code: def _compute_l0_contributions_histogram_on_preaggregated_data(
      col, backend: pipeline_backend.PipelineBackend
  ):
      """Computes histogram of the number of distinct partitions contributed by a privacy id.
  
      This histogram contains: number of privacy ids which contributes to 1
      partition, to 2 partitions etc.
  
      Args:
          col: collection with a pre-aggregated dataset, each element is
          (partition_key, (count, sum, n_partitions, n_contributions)).
          backend: PipelineBackend to run operations on the collection.
      Returns:
          1 element collection, which contains the computed histograms.Histogram.
      """
      col = backend.map_tuple(
          col,
          lambda _, x: (
              x[2],
              1.0 / x[2],
          ),  # x is (count, sum, n_partitions, n_contributions)
          "Extract n_partitions",
      )
      # col: (int,), where each element is the number of partitions the
      # corresponding privacy_id contributes.
      return _compute_weighted_frequency_histogram(
          col, backend, hist.HistogramType.L0_CONTRIBUTIONS
      )
  
imports: import bisect
  import operator
  from typing import List, Tuple
  import numpy
  import pipeline_dp
  from pipeline_dp import pipeline_backend, pipeline_functions
  from pipeline_dp.dataset_histograms import histograms
label_desc: Use image node_id 19 for calling a global function with example usage: _compute_l0_contributions_histogram_on_preaggregated_data(col, backend) and returns: _compute_weighted_frequency_histogram