code: def transform(self, X):
          
          N, D = X.shape
          return np.hstack((np.ones((N, 1)), X)) if self.onescol else X
docs: Return this basis applied to X.
  
          Parameters
          ----------
          X: ndarray
              of shape (N, d) of observations where N is the number of samples,
              and d is the dimensionality of X.
  
          Returns
          -------
          ndarray:
              of shape (N, d+1), or (N, d) depending on onescol.