code: def infer_init_method(config):
      if config.distributed.init_method is not None:
          return
  
      registry.register(
          "is_xla", config.training.get("device", "cuda") == "xla"
      )
  
      # support torch.distributed.launch
      if all(
          key in os.environ
          for key in [
              "MASTER_ADDR",
              "MASTER_PORT",
              "WORLD_SIZE",
              "RANK",
          ]
      ):
          config.distributed.init_method = "env://"
          config.distributed.world_size = int(os.environ["WORLD_SIZE"])
          config.distributed.rank = int(os.environ["RANK"])
          config.distributed.no_spawn = True
  
      # we can determine the init method automatically for Slurm
      elif config.distributed.port > 0:
          node_list = os.environ.get("SLURM_STEP_NODELIST")
          if node_list is None:
              node_list = os.environ.get("SLURM_JOB_NODELIST")
          if node_list is not None:
              try:
                  hostnames = subprocess.check_output(
                      ["scontrol", "show", "hostnames", node_list]
                  )
                  config.distributed.init_method = (
                      "tcp://{host}:{port}".format(
                          host=hostnames.split()[0].decode("utf-8"),
                          port=config.distributed.port,
                      )
                  )
                  nnodes = int(os.environ.get("SLURM_NNODES"))
                  ntasks_per_node = os.environ.get(
                      "SLURM_NTASKS_PER_NODE"
                  )
                  if ntasks_per_node is not None:
                      ntasks_per_node = int(ntasks_per_node)
                  else:
                      ntasks = int(os.environ.get("SLURM_NTASKS"))
                      nnodes = int(os.environ.get("SLURM_NNODES"))
                      assert ntasks % nnodes == 0
                      ntasks_per_node = int(ntasks / nnodes)
                  if ntasks_per_node == 1:
                      assert config.distributed.world_size % nnodes == 0
                      gpus_per_node = (
                          config.distributed.world_size // nnodes
                      )
                      node_id = int(os.environ.get("SLURM_NODEID"))
                      config.distributed.rank = node_id * gpus_per_node
                  else:
                      assert (
                          ntasks_per_node
                          == config.distributed.world_size // nnodes
                      )
                      config.distributed.no_spawn = True
                      config.distributed.rank = int(
                          os.environ.get("SLURM_PROCID")
                      )
                      config.device_id = int(
                          os.environ.get("SLURM_LOCALID")
                      )
              except (
                  subprocess.CalledProcessError
              ) as e:  # scontrol failed
                  raise e
              except FileNotFoundError:  # Slurm is not installed
                  pass
  
imports: import contextlib
  import logging
  import os
  import pickle
  import socket
  import subprocess
  import warnings
  from itertools import chain
  import torch
  from mmf.common.registry import registry
  from torch import distributed
label_desc: Use image node_id 17 for calling a global function with example usage: infer_init_method(config) without return types