code: def get_hyperparameter_search_space(
      feat_type: Optional[FEAT_TYPE_TYPE] = None,
      dataset_properties=None,
  ):
      cs = ConfigurationSpace()
  
      n_estimators = Constant("n_estimators", 100)
      criterion = CategoricalHyperparameter(
          "criterion", ["mse", "friedman_mse", "mae"]
      )
      max_features = UniformFloatHyperparameter(
          "max_features", 0.1, 1.0, default_value=1.0
      )
  
      max_depth = UnParametrizedHyperparameter(
          name="max_depth", value="None"
      )
      max_leaf_nodes = UnParametrizedHyperparameter(
          "max_leaf_nodes", "None"
      )
  
      min_samples_split = UniformIntegerHyperparameter(
          "min_samples_split", 2, 20, default_value=2
      )
      min_samples_leaf = UniformIntegerHyperparameter(
          "min_samples_leaf", 1, 20, default_value=1
      )
      min_weight_fraction_leaf = Constant(
          "min_weight_fraction_leaf", 0.0
      )
  
      bootstrap = CategoricalHyperparameter(
          "bootstrap", ["True", "False"], default_value="False"
      )
  
      cs.add_hyperparameters(
          [
              n_estimators,
              criterion,
              max_features,
              max_depth,
              max_leaf_nodes,
              min_samples_split,
              min_samples_leaf,
              min_weight_fraction_leaf,
              bootstrap,
          ]
      )
  
      return cs
  
file_path: auto-sklearn/autosklearn/pipeline/components/feature_preprocessing/extra_trees_preproc_for_regression.py
imports: from typing import Optional
  import numpy
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import CategoricalHyperparameter, Constant, UniformFloatHyperparameter, UniformIntegerHyperparameter, UnParametrizedHyperparameter
  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, check_none
label_desc: Use image node_id 5 for calling the ExtraTreesPreprocessorRegression obj's underlying member method code with example usage: obj.get_hyperparameter_search_space(feat_type, dataset_properties) and returns: cs