How to write custom objective and custom eval metric in lightgbm

custom_loss

def custom_asymmetric_train(y_true, y_pred):
    residual = (y_true - y_pred).astype("float")
    grad = np.where(residual<0, -2*10.0*residual, -2*residual)
    hess = np.where(residual<0, 2*10.0, 2.0)
    return grad, hess

def custom_asymmetric_valid(y_true, y_pred):
    residual = (y_true - y_pred).astype("float")
    loss = np.where(residual < 0, (residual**2)*10.0, residual**2) 
    return "custom_asymmetric_eval", np.mean(loss), False