human
I wrote the below code

    def check(input_dim, variable_dim, broadcastable=None, conserve_memory=True, mean_only=False, learn_scale=True, learn_shift=True):
        (bn, x) = apply_setup(input_dim, broadcastable, conserve_memory, mean_only, learn_scale, learn_shift)
        y = bn.apply(x)
        rng = numpy.random.RandomState((2015, 12, 16))
        input_ = random_unif(rng, ((9,) + (input_dim if isinstance(input_dim, collections.Sequence) else (input_dim,))))
        assert_allclose(y.eval({x: input_}), input_, rtol=0.0001)
        pop_mean = random_unif(rng, variable_dim)
        bn.population_mean.set_value(pop_mean)
        assert_allclose(y.eval({x: input_}), (input_ - pop_mean), rtol=0.0001)
        if (not mean_only):
            pop_stdev = random_unif(rng, variable_dim)
            bn.population_stdev.set_value(pop_stdev)
            assert_allclose(y.eval({x: input_}), ((input_ - pop_mean) / pop_stdev), rtol=0.0001)
            if learn_scale:
                gamma = random_unif(rng, variable_dim)
                bn.scale.set_value(gamma)
            else:
                gamma = 1
            assert_allclose(y.eval({x: input_}), ((input_ - pop_mean) * (gamma / pop_stdev)), rtol=0.0001)
            if learn_shift:
                beta = random_unif(rng, variable_dim)
                bn.shift.set_value(beta)
            else:
                beta = 0.0
            assert_allclose(y.eval({x: input_}), (((input_ - pop_mean) * (gamma / pop_stdev)) + beta), rtol=0.0001)
        else:
            if learn_shift:
                beta = random_unif(rng, variable_dim)
                bn.shift.set_value(beta)
            else:
                beta = 0.0
            assert_allclose(y.eval({x: input_}), ((input_ - pop_mean) + beta), rtol=0.0001)
    (yield (check, 9, (9,)))
    (yield (check, 9, (9,), None, True, True))
    (yield (check, (5, 4), (5, 4), None, False))
    (yield (check, (5, 4), (5, 4), None, False, True))
    (yield (check, (2, 9, 7), (2, 1, 1), (False, True, True)))
    (yield (check, (2, 9, 7), (2, 1, 1),...

What's a good function header?

=====
Here's a good function header?
assistant
#Here's a good function header.

def test_batch_normalization_inference_apply():