code: def run(rank, world_size, args, devices, dataset):
      # Set up multiprocessing environment.
      device = devices[rank]
      torch.cuda.set_device(device)
      dist.init_process_group(
          backend="nccl",  # Use NCCL backend for distributed GPU training
          init_method="tcp://127.0.0.1:12345",
          world_size=world_size,
          rank=rank,
      )
  
      graph = dataset.graph
      features = dataset.feature
      train_set = dataset.tasks[0].train_set
      valid_set = dataset.tasks[0].validation_set
      test_set = dataset.tasks[0].test_set
      args.fanout = list(map(int, args.fanout.split(",")))
      num_classes = dataset.tasks[0].metadata["num_classes"]
  
      in_size = features.size("node", None, "feat")[0]
      hidden_size = 256
      out_size = num_classes
  
      # Create GraphSAGE model. It should be copied onto a GPU as a replica.
      model = SAGE(in_size, hidden_size, out_size).to(device)
      model = DDP(model)
  
      # Create data loaders.
      train_dataloader = create_dataloader(
          args,
          graph,
          features,
          train_set,
          device,
          drop_last=False,
          shuffle=True,
          drop_uneven_inputs=False,
      )
      valid_dataloader = create_dataloader(
          args,
          graph,
          features,
          valid_set,
          device,
          drop_last=False,
          shuffle=False,
          drop_uneven_inputs=False,
      )
      test_dataloader = create_dataloader(
          args,
          graph,
          features,
          test_set,
          device,
          drop_last=False,
          shuffle=False,
          drop_uneven_inputs=False,
      )
  
      # Model training.
      if rank == 0:
          print("Training...")
      train(
          world_size,
          rank,
          args,
          train_dataloader,
          valid_dataloader,
          num_classes,
          model,
          device,
      )
  
      # Test the model.
      if rank == 0:
          print("Testing...")
      test_acc = (
          evaluate(
              rank,
              model,
              test_dataloader,
              num_classes,
              device,
          )
          / world_size
      )
      dist.reduce(tensor=test_acc, dst=0)
      dist.barrier()
      if rank == 0:
          print(f"Test Accuracy {test_acc.item():.4f}")
  
imports: import argparse
  import os
  import time
  import dgl.graphbolt
  import dgl.nn
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.nn
  import torch.nn.functional
  import torchmetrics.functional
  import tqdm
  from torch.distributed.algorithms.join import Join
  from torch.nn.parallel import DistributedDataParallel
label_desc: Use image node_id 4 for calling a global function with example usage: run(rank, world_size, args, devices, dataset) without return types