梯度(2d)搜索函数返回None。有什么问题吗？

import numpy as np

def gradient_descent_2d(f, grad_f, x0, y0, learning_rate=0.01, max_iter=1000, tol=1e-6):
    x, y = x0, y0
    for i in range(max_iter):
        grad_x, grad_y = grad_f(x, y)
        x_new = x - learning_rate * grad_x
        y_new = y - learning_rate * grad_y
        
        if np.linalg.norm([x_new - x, y_new - y]) < tol:
            return x_new, y_new
        
        x, y = x_new, y_new
    
    return None

# 示例函数和梯度
def f(x, y):
    return x**2 + y**2

def grad_f(x, y):
    return 2*x, 2*y

# 运行梯度下降
result = gradient_descent_2d(f, grad_f, x0=10, y0=10)
print(result)  # 应该输出接近 (0, 0) 的值