code: def _get_random_item(
      self, dct: Dict[str, Tuple[Callable, bool]]
  ) -> Tuple[str, Tuple[Callable, bool]]:
      keys = tuple(dct.keys())
      key = keys[int(torch.randint(len(keys), ()))]
      return key, dct[key]
  
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 3 for calling the _AutoAugmentBase obj's underlying member method code with example usage: obj._get_random_item(dct) and returns: key,  dct