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 __init__(
      self,
      base_std: float,
      metric: pipeline_dp.Metric,
      noise: pipeline_dp.NoiseKind,
      l0_ratios_dropped: Sequence[Tuple[int, float]],
      linf_ratios_dropped: Sequence[Tuple[int, float]],
      partition_histogram: hist.Histogram,
  ):
      self._base_std = base_std
      self._metric = metric
      self._noise = noise
      self._l0_ratios_dropped = l0_ratios_dropped
      self._linf_ratios_dropped = linf_ratios_dropped
      self._partition_histogram = partition_histogram
  
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 1 to create a new CountErrorEstimator object  with example: obj = CountErrorEstimator(base_std, metric, noise, l0_ratios_dropped, linf_ratios_dropped, partition_histogram)