unnecessary wrap on BatchNorm?

[johnnychen94]

BatchNormWrap seems not necessary given that Conv requires 4D data as input:

UNet.jl/src/model.jl

Lines 1 to 5 in a5ac874

	function BatchNormWrap(out_ch)
	Chain(x->expand_dims(x,2),
	BatchNorm(out_ch),
	x->squeeze(x))
	end

I guess I must misunderstand the idea here, can you explain it a bit why we need to unsqueeze the 4D input to 6D before feeding into BN? I didn't see this operation in other UNet implementations, for example, in pytorch-unet