=False
    )
    uma_backend = VanillaAcceleratorBackend()
    uma_backend.register()
    target = tvm.target.Target("vanilla_accelerator", host=tvm.target.Target("c"))
    target_c = tvm.target.Target("c")
    # Generation of test input and output
    data_shape = [int(x) for x in mod["main"].params[0].type_annotation.shape]
    data = np.random.uniform(size=data_shape).astype("float32")
    input_list = {str(tf_model_details[0]["name"]): data}
    output_list = generate_ref_data(mod, input_list, params)
    # UMA partitioning (needs to be done after generate_ref_data)
    mod = uma_backend.partition(mod)
    aot_test_model = AOTTestModel(module=mod, inputs=input_list, outputs=output_list, params=params)
    test_runner = AOTTestRunner(
        pass_config={"tir.usmp.enable": True, "tir.usmp.algorithm": "greedy_by_size"}
    )
    compile_and_run(
        aot_test_model,
        test_runner,
        interface_api="c",
        use_unpacked_api=True,
        workspace_byte_alignment=1,
        debug_calculated_workspaces=False,
        target=[target_c, target],
    )