def METHOD_NAME(self,):
    cfg = CTCDecodingConfig(strategy='greedy')
    vocab = char_vocabulary()
    decoding = CTCDecoding(decoding_cfg=cfg, vocabulary=vocab)
    B, T = 4, 20
    V = len(char_vocabulary()) + 1
    input_signal = torch.randn(size=(B, T, V))
    length = torch.randint(low=1, high=T, size=[B])
    with torch.no_grad():
        texts, _ = decoding.ctc_decoder_predictions_tensor(
            input_signal, length, fold_consecutive=True, return_hypotheses=False
        )
        for text in texts:
            assert isinstance(text, str)