code: def _unflatten_and_insert_image_or_video(
      self,
      flat_inputs_with_spec: Tuple[List[Any], TreeSpec, int],
      image_or_video: ImageOrVideo,
  ) -> Any:
      flat_inputs, spec, idx = flat_inputs_with_spec
      flat_inputs[idx] = image_or_video
      return tree_unflatten(flat_inputs, spec)
  
imports: import math
  from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
  import PIL.Image
  import torch
  from torch.utils._pytree import tree_flatten, tree_unflatten, TreeSpec
  from torchvision import transforms, tv_tensors
  from torchvision.transforms import _functional_tensor
  from torchvision.transforms.v2 import AutoAugmentPolicy, functional, InterpolationMode, Transform
  from torchvision.transforms.v2.functional._geometry import _check_interpolation
  from torchvision.transforms.v2.functional._meta import get_size
  from torchvision.transforms.v2.functional._utils import _FillType, _FillTypeJIT
  from ._utils import _get_fill, _setup_fill_arg, check_type, is_pure_tensor
label_desc: Use image node_id 5 for calling the _AutoAugmentBase obj's underlying member method code with example usage: obj._unflatten_and_insert_image_or_video(flat_inputs_with_spec, image_or_video) and returns: tree_unflatten