Add DConv docstring

Author: aurorarossi

GNNLux/src/layers/conv.jl

@@ -710,6 +710,40 @@ function Base.show(io::IO, l::EGNNConv)
     print(io, ")")
 end
 
+"""
+    DConv(in => out, k; init_weight = glorot_uniform, init_bias = zeros32, use_bias = true)
+
+Diffusion convolution layer from the paper [Diffusion Convolutional Recurrent Neural Networks: Data-Driven Traffic Forecasting](https://arxiv.org/pdf/1707.01926).
+
+# Arguments
+
+- `in`: The dimension of input features.
+- `out`: The dimension of output features.
+- `k`: Number of diffusion steps.
+- `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 = GNNGraph(rand(rng, 10, 10), ndata = rand(rng, Float32, 2, 10))
+
+dconv = DConv(2 => 4, 4)
+
+# setup layer
+ps, st = LuxCore.setup(rng, dconv)
+
+# forward pass
+y, st = dconv(g, g.ndata.x, ps, st)   # size: (4, num_nodes)
+```
+"""
 @concrete struct DConv <: GNNLayer
     in_dims::Int
     out_dims::Int