code: def forward(self, data):
      out = F.relu(self.lin0(data.x))
      h = out.unsqueeze(0)
  
      for i in range(3):
          m = F.relu(self.conv(out, data.edge_index, data.edge_attr))
          out, h = self.gru(m.unsqueeze(0), h)
          out = out.squeeze(0)
  
      out = self.set2set(out, data.batch)
      out = F.relu(self.lin1(out))
      out = self.lin2(out)
      return out.view(-1)
  
file_path: pytorch_geometric/examples/qm9_nn_conv.py
imports: import copy
  import os.path
  import torch
  import torch.nn.functional
  from torch.nn import GRU, Linear, ReLU, Sequential
  import torch_geometric.transforms
  from torch_geometric.datasets import QM9
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import NNConv, Set2Set
  from torch_geometric.utils import remove_self_loops
label_desc: Use image node_id 2 for calling the Net obj's underlying member method code with example usage: obj.forward(data) and returns: out