如何完成梯度下降算法的代码？

import numpy as np

def gradient_descent(X, y, learning_rate, num_iterations):
    num_samples, num_features = X.shape
    theta = np.zeros(num_features)  # 初始化参数

    for i in range(num_iterations):
        # 计算预测值
        y_pred = np.dot(X, theta)
        # 计算误差
        error = y_pred - y
        # 计算梯度
        gradient = np.dot(X.T, error) / num_samples
        # 更新参数
        theta -= learning_rate * gradient

    return theta

# 示例数据
X = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
y = np.array([4, 7, 10])

# 调用梯度下降算法
learning_rate = 0.01
num_iterations = 1000
theta = gradient_descent(X, y, learning_rate, num_iterations)

print("最优参数：", theta)