code: def aggregate_utility_analysis():
      # Load data
      restaurant_visits_rows = load_data(FLAGS.input_file)
      # Create aggregate_params, data_extractors and public partitions.
      aggregate_params = get_aggregate_params()
      data_extractors = get_data_extractors()
      public_partitions = (
          list(range(1, 8)) if FLAGS.public_partitions else None
      )
  
      options = analysis.UtilityAnalysisOptions(
          1, 1e-5, aggregate_params, get_multi_params()
      )
  
      result = analysis.perform_utility_analysis(
          restaurant_visits_rows,
          pipeline_dp.LocalBackend(),
          options,
          data_extractors,
          public_partitions,
      )
  
      # Here's where the lazy iterator initiates computations and gets transformed
      # into actual results
      result = list(result)
  
      # Save the results
      write_to_file(result, FLAGS.output_file)
  
file_path: pipelinedp/examples/restaurant_visits/run_without_frameworks_utility_analysis.py
imports: from absl import app
  from absl import flags
  import pipeline_dp
  import pandas
  import analysis.utility_analysis
  from analysis.utility_analysis_engine import UtilityAnalysisEngine
label_desc: Use image node_id 7 for calling a global function with example usage: aggregate_utility_analysis() without return types