code: def main(unused_argv):
      # Here, we use a local backend for computations. This does not depend on
      # any pipeline framework and it is implemented in pure Python in
      # PipelineDP. It keeps all data in memory and is not optimized for large data.
      # For datasets smaller than ~tens of megabytes, local execution without any
      # framework is faster than local mode with Beam or Spark.
      backend = pipeline_dp.LocalBackend()
  
      # Define the privacy budget available for our computation.
      budget_accountant = pipeline_dp.NaiveBudgetAccountant(
          total_epsilon=1, total_delta=1e-6
      )
  
      # Load and parse input data
      df = pd.read_csv(FLAGS.input_file)
      df.rename(
          inplace=True,
          columns={
              "VisitorId": "user_id",
              "Time entered": "enter_time",
              "Time spent (minutes)": "spent_minutes",
              "Money spent (euros)": "spent_money",
              "Day": "day",
          },
      )
      df["count"] = 0
      df_day_count = df[["day", "count"]].groupby("day").count()
      day_count = [
          (row[0], row[1]["count"]) for row in df_day_count.iterrows()
      ]
      # day_count has data [(1, 300), ...]
  
      # Create a DPEngine instance.
      dp_engine = pipeline_dp.DPEngine(budget_accountant, backend)
  
      params = pipeline_dp.AddDPNoiseParams(
          noise_kind=pipeline_dp.NoiseKind.GAUSSIAN,
          # there are data for 7 days (partitions), so each visitor can contribute
          # to not more than to 7 partitions
          l0_sensitivity=7,
          # Assume visitor can not go to more than 5 restaurants per day.
          linf_sensitivity=5,
      )
  
      # A report that helps to explain computations in the pipeline.
      explain_computation_report = (
          pipeline_dp.ExplainComputationReport()
      )
  
      # Create a computational graph for the aggregation.
      # All computations are lazy. dp_result is iterable, but iterating it would
      # fail until budget is computed (below).
      # It’s possible to call DPEngine.aggregate multiple times with different
      # metrics to compute.
      dp_result = dp_engine.add_dp_noise(
          day_count,
          params,
          out_explain_computation_report=explain_computation_report,
      )
  
      budget_accountant.compute_budgets()
  
      print(explain_computation_report.text())
  
      # Here's where the lazy iterator initiates computations and gets transformed
      # into actual results
      dp_result = list(dp_result)
  
      # Save the results
      write_to_file(dp_result, FLAGS.output_file)
  
      return 0
  
imports: from absl import app
  from absl import flags
  import pipeline_dp
  import pandas
label_desc: Use image node_id 2 for calling a global function with example usage: main(unused_argv) and returns: int