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

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)