code: def _flatten_and_extract_image_or_video(
      self,
      inputs: Any,
      unsupported_types: Tuple[Type, ...] = (
          tv_tensors.BoundingBoxes,
          tv_tensors.Mask,
      ),
  ) -> Tuple[Tuple[List[Any], TreeSpec, int], ImageOrVideo]:
      flat_inputs, spec = tree_flatten(
          inputs if len(inputs) > 1 else inputs[0]
      )
      needs_transform_list = self._needs_transform_list(flat_inputs)
  
      image_or_videos = []
      for idx, (inpt, needs_transform) in enumerate(
          zip(flat_inputs, needs_transform_list)
      ):
          if needs_transform and check_type(
              inpt,
              (
                  tv_tensors.Image,
                  PIL.Image.Image,
                  is_pure_tensor,
                  tv_tensors.Video,
              ),
          ):
              image_or_videos.append((idx, inpt))
          elif isinstance(inpt, unsupported_types):
              raise TypeError(
                  f"Inputs of type {type(inpt).__name__} are not supported by {type(self).__name__}()"
              )
  
      if not image_or_videos:
          raise TypeError("Found no image in the sample.")
      if len(image_or_videos) > 1:
          raise TypeError(
              f"Auto augment transformations are only properly defined for a single image or video, "
              f"but found {len(image_or_videos)}."
          )
  
      idx, image_or_video = image_or_videos[0]
      return (flat_inputs, spec, idx), image_or_video
  
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 4 for calling the _AutoAugmentBase obj's underlying member method code with example usage: obj._flatten_and_extract_image_or_video(inputs, unsupported_types) and returns: flat_inputs,  spec,  idx,  image_or_video