I have met a question:

Suppose I use a batch of data with torch.Tensor type which represents a set of batched nodes for a batch of graphs(size of data: batch_size* max_node_num * emb). Also the data is attached with a mask tensor due to the unequal number of nodes for different graphs in a batch. Currently I use graph attention network(GAT) to build my model. So how to intergrate mask tensor(batch_size * max_node_num) along with data tensor(batch_size* max_node_num * emb) into GATConv to adapt for a batch of data computation for a model?

Hope for the answer.

Thank you!