code: def run(proc_id, nprocs, devices, g, data, mode):
      # find corresponding device for my rank
      device = devices[proc_id]
      torch.cuda.set_device(device)
      # initialize process group and unpack data for sub-processes
      dist.init_process_group(
          backend="nccl",
          init_method="tcp://127.0.0.1:12345",
          world_size=nprocs,
          rank=proc_id,
      )
      out_size, train_idx, val_idx, test_idx = data
      g = g.to(device if mode == "puregpu" else "cpu")
      # create GraphSAGE model (distributed)
      in_size = g.ndata["feat"].shape[1]
      model = SAGE(in_size, 16, 16).to(device)
      model = DistributedDataParallel(
          model, device_ids=[device], output_device=device
      )
      # training + testing
      use_uva = mode == "mixed"
      train(
          proc_id,
          nprocs,
          device,
          g,
          train_idx,
          val_idx,
          test_idx,
          model,
          use_uva,
      )
      # cleanup process group
      dist.destroy_process_group()
  
imports: import argparse
  import os
  import time
  import dgl.function
  import dgl.nn
  import numpy
  import sklearn.linear_model
  import sklearn.metrics
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.nn
  import torch.nn.functional
  import tqdm
  from dgl.data import AsNodePredDataset, RedditDataset
  from dgl.dataloading import as_edge_prediction_sampler, DataLoader, MultiLayerFullNeighborSampler, NeighborSampler
  from dgl.multiprocessing import shared_tensor
  from ogb.nodeproppred import DglNodePropPredDataset
  from torch.nn.parallel import DistributedDataParallel
label_desc: Use image node_id 4 for calling a global function with example usage: run(proc_id, nprocs, devices, g, data, mode) without return types