code: def __reduce__(self):
      # __reduce__ gets called when we try to pickle the dataset.
      # In a DataLoader with spawn context, this gets called `num_workers` times from the main process.
  
      # We have to reset the [target_]transform[s] attributes of the dataset
      # to their original values, because we previously set them to None in __init__().
      dataset = copy(self._dataset)
      dataset.transform = self.transform
      dataset.transforms = self.transforms
      dataset.target_transform = self.target_transform
  
      return wrap_dataset_for_transforms_v2, (
          dataset,
          self._target_keys,
      )
  
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 5 for calling the VisionDatasetTVTensorWrapper obj's underlying member method code with example usage: obj.__reduce__() and returns: wrap_dataset_for_transforms_v2,  dataset,  self