def METHOD_NAME():
    W = numpy.asarray([1.0, 0.0, 0.0, 1.0], dtype="f").reshape((2, 2))
    bias = numpy.asarray([0.0, 0.0], dtype="f")
    model = Linear(2, 2)
    model.set_param("W", W)
    model.set_param("b", bias)
    sgd = SGD(1.0, L2=0.0, grad_clip=0.0)
    sgd.averages = None
    ff = numpy.asarray([[0.0, 0.0]], dtype="f")
    tf = numpy.asarray([[1.0, 0.0]], dtype="f")
    ft = numpy.asarray([[0.0, 1.0]], dtype="f")  # noqa: F841
    tt = numpy.asarray([[1.0, 1.0]], dtype="f")  # noqa: F841
    # ff, i.e. 0, 0
    scores, backprop = model.begin_update(ff)
    assert_allclose(scores[0, 0], scores[0, 1])
    # Tell it the answer was 'f'
    gradient = numpy.asarray([[-1.0, 0.0]], dtype="f")
    backprop(gradient)
    for key, (param, d_param) in model.get_gradients().items():
        param, d_param = sgd(key, param, d_param)
        model.set_param(key[1], param)
        model.set_grad(key[1], d_param)
    b = model.get_param("b")
    W = model.get_param("W")
    assert b[0] == 1.0
    assert b[1] == 0.0
    # Unchanged -- input was zeros, so can't get gradient for weights.
    assert W[0, 0] == 1.0
    assert W[0, 1] == 0.0
    assert W[1, 0] == 0.0
    assert W[1, 1] == 1.0
    # tf, i.e. 1, 0
    scores, finish_update = model.begin_update(tf)
    # Tell it the answer was 'T'
    gradient = numpy.asarray([[0.0, -1.0]], dtype="f")
    finish_update(gradient)
    for key, (W, dW) in model.get_gradients().items():
        sgd(key, W, dW)
    b = model.get_param("b")
    W = model.get_param("W")
    assert b[0] == 1.0
    assert b[1] == 1.0
    # Gradient for weights should have been outer(gradient, input)
    # so outer([0, -1.], [1., 0.])
    # =  [[0., 0.], [-1., 0.]]
    assert W[0, 0] == 1.0 - 0.0
    assert W[0, 1] == 0.0 - 0.0
    assert W[1, 0] == 0.0 - -1.0
    assert W[1, 1] == 1.0 - 0.0