code: def convert(model, feature_names, target):
      """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
      """
      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)
  
file_path: turicreate/src/external/coremltools_wrap/coremltools/coremltools/converters/sklearn/_SVC.py
imports: from ...proto import Model_pb2
  from ...proto import SVM_pb2
  from ...None import SPECIFICATION_VERSION
  from ...models._interface_management import set_classifier_interface_params
  from ..._deps import _HAS_SKLEARN
  from ...models import MLModel
  from ._svm_common import _set_kernel
label_desc: Use image node_id 2 for calling a global function with example usage: convert(model, feature_names, target) and returns: _MLModel