class_docstr: Representing a sequence of fx.Nodes to be formed into a fx.GraphModule.
  
  This class encapsulates metadata and provides building block methods to construct this
  layered abstraction from a sequence of flat fx.Nodes.
  
  Attributes:
  - _stack_meta: Metadata of the module stack.
  - _nodes: List of IR nodes in the module.
  - _reference_root_module: Reference to the root flat fx.GraphModule instance.
class_docstr_tok: ["Representing","a","sequence","of","fx.Nodes","to","be","formed","into","a","fx.GraphModule",".","This","class","encapsulates","metadata","and","provides","building","block","methods","to","construct","this","layered","abstraction","from","a","sequence","of","flat","fx.Nodes",".","Attributes",":","-","_stack_meta",":","Metadata","of","the","module","stack",".","-","_nodes",":","List","of","IR","nodes","in","the","module",".","-","_reference_root_module",":","Reference","to","the","root","flat","fx.GraphModule","instance","."]
code: def __init__(
      self,
      reference_root_module: torch.fx.GraphModule,
      stack_meta: _ModuleStackMeta,
  ):
      self._stack_meta = stack_meta
      self._nodes: List[_IRNode] = []
      self._reference_module = reference_root_module
  
imports: from __future__ import annotations
  import abc
  import collections
  import copy
  import operator
  from typing import Any, Dict, Final, Generator, Iterator, List, Optional, Sequence, Set, Tuple, Union
  import torch
  import torch.fx
  from torch.onnx._internal import _beartype
  from torch.onnx._internal.fx import _pass
  from torch.utils import _pytree
label_desc: Use image node_id 1 to create a new _ModuleNode object from inherited base classes: _IRNode with example: obj = _ModuleNode(reference_root_module, stack_meta)