code: def __init__(self, dataset, num_layers, hidden):
      super().__init__()
      self.conv1 = GraphConv(dataset.num_features, hidden, aggr="mean")
      self.convs = torch.nn.ModuleList()
      for i in range(num_layers - 1):
          self.convs.append(GraphConv(hidden, hidden, aggr="mean"))
      self.jump = JumpingKnowledge(mode="cat")
      self.lin1 = Linear(num_layers * hidden, hidden)
      self.lin2 = Linear(hidden, dataset.num_classes)
  
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 1 to create a new Graclus object from inherited base classes: torch.nn with example: obj = Graclus(dataset, num_layers, hidden)