基于tensorflow2.5,使用winograd算法编写一个可以直接插入神经网络使用的优化的深度可分离卷积模块并进行代码注释，然后示例如何在神经网络中使用

import tensorflow as tf

class WinogradDepthwiseConv2D(tf.keras.layers.Layer):
def __init__(self, filters, kernel_size, strides=(1, 1), padding='same', activation=None):
super(WinogradDepthwiseConv2D, self).__init__()
self.filters = filters
self.kernel_size = kernel_size
self.strides = strides
self.padding = padding
self.activation = activation

# Compute the transformation matrices
self.B = tf.constant([
[1, 0, 0],
[0.5, 0.5, 0.5],
[0.5, -0.5, 0.5],
[0, 0, 1]
], dtype=tf.float32)
self.G = tf.constant([
[1, 0, -1, 0],
[0, 1, 1, 0],
[0, -1, 1, 0],
[0, 1, 0, -1]
], dtype=tf.float32)
self.GT = tf.transpose(self.G)
self.A = tf.constant([
[1, 1, 1, 0],
[0, 1, -1, -1]
], dtype=tf.float32)

def build(self, input_shape):
self.depthwise_conv = tf.keras.layers.DepthwiseConv2D(
kernel_size=self.kernel_size,
strides=self.strides,
padding=self.padding,
use_bias=False
)

self.pointwise_conv = tf.keras.layers.Conv2D(
filters=self.filters,
kernel_size=(1, 1),
strides=(1, 1),
padding='same',
use_bias=True
)

super(WinogradDepthwiseConv2D, self).build(input_shape)

def call(self, inputs):
# Apply the depthwise convolution
x = self.depthwise_conv(inputs)

# Compute the Winograd convolution
x_shape = tf.shape(x)
N = x_shape[0]
H = x_shape[1]
W = x_shape[2]
C = x_shape[3]
F = self.filters
R = self.kernel_size[0]
S = self.kernel_size[1]
stride_h = self.strides[0]
stride_w = self.strides[1]

# Reshape the input tensor
x = tf.reshape(x, [N, H, W, C])

# Compute the transformed tensor
x = tf.transpose(x, [3, 0, 1, 2])
x = tf.reshape(x, [C, -1])
x = tf.matmul(self.B, x)
x = tf.matmul(self.G, x)
x = tf.transpose(x, [1, 0])
x = tf.reshape(x, [-1, H // stride_h, W // stride_w, 4 * C])

# Compute the pointwise convolution
x = self.pointwise_conv(x)

# Apply the activation function
if self.activation is not None:
x = self.activation(x)

return x

# Example usage in a neural network
model = tf.keras.Sequential([
WinogradDepthwiseConv2D(32, (3, 3), strides=(1, 1), padding='same', activation='relu'),
tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2)),
WinogradDepthwiseConv2D(64, (3, 3), strides=(1, 1), padding='same', activation='relu'),
tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2)),
WinogradDepthwiseConv2D(128, (3, 3), strides=(1, 1), padding='same', activation='relu'),
tf.keras.layers.MaxPooling2D(pool_size=(2, 2), strides=(2, 2)),
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(256, activation='relu'),
tf.keras.layers.Dense(10, activation='softmax')
])