code: def __init__(self, input_dim, hidden_dim, output_dim):
      super().__init__()
      self.ginlayers = nn.ModuleList()
      self.batch_norms = nn.ModuleList()
      num_layers = 5
      # five-layer GCN with two-layer MLP aggregator and sum-neighbor-pooling scheme
      for layer in range(num_layers - 1):  # excluding the input layer
          if layer == 0:
              mlp = MLP(input_dim, hidden_dim, hidden_dim)
          else:
              mlp = MLP(hidden_dim, hidden_dim, hidden_dim)
          self.ginlayers.append(
              GINConv(mlp, learn_eps=False)
          )  # set to True if learning epsilon
          self.batch_norms.append(nn.BatchNorm1d(hidden_dim))
      # linear functions for graph sum poolings of output of each layer
      self.linear_prediction = nn.ModuleList()
      for layer in range(num_layers):
          if layer == 0:
              self.linear_prediction.append(
                  nn.Linear(input_dim, output_dim)
              )
          else:
              self.linear_prediction.append(
                  nn.Linear(hidden_dim, output_dim)
              )
      self.drop = nn.Dropout(0.5)
      self.pool = SumPooling()
  
imports: import argparse
  import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from dgl.data import GINDataset
  from dgl.dataloading import GraphDataLoader
  from dgl.nn.pytorch.conv import GINConv
  from dgl.nn.pytorch.glob import SumPooling
  from sklearn.model_selection import StratifiedKFold
  from torch.utils.data.sampler import SubsetRandomSampler
label_desc: Use image node_id 1 to create a new GIN object from inherited base classes: nn with example: obj = GIN(input_dim, hidden_dim, output_dim)