class_docstr: Specifies parameters for function DPEngine.aggregate()
  
  Attributes:
      metrics: A list of metrics to compute.
      noise_kind: The type of noise to use for the DP calculations.
      max_partitions_contributed: A bound on the number of partitions to
        which one unit of privacy (e.g., a user) can contribute.
      max_contributions_per_partition: A bound on the number of times one
        unit of privacy (e.g. a user) can contribute to a partition.
      max_contributions: A bound on the total number of times one unit of
        privacy (e.g., a user) can contribute.
      budget_weight: Relative weight of the privacy budget allocated to this
        aggregation.
      min_value: Lower bound on each value.
      max_value: Upper bound on each value.
      min_sum_per_partition: Lower bound on sum per partition. Used only for
      SUM metric calculations. It can not be set when min_value/max_value is
       set.
      max_sum_per_partition: Upper bound on sum per partition. Used only for
      SUM metric calculations. It can not be set when min_value/max_value is
       set.
      custom_combiners: Warning: experimental@ Combiners for computing custom
        metrics.
      vector_norm_kind: The type of norm. Used only for VECTOR_SUM metric
      calculations.
      vector_max_norm: Bound on each value of a vector. Used only for
       VECTOR_SUM metric calculations.
      vector_size: Number of coordinates in a vector. Used only for VECTOR_SUM
       metric calculations.
      contribution_bounds_already_enforced: assume that the input dataset
       complies with the bounds provided in max_partitions_contributed and
       max_contributions_per_partition. This option can be used if the dataset
       does not contain any identifiers that can be used to enforce
       contribution bounds automatically.
      public_partitions_already_filtered: if true, it is assumed that records
       with partition_key which are not in public_partitions are already
       removed from the dataset. It can only be used with public partitions.
      partition_selection_strategy: which strategy to use for private
       partition selection. It is ignored when public partitions are used.
      pre_threshold: the minimum amount of privacy units which are required
       for keeping a partition in private partition selection. Note that this
       is in addition to a differentially private partition selection, so a
       partition with pre_threshold privacy units isn't necessarily kept. It
       is ignored when public partitions are used.
       More details on pre-thresholding are in
       https://github.com/google/differential-privacy/blob/main/common_docs/pre_thresholding.md
code: def __str__(self):
      return parameters_to_readable_string(self)
  
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 7 for calling the AggregateParams obj's underlying member method code with example usage: obj.__str__() and returns: parameters_to_readable_string