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 transform(self, X: SUPPORTED_FEAT_TYPES) -> PIPELINE_DATA_DTYPE:
      if self.column_transformer is None:
          raise ValueError(
              "Cannot call transform on a Datapreprocessor that has not"
              "yet been fit. Please check the log files for errors "
              "while trying to fit the model."
          )
      return self.column_transformer.transform(X)
  
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 3 for calling the FeatTypeSplit obj's underlying member method code with example usage: obj.transform(X) and returns: self