code: def train_tf_ray_air(
      *,
      config: dict,
      num_workers: int = 4,
      cpus_per_worker: int = 8,
      use_gpu: bool = False,
  ) -> Tuple[float, float, float]:
      # This function is kicked off by the main() function and runs a full training
      # run using Ray AIR.
      from ray.train.tensorflow import TensorflowTrainer
      from ray.train import ScalingConfig
  
      def train_loop(config):
          train_func(use_ray=True, config=config)
  
      start_time = time.monotonic()
      trainer = TensorflowTrainer(
          train_loop_per_worker=train_loop,
          train_loop_config=config,
          scaling_config=ScalingConfig(
              trainer_resources={"CPU": 0},
              num_workers=num_workers,
              resources_per_worker={"CPU": cpus_per_worker},
              use_gpu=use_gpu,
          ),
      )
      result = trainer.fit()
      time_taken = time.monotonic() - start_time
  
      print(f"Last result: {result.metrics}")
      return (
          time_taken,
          result.metrics["local_time_taken"],
          result.metrics["loss"],
      )
  
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 4 for calling a global function with example usage: train_tf_ray_air() and returns: time_taken,  result,  result