code: def compute_on_spark():
      master = "local[1]"  # run Spark locally with one worker thread to load the input file into 1 partition
      conf = pyspark.SparkConf().setMaster(master)
      sc = pyspark.SparkContext(conf=conf)
      movie_views = sc.textFile(FLAGS.input_file).mapPartitions(
          parse_partition
      )
      pipeline_backend = pipeline_dp.SparkRDDBackend(sc)
      dp_result = calculate_private_result(
          movie_views, pipeline_backend
      )
  
      delete_if_exists(FLAGS.output_file)
      dp_result.saveAsTextFile(FLAGS.output_file)
  
imports: from absl import app
  from absl import flags
  from apache_beam.runners.portability import fn_api_runner
  import pyspark
  from examples.movie_view_ratings.common_utils import *
  import pipeline_dp
  import numpy
label_desc: Use image node_id 5 for calling a global function with example usage: compute_on_spark() without return types