帮我用pytorch写一下ASSF模块

好的，我可以回答这个问题。以下是一个简单的 ASSF 模块的 PyTorch 实现：

import torch
import torch.nn as nn

class ASSF(nn.Module):
    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1):
        super(ASSF, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding)
        self.conv2 = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding)
        self.conv3 = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding)
        self.conv4 = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding)
        self.conv5 = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding)
        self.conv6 = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding)
        self.conv7 = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding)
        self.conv8 = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding)
        self.conv9 = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding)
        self.conv10 = nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding)

    def forward(self, x):
        x1 = self.conv1(x)
        x2 = self.conv2(x1)
        x3 = self.conv3(x2)
        x4 = self.conv4(x3)
        x5 = self.conv5(x4)
        x6 = self.conv6(x5)
        x7 = self.conv7(x6)
        x8 = self.conv8(x7)
        x9 = self.conv9(x8)
        x10 = self.conv10(x9)
        out = torch.cat((x1, x2, x3, x4, x5, x6, x7, x8, x9, x10), dim=1)
        return out

这个模块包含了 10 个卷积层，每个卷积层都使用相同的卷积核大小、步长和填充。在前向传播过程中，我们将每个卷积层的输出拼接在一起，形成一个更大的特征图。这个模块可以用于增加模型的感受野和提高模型的性能。