code: def load_data_in_spark_dataframe(
      spark: SparkSession,
  ) -> pyspark.sql.dataframe.DataFrame:
      df = spark.read.csv(
          FLAGS.input_file, header=True, inferSchema=True
      )
      return (
          df.withColumnRenamed("VisitorId", "visitor_id")
          .withColumnRenamed("Time entered", "enter_time")
          .withColumnRenamed("Time spent (minutes)", "spent_minutes")
          .withColumnRenamed("Money spent (euros)", "spent_money")
          .withColumnRenamed("Day", "day")
      )
  
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 2 for calling a global function with example usage: load_data_in_spark_dataframe(spark) and returns: df