code: def widerface_wrapper(dataset, target_keys):
      target_keys = parse_target_keys(
          target_keys,
          available={
              "bbox",
              "blur",
              "expression",
              "illumination",
              "occlusion",
              "pose",
              "invalid",
          },
          default="all",
      )
  
      def wrapper(idx, sample):
          image, target = sample
  
          if target is None:
              return image, target
  
          target = {key: target[key] for key in target_keys}
  
          if "bbox" in target_keys:
              target["bbox"] = F.convert_bounding_box_format(
                  tv_tensors.BoundingBoxes(
                      target["bbox"],
                      format=tv_tensors.BoundingBoxFormat.XYWH,
                      canvas_size=(image.height, image.width),
                  ),
                  new_format=tv_tensors.BoundingBoxFormat.XYXY,
              )
  
          return image, target
  
      return wrapper
  
imports: from __future__ import annotations
  import collections.abc
  import contextlib
  from collections import defaultdict
  from copy import copy
  import torch
  from torchvision import datasets, tv_tensors
  from torchvision.transforms.v2 import functional
label_desc: Use image node_id 20 for calling a global function with example usage: widerface_wrapper(dataset, target_keys) and returns: wrapper, image,  target, image,  target