def METHOD_NAME():
    """Make sure that sotfmax expressions are turned into
    a softmax Op.
    """
    rng = np.random.default_rng(utt.fetch_seed())
    x = aesara.shared(rng.normal(size=(3, 4)))
    def softmax_graph(c):
        return exp(c) / exp(c).sum(axis=-1, keepdims=True)
    def f(inputs):
        y = softmax_graph(x)
        return aesara.grad(None, x, known_grads={y: inputs})
    utt.verify_grad(f, [rng.random((3, 4))])