def METHOD_NAME():
    """Tests same configuration as control of InputPort in tests/mechansims/test_identicalness_of_control_and_gating
    """
    Input_Layer = TransferMechanism(name='Input Layer', function=Logistic, size=2)
    Hidden_Layer_1 = TransferMechanism(name='Hidden Layer_1', function=Logistic, size=5)
    Hidden_Layer_2 = TransferMechanism(name='Hidden Layer_2', function=Logistic, size=4)
    Output_Layer = TransferMechanism(name='Output Layer', function=Logistic, size=3)
    Gating_Mechanism = GatingMechanism(size=[1], gate=[Hidden_Layer_1, Hidden_Layer_2, Output_Layer])
    Input_Weights_matrix = (np.arange(2 * 5).reshape((2, 5)) + 1) / (2 * 5)
    Middle_Weights_matrix = (np.arange(5 * 4).reshape((5, 4)) + 1) / (5 * 4)
    Output_Weights_matrix = (np.arange(4 * 3).reshape((4, 3)) + 1) / (4 * 3)
    # This projection is specified in add_backpropagation_learning_pathway method below
    Input_Weights = MappingProjection(name='Input Weights',matrix=Input_Weights_matrix)
    # This projection is "discovered" by add_backpropagation_learning_pathway method below
    Middle_Weights = MappingProjection(name='Middle Weights',sender=Hidden_Layer_1,receiver=Hidden_Layer_2,
        matrix={
            VALUE: Middle_Weights_matrix,
            FUNCTION: AccumulatorIntegrator,
            FUNCTION_PARAMS: {
                DEFAULT_VARIABLE: Middle_Weights_matrix,
                INITIALIZER: Middle_Weights_matrix,
                RATE: Middle_Weights_matrix
            },
        }
    )
    Output_Weights = MappingProjection(sender=Hidden_Layer_2, receiver=Output_Layer, matrix=Output_Weights_matrix)
    pathway = [Input_Layer, Input_Weights, Hidden_Layer_1, Hidden_Layer_2, Output_Layer]
    comp = Composition()
    backprop_pathway = comp.add_backpropagation_learning_pathway(pathway=pathway,
                                                                 loss_spec=None)
    # c.add_linear_processing_pathway(pathway=z)
    comp.add_node(Gating_Mechanism)
    stim_list = {
        Input_Layer: [[-1, 30]],
        Gating_Mechanism: [1.0],
        backprop_pathway.target: [[0, 0, 1]]}
    comp.learn(num_trials=3, inputs=stim_list)
    expected_results = [[[0.81493513, 0.85129046, 0.88154205]],
                        [[0.81331773, 0.85008207, 0.88157851]],
                        [[0.81168332, 0.84886047, 0.88161468]]]
    np.testing.assert_allclose(comp.results, expected_results)
    stim_list[Gating_Mechanism]=[0.0]
    results = comp.learn(num_trials=1, inputs=stim_list)
    expected_results = [[0.5, 0.5, 0.5]]
    np.testing.assert_allclose(results, expected_results)
    stim_list[Gating_Mechanism]=[2.0]
    results = comp.learn(num_trials=1, inputs=stim_list)
    expected_results = [[0.96941429, 0.9837254 , 0.99217549]]
    np.testing.assert_allclose(results, expected_results)