class_docstr: Function to score different max_partitions_contributed bounds.
  
  Suitable only for COUNT and PRIVACY_ID_COUNT aggregations.
code: def _l0_impact_noise(self, k: int):
      noise_params = dp_computations.ScalarNoiseParams(
          eps=self._params.aggregation_eps,
          delta=self._params.aggregation_delta,
          max_partitions_contributed=k,
          max_contributions_per_partition=1,
          noise_kind=self._params.aggregation_noise_kind,
          min_value=None,
          max_value=None,
          min_sum_per_partition=None,
          max_sum_per_partition=None,
      )
      return (
          self._number_of_partitions
          * dp_computations.compute_dp_count_noise_std(noise_params)
      )
  
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 6 for calling the L0ScoringFunction obj's underlying member method code with example usage: obj._l0_impact_noise(k) and returns: unknown