class_docstr: Encode categorical columns with `M` categories into `M-1` columns according
  to the one-hot scheme.
  
  The order of non-categorical columns is preserved, encoded columns are inserted
  inplace of the original column.
  
  Parameters
  ----------
  allow_drop : boolean, optional, default: True
      Whether to allow dropping categorical columns that only consist
      of a single category.
  
  Attributes
  ----------
  feature_names_ : pandas.Index
      List of encoded columns.
  
  categories_ : dict
      Categories of encoded columns.
  
  encoded_columns_ : list
      Name of columns after encoding.
      Includes names of non-categorical columns.
  
  n_features_in_ : int
      Number of features seen during ``fit``.
  
  feature_names_in_ : ndarray of shape (`n_features_in_`,)
      Names of features seen during ``fit``. Defined only when `X`
      has feature names that are all strings.
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docstr: Get output feature names for transformation.
  
  Parameters
  ----------
  input_features : array-like of str or None, default=None
      Input features.
  
      - If `input_features` is `None`, then `feature_names_in_` is
        used as feature names in.
      - If `input_features` is an array-like, then `input_features` must
        match `feature_names_in_` if `feature_names_in_` is defined.
  
  Returns
  -------
  feature_names_out : ndarray of str objects
      Transformed feature names.
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code: def get_feature_names_out(self, input_features=None):
      """Get output feature names for transformation.
  
      Parameters
      ----------
      input_features : array-like of str or None, default=None
          Input features.
  
          - If `input_features` is `None`, then `feature_names_in_` is
            used as feature names in.
          - If `input_features` is an array-like, then `input_features` must
            match `feature_names_in_` if `feature_names_in_` is defined.
  
      Returns
      -------
      feature_names_out : ndarray of str objects
          Transformed feature names.
      """
      check_is_fitted(self, "encoded_columns_")
      input_features = _check_feature_names_in(self, input_features)
  
      return self.encoded_columns_.values.copy()
  
file_path: scikit-survival/sksurv/preprocessing.py
imports: from sklearn.base import BaseEstimator, TransformerMixin
  from sklearn.utils.validation import _check_feature_names_in, check_is_fitted
  from .column import encode_categorical
label_desc: Use image node_id 6 for calling the OneHotEncoder obj's underlying member method code with example usage: obj.get_feature_names_out(input_features) and returns: self