code: def _process_image_files_batch(
      coder: ImageCoder,
      output_file: str,
      filenames: Iterable[str],
      synsets: Iterable[Union[str, bytes]],
      labels: Mapping[str, int],
  ):
      """
      Processes and saves a list of images as TFRecords.
  
      Args:
      coder: instance of ImageCoder to provide TensorFlow image coding utils.
      output_file: string, unique identifier specifying the data set.
      filenames: list of strings; each string is a path to an image file.
      synsets: list of strings; each string is a unique WordNet ID.
      labels: map of string to integer; id for all synset labels.
  
      """
      writer = tf.python_io.TFRecordWriter(output_file)
  
      for filename, synset in zip(filenames, synsets):
          image_buffer, height, width = _process_image(filename, coder)
          label = labels[synset]
          example = _convert_to_example(
              filename, image_buffer, label, synset, height, width
          )
          writer.write(example.SerializeToString())
  
      writer.close()
  
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 4 for calling a global function with example usage: _process_image_files_batch(coder, output_file, filenames, synsets, labels) without return types