code: def build_model(self):  # 传的还是全局
      label_feature = self.embed_input(
          self.gw.node_feat["feat"], name="node"
      )
      edge_feature = [
          (
              "edge",
              self.embed_input(
                  self.gw.edge_feat["feat"], name="edge", norm=False
              ),
          )
      ]
      feature_batch = label_feature
      for i in range(self.num_layers):
          feature_batch = self.get_gat_layer(
              i,
              self.gw,
              feature_batch,
              hidden_size=self.hidden_size,
              num_heads=self.num_heads,
              concat=True,
              edge_feature=edge_feature,
              layer_norm=True,
              relu=True,
          )
          if self.dropout > 0:
              feature_batch = L.dropout(
                  feature_batch,
                  dropout_prob=self.dropout,
                  dropout_implementation="upscale_in_train",
              )
  
      feature_batch = linear(
          feature_batch, hidden_size=self.out_size, name="final"
      )
  
      self.out_feat = feature_batch
  
imports: import math
  import paddle.fluid
  import paddle.fluid.layers
  from module.transformer_gat_pgl import transformer_gat_pgl
label_desc: Use image node_id 4 for calling the Proteins_baseline_model obj's underlying member method code with example usage: obj.build_model() without return types