Generally, the forward() function can be defined to have multiple input arguments. However, I think it is not possible to pass multiple arguments to models wrapped by Sequential() function.
Furthermore, I see that in the following code from the BasicBlock:



 yclin:

def forward(self, x,y): out1 = self.conv1(self.relu(self.bn1(x))) out2 = y return torch.cat([x, out1], 1), out2


the input tensor y is just passed along as output without any changes. So, in fact you would not even need to pass y along with x.