code: def test_find_candidate_parameters_both_l0_and_linf_sum_to_be_tuned(
      self,
  ):
      mock_l0_histogram = histograms.Histogram(
          histograms.HistogramType.L0_CONTRIBUTIONS, bins=[]
      )
      mock_l0_histogram.max_value = mock.Mock(return_value=6)
      mock_linf_sum_contributions_histogram = histograms.Histogram(
          histograms.HistogramType.LINF_SUM_CONTRIBUTIONS,
          [
              _frequency_bin(max_value=1),
              _frequency_bin(max_value=2),
              _frequency_bin(max_value=3),
          ],
      )
  
      mock_histograms = histograms.DatasetHistograms(
          mock_l0_histogram,
          None,
          None,
          mock_linf_sum_contributions_histogram,
          None,
          None,
          None,
      )
      parameters_to_tune = parameter_tuning.ParametersToTune(
          max_partitions_contributed=True,
          min_sum_per_partition=False,
          max_sum_per_partition=True,
      )
  
      candidates = parameter_tuning._find_candidate_parameters(
          mock_histograms,
          parameters_to_tune,
          pipeline_dp.Metrics.SUM,
          max_candidates=5,
      )
      self.assertEqual(
          [1, 1, 6, 6], candidates.max_partitions_contributed
      )
      self.assertEqual([1, 3, 1, 3], candidates.max_sum_per_partition)
      self.assertEqual([0, 0, 0, 0], candidates.min_sum_per_partition)
  
imports: from absl.testing import absltest
  from absl.testing import parameterized
  from unittest import mock
  from unittest.mock import patch
  from typing import List, Optional
  import pipeline_dp
  import analysis
  from analysis import metrics
  from analysis import parameter_tuning
  from pipeline_dp.dataset_histograms import histograms
  from pipeline_dp.dataset_histograms import computing_histograms
label_desc: Use image node_id 6 for calling the ParameterTuning obj's underlying member method code with example usage: obj.test_find_candidate_parameters_both_l0_and_linf_sum_to_be_tuned() without return types