code: def forward(self, x, adj, mask=None):
      x1 = F.relu(self.conv1(x, adj, mask))
      x2 = F.relu(self.conv2(x1, adj, mask))
      return self.lin(self.jump([x1, x2]))
  
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 Block obj's underlying member method code with example usage: obj.forward(x, adj, mask) and returns: self