code: def __init__(
      self, in_channels, hidden_channels, out_channels, mode="cat"
  ):
      super().__init__()
  
      self.conv1 = DenseSAGEConv(in_channels, hidden_channels)
      self.conv2 = DenseSAGEConv(hidden_channels, out_channels)
      self.jump = JumpingKnowledge(mode)
      if mode == "cat":
          self.lin = Linear(
              hidden_channels + out_channels, out_channels
          )
      else:
          self.lin = Linear(out_channels, out_channels)
  
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 1 to create a new Block object from inherited base classes: torch.nn with example: obj = Block(in_channels, hidden_channels, out_channels, mode)