Fix a few typos (#281)

* Fix a typo in docs/tutorials/index.md

* Fix a few typos in node_classification_pluto.jl

* Fix a typo in node_classification_cora.jl

* Fix a typo in gnngraph.jl

* Fix a typo in gnnheterograph.jl

* Fix a typo in test_utils.jl

Author: pitmonticone

docs/tutorials/index.md

@@ -16,4 +16,4 @@ checkout these tutorials from
 [PyTorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/notes/colabs.html).
 You are expected to use [Pluto.jl](https://github.com/fonsp/Pluto.jl) notebooks
 with [DemoCards.jl](https://github.com/JuliaDocs/DemoCards.jl).
-Please check out exsisting tutorials for more details.
+Please check out existing tutorials for more details.

docs/tutorials/introductory_tutorials/node_classification_pluto.jl

@@ -44,7 +44,7 @@ Let us start off by importing some libraries. We will be using Flux.jl and `Grap
 # ╔═╡ 0d556a7c-d4b6-4cef-806c-3e1712de0791
 md"""
 ## Visualize
-We want to visualize the the outputs of the resutls using t-distributed stochastic neighbor embedding (tsne) to embed our output embeddings onto a 2D plane.
+We want to visualize the the outputs of the results using t-distributed stochastic neighbor embedding (tsne) to embed our output embeddings onto a 2D plane.
 """
 
 # ╔═╡ 997b5387-3811-4998-a9d1-7981b58b9e09
@@ -57,11 +57,11 @@ end
 md"""
 ## Dataset: Cora
 
-For our tutorial, we will be using the `Cora` dataset. `Cora` is a citaton network of 2708 documents classified into one of seven classes and 5429 links. Each node represent articles/documents and the edges between these nodes if one of them cite each other.
+For our tutorial, we will be using the `Cora` dataset. `Cora` is a citation network of 2708 documents classified into one of seven classes and 5429 links. Each node represent articles/documents and the edges between these nodes if one of them cite each other.
 
 Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 1433 unique words.
 
-This dataset was first introduced by [Yang et al. (2016)](https://arxiv.org/abs/1603.08861) as one of the datasets of the `Planetoid` benchmark suite. We will be using [MLDatasets.jl](https://juliaml.github.io/MLDatasets.jl/stable/) for an easy accss to this dataset.
+This dataset was first introduced by [Yang et al. (2016)](https://arxiv.org/abs/1603.08861) as one of the datasets of the `Planetoid` benchmark suite. We will be using [MLDatasets.jl](https://juliaml.github.io/MLDatasets.jl/stable/) for an easy access to this dataset.
 """
 
 # ╔═╡ edab1e3a-31f6-471f-9835-5b1f97e5cf3f
@@ -77,15 +77,15 @@ dataset.metadata
 
 # ╔═╡ 3438ee7f-bfca-465d-85df-13379622d415
 md"""
-The `graphs` variable GraphDataset contains the graph. The `Cora` dataaset contains only 1 graph.
+The `graphs` variable GraphDataset contains the graph. The `Cora` dataset contains only 1 graph.
 """
 
 # ╔═╡ eec6fb60-0774-4f2a-bcb7-dbc28ab747a6
 dataset.graphs
 
 # ╔═╡ bd2fd04d-7fb0-4b31-959b-bddabe681754
 md"""
-There is only one graph of the dataset. The `node_data` contians `features` indicating if certain words are present or not and `targets` indicating the class for each document. We convert the single-graph dataset to a `GNNGraph`.
+There is only one graph of the dataset. The `node_data` contains `features` indicating if certain words are present or not and `targets` indicating the class for each document. We convert the single-graph dataset to a `GNNGraph`.
 """
 
 # ╔═╡ b29c3a02-c21b-4b10-aa04-b90bcc2931d8

test/GNNGraphs/gnngraph.jl

@@ -338,7 +338,7 @@ end
     @test g1 !== g2
 end
 
-## Cannot test this because DataStore is not an oredered collection
+## Cannot test this because DataStore is not an ordered collection
 ## Uncomment when/if it will be based on OrderedDict
 # @testset "show" begin
 #     @test sprint(show, rand_graph(10, 20)) == "GNNGraph(10, 20) with no data"

test/GNNGraphs/gnnheterograph.jl

@@ -69,7 +69,7 @@ end
     @test hg.num_edges == Dict((:A, :rel1, :B) => 20, (:B, :rel2, :A) => 30)
 end
 
-## Cannot test this because DataStore is not an oredered collection
+## Cannot test this because DataStore is not an ordered collection
 ## Uncomment when/if it will be based on OrderedDict
 # @testset "show" begin
 #     num_nodes = Dict(:A => 10, :B => 20);

test/examples/node_classification_cora.jl

@@ -92,7 +92,7 @@ function train_many(; usecuda = false)
         ## ("ChebConv", (nin, nout) -> ChebConv(nin => nout, 2)), # not working on gpu
         ## ("NNConv", (nin, nout) -> NNConv(nin => nout)),  # needs edge features
         ## ("GatedGraphConv", (nin, nout) -> GatedGraphConv(nout, 2)),  # needs nin = nout
-        ## ("EdgeConv",(nin, nout) -> EdgeConv(Dense(2nin, nout, relu))), # Fits the traning set but does not generalize well
+        ## ("EdgeConv",(nin, nout) -> EdgeConv(Dense(2nin, nout, relu))), # Fits the training set but does not generalize well
     ]
         @show layer
         @time train_res, test_res = train(Layer; usecuda, verbose = false)

test/test_utils.jl

@@ -20,7 +20,7 @@ end
 # Test gradients with respects to layer weights and to input. 
 # If `g` has edge features, it is assumed that the layer can 
 # use them in the forward pass as `l(g, x, e)`.
-# Test also gradient with repspect to `e`. 
+# Test also gradient with respect to `e`. 
 function test_layer(l, g::GNNGraph; atol = 1e-5, rtol = 1e-5,
                     exclude_grad_fields = [],
                     verbose = false,