code: def get_distributed_launch_cmd(
      num_processes: int, trainer_path: str, config_path: str
  ):
      return f"""{sys.executable} -m torch.distributed.launch \
          --nnodes=1 \
          --nproc_per_node={num_processes} \
          --master_addr=localhost \
          --master_port=29500 \
          --use_env \
          {trainer_path} \
          --config={config_path} \
          --log_freq=100 \
          --distributed_backend=ddp
          """
  
file_path: ClassyVision/test/generic/config_utils.py
imports: import sys
  from classy_vision.tasks import build_task
label_desc: Use image node_id 8 for calling a global function with example usage: get_distributed_launch_cmd(num_processes, trainer_path, config_path) and returns: sys, str, num_processes, str, trainer_path, str, config_path, str