docstr: Re-construct an instance of TopLevelTracedModule using an instance of a C++ module.
  
  Args:
      cpp_module: The C++ module that this TopLevelTracedModule will be rebuilt around.
docstr_tok: ["Re-construct","an","instance","of","TopLevelTracedModule","using","an","instance","of","a","C++","module",".","Args",":","cpp_module",":","The","C++","module","that","this","TopLevelTracedModule","will","be","rebuilt","around","."]
code: def _reconstruct(self, cpp_module):
      """
      Re-construct an instance of TopLevelTracedModule using an instance of a C++ module.
  
      Args:
          cpp_module: The C++ module that this TopLevelTracedModule will be rebuilt around.
      """
      self.__dict__["_actual_script_module"]._reconstruct(cpp_module)
  
imports: import contextlib
  import copy
  import functools
  import inspect
  import os
  import re
  import warnings
  from typing import Any, Callable, Dict, List, Optional, Set, TypeVar
  from typing_extensions import ParamSpec
  import torch
  from torch._jit_internal import _qualified_name, get_callable_argument_names, is_scripting
  from torch.autograd import function
  from torch.jit._script import _CachedForward, script, ScriptModule
  from torch.jit._state import _enabled, _python_cu
  from torch.nn import Module
  from torch.testing._comparison import default_tolerances
label_desc: Use image node_id 1 for calling the TopLevelTracedModule obj's underlying member method code with example usage: obj._reconstruct(cpp_module) without return types