def METHOD_NAME():
    if version.parse(tf.version.VERSION) >= version.parse("2.00"):
        keras_model = tf.keras.applications.ResNet50(input_shape=(224, 224, 3))
        connected_graph_from_keras_model = KerasConnectedGraph(keras_model)
        ops_from_keras_model = connected_graph_from_keras_model.get_all_ops()
        products_from_keras_model = connected_graph_from_keras_model.get_all_products()
        # Approx number of ops
        assert len(ops_from_keras_model) > 180
        # Approx number of products
        assert len(products_from_keras_model) > 450
        assert connected_graph_from_keras_model._split_count == 16
        split_product = products_from_keras_model["Split_0__to__multiple_ops"]
        assert ops_from_keras_model["Conv_6"] in split_product.consumers
        assert ops_from_keras_model["Conv_12"] in split_product.consumers
        add_op = ops_from_keras_model["Add_16"]
        assert ops_from_keras_model["BatchNormalization_14"] in add_op.input_ops
        assert ops_from_keras_model["BatchNormalization_15"] in add_op.input_ops
        assert products_from_keras_model["BatchNormalization_14_to_Add_16"] in add_op.inputs
        assert products_from_keras_model["BatchNormalization_15_to_Add_16"] in add_op.inputs