def decode(self, token_ids, skip_special_tokens=False, clean_up_tokenization_spaces=True):
        """
        Converts a sequence of ids (integer) in a string, using the tokenizer and vocabulary
        with options to remove special tokens and clean up tokenization spaces.
        Similar to doing ``self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))``.

        Args:
            token_ids: list of tokenized input ids. Can be obtained using the `encode` or `encode_plus` methods.
            skip_special_tokens: if set to True, will replace special tokens.
            clean_up_tokenization_spaces: if set to True, will clean up the tokenization spaces.
        """
        filtered_tokens = self.convert_ids_to_tokens(token_ids, skip_special_tokens=skip_special_tokens)

        # To avoid mixing byte-level and unicode for byte-level BPT
        # we need to build string separatly for added tokens and byte-level tokens
        # cf. https://github.com/huggingface/transformers/issues/1133
        sub_texts = []
        current_sub_text = []
        for token in filtered_tokens:
            if skip_special_tokens and token in self.all_special_ids:
                continue
            if token in self.added_tokens_encoder:
                if current_sub_text:
                    sub_texts.append(self.convert_tokens_to_string(current_sub_text))
                    current_sub_text = []
                sub_texts.append(token)
            else:
                current_sub_text.append(token)
        if current_sub_text:
            sub_texts.append(self.convert_tokens_to_string(current_sub_text))
        text = " ".join(sub_texts)

        if clean_up_tokenization_spaces:
            clean_text = self.clean_up_tokenization(text)
            return clean_text
        else:
            return text

        Converts a sequence of ids (integer) in a string, using the tokenizer and vocabulary
        with options to remove special tokens and clean up tokenization spaces.
        Similar to doing ``self.convert_tokens_to_string(self.convert_ids_to_tokens(token_ids))``.

        Args:
            token_ids: list of tokenized input ids. Can be obtained using the `encode` or `encode_plus` methods.
            skip_special_tokens: if set to True, will replace special tokens.
            clean_up_tokenization_spaces: if set to True, will clean up the tokenization spaces.
        
xtune/src/transformers/tokenization_utils.py