code: def _compute_partition_sum_histogram(
      col, backend: pipeline_backend.PipelineBackend
  ):
      """Computes histogram of sum per partition.
  
      This histogram contains: the number of partition_keys which have sum of
      values X_1, X_2, ..., X_n, where X_1 = min_sum, X_n = one before max sum and
      n is equal to NUMBER_OF_BUCKETS_SUM_HISTOGRAM.
  
      Args:
          col: collection with elements ((privacy_id, partition_key), value).
          backend: PipelineBackend to run operations on the collection.
      Returns:
          1 element collection, which contains the computed hist.Histogram.
      """
  
      col = backend.map_tuple(
          col,
          lambda pid_pk, value: (pid_pk[1], value),
          "Drop privacy id",
      )
      col = backend.sum_per_key(
          col, "Sum of contributions per partition"
      )
      # col: (pk, sum_per_partition)
      col = backend.values(col, "Drop keys")
      # col: (float)
      col = backend.to_multi_transformable_collection(col)
      lowers = _min_max_lowers(
          col, NUMBER_OF_BUCKETS_SUM_HISTOGRAM, backend
      )
  
      return _compute_frequency_histogram_helper_with_lowers(
          col, backend, hist.HistogramType.SUM_PER_PARTITION, lowers
      )
  
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 17 for calling a global function with example usage: _compute_partition_sum_histogram(col, backend) and returns: _compute_frequency_histogram_helper_with_lowers