Convert a normalizer model to the protobuf spec.

    Parameters
    ----------
    model: Normalizer
        A Normalizer.

    input_features: str
        Name of the input column.

    output_features: str
        Name of the output column.

    Returns
    -------
    model_spec: An object of type Model_pb.
        Protobuf representation of the model

def convert(model, input_features, output_features):
    """Convert a normalizer model to the protobuf spec.

    Parameters
    ----------
    model: Normalizer
        A Normalizer.

    input_features: str
        Name of the input column.

    output_features: str
        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.')

    # Test the scikit-learn model
    _sklearn_util.check_expected_type(model, Normalizer)
    _sklearn_util.check_fitted(model, lambda m: hasattr(m, 'norm'))

    # Set the interface params.
    spec = _Model_pb2.Model()
    spec.specificationVersion = SPECIFICATION_VERSION
    spec = _set_transform_interface_params(spec, input_features, output_features)

    # Set the one hot encoder parameters
    _normalizer_spec = spec.normalizer
    if model.norm == 'l1':
        _normalizer_spec.normType = _proto__normalizer.L1
    elif model.norm == 'l2':
        _normalizer_spec.normType = _proto__normalizer.L2
    elif model.norm == 'max':
        _normalizer_spec.normType = _proto__normalizer.LMax
    return _MLModel(spec)