class_docstr: Function to score different max_partitions_contributed bounds.
  
  Suitable only for COUNT and PRIVACY_ID_COUNT aggregations.
docstr: Global sensitivity of the scoring function.
  
  Equals min(l0_upper_bound, number_of_partitions), because
  max_partitions_contributed upper bound is always at least
  number of partitions.
docstr_tok: ["Global","sensitivity","of","the","scoring","function",".","Equals","min","(","l0_upper_bound",",","number_of_partitions",")",",","because","max_partitions_contributed","upper","bound","is","always","at","least","number","of","partitions","."]
code: def global_sensitivity(self) -> float:
      """Global sensitivity of the scoring function.
  
      Equals min(l0_upper_bound, number_of_partitions), because
      max_partitions_contributed upper bound is always at least
      number of partitions."""
      return self._max_partitions_contributed_best_upper_bound()
  
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 4 for calling the L0ScoringFunction obj's underlying member method code with example usage: obj.global_sensitivity() and returns: self