Add SAGEConv docs

Author: aurorarossi

GNNLux/src/layers/conv.jl

@@ -1771,6 +1771,54 @@ function Base.show(io::IO, l::ResGatedGraphConv)
     print(io, ")")
 end
 
+@doc raw"""
+    SAGEConv(in => out, σ=identity; aggr=mean, init_weight = glorot_uniform, init_bias = zeros32, use_bias=true)
+    
+GraphSAGE convolution layer from paper [Inductive Representation Learning on Large Graphs](https://arxiv.org/pdf/1706.02216.pdf).
+
+Performs:
+```math
+\mathbf{x}_i' = W \cdot [\mathbf{x}_i; \square_{j \in \mathcal{N}(i)} \mathbf{x}_j]
+```
+
+where the aggregation type is selected by `aggr`.
+
+# Arguments
+
+- `in`: The dimension of input features.
+- `out`: The dimension of output features.
+- `σ`: Activation function.
+- `aggr`: Aggregation operator for the incoming messages (e.g. `+`, `*`, `max`, `min`, and `mean`).
+- `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 = rand(rng, Float32, in_channel, g.num_nodes)
+
+# create layer
+l = SAGEConv(in_channel => out_channel, tanh, use_bias = false, aggr = +)
+
+# setup layer
+ps, st = LuxCore.setup(rng, l)
+
+# forward pass
+y, st = l(g, x, ps, st)       # size:  out_channel × num_nodes   
+```
+"""
 @concrete struct SAGEConv <: GNNLayer
     in_dims::Int
     out_dims::Int