code: def __getitem__(self, idx):
      # This gets us the raw sample since we disabled the transforms for the underlying dataset in the constructor
      # of this class
      sample = self._dataset[idx]
  
      sample = self._wrapper(idx, sample)
  
      # Regardless of whether the user has supplied the transforms individually (`transform` and `target_transform`)
      # or joint (`transforms`), we can access the full functionality through `transforms`
      if self.transforms is not None:
          sample = self.transforms(*sample)
  
      return sample
  
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 3 for calling the VisionDatasetTVTensorWrapper obj's underlying member method code with example usage: obj.__getitem__(idx) and returns: sample