code: def save_sample_images(
      dataset: tf.distribute.DistributedDataset,
      summary_writer: tf.summary.SummaryWriter,
      strategy: tf.distribute.Strategy,
  ) -> None:
      """Save some sample images from the dataset to a TensorBoard summary.
  
      Args:
        dataset: Dataset from which to retrieve images.
        summary_writer: Summary writer object to log information. This will save the
          images to the corresponding TensorBoard summary.
        strategy: Strategy used for distributed training and evaluation.
      """
      iterator = iter(dataset)
      logging.info("Saving sample images.")
      for i in range(_NUM_SAVE_IMG.value):
          items = next(iterator)
          items = strategy.experimental_local_results(items)[0]
          logging.info("Loaded data.")
  
          image_set = [
              items["image_{}".format(obf)][0, ...]
              for obf in _OBFUSCATIONS_TRAIN.value
          ]
  
          with summary_writer.as_default():
              tf.summary.image(
                  "Train Image {}".format(i), image_set, step=i
              )
          logging.info("Saved image %s.", i)
  
file_path: neural-structured-learning/research/meo/mlp_baseline/multiple_decoders.py
imports: import os
  from typing import Optional, Sequence
  from absl import app
  from absl import flags
  from absl import logging
  from meo.mlp_baseline import configs
  from meo.mlp_baseline import data_utils
  from meo.mlp_baseline import extended_model
  from meo.mlp_baseline import losses
  from meo.mlp_baseline import obfuscations
  import numpy
  import tensorflow
  import tensorflow_hub
label_desc: Use image node_id 2 for calling a global function with example usage: save_sample_images(dataset, summary_writer, strategy) without return types