code: def __prepare_scriptable__(self) -> nn.Module:
      # This method is called early on when `torch.jit.script`'ing an `nn.Module` instance. If it succeeds, the return
      # value is used for scripting over the original object that should have been scripted. Since the v1 transforms
      # are JIT scriptable, and we made sure that for single image inputs v1 and v2 are equivalent, we just return the
      # equivalent v1 transform here. This of course only makes transforms v2 JIT scriptable as long as transforms v1
      # is around.
      if self._v1_transform_cls is None:
          raise RuntimeError(
              f"Transform {type(self).__name__} cannot be JIT scripted. "
              "torchscript is only supported for backward compatibility with transforms "
              "which are already in torchvision.transforms. "
              "For torchscript support (on tensors only), you can use the functional API instead."
          )
  
      return self._v1_transform_cls(
          **self._extract_params_for_v1_transform()
      )
  
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 11 for calling the Transform obj's underlying member method code with example usage: obj.__prepare_scriptable__() and returns: self