code: def train_torch_vanilla_worker(
      *,
      config: dict,
      rank: int,
      world_size: int,
      master_addr: str,
      master_port: int,
      use_gpu: bool = False,
      gpu_id: int = 0,
  ):
      # This function is kicked off by the main() function and runs the vanilla
      # training script on a single worker.
      backend = "nccl" if use_gpu else "gloo"
  
      os.environ["MASTER_ADDR"] = master_addr
      os.environ["MASTER_PORT"] = str(master_port)
      os.environ["NCCL_BLOCKING_WAIT"] = "1"
      distributed.init_process_group(
          backend=backend,
          rank=rank,
          world_size=world_size,
          init_method="env://",
      )
  
      config["use_gpu"] = use_gpu
      config["gpu_id"] = gpu_id
      train_func(use_ray=False, config=config)
  
      distributed.destroy_process_group()
  
imports: import json
  import os
  import time
  from pathlib import Path
  from typing import Dict, Tuple
  import click
  import numpy
  import torch
  from torch import nn, distributed
  from torch.utils.data import DataLoader, DistributedSampler
  from torch.utils.data.dataloader import default_collate
  from torchvision import datasets
  from torchvision.transforms import ToTensor
label_desc: Use image node_id 6 for calling a global function with example usage: train_torch_vanilla_worker() without return types