IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1)

When coco dataset has empty bbox annotation following error occured. How should I handle the empty bbox dataset in cocodataset

IndexError: Dimension out of range (expected to be in range of [-1, 0], but got 1)

[{'boxes': tensor([], device='cuda:1'), 'labels': tensor([], device='cuda:1', dtype=torch.int64), 'image_id': tensor([311877], device='cuda:1'), 'area': tensor([], device='cuda:1'), 'iscrowd': tensor([], device='cuda:1', dtype=torch.int64)}] len_> 0

class CocoDataset(torch.utils.data.Dataset):
    def __init__(self, root, annotation, transforms=None):

        self.root = root
        self.transforms = transforms
        self.coco = COCO(annotation)
        self.ids = list(sorted(self.coco.imgs.keys()))

    def __getitem__(self, index):
        # Own coco file
        coco = self.coco
        # Image ID
        img_id = self.ids[index]
        # List: get annotation id from coco
        ann_ids = coco.getAnnIds(imgIds=img_id)
        # Dictionary: target coco_annotation file for an image
        coco_annotation = coco.loadAnns(ann_ids)
        # path for input image
        path = coco.loadImgs(img_id)[0]['file_name']
        # open the input image
        img = Image.open(os.path.join(self.root, path))

        # number of objects in the image
        num_objs = len(coco_annotation)

        # Bounding boxes for objects
        # In coco format, bbox = [xmin, ymin, width, height]
        # In pytorch, the input should be [xmin, ymin, xmax, ymax]
        boxes = []
        for i in range(num_objs):
            xmin = coco_annotation[i]['bbox'][0]
            ymin = coco_annotation[i]['bbox'][1]
            xmax = xmin + coco_annotation[i]['bbox'][2]
            ymax = ymin + coco_annotation[i]['bbox'][3]
            boxes.append([xmin, ymin, xmax, ymax])
        boxes = torch.as_tensor(boxes, dtype=torch.float32)
        # Labels (In my case, I only one class: target class or background)
        labels = torch.ones((num_objs,), dtype=torch.int64)
        # Tensorise img_id
        img_id = torch.tensor([img_id])
        # Size of bbox (Rectangular)
        areas = []
        for i in range(num_objs):
            areas.append(coco_annotation[i]['area'])
        areas = torch.as_tensor(areas, dtype=torch.float32)
        # Iscrowd
        iscrowd = torch.zeros((num_objs,), dtype=torch.int64)

        # Annotation is in dictionary format
        my_annotation = {}
        my_annotation["boxes"] = boxes
        my_annotation["labels"] = labels
        my_annotation["image_id"] = img_id
        my_annotation["area"] = areas
        my_annotation["iscrowd"] = iscrowd

        if self.transforms is not None:
            img = self.transforms(img)
        return img, my_annotation


def resize_boxes(boxes, original_size, new_size):
    ratios = tuple(float(s) / float(s_orig) for s, s_orig in zip(new_size, original_size))
    ratio_height, ratio_width = ratios
    #print("=============================================================")
    #print('ratio_height->',ratio_height,'ratio_width->',ratio_width,'boxes->',boxes)
    print('len_>',len(boxes))

    xmin, ymin, xmax, ymax = boxes.unbind(1)
    xmin = xmin * ratio_width
    xmax = xmax * ratio_width
    ymin = ymin * ratio_height
    ymax = ymax * ratio_height

    return torch.stack((xmin, ymin, xmax, ymax), dim=1)