code: def test_calculate_returns_one_of_the_lower_bounds(self):
      # Arrange
      params = construct_params(
          aggregation_eps=0.9,
          aggregation_delta=1e-10,
          calculation_eps=0.1,
          aggregation_noise_kind=pipeline_dp.NoiseKind.GAUSSIAN,
          max_partitions_contributed_upper_bound=100,
      )
      partitions = [i + 1 for i in range(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
              ),
          ],
      )
      histograms = [
          hist.DatasetHistograms(
              l0_histogram,
              l1_contributions_histogram=None,
              linf_contributions_histogram=None,
              linf_sum_contributions_histogram=None,
              count_per_partition_histogram=None,
              count_privacy_id_per_partition=None,
              sum_per_partition_histogram=None,
          )
      ]
      backend = pipeline_dp.LocalBackend()
      calculator = private_contribution_bounds.PrivateL0Calculator(
          params, partitions, histograms, backend
      )
  
      # Act
      l0_bound = list(calculator.calculate())[0]
  
      # Assert
      self.assertIn(l0_bound, list(range(1, 101)))
  
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 1 for calling the PrivateL0CalculatorTest obj's underlying member method code with example usage: obj.test_calculate_returns_one_of_the_lower_bounds() without return types