code: def __init__(self):
      super(TopKPool, self).__init__()
  
      self.conv1 = GraphConv(dataset.num_features, 128)
      self.pool1 = TopKPooling(128, ratio=0.8)
      self.conv2 = GraphConv(128, 128)
      self.pool2 = TopKPooling(128, ratio=0.8)
      self.conv3 = GraphConv(128, 128)
      self.pool3 = TopKPooling(128, ratio=0.8)
  
      self.lin1 = torch.nn.Linear(256, 128)
      self.lin2 = torch.nn.Linear(128, 64)
      self.lin3 = torch.nn.Linear(64, dataset.num_classes)
  
imports: import os
  import random
  import numpy
  from tqdm import tqdm
  import torch
  import torch.nn.functional
  from torch.nn import Sequential, Linear, ReLU
  import torch_geometric
  from torch_geometric.datasets import TUDataset
  from torch_geometric.nn import GINConv, global_add_pool, GraphConv, TopKPooling
  from torch_geometric.nn import global_mean_pool, global_max_pool
  import logging
label_desc: Use image node_id 1 to create a new TopKPool object from inherited base classes: torch.nn with example: obj = TopKPool()