class_docstr: FoldedGraphModule is a GraphModule which also contains another
  `const_subgraph_module` representing a subgraph which has all const attr
  inputs and which can be run once before running the main standard
  `graph`. The `const_output_names` are the ordered list names of attrs which
  represent what each respective output from the const_subgraph should be set
  on which attrs.
class_docstr_tok: ["FoldedGraphModule","is","a","GraphModule","which","also","contains","another","`","const_subgraph_module","`","representing","a","subgraph","which","has","all","const","attr","inputs","and","which","can","be","run","once","before","running","the","main","standard","`","graph","`",".","The","`","const_output_names","`","are","the","ordered","list","names","of","attrs","which","represent","what","each","respective","output","from","the","const_subgraph","should","be","set","on","which","attrs","."]
code: def __call__(self, *args, **kwargs):
      if not self.has_folding_been_run:
          self.run_folding()
      return super().__call__(*args)
  
file_path: pytorch/torch/fx/experimental/const_fold.py
imports: import re
  from typing import Callable, Dict, Optional, Set, Union
  import torch.fx
  from torch.fx.node import map_arg
  from torch.fx.passes.split_module import split_module
label_desc: Use image node_id 2 for calling the FoldedGraphModule obj's underlying member method code with example usage: obj.__call__() and returns: super