Add MEGNetConv docs

Author: aurorarossi

GNNLux/src/layers/conv.jl

@@ -1458,6 +1458,50 @@ function Base.show(io::IO, l::GMMConv)
     print(io, ")")
 end
 
+@doc raw"""
+    MEGNetConv(ϕe, ϕv; aggr=mean)
+    MEGNetConv(in => out; aggr=mean)
+
+Convolution from [Graph Networks as a Universal Machine Learning Framework for Molecules and Crystals](https://arxiv.org/pdf/1812.05055.pdf)
+paper. In the forward pass, takes as inputs node features `x` and edge features `e` and returns
+updated features `x'` and `e'` according to 
+
+```math
+\begin{aligned}
+\mathbf{e}_{i\to j}'  = \phi_e([\mathbf{x}_i;\,  \mathbf{x}_j;\,  \mathbf{e}_{i\to j}]),\\
+\mathbf{x}_{i}'  = \phi_v([\mathbf{x}_i;\, \square_{j\in \mathcal{N}(i)}\,\mathbf{e}_{j\to i}']).
+\end{aligned}
+```
+
+`aggr` defines the aggregation to be performed.
+
+If the neural networks `ϕe` and  `ϕv` are not provided, they will be constructed from
+the `in` and `out` arguments instead as multi-layer perceptron with one hidden layer and `relu` 
+activations.
+
+# Examples
+
+```julia
+using GNNLux, Lux, Random
+
+# initialize random number generator
+rng = Random.default_rng()
+
+# create a random graph
+g = rand_graph(rng, 10, 30)
+x = randn(rng, Float32, 3, 10)
+e = randn(rng, Float32, 3, 30)
+
+# create a MEGNetConv layer
+m = MEGNetConv(3 => 3)
+
+# setup layer
+ps, st = LuxCore.setup(rng, m)
+
+# forward pass
+(x′, e′), st = m(g, x, e, ps, st)
+```
+"""
 @concrete struct MEGNetConv{TE, TV, A} <: GNNContainerLayer{(:ϕe, :ϕv)}
     in_dims::Int
     out_dims::Int