code: def train_tf_vanilla_worker(
      *,
      config: dict,
      rank: int,
      world_size: int,
      worker_ip_port_list: List[str],
      use_gpu: bool = False,
  ):
      # This function is kicked off by the main() function and runs the vanilla
      # training script on a single worker.
      assert world_size == len(worker_ip_port_list)
  
      tf_config = {
          "cluster": {"worker": worker_ip_port_list},
          "task": {"type": "worker", "index": rank},
      }
      os.environ["TF_CONFIG"] = json.dumps(tf_config)
  
      train_func(use_ray=False, config=config)
  
file_path: ray/release/air_tests/air_benchmarks/workloads/tensorflow_benchmark.py
imports: import json
  import os
  import time
  from pathlib import Path
  import click
  import numpy
  import tensorflow
  from typing import List, Tuple
label_desc: Use image node_id 5 for calling a global function with example usage: train_tf_vanilla_worker() without return types