docstr: Return the configuration space of this classification algorithm.
  
  Parameters
  ----------
  feat_type : FEAT_TYPE_TYPE (default=None)
  dataset_properties : dict, optional (default=None)
  
  Returns
  -------
  Configspace.configuration_space.ConfigurationSpace
      The configuration space of this classification algorithm.
docstr_tok: ["Return","the","configuration","space","of","this","classification","algorithm",".","Parameters","--","--","--","--","--","feat_type",":","FEAT_TYPE_TYPE","(","default=None",")","dataset_properties",":","dict",",","optional","(","default=None",")","Returns","--","--","--","-","Configspace.configuration_space.ConfigurationSpace","The","configuration","space","of","this","classification","algorithm","."]
code: def get_hyperparameter_search_space(
      feat_type: Optional[FEAT_TYPE_TYPE] = None,
      dataset_properties=None,
  ):
      """Return the configuration space of this classification algorithm.
  
      Parameters
      ----------
      feat_type : FEAT_TYPE_TYPE (default=None)
      dataset_properties : dict, optional (default=None)
  
      Returns
      -------
      Configspace.configuration_space.ConfigurationSpace
          The configuration space of this classification algorithm.
      """
      raise NotImplementedError()
  
imports: from typing import Dict, Optional
  import importlib
  import inspect
  import pkgutil
  import sys
  from collections import OrderedDict
  from sklearn.base import BaseEstimator, TransformerMixin
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.pipeline.constants import SPARSE
label_desc: Use image node_id 2 for calling the AutoSklearnComponent obj's underlying member method code with example usage: obj.get_hyperparameter_search_space(feat_type, dataset_properties) without return types