code: def train(
      proc_id,
      nprocs,
      device,
      args,
      g,
      num_classes,
      train_idx,
      val_idx,
      model,
      use_uva,
  ):
      # Instantiate a neighbor sampler
      sampler = NeighborSampler(
          [10, 10, 10],
          prefetch_node_feats=["feat"],
          prefetch_labels=["label"],
          fused=(args.mode != "benchmark"),
      )
      train_dataloader = DataLoader(
          g,
          train_idx,
          sampler,
          device=device,
          batch_size=1024,
          shuffle=True,
          drop_last=False,
          num_workers=args.num_workers,
          use_ddp=True,  # To split the set for each process
          use_uva=use_uva,
      )
      val_dataloader = DataLoader(
          g,
          val_idx,
          sampler,
          device=device,
          batch_size=1024,
          shuffle=True,
          drop_last=False,
          num_workers=args.num_workers,
          use_ddp=True,
          use_uva=use_uva,
      )
      opt = torch.optim.Adam(
          model.parameters(), lr=0.001, weight_decay=5e-4
      )
      for epoch in range(args.num_epochs):
          t0 = time.time()
          model.train()
          total_loss = 0
          for it, (input_nodes, output_nodes, blocks) in enumerate(
              train_dataloader
          ):
              x = blocks[0].srcdata["feat"]
              y = blocks[-1].dstdata["label"].to(torch.int64)
              y_hat = model(blocks, x)
              loss = F.cross_entropy(y_hat, y)
              opt.zero_grad()
              loss.backward()
              opt.step()  # Gradients are synchronized in DDP
              total_loss += loss
          #####################################################################
          # (HIGHLIGHT) Collect accuracy values from sub-processes and obtain
          # overall accuracy.
          #
          # `torch.distributed.reduce` is used to reduce tensors from all the
          # sub-processes to a specified process, ReduceOp.SUM is used by default.
          #
          # Other multiprocess functions supported by the backend are also
          # available. Please refer to
          # https://pytorch.org/docs/stable/distributed.html
          # for more information.
          #####################################################################
          acc = (
              evaluate(
                  device, model, g, num_classes, val_dataloader
              ).to(device)
              / nprocs
          )
          t1 = time.time()
          # Reduce `acc` tensors to process 0.
          dist.reduce(tensor=acc, dst=0)
          if proc_id == 0:
              print(
                  f"Epoch {epoch:05d} | Loss {total_loss / (it + 1):.4f} | "
                  f"Accuracy {acc.item():.4f} | Time {t1 - t0:.4f}"
              )
  
imports: import argparse
  import os
  import time
  import dgl
  import dgl.nn
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.nn
  import torch.nn.functional
  import torchmetrics.functional
  import tqdm
  from dgl.data import AsNodePredDataset
  from dgl.dataloading import 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 3 for calling a global function with example usage: train(proc_id, nprocs, device, args, g, num_classes, train_idx, val_idx, model, use_uva) without return types