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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 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)