code: def predict(self, X, alpha=None):
          
          X = check_array(X)
          coef = self._get_coef(alpha)
          return numpy.dot(X, coef)
docs: The linear predictor of the model.
  
          Parameters
          ----------
          X : array-like, shape = (n_samples, n_features)
              Test data of which to calculate log-likelihood from
  
          alpha : float, optional
              Constant that multiplies the penalty terms. If the same alpha was used during training, exact
              coefficients are used, otherwise coefficients are interpolated from the closest alpha values that
              were used during training. If set to ``None``, the last alpha in the solution path is used.
  
          Returns
          -------
          T : array, shape = (n_samples,)
              The predicted decision function