code: def forward(self, subgraph, blocks, f_feat, b_feat, u_feat, v_feat):
      # Forward of n layers of GAS
      for layer, block in zip(self.layers, blocks):
          f_feat, b_feat, u_feat, v_feat = layer(
              block,
              f_feat[: block.num_edges(etype="forward")],
              b_feat[: block.num_edges(etype="backward")],
              u_feat,
              v_feat,
          )
  
      # return the result of final prediction layer
      return self.predictor(
          subgraph,
          f_feat[: subgraph.num_edges(etype="forward")],
          u_feat,
          v_feat,
      )
  
file_path: dgl/examples/pytorch/gas/model_sampling.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(subgraph, blocks, f_feat, b_feat, u_feat, v_feat) and returns: self