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().
docstr: Returns the noise with the minimum standard deviation.
docstr_tok: ["Returns","the","noise","with","the","minimum","standard","deviation","."]
code: def select_noise_kind(
      self,
      epsilon: float,
      delta: float,
      sensitivities: dp_computations.Sensitivities,
  ) -> pipeline_dp.NoiseKind:
      """Returns the noise with the minimum standard deviation."""
      if delta == 0:
          return pipeline_dp.NoiseKind.LAPLACE
      gaussian_std = self._get_gaussian_std(
          epsilon, delta, sensitivities
      )
      laplace_std = self._get_laplace_std(epsilon, sensitivities)
      if gaussian_std < laplace_std:
          return pipeline_dp.NoiseKind.GAUSSIAN
      return pipeline_dp.NoiseKind.LAPLACE
  
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 11 for calling the DPStrategySelector obj's underlying member method code with example usage: obj.select_noise_kind(epsilon, delta, sensitivities) and returns: pipeline_dp, pipeline_dp, pipeline_dp