You can directly add PyTorch tensors in your forward without the need to use a layer:
def forward(self, x):
    out1 = self.layer1(x)
    out2 = self.layer1(x)
    out3 = self.layer1(x)
    out = out1 + out2 + out3
    ...

Alternatively, you could also create a custom nn.Module, pass the inputs to this module, and add them in the forward (although it sounds quite cumbersome given that a + will also do it  ).