code: def predict(self, train_x):
          
          k_trans = np.exp(-np.power(edit_distance_matrix(train_x, self._x), 2))
          y_mean = k_trans.dot(self._alpha_vector)  # Line 4 (y_mean = f_star)
  
          # compute inverse K_inv of K based on its Cholesky
          # decomposition L and its inverse L_inv
          l_inv = solve_triangular(self._l_matrix.T, np.eye(self._l_matrix.shape[0]))
          k_inv = l_inv.dot(l_inv.T)
          # Compute variance of predictive distribution
          y_var = np.ones(len(train_x), dtype=np.float)
          y_var -= np.einsum("ij,ij->i", np.dot(k_trans, k_inv), k_trans)
  
          # Check if any of the variances is negative because of
          # numerical issues. If yes: set the variance to 0.
          y_var_negative = y_var < 0
          if np.any(y_var_negative):
              y_var[y_var_negative] = 0.0
          return y_mean, np.sqrt(y_var)
docs: Predict the result.
          Args:
              train_x: A list of NetworkDescriptor.
          Returns:
              y_mean: The predicted mean.
              y_std: The predicted standard deviation.