class_docstr: FX graph transformation during ONNX export before converting from FX IR to ONNX IR.
class_docstr_tok: ["FX","graph","transformation","during","ONNX","export","before","converting","from","FX","IR","to","ONNX","IR","."]
docstr: Returns a tuple of (Rule, Level, message) for this Rule.
  
  Message template: 'Running {pass_name} pass.'
docstr_tok: ["Returns","a","tuple","of","(","Rule",",","Level",",","message",")","for","this","Rule",".","Message","template",":","'Running","{","pass_name","}","pass",".","'"]
code: def format(  # type: ignore[override]
      self, level: infra.Level, pass_name
  ) -> Tuple[infra.Rule, infra.Level, str]:
      """Returns a tuple of (Rule, Level, message) for this Rule.
  
      Message template: 'Running {pass_name} pass.'
      """
      return self, level, self.format_message(pass_name=pass_name)
  
imports: import dataclasses
  from typing import Tuple
  from torch.onnx._internal.diagnostics import infra
label_desc: Use image node_id 2 for calling the _FxPass obj's underlying member method code with example usage: obj.format(level, pass_name) and returns: self,  level,  self