code: def __init__(self, dataset, num_layers, hidden, mode="cat"):
      super().__init__()
      self.conv1 = GCNConv(dataset.num_features, hidden)
      self.convs = torch.nn.ModuleList()
      for i in range(num_layers - 1):
          self.convs.append(GCNConv(hidden, hidden))
      self.jump = JumpingKnowledge(mode)
      if mode == "cat":
          self.lin1 = Linear(num_layers * hidden, hidden)
      else:
          self.lin1 = Linear(hidden, hidden)
      self.lin2 = Linear(hidden, dataset.num_classes)
  
file_path: pytorch_geometric/benchmark/kernel/gcn.py
imports: import torch
  import torch.nn.functional
  from torch.nn import Linear
  from torch_geometric.nn import GCNConv, JumpingKnowledge, global_mean_pool
label_desc: Use image node_id 1 to create a new GCNWithJK object from inherited base classes: torch.nn with example: obj = GCNWithJK(dataset, num_layers, hidden, mode)