如何在Tensorflow中制作2D高斯滤波器？

import tensorflow as tf
import numpy as np

def gaussian_filter(size, sigma):
    kernel = np.fromfunction(lambda x, y: (1/(2*np.pi*sigma**2)) * np.exp(-((x-size//2)**2 + (y-size//2)**2)/(2*sigma**2)), (size, size))
    return kernel / np.sum(kernel)

def convert_to_tensor(kernel):
    kernel = tf.convert_to_tensor(kernel, dtype=tf.float32)
    kernel = tf.reshape(kernel, [kernel.shape[0], kernel.shape[1], 1, 1])
    return kernel

def apply_gaussian_filter(image, kernel):
    filtered_image = tf.nn.conv2d(image, kernel, strides=[1, 1, 1, 1], padding='SAME')
    return filtered_image

# 定义输入图像
image = tf.placeholder(tf.float32, shape=[None, height, width, channels])

# 定义高斯滤波器参数
filter_size = 5
sigma = 1.0

# 创建高斯滤波器
gaussian_kernel = gaussian_filter(filter_size, sigma)
gaussian_kernel = convert_to_tensor(gaussian_kernel)

# 对图像应用高斯滤波器
filtered_image = apply_gaussian_filter(image, gaussian_kernel)