code: def build_model(self):  # 传的还是全局
      label_feature = self.embed_input(self.gw.node_feat["feat"])
      feature_batch = L.dropout(
          label_feature,
          dropout_prob=self.dropout,
          dropout_implementation="upscale_in_train",
      )
      for i in range(self.num_layers - 1):
          feature_batch = self.get_gat_layer(
              i,
              self.gw,
              feature_batch,
              hidden_size=self.hidden_size,
              num_heads=self.num_heads,
              concat=True,
              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 = self.get_gat_layer(
          self.num_layers - 1,
          self.gw,
          feature_batch,
          hidden_size=self.out_size,
          num_heads=self.num_heads,
          concat=False,
          layer_norm=False,
          relu=False,
          gate=True,
      )
  
      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 Arxiv_baseline_model obj's underlying member method code with example usage: obj.build_model() without return types