code: def get_hyperparameter_search_space(
      feat_type: Optional[FEAT_TYPE_TYPE] = None,
      dataset_properties=None,
  ):
      C = UniformFloatHyperparameter("C", 1e-5, 10, 1.0, log=True)
      fit_intercept = UnParametrizedHyperparameter(
          "fit_intercept", "True"
      )
      loss = CategoricalHyperparameter(
          "loss", ["hinge", "squared_hinge"], default_value="hinge"
      )
  
      tol = UniformFloatHyperparameter(
          "tol", 1e-5, 1e-1, default_value=1e-4, log=True
      )
      # Note: Average could also be an Integer if > 1
      average = CategoricalHyperparameter(
          "average", ["False", "True"], default_value="False"
      )
  
      cs = ConfigurationSpace()
      cs.add_hyperparameters([loss, fit_intercept, tol, C, average])
      return cs
  
imports: from typing import Optional
  import numpy
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import CategoricalHyperparameter, UniformFloatHyperparameter, UnParametrizedHyperparameter
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.pipeline.components.base import AutoSklearnClassificationAlgorithm, IterativeComponentWithSampleWeight
  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
label_desc: Use image node_id 9 for calling the PassiveAggressive obj's underlying member method code with example usage: obj.get_hyperparameter_search_space(feat_type, dataset_properties) and returns: cs