Sure, I will do that. Here is what I have so far.
class toy_resnet(nn.Module):
def __init__(self):
    super(toy_resnet,self).__init__()

    self.conv1 = nn.Sequential(
        Conv2d(3, 8, kernel_size=3, stride=1, padding='same'),
        BatchNorm2d(8),
        ReLU(inplace=True),
        Conv2d(8, 8, kernel_size=3, stride=1, padding='same'),
        BatchNorm2d(8),
        ReLU(inplace=True)
         )

    self.conv2 = nn.Sequential(
        Conv2d(8, 8, kernel_size=3, stride=1, padding='same'),
        BatchNorm2d(8),
        ReLU(inplace=True),
        Conv2d(8, 8, kernel_size=3, stride=1, padding='same'),
        BatchNorm2d(8),
        ReLU(inplace=True)
           )

    self.classifier = nn.Sequential(
        nn.Flatten(),
        nn.Linear(24200,37*10),
        nn.Dropout(0.5)
    )
    self.max_pool = MaxPool2d(kernel_size=3, stride=2)
def forward(self,input):
    # x = self.conv1(input.float().to('cuda'))
    # #print(x.shape)
    # x = self.conv2(x.float().to('cuda'))
    # #print(x.shape)
    # x = self.conv3(x.float().to('cuda'))
    #res = input
    x = self.conv1(input.float())
    x = self.max_pool(x)
    res= x

    #x+=input
    #print(x.shape)
    x = self.conv2(x.float())
    out = x+res
    
    x = self.conv2(out.float())
    res = x+out

    out = self.max_pool(res)
    #print(x.shape)
    #x = self.conv3(x.float())
    #print(x.shape)
    x = self.classifier(out)
    return x