code: def forward(self, data):
      x, edge_index = data.x, data.edge_index
      x = self.embedding(x)
      if self.use_virtual_node:
          virtual_embed = self.virtual_init(
              torch.zeros(1).long().to(x.device)
          )
  
      for i, conv in enumerate(self.convs):
          if self.use_virtual_node:
              x = x + virtual_embed
              virtual_embed = (
                  torch.sum(x, dim=0, keepdim=True) + virtual_embed
              )
          x = conv(x, edge_index)
          if self.use_virtual_node:
              virtual_embed = self.virtual_mlps[i](virtual_embed)
  
      if self.use_virtual_node:
          return x, virtual_embed
      return x
  
file_path: DIG/dig/auggraph/method/GraphAug/aug/aug_encoder.py
imports: import torch
  from torch.nn import Linear, Sequential, ReLU, BatchNorm1d
  from torch_geometric.nn import GINConv
  from torch_scatter import scatter
  from ..constants import *
label_desc: Use image node_id 2 for calling the AugEncoder obj's underlying member method code with example usage: obj.forward(data) and returns: x, x,  virtual_embed