How to use the gluoncv.nn.bbox.BBoxClipToImage function in gluoncv

def validate(net, val_data, async_eval_processes, ctx, eval_metric, logger, epoch, best_map, args):
    """Test on validation dataset."""
    clipper = gcv.nn.bbox.BBoxClipToImage()
    eval_metric.reset()
    if not args.disable_hybridization:
        net.hybridize(static_alloc=args.static_alloc)
    tic = time.time()
    for ib, batch in enumerate(val_data):
        batch = split_and_load(batch, ctx_list=ctx)
        det_bboxes = []
        det_ids = []
        det_scores = []
        det_masks = []
        det_infos = []
        for x, im_info in zip(*batch):
            # get prediction results
            ids, scores, bboxes, masks = net(x)
            det_bboxes.append(clipper(bboxes, x))
            det_ids.append(ids)

def validate(net, val_data, ctx, eval_metric, size):
    """Test on validation dataset."""
    clipper = gcv.nn.bbox.BBoxClipToImage()
    eval_metric.reset()
    net.hybridize(static_alloc=True)
    with tqdm(total=size) as pbar:
        for ib, batch in enumerate(val_data):
            batch = split_and_load(batch, ctx_list=ctx)
            det_bboxes = []
            det_ids = []
            det_scores = []
            det_masks = []
            det_infos = []
            for x, im_info in zip(*batch):
                # get prediction results
                ids, scores, bboxes, masks = net(x)
                det_bboxes.append(clipper(bboxes, x))
                det_ids.append(ids)
                det_scores.append(scores)

def validate(net, val_data, ctx, eval_metric, size):
    """Test on validation dataset."""
    clipper = gcv.nn.bbox.BBoxClipToImage()
    eval_metric.reset()
    net.hybridize(static_alloc=True)
    with tqdm(total=size) as pbar:
        for ib, batch in enumerate(val_data):
            batch = split_and_load(batch, ctx_list=ctx)
            det_bboxes = []
            det_ids = []
            det_scores = []
            gt_bboxes = []
            gt_ids = []
            gt_difficults = []
            for x, y, im_scale in zip(*batch):
                # get prediction results
                ids, scores, bboxes = net(x)
                det_ids.append(ids)
                det_scores.append(scores)

def validate(net, val_data, ctx, eval_metric, args):
    """Test on validation dataset."""
    clipper = gcv.nn.bbox.BBoxClipToImage()
    eval_metric.reset()
    if not args.disable_hybridization:
        # input format is differnet than training, thus rehybridization is needed.
        net.hybridize(static_alloc=args.static_alloc)
    for batch in val_data:
        batch = split_and_load(batch, ctx_list=ctx)
        det_bboxes = []
        det_ids = []
        det_scores = []
        gt_bboxes = []
        gt_ids = []
        gt_difficults = []
        for x, y, im_scale in zip(*batch):
            # get prediction results
            ids, scores, bboxes = net(x)
            det_ids.append(ids)

def validate(net, val_data, ctx, eval_metric):
    """Test on validation dataset."""
    clipper = gcv.nn.bbox.BBoxClipToImage()
    eval_metric.reset()
    net.hybridize(static_alloc=True)
    for batch in val_data:
        batch = split_and_load(batch, ctx_list=ctx)
        det_bboxes = []
        det_ids = []
        det_scores = []
        gt_bboxes = []
        gt_ids = []
        gt_difficults = []
        for x, y, im_scale in zip(*batch):
            # get prediction results
            ids, scores, bboxes = net(x)
            det_ids.append(ids)
            det_scores.append(scores)
            # clip to image size

def validate(net, val_data, ctx, eval_metric, size):
    """Test on validation dataset."""
    clipper = gcv.nn.bbox.BBoxClipToImage()
    eval_metric.reset()
    net.hybridize(static_alloc=True)
    with tqdm(total=size) as pbar:
        for ib, batch in enumerate(val_data):
            batch = split_and_load(batch, ctx_list=ctx)
            det_bboxes = []
            det_ids = []
            det_scores = []
            gt_bboxes = []
            gt_ids = []
            gt_difficults = []
            for x, y, im_scale in zip(*batch):
                # get prediction results
                ids, scores, bboxes = net(x)
                det_ids.append(ids)
                det_scores.append(scores)

def validate(net, val_data, ctx, eval_metric, size):
    """Test on validation dataset."""
    clipper = gcv.nn.bbox.BBoxClipToImage()
    eval_metric.reset()
    net.hybridize(static_alloc=True)
    with tqdm(total=size) as pbar:
        for ib, batch in enumerate(val_data):
            batch = split_and_load(batch, ctx_list=ctx)
            det_bboxes = []
            det_ids = []
            det_scores = []
            gt_bboxes = []
            gt_ids = []
            gt_difficults = []
            for x, y, im_scale in zip(*batch):
                # get prediction results
                ids, scores, bboxes = net(x)
                det_ids.append(ids)
                det_scores.append(scores)

def validate(net, val_data, ctx, eval_metric, args):
    """Test on validation dataset."""
    clipper = gcv.nn.bbox.BBoxClipToImage()
    eval_metric.reset()
    net.hybridize(static_alloc=True)
    for ib, batch in enumerate(val_data):
        batch = split_and_load(batch, ctx_list=ctx)
        det_bboxes = []
        det_ids = []
        det_scores = []
        det_masks = []
        det_segms = []
        det_infos = []
        for x, im_info in zip(*batch):
            # get prediction results
            ids, scores, bboxes, masks, segms = net(x)
            det_bboxes.append(clipper(bboxes, x))
            det_ids.append(ids)
            det_scores.append(scores)