code: def test_score_gaussian_noise_valid_values_calculates_score_correctly(
      self,
  ):
      params = construct_params(
          aggregation_noise_kind=pipeline_dp.NoiseKind.GAUSSIAN,
          aggregation_eps=0.9,
          aggregation_delta=0.001,
          max_partitions_contributed_upper_bound=100,
      )
      number_of_partitions = 200
      l0_histogram = hist.Histogram(
          name=hist.HistogramType.L0_CONTRIBUTIONS,
          bins=[
              hist.FrequencyBin(
                  lower=1, upper=2, count=100, sum=100, max=1
              ),
              hist.FrequencyBin(
                  lower=2, upper=6, count=10, sum=10, max=5
              ),
              hist.FrequencyBin(
                  lower=6, upper=100, count=20, sum=20, max=60
              ),
          ],
      )
      l0_scoring_function = (
          private_contribution_bounds.L0ScoringFunction(
              params, number_of_partitions, l0_histogram
          )
      )
  
      score_1 = l0_scoring_function.score(1)
      score_2 = l0_scoring_function.score(2)
      score_5 = l0_scoring_function.score(5)
      score_6 = l0_scoring_function.score(6)
      score_60 = l0_scoring_function.score(60)
      score_61 = l0_scoring_function.score(61)
      score_101 = l0_scoring_function.score(101)
      score_200 = l0_scoring_function.score(200)
  
      # sigma = gaussian_std(eps=0.9, delta=0.001) = 2.8127255538420286
      # -0.5 * 200 * sqrt(1) * sigma - 0.5 * ((2 - 1) * 10 + (6 - 1) * 20)
      self.assertAlmostEqual(-336, score_1, delta=10)
      # -0.5 * 200 * sqrt(2) * sigma - 0.5 * ((6 - 2) * 20)
      self.assertAlmostEqual(-437, score_2, delta=10)
      # -0.5 * 200 * sqrt(5) * sigma - 0.5 * ((6 - 5) * 20)
      self.assertAlmostEqual(-638, score_5, delta=10)
      # -0.5 * 200 * sqrt(6) * sigma - 0.5 * 0
      self.assertAlmostEqual(-688, score_6, delta=10)
      # -0.5 * 200 * sqrt(60) * sigma - 0.5 * 0
      self.assertAlmostEqual(-2178, score_60, delta=10)
      # -0.5 * 200 * sqrt(61) * sigma - 0.5 * 0
      self.assertAlmostEqual(-2196, score_61, delta=10)
      # -0.5 * 200 * sqrt(101) * sigma - 0.5 * 0
      self.assertAlmostEqual(-2826, score_101, delta=10)
      # -0.5 * 200 * sqrt(200) * sigma - 0.5 * 0
      self.assertAlmostEqual(-3977, score_200, delta=10)
  
file_path: pipelinedp/tests/private_contribution_bounds_test.py
imports: import unittest
  import pipeline_dp
  from pipeline_dp.dataset_histograms import histograms
  from pipeline_dp import private_contribution_bounds
label_desc: Use image node_id 6 for calling the L0ScoringFunctionTest obj's underlying member method code with example usage: obj.test_score_gaussian_noise_valid_values_calculates_score_correctly() without return types