code: def forward(self, data):
      x, edge_index, batch = data.x, data.edge_index, data.batch
  
      x = F.relu(self.conv1(x, edge_index))
      x, edge_index, _, batch, _, _ = self.pool1(
          x, edge_index, None, batch
      )
      x1 = torch.cat([gmp(x, batch), gap(x, batch)], dim=1)
  
      x = F.relu(self.conv2(x, edge_index))
      x, edge_index, _, batch, _, _ = self.pool2(
          x, edge_index, None, batch
      )
      x2 = torch.cat([gmp(x, batch), gap(x, batch)], dim=1)
  
      x = F.relu(self.conv3(x, edge_index))
      x, edge_index, _, batch, _, _ = self.pool3(
          x, edge_index, None, batch
      )
      x3 = torch.cat([gmp(x, batch), gap(x, batch)], dim=1)
  
      x = x1 + x2 + x3
  
      x = F.relu(self.lin1(x))
      x = F.dropout(x, p=0.5, training=self.training)
      x = F.relu(self.lin2(x))
      x = F.log_softmax(self.lin3(x), dim=-1)
  
      return x
  
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 2 for calling the TopKPool obj's underlying member method code with example usage: obj.forward(data) and returns: x