human
Can you provide a the Python program you requested given the following:
I am working on the file "FADL1/L7CA_lesson7-cifar10.ipynb".
The first task is:
Notice making the model deeper hasn't helped. It's possible to train a standard ConvNet 12 layers deep, but it's hard to do properly. Instead we're going to replace the ConvNet with a ResNet.
7. ResNet
The ResnetLayer class is going to inherit from BnLayer and replace our forward with return x + super().forward(x). And that's it. Everything else is going to be identical -- except that we're now going to make the network 4x deeper.
Can you write Python program for it?
assistant
#Here is the Python program you requested.

class ResnetLayer(BnLayer):
    def forward(self, x): return x + super().forward(x)

class Resnet(nn.Module):
    def __init__(self, layers, c):
        super().__init__()
        self.conv1 = nn.Conv2d(3, 10, kernel_size=5, stride=1, padding=2)
        self.layers = nn.ModuleList([BnLayer(layers[i], layers[i+1])
            for i in range(len(layers) - 1)])
        self.layers2 = nn.ModuleList([ResnetLayer(layers[i+1], layers[i + 1], 1)
            for i in range(len(layers) - 1)])
        self.layers3 = nn.ModuleList([ResnetLayer(layers[i+1], layers[i + 1], 1)
            for i in range(len(layers) - 1)])
        self.out = nn.Linear(layers[-1], c)
    
    def forward(self, x):
        x = self.conv1(x)
        for λ,λ2,λ3 in zip(self.layers, self.layers2, self.layers3):
            x = λ3(λ2(λ(x))) # function of a function of a function
        x = F.adaptive_max_pool2d(x, 1)
        x = x.view(x.size(0), -1)
        return F.log_softmax(self.out(x), dim=-1)