Sure, here is a simple one:
class block1(nn.Module):
    def __init__(self,ni):
        super(block1, self).__init__()
        self.conv1 = nn.Conv2d(ni, ni, 1)
        self.conv2 = nn.Conv2d(ni, ni, 3, 1, 1)
        self.classifier = nn.Linear(ni*24*24,751)

    def forward(self,x):
        residual = x
        out = F.relu(self.conv1(x))
        out = F.relu(self.conv2(out))
        
        out += residual
        
        out = out.view(out.size(0),-1)
        return self.classifier(out)

block = block1(16)
x = torch.randn(1, 16, 24, 24)
output = block(x)

Depending on your number of in and out channels, you might need to add a downsample layer as in the examples.