class_docstr: Specifies parameters for differentially-private partition selection.
  
  Attributes:
      max_partitions_contributed: Maximum number of partitions per privacy ID.
          The algorithm will drop contributions over this limit. To keep more
          data, this should be a good estimate of the realistic upper bound.
          Significantly over- or under-estimating this may increase the number
          of dropped partitions.
      budget_weight: Relative weight of the privacy budget allocated to
          partition selection.
      partition_selection_strategy: which strategy to use for private
       partition selection.
code: def __post_init__(self):
      if self.pre_threshold is not None:
          _check_is_positive_int(self.pre_threshold, "pre_threshold")
  
imports: import logging
  import math
  from dataclasses import dataclass
  from enum import Enum
  from typing import Any, Sequence, Callable, Optional, List
  import numpy
  from pipeline_dp import input_validators
label_desc: Use image node_id 1 for calling the SelectPartitionsParams obj's underlying member method code with example usage: obj.__post_init__() without return types