diff --git a/GNNGraphs/docs/src/guides/temporalgraph.md b/GNNGraphs/docs/src/guides/temporalgraph.md
index 53c8543b1..3f66d160a 100644
--- a/GNNGraphs/docs/src/guides/temporalgraph.md
+++ b/GNNGraphs/docs/src/guides/temporalgraph.md
@@ -85,6 +85,29 @@ GNNGraph:
   num_nodes: 10
   num_edges: 16
 ```
+## Iteration and Broadcasting
+
+Iteration and broadcasting over a temporal graph is similar to that of a vector of snapshots:
+
+```jldoctest temporal
+julia> snapshots = [rand_graph(10, 20), rand_graph(10, 14), rand_graph(10, 22)];
+
+julia> tg = TemporalSnapshotsGNNGraph(snapshots);
+
+julia> [g for g in tg] # iterate over snapshots
+3-element Vector{GNNGraph{Tuple{Vector{Int64}, Vector{Int64}, Nothing}}}:
+ GNNGraph(10, 20) with no data
+ GNNGraph(10, 14) with no data
+ GNNGraph(10, 22) with no data
+
+julia> f(g) = g isa GNNGraph;
+
+julia> f.(tg) # broadcast over snapshots
+3-element BitVector:
+ 1
+ 1
+ 1
+```
 
 ## Basic Queries
 
diff --git a/GNNGraphs/src/temporalsnapshotsgnngraph.jl b/GNNGraphs/src/temporalsnapshotsgnngraph.jl
index 3491cfc4b..30f854730 100644
--- a/GNNGraphs/src/temporalsnapshotsgnngraph.jl
+++ b/GNNGraphs/src/temporalsnapshotsgnngraph.jl
@@ -91,7 +91,13 @@ end
 
 function Base.length(tg::TemporalSnapshotsGNNGraph)
     return tg.num_snapshots
-end
+end 
+
+# Allow broadcasting over the temporal snapshots
+Base.broadcastable(tg::TemporalSnapshotsGNNGraph) = tg.snapshots
+
+Base.iterate(tg::TemporalSnapshotsGNNGraph) = Base.iterate(tg.snapshots)
+Base.iterate(tg::TemporalSnapshotsGNNGraph, i) = Base.iterate(tg.snapshots, i)
 
 function Base.setindex!(tg::TemporalSnapshotsGNNGraph, g::GNNGraph, t::Int)
     tg.snapshots[t] = g
diff --git a/GNNGraphs/test/temporalsnapshotsgnngraph.jl b/GNNGraphs/test/temporalsnapshotsgnngraph.jl
index 3d433bd7b..352dbbedd 100644
--- a/GNNGraphs/test/temporalsnapshotsgnngraph.jl
+++ b/GNNGraphs/test/temporalsnapshotsgnngraph.jl
@@ -1,3 +1,5 @@
+#TODO add graph_type = GRAPH_TYPE to all constructor calls
+
 @testset "Constructor array TemporalSnapshotsGNNGraph" begin
     snapshots = [rand_graph(10, 20) for i in 1:5]
     tg = TemporalSnapshotsGNNGraph(snapshots)
@@ -12,6 +14,7 @@
     @test tg.num_snapshots == 5
 end
 
+
 @testset "==" begin
     snapshots = [rand_graph(10, 20) for i in 1:5]
     tsg1 = TemporalSnapshotsGNNGraph(snapshots)
@@ -41,7 +44,7 @@ end
 end
 
 @testset "getproperty" begin
-    x = rand(10)
+    x = rand(Float32, 10)
     snapshots = [rand_graph(10, 20, ndata = x) for i in 1:5]
     tsg = TemporalSnapshotsGNNGraph(snapshots)
     @test tsg.tgdata == DataStore()
@@ -111,18 +114,31 @@ end
 @testset "show" begin
     snapshots = [rand_graph(10, 20) for i in 1:5]
     tsg = TemporalSnapshotsGNNGraph(snapshots)
-    @test sprint(show,tsg) == "TemporalSnapshotsGNNGraph(5) with no data"
-    @test sprint(show, MIME("text/plain"), tsg; context=:compact => true) == "TemporalSnapshotsGNNGraph(5) with no data"
+    @test sprint(show,tsg) == "TemporalSnapshotsGNNGraph(5)"
+    @test sprint(show, MIME("text/plain"), tsg; context=:compact => true) == "TemporalSnapshotsGNNGraph(5)"
