def __call__(self, *args, **kwargs):
        """
        Answer the question(s) given as inputs by using the context(s).

        Args:
            args ([`SquadExample`] or a list of [`SquadExample`]):
                One or several [`SquadExample`] containing the question and context.
            X ([`SquadExample`] or a list of [`SquadExample`], *optional*):
                One or several [`SquadExample`] containing the question and context (will be treated the same way as if
                passed as the first positional argument).
            data ([`SquadExample`] or a list of [`SquadExample`], *optional*):
                One or several [`SquadExample`] containing the question and context (will be treated the same way as if
                passed as the first positional argument).
            question (`str` or `List[str]`):
                One or several question(s) (must be used in conjunction with the `context` argument).
            context (`str` or `List[str]`):
                One or several context(s) associated with the question(s) (must be used in conjunction with the
                `question` argument).
            topk (`int`, *optional*, defaults to 1):
                The number of answers to return (will be chosen by order of likelihood). Note that we return less than
                topk answers if there are not enough options available within the context.
            doc_stride (`int`, *optional*, defaults to 128):
                If the context is too long to fit with the question for the model, it will be split in several chunks
                with some overlap. This argument controls the size of that overlap.
            max_answer_len (`int`, *optional*, defaults to 15):
                The maximum length of predicted answers (e.g., only answers with a shorter length are considered).
            max_seq_len (`int`, *optional*, defaults to 384):
                The maximum length of the total sentence (context + question) in tokens of each chunk passed to the
                model. The context will be split in several chunks (using `doc_stride` as overlap) if needed.
            max_question_len (`int`, *optional*, defaults to 64):
                The maximum length of the question after tokenization. It will be truncated if needed.
            handle_impossible_answer (`bool`, *optional*, defaults to `False`):
                Whether or not we accept impossible as an answer.
            align_to_words (`bool`, *optional*, defaults to `True`):
                Attempts to align the answer to real words. Improves quality on space separated langages. Might hurt on
                non-space-separated languages (like Japanese or Chinese)

        Return:
            A `dict` or a list of `dict`: Each result comes as a dictionary with the following keys:

            - **score** (`float`) -- The probability associated to the answer.
            - **start** (`int`) -- The character start index of the answer (in the tokenized version of the input).
            - **end** (`int`) -- The character end index of the answer (in the tokenized version of the input).
            - **answer** (`str`) -- The answer to the question.
        """

        # Convert inputs to features

        examples = self._args_parser(*args, **kwargs)
        if isinstance(examples, (list, tuple)) and len(examples) == 1:
            return super().__call__(examples[0], **kwargs)
        return super().__call__(examples, **kwargs)

        Answer the question(s) given as inputs by using the context(s).

        Args:
            args ([`SquadExample`] or a list of [`SquadExample`]):
                One or several [`SquadExample`] containing the question and context.
            X ([`SquadExample`] or a list of [`SquadExample`], *optional*):
                One or several [`SquadExample`] containing the question and context (will be treated the same way as if
                passed as the first positional argument).
            data ([`SquadExample`] or a list of [`SquadExample`], *optional*):
                One or several [`SquadExample`] containing the question and context (will be treated the same way as if
                passed as the first positional argument).
            question (`str` or `List[str]`):
                One or several question(s) (must be used in conjunction with the `context` argument).
            context (`str` or `List[str]`):
                One or several context(s) associated with the question(s) (must be used in conjunction with the
                `question` argument).
            topk (`int`, *optional*, defaults to 1):
                The number of answers to return (will be chosen by order of likelihood). Note that we return less than
                topk answers if there are not enough options available within the context.
            doc_stride (`int`, *optional*, defaults to 128):
                If the context is too long to fit with the question for the model, it will be split in several chunks
                with some overlap. This argument controls the size of that overlap.
            max_answer_len (`int`, *optional*, defaults to 15):
                The maximum length of predicted answers (e.g., only answers with a shorter length are considered).
            max_seq_len (`int`, *optional*, defaults to 384):
                The maximum length of the total sentence (context + question) in tokens of each chunk passed to the
                model. The context will be split in several chunks (using `doc_stride` as overlap) if needed.
            max_question_len (`int`, *optional*, defaults to 64):
                The maximum length of the question after tokenization. It will be truncated if needed.
            handle_impossible_answer (`bool`, *optional*, defaults to `False`):
                Whether or not we accept impossible as an answer.
            align_to_words (`bool`, *optional*, defaults to `True`):
                Attempts to align the answer to real words. Improves quality on space separated langages. Might hurt on
                non-space-separated languages (like Japanese or Chinese)

        Return:
            A `dict` or a list of `dict`: Each result comes as a dictionary with the following keys:

            - **score** (`float`) -- The probability associated to the answer.
            - **start** (`int`) -- The character start index of the answer (in the tokenized version of the input).
            - **end** (`int`) -- The character end index of the answer (in the tokenized version of the input).
            - **answer** (`str`) -- The answer to the question.
        
dpkd/transformers/src/transformers/pipelines/question_answering.py