fix docs

GNNlib/src/msgpass.jl

@@ -1,15 +1,12 @@
 """
-    propagate(fmsg, g, aggr [layer]; [xi, xj, e])
-    propagate(fmsg, g, aggr, [layer,] xi, xj, e=nothing) 
+    propagate(fmsg, g, aggr; [xi, xj, e])
+    propagate(fmsg, g, aggr xi, xj, e=nothing) 
 
 Performs message passing on graph `g`. Takes care of materializing the node features on each edge, 
 applying the message function `fmsg`, and returning an aggregated message ``\\bar{\\mathbf{m}}`` 
 (depending on the return value of `fmsg`, an array or a named tuple of 
 arrays with last dimension's size `g.num_nodes`).
 
-If also a [`GNNLayer`](@ref) `layer` is provided, it will be passed to `fmsg` 
-as a first argument.
-
 It can be decomposed in two steps:
 
 ```julia
@@ -35,10 +32,8 @@ providing as input `f` a closure.
       with the same batch size. If also `layer` is passed to propagate,
       the signature of `fmsg` has to be `fmsg(layer, xi, xj, e)` 
       instead of `fmsg(xi, xj, e)`.
-- `layer`: A [`GNNLayer`](@ref). If provided it will be passed to `fmsg` as a first argument.
 - `aggr`: Neighborhood aggregation operator. Use `+`, `mean`, `max`, or `min`. 
 
-
 # Examples
 
 ```julia
@@ -86,8 +81,8 @@ end
 ## APPLY EDGES
 
 """
-    apply_edges(fmsg, g, [layer]; [xi, xj, e])
-    apply_edges(fmsg, g, [layer,] xi, xj, e=nothing)
+    apply_edges(fmsg, g; [xi, xj, e])
+    apply_edges(fmsg, g, xi, xj, e=nothing)
 
 Returns the message from node `j` to node `i` applying
 the message function `fmsg` on the edges in graph `g`.
@@ -99,9 +94,6 @@ The function `fmsg` operates on batches of edges, therefore
 `xi`, `xj`, and `e` are tensors whose last dimension
 is the batch size, or can be named tuples of 
 such tensors.
-
-If also a [`GNNLayer`](@ref) `layer` is provided, it will be passed to `fmsg` 
-as a first argument.
 
 # Arguments
 
@@ -117,7 +109,6 @@ as a first argument.
        with the same batch size. If also `layer` is passed to propagate,
       the signature of `fmsg` has to be `fmsg(layer, xi, xj, e)` 
       instead of `fmsg(xi, xj, e)`.
-- `layer`: A [`GNNLayer`](@ref). If provided it will be passed to `fmsg` as a first argument.
 
 See also [`propagate`](@ref) and [`aggregate_neighbors`](@ref).
 """

docs/Project.toml

@@ -4,6 +4,7 @@ Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 DocumenterInterLinks = "d12716ef-a0f6-4df4-a9f1-a5a34e75c656"
 Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
 GNNGraphs = "aed8fd31-079b-4b5a-b342-a13352159b8c"
+GNNlib = "a6a84749-d869-43f8-aacc-be26a1996e48"
 GraphNeuralNetworks = "cffab07f-9bc2-4db1-8861-388f63bf7694"
 Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"

docs/make.jl

@@ -26,7 +26,7 @@ prettyurls = get(ENV, "CI", nothing) == "true"
 mathengine = MathJax3()
 
 makedocs(;
-         modules = [GraphNeuralNetworks, GNNGraphs],
+         modules = [GraphNeuralNetworks, GNNGraphs, GNNlib],
          doctest = false,
          clean = true,
          plugins = [interlinks],

docs/src/api/messagepassing.md

@@ -14,19 +14,19 @@ Pages   = ["messagepassing.md"]
 ## Interface
 
 ```@docs
-apply_edges
-aggregate_neighbors
-propagate
+GNNlib.apply_edges
+GNNlib.aggregate_neighbors
+GNNlib.propagate
 ```
 
 ## Built-in message functions
 
 ```@docs
-copy_xi
-copy_xj
-xi_dot_xj
-xi_sub_xj
-xj_sub_xi
-e_mul_xj
-w_mul_xj
+GNNlib.copy_xi
+GNNlib.copy_xj
+GNNlib.xi_dot_xj
+GNNlib.xi_sub_xj
+GNNlib.xj_sub_xi
+GNNlib.e_mul_xj
+GNNlib.w_mul_xj
 ```