src/transformers/models/deberta_v2/tokenization_deberta_v2.py/SPMTokenizer/decode
class SPMTokenizer:    def decode(self, tokens, start=-1, end=-1, raw_text=None):
        if raw_text is None:
            return self.spm.decode_pieces([t for t in tokens])
        else:
            words = self.split_to_words(raw_text)
            word_tokens = [self.tokenize(w) for w in words]
            token2words = [0] * len(tokens)
            tid = 0
            for i, w in enumerate(word_tokens):
                for k, t in enumerate(w):
                    token2words[tid] = i
                    tid += 1
            word_start = token2words[start]
            word_end = token2words[end] if end < len(tokens) else len(words)
            text = "".join(words[word_start:word_end])
            return text