code: def fit_transform(self, X, y=None):
          
          if self.categorical_features == "auto":
              self.categorical_features = auto_select_categorical_features(X, threshold=self.threshold)
  
          return _transform_selected(
              X,
              self._fit_transform,
              self.categorical_features,
              copy=True
          )
docs: Fit OneHotEncoder to X, then transform X.
  
          Equivalent to self.fit(X).transform(X), but more convenient and more
          efficient. See fit for the parameters, transform for the return value.
  
          Parameters
          ----------
          X : array-like or sparse matrix, shape=(n_samples, n_features)
              Dense array or sparse matrix.
          y: array-like {n_samples,} (Optional, ignored)
              Feature labels