def preprocess(self,
                   inputs: InputsType,
                   batch_size: int = 1,
                   bboxes: Optional[List] = None,
                   **kwargs):
        """Process the inputs into a model-feedable format.

        Args:
            inputs (InputsType): Inputs given by user.
            batch_size (int): batch size. Defaults to 1.

        Yields:
            Any: Data processed by the ``pipeline`` and ``collate_fn``.
            List[str or np.ndarray]: List of original inputs in the batch
        """

        for i, input in enumerate(inputs):
            bbox = bboxes[i] if bboxes else []
            data_infos = self.preprocess_single(
                input, index=i, bboxes=bbox, **kwargs)
            # only supports inference with batch size 1
            yield self.collate_fn(data_infos), [input]
Process the inputs into a model-feedable format.

        Args:
            inputs (InputsType): Inputs given by user.
            batch_size (int): batch size. Defaults to 1.

        Yields:
            Any: Data processed by the ``pipeline`` and ``collate_fn``.
            List[str or np.ndarray]: List of original inputs in the batch
        
mmpose/mmpose/apis/inferencers/base_mmpose_inferencer.py