@roy
The following code does it:
full_network.to(device)
full_network.eval()
res = []
tags_set = {
            "StreetNumber": 0,
            "StreetName": 1,
            "Unit": 2,
            "Municipality": 3,
            "Province": 4,
            "PostalCode": 5,
            "Orientation": 6,
            "GeneralDelivery": 7
        }
test_sent ='35 r de percé gatineau qc j8r 2e6'
test_sent_vec = embedding_vectorizer(test_sent)
test_sent_vectorizer = torch.tensor([test_sent_vec], dtype=torch.float32).to(device)
test_sent_vectorizer_len = torch.tensor([test_sent_vectorizer.size()[1]], dtype=torch.long).to(device) 

with torch.no_grad():
  test_sent_res = full_network(test_sent_vectorizer, test_sent_vectorizer_len)
  out = test_sent_res.cpu()[0]
  out = torch.argmax(out,dim=1)
  for c in out:
            res.append(list(tags_set.keys())[list(tags_set.values()).index(c.item())])

  print(f'Predicted: {res}')


P.S. I had to run and train the code and then worked on the above code