ResGatedGraphConv docs

Author: aurorarossi

GNNLux/src/layers/conv.jl

@@ -1667,6 +1667,58 @@ function Base.show(io::IO, l::NNConv)
     print(io, ")")
 end
 
+@doc raw"""
+    ResGatedGraphConv(in => out, act=identity; init_weight = glorot_uniform, init_bias = zeros32, use_bias = true)
+
+The residual gated graph convolutional operator from the [Residual Gated Graph ConvNets](https://arxiv.org/abs/1711.07553) paper.
+
+The layer's forward pass is given by
+
+```math
+\mathbf{x}_i' = act\big(U\mathbf{x}_i + \sum_{j \in N(i)} \eta_{ij} V \mathbf{x}_j\big),
+```
+where the edge gates ``\eta_{ij}`` are given by
+
+```math
+\eta_{ij} = sigmoid(A\mathbf{x}_i + B\mathbf{x}_j).
+```
+
+# Arguments
+
+- `in`: The dimension of input features.
+- `out`: The dimension of output features.
+- `act`: Activation function.
+- `init_weight`: Weights' initializer. Default `glorot_uniform`.
+- `init_bias`: Bias initializer. Default `zeros32`.
+- `use_bias`: Add learnable bias. Default `true`.
+
+
+# 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]
+in_channel = 3
+out_channel = 5
+g = GNNGraph(s, t)
+x = randn(rng, Float32, in_channel, g.num_nodes)
+
+# create layer
+l = ResGatedGraphConv(in_channel => out_channel, tanh, use_bias = true)
+
+# setup layer
+ps, st = LuxCore.setup(rng, l)
+
+# forward pass
+y, st = l(g, x, ps, st)       # size:  out_channel × num_nodes  
+```
+"""
 @concrete struct ResGatedGraphConv <: GNNLayer
     in_dims::Int
     out_dims::Int