def METHOD_NAME():
    service_name = "isvc-pmml-v2-grpc"
    model_name = "pmml"
    predictor = V1beta1PredictorSpec(
        min_replicas=1,
        model=V1beta1ModelSpec(
            model_format=V1beta1ModelFormat(
                name="pmml",
            ),
            runtime="kserve-pmmlserver",
            storage_uri='gs://kfserving-examples/models/pmml',
            resources=V1ResourceRequirements(
                requests={'cpu': '10m', 'memory': '128Mi'},
                limits={'cpu': '100m', 'memory': '256Mi'}
            ),
            ports=[
                V1ContainerPort(
                    container_port=8081,
                    name="h2c",
                    protocol="TCP"
                )],
            args=["--model_name", model_name]
        )
    )
    isvc = V1beta1InferenceService(api_version=constants.KSERVE_V1BETA1,
                                   kind=constants.KSERVE_KIND,
                                   metadata=client.V1ObjectMeta(
                                       name=service_name, namespace=KSERVE_TEST_NAMESPACE),
                                   spec=V1beta1InferenceServiceSpec(predictor=predictor))
    kserve_client = KServeClient(
        config_file=os.environ.get("KUBECONFIG", "~/.kube/config"))
    kserve_client.create(isvc)
    kserve_client.wait_isvc_ready(
        service_name, namespace=KSERVE_TEST_NAMESPACE)
    json_file = open("./data/pmml_input_v2_grpc.json")
    payload = json.load(json_file)["inputs"]
    response = predict_grpc(service_name=service_name,
                            payload=payload, model_name=model_name)
    assert response.outputs[0].contents.bytes_contents[0] == b'setosa'
    assert response.outputs[1].contents.fp64_contents[0] == 1.0
    assert response.outputs[2].contents.fp64_contents[0] == 0.0
    assert response.outputs[3].contents.fp64_contents[0] == 0.0
    kserve_client.delete(service_name, KSERVE_TEST_NAMESPACE)