def __call__(self, inputs: Union[str, List[str]], **kwargs):
        """
        Classify each token of the text(s) given as inputs.

        Args:
            inputs (`str` or `List[str]`):
                One or several texts (or one list of texts) for token classification.

        Return:
            A list or a list of list of `dict`: Each result comes as a list of dictionaries (one for each token in the
            corresponding input, or each entity if this pipeline was instantiated with an aggregation_strategy) with
            the following keys:

            - **word** (`str`) -- The token/word classified. This is obtained by decoding the selected tokens. If you
              want to have the exact string in the original sentence, use `start` and `end`.
            - **score** (`float`) -- The corresponding probability for `entity`.
            - **entity** (`str`) -- The entity predicted for that token/word (it is named *entity_group* when
              *aggregation_strategy* is not `"none"`.
            - **index** (`int`, only present when `aggregation_strategy="none"`) -- The index of the corresponding
              token in the sentence.
            - **start** (`int`, *optional*) -- The index of the start of the corresponding entity in the sentence. Only
              exists if the offsets are available within the tokenizer
            - **end** (`int`, *optional*) -- The index of the end of the corresponding entity in the sentence. Only
              exists if the offsets are available within the tokenizer
        """

        _inputs, offset_mapping = self._args_parser(inputs, **kwargs)
        if offset_mapping:
            kwargs["offset_mapping"] = offset_mapping

        return super().__call__(inputs, **kwargs)

        Classify each token of the text(s) given as inputs.

        Args:
            inputs (`str` or `List[str]`):
                One or several texts (or one list of texts) for token classification.

        Return:
            A list or a list of list of `dict`: Each result comes as a list of dictionaries (one for each token in the
            corresponding input, or each entity if this pipeline was instantiated with an aggregation_strategy) with
            the following keys:

            - **word** (`str`) -- The token/word classified. This is obtained by decoding the selected tokens. If you
              want to have the exact string in the original sentence, use `start` and `end`.
            - **score** (`float`) -- The corresponding probability for `entity`.
            - **entity** (`str`) -- The entity predicted for that token/word (it is named *entity_group* when
              *aggregation_strategy* is not `"none"`.
            - **index** (`int`, only present when `aggregation_strategy="none"`) -- The index of the corresponding
              token in the sentence.
            - **start** (`int`, *optional*) -- The index of the start of the corresponding entity in the sentence. Only
              exists if the offsets are available within the tokenizer
            - **end** (`int`, *optional*) -- The index of the end of the corresponding entity in the sentence. Only
              exists if the offsets are available within the tokenizer
        
dpkd/transformers/src/transformers/pipelines/token_classification.py