code: def _compute_linf_contributions_histogram_on_preaggregated_data(
      col, backend: pipeline_backend.PipelineBackend
  ):
      """Computes histogram of per partition privacy id contributions.
  
      This histogram contains: the number of (privacy id, partition_key)-pairs
      which have 1 row in the datasets, 2 rows 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.
      """
      linf = backend.map_tuple(
          col,
          lambda _, x: x[
              0
          ],  # x is (count, sum, n_partitions, n_contributions)
          "Extract count per partition contribution",
      )
      # linf: (int,) where each element is the count of elements the
      # corresponding privacy_id contributes to the partition.
      return _compute_frequency_histogram(
          linf, backend, hist.HistogramType.LINF_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 21 for calling a global function with example usage: _compute_linf_contributions_histogram_on_preaggregated_data(col, backend) and returns: _compute_frequency_histogram