code: def launch_job(cfg, init_method, func, daemon=False):
      """
      Run 'func' on one or more GPUs, specified in cfg
      Args:
          cfg (CfgNode): configs. Details can be found in
              slowfast/config/defaults.py
          init_method (str): initialization method to launch the job with multiple
              devices.
          func (function): job to run on GPU(s)
          daemon (bool): The spawned processes’ daemon flag. If set to True,
              daemonic processes will be created
      """
      if cfg.NUM_GPUS > 1:
          torch.multiprocessing.spawn(
              mpu.run,
              nprocs=cfg.NUM_GPUS,
              args=(
                  cfg.NUM_GPUS,
                  func,
                  init_method,
                  cfg.SHARD_ID,
                  cfg.NUM_SHARDS,
                  cfg.DIST_BACKEND,
                  cfg,
              ),
              daemon=daemon,
          )
      else:
          func(cfg=cfg)
  
imports: import json
  import logging
  import math
  import numpy
  import os
  from datetime import datetime
  import psutil
  import torch
  import torchvision.io
  from fvcore.nn.activation_count import activation_count
  from fvcore.nn.flop_count import flop_count
  from matplotlib import pyplot
  from torch import nn
  from torchvision.utils import make_grid
  import slowfast.utils.logging
  import slowfast.utils.multiprocessing
  from slowfast.datasets.utils import pack_pathway_output
  from slowfast.models.batchnorm_helper import SubBatchNorm3d
  from slowfast.utils.env import pathmgr
label_desc: Use image node_id 14 for calling a global function with example usage: launch_job(cfg, init_method, func, daemon) without return types