class_docstr: A combiner for utility analysis privacy ID counts.
class_docstr_tok: ["A","combiner","for","utility","analysis","privacy","ID","counts","."]
code: def create_accumulator(
      self, sparse_acc: Tuple[np.ndarray, np.ndarray, np.ndarray]
  ) -> AccumulatorType:
      counts, _sum, n_partitions = sparse_acc
      counts = np.where(counts > 0, 1, 0)
      data = None, counts, n_partitions
      self._params.min_sum_per_partition = 0.0
      self._params.max_sum_per_partition = 1.0
      return super().create_accumulator(data)
  
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 PrivacyIdCountCombiner obj's underlying member method code with example usage: obj.create_accumulator(sparse_acc) and returns: super