code: def __init__(
      self,
      inner,
      strict=True,
      force_outplace=False,
      return_inputs=False,
      return_inputs_states=False,
  ):
      super().__init__()
      # inner may be a Module, or it may be an arbitrary callable
      # If it's a Module, we get its parameters automatically, which lets
      # us avoid a special casing functions versus modules.
      self.inner = inner
      self.strict = strict
      self._force_outplace = force_outplace
      self._return_inputs = return_inputs
      self._return_inputs_states = return_inputs_states
  
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 to create a new ONNXTracedModule object from inherited base classes: torch.nn with example: obj = ONNXTracedModule(inner, strict, force_outplace, return_inputs, return_inputs_states)