class_docstr: Calculates differentially-private l0 bound (i.e. max_partitions_contributed).
docstr: Args:
    params: calculation parameters.
    partitions: pCollection of all partitions present in the data
      the calculated bound will be used for.
    histograms: pCollection consisting of one element of DatasetHistograms
      object.
    backend: pipeline backend to use for calculations.
docstr_tok: ["Args",":","params",":","calculation","parameters",".","partitions",":","pCollection","of","all","partitions","present","in","the","data","the","calculated","bound","will","be","used","for",".","histograms",":","pCollection","consisting","of","one","element","of","DatasetHistograms","object",".","backend",":","pipeline","backend","to","use","for","calculations","."]
code: def __init__(
      self,
      params: pipeline_dp.CalculatePrivateContributionBoundsParams,
      partitions,
      histograms,
      backend,
  ) -> None:
      """
      Args:
        params: calculation parameters.
        partitions: pCollection of all partitions present in the data
          the calculated bound will be used for.
        histograms: pCollection consisting of one element of DatasetHistograms
          object.
        backend: pipeline backend to use for calculations.
      """
      self._params = params
      self._backend = backend
      self._partitions = partitions
      self._histograms = histograms
  
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 1 to create a new PrivateL0Calculator object  with example: obj = PrivateL0Calculator(params, partitions, histograms, backend)