def METHOD_NAME(self, model_name):
    # create
    self.client_.create_deployment(
        model_name, "models:/{}/1".format(model_name), flavor="onnx"
    )
    # list
    deployment_list = self.client_.list_deployments()
    self.assertEqual(len(deployment_list), 1)
    self.assertEqual(deployment_list[0]["name"], model_name)
    # get
    deployment = self.client_.get_deployment(model_name)
    self.assertEqual(deployment["name"], model_name)
    # predict
    inputs = {}
    with open("./mlflow-triton-plugin/examples/input.json", "r") as f:
        input_json = json.load(f)
        for key, value in input_json["inputs"].items():
            inputs[key] = np.array(value, dtype=np.float32)
    output = self.client_.predict(model_name, inputs)
    with open("./mlflow-triton-plugin/examples/expected_output.json", "r") as f:
        output_json = json.load(f)
        for key, value in output_json["outputs"].items():
            np.testing.assert_allclose(
                output["outputs"][key],
                np.array(value, dtype=np.int32),
                err_msg="Inference result is not correct",
            )
    # delete
    self.client_.delete_deployment(model_name)