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 set_hyperparameters(
      self,
      feat_type: FEAT_TYPE_TYPE,
      configuration: Configuration,
      init_params: Optional[Dict[str, Any]] = None,
  ) -> "FeatTypeSplit":
      if init_params is not None and "feat_type" in init_params.keys():
          self.feat_type = init_params["feat_type"]
  
      self.config = configuration
  
      for transf_name, transf_op in self._transformers:
          sub_configuration_space = (
              transf_op.get_hyperparameter_search_space(
                  dataset_properties=self.dataset_properties,
                  feat_type=feat_type,
              )
          )
          sub_config_dict = {}
          for param in configuration:
              if param.startswith("%s:" % transf_name):
                  value = configuration[param]
                  new_name = param.replace("%s:" % transf_name, "", 1)
                  sub_config_dict[new_name] = value
  
          sub_configuration = Configuration(
              sub_configuration_space,
              values=sub_config_dict,
              allow_inactive_with_values=True,
          )
  
          sub_init_params_dict: Optional[Dict[str, Any]] = None
          if init_params is not None:
              sub_init_params_dict = {}
              for param in init_params:
                  if param.startswith("%s:" % transf_name):
                      value = init_params[param]
                      new_name = param.replace(
                          "%s:" % transf_name, "", 1
                      )
                      sub_init_params_dict[new_name] = value
  
          if isinstance(
              transf_op,
              (AutoSklearnChoice, AutoSklearnComponent, BasePipeline),
          ):
              transf_op.set_hyperparameters(
                  feat_type=feat_type,
                  configuration=sub_configuration,
                  init_params=sub_init_params_dict,
              )
          else:
              raise NotImplementedError("Not supported yet!")
  
      return self
  
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 5 for calling the FeatTypeSplit obj's underlying member method code with example usage: obj.set_hyperparameters(feat_type, configuration, init_params) and returns: self