How do you use `bypass_graph` with a `Flux.Chain`?

[emsal0]

GeometricFlux.jl/src/layers/misc.jl

Lines 6 to 13 in c8396e4

	function bypass_graph(nf_func=identity, ef_func=identity, gf_func=identity)
	return function (fg::FeaturedGraph)
	FeaturedGraph(fg,
	nf=nf_func(node_feature(fg)),
	ef=ef_func(edge_feature(fg)),
	gf=gf_func(global_feature(fg)))
	end
	end

Say I want to use a Flux.Chain to apply a neural network to each node feature vector. I don't think I can just pass in the Chain here, because it will be applied to the whole node feature matrix, which a) doesn't separate the node features and b) might be of variable size. Should the functionality of bypass_graph be changed so that it applies a mapping over node feature vectors instead?

[yuehhua]

The bypass_graph is designed to pass node/edge/global features over distinct networks. You want to pass each node features over a neural network. I am curious what is the difference between passing node features one by one and passing whole node features as a matrix? Computationally, it should be the same and we don't need to separate node features for that.

[yuehhua]

In the latest version, bypass_graph has been deprecated. The functionality is replaced by GraphParallel layer.