docstr: Gets the OnnxConfig for a model_type and feature combination.
  
  Args:
      model_type (`str`):
          The model type to retrieve the config for.
      feature (`str`):
          The feature to retrieve the config for.
  
  Returns:
      `OnnxConfig`: config for the combination
docstr_tok: ["Gets","the","OnnxConfig","for","a","model_type","and","feature","combination",".","Args",":","model_type","(","`","str","`",")",":","The","model","type","to","retrieve","the","config","for",".","feature","(","`","str","`",")",":","The","feature","to","retrieve","the","config","for",".","Returns",":","`","OnnxConfig","`",":","config","for","the","combination"]
code: def get_config(model_type: str, feature: str) -> OnnxConfig:
      """
      Gets the OnnxConfig for a model_type and feature combination.
  
      Args:
          model_type (`str`):
              The model type to retrieve the config for.
          feature (`str`):
              The feature to retrieve the config for.
  
      Returns:
          `OnnxConfig`: config for the combination
      """
      return FeaturesManager._SUPPORTED_MODEL_TYPE[model_type][feature]
  
file_path: H2O/h2o_flexgen/benchmark/third_party/transformers/src/transformers/onnx/features.py
imports: import os
  from functools import partial, reduce
  from typing import TYPE_CHECKING, Callable, Dict, Optional, Tuple, Type, Union
  import transformers
  from ..None import PretrainedConfig, is_tf_available, is_torch_available
  from ..utils import TF2_WEIGHTS_NAME, WEIGHTS_NAME, logging
  from .config import OnnxConfig
label_desc: Use image node_id 8 for calling the FeaturesManager obj's underlying member method code with example usage: obj.get_config(model_type, feature) and returns: FeaturesManager