code: def test_promptflow_model_sparkudf_predict():
      # Assert predict with promptflow model
      logged_model = log_promptflow_example_model()
      # Assert predict with spark udf
      udf = mlflow.pyfunc.spark_udf(
          spark, logged_model.model_uri, result_type="string"
      )
      input_value = "Python Hello World!"
      df = spark.createDataFrame([{"text": input_value}])
      df = df.withColumn("output", udf("text"))
      pdf = df.toPandas()
      expected_result = f"Write a simple {input_value} program that displays the greeting message when executed.\n"
      assert pdf["output"].tolist() == [expected_result]
  
file_path: mlflow/tests/promptflow/test_promptflow_model_export.py
imports: import json
  from pathlib import Path
  import pytest
  from promptflow import load_flow
  from promptflow._sdk.entities._flow import Flow
  from pyspark.sql import SparkSession
  import mlflow
  from mlflow import MlflowException
  from mlflow.deployments import PredictionsResponse
  from mlflow.pyfunc.scoring_server import CONTENT_TYPE_JSON
  from tests.helper_functions import pyfunc_serve_and_score_model
label_desc: Use image node_id 8 for calling a global function with example usage: test_promptflow_model_sparkudf_predict() without return types