Add DCGRU docs

Author: aurorarossi

GNNLux/src/layers/temporalconv.jl

@@ -438,6 +438,46 @@ end
 
 LuxCore.outputsize(l::DCGRUCell) = (l.out_dims,)
 
+"""
+    DCGRU(in => out, k; use_bias = true, init_weight = glorot_uniform, init_state = zeros32, init_bias = zeros32) 
+
+Diffusion Convolutional Recurrent Neural Network (DCGRU) layer from the paper [Diffusion Convolutional Recurrent Neural
+Network: Data-driven Traffic Forecasting](https://arxiv.org/pdf/1707.01926).
+
+Performs a Diffusion Convolutional layer to model spatial dependencies, followed by a Gated Recurrent Unit (GRU) cell to model temporal dependencies.
+
+# Arguments
+
+- `in`: Number of input features.
+- `out`: Number of output features.
+- `k`: Diffusion step.
+- `use_bias`: Add learnable bias. Default `true`.
+- `init_weight`: Weights' initializer. Default `glorot_uniform`.
+- `init_state`: Initial state of the hidden stat of the GRU layer. Default `zeros32`.
+- `init_bias`: Bias initializer. Default `zeros32`.
+
+# Examples
+
+```julia
+using GNNLux, Lux, Random
+
+# initialize random number generator
+rng = Random.default_rng()
+
+# create data
+g = rand_graph(rng, 5, 10)
+x = rand(rng, Float32, 2, 5)
+
+# create layer
+l = DCGRU(2 => 5, 2)
+
+# setup layer
+ps, st = LuxCore.setup(rng, l)
+
+# forward pass
+y, st = l(g, x, ps, st)      # result size (5, 5)
+```
+"""
 DCGRU(ch::Pair{Int, Int}, k::Int; kwargs...) = GNNLux.StatefulRecurrentCell(DCGRUCell(ch, k; kwargs...))
 
 @concrete struct EvolveGCNO <: GNNLayer