class_docstr: A class that contains all the information needed by ONNX Runtime optimization for a given model type.
  Attributes:
      _conf (`Dict[str, tuple]`):
          A dictionary mapping each supported model type to a tuple containing the number of attention heads
          and the hidden size model config attribute names as well as the corresponding ONNX Runtime model type.
class_docstr_tok: ["A","class","that","contains","all","the","information","needed","by","ONNX","Runtime","optimization","for","a","given","model","type",".","Attributes",":","_conf","(","`","Dict","[","str",",","tuple","]","`",")",":","A","dictionary","mapping","each","supported","model","type","to","a","tuple","containing","the","number","of","attention","heads","and","the","hidden","size","model","config","attribute","names","as","well","as","the","corresponding","ONNX","Runtime","model","type","."]
code: def get_normalized_config_class(cls, model_type: str) -> Type:
      model_type = model_type.replace("_", "-")
      cls.check_supported_model(model_type)
      return cls._conf[model_type]
  
imports: import functools
  from typing import Callable, Dict, Type, Union
  from transformers import PretrainedConfig
label_desc: Use image node_id 2 for calling the NormalizedConfigManager obj's underlying member method code with example usage: obj.get_normalized_config_class(cls, model_type) and returns: cls