code: def _process_image_files_batch(
      coder: ImageCoder,
      output_file: str,
      filenames: List[str],
      synsets: List[str],
      labels: Mapping[str, int],
  ) -> None:
      """
      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.
  
      """
      import tensorflow as tf
  
      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: import math
  import os
  import random
  from typing import TYPE_CHECKING, Iterable, List, Mapping, Union, Tuple
  from bigdl.dllib.utils.log4Error import invalidInputError
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