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 __str__(self):
      return "Private Partitions"
  
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 2 for calling the SelectPartitionsParams obj's underlying member method code with example usage: obj.__str__() and returns: str