code: def convert_sklearn_svc_model(operator, device, extra_config):
      """
      Converter for `sklearn.svm.SVC` and `sklearn.svm.NuSVC`
  
      Args:
          operator: An operator wrapping a `sklearn.svm.SVC` or `sklearn.svm.NuSVC` model
          device: String defining the type of device the converted operator should be run on
          extra_config: Extra configuration used to select the best conversion strategy
  
      Returns:
          A PyTorch model
      """
      if operator.raw_operator.kernel in [
          "linear",
          "poly",
          "rbf",
          "sigmoid",
      ]:
          # https://stackoverflow.com/questions/20113206/scikit-learn-svc-decision-function-and-predict
          kernel = operator.raw_operator.kernel
          degree = operator.raw_operator.degree
          classes = operator.raw_operator.classes_
          sv = operator.raw_operator.support_vectors_
          nv = operator.raw_operator.n_support_
          a = operator.raw_operator.dual_coef_
          b = operator.raw_operator.intercept_
          coef0 = operator.raw_operator.coef0
  
          if hasattr(operator.raw_operator, "_gamma"):
              gamma = operator.raw_operator._gamma
          else:
              # TODO: which versions is this case for, and how to test?
              gamma = operator.raw_operator.gamma
  
          return SVC(
              operator,
              kernel,
              degree,
              sv,
              nv,
              a,
              b,
              gamma,
              coef0,
              classes,
              device,
          )
      else:
          raise RuntimeError(
              "Unsupported kernel for SVC: {}".format(
                  operator.raw_operator.kernel
              )
          )
  
label_desc: Use image node_id 1 for calling a global function with example usage: convert_sklearn_svc_model(operator, device, extra_config) and returns: SVC