tup.py

import torch

class FooMod(torch.nn.Module):
    def forward(self, x, tup):
        return x + tup[0]

traced = torch.jit.trace(FooMod(), (torch.rand(3, 4), (torch.rand(3, 4), torch.rand(4))))
print(traced.graph)

===

graph(%x : Float(3, 4)
      %1 : Tuple) {
  %2 : Float(3, 4), %3 : Float(4) = prim::TupleUnpack(%1)
  %4 : int = prim::Constant[value=1](), scope: FooMod
  %5 : Float(3, 4) = aten::add(%x, %2, %4), scope: FooMod
  return (%5);
}