class_docstr: Performs DP aggregations.
class_docstr_tok: ["Performs","DP","aggregations","."]
docstr: Retrieves a collection of differentially-private partitions.
  
  Args:
    col: collection where all elements are of the same type.
    params: parameters, see doc for SelectPrivatePartitionsParams.
    data_extractors: functions that extract needed pieces of information
      from elements of 'col'. Only `privacy_id_extractor` and
      `partition_extractor` are required.
      `value_extractor` is not required.
docstr_tok: ["Retrieves","a","collection","of","differentially-private","partitions",".","Args",":","col",":","collection","where","all","elements","are","of","the","same","type",".","params",":","parameters",",","see","doc","for","SelectPrivatePartitionsParams",".","data_extractors",":","functions","that","extract","needed","pieces","of","information","from","elements","of","'col","'",".","Only","`","privacy_id_extractor","`","and","`","partition_extractor","`","are","required",".","`","value_extractor","`","is","not","required","."]
code: def select_partitions(
      self,
      col,
      params: pipeline_dp.SelectPartitionsParams,
      data_extractors: pipeline_dp.DataExtractors,
  ):
      """Retrieves a collection of differentially-private partitions.
  
      Args:
        col: collection where all elements are of the same type.
        params: parameters, see doc for SelectPrivatePartitionsParams.
        data_extractors: functions that extract needed pieces of information
          from elements of 'col'. Only `privacy_id_extractor` and
          `partition_extractor` are required.
          `value_extractor` is not required.
      """
      self._check_select_private_partitions(
          col, params, data_extractors
      )
      self._check_budget_accountant_compatibility(False, [], False)
  
      with self._budget_accountant.scope(weight=params.budget_weight):
          self._add_report_generator(params, "select_partitions")
          col = self._select_partitions(col, params, data_extractors)
          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 10 for calling the DPEngine obj's underlying member method code with example usage: obj.select_partitions(col, params, data_extractors) and returns: self