code: def __call__(self, image, target=None):
      image = F.normalize(image, mean=self.mean, std=self.std)
      if target is None:
          return image, None
      target = target.copy()
      h, w = image.shape[-2:]
      if "boxes" in target:
          boxes = target["boxes"]
          boxes = box_xyxy_to_cxcywh(boxes)
          boxes = boxes / torch.tensor(
              [w, h, w, h], dtype=torch.float32
          )
          target["boxes"] = boxes
      return 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 the Normalize obj's underlying member method code with example usage: obj.__call__(image, target) and returns: image,  target, image,  None