code: def get_hyperparameter_search_space(
      feat_type: Optional[FEAT_TYPE_TYPE] = None,
      dataset_properties=None,
  ):
      cs = ConfigurationSpace()
  
      criterion = CategoricalHyperparameter(
          "criterion", ["gini", "entropy"], default_value="gini"
      )
      max_depth_factor = UniformFloatHyperparameter(
          "max_depth_factor", 0.0, 2.0, default_value=0.5
      )
      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
      )
      max_features = UnParametrizedHyperparameter("max_features", 1.0)
      max_leaf_nodes = UnParametrizedHyperparameter(
          "max_leaf_nodes", "None"
      )
      min_impurity_decrease = UnParametrizedHyperparameter(
          "min_impurity_decrease", 0.0
      )
  
      cs.add_hyperparameters(
          [
              criterion,
              max_features,
              max_depth_factor,
              min_samples_split,
              min_samples_leaf,
              min_weight_fraction_leaf,
              max_leaf_nodes,
              min_impurity_decrease,
          ]
      )
  
      return cs
  
file_path: auto-sklearn/autosklearn/pipeline/components/classification/decision_tree.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 AutoSklearnClassificationAlgorithm
  from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA
  from autosklearn.pipeline.implementations.util import convert_multioutput_multiclass_to_multilabel
  from autosklearn.util.common import check_none
label_desc: Use image node_id 6 for calling the DecisionTree obj's underlying member method code with example usage: obj.get_hyperparameter_search_space(feat_type, dataset_properties) and returns: cs