old_contents: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  from __future__ import print_function
  import tensorflow as tf
  import numpy as np
  
  FLAGS = tf.app.flags.FLAGS
  
  tf.app.flags.DEFINE_string(
      "train_dir", "logo_images/cropped",
      "Directory where to write event logs and checkpoint.")
  tf.app.flags.DEFINE_integer("max_steps", 10000, "Number of batches to run.")
  tf.app.flags.DEFINE_integer("image_size", 64, "Size of an input image.")
  
  
  def inference():
      pass
  
  
  def train():
      with tf.Graph().as_default():
          logits = inference()
  
  
  def main():
      if not tf.gfile.Exists(FLAGS.train_dir):
          print("Not found: %s" % (FLAGS.train_dir))
      train()
  
  
  if __name__ == '__main__':
      main()
  
new_contents: #!/usr/bin/env python
  # -*- coding: utf-8 -*-
  from __future__ import print_function
  import tensorflow as tf
  import numpy as np
  
  FLAGS = tf.app.flags.FLAGS
  
  tf.app.flags.DEFINE_string(
      "train_dir", "logo_images/cropped",
      "Directory where to write event logs and checkpoint.")
  tf.app.flags.DEFINE_integer("max_steps", 10000, "Number of batches to run.")
  tf.app.flags.DEFINE_integer("image_size", 64, "Size of an input image.")
  
  
  def read_flickrlogos27(train_dir):
      class FlickrLogos27Record():
          pass
  
      result = FlickrLogos27Record()
  
      label_bytes = 1
      result.width = 32
      result.height = 32
      result.depth = 3
      image_bytes = result.width * result.height * result.depth
      record_bytes = label_bytes + image_bytes
  
  
  
  def inference():
      pass
  
  
  def train():
      with tf.Graph().as_default():
          logits = inference()
  
  
  def main():
      if not tf.gfile.Exists(FLAGS.train_dir):
          print("Not found: %s" % (FLAGS.train_dir))
      read_flickrlogos27(FLAGS.train_dir)
      train()
  
  
  if __name__ == '__main__':
      main()
  
subject: Add read_FlickrLogos27 method