code: def __init__(self, dataset, target_keys):
      dataset_cls = type(dataset)
  
      if not isinstance(dataset, datasets.VisionDataset):
          raise TypeError(
              f"This wrapper is meant for subclasses of `torchvision.datasets.VisionDataset`, "
              f"but got a '{dataset_cls.__name__}' instead.\n"
              f"For an example of how to perform the wrapping for custom datasets, see\n\n"
              "https://pytorch.org/vision/main/auto_examples/plot_tv_tensors.html#do-i-have-to-wrap-the-output-of-the-datasets-myself"
          )
  
      for cls in dataset_cls.mro():
          if cls in WRAPPER_FACTORIES:
              wrapper_factory = WRAPPER_FACTORIES[cls]
              if target_keys is not None and cls not in {
                  datasets.CocoDetection,
                  datasets.VOCDetection,
                  datasets.Kitti,
                  datasets.WIDERFace,
              }:
                  raise ValueError(
                      f"`target_keys` is currently only supported for `CocoDetection`, `VOCDetection`, `Kitti`, "
                      f"and `WIDERFace`, but got {cls.__name__}."
                  )
              break
          elif cls is datasets.VisionDataset:
              # TODO: If we have documentation on how to do that, put a link in the error message.
              msg = f"No wrapper exists for dataset class {dataset_cls.__name__}. Please wrap the output yourself."
              if dataset_cls in datasets.__dict__.values():
                  msg = (
                      f"{msg} If an automated wrapper for this dataset would be useful for you, "
                      f"please open an issue at https://github.com/pytorch/vision/issues."
                  )
              raise TypeError(msg)
  
      self._dataset = dataset
      self._target_keys = target_keys
      self._wrapper = wrapper_factory(dataset, target_keys)
  
      # We need to disable the transforms on the dataset here to be able to inject the wrapping before we apply them.
      # Although internally, `datasets.VisionDataset` merges `transform` and `target_transform` into the joint
      # `transforms`
      # https://github.com/pytorch/vision/blob/135a0f9ea9841b6324b4fe8974e2543cbb95709a/torchvision/datasets/vision.py#L52-L54
      # some (if not most) datasets still use `transform` and `target_transform` individually. Thus, we need to
      # disable all three here to be able to extract the untransformed sample to wrap.
      self.transform, dataset.transform = dataset.transform, None
      self.target_transform, dataset.target_transform = (
          dataset.target_transform,
          None,
      )
      self.transforms, dataset.transforms = dataset.transforms, None
  
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 1 to create a new VisionDatasetTVTensorWrapper object  with example: obj = VisionDatasetTVTensorWrapper(dataset, target_keys)