code: def get_hyperparameter_search_space(
      feat_type: Optional[FEAT_TYPE_TYPE] = None,
      dataset_properties=None,
  ):
      C = UniformFloatHyperparameter(
          "C", 0.03125, 32768, log=True, default_value=1.0
      )
      # No linear kernel here, because we have liblinear
      kernel = CategoricalHyperparameter(
          name="kernel",
          choices=["rbf", "poly", "sigmoid"],
          default_value="rbf",
      )
      degree = UniformIntegerHyperparameter(
          "degree", 2, 5, default_value=3
      )
      gamma = UniformFloatHyperparameter(
          "gamma", 3.0517578125e-05, 8, log=True, default_value=0.1
      )
      # TODO this is totally ad-hoc
      coef0 = UniformFloatHyperparameter(
          "coef0", -1, 1, default_value=0
      )
      # probability is no hyperparameter, but an argument to the SVM algo
      shrinking = CategoricalHyperparameter(
          "shrinking", ["True", "False"], default_value="True"
      )
      tol = UniformFloatHyperparameter(
          "tol", 1e-5, 1e-1, default_value=1e-3, log=True
      )
      # cache size is not a hyperparameter, but an argument to the program!
      max_iter = UnParametrizedHyperparameter("max_iter", -1)
  
      cs = ConfigurationSpace()
      cs.add_hyperparameters(
          [C, kernel, degree, gamma, coef0, shrinking, tol, max_iter]
      )
  
      degree_depends_on_poly = EqualsCondition(degree, kernel, "poly")
      coef0_condition = InCondition(coef0, kernel, ["poly", "sigmoid"])
      cs.add_condition(degree_depends_on_poly)
      cs.add_condition(coef0_condition)
  
      return cs
  
file_path: auto-sklearn/autosklearn/pipeline/components/classification/libsvm_svc.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 AutoSklearnClassificationAlgorithm
  from autosklearn.pipeline.constants import DENSE, PREDICTIONS, SPARSE, UNSIGNED_DATA
  from autosklearn.pipeline.implementations.util import softmax
  from autosklearn.util.common import check_for_bool, check_none
label_desc: Use image node_id 6 for calling the LibSVM_SVC obj's underlying member method code with example usage: obj.get_hyperparameter_search_space(feat_type, dataset_properties) and returns: cs