     @test sprint(show, MIME("text/plain"), tsg; context=:compact =>  false) == "TemporalSnapshotsGNNGraph:\n  num_nodes: [10, 10, 10, 10, 10]\n  num_edges: [20, 20, 20, 20, 20]\n  num_snapshots: 5"
-    tsg.tgdata.x=rand(4)
-    @test sprint(show,tsg) == "TemporalSnapshotsGNNGraph(5) with x: 4-element data"
+    tsg.tgdata.x = rand(Float32, 4)
+    @test sprint(show,tsg) == "TemporalSnapshotsGNNGraph(5)"
+end
+
+@testset "broadcastable" begin
+    snapshots = [rand_graph(10, 20) for i in 1:5]
+    tsg = TemporalSnapshotsGNNGraph(snapshots)
+    f(g) = g isa GNNGraph
+    @test f.(tsg) == trues(5)
+end
+
+@testset "iterate" begin
+    snapshots = [rand_graph(10, 20) for i in 1:5]
+    tsg = TemporalSnapshotsGNNGraph(snapshots)
+    @test [g for g in tsg] isa Vector{<:GNNGraph}
 end
 
 if TEST_GPU
     @testset "gpu" begin
-        snapshots = [rand_graph(10, 20; ndata = rand(5,10)) for i in 1:5]
+        snapshots = [rand_graph(10, 20; ndata = rand(Float32, 5,10)) for i in 1:5]
         tsg = TemporalSnapshotsGNNGraph(snapshots)
-        tsg.tgdata.x = rand(5)
+        tsg.tgdata.x = rand(Float32, 5)
         dev = CUDADevice() #TODO replace with `gpu_device()`
         tsg = tsg |> dev
         @test tsg.snapshots[1].ndata.x isa CuArray
diff --git a/GNNLux/docs/src_tutorials/gnn_intro.jl b/GNNLux/docs/src_tutorials/gnn_intro.jl
index 1fa18e41a..d09cae3e7 100644
--- a/GNNLux/docs/src_tutorials/gnn_intro.jl
+++ b/GNNLux/docs/src_tutorials/gnn_intro.jl
@@ -220,7 +220,7 @@ visualize_embeddings(emb_init, colors = labels)
 # If you are not new to Lux, this scheme should appear familiar to you.
 
 # Note that our semi-supervised learning scenario is achieved by the following line:
-# ```
+# ```julia
 # logitcrossentropy(ŷ[:,train_mask], y[:,train_mask])
 # ```
 # While we compute node embeddings for all of our nodes, we **only make use of the training nodes for computing the loss**.
diff --git a/GraphNeuralNetworks/docs/make_tutorials.jl b/GraphNeuralNetworks/docs/make_tutorials_pluto.jl
similarity index 100%
rename from GraphNeuralNetworks/docs/make_tutorials.jl
rename to GraphNeuralNetworks/docs/make_tutorials_pluto.jl
diff --git a/GraphNeuralNetworks/docs/src_tutorials/temporalconv_tutorials/temporal_graph_classification_pluto.jl b/GraphNeuralNetworks/docs/src_tutorials/temporalconv_tutorials/temporal_graph_classification_pluto.jl
index 7a664869a..ff0cf439d 100644
--- a/GraphNeuralNetworks/docs/src_tutorials/temporalconv_tutorials/temporal_graph_classification_pluto.jl
+++ b/GraphNeuralNetworks/docs/src_tutorials/temporalconv_tutorials/temporal_graph_classification_pluto.jl
@@ -1,64 +1,36 @@
-### A Pluto.jl notebook ###
-# v0.19.45
 
-#> [frontmatter]
-#> author = "[Aurora Rossi](https://github.com/aurorarossi)"
-#> title = "Temporal Graph classification with Graph Neural Networks"
-#> date = "2024-03-06"
-#> description = "Temporal Graph classification with GraphNeuralNetworks.jl"
-#> cover = "assets/brain_gnn.gif"
+# # Temporal Graph classification with GraphNeuralNetworks.jl
+#
+# In this tutorial, we will learn how to extend the graph classification task to the case of temporal graphs, i.e., graphs whose topology and features are time-varying.
