def METHOD_NAME(
    use_vm, tflite_mobilenet_v1_1_quant, tflite_compile_model, imagenet_cat
):
    # some CI environments wont offer TFLite, so skip in case it is not present
    pytest.importorskip("tflite")
    inputs = np.load(imagenet_cat)
    input_dict = {"input": inputs["input"].astype("uint8")}
    tflite_compiled_model = tflite_compile_model(tflite_mobilenet_v1_1_quant, use_vm=use_vm)
    result = tvmc.run(
        tflite_compiled_model,
        inputs=input_dict,
        benchmark=True,
        hostname=None,
        device="cpu",
        profile=True,
    )
    # collect the top 5 results
    top_5_results = get_top_results(result, 5)
    top_5_ids = top_5_results[0]
    # IDs were collected from this reference:
    # https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/
    # java/demo/app/src/main/assets/labels_mobilenet_quant_v1_224.txt
    tiger_cat_mobilenet_id = 283
    assert (
        tiger_cat_mobilenet_id in top_5_ids
    ), "tiger cat is expected in the top-5 for mobilenet v1"
    assert isinstance(result.outputs, dict)
    assert isinstance(result.times, BenchmarkResult)
    assert "output_0" in result.outputs.keys()