docstr: Returns the names of all input columns present when fitting.
  These columns are necessary for the transform step.
docstr_tok: ["Returns","the","names","of","all","input","columns","present","when","fitting",".","These","columns","are","necessary","for","the","transform","step","."]
code: def get_feature_names_in(self) -> List[str]:
      """
      Returns the names of all input columns present when fitting.
      These columns are necessary for the transform step.
      """
      in_feats = getattr(self, "feature_names_in_", None)
      if not isinstance(in_feats, list):
          raise NotFittedError(
              "Estimator has to be fitted to return feature names."
          )
      else:
          return in_feats
  
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 9 for calling the BaseEncoder obj's underlying member method code with example usage: obj.get_feature_names_in() and returns: in_feats