Hi all.
I have created a network with 2 inputs to pass to forward, which are then concatenated later to be fed into another network.
def forward(self, latent, image):
    # latent, image = input_dict["latent"], input_dict["image"]
    x1 = self.net1(latent)
    print(x1.size())
    x2 = self.net2(image)
    print(x2.size())
    x = torch.cat([x1, x2], 1)
    return self.merge(x)

After passing it to torchsummary, it is able to compile(?) the network with given inputs.
summary(netD, [(1024,1,1), (3,64,64)])
However, I have tried various methods to pass on 2 torch.Tensors to the network for feedfoward but they all throw errors.
 dlXGenerated = netG(dlZ, LR)
Can anyone point me to the correct way of passing 2 tensors to a network?