code: def train_tensorflow_regression(
      num_workers: int = 2, use_gpu: bool = False
  ) -> Result:
      dataset = ray.data.read_csv(
          "s3://anonymous@air-example-data/regression.csv"
      )
      preprocessor = Concatenator(
          exclude=["", "y"], output_column_name="x"
      )
      dataset = preprocessor.fit_transform(dataset)
  
      config = {"lr": 1e-3, "batch_size": 32, "epochs": 4}
      scaling_config = ScalingConfig(
          num_workers=num_workers, use_gpu=use_gpu
      )
      trainer = TensorflowTrainer(
          train_loop_per_worker=train_func,
          train_loop_config=config,
          scaling_config=scaling_config,
          datasets={"train": dataset},
      )
      results = trainer.fit()
      print(results.metrics)
      return results
  
file_path: ray/python/ray/train/examples/tf/tensorflow_regression_example.py
imports: import argparse
  import tensorflow
  import ray
  from ray import train
  from ray.air.integrations.keras import ReportCheckpointCallback
  from ray.data.preprocessors import Concatenator
  from ray.train import Result, ScalingConfig
  from ray.train.tensorflow import TensorflowTrainer
label_desc: Use image node_id 3 for calling a global function with example usage: train_tensorflow_regression(num_workers, use_gpu) and returns: results