code: def create_bottleneck_file(
      bottleneck_path,
      image_lists,
      label_name,
      index,
      image_dir,
      category,
      sess,
      jpeg_data_tensor,
      decoded_image_tensor,
      resized_input_tensor,
      bottleneck_tensor,
  ):
      """Create a single bottleneck file."""
      logging.debug("Creating bottleneck at %s", bottleneck_path)
      image_path = get_image_path(
          image_lists, label_name, index, image_dir, category
      )
      if not tf.gfile.Exists(image_path):
          logging.fatal("File does not exist %s", image_path)
      image_data = tf.gfile.GFile(image_path, "rb").read()
      try:
          bottleneck_values = run_bottleneck_on_image(
              sess,
              image_data,
              jpeg_data_tensor,
              decoded_image_tensor,
              resized_input_tensor,
              bottleneck_tensor,
          )
      except Exception as e:
          raise RuntimeError(
              "Error during processing file %s (%s)"
              % (image_path, str(e))
          )
      bottleneck_string = ",".join(str(x) for x in bottleneck_values)
      with tf.gfile.GFile(bottleneck_path, "w") as bottleneck_file:
          bottleneck_file.write(bottleneck_string)
  
imports: from absl import logging
  import argparse
  import collections
  from datetime import datetime
  import hashlib
  import os.path
  import random
  import re
  import sys
  import numpy
  import tensorflow
  import tensorflow_hub
  from tensorflow.contrib import quantize
label_desc: Use image node_id 7 for calling a global function with example usage: create_bottleneck_file(bottleneck_path, image_lists, label_name, index, image_dir, category, sess, jpeg_data_tensor, decoded_image_tensor, resized_input_tensor, bottleneck_tensor) without return types