class_docstr: Estimator of the error from DP pipeline from DatasetHistograms.
  
  The recommended way to create this object is to use create_error_estimator.
  It works only for COUNT and PRIVACY_ID_COUNT.
  
  Partition selection error is not implemented yet. Now only contribution
  bounding and noise error are taken into consideration.
code: def _get_stddev(
      self, l0_bound: int, linf_bound: Optional[int] = None
  ) -> float:
      if self._metric == pipeline_dp.Metrics.PRIVACY_ID_COUNT:
          linf_bound = 1
      if self._noise == pipeline_dp.NoiseKind.LAPLACE:
          return self._base_std * l0_bound * linf_bound
      # Gaussian noise case.
      return self._base_std * math.sqrt(l0_bound) * linf_bound
  
file_path: pipelinedp/pipeline_dp/dataset_histograms/histogram_error_estimator.py
imports: import pipeline_dp
  from pipeline_dp.dataset_histograms import histograms
  from typing import Optional, Sequence, Tuple
  import math
  import bisect
label_desc: Use image node_id 6 for calling the CountErrorEstimator obj's underlying member method code with example usage: obj._get_stddev(l0_bound, linf_bound) and returns: unknown, unknown