code: def _compute_partition_privacy_id_count_histogram_on_preaggregated_data(
      col, backend: pipeline_backend.PipelineBackend
  ):
      """Computes a histogram of privacy id counts per partition.
  
      This histogram contains: the number of partitions with privacy_id_count=1,
      with privacy_id_count=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.keys(col, "Extract partition keys")
      # col: (pk)
      col = backend.count_per_element(
          col, "Count privacy IDs per partition key"
      )
      # col: (pk, n)
      col = backend.values(col, "Drop partition keys")
  
      return _compute_frequency_histogram(
          col,
          backend,
          hist.HistogramType.COUNT_PRIVACY_ID_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 25 for calling a global function with example usage: _compute_partition_privacy_id_count_histogram_on_preaggregated_data(col, backend) and returns: _compute_frequency_histogram