How to use the nni.networkmorphism_tuner.graph.json_to_graph function in nni

def test_graph_json_transform(self):
        """ unittest for graph_json_transform function
        """

        graph_init = CnnGenerator(10, (32, 32, 3)).generate()
        graph_init = to_wider_graph(deepcopy(graph_init))
        graph_init = to_deeper_graph(deepcopy(graph_init))
        graph_init = to_skip_connection_graph(deepcopy(graph_init))
        json_out = graph_to_json(graph_init, "temp.json")

        graph_recover = json_to_graph(json_out)

        # compare all data in graph
        self.assertEqual(graph_init.input_shape, graph_recover.input_shape)
        self.assertEqual(graph_init.weighted, graph_recover.weighted)
        self.assertEqual(
            graph_init.layer_id_to_input_node_ids,
            graph_recover.layer_id_to_input_node_ids,
        )
        self.assertEqual(graph_init.adj_list, graph_recover.adj_list)
        self.assertEqual(
            graph_init.reverse_adj_list,
            graph_recover.reverse_adj_list)
        self.assertEqual(
            len(graph_init.operation_history), len(
                graph_recover.operation_history)
        )

def test_to_skip_connection_graph(self):
        """ unittest for to_skip_connection_graph function
        """

        graph_init = CnnGenerator(10, (32, 32, 3)).generate()
        json_out = graph_to_json(graph_init, "temp.json")
        graph_recover = json_to_graph(json_out)
        skip_connection_graph = to_wider_graph(deepcopy(graph_recover))
        model = skip_connection_graph.produce_torch_model()
        out = model(torch.ones(1, 3, 32, 32))
        self.assertEqual(out.shape, torch.Size([1, 10]))

def test_to_wider_graph(self):
        """ unittest for to_wider_graph function
        """

        graph_init = CnnGenerator(10, (32, 32, 3)).generate()
        json_out = graph_to_json(graph_init, "temp.json")
        graph_recover = json_to_graph(json_out)
        wider_graph = to_wider_graph(deepcopy(graph_recover))
        model = wider_graph.produce_torch_model()
        out = model(torch.ones(1, 3, 32, 32))
        self.assertEqual(out.shape, torch.Size([1, 10]))

def build_graph_from_json(ir_model_json):
    """build model from json representation
    """
    graph = json_to_graph(ir_model_json)
    logging.debug(graph.operation_history)
    model = graph.produce_torch_model()
    return model

def build_graph_from_json(ir_model_json):
    """build model from json representation
    """
    graph = json_to_graph(ir_model_json)
    logging.debug(graph.operation_history)
    model = graph.produce_keras_model()
    return model

def build_graph_from_json(ir_model_json):
    """build model from json representation
    """
    graph = json_to_graph(ir_model_json)
    logging.debug(graph.operation_history)
    model = graph.produce_keras_model()
    return model

def build_graph_from_json(ir_model_json):
    """build model from json representation
    """
    graph = json_to_graph(ir_model_json)
    logging.debug(graph.operation_history)
    model = graph.produce_torch_model()
    return model

Parameters
        ----------
        model_id : int
            model index

        Returns
        -------
        load_model : Graph
            the model graph representation
        """

        with open(os.path.join(self.path, str(model_id) + ".json")) as fin:
            json_str = fin.read().replace("\n", "")

        load_model = json_to_graph(json_str)
        return load_model