class_docstr: Node is missing ONNX shape inference.
class_docstr_tok: ["Node","is","missing","ONNX","shape","inference","."]
docstr: Returns a tuple of (Rule, Level, message) for this Rule.
  
  Message template: 'The shape inference of {op_name} type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.'
docstr_tok: ["Returns","a","tuple","of","(","Rule",",","Level",",","message",")","for","this","Rule",".","Message","template",":","'The","shape","inference","of","{","op_name","}","type","is","missing",",","so","it","may","result","in","wrong","shape","inference","for","the","exported","graph",".","Please","consider","adding","it","in","symbolic","function",".","'"]
code: def format(  # type: ignore[override]
      self, level: infra.Level, op_name
  ) -> Tuple[infra.Rule, infra.Level, str]:
      """Returns a tuple of (Rule, Level, message) for this Rule.
  
      Message template: 'The shape inference of {op_name} type is missing, so it may result in wrong shape inference for the exported graph. Please consider adding it in symbolic function.'
      """
      return self, level, self.format_message(op_name=op_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 _NodeMissingOnnxShapeInference obj's underlying member method code with example usage: obj.format(level, op_name) and returns: self,  level,  self