def METHOD_NAME():
    import tensorflow as tf
    try:
        graph = tf.Graph()
        graph_def = tf.GraphDef()
        with tf.Session() as sess:
            x = tf.placeholder(tf.float64, shape=(1, 3, 3, 1), name="x")
            y = tf.constant(np.random.random((2, 2, 1, 1)), name="y")
            op = tf.nn.conv2d(input=x, filter=y, strides=[1, 1, 1, 1], padding="VALID", name="op_to_store")
            sess.run(tf.global_variables_initializer())
            constant_graph = tf.graph_util.convert_variables_to_constants(sess, sess.graph_def, ["op_to_store"])
        graph_def.ParseFromString(constant_graph.SerializeToString())
        with graph.as_default():
            tf.import_graph_def(graph_def, name="")
    except:
        graph = tf.Graph()
        graph_def = tf.compat.v1.GraphDef()
        with tf.compat.v1.Session() as sess:
            x = tf.compat.v1.placeholder(tf.float64, shape=(1, 3, 3, 1), name="x")
            y = tf.compat.v1.constant(np.random.random((2, 2, 1, 1)), name="y")
            op = tf.nn.conv2d(input=x, filters=y, strides=[1, 1, 1, 1], padding="VALID", name="op_to_store")
            sess.run(tf.compat.v1.global_variables_initializer())
            constant_graph = tf.compat.v1.graph_util.convert_variables_to_constants(
                sess, sess.graph_def, ["op_to_store"]
            )
        graph_def.ParseFromString(constant_graph.SerializeToString())
        with graph.as_default():
            tf.import_graph_def(graph_def, name="")
    return graph