You could just define your model and concat the tensors in the forward function.
Here is a small example:
class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.fc1a = nn.Linear(20, 10)
        self.fc1b = nn.Linear(20, 10)
        self.fc2 = nn.Linear(20, 2)
        
    def forward(self, x):
        x1 = F.relu(self.fc1a(x))
        x2 = F.relu(self.fc1b(x))
        x = torch.cat((x1, x2), dim=1)
        x = self.fc2(x)
        return x

model = MyModel()
x = torch.randn(1, 20)
output = model(x)