code: def get_hyperparameter_search_space(
      feat_type: Optional[FEAT_TYPE_TYPE] = None,
      dataset_properties=None,
  ):
      gamma = UniformFloatHyperparameter(
          "gamma", 3.0517578125e-05, 8, default_value=1.0, log=True
      )
      n_components = UniformIntegerHyperparameter(
          "n_components", 50, 10000, default_value=100, log=True
      )
      cs = ConfigurationSpace()
      cs.add_hyperparameters([gamma, n_components])
      return cs
  
file_path: auto-sklearn/autosklearn/pipeline/components/feature_preprocessing/kitchen_sinks.py
imports: from typing import Optional, Union
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import UniformFloatHyperparameter, UniformIntegerHyperparameter
  from numpy.random import RandomState
  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
label_desc: Use image node_id 5 for calling the RandomKitchenSinks obj's underlying member method code with example usage: obj.get_hyperparameter_search_space(feat_type, dataset_properties) and returns: cs