code: def get_hyperparameter_search_space(
      feat_type: Optional[FEAT_TYPE_TYPE] = None,
      dataset_properties=None,
  ):
      cs = ConfigurationSpace()
  
      # the smoothing parameter is a non-negative float
      # I will limit it to 1000 and put it on a logarithmic scale. (SF)
      # Please adjust that, if you know a proper range, this is just a guess.
      alpha = UniformFloatHyperparameter(
          name="alpha", lower=1e-2, upper=100, default_value=1, log=True
      )
  
      fit_prior = CategoricalHyperparameter(
          name="fit_prior",
          choices=["True", "False"],
          default_value="True",
      )
  
      cs.add_hyperparameters([alpha, fit_prior])
  
      return cs
  
file_path: auto-sklearn/autosklearn/pipeline/components/classification/bernoulli_nb.py
imports: from typing import Optional
  import numpy
  from ConfigSpace.configuration_space import ConfigurationSpace
  from ConfigSpace.hyperparameters import CategoricalHyperparameter, UniformFloatHyperparameter
  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.util.common import check_for_bool
label_desc: Use image node_id 6 for calling the BernoulliNB obj's underlying member method code with example usage: obj.get_hyperparameter_search_space(feat_type, dataset_properties) and returns: cs