def __call__(self,
                 inputs: InputsType,
                 return_datasamples: bool = False,
                 batch_size: int = 1,
                 **kwargs) -> dict:
        """Call the inferencer.

        Args:
            inputs (tuple, List[tuple]): The input data tuples, every tuple
                should include three items (left image, right image, text).
                The image can be a path or numpy array.
            return_datasamples (bool): Whether to return results as
                :obj:`DataSample`. Defaults to False.
            batch_size (int): Batch size. Defaults to 1.
            resize (int, optional): Resize the short edge of the image to the
                specified length before visualization. Defaults to None.
            draw_score (bool): Whether to draw the prediction scores
                of prediction categories. Defaults to True.
            show (bool): Whether to display the visualization result in a
                window. Defaults to False.
            wait_time (float): The display time (s). Defaults to 0, which means
                "forever".
            show_dir (str, optional): If not None, save the visualization
                results in the specified directory. Defaults to None.

        Returns:
            list: The inference results.
        """
        assert isinstance(inputs, (tuple, list))
        if isinstance(inputs, tuple):
            inputs = [inputs]
        for input_ in inputs:
            assert isinstance(input_, tuple)
            assert len(input_) == 3

        return super().__call__(
            inputs,
            return_datasamples=return_datasamples,
            batch_size=batch_size,
            **kwargs)
Call the inferencer.

        Args:
            inputs (tuple, List[tuple]): The input data tuples, every tuple
                should include three items (left image, right image, text).
                The image can be a path or numpy array.
            return_datasamples (bool): Whether to return results as
                :obj:`DataSample`. Defaults to False.
            batch_size (int): Batch size. Defaults to 1.
            resize (int, optional): Resize the short edge of the image to the
                specified length before visualization. Defaults to None.
            draw_score (bool): Whether to draw the prediction scores
                of prediction categories. Defaults to True.
            show (bool): Whether to display the visualization result in a
                window. Defaults to False.
            wait_time (float): The display time (s). Defaults to 0, which means
                "forever".
            show_dir (str, optional): If not None, save the visualization
                results in the specified directory. Defaults to None.

        Returns:
            list: The inference results.
        
mmpretrain/apis/nlvr.py