code: def run(rank, world_size, dataset, quiver_feature, quiver_sampler):
      os.environ["MASTER_ADDR"] = "localhost"
      os.environ["MASTER_PORT"] = "12355"
      dist.init_process_group("nccl", rank=rank, world_size=world_size)
      torch.cuda.set_device(rank)
  
      data = dataset[0]
      train_idx = data.train_mask.nonzero(as_tuple=False).view(-1)
      train_idx = train_idx.split(train_idx.size(0) // world_size)[rank]
  
      train_loader = torch.utils.data.DataLoader(
          train_idx, batch_size=1024, shuffle=True, num_workers=0
      )
  
      if rank == 0:
          subgraph_loader = NeighborSampler(
              data.edge_index,
              node_idx=None,
              sizes=[-1],
              batch_size=2048,
              shuffle=False,
              num_workers=6,
          )
  
      torch.manual_seed(12345)
      model = SAGE(dataset.num_features, 256, dataset.num_classes).to(
          rank
      )
      model = DistributedDataParallel(model, device_ids=[rank])
      optimizer = torch.optim.Adam(model.parameters(), lr=0.01)
  
      y = data.y.to(rank)
  
      for epoch in range(1, 21):
          model.train()
  
          for seeds in train_loader:
              n_id, batch_size, adjs = quiver_sampler.sample(seeds)
              adjs = [adj.to(rank) for adj in adjs]
  
              optimizer.zero_grad()
              out = model(quiver_feature[n_id].to(rank), adjs)
              loss = F.nll_loss(out, y[n_id[:batch_size]])
              loss.backward()
              optimizer.step()
  
          dist.barrier()
  
          if rank == 0:
              print(f"Epoch: {epoch:03d}, Loss: {loss:.4f}")
  
          if (
              rank == 0 and epoch % 5 == 0
          ):  # We evaluate on a single GPU for now
              model.eval()
              with torch.no_grad():
                  out = model.module.inference(
                      quiver_feature, rank, subgraph_loader
                  )
              res = out.argmax(dim=-1) == data.y
              acc1 = int(res[data.train_mask].sum()) / int(
                  data.train_mask.sum()
              )
              acc2 = int(res[data.val_mask].sum()) / int(
                  data.val_mask.sum()
              )
              acc3 = int(res[data.test_mask].sum()) / int(
                  data.test_mask.sum()
              )
              print(
                  f"Train: {acc1:.4f}, Val: {acc2:.4f}, Test: {acc3:.4f}"
              )
  
          dist.barrier()
  
      dist.destroy_process_group()
  
file_path: pytorch_geometric/examples/quiver/multi_gpu_quiver.py
imports: import os
  import quiver
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.nn.functional
  from torch.nn.parallel import DistributedDataParallel
  from tqdm import tqdm
  from torch_geometric.datasets import Reddit
  from torch_geometric.loader import NeighborSampler
  from torch_geometric.nn import SAGEConv
label_desc: Use image node_id 1 for calling a global function with example usage: run(rank, world_size, dataset, quiver_feature, quiver_sampler) without return types