def METHOD_NAME():
    y = vector("y")
    y.tag.test_value = np.r_[1.0, 2.0].astype(config.floatX)
    x = vector("x")
    x.tag.test_value = np.r_[3.0, 4.0].astype(config.floatX)
    A = matrix("A")
    A.tag.test_value = np.empty((2, 2), dtype=config.floatX)
    alpha = scalar("alpha")
    alpha.tag.test_value = np.array(3.0, dtype=config.floatX)
    beta = scalar("beta")
    beta.tag.test_value = np.array(5.0, dtype=config.floatX)
    # This should be converted into a `Gemv` `Op` when the non-JAX compatible
    # optimizations are turned on; however, when using JAX mode, it should
    # leave the expression alone.
    out = y.dot(alpha * A).dot(x) + beta * y
    fgraph = FunctionGraph([y, x, A, alpha, beta], [out])
    compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
    out = maximum(y, x)
    fgraph = FunctionGraph([y, x], [out])
    compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])
    out = at_max(y)
    fgraph = FunctionGraph([y], [out])
    compare_jax_and_py(fgraph, [get_test_value(i) for i in fgraph.inputs])