code: def init_torch_distributed(backend):
      global dist
      import torch.distributed as dist
  
      torch.distributed.init_process_group(backend)
      local_rank = int(os.environ["LOCAL_RANK"])
      torch.cuda.set_device(local_rank)
  
file_path: H2O/h2o_flexgen/benchmark/third_party/DeepSpeed/benchmarks/communication/utils.py
imports: import torch
  import os
  import math
  import argparse
  from benchmarks.communication.constants import *
label_desc: Use image node_id 1 for calling a global function with example usage: init_torch_distributed(backend) without return types