+#
+# We will design and train a simple temporal graph neural network architecture to classify subjects' gender (female or male) using the temporal graphs extracted from their brain fMRI scan signals. Given the large amount of data, we will implement the training so that it can also run on the GPU.
+
+# ## Import
+#
+# We start by importing the necessary libraries. We use `GraphNeuralNetworks.jl`, `Flux.jl` and `MLDatasets.jl`, among others.
+
+using Flux
+using GraphNeuralNetworks
+using Statistics, Random
+using LinearAlgebra
+using MLDatasets: TemporalBrains
+using CUDA # comment out if you don't have a CUDA GPU
+
+# ## Dataset: TemporalBrains
+# The TemporalBrains dataset contains a collection of functional brain connectivity networks from 1000 subjects obtained from resting-state functional MRI data from the [Human Connectome Project (HCP)](https://www.humanconnectome.org/study/hcp-young-adult/document/extensively-processed-fmri-data-documentation). 
+# Functional connectivity is defined as the temporal dependence of neuronal activation patterns of anatomically separated brain regions.
+#
+# The graph nodes represent brain regions and their number is fixed at 102 for each of the 27 snapshots, while the edges, representing functional connectivity, change over time.
+# For each snapshot, the feature of a node represents the average activation of the node during that snapshot.
+# Each temporal graph has a label representing gender ('M' for male and 'F' for female) and age group (22-25, 26-30, 31-35, and 36+).
+# The network's edge weights are binarized, and the threshold is set to 0.6 by default.
 
-using Markdown
-using InteractiveUtils
-
-# ╔═╡ b8df1800-c69d-4e18-8a0a-097381b62a4c
-begin
-	using Flux
-	using GraphNeuralNetworks
-	using Statistics, Random
-	using LinearAlgebra
-	using MLDatasets: TemporalBrains
-	using CUDA
-	using cuDNN
-end
-
-# ╔═╡ 69d00ec8-da47-11ee-1bba-13a14e8a6db2
-md"
-# Temporal Graph classification with GraphNeuralNetworks.jl
-
-In this tutorial, we will learn how to extend the graph classification task to the case of temporal graphs, i.e., graphs whose topology and features are time-varying.
-
-We will design and train a simple temporal graph neural network architecture to classify subjects' gender (female or male) using the temporal graphs extracted from their brain fMRI scan signals. Given the large amount of data, we will implement the training so that it can also run on the GPU.
-"
-
-# ╔═╡ ef8406e4-117a-4cc6-9fa5-5028695b1a4f
-md"
-## Import
-
-We start by importing the necessary libraries. We use `GraphNeuralNetworks.jl`, `Flux.jl` and `MLDatasets.jl`, among others.
-"
-
-# ╔═╡ 2544d468-1430-4986-88a9-be4df2a7cf27
-md"
-## Dataset: TemporalBrains
-The TemporalBrains dataset contains a collection of functional brain connectivity networks from 1000 subjects obtained from resting-state functional MRI data from the [Human Connectome Project (HCP)](https://www.humanconnectome.org/study/hcp-young-adult/document/extensively-processed-fmri-data-documentation). 
-Functional connectivity is defined as the temporal dependence of neuronal activation patterns of anatomically separated brain regions.
-
-The graph nodes represent brain regions and their number is fixed at 102 for each of the 27 snapshots, while the edges, representing functional connectivity, change over time.
-For each snapshot, the feature of a node represents the average activation of the node during that snapshot.
-Each temporal graph has a label representing gender ('M' for male and 'F' for female) and age group (22-25, 26-30, 31-35, and 36+).
-The network's edge weights are binarized, and the threshold is set to 0.6 by default.
-"
-
-# ╔═╡ f2dbc66d-b8b7-46ae-ad5b-cbba1af86467
 brain_dataset = TemporalBrains()
 
-# ╔═╡ d9e4722d-6f02-4d41-955c-8bb3e411e404
-md"After loading the dataset from the MLDatasets.jl package, we see that there are 1000 graphs and we need to convert them to the `TemporalSnapshotsGNNGraph` format.
