code: def forward(self, g, x, edge_attr):
      h_node = self.gnn_node(g, x, edge_attr)
  
      h_graph = self.pool(g, h_node)
      output = self.graph_pred_linear(h_graph)
  
      if self.training:
          return output
      else:
          return torch.clamp(output, min=0, max=50)
  
file_path: dgl/examples/pytorch/ogb_lsc/PCQM4M/gnn.py
imports: import torch
  import torch.nn
  from conv import GNN_node, GNN_node_Virtualnode
  from dgl.nn.pytorch import AvgPooling, GlobalAttentionPooling, MaxPooling, Set2Set, SumPooling
label_desc: Use image node_id 2 for calling the GNN obj's underlying member method code with example usage: obj.forward(g, x, edge_attr) and returns: output, torch