code: def main():
      if not torch.cuda.is_available():
          print("No GPU found!")
          return
  
      devices = [
          torch.device(f"cuda:{i}")
          for i in range(torch.cuda.device_count())
      ]
      world_size = len(devices)
  
      print(f"Training with {world_size} gpus.")
  
      # Load and preprocess dataset.
      dataset = gb.BuiltinDataset("ogbn-arxiv").load()
  
      # Thread limiting to avoid resource competition.
      os.environ["OMP_NUM_THREADS"] = str(
          mp.cpu_count() // 2 // world_size
      )
  
      mp.set_sharing_strategy("file_system")
      mp.spawn(
          run,
          args=(world_size, devices, dataset),
          nprocs=world_size,
          join=True,
      )
  
imports: 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 5 for calling a global function with example usage: main() without return types