class_docstr: This class implements a pipeline for data preprocessing of text features.
  It assumes that the data to be transformed is made only of text features.
  The steps of this pipeline are:
      1 - Vectorize: Fits a *Vecotrizer object and apply this
      2 - text feature reduction: TruncatedSVD
  
  Parameters
  ----------
  config : ConfigSpace.configuration_space.Configuration
      The configuration to evaluate.
  
  random_state : Optional[int | RandomState]
      If int, random_state is the seed used by the random number generator;
      If RandomState instance, random_state is the random number generator;
      If None, the random number generator is the RandomState instance
      used by `np.random`.
code: def get_properties(
      dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None,
  ) -> Dict[str, Optional[Union[str, int, bool, Tuple]]]:
      return {
          "shortname": "txt_datapreproc",
          "name": "text data preprocessing",
          "handles_missing_values": True,
          "handles_nominal_values": False,
          "handles_numerical_features": False,
          "prefers_data_scaled": False,
          "prefers_data_normalized": False,
          "handles_regression": True,
          "handles_classification": True,
          "handles_multiclass": True,
          "handles_multilabel": True,
          "is_deterministic": True,
          "handles_sparse": True,
          "handles_dense": True,
          "input": (DENSE, SPARSE, UNSIGNED_DATA),
          "output": (INPUT,),
          "preferred_dtype": None,
      }
  
file_path: auto-sklearn/autosklearn/pipeline/components/data_preprocessing/feature_type_text.py
imports: from typing import Any, Dict, List, Optional, Tuple, Union
  import numpy
  from ConfigSpace.configuration_space import Configuration, ConfigurationSpace
  from sklearn.base import BaseEstimator
  from autosklearn.askl_typing import FEAT_TYPE_TYPE
  from autosklearn.pipeline.base import DATASET_PROPERTIES_TYPE, BasePipeline
  from autosklearn.pipeline.components.data_preprocessing.text_encoding import BagOfWordChoice
  from autosklearn.pipeline.components.data_preprocessing.text_feature_reduction.truncated_svd import TextFeatureReduction
  from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA
label_desc: Use image node_id 2 for calling the TextPreprocessingPipeline obj's underlying member method code with example usage: obj.get_properties(dataset_properties) and returns: dict