code: def forward(self, x, edge_index, batch, batch_size):
      for conv in self.convs:
          x = conv(x, edge_index).relu()
      # Pass the batch size to avoid CPU communication/graph breaks:
      x = global_add_pool(x, batch, size=batch_size)
      return self.mlp(x)
  
file_path: pytorch_geometric/examples/compile/gin.py
imports: import os.path
  import time
  import torch
  import torch.nn.functional
  import torch_geometric
  from torch_geometric.datasets import TUDataset
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import MLP, GINConv, global_add_pool
label_desc: Use image node_id 2 for calling the GIN obj's underlying member method code with example usage: obj.forward(x, edge_index, batch, batch_size) and returns: self