class_docstr: This component is used to apply distinct transformations to categorical,
  numerical and text features of a dataset. It is built on top of sklearn's
  ColumnTransformer.
class_docstr_tok: ["This","component","is","used","to","apply","distinct","transformations","to","categorical",",","numerical","and","text","features","of","a","dataset",".","It","is","built","on","top","of","sklearn's","ColumnTransformer","."]
code: def _get_hyperparameter_search_space_recursevely(
      dataset_properties: DATASET_PROPERTIES_TYPE,
      cs: ConfigurationSpace,
      transformer: BaseEstimator,
      feat_type: Optional[FEAT_TYPE_TYPE] = None,
  ) -> ConfigurationSpace:
      for st_name, st_operation in transformer:
          if hasattr(st_operation, "get_hyperparameter_search_space"):
              cs.add_configuration_space(
                  st_name,
                  st_operation.get_hyperparameter_search_space(
                      dataset_properties=dataset_properties
                  ),
              )
          else:
              return FeatTypeSplit._get_hyperparameter_search_space_recursevely(
                  dataset_properties, cs, st_operation
              )
      return cs
  
imports: from typing import Any, Dict, List, Optional, Tuple, Union
  import numpy
  import sklearn.compose
  from ConfigSpace import Configuration
  from ConfigSpace.configuration_space import ConfigurationSpace
  from scipy import sparse
  from sklearn.base import BaseEstimator
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.data.validation import SUPPORTED_FEAT_TYPES, SUPPORTED_TARGET_TYPES
  from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, PIPELINE_DATA_DTYPE, BasePipeline
  from autosklearn.pipeline.components.base import AutoSklearnChoice, AutoSklearnComponent, AutoSklearnPreprocessingAlgorithm
  from autosklearn.pipeline.components.data_preprocessing.feature_type_categorical import CategoricalPreprocessingPipeline
  from autosklearn.pipeline.components.data_preprocessing.feature_type_numerical import NumericalPreprocessingPipeline
  from autosklearn.pipeline.components.data_preprocessing.feature_type_text import TextPreprocessingPipeline
  from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA
label_desc: Use image node_id 7 for calling the FeatTypeSplit obj's underlying member method code with example usage: obj._get_hyperparameter_search_space_recursevely(dataset_properties, cs, transformer, feat_type) and returns: cs, FeatTypeSplit