Построить график

[Alduin]

Нужна помощь! Мне нужно отобразить график.Но я не знаю ка кэто правильно сделать с учетом множества функции.
Начальное приближение определять графически. Вывести количество
итераций

def function_exercise(xy):
    x, y = xy
    return [np.tan(x * y) - x * x,
            0.7 * x * x + 2 * y * y - 1]


def jacobian_exercise(xy):
    x, y = xy
    return [[(y / (np.cos(x * y) ** 2)) - 2 * x, (x / (np.cos(x * y) ** 2))],
            [1.4 * x, 4 * y]]


def iter_newton(X, function, jacobian, imax=1e6, tol=1e-5):
    for i in range(int(imax)):
        J = jacobian(X)  # calculate jacobian J = df(X)/dY(X)
        Y = function(X)  # calculate function Y = f(X)
        dX = np.linalg.solve(J, Y)  # solve for increment from JdX = Y
        X -= dX  # step X by dX
        i += 1
        print(f'Number of iterations: {i=}\t Result: {X=}')
        if np.linalg.norm(dX) < tol:  # break if converged
            print('Converged')
            break
    return X


def Newton_sys_graphic():
    fig1, ax = plt.subplots(figsize=(8, 6))
    plt.ylabel(r'$y$', fontsize=13, fontname='Arial', color='white')
    plt.xlabel(r'$x$', fontsize=13, fontname='Arial', color='white')
    plt.grid(which='both', linewidth=1.5, linestyle='-', color='gray')
    ax.tick_params(which='major', length=8, width=2)
    ax.tick_params(which='minor', length=8, width=2)
    ax.minorticks_on()
    ax.grid(which='major',
            linewidth=2)
    ax.grid(which='minor',
            linestyle=':')
    plt.scatter(*function_exercise(11), color="blue", marker="s", edgecolors='yellow', linewidth=3, s=60, hatch='+o')
    # plt.plot(*function_exercise(1, 1))
    legend = plt.legend(['Initial approximation'], loc='upper right', shadow=True, fontsize='x-large',
                        frameon=True, title="Легенда", title_fontsize=15, framealpha=1)
    frame = legend.get_frame()
    frame.set_facecolor('black')
    frame.set_edgecolor('red')
    plt.title("Initial approximation")
    mplcyberpunk.add_gradient_fill(alpha_gradientglow=0.5)
    mplcyberpunk.add_glow_effects()
    plt.show()
    fig1.savefig('newton_point.png', dpi=300, bbox_inches='tight')


X_0 = np.array([1, 1], dtype=float)
print(iter_newton(X_0, function_exercise, jacobian_exercise))
Newton_sys_graphic()