code: def forward(self, graph, feature, edge_feat):
      feature = self.linear(feature)
      edge_embedding = self.bond_encoder(edge_feat)
  
      norm = GF.degree_norm(graph)
  
      msg = graph.send(
          src_feat={"x": feature, "norm": norm},
          edge_feat={"e": edge_embedding},
          message_func=self.send_func,
      )
  
      output = graph.recv(msg=msg, reduce_func=self.recv_sum)
  
      output = output + self.bias
      output = output * norm
  
      return output
  
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  import pgl
  import pgl.nn
  import pgl.nn.functional
  from mol_encoder import AtomEncoder, BondEncoder
label_desc: Use image node_id 4 for calling the GCNConv obj's underlying member method code with example usage: obj.forward(graph, feature, edge_feat) and returns: output