code: def forward(self, x, edge_index, batch):
      x = self.conv1(x, edge_index).relu()
  
      x, mask = to_dense_batch(x, batch)
      adj = to_dense_adj(edge_index, batch)
  
      s = self.pool1(x)
      x, adj, mc1, o1 = dense_mincut_pool(x, adj, s, mask)
  
      x = self.conv2(x, adj).relu()
      s = self.pool2(x)
  
      x, adj, mc2, o2 = dense_mincut_pool(x, adj, s)
  
      x = self.conv3(x, adj)
  
      x = x.mean(dim=1)
      x = self.lin1(x).relu()
      x = self.lin2(x)
      return F.log_softmax(x, dim=-1), mc1 + mc2, o1 + o2
  
file_path: pytorch_geometric/examples/proteins_mincut_pool.py
imports: import os.path
  import time
  from math import ceil
  import torch
  import torch.nn.functional
  from torch.nn import Linear
  from torch_geometric.datasets import TUDataset
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import DenseGraphConv, GCNConv, dense_mincut_pool
  from torch_geometric.utils import to_dense_adj, to_dense_batch
label_desc: Use image node_id 2 for calling the Net obj's underlying member method code with example usage: obj.forward(x, edge_index, batch) and returns: F,  unknown,  unknown