Add GatedGraphConv docs

Author: aurorarossi

GNNLux/src/layers/conv.jl

@@ -1186,6 +1186,54 @@ function (l::SGConv)(g, x, edge_weight, ps, st)
     return y, st
 end
 
+@doc raw"""
+    GatedGraphConv(out, num_layers; 
+            aggr = +, init_weight = glorot_uniform)
+
+Gated graph convolution layer from [Gated Graph Sequence Neural Networks](https://arxiv.org/abs/1511.05493).
+
+Implements the recursion
+```math
+\begin{aligned}
+\mathbf{h}^{(0)}_i &= [\mathbf{x}_i; \mathbf{0}] \\
+\mathbf{h}^{(l)}_i &= GRU(\mathbf{h}^{(l-1)}_i, \square_{j \in N(i)} W \mathbf{h}^{(l-1)}_j)
+\end{aligned}
+```
+
+where ``\mathbf{h}^{(l)}_i`` denotes the ``l``-th hidden variables passing through GRU. The dimension of input ``\mathbf{x}_i`` needs to be less or equal to `out`.
+
+# Arguments
+
+- `out`: The dimension of output features.
+- `num_layers`: The number of recursion steps.
+- `aggr`: Aggregation operator for the incoming messages (e.g. `+`, `*`, `max`, `min`, and `mean`).
+- `init_weight`: Weights' initializer. Default `glorot_uniform`.
+
+# Examples:
+
+```julia
+using GNNLux, Lux, Random
+
+# initialize random number generator
+rng = Random.default_rng()
+
+# create data
+s = [1,1,2,3]
+t = [2,3,1,1]
+out_channel = 5
+num_layers = 3
+g = GNNGraph(s, t)
+
+# create layer
+l = GatedGraphConv(out_channel, num_layers)
+
+# setup layer
+ps, st = LuxCore.setup(rng, l)
+
+# forward pass
+y, st = l(g, x, ps, st)       # size:  out_channel × num_nodes  
+```
+"""
 @concrete struct GatedGraphConv <: GNNLayer
     gru
     init_weight
@@ -1194,7 +1242,6 @@ end
     aggr
 end
 
-
 function GatedGraphConv(dims::Int, num_layers::Int; 
             aggr = +, init_weight = glorot_uniform)
     gru = GRUCell(dims => dims)