class_docstr: Performs DP aggregations.
class_docstr_tok: ["Performs","DP","aggregations","."]
docstr: Verifies that arguments for select_partitions are correct.
docstr_tok: ["Verifies","that","arguments","for","select_partitions","are","correct","."]
code: def _check_select_private_partitions(
      self,
      col,
      params: pipeline_dp.SelectPartitionsParams,
      data_extractors: pipeline_dp.DataExtractors,
  ):
      """Verifies that arguments for select_partitions are correct."""
      if col is None or not col:
          raise ValueError("col must be non-empty")
      if params is None:
          raise ValueError(
              "params must be set to a valid SelectPrivatePartitionsParams"
          )
      if not isinstance(params, pipeline_dp.SelectPartitionsParams):
          raise TypeError(
              "params must be set to a valid SelectPrivatePartitionsParams"
          )
      if (
          not isinstance(params.max_partitions_contributed, int)
          or params.max_partitions_contributed <= 0
      ):
          raise ValueError(
              "params.max_partitions_contributed must be set "
              "(to a positive integer)"
          )
      if data_extractors is None:
          raise ValueError(
              "data_extractors must be set to a pipeline_dp.DataExtractors"
          )
      if not isinstance(data_extractors, pipeline_dp.DataExtractors):
          raise TypeError(
              "data_extractors must be set to a pipeline_dp.DataExtractors"
          )
  
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 9 for calling the DPEngine obj's underlying member method code with example usage: obj._check_select_private_partitions(col, params, data_extractors) without return types