code: def forward(self, x, edge_index, batch):
      for conv, batch_norm in zip(self.convs, self.batch_norms):
          x = F.relu(batch_norm(conv(x, edge_index)))
      x = global_add_pool(x, batch)
      x = F.relu(self.batch_norm1(self.lin1(x)))
      x = F.dropout(x, p=0.5, training=self.training)
      x = self.lin2(x)
      return F.log_softmax(x, dim=-1)
  
file_path: pytorch_geometric/examples/jit/gin.py
imports: import os.path
  import torch
  import torch.nn.functional
  from torch.nn import BatchNorm1d
  from torch.nn import Linear, ReLU, Sequential
  from torch_geometric.datasets import TUDataset
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import GINConv, global_add_pool
  from torch_geometric.transforms import OneHotDegree
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) and returns: F