class_docstr: Function to score different max_partitions_contributed bounds.
  
  Suitable only for COUNT and PRIVACY_ID_COUNT aggregations.
code: def _l0_impact_dropped(self, k: int):
      # TODO: precalculate it and make it work in O(1) time.
      capped_contributions = map(
          lambda bin: max(
              min(
                  bin.lower,
                  self._max_partitions_contributed_best_upper_bound(),
              )
              - k,
              0,
          )
          * bin.count,
          self._l0_histogram.bins,
      )
      return sum(capped_contributions)
  
imports: from dataclasses import dataclass
  from functools import lru_cache
  from typing import List
  import pipeline_dp
  from pipeline_dp import dp_computations
  from pipeline_dp.dataset_histograms.histograms import Histogram
  from pipeline_dp import pipeline_functions
label_desc: Use image node_id 7 for calling the L0ScoringFunction obj's underlying member method code with example usage: obj._l0_impact_dropped(k) and returns: sum