code: def get_hyperparameter_search_space(
      feat_type: Optional[FEAT_TYPE_TYPE] = None,
      dataset_properties=None,
  ):
      n_estimators = UniformIntegerHyperparameter(
          name="n_estimators", lower=10, upper=100, default_value=10
      )
      max_depth = UniformIntegerHyperparameter(
          name="max_depth", lower=2, upper=10, default_value=5
      )
      min_samples_split = UniformIntegerHyperparameter(
          name="min_samples_split", lower=2, upper=20, default_value=2
      )
      min_samples_leaf = UniformIntegerHyperparameter(
          name="min_samples_leaf", lower=1, upper=20, default_value=1
      )
      min_weight_fraction_leaf = Constant(
          "min_weight_fraction_leaf", 1.0
      )
      max_leaf_nodes = UnParametrizedHyperparameter(
          name="max_leaf_nodes", value="None"
      )
      bootstrap = CategoricalHyperparameter(
          "bootstrap", ["True", "False"]
      )
      cs = ConfigurationSpace()
      cs.add_hyperparameters(
          [
              n_estimators,
              max_depth,
              min_samples_split,
              min_samples_leaf,
              min_weight_fraction_leaf,
              max_leaf_nodes,
              bootstrap,
          ]
      )
      return cs
  
file_path: auto-sklearn/autosklearn/pipeline/components/feature_preprocessing/random_trees_embedding.py
imports: from typing import Optional
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import CategoricalHyperparameter, Constant, UniformIntegerHyperparameter, UnParametrizedHyperparameter
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm
  from autosklearn.pipeline.constants import DENSE, SIGNED_DATA, SPARSE, UNSIGNED_DATA
  from autosklearn.util.common import check_for_bool, check_none
label_desc: Use image node_id 7 for calling the RandomTreesEmbedding obj's underlying member method code with example usage: obj.get_hyperparameter_search_space(feat_type, dataset_properties) and returns: cs