Return a view of the destination node features for the edges in the batch.
Examples
The following example uses PyTorch backend.
>>> importdgl>>> importtorch
>>> # Instantiate a graph and set a node feature 'h'>>> g=dgl.graph((torch.tensor([0,1,1]),torch.tensor([1,1,0])))>>> g.ndata['h']=torch.tensor([[0.],[1.]])
>>> # Define a UDF that retrieves the destination node features for edges>>> defedge_udf(edges):>>> # edges.dst['h'] is a tensor of shape (E, 1),>>> # where E is the number of edges in the batch.>>> return{'dst':edges.dst['h']}
>>> # Copy features from destination nodes to edges>>> g.apply_edges(edge_udf)>>> g.edata['dst']tensor([[1.], [1.], [1.]])
>>> # Use edge UDF in message passing>>> importdgl.functionasfn>>> g.update_all(edge_udf,fn.sum('dst','h'))>>> g.ndata['h']tensor([[0.], [2.]])