Add CGConv docstring

Author: aurorarossi

GNNLux/src/layers/conv.jl

@@ -427,6 +427,65 @@ function (l::AGNNConv)(g, x::AbstractMatrix, ps, st)
     return GNNlib.agnn_conv(m, g, x), st
 end
 
+@doc raw"""
+    CGConv((in, ein) => out, act = identity; residual = false,
+                use_bias = true, init_weight = glorot_uniform, init_bias = zeros32)
+    CGConv(in => out, ...)
+
+The crystal graph convolutional layer from the paper
+[Crystal Graph Convolutional Neural Networks for an Accurate and
+Interpretable Prediction of Material Properties](https://arxiv.org/pdf/1710.10324.pdf).
+Performs the operation
+
+```math
+\mathbf{x}_i' = \mathbf{x}_i + \sum_{j\in N(i)}\sigma(W_f \mathbf{z}_{ij} + \mathbf{b}_f)\, act(W_s \mathbf{z}_{ij} + \mathbf{b}_s)
+```
+
+where ``\mathbf{z}_{ij}``  is the node and edge features concatenation 
+``[\mathbf{x}_i; \mathbf{x}_j; \mathbf{e}_{j\to i}]`` 
+and ``\sigma`` is the sigmoid function.
+The residual ``\mathbf{x}_i`` is added only if `residual=true` and the output size is the same 
+as the input size.
+
+# Arguments
+
+- `in`: The dimension of input node features.
+- `ein`: The dimension of input edge features. 
+If `ein` is not given, assumes that no edge features are passed as input in the forward pass.
+- `out`: The dimension of output node features.
+- `act`: Activation function.
+- `residual`: Add a residual connection.
+- `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 random graph
+g = rand_graph(rng, 5, 6)
+x = rand(rng, Float32, 2, g.num_nodes)
+e = rand(rng, Float32, 3, g.num_edges)
+
+l = CGConv((2, 3) => 4, tanh)
+
+# setup layer
+ps, st = LuxCore.setup(rng, l)
+
+# forward pass
+y, st = l(g, x, e, ps, st)    # size: (4, num_nodes)
+
+# No edge features
+l = CGConv(2 => 4, tanh)
+ps, st = LuxCore.setup(rng, l)
+y, st = l(g, x, ps, st)    # size: (4, num_nodes)
+```
+"""
 @concrete struct CGConv <: GNNContainerLayer{(:dense_f, :dense_s)}
     in_dims::NTuple{2, Int}
     out_dims::Int