code: def initialize_distributed(backend="nccl"):
      """Initialize torch.distributed."""
      # Get local rank in case it is provided.
      # parser = argparse.ArgumentParser()
      # parser.add_argument('--local_rank', type=int, default=None,
      #                    help='local rank passed from distributed launcher')
      # args = parser.parse_args()
      if backend not in ("nccl", "ucc"):
          raise RuntimeError(
              f"Currently only nccl & ucc are supported but {backend}"
          )
      if backend == "ucc":
          if not HAS_UCC:
              raise ImportError(
                  "UCC backend requires pytorch source build with UCC installed and enabled"
              )
      args = global_vars.get_args()
      local_rank = args.local_rank
  
      # Get rank and world size.
      rank = int(os.getenv("RANK", "0"))
      world_size = int(os.getenv("WORLD_SIZE", "1"))
  
      print(
          "> initializing torch.distributed with local rank: {}, "
          "rank: {}, world size: {}".format(
              local_rank, rank, world_size
          )
      )
  
      # Set the device id.
      device = rank % torch.cuda.device_count()
      if local_rank is not None:
          device = local_rank
      torch.cuda.set_device(device)
  
      # Call the init process.
      init_method = "tcp://"
      master_ip = os.getenv("MASTER_ADDR", "localhost")
      master_port = os.getenv("MASTER_PORT", "6000")
      init_method += master_ip + ":" + master_port
      torch.distributed.init_process_group(
          backend=backend,
          world_size=world_size,
          rank=rank,
          init_method=init_method,
          timeout=datetime.timedelta(seconds=60),
      )
  
imports: from dataclasses import dataclass
  import datetime
  import os
  import random
  from typing import Optional, Union, List, Tuple, Callable, Dict
  import numpy
  import torch
  import torch.nn
  from apex import transformer
  from apex.transformer.tensor_parallel import ColumnParallelLinear, RowParallelLinear, scatter_to_sequence_parallel_region
  from apex.transformer.pipeline_parallel.utils import average_losses_across_data_parallel_group
  from apex.transformer.pipeline_parallel.schedules.common import Batch
  from apex.transformer.testing import global_vars
  from apex.transformer._ucc_util import HAS_UCC
label_desc: Use image node_id 6 for calling a global function with example usage: initialize_distributed(backend) without return types