code: def initialize_distributed(args, backend="nccl"):
      """Initialize torch.distributed."""
      # Get local rank in case it is provided.
      local_rank = args.local_rank
  
      # Get rank and world size.
      rank = int(os.getenv("RANK", "0"))
      world_size = int(os.getenv("WORLD_SIZE", "1"))
  
      logging.info(
          f"Initializing torch.distributed with local_rank: {local_rank}, rank: {rank}, world_size: {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,
      )
      return local_rank, rank, world_size
  
imports: import os
  import torch
  from nemo.utils import logging
label_desc: Use image node_id 1 for calling a global function with example usage: initialize_distributed(args, backend) and returns: local_rank,  rank,  world_size