code: def __init__(self):
      super(GIN, self).__init__()
  
      num_features = dataset.num_features
      dim = 32
  
      nn1 = Sequential(
          Linear(num_features, dim), ReLU(), Linear(dim, dim)
      )
      self.conv1 = GINConv(nn1)
      self.bn1 = torch.nn.BatchNorm1d(dim)
  
      nn2 = Sequential(Linear(dim, dim), ReLU(), Linear(dim, dim))
      self.conv2 = GINConv(nn2)
      self.bn2 = torch.nn.BatchNorm1d(dim)
  
      nn3 = Sequential(Linear(dim, dim), ReLU(), Linear(dim, dim))
      self.conv3 = GINConv(nn3)
      self.bn3 = torch.nn.BatchNorm1d(dim)
  
      nn4 = Sequential(Linear(dim, dim), ReLU(), Linear(dim, dim))
      self.conv4 = GINConv(nn4)
      self.bn4 = torch.nn.BatchNorm1d(dim)
  
      nn5 = Sequential(Linear(dim, dim), ReLU(), Linear(dim, dim))
      self.conv5 = GINConv(nn5)
      self.bn5 = torch.nn.BatchNorm1d(dim)
  
      self.fc1 = Linear(dim, dim)
      self.fc2 = Linear(dim, 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 GIN object from inherited base classes: torch.nn with example: obj = GIN()