code: def test_sum_returns_sensible_result(self):
      # Arrange
      col = [(f"{u}", "pk1", 100.0) for u in range(30)]
      col += [(f"{u + 30}", "pk1", -100.0) for u in range(30)]
  
      dist_data = PrivateRDDTest.sc.parallelize(col)
      # Use very high epsilon and delta to minimize noise and test
      # flakiness.
      budget_accountant = budget_accounting.NaiveBudgetAccountant(
          total_epsilon=800, total_delta=0.999
      )
  
      def privacy_id_extractor(x):
          return x[0]
  
      prdd = private_spark.make_private(
          dist_data, budget_accountant, privacy_id_extractor
      )
      sum_params = agg.SumParams(
          noise_kind=pipeline_dp.NoiseKind.GAUSSIAN,
          max_partitions_contributed=2,
          max_contributions_per_partition=3,
          min_value=1.55,
          max_value=2.7889,
          budget_weight=1,
          partition_extractor=lambda x: x[1],
          value_extractor=lambda x: x[2],
      )
  
      # Act
      actual_result = prdd.sum(sum_params)
      budget_accountant.compute_budgets()
  
      # Assert
      # This is a health check to validate that the result is sensible.
      # Hence, we use a very large tolerance to reduce test flakiness.
      expected_result_dict = {"pk1": 130.167}
      actual_result_dict = self.to_dict(actual_result.collect())
  
      for pk, sum in actual_result_dict.items():
          self.assertTrue(
              self.value_per_key_within_tolerance(
                  sum, expected_result_dict[pk], 5.0
              )
          )
  
imports: import pyspark
  from pyspark import SparkContext
  from absl.testing import parameterized
  from unittest.mock import patch
  import unittest
  import sys
  import collections
  import pipeline_dp
  from pipeline_dp import aggregate_params
  from pipeline_dp import budget_accounting, private_spark
label_desc: Use image node_id 13 for calling the PrivateRDDTest obj's underlying member method code with example usage: obj.test_sum_returns_sensible_result() and returns: x