code: def forward(self, graph, e_feat, u_feat, v_feat):
      # For full graph training, directly use the graph
      # Forward of n layers of GAS
      for layer in self.layers:
          e_feat, u_feat, v_feat = layer(graph, e_feat, u_feat, v_feat)
  
      # return the result of final prediction layer
      return self.predictor(graph, e_feat, u_feat, v_feat)
  
file_path: dgl/examples/pytorch/gas/model.py
imports: import dgl.function
  import torch
  import torch.nn
  from dgl.nn.functional import edge_softmax
label_desc: Use image node_id 2 for calling the GAS obj's underlying member method code with example usage: obj.forward(graph, e_feat, u_feat, v_feat) and returns: self