code: def compute_on_spark_dataframes() -> None:
      spark = (
          SparkSession.builder.master("local[1]")
          .appName("Restaurant")
          .getOrCreate()
      )
      df = load_data_in_spark_dataframe(spark)
      result_df = compute_private_result(df)
      result_df.printSchema()
      delete_if_exists(FLAGS.output_file)
      result_df.write.format("csv").option("header", True).save(
          FLAGS.output_file
      )
  
file_path: pipelinedp/examples/restaurant_visits/run_on_dataframes.py
imports: from absl import app
  from absl import flags
  import os
  import shutil
  import pandas
  from pyspark.sql import SparkSession
  import pyspark
  import apache_beam
  from apache_beam.runners.portability import fn_api_runner
  from apache_beam.dataframe.io import read_csv
  import pipeline_dp
  from pipeline_dp import dataframes
  from typing import Union
label_desc: Use image node_id 4 for calling a global function with example usage: compute_on_spark_dataframes() without return types