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_properties(
      dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None,
  ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]:
      return {
          "shortname": "FeatTypeSplit",
          "name": "Feature Type Splitter",
          "handles_regression": True,
          "handles_classification": True,
          "handles_multiclass": True,
          "handles_multilabel": True,
          "handles_multioutput": True,
          # TODO find out of this is right!
          "handles_sparse": True,
          "handles_dense": True,
          "input": (DENSE, SPARSE, UNSIGNED_DATA),
          "output": (INPUT,),
      }
  
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 4 for calling the FeatTypeSplit obj's underlying member method code with example usage: obj.get_properties(dataset_properties) and returns: dict