message: Fix lint errors and improve docs in fully_connected_reader.py.
diff: -# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
  +# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  @@ -45,6 +45,7 @@ VALIDATION_FILE = 'validation.tfrecords'
  def decode(serialized_example):
  + """Parses an image and label from the given `serialized_example`."""
  features = tf.parse_single_example(
  serialized_example,
  # Defaults are not specified since both keys are required.
  @@ -66,6 +67,7 @@ def decode(serialized_example):
  def augment(image, label):
  + """Placeholder for data augmentation."""
  # OPTIONAL: Could reshape into a 28x28 image and apply distortions
  # here. Since we are not applying any distortions in this
  # example, and the next step expects the image to be flattened
  @@ -74,9 +76,8 @@ def augment(image, label):
  def normalize(image, label):
  - # Convert from [0, 255] -> [-0.5, 0.5] floats.
  + """Convert `image` from [0, 255] -> [-0.5, 0.5] floats."""
  image = tf.cast(image, tf.float32) * (1. / 255) - 0.5
  -
  return image, label
  @@ -106,18 +107,23 @@ def inputs(train, batch_size, num_epochs):
  if train else VALIDATION_FILE)
  with tf.name_scope('input'):
  - # TFRecordDataset opens a protobuf and reads entries line by line
  - # could also be [list, of, filenames]
  + # TFRecordDataset opens a binary file and reads one record at a time.
  + # `filename` could also be a list of filenames, which will be read in order.
  dataset = tf.data.TFRecordDataset(filename)
  - dataset = dataset.repeat(num_epochs)
  - # map takes a python function and applies it to every sample
  + # The map transformation takes a function and applies it to every element
  + # of the dataset.
  dataset = dataset.map(decode)
  dataset = dataset.map(augment)
  dataset = dataset.map(normalize)
  - #the parameter is the queue size
  + # The shuffle transformation uses a finite-sized buffer to shuffle elements
  + # in memory. The parameter is the number of elements in the buffer. For
  + # completely uniform shuffling, set the parameter to be the same as the
  + # number of elements in the dataset.
  dataset = dataset.shuffle(1000 + 3 * batch_size)
  +
  + dataset = dataset.repeat(num_epochs)
  dataset = dataset.batch(batch_size)
  iterator = dataset.make_one_shot_iterator()
  @@ -153,7 +159,7 @@ def run_training():
  sess.run(init_op)
  try:
  step = 0
  - while True: #train until OutOfRangeError
  + while True: # Train until OutOfRangeError
  start_time = time.time()
  # Run one step of the model. The return values are
  