def predict(device, net, words, n_vocab, vocab_to_int, int_to_vocab, top_k=5):
    net.eval()

    state_h, state_c = net.zero_state(1)
    state_h = state_h.to(device)
    state_c = state_c.to(device)
    for w in words:
        ix = torch.tensor([[vocab_to_int[w]]]).to(device)
        output, (state_h, state_c) = net(ix, (state_h, state_c))

    _, top_ix = torch.topk(output[0], k=top_k)
    choices = top_ix.tolist()
    choice = np.random.choice(choices[0])

    words.append(int_to_vocab[choice])