code: def get_data():
      """Creates datasets."""
      datasets, _ = tfds.load(
          name="mnist", with_info=True, as_supervised=True
      )
      mnist_train, mnist_test = datasets["train"], datasets["test"]
  
      buffer_size = 10000
  
      batch_size_per_replica = 64
      num_replicas = mirrored_strategy.num_replicas_in_sync
      batch_size = batch_size_per_replica * num_replicas
  
      def scale(image, label):
          image = tf.cast(image, tf.float32)
          image /= 255
  
          return image, label
  
      train_dataset_ = mnist_train.map(scale).cache()
      train_dataset_ = train_dataset_.shuffle(buffer_size).batch(
          batch_size
      )
      eval_dataset_ = mnist_test.map(scale).batch(batch_size)
  
      return train_dataset_, eval_dataset_
  
file_path: cloud/src/python/tensorflow_cloud/core/tests/testdata/save_and_load.py
imports: from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  import argparse
  import tensorflow
  import tensorflow_datasets
label_desc: Use image node_id 1 for calling a global function with example usage: get_data() and returns: train_dataset_,  eval_dataset_, image,  label