def predict(self, text:str, n_words:int=1, no_unk:bool=True, temperature:float=1., min_p:float=None, sep:str=' ',
                decoder=decode_spec_tokens):
        "Return the `n_words` that come after `text`."
        ds = self.data.single_dl.dataset
        self.model.reset()
        xb,yb = self.data.one_item(text)
        new_idx = []
        for _ in range(n_words): #progress_bar(range(n_words), leave=False):
            res = self.pred_batch(batch=(xb,yb))[0][-1]
            #if len(new_idx) == 0: self.model[0].select_hidden([0])
            if no_unk: res[self.data.vocab.stoi[UNK]] = 0.
            if min_p is not None: 
                if (res >= min_p).float().sum() == 0:
                    warn(f"There is no item with probability >= {min_p}, try a lower value.")
                else: res[res < min_p] = 0.
            if temperature != 1.: res.pow_(1 / temperature)
            idx = torch.multinomial(res, 1).item()
            new_idx.append(idx)
            xb = xb.new_tensor([idx])[None]
        return text + sep + sep.join(decoder(self.data.vocab.textify(new_idx, sep=None)))
Return the `n_words` that come after `text`.