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 = self.bn1(x)
      x = F.relu(self.conv2(x, edge_index))
      x = self.bn2(x)
      x = F.relu(self.conv3(x, edge_index))
      x = self.bn3(x)
      x = F.relu(self.conv4(x, edge_index))
      x = self.bn4(x)
      x = F.relu(self.conv5(x, edge_index))
      x = self.bn5(x)
      x = global_add_pool(x, batch)
      x = F.relu(self.fc1(x))
      x = F.dropout(x, p=0.5, training=self.training)
      x = self.fc2(x)
      return F.log_softmax(x, dim=-1)
  
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 GIN obj's underlying member method code with example usage: obj.forward(data) and returns: F