-So we create a function called `data_loader` that implements the latter and splits the dataset into the training set that will be used to train the model and the test set that will be used to test the performance of the model.
-"
+# After loading the dataset from the MLDatasets.jl package, we see that there are 1000 graphs and we need to convert them to the `TemporalSnapshotsGNNGraph` format.
+# So we create a function called `data_loader` that implements the latter and splits the dataset into the training set that will be used to train the model and the test set that will be used to test the performance of the model.
+
 
-# ╔═╡ bb36237a-5545-47d0-a873-7ddff3efe8ba
 function data_loader(brain_dataset)
 	graphs = brain_dataset.graphs
     dataset = Vector{TemporalSnapshotsGNNGraph}(undef, length(graphs))
@@ -76,98 +48,68 @@ function data_loader(brain_dataset)
     train_loader = dataset[1:200]
     test_loader = dataset[201:250]
     return train_loader, test_loader
-end;
-
-# ╔═╡ d4732340-9179-4ada-b82e-a04291d745c2
-md"
-The first part of the `data_loader` function calls the `mlgraph2gnngraph` function for each snapshot, which takes the graph and converts it to a `GNNGraph`. The vector of `GNNGraph`s is then rewritten to a `TemporalSnapshotsGNNGraph`.
-
-The second part adds the graph and node features to the temporal graphs, in particular it adds the one-hot encoding of the label of the graph (in this case we directly use the identity matrix) and appends the mean activation of the node of the snapshot (which is contained in the vector `dataset[i].snapshots[t].ndata.x`, where `i` is the index indicating the subject and `t` is the snapshot). For the graph feature, it adds the one-hot encoding of gender.
-
-The last part splits the dataset.
-"
-
-
-# ╔═╡ ec088a59-2fc2-426a-a406-f8f8d6784128
-md"
-## Model
+end
 
-We now implement a simple model that takes a `TemporalSnapshotsGNNGraph` as input.
-It consists of a `GINConv` applied independently to each snapshot, a `GlobalPool` to get an embedding for each snapshot, a pooling on the time dimension to get an embedding for the whole temporal graph, and finally a `Dense` layer.
+# The first part of the `data_loader` function calls the `mlgraph2gnngraph` function for each snapshot, which takes the graph and converts it to a `GNNGraph`. The vector of `GNNGraph`s is then rewritten to a `TemporalSnapshotsGNNGraph`.
+#
+# The second part adds the graph and node features to the temporal graphs, in particular it adds the one-hot encoding of the label of the graph (in this case we directly use the identity matrix) and appends the mean activation of the node of the snapshot (which is contained in the vector `dataset[i].snapshots[t].ndata.x`, where `i` is the index indicating the subject and `t` is the snapshot). For the graph feature, it adds the one-hot encoding of gender.
+#
+# The last part splits the dataset.
 
-First, we start by adapting the `GlobalPool` to the `TemporalSnapshotsGNNGraphs`.
-"
+# ## Model
+#
+# We now implement a simple model that takes a `TemporalSnapshotsGNNGraph` as input.
+# It consists of a `GINConv` applied independently to each snapshot, a `GlobalPool` to get an embedding for each snapshot, a pooling on the time dimension to get an embedding for the whole temporal graph, and finally a `Dense` layer.
+#
+# First, we start by adapting the `GlobalPool` to the `TemporalSnapshotsGNNGraphs`.
 
-# ╔═╡ 5ea98df9-4920-4c94-9472-3ef475af89fd
 function (l::GlobalPool)(g::TemporalSnapshotsGNNGraph, x::AbstractVector)
     h = [reduce_nodes(l.aggr, g[i], x[i]) for i in 1:(g.num_snapshots)]
     sze = size(h[1])
     reshape(reduce(hcat, h), sze[1], length(h))
 end
 
-# ╔═╡ cfda2cf4-d08b-4f46-bd39-02ae3ed53369
-md"
-Then we implement the constructor of the model, which we call `GenderPredictionModel`, and the foward pass.
-"
+# Then we implement the constructor of the model, which we call `GenderPredictionModel`, and the foward pass.
