code: def test_tune_min_sum_per_partition_is_not_supported(self):
      tune_options = _get_tune_options(
          [pipeline_dp.Metrics.SUM],
          parameter_tuning.ParametersToTune(
              max_partitions_contributed=True,
              min_sum_per_partition=True,
              max_sum_per_partition=True,
          ),
      )
      contribution_histograms = histograms.DatasetHistograms(
          None, None, None, None, None, None, None
      )
      data_extractors = pipeline_dp.DataExtractors(
          privacy_id_extractor=lambda _: 0,
          partition_extractor=lambda _: 0,
      )
      with self.assertRaisesRegex(
          ValueError,
          "Tuning of min_sum_per_partition is not supported yet.",
      ):
          parameter_tuning.tune(
              input,
              pipeline_dp.LocalBackend(),
              contribution_histograms,
              tune_options,
              data_extractors,
          )
  
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 13 for calling the ParameterTuning obj's underlying member method code with example usage: obj.test_tune_min_sum_per_partition_is_not_supported() without return types