code: func (nn *neuralnetwork2) pred(ind []int) (int, error) {
  	nn.feats2vec(ind)
  
  	// f, _ := os.OpenFile("LOOOOOG", os.O_APPEND|os.O_CREATE|os.O_RDWR, 0644)
  	// logger := log.New(f, "", 0)
  	// logger := log.New(os.Stderr, "", 0)
  
  	// m := G.NewLispMachine(nn.sub, G.ExecuteFwdOnly(), G.WithLogger(logger), G.WithWatchlist(), G.LogBothDir(), G.WithValueFmt("%+3.3v"))
  	m := G.NewLispMachine(nn.sub, G.ExecuteFwdOnly())
  	if err := m.RunAll(); err != nil {
  		return 0, err
  	}
  	// logger.Println("========================\n")
  
  	val := nn.scores.Value().(tensor.Tensor)
  	t, err := tensor.Argmax(val, tensor.AllAxes)
  	if err != nil {
  		return 0, err
  	}
  
  	return t.ScalarValue().(int), nil
  }
docs: // pred predicts the index of the transitions