code: def get_hyperparameter_search_space(
      feat_type: Optional[FEAT_TYPE_TYPE] = None,
      dataset_properties=None,
  ):
      C = UniformFloatHyperparameter(
          name="C",
          lower=0.03125,
          upper=32768,
          log=True,
          default_value=1.0,
      )
      # Random Guess
      epsilon = UniformFloatHyperparameter(
          name="epsilon",
          lower=0.001,
          upper=1,
          default_value=0.1,
          log=True,
      )
  
      kernel = CategoricalHyperparameter(
          name="kernel",
          choices=["linear", "poly", "rbf", "sigmoid"],
          default_value="rbf",
      )
      degree = UniformIntegerHyperparameter(
          name="degree", lower=2, upper=5, default_value=3
      )
  
      gamma = UniformFloatHyperparameter(
          name="gamma",
          lower=3.0517578125e-05,
          upper=8,
          log=True,
          default_value=0.1,
      )
  
      # TODO this is totally ad-hoc
      coef0 = UniformFloatHyperparameter(
          name="coef0", lower=-1, upper=1, default_value=0
      )
      # probability is no hyperparameter, but an argument to the SVM algo
      shrinking = CategoricalHyperparameter(
          name="shrinking",
          choices=["True", "False"],
          default_value="True",
      )
      tol = UniformFloatHyperparameter(
          name="tol",
          lower=1e-5,
          upper=1e-1,
          default_value=1e-3,
          log=True,
      )
      max_iter = UnParametrizedHyperparameter("max_iter", -1)
  
      cs = ConfigurationSpace()
      cs.add_hyperparameters(
          [
              C,
              kernel,
              degree,
              gamma,
              coef0,
              shrinking,
              tol,
              max_iter,
              epsilon,
          ]
      )
  
      degree_depends_on_poly = EqualsCondition(degree, kernel, "poly")
      gamma_depends_on_kernel = InCondition(
          child=gamma, parent=kernel, values=("poly", "rbf")
      )
      coef0_depends_on_kernel = InCondition(
          child=coef0, parent=kernel, values=("poly", "sigmoid")
      )
      cs.add_conditions(
          [
              degree_depends_on_poly,
              gamma_depends_on_kernel,
              coef0_depends_on_kernel,
          ]
      )
  
      return cs
  
file_path: auto-sklearn/autosklearn/pipeline/components/regression/libsvm_svr.py
imports: from typing import Optional
  import resource
  import sys
  from ConfigSpace.conditions import EqualsCondition, InCondition
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import CategoricalHyperparameter, UniformFloatHyperparameter, UniformIntegerHyperparameter, UnParametrizedHyperparameter
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.pipeline.components.base import AutoSklearnRegressionAlgorithm
  from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA
  from autosklearn.util.common import check_for_bool, check_none
label_desc: Use image node_id 5 for calling the LibSVM_SVR obj's underlying member method code with example usage: obj.get_hyperparameter_search_space(feat_type, dataset_properties) and returns: cs