code: def _compute_partition_count_histogram_on_preaggregated_data(
      col, backend: pipeline_backend.PipelineBackend
  ):
      """Computes histogram of counts per partition.
  
      This histogram contains: the number of partitions with total count of
      contributions = 1, 2 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_values(
          col,
          lambda x: x[
              0
          ],  # x is (count, sum, n_partitions, n_contributions)
          "Extract partition key and count of privacy ID contributions",
      )
      # col: (pk, int)
      col = backend.sum_per_key(col, "Sum per partition")
      # col: (pk, int), where each element is the total count per partition.
      col = backend.values(col, "Drop partition keys")
      # col: (int,)
      return _compute_frequency_histogram(
          col, backend, hist.HistogramType.COUNT_PER_PARTITION
      )
  
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 23 for calling a global function with example usage: _compute_partition_count_histogram_on_preaggregated_data(col, backend) and returns: _compute_frequency_histogram