code: def hflip(image, target):
      flipped_image = F.hflip(image)
  
      w, h = image.size
  
      target = target.copy()
      if "boxes" in target:
          boxes = target["boxes"]
          boxes = boxes[:, [2, 1, 0, 3]] * torch.as_tensor(
              [-1, 1, -1, 1]
          ) + torch.as_tensor([w, 0, w, 0])
          target["boxes"] = boxes
  
      if "masks" in target:
          target["masks"] = target["masks"].flip(-1)
  
      return flipped_image, target
  
imports: import random
  import PIL
  import torch
  import torchvision.transforms
  import torchvision.transforms.functional
  from util.box_ops import box_xyxy_to_cxcywh
  from util.misc import interpolate
label_desc: Use image node_id 2 for calling a global function with example usage: hflip(image, target) and returns: flipped_image,  target