class_docstr: This class implements a pipeline for data preprocessing of categorical features.
  It assumes that the data to be transformed is made only of categorical features.
  The steps of this pipeline are:
      1 - Category shift: Adds 3 to every category value
      2 - Imputation: Assign category 2 to missing values (NaN).
      3 - Minority coalescence: Assign category 1 to all categories whose occurrence
          don't sum-up to a certain minimum fraction
      4 - One hot encoding: usual sklearn one hot encoding
  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`.
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code: def __init__(
      self,
      feat_type: Optional[FEAT_TYPE_TYPE] = None,
      config: Optional[Configuration] = None,
      steps: Optional[List[Tuple[str, BaseEstimator]]] = None,
      dataset_properties: Optional[DATASET_PROPERTIES_TYPE] = None,
      include: Optional[Dict[str, str]] = None,
      exclude: Optional[Dict[str, str]] = None,
      random_state: Optional[Union[int, np.random.RandomState]] = None,
      init_params: Optional[Dict[str, Any]] = None,
  ) -> None:
      self._output_dtype = np.int32
      super().__init__(
          config=config,
          steps=steps,
          dataset_properties=dataset_properties,
          include=include,
          exclude=exclude,
          random_state=random_state,
          init_params=init_params,
          feat_type=feat_type,
      )
  
file_path: auto-sklearn/autosklearn/pipeline/components/data_preprocessing/feature_type_categorical.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.categorical_encoding import OHEChoice
  from autosklearn.pipeline.components.data_preprocessing.categorical_encoding.encoding import OrdinalEncoding
  from autosklearn.pipeline.components.data_preprocessing.category_shift.category_shift import CategoryShift
  from autosklearn.pipeline.components.data_preprocessing.imputation.categorical_imputation import CategoricalImputation
  from autosklearn.pipeline.components.data_preprocessing.minority_coalescense import CoalescenseChoice
  from autosklearn.pipeline.constants import DENSE, INPUT, SPARSE, UNSIGNED_DATA
label_desc: Use image node_id 1 to create a new CategoricalPreprocessingPipeline object from inherited base classes: BasePipeline with example: obj = CategoricalPreprocessingPipeline(feat_type, config, steps, dataset_properties, include, exclude, random_state, init_params)