src/transformers/models/marian/tokenization_marian.py/MarianTokenizer/decode
class MarianTokenizer:    def decode(self, token_ids, **kwargs):
        """
        Converts a sequence of ids 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 (`Union[int, 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*, defaults to `True`):
                Whether or not to clean up the tokenization spaces.
            use_source_tokenizer (`bool`, *optional*, defaults to `False`):
                Whether or not to use the source tokenizer to decode sequences (only applicable in sequence-to-sequence
                problems).
            kwargs (additional keyword arguments, *optional*):
                Will be passed to the underlying model specific decode method.

        Returns:
            `str`: The decoded sentence.
        """
        return super().decode(token_ids, **kwargs)