 
-# ╔═╡ 2eedd408-67ee-47b2-be6f-2caec94e95b5
-begin
-	struct GenderPredictionModel
-	    gin::GINConv
-	    mlp::Chain
-	    globalpool::GlobalPool
-	    f::Function
-	    dense::Dense
-	end
-	
-	Flux.@layer GenderPredictionModel
-	
-	function GenderPredictionModel(; nfeatures = 103, nhidden = 128, activation = relu)
-	    mlp = Chain(Dense(nfeatures, nhidden, activation), Dense(nhidden, nhidden, activation))
-	    gin = GINConv(mlp, 0.5)
-	    globalpool = GlobalPool(mean)
-	    f = x -> mean(x, dims = 2)
-	    dense = Dense(nhidden, 2)
-	    GenderPredictionModel(gin, mlp, globalpool, f, dense)
-	end
-	
-	function (m::GenderPredictionModel)(g::TemporalSnapshotsGNNGraph)
-		h = m.gin(g, g.ndata.x)
-		h = m.globalpool(g, h)
-		h = m.f(h)
-		m.dense(h)
-	end
-	
+struct GenderPredictionModel
+    gin::GINConv
+    mlp::Chain
+    globalpool::GlobalPool
+    dense::Dense
 end
 
-# ╔═╡ 76780020-406d-4803-9af0-d928e54fc18c
-md"
-## Training
+Flux.@layer GenderPredictionModel
+
+function GenderPredictionModel(; nfeatures = 103, nhidden = 128, σ = relu)
+    mlp = Chain(Dense(nfeatures => nhidden, σ), Dense(nhidden => nhidden, σ))
+    gin = GINConv(mlp, 0.5)
+    globalpool = GlobalPool(mean)
+    dense = Dense(nhidden => 2)
+    return GenderPredictionModel(gin, mlp, globalpool, dense)
+end
 
-We train the model for 100 epochs, using the Adam optimizer with a learning rate of 0.001. We use the `logitbinarycrossentropy` as the loss function, which is typically used as the loss in two-class classification, where the labels are given in a one-hot format.
-The accuracy expresses the number of correct classifications. 
-"
+function (m::GenderPredictionModel)(g::TemporalSnapshotsGNNGraph)
+    h = m.gin(g, g.ndata.x)
+    h = m.globalpool(g, h)
+    h = mean(h, dims=2)
+    return m.dense(h)
+end
+	
+# ## Training
+#
+# We train the model for 100 epochs, using the Adam optimizer with a learning rate of 0.001. We use the `logitbinarycrossentropy` as the loss function, which is typically used as the loss in two-class classification, where the labels are given in a one-hot format.
+# The accuracy expresses the number of correct classifications. 
 
-# ╔═╡ 0a1e07b0-a4f3-4a4b-bcd1-7fe200967cf8
 lossfunction(ŷ, y) = Flux.logitbinarycrossentropy(ŷ, y);
 
-# ╔═╡ cc2ebdcf-72de-4a3b-af46-5bddab6689cc
 function eval_loss_accuracy(model, data_loader)
     error = mean([lossfunction(model(g), g.tgdata.g) for g in data_loader])
     acc = mean([round(100 * mean(Flux.onecold(model(g)) .==     Flux.onecold(g.tgdata.g)); digits = 2) for g in data_loader])
     return (loss = error, acc = acc)
-end;
-
-# ╔═╡ d64be72e-8c1f-4551-b4f2-28c8b78466c0
-function train(dataset; usecuda::Bool, kws...)
