class_docstr: Output:
      node representations
class_docstr_tok: ["Output",":","node","representations"]
code: def forward(self, batched_data):
      x, edge_index, edge_attr, batch = (
          batched_data.x,
          batched_data.edge_index,
          batched_data.edge_attr,
          batched_data.batch,
      )
  
      ### virtual node embeddings for graphs
      virtualnode_embedding = self.virtualnode_embedding(
          torch.zeros(batch[-1].item() + 1)
          .to(edge_index.dtype)
          .to(edge_index.device)
      )
  
      h_list = [self.atom_encoder(x)]
      for layer in range(self.num_layer):
          ### add message from virtual nodes to graph nodes
          h_list[layer] = h_list[layer] + virtualnode_embedding[batch]
  
          ### Message passing among graph nodes
          h = self.convs[layer](h_list[layer], edge_index, edge_attr)
  
          h = self.batch_norms[layer](h)
          if layer == self.num_layer - 1:
              # remove relu for the last layer
              h = F.dropout(h, self.drop_ratio, training=self.training)
          else:
              h = F.dropout(
                  F.relu(h), self.drop_ratio, training=self.training
              )
  
          if self.residual:
              h = h + h_list[layer]
  
          h_list.append(h)
  
          ### update the virtual nodes
          if layer < self.num_layer - 1:
              ### add message from graph nodes to virtual nodes
              virtualnode_embedding_temp = (
                  global_add_pool(h_list[layer], batch)
                  + virtualnode_embedding
              )
              ### transform virtual nodes using MLP
  
              if self.residual:
                  virtualnode_embedding = (
                      virtualnode_embedding
                      + F.dropout(
                          self.mlp_virtualnode_list[layer](
                              virtualnode_embedding_temp
                          ),
                          self.drop_ratio,
                          training=self.training,
                      )
                  )
              else:
                  virtualnode_embedding = F.dropout(
                      self.mlp_virtualnode_list[layer](
                          virtualnode_embedding_temp
                      ),
                      self.drop_ratio,
                      training=self.training,
                  )
  
      ### Different implementations of Jk-concat
      if self.JK == "last":
          node_representation = h_list[-1]
      elif self.JK == "sum":
          node_representation = 0
          for layer in range(self.num_layer + 1):
              node_representation += h_list[layer]
  
      return node_representation
  
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 GNN_node_Virtualnode obj's underlying member method code with example usage: obj.forward(batched_data) and returns: node_representation