code: def __init__(
      self,
      preprocessing_params={
          "target_preprocessing": [],
          "columns_preprocessing": {},
      },
      model_name=None,
      k_fold=None,
      repeat=None,
  ):
      self._params = preprocessing_params
  
      if "target_preprocessing" not in preprocessing_params:
          self._params["target_preprocessing"] = []
      if "columns_preprocessing" not in preprocessing_params:
          self._params["columns_preprocessing"] = {}
  
      # preprocssing step attributes
      self._categorical_y = None
      self._scale_y = None
      self._missing_values = []
      self._categorical = []
      self._scale = []
      self._remove_columns = []
      self._datetime_transforms = []
      self._text_transforms = []
      self._golden_features = None
      self._kmeans = None
      self._add_random_feature = self._params.get(
          "add_random_feature", False
      )
      self._drop_features = self._params.get("drop_features", [])
      self._model_name = model_name
      self._k_fold = k_fold
      self._repeat = repeat
  
imports: import copy
  import logging
  import warnings
  import numpy
  import pandas
  from supervised.algorithms.registry import BINARY_CLASSIFICATION, MULTICLASS_CLASSIFICATION, REGRESSION
  from supervised.exceptions import AutoMLException
  from supervised.preprocessing.datetime_transformer import DateTimeTransformer
  from supervised.preprocessing.exclude_missing_target import ExcludeRowsMissingTarget
  from supervised.preprocessing.goldenfeatures_transformer import GoldenFeaturesTransformer
  from supervised.preprocessing.kmeans_transformer import KMeansTransformer
  from supervised.preprocessing.label_binarizer import LabelBinarizer
  from supervised.preprocessing.label_encoder import LabelEncoder
  from supervised.preprocessing.preprocessing_categorical import PreprocessingCategorical
  from supervised.preprocessing.preprocessing_missing import PreprocessingMissingValues
  from supervised.preprocessing.preprocessing_utils import PreprocessingUtils
  from supervised.preprocessing.scale import Scale
  from supervised.preprocessing.text_transformer import TextTransformer
  from supervised.utils.config import LOG_LEVEL
label_desc: Use image node_id 1 to create a new Preprocessing object from inherited base classes: object with example: obj = Preprocessing(preprocessing_params, model_name, k_fold, repeat)