+end
 
-	if usecuda && CUDA.functional() #check if GPU is available 
-        my_device = gpu
-        @info "Training on GPU"
-    else
-        my_device = cpu
-        @info "Training on CPU"
-    end
+function train(dataset)
+    device = gpu_device()
 	
     function report(epoch)
         train_loss, train_acc = eval_loss_accuracy(model, train_loader)
@@ -176,13 +118,13 @@ function train(dataset; usecuda::Bool, kws...)
         return (train_loss, train_acc, test_loss, test_acc)
     end
 
-    model = GenderPredictionModel() |> my_device
+    model = GenderPredictionModel() |> device
 
     opt = Flux.setup(Adam(1.0f-3), model)
 
     train_loader, test_loader = data_loader(dataset)
-	train_loader = train_loader |> my_device
-	test_loader = test_loader |> my_device
+	train_loader = train_loader |> device
+	test_loader = test_loader |> device
 
     report(0)
     for epoch in 1:100
@@ -198,1538 +140,11 @@ function train(dataset; usecuda::Bool, kws...)
         end
     end
     return model
-end;
-
+end
 
-# ╔═╡ 483f17ba-871c-4769-88bd-8ec781d1909d
-train(brain_dataset; usecuda = true)
 
-# ╔═╡ b4a3059a-db7d-47f1-9ae5-b8c3d896c5e5
-md"
-We set up the training on the GPU because training takes a lot of time, especially when working on the CPU.
-"
+train(brain_dataset)
 
-# ╔═╡ cb4eed19-2658-411d-886c-e0c9c2b44219
-md"
 ## Conclusions
-
-In this tutorial, we implemented a very simple architecture to classify temporal graphs in the context of gender classification using brain data. We then trained the model on the GPU for 100 epochs on the TemporalBrains dataset. The accuracy of the model is approximately 75-80%, but can be improved by fine-tuning the parameters and training on more data.
-"
-
-# ╔═╡ 00000000-0000-0000-0000-000000000001
-PLUTO_PROJECT_TOML_CONTENTS = """
-[deps]
-CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
-Flux = "587475ba-b771-5e3f-ad9e-33799f191a9c"
-GraphNeuralNetworks = "cffab07f-9bc2-4db1-8861-388f63bf7694"
-LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
-MLDatasets = "eb30cadb-4394-5ae3-aed4-317e484a6458"
-Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
-Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
-cuDNN = "02a925ec-e4fe-4b08-9a7e-0d78e3d38ccd"
-
-[compat]
-CUDA = "~5.4.3"
-Flux = "~0.14.16"
-GraphNeuralNetworks = "~0.6.19"
-MLDatasets = "~0.7.16"
-cuDNN = "~1.3.2"
-"""
-
-# ╔═╡ 00000000-0000-0000-0000-000000000002
-PLUTO_MANIFEST_TOML_CONTENTS = """
-# This file is machine-generated - editing it directly is not advised
-
-julia_version = "1.10.4"
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-
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-"""
-
-# ╔═╡ Cell order:
-# ╟─69d00ec8-da47-11ee-1bba-13a14e8a6db2
-# ╟─ef8406e4-117a-4cc6-9fa5-5028695b1a4f
-# ╠═b8df1800-c69d-4e18-8a0a-097381b62a4c
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-# ╠═f2dbc66d-b8b7-46ae-ad5b-cbba1af86467
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-# ╠═bb36237a-5545-47d0-a873-7ddff3efe8ba
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-# ╟─ec088a59-2fc2-426a-a406-f8f8d6784128
-# ╠═5ea98df9-4920-4c94-9472-3ef475af89fd
-# ╟─cfda2cf4-d08b-4f46-bd39-02ae3ed53369
-# ╠═2eedd408-67ee-47b2-be6f-2caec94e95b5
-# ╟─76780020-406d-4803-9af0-d928e54fc18c
-# ╠═0a1e07b0-a4f3-4a4b-bcd1-7fe200967cf8
-# ╠═cc2ebdcf-72de-4a3b-af46-5bddab6689cc
-# ╠═d64be72e-8c1f-4551-b4f2-28c8b78466c0
-# ╠═483f17ba-871c-4769-88bd-8ec781d1909d
-# ╟─b4a3059a-db7d-47f1-9ae5-b8c3d896c5e5
-# ╟─cb4eed19-2658-411d-886c-e0c9c2b44219
-# ╟─00000000-0000-0000-0000-000000000001
-# ╟─00000000-0000-0000-0000-000000000002
+#
+# In this tutorial, we implemented a very simple architecture to classify temporal graphs in the context of gender classification using brain data. We then trained the model on the GPU for 100 epochs on the TemporalBrains dataset. The accuracy of the model is approximately 75-80%, but can be improved by fine-tuning the parameters and training on more data.