code: def oxford_iiit_pet_wrapper_factor(dataset, target_keys):
      def wrapper(idx, sample):
          image, target = sample
  
          if target is not None:
              target = wrap_target_by_type(
                  target,
                  target_types=dataset._target_types,
                  type_wrappers={
                      "segmentation": pil_image_to_mask,
                  },
              )
  
          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 18 for calling a global function with example usage: oxford_iiit_pet_wrapper_factor(dataset, target_keys) and returns: wrapper, image,  target