code: def _setup_tensorflow_environment(
      worker_addresses: List[str], index: int
  ):
      """Set up distributed Tensorflow training information.
  
      This function should be called on each worker.
  
      Args:
          worker_addresses: Addresses of all the workers.
          index: Index (i.e. world rank) of the current worker.
      """
      tf_config = {
          "cluster": {"worker": worker_addresses},
          "task": {"type": "worker", "index": index},
      }
      os.environ["TF_CONFIG"] = json.dumps(tf_config)
  
file_path: ray/python/ray/train/tensorflow/config.py
imports: import json
  import logging
  import os
  from dataclasses import dataclass
  from typing import List
  import ray
  from ray.train._internal.utils import get_address_and_port
  from ray.train._internal.worker_group import WorkerGroup
  from ray.train.backend import Backend, BackendConfig
  from ray.util import PublicAPI
label_desc: Use image node_id 1 for calling a global function with example usage: _setup_tensorflow_environment(worker_addresses, index) without return types