code: def run_all_analyses(votes, lambda_laplace, gnmax_parameters, sigma2):
      """Sequentially runs all analyses.
  
      Args:
        votes: A matrix of votes, where each row contains votes in one instance.
        lambda_laplace: The scale of the Laplace noise (lambda).
        gnmax_parameters: A list of parameters for GNMax.
        sigma2: Shared parameter for the GNMax mechanisms.
  
      Returns:
        Five lists whose length is the number of queries.
      """
      print("=== Laplace Mechanism ===")
      eps_lap, _, _, _ = run_analysis(
          votes, "lnmax", lambda_laplace, None
      )
      print()
  
      # Does not go anywhere, for now
      # print('=== Gaussian Mechanism (simple) ===')
      # eps, _, _, _ = run_analysis(votes[:n,], 'gnmax', sigma1, None)
  
      eps_gnmax = [[] for p in gnmax_parameters]
      partition_gmax = [[] for p in gnmax_parameters]
      answered = [[] for p in gnmax_parameters]
      order_opt = [[] for p in gnmax_parameters]
      for i, p in enumerate(gnmax_parameters):
          print("=== Gaussian Mechanism (confident) {}: ===".format(p))
          (
              eps_gnmax[i],
              partition_gmax[i],
              answered[i],
              order_opt[i],
          ) = run_analysis(votes, "gnmax_conf", sigma2, p)
          print()
  
      return eps_lap, eps_gnmax, partition_gmax, answered, order_opt
  
file_path: privacy/research/pate_2018/ICLR2018/rdp_cumulative.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import math
  import os
  import pickle
  import sys
  from absl import app
  from absl import flags
  import matplotlib
  import matplotlib.pyplot
  import numpy
  import core
label_desc: Use image node_id 4 for calling a global function with example usage: run_all_analyses(votes, lambda_laplace, gnmax_parameters, sigma2) and returns: eps_lap,  eps_gnmax,  partition_gmax,  answered,  order_opt