class_docstr: Performs DP aggregations.
class_docstr_tok: ["Performs","DP","aggregations","."]
code: def _check_aggregate_params(
      self,
      col,
      params: pipeline_dp.AggregateParams,
      data_extractors: pipeline_dp.DataExtractors,
      check_data_extractors: bool = True,
  ):
      if params.max_contributions is not None:
          supported_metrics = [
              pipeline_dp.Metrics.PRIVACY_ID_COUNT,
              pipeline_dp.Metrics.COUNT,
              pipeline_dp.Metrics.SUM,
              pipeline_dp.Metrics.MEAN,
          ]
          not_supported_metrics = set(params.metrics).difference(
              supported_metrics
          )
          if not_supported_metrics:
              raise NotImplementedError(
                  f"max_contributions is not supported for {not_supported_metrics}"
              )
      _check_col(col)
      if params is None:
          raise ValueError(
              "params must be set to a valid AggregateParams"
          )
      if not isinstance(params, pipeline_dp.AggregateParams):
          raise TypeError(
              "params must be set to a valid AggregateParams"
          )
      if check_data_extractors:
          _check_data_extractors(data_extractors)
      if params.contribution_bounds_already_enforced:
          if pipeline_dp.Metrics.PRIVACY_ID_COUNT in params.metrics:
              raise ValueError(
                  "PRIVACY_ID_COUNT cannot be computed when "
                  "contribution_bounds_already_enforced is True."
              )
      if params.post_aggregation_thresholding:
          if pipeline_dp.Metrics.PRIVACY_ID_COUNT not in params.metrics:
              raise ValueError(
                  "When post_aggregation_thresholding = True, "
                  "PRIVACY_ID_COUNT must be in metrics"
              )
  
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 18 for calling the DPEngine obj's underlying member method code with example usage: obj._check_aggregate_params(col, params, data_extractors, check_data_extractors) without return types