class_docstr: A combiner for utility analysis counts.
class_docstr_tok: ["A","combiner","for","utility","analysis","counts","."]
code: def create_accumulator(
      self, sparse_acc: Tuple[np.ndarray, np.ndarray, np.ndarray]
  ):
      count, sum_, n_partitions = sparse_acc
      max_partitions = self._params.max_partitions_contributed
      prob_keep_partition = np.where(
          n_partitions > 0,
          np.minimum(1, max_partitions / n_partitions),
          0,
      )
      acc = (list(prob_keep_partition), None)
      empty_acc = ([], None)
      # 'acc' can contain many probabilities and in that case it is better to
      # convert it to moments. The next line achieves this.
      return _merge_partition_selection_accumulators(acc, empty_acc)
  
imports: import abc
  import copy
  from dataclasses import dataclass
  from typing import Any, List, Optional, Tuple
  import numpy
  import math
  import pipeline_dp
  from pipeline_dp import budget_accounting
  from pipeline_dp import dp_computations
  from analysis import metrics
  from analysis import poisson_binomial
  from pipeline_dp import partition_selection
label_desc: Use image node_id 2 for calling the PartitionSelectionCombiner obj's underlying member method code with example usage: obj.create_accumulator(sparse_acc) and returns: _merge_partition_selection_accumulators