def batch_decode(
        self,
        sequences: Union[List[int], List[List[int]], "np.ndarray", "torch.Tensor", "tf.Tensor"],
        skip_special_tokens: bool = False,
        clean_up_tokenization_spaces: bool = None,
        output_char_offsets: bool = False,
        **kwargs,
    ) -> List[str]:
        """
        Convert a list of lists of token ids into a list of strings by calling decode.

        Args:
            sequences (`Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]`):
                List of tokenized input ids. Can be obtained using the `__call__` method.
            skip_special_tokens (`bool`, *optional*, defaults to `False`):
                Whether or not to remove special tokens in the decoding.
            clean_up_tokenization_spaces (`bool`, *optional*):
                Whether or not to clean up the tokenization spaces.
            output_char_offsets (`bool`, *optional*, defaults to `False`):
                Whether or not to output character offsets. Character offsets can be used in combination with the
                sampling rate and model downsampling rate to compute the time-stamps of transcribed characters.

                <Tip>

                Please take a look at the Example of [`~models.wav2vec2.tokenization_wav2vec2.decode`] to better
                understand how to make use of `output_word_offsets`.
                [`~model.wav2vec2_phoneme.tokenization_wav2vec2_phoneme.batch_decode`] works analogous with phonemes
                and batched output.

                </Tip>

            kwargs (additional keyword arguments, *optional*):
                Will be passed to the underlying model specific decode method.

        Returns:
            `List[str]` or [`~models.wav2vec2.tokenization_wav2vec2_phoneme.Wav2Vec2PhonemeCTCTokenizerOutput`]: The
            decoded sentence. Will be a
            [`~models.wav2vec2.tokenization_wav2vec2_phoneme.Wav2Vec2PhonemeCTCTokenizerOutput`] when
            `output_char_offsets == True`.
        """
        batch_decoded = [
            self.decode(
                seq,
                skip_special_tokens=skip_special_tokens,
                clean_up_tokenization_spaces=clean_up_tokenization_spaces,
                output_char_offsets=output_char_offsets,
                **kwargs,
            )
            for seq in sequences
        ]
        if output_char_offsets:
            # transform list of dicts to dict of lists
            return Wav2Vec2PhonemeCTCTokenizerOutput({k: [d[k] for d in batch_decoded] for k in batch_decoded[0]})

        return batch_decoded

        Convert a list of lists of token ids into a list of strings by calling decode.

        Args:
            sequences (`Union[List[int], List[List[int]], np.ndarray, torch.Tensor, tf.Tensor]`):
                List of tokenized input ids. Can be obtained using the `__call__` method.
            skip_special_tokens (`bool`, *optional*, defaults to `False`):
                Whether or not to remove special tokens in the decoding.
            clean_up_tokenization_spaces (`bool`, *optional*):
                Whether or not to clean up the tokenization spaces.
            output_char_offsets (`bool`, *optional*, defaults to `False`):
                Whether or not to output character offsets. Character offsets can be used in combination with the
                sampling rate and model downsampling rate to compute the time-stamps of transcribed characters.

                <Tip>

                Please take a look at the Example of [`~models.wav2vec2.tokenization_wav2vec2.decode`] to better
                understand how to make use of `output_word_offsets`.
                [`~model.wav2vec2_phoneme.tokenization_wav2vec2_phoneme.batch_decode`] works analogous with phonemes
                and batched output.

                </Tip>

            kwargs (additional keyword arguments, *optional*):
                Will be passed to the underlying model specific decode method.

        Returns:
            `List[str]` or [`~models.wav2vec2.tokenization_wav2vec2_phoneme.Wav2Vec2PhonemeCTCTokenizerOutput`]: The
            decoded sentence. Will be a
            [`~models.wav2vec2.tokenization_wav2vec2_phoneme.Wav2Vec2PhonemeCTCTokenizerOutput`] when
            `output_char_offsets == True`.
        
dpkd/transformers/src/transformers/models/wav2vec2_phoneme/tokenization_wav2vec2_phoneme.py