code: def _get_custom_params(cls):
      """
      :param cls: the original h2o estimator class.
      :return: a dictionary of params used to customize the behaviour of the sklearn wrapper.
      """
      custom = {}
      # if cls.__name__ in ['H2OGeneralizedLowRankEstimator']:
      #     custom.update(predictions_col=-1)
      if cls.__name__ in [
          "H2OAutoEncoderEstimator",
          "H2OGeneralizedLowRankEstimator",
          "H2OPrincipalComponentAnalysisEstimator",
          "H2OSingularValueDecompositionEstimator",
          "H2OTargetEncoderEstimator",
      ]:
          custom.update(
              predictions_col="all"
          )  # `predict` will return all columns (instead of a vector by default).
      if cls.__name__ in [
          "H2OGeneralizedLinearEstimator",
          "H2OGeneralizedAdditiveEstimator",
      ]:
          custom.update(
              distribution_param="family"
          )  # use algo `family` param to identify distribution (default is `distribution`).
      if cls.__name__ in [
          "H2ONaiveBayesEstimator",
          "H2OSupportVectorMachineEstimator",
      ]:
          custom.update(
              default_estimator_type="classifier"
          )  #  makes the generic sklearn estimator a `classifier` by default.
  
      if cls.__name__ in ["H2OAggregatorEstimator"]:
  
          def _transform(self, X):
              self._estimator.train(training_frame=X)
              return self._estimator.aggregated_frame
  
          custom.update(
              _fit=_noop,
              # _transform=attrgetter('aggregated_frame'),
              _transform=_transform,
          )
      return custom or None
  
imports: import inspect
  from operator import attrgetter
  import sys
  from sklearn.base import ClassifierMixin, RegressorMixin
  from h2o.utils.mixin import register_submodule
  from ..None import automl
  from ..None import estimators
  from ..None import transforms
  from .wrapper import estimator, params_as_h2o_frames, transformer, H2OConnectionMonitorMixin, H2OEstimatorPredictProbabilitiesSupport, H2OEstimatorScoreSupport, H2OEstimatorTransformSupport
label_desc: Use image node_id 3 for calling a global function with example usage: _get_custom_params(cls) and returns: unknown, self