human
I wrote the below code

    document = torch.LongTensor([word_dict[w] for w in ex['document']])
    question = torch.LongTensor([word_dict[w] for w in ex['question']])
    features = torch.zeros(len(ex['document']), len(feature_dict))
    if opt['use_in_question']:
        q_words_cased = set([w for w in ex['question']])
        q_words_uncased = set([w.lower() for w in ex['question']])
        for i in range(len(ex['document'])):
            if (ex['document'][i] in q_words_cased):
                features[i][feature_dict['in_question']] = 1.0
            if (ex['document'][i].lower() in q_words_uncased):
                features[i][feature_dict['in_question_uncased']] = 1.0
    if opt['use_tf']:
        counter = Counter([w.lower() for w in ex['document']])
        l = len(ex['document'])
        for (i, w) in enumerate(ex['document']):
            features[i][feature_dict['tf']] = ((counter[w.lower()] * 1.0) / l)
    if (opt['use_time'] > 0):
        sent_idx = 0
        def _full_stop(w):
            return (w in {'.', '?', '!'})
        for (i, w) in reversed(list(enumerate(ex['document']))):
            sent_idx = ((sent_idx + 1) if _full_stop(w) else max(sent_idx, 1))
            if (sent_idx < opt['use_time']):
                features[i][feature_dict[('time=T%d' % sent_idx)]] = 1.0
            else:
                features[i][feature_dict[('time>=T%d' % opt['use_time'])]] = 1.0
    if (ex['target'] is None):
        return (document, features, question)
    start = torch.LongTensor(1).fill_(ex['target'][0])
    end = torch.LongTensor(1).fill_(ex['target'][1])
    return (document, features, question, start, end)

What's a good function header?
 Here's a good function header?
assistant
#Here's a good function header.

def vectorize(opt, ex, word_dict, feature_dict):