def _process_yolox(output: ndarray, ratio: float, size: Tuple = (384, 384)) -> Tuple[ndarray, ndarray]:
    """takes the outputs and processes them 

    Args:
        output (ndarray): processes the outpus of the yolox inference
        ratio (float): the ratio between original and inferred sizes.
        size (Tuple, optional): . Defaults to (384, 384).

    Returns:
        Tuple[ndarray, ndarray]: _description_
    """
    predictions = demo_postprocess(output[0], size)[0]
    boxes = predictions[:, :4]
    scores = predictions[:, 4:5] 

    boxes_xyxy = np.ones_like(boxes)
    boxes_xyxy[:, 0] = boxes[:, 0] - boxes[:, 2]/2.
    boxes_xyxy[:, 1] = boxes[:, 1] - boxes[:, 3]/2.
    boxes_xyxy[:, 2] = boxes[:, 0] + boxes[:, 2]/2.
    boxes_xyxy[:, 3] = boxes[:, 1] + boxes[:, 3]/2.
    boxes_xyxy /= ratio

    return boxes_xyxy, scores
takes the outputs and processes them 

    Args:
        output (ndarray): processes the outpus of the yolox inference
        ratio (float): the ratio between original and inferred sizes.
        size (Tuple, optional): . Defaults to (384, 384).

    Returns:
        Tuple[ndarray, ndarray]: _description_
    
src/marqo/s2_inference/processing/yolox_utils.py