class_docstr: Performs DP aggregations.
class_docstr_tok: ["Performs","DP","aggregations","."]
docstr: Computes DP aggregate metrics.
  
  Args:
    col: collection where all elements are of the same type.
    params: specifies which metrics to compute and computation parameters.
    data_extractors: functions that extract needed pieces of information
      from elements of 'col'.
    public_partitions: A collection of partition keys that will be present
      in the result. If not provided, partitions will be selected in a DP
      manner.
    out_explain_computation_report: an output argument, if specified,
      it will contain the Explain Computation report for this aggregation.
      For more details see the docstring to report_generator.py.
  
  Returns:
    Collection of (partition_key, result_dictionary), where
    'result_dictionary' contains computed metrics per partition_key.
    Keys of 'result_dictionary' correspond to computed metrics, e.g.
    'count' for COUNT metrics etc.
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code: def aggregate(
      self,
      col,
      params: pipeline_dp.AggregateParams,
      data_extractors: pipeline_dp.DataExtractors,
      public_partitions=None,
      out_explain_computation_report: Optional[
          pipeline_dp.ExplainComputationReport
      ] = None,
  ):
      """Computes DP aggregate metrics.
  
      Args:
        col: collection where all elements are of the same type.
        params: specifies which metrics to compute and computation parameters.
        data_extractors: functions that extract needed pieces of information
          from elements of 'col'.
        public_partitions: A collection of partition keys that will be present
          in the result. If not provided, partitions will be selected in a DP
          manner.
        out_explain_computation_report: an output argument, if specified,
          it will contain the Explain Computation report for this aggregation.
          For more details see the docstring to report_generator.py.
  
      Returns:
        Collection of (partition_key, result_dictionary), where
        'result_dictionary' contains computed metrics per partition_key.
        Keys of 'result_dictionary' correspond to computed metrics, e.g.
        'count' for COUNT metrics etc.
      """
      self._check_aggregate_params(col, params, data_extractors)
      self._check_budget_accountant_compatibility(
          public_partitions is not None,
          params.metrics,
          params.custom_combiners is not None,
      )
  
      with self._budget_accountant.scope(weight=params.budget_weight):
          self._add_report_generator(
              params, "aggregate", public_partitions is not None
          )
          if out_explain_computation_report is not None:
              out_explain_computation_report._set_report_generator(
                  self._current_report_generator
              )
          col = self._aggregate(
              col, params, data_extractors, public_partitions
          )
          budget = (
              self._budget_accountant._compute_budget_for_aggregation(
                  params.budget_weight
              )
          )
          return self._annotate(col, params=params, budget=budget)
  
imports: import functools
  from typing import Any, Callable, Optional, Sequence, Tuple, Union
  import pipeline_dp
  from pipeline_dp import budget_accounting
  from pipeline_dp import combiners
  from pipeline_dp import contribution_bounders
  from pipeline_dp import dp_computations
  from pipeline_dp import partition_selection
  from pipeline_dp import pipeline_functions
  from pipeline_dp import report_generator
  from pipeline_dp import sampling_utils
  from pipeline_dp.dataset_histograms import computing_histograms
  from pipeline_dp.private_contribution_bounds import PrivateL0Calculator
label_desc: Use image node_id 7 for calling the DPEngine obj's underlying member method code with example usage: obj.aggregate(col, params, data_extractors, public_partitions, out_explain_computation_report) and returns: self