code: def forward(self, data):
      x, adj, mask = data.x, data.adj, data.mask
  
      s = self.pool_block1(x, adj, mask)
      x = F.relu(self.embed_block1(x, adj, mask))
      xs = [x.mean(dim=1)]
      x, adj, _, _ = dense_diff_pool(x, adj, s, mask)
  
      for i, (embed_block, pool_block) in enumerate(
          zip(self.embed_blocks, self.pool_blocks)
      ):
          s = pool_block(x, adj)
          x = F.relu(embed_block(x, adj))
          xs.append(x.mean(dim=1))
          if i < len(self.embed_blocks) - 1:
              x, adj, _, _ = dense_diff_pool(x, adj, s)
  
      x = self.jump(xs)
      x = F.relu(self.lin1(x))
      x = F.dropout(x, p=0.5, training=self.training)
      x = self.lin2(x)
      return F.log_softmax(x, dim=-1)
  
file_path: pytorch_geometric/benchmark/kernel/diff_pool.py
imports: from math import ceil
  import torch
  import torch.nn.functional
  from torch.nn import Linear
  from torch_geometric.nn import DenseSAGEConv, JumpingKnowledge, dense_diff_pool
label_desc: Use image node_id 3 for calling the DiffPool obj's underlying member method code with example usage: obj.forward(data) and returns: F