code: def setup_distributed(
      world_size, rank, local_rank, backend="nccl", init_method="env://"
  ):
      """Perform env setup and initialization for distributed training"""
      if init_method == "env://":
          _set_env_vars(world_size, rank, local_rank)
      if world_size > 1 and "OMP_NUM_THREADS" not in os.environ:
          _LG.info("Setting OMP_NUM_THREADS == 1")
          os.environ["OMP_NUM_THREADS"] = "1"
      params = {
          "backend": backend,
          "init_method": init_method,
          "world_size": world_size,
          "rank": rank,
      }
      _LG.info("Initializing distributed process group with %s", params)
      dist.init_process_group(**params)
      _LG.info("Initialized distributed process group.")
  
file_path: audio/examples/source_separation/utils/dist_utils.py
imports: import csv
  import logging
  import os
  import types
  import torch
  import torch.distributed
label_desc: Use image node_id 3 for calling a global function with example usage: setup_distributed(world_size, rank, local_rank, backend, init_method) without return types