code: def convert(model, feature_names, target):
      
      if not(_HAS_SKLEARN):
          raise RuntimeError('scikit-learn not found. scikit-learn conversion API is disabled.')
      spec = _generate_base_svm_classifier_spec(model)
      spec = set_classifier_interface_params(spec, feature_names, model.classes_, 'supportVectorClassifier', output_features = target)
  
      svm = spec.supportVectorClassifier
      for i in model.n_support_:
          svm.numberOfSupportVectorsPerClass.append(int(i))
  
      if len(model.probA_) != 0 and len(model.classes_) == 2:
          print("[WARNING] Scikit Learn uses a technique to normalize pairwise probabilities even for binary classification. "
                "This can cause differences in predicted probabilities, usually less than 0.5%.")
  
      # If this is an empty list, then model.probA_ will be an empty list.
      if len(model.probA_) != 0:
          for i in model.probA_:
              svm.probA.append(i)
  
      for i in model.probB_:
          svm.probB.append(i)
  
      return _MLModel(spec)
docs: Convert a Support Vector Classtion (SVC) model to the protobuf spec.
      Parameters
      ----------
      model: SVC
          A trained SVC encoder model.
  
      feature_names: [str], optional (default=None)
          Name of the input columns.
  
      target: str, optional (default=None)
          Name of the output column.
  
      Returns
      -------
      model_spec: An object of type Model_pb.
          Protobuf representation of the model