code: def rebuild_from_type_v2(
      cls,
      func: Callable,
      new_type: _TensorMeta,
      args: tuple,
      state: dict,
      *,
      archiveinfo: Optional["_LazyLoadingUnpickler"] = None,
  ) -> Any:
      ret = func(*args)
      if isinstance(ret, _NotYetLoadedTensor):
          old_lt = ret._load_tensor
  
          def _load_tensor() -> Any:
              t = old_lt()
              return torch._tensor._rebuild_from_type_v2(
                  lambda: t, new_type, (), state
              )
  
          ret._load_tensor = _load_tensor  # type: ignore[method-assign]
          return ret
      return torch._tensor._rebuild_from_type_v2(
          func, new_type, args, state
      )
  
imports: import os
  import pickle
  import warnings
  from functools import partial
  from io import BytesIO
  from typing import IO, TYPE_CHECKING, Any, Callable, Dict, Optional, OrderedDict, Sequence, Set, Union
  import torch
  from lightning_utilities.core.apply_func import apply_to_collection
  from torch import Tensor
  from torch._C import _TensorMeta
  from torch.nn import Parameter
  from typing_extensions import override
  from lightning.fabric.utilities.imports import _TORCH_GREATER_EQUAL_2_0
  from lightning.fabric.utilities.types import _PATH, _Stateful
label_desc: Use image node_id 2 for calling the _NotYetLoadedTensor obj's underlying member method code with example usage: obj.rebuild_from_type_v2(cls, func, new_type, args, state) and returns: torch, ret, torch