code: def get_hyperparameter_search_space(
      feat_type: Optional[FEAT_TYPE_TYPE] = None,
      dataset_properties=None,
  ):
      if dataset_properties is not None and (
          dataset_properties.get("sparse") is True
          or dataset_properties.get("signed") is False
      ):
          allow_chi2 = False
      else:
          allow_chi2 = True
  
      possible_kernels = ["poly", "rbf", "sigmoid", "cosine"]
      if allow_chi2:
          possible_kernels.append("chi2")
      kernel = CategoricalHyperparameter(
          "kernel", possible_kernels, "rbf"
      )
      n_components = UniformIntegerHyperparameter(
          "n_components", 50, 10000, default_value=100, log=True
      )
      gamma = UniformFloatHyperparameter(
          "gamma", 3.0517578125e-05, 8, log=True, default_value=0.1
      )
      degree = UniformIntegerHyperparameter("degree", 2, 5, 3)
      coef0 = UniformFloatHyperparameter(
          "coef0", -1, 1, default_value=0
      )
  
      cs = ConfigurationSpace()
      cs.add_hyperparameters(
          [kernel, degree, gamma, coef0, n_components]
      )
  
      degree_depends_on_poly = EqualsCondition(degree, kernel, "poly")
      coef0_condition = InCondition(coef0, kernel, ["poly", "sigmoid"])
  
      gamma_kernels = ["poly", "rbf", "sigmoid"]
      if allow_chi2:
          gamma_kernels.append("chi2")
      gamma_condition = InCondition(gamma, kernel, gamma_kernels)
      cs.add_conditions(
          [degree_depends_on_poly, coef0_condition, gamma_condition]
      )
      return cs
  
file_path: auto-sklearn/autosklearn/pipeline/components/feature_preprocessing/nystroem_sampler.py
imports: from typing import Optional
  from ConfigSpace.conditions import EqualsCondition, InCondition
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import CategoricalHyperparameter, UniformFloatHyperparameter, UniformIntegerHyperparameter
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.pipeline.components.base import AutoSklearnPreprocessingAlgorithm
  from autosklearn.pipeline.constants import DENSE, INPUT, SIGNED_DATA, SPARSE, UNSIGNED_DATA
label_desc: Use image node_id 5 for calling the Nystroem obj's underlying member method code with example usage: obj.get_hyperparameter_search_space(feat_type, dataset_properties) and returns: cs