code: def forward(self, x, edge_index, edge_attr):
      x = self.linear(x)
      edge_embedding = self.bond_encoder(edge_attr)
  
      row, col = edge_index
  
      # edge_weight = torch.ones((edge_index.size(1), ), device=edge_index.device)
      deg = degree(row, x.size(0), dtype=x.dtype) + 1
      deg_inv_sqrt = deg.pow(-0.5)
      deg_inv_sqrt[deg_inv_sqrt == float("inf")] = 0
  
      norm = deg_inv_sqrt[row] * deg_inv_sqrt[col]
  
      return self.propagate(
          edge_index, x=x, edge_attr=edge_embedding, norm=norm
      ) + F.relu(x + self.root_emb.weight) * 1.0 / deg.view(-1, 1)
  
imports: import torch
  from torch_geometric.nn import MessagePassing
  import torch.nn.functional
  from torch_geometric.nn import global_mean_pool, global_add_pool
  from ogb.graphproppred.mol_encoder import AtomEncoder, BondEncoder
  from torch_geometric.utils import degree
  import math
label_desc: Use image node_id 2 for calling the GCNConv obj's underlying member method code with example usage: obj.forward(x, edge_index, edge_attr) and returns: unknown