First of all, thank you for your prompt response!
I understand the problem better but I still don’t know how to implement my model.
My problem is that I have to implement in pyTorch a model that worked perfectly and gave excellent results with TorchNN and LUA language.
This is the TorchNN model:
nn.Sequential {
  [input -> (1) -> (2) -> (3) -> (4) -> (5) -> (6) -> (7) -> (8) -> (9) -> (10) -> (11) -> output]
  (1): nn.ParallelTable {
    input
      |`-> (1): nn.Sequential {
      |      [input -> (1) -> (2) -> (3) -> output]
      |      (1): nn.SplitTable
      |      (2): nn.ParallelTable {
      |        input
      |          |`-> (1): nn.Linear(14 -> 42)
      |          |`-> (2): nn.Linear(14 -> 42)
      |          |`-> (3): nn.Linear(14 -> 42)
      |          |`-> (4): nn.Linear(14 -> 42)
      |          |`-> (5): nn.Linear(14 -> 42)
      |          |`-> (6): nn.Linear(14 -> 42)
      |          |`-> (7): nn.Linear(14 -> 42)
      |          |`-> (8): nn.Linear(14 -> 42)
      |          |`-> (9): nn.Linear(14 -> 42)
      |          |`-> (10): nn.Linear(14 -> 42)
      |          |`-> (11): nn.Linear(14 -> 42)
      |           `-> (12): nn.Linear(14 -> 42)
      |           ... -> output
      |      }
      |      (3): nn.JoinTable
      |    }
       `-> (2): nn.Sequential {
             [input -> (1) -> output]
             (1): nn.Linear(5 -> 15)
           }
       ... -> output
  }
  (2): nn.JoinTable
  (3): nn.Linear(519 -> 780)
  (4): nn.PReLU
  (5): nn.Linear(780 -> 520)
  (6): nn.PReLU
  (7): nn.Linear(520 -> 160)
  (10): nn.Tanh
  (11): nn.Linear(160 -> 12)

For various reasons I had to switch to pyTorch, but despite my efforts, the performance of the network has completely collapsed, because of the problem I’m stumbling upon: how to make my pyTorch model conform to the TorchNN nodel.
You understand that I’m not a great pyTorch expert but I’m eager to learn, so I’d be very grateful if you could help me some good advice, a contribution, or a piece of code that would get me out of this predicament.
Thank you in advance for your help.
Friendly
GP