Creating and using TensorFlow TFRecords

tfrecord_utils.py

def list_to_tfrecord(list, tfrecord_filename):
  """
  Convert a list of (features, labels) to a TFRecord file.
  
  param list: a list of tuples with (feature, label)
  """
   with python_io.TFRecordWriter(tfrecord_filename) as writer:
      for feature, label in list:
        example = tf.train.Example()
        example.features.feature['x'].float_list.value.extend(features)
        example.features.feature['y'].int64_list.value.append(label)
        writer.write(example.SerializeToString())
        

def gen_input_fn(tfrecord, epochs=1, batch_size=16, buffer_size=64, feature_shape=(299 * 299,), label_shape=()):
  """
  Return an input_fn that uses TFRecords for use with TensorFlow's estimator API
  """
  def parse(example):
    features = {
        'x': tf.FixedLenFeature(feature_shape, tf.float32),
        'y': tf.FixedLenFeature(label_shape, tf.int64),
    }
    parsed_features = tf.parse_single_example(example_proto, features)
    return parsed_features['x'], parsed_features['y']

  def input_fn():
    dataset = tf.contrib.data.TFRecordDataset(['train_aug_12.tfrecords'])
    dataset = dataset.map(parse)
    if buffer_size > 0:
      dataset = dataset.shuffle(buffer_size)
    dataset = dataset.repeat(epochs)
    dataset = dataset.batch(batch_size)
    features, label = dataset.make_one_shot_iterator().get_next()
    return features, label
  return input_fn

There are some errors in your code like python_io,example_proto,...
Also, please add a readme file to understand how to use your python file