code: def _extract_params_for_v1_transform(self) -> Dict[str, Any]:
      params = super()._extract_params_for_v1_transform()
  
      if isinstance(params["fill"], dict):
          raise ValueError(
              f"{type(self).__name__}() can not be scripted for when `fill` is a dictionary."
          )
  
      return params
  
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 2 for calling the _AutoAugmentBase obj's underlying member method code with example usage: obj._extract_params_for_v1_transform() and returns: params