Summarize network into the following structure.
    Args:
    mlmodel_spec : mlmodel spec
    Returns:
    inputs : list[(str, str)] - a list of two tuple (name, descriptor) for each input blob.
    outputs : list[(str, str)] - a list of two tuple (name, descriptor) for each output blob
    layers : list[(str, list[str], list[str], list[(str, str)])] - a list of layers represented by
        layer name, input blobs, output blobs, a list of (parameter name, content)

def summarize_neural_network_spec(mlmodel_spec):
    """ Summarize network into the following structure.
    Args:
    mlmodel_spec : mlmodel spec
    Returns:
    inputs : list[(str, str)] - a list of two tuple (name, descriptor) for each input blob.
    outputs : list[(str, str)] - a list of two tuple (name, descriptor) for each output blob
    layers : list[(str, list[str], list[str], list[(str, str)])] - a list of layers represented by
        layer name, input blobs, output blobs, a list of (parameter name, content)
    """
    inputs = [(blob.name, _get_feature_description_summary(blob)) for blob in mlmodel_spec.description.input]
    outputs = [(blob.name, _get_feature_description_summary(blob)) for blob in mlmodel_spec.description.output]
    nn = None

    if mlmodel_spec.HasField('neuralNetwork'):
        nn = mlmodel_spec.neuralNetwork
    elif mlmodel_spec.HasField('neuralNetworkClassifier'):
        nn = mlmodel_spec.neuralNetworkClassifier
    elif mlmodel_spec.HasField('neuralNetworkRegressor'):
        nn = mlmodel_spec.neuralNetworkRegressor

    layers = [_summarize_network_layer_info(layer) for layer in nn.layers] if nn != None else None
    return (inputs, outputs, layers)