code: def train_tensorflow_mnist(
      num_workers: int = 2, use_gpu: bool = False, epochs: int = 4
  ) -> Result:
      train_dataset = get_dataset(split_type="train")
      config = {"lr": 1e-3, "batch_size": 64, "epochs": epochs}
      scaling_config = dict(num_workers=num_workers, use_gpu=use_gpu)
      trainer = TensorflowTrainer(
          train_loop_per_worker=train_func,
          train_loop_config=config,
          datasets={"train": train_dataset},
          scaling_config=scaling_config,
      )
  
      results = trainer.fit()
      print(results.metrics)
      return results
  
file_path: ray/python/ray/train/examples/tf/tensorflow_autoencoder_example.py
imports: import argparse
  import numpy
  import pandas
  import tensorflow
  import tensorflow_datasets
  import ray
  from ray import train
  from ray.air.integrations.keras import ReportCheckpointCallback
  from ray.data.datasource import SimpleTensorFlowDatasource
  from ray.data.extensions import TensorArray
  from ray.train import Result
  from ray.train.tensorflow import TensorflowTrainer, prepare_dataset_shard
label_desc: Use image node_id 4 for calling a global function with example usage: train_tensorflow_mnist(num_workers, use_gpu, epochs) and returns: results