code: def test_select_partitions_returns_sensible_result(self):
      # Arrange
      col = [(u, "pk1") for u in range(50)]
      col += [(50 + u, "pk2") for u in range(50)]
      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
      )
      max_partitions_contributed = 2
  
      def privacy_id_extractor(x):
          return x[0]
  
      def partition_extractor(x):
          return x[1]
  
      # Act
      prdd = private_spark.make_private(
          dist_data, budget_accountant, privacy_id_extractor
      )
  
      select_partitions_params = agg.SelectPartitionsParams(
          max_partitions_contributed=max_partitions_contributed
      )
      actual_result = prdd.select_partitions(
          select_partitions_params, partition_extractor
      )
      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.
      self.assertEqual(sorted(actual_result.collect()), ["pk1", "pk2"])
  
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 22 for calling the PrivateRDDTest obj's underlying member method code with example usage: obj.test_select_partitions_returns_sensible_result() and returns: x, x