code: def _check_fit_inputs(self, X, y):
      if self._get_tags().get("supervised_encoder"):
          if y is None:
              raise ValueError(
                  "Supervised encoders need a target for the fitting. The target cannot be None"
              )
          else:
              if (
                  y.isna().any()
              ):  # Target column should never have missing values
                  raise ValueError(
                      "The target column y must not contain missing values."
                  )
  
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 3 for calling the BaseEncoder obj's underlying member method code with example usage: obj._check_fit_inputs(X, y) without return types