code: def get_num_nodes(prior=None, launcher: str = None) -> Optional[int]:
      """Get the number of nodes.
  
      Use for "multiprocessing distributed" mode.
      RANK equals to the Node id in this case and
      the real Rank is set as (nGPU * NodeID) + LOCAL_RANK in torch.distributed.
  
      """
      if prior is not None:
          return prior
      elif launcher == "slurm":
          if not is_in_slurm_step():
              raise RuntimeError(
                  "This process seems not to be launched by 'srun'"
              )
  
          # Assume ntasks_per_node == 1
          if (
              os.environ["SLURM_STEP_NUM_NODES"]
              != os.environ["SLURM_NTASKS"]
          ):
              raise RuntimeError(
                  "Run with --ntasks_per_node=1 if mutliprocessing_distributed=true"
              )
          return int(os.environ["SLURM_STEP_NUM_NODES"])
      elif launcher == "mpi":
          # Use mpi4py only for initialization and not using for communication
          from mpi4py import MPI
  
          comm = MPI.COMM_WORLD
          # Assume ntasks_per_node == 1 (We can't check whether it is or not)
          return comm.Get_size()
      elif launcher is not None:
          raise RuntimeError(f"launcher='{launcher}' is not supported")
      else:
          # prior is None -> NUM_NODES = 1
          return int(os.environ.get("WORLD_SIZE", 1))
  
imports: import dataclasses
  import os
  import socket
  from typing import Optional
  import torch
  import torch.distributed
label_desc: Use image node_id 12 for calling a global function with example usage: get_num_nodes(prior, launcher) and returns: prior, int, comm, int