code: def forward(self, g):
      h_node = self.gnn_node(g)
  
      h_graph = self.pool(g, h_node)
      output = self.graph_pred_linear(h_graph)
  
      if self.training:
          return output
      else:
          # At inference time, relu is applied to output to ensure positivity
          return paddle.clip(output, min=0, max=50)
  
file_path: PGL/ogb_examples/lsc/PCQM4M/gnn.py
imports: import paddle
  import paddle.nn
  import paddle.nn.functional
  import pgl.nn
  from conv import GNN_node, GNN_node_Virtualnode
label_desc: Use image node_id 2 for calling the GNN obj's underlying member method code with example usage: obj.forward(g) and returns: output, paddle