Test calculation of running variance in BatchNormedConv1d/Linear

[julianhoever]

Test the calculation of running variance in the BatchNormedConv1d and BatchNormedLinear (see the following integration test which fails):

elastic-ai.creator/elasticai/creator/nn/fixed_point/conv1d/layer/batch_normed_conv1d_test.py

Lines 52 to 69 in 94269d2

	@pytest.mark.parametrize(
	"signal_length, inputs, expected",
	[
	(3, [1.0, 1.0, 1.0], [2.0, 2.0]),
	(4, [0.5, 0.5, 1.0, 1.0], [1.0, 1.5, 2.0]),
	],
	)
	def test_ones_weight_kernel_convolution(
	signal_length: int, inputs: list[float], expected: list[float]
	) -> None:
	# TODO: Find out how the running_var calculation works?
	# BatchNorm layer has a running_var of 0.25 for [1.0, 1.5, 2.0]
	# But the correct running_var should be 0.166667
	conv = conv1d(signal_length=signal_length, bias=False, affine=False)
	conv.conv_weight.data = torch.ones_like(conv.conv_weight.data)
	input_tensor = torch.tensor(inputs)
	prediction = conv(input_tensor)
	assert expected == prediction.tolist()