设计一个扩张卷积模块，具体过程为首先使用1×1DWConv压缩通道，然后使用大小分别为5×5、7×7、9×9的DWConv和由它们构造的不同膨胀速率的扩张卷积形成多个平行分支，膨胀率分别为5、7、9。此外，还有一个残差连接分支。最后，将这些并行分支的输出拼接在一起后输出一个1×1DWConv，最后形成特征图y，请按照上述要求编写出可以应用到yolov5上的代码

下面是一个可以应用到yolov5上的扩张卷积模块的代码示例：

import torch.nn as nn
import torch.nn.functional as F

class ExpandConvModule(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(ExpandConvModule, self).__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2_dw_5 = nn.Conv2d(out_channels, out_channels, kernel_size=5, padding=2, groups=out_channels, bias=False)
        self.bn2_dw_5 = nn.BatchNorm2d(out_channels)
        self.conv2_dw_7 = nn.Conv2d(out_channels, out_channels, kernel_size=7, padding=3, groups=out_channels, bias=False)
        self.bn2_dw_7 = nn.BatchNorm2d(out_channels)
        self.conv2_dw_9 = nn.Conv2d(out_channels, out_channels, kernel_size=9, padding=4, groups=out_channels, bias=False)
        self.bn2_dw_9 = nn.BatchNorm2d(out_channels)
        self.conv2_ex_5 = nn.Conv2d(out_channels, out_channels, kernel_size=1, dilation=5, bias=False)
        self.bn2_ex_5 = nn.BatchNorm2d(out_channels)
        self.conv2_ex_7 = nn.Conv2d(out_channels, out_channels, kernel_size=1, dilation=7, bias=False)
        self.bn2_ex_7 = nn.BatchNorm2d(out_channels)
        self.conv2_ex_9 = nn.Conv2d(out_channels, out_channels, kernel_size=1, dilation=9, bias=False)
        self.bn2_ex_9 = nn.BatchNorm2d(out_channels)
        self.conv3 = nn.Conv2d(out_channels * 7, out_channels, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(out_channels)
        self.relu = nn.ReLU(inplace=True)
        
    def forward(self, x):
        residual = x
        
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        
        out_5 = self.conv2_dw_5(out)
        out_5 = self.bn2_dw_5(out_5)
        out_5 = self.relu(out_5)
        out_5 = self.conv2_ex_5(out_5)
        out_5 = self.bn2_ex_5(out_5)
        out_5 = self.relu(out_5)
        
        out_7 = self.conv2_dw_7(out)
        out_7 = self.bn2_dw_7(out_7)
        out_7 = self.relu(out_7)
        out_7 = self.conv2_ex_7(out_7)
        out_7 = self.bn2_ex_7(out_7)
        out_7 = self.relu(out_7)
        
        out_9 = self.conv2_dw_9(out)
        out_9 = self.bn2_dw_9(out_9)
        out_9 = self.relu(out_9)
        out_9 = self.conv2_ex_9(out_9)
        out_9 = self.bn2_ex_9(out_9)
        out_9 = self.relu(out_9)
        
        out = torch.cat((out_5, out_7, out_9, out), dim=1)
        out = self.conv3(out)
        out = self.bn3(out)
        
        out += residual
        out = self.relu(out)
        
        return out

这个模块包含了多个平行分支，每个分支都是一个由1x1DWConv和DWConv或扩张卷积构成的卷积层。这些分支的输出被拼接在一起，然后再通过1x1DWConv进行压缩，最终形成特征图y。这个模块还包含了一个残差连接分支，用于保留原始特征图的信息。