Dealing with graphs with heterogeneous sizes

I would like to use graph neural networks (from pytorch geometric) for dealing with a dataset of graphs, where each graph has a different number of nodes and edges.

I would like to test graf convolutional networks (GCN) and graph attention networks (GAT). Are these approaches able to deal with such kind of dataset (with a heterogeneous number of nodes and edges)? This is not clear for me.

I plan to use these two layers, specifically: