code: def single_gpu_training(cfg, args, rank, iterations, shared_results):
      is_cuda = torch.cuda.is_available()
      if is_cuda:
          torch.cuda.set_device(rank)
  
      model, loss_fn, optimizer = setup_model_loss_criterion(
          cfg, args, rank, is_cuda
      )
  
      for _ in range(iterations):
          input = torch.randn(1, args.input_size)
          target = torch.empty(
              args.batch_size, dtype=torch.long
          ).random_(args.nb_classes)
  
          if is_cuda:
              input = input.cuda()
              target = target.cuda()
          train_step(input, target, model, loss_fn, optimizer)
  
      results = []
      for param in model.parameters():
          if len(results) == 0:
              results = param.flatten().cpu().data
          else:
              results = torch.cat(
                  (results, param.flatten().cpu().data), 0
              )
  
      shared_results[rank] = results
  
imports: import argparse
  import functools
  import random
  import unittest
  from multiprocessing import Manager
  import torch
  import torch.nn
  from omegaconf import OmegaConf
  from fairseq import optim
  from fairseq.distributed import utils
label_desc: Use image node_id 3 for calling a global function with example usage: single_gpu_training(cfg, args, rank, iterations, shared_results) without return types