How to create a graph neural network dataset? (pytorch geometric)
How can I convert my own dataset to be usable by pytorch geometric for a graph neural network?
All the tutorials use existing dataset already converted to be usable by pytorch. For example if I have my own pointcloud dataset how can i use it to train for classification with graph neural network? What about my own image dataset for classification?

How you need to transform your data depends on what format your model expects.
Graph neural networks typically expect (a subset of):

node features
edges
edge attributes
node targets

depending on the problem. You can create an object with tensors of these values (and extend the attributes as you need) in PyTorch Geometric wth a Data object like so:
data = Data(x=x, edge_index=edge_index, y=y)
data.train_idx = torch.tensor([...], dtype=torch.long)
data.test_mask = torch.tensor([...], dtype=torch.bool)

