I’m not sure, how you’ve processed the text, but the methods from the seq2seq tutorial seem to work for your example tweet:
text = '@user when a father is dysfunctional and is so selfish he drags his kids into his dysfunction. #run'
text = normalizeString(text)
input_lang = Lang('tweet')
input_lang.addSentence(text)

encoded = [input_lang.word2index[w] for w in text.split(' ')]
input = torch.tensor(encoded)
print(input.type())
> torch.LongTensor