def METHOD_NAME(img, conf_thresh, nms_thresh, output):
    # anchors = [12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401]
    # num_anchors = 9
    # anchor_masks = [[0, 1, 2], [3, 4, 5], [6, 7, 8]]
    # strides = [8, 16, 32]
    # anchor_step = len(anchors) // num_anchors
    # [batch, num, 1, 4]
    box_array = output[0]
    # [batch, num, num_classes]
    confs = output[1]
    t1 = time.time()
    if type(box_array).__name__ != 'ndarray':
        box_array = box_array.cpu().detach().numpy()
        confs = confs.cpu().detach().numpy()
    num_classes = confs.shape[2]
    # [batch, num, 4]
    box_array = box_array[:, :, 0]
    # [batch, num, num_classes] --> [batch, num]
    max_conf = np.max(confs, axis=2)
    max_id = np.argmax(confs, axis=2)
    t2 = time.time()
    bboxes_batch = []
    for i in range(box_array.shape[0]):
        argwhere = max_conf[i] > conf_thresh
        l_box_array = box_array[i, argwhere, :]
        l_max_conf = max_conf[i, argwhere]
        l_max_id = max_id[i, argwhere]
        bboxes = []
        # nms for each class
        for j in range(num_classes):
            cls_argwhere = l_max_id == j
            ll_box_array = l_box_array[cls_argwhere, :]
            ll_max_conf = l_max_conf[cls_argwhere]
            ll_max_id = l_max_id[cls_argwhere]
            keep = nms_cpu(ll_box_array, ll_max_conf, nms_thresh)
            if (keep.size > 0):
                ll_box_array = ll_box_array[keep, :]
                ll_max_conf = ll_max_conf[keep]
                ll_max_id = ll_max_id[keep]
                for k in range(ll_box_array.shape[0]):
                    bboxes.append([ll_box_array[k, 0], ll_box_array[k, 1], ll_box_array[k, 2], ll_box_array[k, 3], ll_max_conf[k], ll_max_conf[k], ll_max_id[k]])
        bboxes_batch.append(bboxes)
    t3 = time.time()
    print('-----------------------------------')
    print('       max and argmax : %f' % (t2 - t1))
    print('                  nms : %f' % (t3 - t2))
    print('Post processing total : %f' % (t3 - t1))
    print('-----------------------------------')
    return bboxes_batch