How to use the lightwood.encoders.text.short.ShortTextEncoder function in lightwood

def _test_mean(self, onehot):
        vocab_size = 99 if onehot else 800

        priming_data = generate_sentences(2, 6, vocab_size)
        test_data = random.sample(priming_data, len(priming_data) // 5)

        enc = ShortTextEncoder(combine='mean')
        enc.prepare_encoder(priming_data)

        if onehot:
            assert not enc.cae.use_autoencoder
        else:
            assert enc.cae.use_autoencoder

        encoded_data = enc.encode(test_data)

        assert len(test_data) == len(encoded_data)

        with self.assertRaises(ValueError):
            decoded_data = enc.decode(encoded_data)

def _test_concat(self, onehot):
        vocab_size = 99 if onehot else 800
        
        priming_data = generate_sentences(2, 6, vocab_size)
        test_data = random.sample(priming_data, len(priming_data) // 5)

        enc = ShortTextEncoder(combine='concat')
        enc.prepare_encoder(priming_data)

        if onehot:
            assert not enc.cae.use_autoencoder
        else:
            assert enc.cae.use_autoencoder

        encoded_data = enc.encode(test_data)
        decoded_data = enc.decode(encoded_data)
        
        assert len(test_data) == len(encoded_data) == len(decoded_data)

        for x_sent, y_sent in zip(
            test_data,
            [' '.join(x) for x in decoded_data]
        ):

def _test_concat(self, onehot):
        vocab_size = 99 if onehot else 800
        
        priming_data = generate_sentences(2, 6, vocab_size)
        test_data = random.sample(priming_data, len(priming_data) // 5)

        enc = ShortTextEncoder(combine='concat')
        enc.prepare_encoder(priming_data)

        if onehot:
            assert not enc.cae.use_autoencoder
        else:
            assert enc.cae.use_autoencoder

        encoded_data = enc.encode(test_data)
        decoded_data = enc.decode(encoded_data)
        
        assert len(test_data) == len(encoded_data) == len(decoded_data)

        for x_sent, y_sent in zip(
            test_data,
            [' '.join(x) for x in decoded_data]
        ):

def _test_mean(self, onehot):
        vocab_size = 99 if onehot else 800

        priming_data = generate_sentences(2, 6, vocab_size)
        test_data = random.sample(priming_data, len(priming_data) // 5)

        enc = ShortTextEncoder(combine='mean')
        enc.prepare_encoder(priming_data)

        if onehot:
            assert not enc.cae.use_autoencoder
        else:
            assert enc.cae.use_autoencoder

        encoded_data = enc.encode(test_data)

        assert len(test_data) == len(encoded_data)

        with self.assertRaises(ValueError):
            decoded_data = enc.decode(encoded_data)