code: def main():
      parser = argparse.ArgumentParser(
          description="OGBN-Arxiv (Node2Vec)"
      )
      parser.add_argument("--device", type=int, default=0)
      parser.add_argument("--embedding_dim", type=int, default=128)
      parser.add_argument("--walk_length", type=int, default=80)
      parser.add_argument("--context_size", type=int, default=20)
      parser.add_argument("--walks_per_node", type=int, default=10)
      parser.add_argument("--batch_size", type=int, default=256)
      parser.add_argument("--lr", type=float, default=0.01)
      parser.add_argument("--epochs", type=int, default=5)
      parser.add_argument("--log_steps", type=int, default=1)
      args = parser.parse_args()
  
      device = (
          f"cuda:{args.device}" if torch.cuda.is_available() else "cpu"
      )
      device = torch.device(device)
  
      dataset = PygNodePropPredDataset(name="ogbn-arxiv")
      data = dataset[0]
      data.edge_index = to_undirected(data.edge_index, data.num_nodes)
  
      model = Node2Vec(
          data.edge_index,
          args.embedding_dim,
          args.walk_length,
          args.context_size,
          args.walks_per_node,
          sparse=True,
      ).to(device)
  
      loader = model.loader(
          batch_size=args.batch_size, shuffle=True, num_workers=4
      )
      optimizer = torch.optim.SparseAdam(
          list(model.parameters()), lr=args.lr
      )
  
      model.train()
      for epoch in range(1, args.epochs + 1):
          for i, (pos_rw, neg_rw) in enumerate(loader):
              optimizer.zero_grad()
              loss = model.loss(pos_rw.to(device), neg_rw.to(device))
              loss.backward()
              optimizer.step()
  
              if (i + 1) % args.log_steps == 0:
                  print(
                      f"Epoch: {epoch:02d}, Step: {i+1:03d}/{len(loader)}, "
                      f"Loss: {loss:.4f}"
                  )
  
              if (i + 1) % 100 == 0:  # Save model every 100 steps.
                  save_embedding(model)
          save_embedding(model)
  
imports: import argparse
  import torch
  from torch_geometric.nn import Node2Vec
  from torch_geometric.utils import to_undirected
  from ogb.nodeproppred import PygNodePropPredDataset
label_desc: Use image node_id 2 for calling a global function with example usage: main() without return types