def __call__(self, *args, **kwargs):
        r"""
        Answers queries according to a table. The pipeline accepts several types of inputs which are detailed below:

        - `pipeline(table, query)`
        - `pipeline(table, [query])`
        - `pipeline(table=table, query=query)`
        - `pipeline(table=table, query=[query])`
        - `pipeline({"table": table, "query": query})`
        - `pipeline({"table": table, "query": [query]})`
        - `pipeline([{"table": table, "query": query}, {"table": table, "query": query}])`

        The `table` argument should be a dict or a DataFrame built from that dict, containing the whole table:

        Example:

        ```python
        data = {
            "actors": ["brad pitt", "leonardo di caprio", "george clooney"],
            "age": ["56", "45", "59"],
            "number of movies": ["87", "53", "69"],
            "date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
        }
        ```

        This dictionary can be passed in as such, or can be converted to a pandas DataFrame:

        Example:

        ```python
        import pandas as pd

        table = pd.DataFrame.from_dict(data)
        ```

        Args:
            table (`pd.DataFrame` or `Dict`):
                Pandas DataFrame or dictionary that will be converted to a DataFrame containing all the table values.
                See above for an example of dictionary.
            query (`str` or `List[str]`):
                Query or list of queries that will be sent to the model alongside the table.
            sequential (`bool`, *optional*, defaults to `False`):
                Whether to do inference sequentially or as a batch. Batching is faster, but models like SQA require the
                inference to be done sequentially to extract relations within sequences, given their conversational
                nature.
            padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
                Activates and controls padding. Accepts the following values:

                - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
                  sequence if provided).
                - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
                  acceptable input length for the model if that argument is not provided.
                - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
                  lengths).

            truncation (`bool`, `str` or [`TapasTruncationStrategy`], *optional*, defaults to `False`):
                Activates and controls truncation. Accepts the following values:

                - `True` or `'drop_rows_to_fit'`: Truncate to a maximum length specified with the argument `max_length`
                  or to the maximum acceptable input length for the model if that argument is not provided. This will
                  truncate row by row, removing rows from the table.
                - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
                  greater than the model maximum admissible input size).


        Return:
            A dictionary or a list of dictionaries containing results: Each result is a dictionary with the following
            keys:

            - **answer** (`str`) -- The answer of the query given the table. If there is an aggregator, the answer will
              be preceded by `AGGREGATOR >`.
            - **coordinates** (`List[Tuple[int, int]]`) -- Coordinates of the cells of the answers.
            - **cells** (`List[str]`) -- List of strings made up of the answer cell values.
            - **aggregator** (`str`) -- If the model has an aggregator, this returns the aggregator.
        """
        pipeline_inputs = self._args_parser(*args, **kwargs)

        results = super().__call__(pipeline_inputs, **kwargs)
        if len(results) == 1:
            return results[0]
        return results

        Answers queries according to a table. The pipeline accepts several types of inputs which are detailed below:

        - `pipeline(table, query)`
        - `pipeline(table, [query])`
        - `pipeline(table=table, query=query)`
        - `pipeline(table=table, query=[query])`
        - `pipeline({"table": table, "query": query})`
        - `pipeline({"table": table, "query": [query]})`
        - `pipeline([{"table": table, "query": query}, {"table": table, "query": query}])`

        The `table` argument should be a dict or a DataFrame built from that dict, containing the whole table:

        Example:

        ```python
        data = {
            "actors": ["brad pitt", "leonardo di caprio", "george clooney"],
            "age": ["56", "45", "59"],
            "number of movies": ["87", "53", "69"],
            "date of birth": ["7 february 1967", "10 june 1996", "28 november 1967"],
        }
        ```

        This dictionary can be passed in as such, or can be converted to a pandas DataFrame:

        Example:

        ```python
        import pandas as pd

        table = pd.DataFrame.from_dict(data)
        ```

        Args:
            table (`pd.DataFrame` or `Dict`):
                Pandas DataFrame or dictionary that will be converted to a DataFrame containing all the table values.
                See above for an example of dictionary.
            query (`str` or `List[str]`):
                Query or list of queries that will be sent to the model alongside the table.
            sequential (`bool`, *optional*, defaults to `False`):
                Whether to do inference sequentially or as a batch. Batching is faster, but models like SQA require the
                inference to be done sequentially to extract relations within sequences, given their conversational
                nature.
            padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):
                Activates and controls padding. Accepts the following values:

                - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single
                  sequence if provided).
                - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum
                  acceptable input length for the model if that argument is not provided.
                - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different
                  lengths).

            truncation (`bool`, `str` or [`TapasTruncationStrategy`], *optional*, defaults to `False`):
                Activates and controls truncation. Accepts the following values:

                - `True` or `'drop_rows_to_fit'`: Truncate to a maximum length specified with the argument `max_length`
                  or to the maximum acceptable input length for the model if that argument is not provided. This will
                  truncate row by row, removing rows from the table.
                - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths
                  greater than the model maximum admissible input size).


        Return:
            A dictionary or a list of dictionaries containing results: Each result is a dictionary with the following
            keys:

            - **answer** (`str`) -- The answer of the query given the table. If there is an aggregator, the answer will
              be preceded by `AGGREGATOR >`.
            - **coordinates** (`List[Tuple[int, int]]`) -- Coordinates of the cells of the answers.
            - **cells** (`List[str]`) -- List of strings made up of the answer cell values.
            - **aggregator** (`str`) -- If the model has an aggregator, this returns the aggregator.
        
dpkd/transformers/src/transformers/pipelines/table_question_answering.py