code: def get_hyperparameter_search_space(
      feat_type: Optional[FEAT_TYPE_TYPE] = None,
      dataset_properties=None,
  ):
      keep_variance = UniformFloatHyperparameter(
          "keep_variance", 0.5, 0.9999, default_value=0.9999
      )
      whiten = CategoricalHyperparameter(
          "whiten", ["False", "True"], default_value="False"
      )
      cs = ConfigurationSpace()
      cs.add_hyperparameters([keep_variance, whiten])
      return cs
  
file_path: auto-sklearn/autosklearn/pipeline/components/feature_preprocessing/pca.py
imports: from typing import Optional
  import numpy
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import CategoricalHyperparameter, UniformFloatHyperparameter
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm
  from autosklearn.pipeline.constants import DENSE, UNSIGNED_DATA
  from autosklearn.util.common import check_for_bool
label_desc: Use image node_id 5 for calling the PCA obj's underlying member method code with example usage: obj.get_hyperparameter_search_space(feat_type, dataset_properties) and returns: cs