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 use_post_aggregation_thresholding(
      self, metric: pipeline_dp.Metric
  ) -> bool:
      return metric == pipeline_dp.Metrics.PRIVACY_ID_COUNT
  
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 12 for calling the DPStrategySelector obj's underlying member method code with example usage: obj.use_post_aggregation_thresholding(metric) and returns: unknown