code: def forward(self, g, h):
      # list of hidden representation at each layer (including the input layer)
      hidden_rep = [h]
      for i, layer in enumerate(self.ginlayers):
          h = layer(g, h)
          h = self.batch_norms[i](h)
          h = F.relu(h)
          hidden_rep.append(h)
      score_over_layer = 0
      # perform graph sum pooling over all nodes in each layer
      for i, h in enumerate(hidden_rep):
          pooled_h = self.pool(g, h)
          score_over_layer += self.drop(
              self.linear_prediction[i](pooled_h)
          )
      return score_over_layer
  
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 2 for calling the GIN obj's underlying member method code with example usage: obj.forward(g, h) and returns: score_over_layer