code: def forward(self, data):
      x, edge_index, batch = data.x, data.edge_index, data.batch
      x = F.relu(self.conv1(x, edge_index))
      xs = [global_mean_pool(x, batch)]
      for i, conv in enumerate(self.convs):
          x = F.relu(conv(x, edge_index))
          xs += [global_mean_pool(x, batch)]
          if i % 2 == 0 and i < len(self.convs) - 1:
              cluster = graclus(edge_index, num_nodes=x.size(0))
              data = Batch(x=x, edge_index=edge_index, batch=batch)
              data = max_pool(cluster, data)
              x, edge_index, batch = data.x, data.edge_index, data.batch
      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/graclus.py
imports: import torch
  import torch.nn.functional
  from torch.nn import Linear
  from torch_geometric.data import Batch
  from torch_geometric.nn import GraphConv, JumpingKnowledge, global_mean_pool, graclus, max_pool
label_desc: Use image node_id 3 for calling the Graclus obj's underlying member method code with example usage: obj.forward(data) and returns: F