def METHOD_NAME(self, data_batch: Sequence[dict],
            data_samples: Sequence[dict]) -> None:
    """Process one batch of data samples and predictions. The processed
    results should be stored in ``self.results``, which will be used to
    compute the metrics when all batches have been processed.
    Args:
        data_batch (Sequence[dict]): A batch of data
            from the dataloader.
        data_samples (Sequence[dict]): A batch of outputs from
            the model.
    """
    for data_sample in data_samples:
        # predicted keypoints coordinates, [1, K, D]
        pred_coords = data_sample['pred_instances']['keypoints']
        # ground truth data_info
        gt = data_sample['gt_instances']
        # ground truth keypoints coordinates, [1, K, D]
        gt_coords = gt['lifting_target']
        # ground truth keypoints_visible, [1, K, 1]
        mask = gt['lifting_target_visible'].astype(bool).reshape(1, -1)
        # instance action
        img_path = data_sample['target_img_path']
        _, rest = osp.basename(img_path).split('_', 1)
        action, _ = rest.split('.', 1)
        result = {
            'pred_coords': pred_coords,
            'gt_coords': gt_coords,
            'mask': mask,
            'action': action
        }
        self.results.append(result)