class_docstr: 'Bounds' the contribution by privacy_id per and cross partitions.
  
  Because this is for Utility Analysis, it doesn't actually ensure that
  contribution bounds are enforced. Instead, it keeps track of probabilities
  different data points are kept under per-partition (Linf) and
  cross-partition (L0) contribution bounding.
  
  If partitions_sampling_prob < 1.0, partitions are subsampled. This sampling
  is deterministic and depends on partition key.
  
  Only works for count at the moment.
code: def __init__(self, partitions_sampling_prob: float):
      super().__init__()
      self._sampling_probability = partitions_sampling_prob
  
file_path: pipelinedp/analysis/contribution_bounders.py
imports: from pipeline_dp import contribution_bounders
  from pipeline_dp import sampling_utils
label_desc: Use image node_id 1 to create a new AnalysisContributionBounder object from inherited base classes: contribution_bounders with example: obj = AnalysisContributionBounder(partitions_sampling_prob)