code: def _supported_backend_check_config(model, backend, extra_config):
      """
      Function used to check whether the specified backend and configuration pair is supported or not.
      """
      assert_torch_installed()
      import onnx
      import torch
  
      tvm_backend = None
      if tvm_installed():
          import tvm
  
          tvm_backend = tvm.__name__
  
      if (
          (backend == torch.jit.__name__ and not _is_onnx_model(model))
          or backend == tvm_backend
          or (
              backend == onnx.__name__
              and not _is_onnx_model(model)
              or _is_onnx_model(model)
              and backend == torch.__name__
          )
      ) and constants.TEST_INPUT not in extra_config:
          raise RuntimeError(
              "Backend {} requires test inputs. \
              Please pass some test input to the convert function.".format(
                  backend
              )
          )
  
imports: from copy import deepcopy
  import psutil
  import numpy
  from .operator_converters import constants
  from ._parse import parse_sklearn_api_model, parse_onnx_api_model, parse_sparkml_api_model
  from ._topology import convert
  from ._utils import assert_torch_installed, assert_lightgbm_installed, assert_xgboost_installed, pandas_installed, sparkml_installed, is_pandas_dataframe, is_spark_dataframe, tvm_installed
  from .exceptions import MissingConverter, MissingBackend
  from .supported import backends
  from sklearn.utils import all_estimators
  from sklearn.utils.validation import check_is_fitted
  from .None import operator_converters
  from .supported import xgb_operator_list
  from .supported import lgbm_operator_list
label_desc: Use image node_id 4 for calling a global function with example usage: _supported_backend_check_config(model, backend, extra_config) without return types