code: def run_function(
      local_function, tensor, dp, noise_multiplier=0, max_grad_norm=1e8
  ):
      if os.environ.get("SLURM_NTASKS") is not None:
          world_size = int(os.environ.get("SLURM_NTASKS"))
          rank = int(os.environ.get("SLURM_PROCID"))
          print(f"Running on a Slurm cluster with {world_size} tasks.")
  
          local_function(
              rank,
              world_size,
              tensor,
              dp,
              noise_multiplier,
              max_grad_norm,
          )
      else:
          world_size = torch.cuda.device_count()
          print(
              f"Spawning multiple processes on a local machine with {world_size} GPUs"
          )
  
          # The rank will be passed as the first argument
          mp.spawn(
              local_function,
              args=(
                  world_size,
                  tensor,
                  dp,
                  noise_multiplier,
                  max_grad_norm,
              ),
              nprocs=world_size,
              join=True,
          )
      return world_size
  
imports: import os
  import sys
  import unittest
  import torch
  import torch.distributed
  import torch.multiprocessing
  import torch.nn
  import torch.optim
  from opacus import PrivacyEngine
  from opacus.distributed import DifferentiallyPrivateDistributedDataParallel
  from torch.nn.parallel import DistributedDataParallel
label_desc: Use image node_id 7 for calling a global function with example usage: run_function(local_function, tensor, dp, noise_multiplier, max_grad_norm) and returns: world_size