code: def forward(self, x, adj, mask=None):
      s = self.gnn1_pool(x, adj, mask)
      x = self.gnn1_embed(x, adj, mask)
  
      x, adj, l1, e1 = dense_diff_pool(x, adj, s, mask)
  
      s = self.gnn2_pool(x, adj)
      x = self.gnn2_embed(x, adj)
  
      x, adj, l2, e2 = dense_diff_pool(x, adj, s)
  
      x = self.gnn3_embed(x, adj)
  
      x = x.mean(dim=1)
      x = self.lin1(x).relu()
      x = self.lin2(x)
      return F.log_softmax(x, dim=-1), l1 + l2, e1 + e2
  
file_path: pytorch_geometric/examples/proteins_diff_pool.py
imports: import os.path
  import time
  from math import ceil
  import torch
  import torch.nn.functional
  import torch_geometric.transforms
  from torch_geometric.datasets import TUDataset
  from torch_geometric.loader import DenseDataLoader
  from torch_geometric.nn import DenseSAGEConv, dense_diff_pool
label_desc: Use image node_id 2 for calling the Net obj's underlying member method code with example usage: obj.forward(x, adj, mask) and returns: F,  unknown,  unknown