code: def get_hyperparameter_search_space(
      feat_type: Optional[FEAT_TYPE_TYPE] = None,
      dataset_properties=None,
  ):
      # More than degree 3 is too expensive!
      degree = UniformIntegerHyperparameter("degree", 2, 3, 2)
      interaction_only = CategoricalHyperparameter(
          "interaction_only", ["False", "True"], "False"
      )
      include_bias = CategoricalHyperparameter(
          "include_bias", ["True", "False"], "True"
      )
  
      cs = ConfigurationSpace()
      cs.add_hyperparameters([degree, interaction_only, include_bias])
  
      return cs
  
file_path: auto-sklearn/autosklearn/pipeline/components/feature_preprocessing/polynomial.py
imports: from typing import Optional
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import CategoricalHyperparameter, UniformIntegerHyperparameter
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm
  from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA
  from autosklearn.util.common import check_for_bool
label_desc: Use image node_id 5 for calling the PolynomialFeatures obj's underlying member method code with example usage: obj.get_hyperparameter_search_space(feat_type, dataset_properties) and returns: cs