code: def forward(self, data):
      x, edge_index, batch = (
          data.node_feature,
          data.edge_index,
          data.batch,
      )
      # print(batch)
  
      layers_out = []
      for i in range(self.num_layers):
          x = self.convs[i](x, edge_index)
          layers_out.append(pyg_nn.global_add_pool(x, batch))
          x = F.dropout(x, p=self.dropout, training=self.training)
          x = F.relu(x)
  
      layers_out = torch.cat(layers_out, dim=-1)
  
      x = self.post_mp(layers_out)
  
      return x
  
imports: from functools import reduce
  import random
  import networkx
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch_geometric.nn
  import torch_geometric.utils
  import utils
label_desc: Use image node_id 3 for calling the GNNStack obj's underlying member method code with example usage: obj.forward(data) and returns: x