code: def _process_dataset(
      filenames: Iterable[str],
      synsets: Iterable[str],
      labels: Mapping[str, int],
      output_directory: str,
      prefix: str,
      num_shards: int,
  ) -> List[str]:
      """Processes and saves list of images as TFRecords.
  
      Args:
      filenames: iterable of strings; each string is a path to an image file.
      synsets: iterable of strings; each string is a unique WordNet ID.
      labels: map of string to integer; id for all synset labels.
      output_directory: path where output files should be created.
      prefix: string; prefix for each file.
      num_shards: number of chunks to split the filenames into.
  
      Returns:
      files: list of tf-record filepaths created from processing the dataset.
  
      """
      _check_or_create_dir(output_directory)
      chunksize = int(math.ceil(len(filenames) / num_shards))
      coder = ImageCoder()
  
      files = []
  
      for shard in range(num_shards):
          chunk_files = filenames[
              shard * chunksize : (shard + 1) * chunksize
          ]
          chunk_synsets = synsets[
              shard * chunksize : (shard + 1) * chunksize
          ]
          output_file = os.path.join(
              output_directory,
              "%s-%.5d-of-%.5d" % (prefix, shard, num_shards),
          )
          _process_image_files_batch(
              coder, output_file, chunk_files, chunk_synsets, labels
          )
          print("Finished writing file: %s", output_file)
          files.append(output_file)
      return files
  
imports: from datetime import datetime
  import math
  import os
  import random
  from typing import Iterable, List, Mapping, Union, Tuple
  import sys
  import threading
  import numpy
  import tensorflow
label_desc: Use image node_id 2 for calling a global function with example usage: _process_dataset(filenames, synsets, labels, output_directory, prefix, num_shards) and returns: files