code: def _extract_params_for_v1_transform(self) -> Dict[str, Any]:
      # This method is called by `__prepare_scriptable__` to instantiate the equivalent v1 transform from the current
      # v2 transform instance. It extracts all available public attributes that are specific to that transform and
      # not `nn.Module` in general.
      # Overwrite this method on the v2 transform class if the above is not sufficient. For example, this might happen
      # if the v2 transform introduced new parameters that are not support by the v1 transform.
      common_attrs = nn.Module().__dict__.keys()
      return {
          attr: value
          for attr, value in self.__dict__.items()
          if not attr.startswith("_") and attr not in common_attrs
      }
  
imports: from __future__ import annotations
  import enum
  from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
  import PIL.Image
  import torch
  from torch import nn
  from torch.utils._pytree import tree_flatten, tree_unflatten
  from torchvision import tv_tensors
  from torchvision.transforms.v2._utils import check_type, has_any, is_pure_tensor
  from torchvision.utils import _log_api_usage_once
  from .functional._utils import _get_kernel
label_desc: Use image node_id 10 for calling the Transform obj's underlying member method code with example usage: obj._extract_params_for_v1_transform() and returns: unknown