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_pipeline_steps(
      self,
      feat_type: Optional[FEAT_TYPE_TYPE] = None,
      dataset_properties: Optional[Dict[str, str]] = None,
  ) -> List[Tuple[str, BaseEstimator]]:
      steps = []
  
      default_dataset_properties = {}
      if dataset_properties is not None and isinstance(
          dataset_properties, dict
      ):
          default_dataset_properties.update(dataset_properties)
  
      steps.extend(
          [
              (
                  "text_encoding",
                  BagOfWordChoice(
                      feat_type=feat_type,
                      dataset_properties=default_dataset_properties,
                      random_state=self.random_state,
                  ),
              ),
              (
                  "text_feature_reduction",
                  TextFeatureReduction(random_state=self.random_state),
              ),
          ]
      )
      return steps
  
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 4 for calling the TextPreprocessingPipeline obj's underlying member method code with example usage: obj._get_pipeline_steps(feat_type, dataset_properties) and returns: steps