class_docstr: Chooses DPStrategy based on DP budget, computation sensitivites, etc
  
  It chooses noise_kind to minimize the noise std deviation.
  For non-public partitions it chooses partition selection strategy to
  minimize threshold. For more details see docstring to
  select_partition_selection_strategy().
code: def _get_dp_strategy_with_post_aggregation_threshold(
      self, l0_sensitivity: int
  ) -> DPStrategy:
      assert self._metric == pipeline_dp.Metrics.PRIVACY_ID_COUNT
      # Half delta goes to the noise, the other half for partition selection.
      # For more details see
      # https://github.com/google/differential-privacy/blob/main/common_docs/Delta_For_Thresholding.pdf
      delta_noise = self._delta / 2
      # linf sensitivity = 1, because metric = PRIVACY_ID_COUNT
      sensitivities = dp_computations.Sensitivities(
          l0=l0_sensitivity, linf=1
      )
      noise_kind = self.select_noise_kind(
          self._epsilon, delta_noise, sensitivities
      )
      partition_selection_strategy = (
          aggregate_params.noise_to_thresholding(
              noise_kind
          ).to_partition_selection_strategy()
      )
      return DPStrategy(
          noise_kind=noise_kind,
          partition_selection_strategy=partition_selection_strategy,
          post_aggregation_thresholding=True,
      )
  
imports: from dataclasses import dataclass
  from typing import Optional
  import pipeline_dp
  from pipeline_dp import aggregate_params
  from pipeline_dp import dp_computations
  from pipeline_dp import input_validators
label_desc: Use image node_id 7 for calling the DPStrategySelector obj's underlying member method code with example usage: obj._get_dp_strategy_with_post_aggregation_threshold(l0_sensitivity) and returns: DPStrategy