code: def forward(self, x, edge_index, batch):
      x = self.lin(x)
      for conv in self.convs:
          x = conv(x, edge_index)
  
      x, S1 = self.mem1(x, batch)
      x = F.leaky_relu(x)
      x = F.dropout(x, p=self.dropout)
      x, S2 = self.mem2(x)
  
      return (
          F.log_softmax(x.squeeze(1), dim=-1),
          MemPooling.kl_loss(S1) + MemPooling.kl_loss(S2),
      )
  
file_path: pytorch_geometric/examples/mem_pool.py
imports: import os.path
  import time
  import torch
  import torch.nn.functional
  from torch.nn import BatchNorm1d, LeakyReLU, Linear
  from torch_geometric.datasets import TUDataset
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import DeepGCNLayer, GATConv, MemPooling
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