code: def _get_tune_options(
      metrics: List[pipeline_dp.Metric],
      parameters_to_tune: parameter_tuning.ParametersToTune = parameter_tuning.ParametersToTune(
          max_partitions_contributed=True,
          max_contributions_per_partition=True,
      ),
  ) -> parameter_tuning.TuneOptions:
      return parameter_tuning.TuneOptions(
          epsilon=1,
          delta=1e-10,
          aggregate_params=_get_aggregate_params(metrics),
          function_to_minimize=parameter_tuning.MinimizingFunction.ABSOLUTE_ERROR,
          parameters_to_tune=parameters_to_tune,
          number_of_parameter_candidates=3,
      )
  
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 2 for calling a global function with example usage: _get_tune_options(metrics, parameters_to_tune) and returns: parameter_tuning