code: def run(rank, world_size: int, dataset_name: str, root: str):
      os.environ["MASTER_ADDR"] = "localhost"
      os.environ["MASTER_PORT"] = "12355"
      dist.init_process_group("nccl", rank=rank, world_size=world_size)
  
      dataset = Dataset(
          dataset_name,
          root,
          pre_transform=T.ToSparseTensor(attr="edge_attr"),
      )
      split_idx = dataset.get_idx_split()
      evaluator = Evaluator(dataset_name)
  
      train_dataset = dataset[split_idx["train"]]
      train_sampler = DistributedSampler(
          train_dataset, num_replicas=world_size, rank=rank
      )
      train_loader = DataLoader(
          train_dataset, batch_size=128, sampler=train_sampler
      )
  
      torch.manual_seed(12345)
      model = GIN(128, dataset.num_tasks, num_layers=3, dropout=0.5).to(
          rank
      )
      model = DistributedDataParallel(model, device_ids=[rank])
      optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
      criterion = torch.nn.BCEWithLogitsLoss()
  
      if rank == 0:
          val_loader = DataLoader(
              dataset[split_idx["valid"]], batch_size=256
          )
          test_loader = DataLoader(
              dataset[split_idx["test"]], batch_size=256
          )
  
      for epoch in range(1, 51):
          model.train()
  
          total_loss = torch.zeros(2).to(rank)
          for data in train_loader:
              data = data.to(rank)
              optimizer.zero_grad()
              logits = model(data.x, data.adj_t, data.batch)
              loss = criterion(logits, data.y.to(torch.float))
              loss.backward()
              optimizer.step()
              total_loss[0] += float(loss) * logits.size(0)
              total_loss[1] += data.num_graphs
  
          dist.all_reduce(total_loss, op=dist.ReduceOp.SUM)
          loss = float(total_loss[0] / total_loss[1])
  
          if rank == 0:  # We evaluate on a single GPU for now.
              model.eval()
  
              y_pred, y_true = [], []
              for data in val_loader:
                  data = data.to(rank)
                  with torch.no_grad():
                      y_pred.append(
                          model.module(data.x, data.adj_t, data.batch)
                      )
                      y_true.append(data.y)
              val_rocauc = evaluator.eval(
                  {
                      "y_pred": torch.cat(y_pred, dim=0),
                      "y_true": torch.cat(y_true, dim=0),
                  }
              )["rocauc"]
  
              y_pred, y_true = [], []
              for data in test_loader:
                  data = data.to(rank)
                  with torch.no_grad():
                      y_pred.append(
                          model.module(data.x, data.adj_t, data.batch)
                      )
                      y_true.append(data.y)
              test_rocauc = evaluator.eval(
                  {
                      "y_pred": torch.cat(y_pred, dim=0),
                      "y_true": torch.cat(y_true, dim=0),
                  }
              )["rocauc"]
  
              print(
                  f"Epoch: {epoch:03d}, Loss: {loss:.4f}, "
                  f"Val: {val_rocauc:.4f}, Test: {test_rocauc:.4f}"
              )
  
          dist.barrier()
  
      dist.destroy_process_group()
  
imports: import os
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.nn.functional
  from ogb.graphproppred import Evaluator
  from ogb.graphproppred import PygGraphPropPredDataset
  from ogb.graphproppred.mol_encoder import AtomEncoder, BondEncoder
  from torch.nn import BatchNorm1d
  from torch.nn import Linear, ReLU, Sequential
  from torch.nn.parallel import DistributedDataParallel
  from torch.utils.data.distributed import DistributedSampler
  import torch_geometric.transforms
  from torch_geometric.loader import DataLoader
  from torch_geometric.nn import GINEConv, global_mean_pool
  from torch_geometric.typing import WITH_TORCH_SPARSE
label_desc: Use image node_id 1 for calling a global function with example usage: run(rank, world_size, dataset_name, root) without return types