docstr: Encoders that utilize the target must make sure that the training data are transformed with:
       transform(X, y)
  and not with:
      transform(X)
docstr_tok: ["Encoders","that","utilize","the","target","must","make","sure","that","the","training","data","are","transformed","with",":","transform","(","X",",","y",")","and","not","with",":","transform","(","X",")"]
code: def fit_transform(self, X, y=None, **fit_params):
      """
      Encoders that utilize the target must make sure that the training data are transformed with:
           transform(X, y)
      and not with:
          transform(X)
      """
      if y is None:
          raise TypeError("fit_transform() missing argument: " "y" "")
      return self.fit(X, y, **fit_params).transform(X, y)
  
imports: from abc import abstractmethod
  from enum import Enum, auto
  import warnings
  import pandas
  import numpy
  import sklearn.base
  from pandas.api.types import is_object_dtype, is_string_dtype, is_numeric_dtype
  from pandas.core.dtypes.dtypes import CategoricalDtype
  from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.exceptions import NotFittedError
  from typing import Dict, List, Optional, Union
  from scipy.sparse import csr_matrix
  from sklearn.preprocessing import LabelEncoder
label_desc: Use image node_id 4 for calling the SupervisedTransformerMixin obj's underlying member method code with example usage: obj.fit_transform(X, y) and returns: self