code: def get_hyperparameter_search_space(
      feat_type: Optional[FEAT_TYPE_TYPE] = None,
      dataset_properties=None,
  ):
      cs = ConfigurationSpace()
      alpha = UniformFloatHyperparameter(
          name="alpha",
          lower=10**-5,
          upper=1,
          log=True,
          default_value=1.0,
      )
      kernel = CategoricalHyperparameter(
          name="kernel",
          # We restrict ourselves to two possible kernels for this example
          choices=["polynomial", "rbf"],
          default_value="polynomial",
      )
      gamma = UniformFloatHyperparameter(
          name="gamma",
          lower=0.00001,
          upper=1,
          default_value=0.1,
          log=True,
      )
      degree = UniformIntegerHyperparameter(
          name="degree", lower=2, upper=5, default_value=3
      )
      coef0 = UniformFloatHyperparameter(
          name="coef0",
          lower=1e-2,
          upper=1e2,
          log=True,
          default_value=1,
      )
      cs.add_hyperparameters([alpha, kernel, gamma, degree, coef0])
      degree_condition = EqualsCondition(degree, kernel, "polynomial")
      coef0_condition = EqualsCondition(coef0, kernel, "polynomial")
      cs.add_conditions([degree_condition, coef0_condition])
      return cs
  
file_path: auto-sklearn/examples/80_extending/example_extending_regression.py
imports: from typing import Optional
  from pprint import pprint
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import UniformFloatHyperparameter, UniformIntegerHyperparameter, CategoricalHyperparameter
  from ConfigSpace.conditions import EqualsCondition
  import sklearn.metrics
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  import autosklearn.regression
  import autosklearn.pipeline.components.regression
  from autosklearn.pipeline.components.base import AutoSklearnRegressionAlgorithm
  from autosklearn.pipeline.constants import SPARSE, DENSE, SIGNED_DATA, UNSIGNED_DATA, PREDICTIONS
  from sklearn.datasets import load_diabetes
  from sklearn.model_selection import train_test_split
label_desc: Use image node_id 5 for calling the KernelRidgeRegression obj's underlying member method code with example usage: obj.get_hyperparameter_search_space(feat_type, dataset_properties) and returns: cs