code: def run(result_queue, proc_id, n_gpus, args, devices, data):
      dev_id = devices[proc_id]
      timing_records = []
      if n_gpus > 1:
          dist_init_method = "tcp://{master_ip}:{master_port}".format(
              master_ip="127.0.0.1", master_port="12345"
          )
          world_size = n_gpus
          th.distributed.init_process_group(
              backend="nccl",
              init_method=dist_init_method,
              world_size=world_size,
              rank=proc_id,
          )
      th.cuda.set_device(dev_id)
  
      n_classes, train_g, _, _ = data
  
      train_nfeat = train_g.ndata.pop("feat")
      train_labels = train_g.ndata.pop("label")
  
      train_nfeat = train_nfeat.to(dev_id)
      train_labels = train_labels.to(dev_id)
  
      in_feats = train_nfeat.shape[1]
  
      train_mask = train_g.ndata["train_mask"]
      train_nid = train_mask.nonzero().squeeze()
  
      # Split train_nid
      train_nid = th.split(
          train_nid, math.ceil(len(train_nid) / n_gpus)
      )[proc_id]
  
      # Create PyTorch DataLoader for constructing blocks
      sampler = dgl.dataloading.MultiLayerNeighborSampler(
          [int(fanout) for fanout in args.fan_out.split(",")]
      )
      dataloader = dgl.dataloading.DataLoader(
          train_g,
          train_nid,
          sampler,
          batch_size=args.batch_size,
          shuffle=True,
          drop_last=False,
          num_workers=args.num_workers,
      )
  
      # Define model and optimizer
      model = SAGE(
          in_feats,
          args.num_hidden,
          n_classes,
          args.num_layers,
          F.relu,
          args.dropout,
      )
      model = model.to(dev_id)
      if n_gpus > 1:
          model = DistributedDataParallel(
              model, device_ids=[dev_id], output_device=dev_id
          )
      loss_fcn = nn.CrossEntropyLoss()
      optimizer = optim.Adam(model.parameters(), lr=args.lr)
  
      # Training loop
      for step, (input_nodes, seeds, blocks) in enumerate(dataloader):
          if proc_id == 0:
              tic_step = time.time()
  
          batch_inputs, batch_labels = load_subtensor(
              train_nfeat, train_labels, seeds, input_nodes, dev_id
          )
          blocks = [block.int().to(dev_id) for block in blocks]
          batch_pred = model(blocks, batch_inputs)
          loss = loss_fcn(batch_pred, batch_labels)
          optimizer.zero_grad()
          loss.backward()
          optimizer.step()
  
          if proc_id == 0:
              timing_records.append(time.time() - tic_step)
  
          if step >= 50:
              break
  
      if n_gpus > 1:
          th.distributed.barrier()
      if proc_id == 0:
          result_queue.put(np.array(timing_records))
  
imports: import argparse
  import math
  import time
  from types import SimpleNamespace
  from typing import NamedTuple
  import dgl
  import dgl.nn.pytorch
  import numpy
  import torch
  import torch.multiprocessing
  import torch.nn
  import torch.nn.functional
  import torch.optim
  from torch.nn.parallel import DistributedDataParallel
  from ..None import utils
label_desc: Use image node_id 2 for calling a global function with example usage: run(result_queue, proc_id, n_gpus, args, devices, data) without return types