human
I wrote the below code

    def check(input_dim, expected_shape, broadcastable=None, conserve_memory=True):
        bn = BatchNormalization(input_dim=input_dim, broadcastable=broadcastable, conserve_memory=conserve_memory)
        if (broadcastable is None):
            if (not isinstance(input_dim, collections.Sequence)):
                b_input_dim = (input_dim,)
            else:
                b_input_dim = input_dim
            input_broadcastable = tuple((False for _ in range(len(b_input_dim))))
        else:
            input_broadcastable = broadcastable
        bn.allocate()
        assert (conserve_memory == bn.conserve_memory)
        assert (input_dim == bn.input_dim)
        assert (bn.broadcastable == broadcastable)
        assert (bn.scale.broadcastable == input_broadcastable)
        assert (bn.shift.broadcastable == input_broadcastable)
        assert (bn.population_mean.broadcastable == input_broadcastable)
        assert (bn.population_stdev.broadcastable == input_broadcastable)
        assert_allclose(bn.population_mean.get_value(borrow=True), 0.0)
        assert_allclose(bn.population_stdev.get_value(borrow=True), 1.0)
        assert_equal(bn.scale.get_value(borrow=True).shape, expected_shape)
        assert_equal(bn.shift.get_value(borrow=True).shape, expected_shape)
        assert_equal(bn.population_mean.get_value(borrow=True).shape, expected_shape)
        assert_equal(bn.population_stdev.get_value(borrow=True).shape, expected_shape)
        assert numpy.isnan(bn.shift.get_value(borrow=True)).all()
        assert numpy.isnan(bn.scale.get_value(borrow=True)).all()
        bn.initialize()
        assert_allclose(bn.shift.get_value(borrow=True), 0.0)
        assert_allclose(bn.scale.get_value(borrow=True), 1.0)
    (yield (check, 5, (5,)))
    (yield (check, (6, 7, 9), (6, 7, 9), (False, False, False)))
    (yield (check, (7, 4, 3), (1, 4, 3), (True, False, False)))
    (yield (check, (9, 3, 6), (9, 1, 1), (False, True, True)))
    (yield (check, (7, 4, 5), (7, 1, 5), (False, True, False), False))

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

def test_batch_normalization_allocation_initialization():