def METHOD_NAME(self):
    o = pnl.TransferMechanism()
    m = pnl.ContrastiveHebbianMechanism(
            input_size=2,
            hidden_size=0,
            target_size=2,
            separated=False,
            mode=pnl.SIMPLE_HEBBIAN,
            integrator_mode=True,
            enable_learning=False,
            matrix=[[0,-1],[-1, 0]],
        # auto=0,
        # hetero=-1,
    )
    # set max passes to ensure failure if no convergence instead of infinite loop
    m.max_passes = 1000
    c = pnl.Composition()
    c.add_linear_processing_pathway([m, o])
    c.scheduler.add_condition(o, pnl.WhenFinished(m))
    c._analyze_graph()
    print('matrix:\n', m.afferents[1].matrix)
    c.run(inputs={m:[2, 2]}, num_trials=4)
    results = c.results
    print(results)
    np.testing.assert_allclose(results, [[np.array([2.])], [np.array([2.])], [np.array([2.])], [np.array([2.])]])