code: def build_model(self):
      label_feature = self.label_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, _, cks = graph_transformer(
              str(i),
              self.gw,
              feature_batch,
              hidden_size=self.hidden_size,
              num_heads=self.num_heads,
              attn_drop=True,
              concat=True,
              skip_feat=True,
              layer_norm=True,
              relu=True,
              gate=True,
          )
          if self.dropout > 0:
              feature_batch = L.dropout(
                  feature_batch,
                  dropout_prob=self.dropout,
                  dropout_implementation="upscale_in_train",
              )
          self.checkpoints = self.checkpoints + cks
  
      feature_batch, attn, cks = graph_transformer(
          str(self.num_layers - 1),
          self.gw,
          feature_batch,
          hidden_size=self.out_size,
          num_heads=self.num_heads + 1,
          concat=False,
          skip_feat=True,
          layer_norm=False,
          relu=False,
          gate=True,
      )
      self.checkpoints.append(feature_batch)
      feature_batch = attn_appnp(
          self.gw, feature_batch, attn, alpha=0.2, k_hop=10
      )
  
      self.checkpoints.append(feature_batch)
      self.out_feat = feature_batch
  
file_path: PGL/ogb_examples/nodeproppred/unimp/model_large.py
imports: import math
  import pgl
  import paddle.fluid
  import paddle.fluid.layers
  from pgl.utils import paddle_helper
  from module.transformer_gat_pgl import transformer_gat_pgl
  from module.model_unimp_large import graph_transformer, linear, attn_appnp
label_desc: Use image node_id 3 for calling the Arxiv_label_embedding_model obj's underlying member method code with example usage: obj.build_model() without return types