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movie_name_list = tf.train.BytesList(value=[b'The Shawshank Redemption', b'Fight Club'])
movie_rating_list = tf.train.FloatList(value=[9.0, 9.7])

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movie_names = tf.train.Feature(bytes_list=movie_name_list)
movie_ratings = tf.train.Feature(float_list=movie_rating_list)

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movie_dict = {
  'Movie Names': movie_names,
  'Movie Ratings': movie_ratings
}
movies = tf.train.Features(feature=movie_dict)

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example = tf.train.Example(features=movies)

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movie_1_actors = tf.train.Feature(
  bytes_list=tf.train.BytesList(
    value=[b'Tim Robbins', b'Morgan Freeman']))
movie_2_actors = tf.train.Feature(
  bytes_list=tf.train.BytesList(
    value=[b'Brad Pitt', b'Edward Norton', b'Helena Bonham Carter']))
movie_actors_list = [movie_1_actors, movie_2_actors]       
movie_actors = tf.train.FeatureList(feature=movie_actors_list)

# Short form

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movies_dict = {
  'Movie Names': movie_names,
  'Movie Ratings': movie_ratings,
  'Movie Actors': movie_actors
}

movies = tf.train.FeatureLists(feature_list=movies_dict)

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# We can also add context features (short form)
customer = tf.train.Features(feature={
    'Age': tf.train.Feature(int64_list=tf.train.Int64List(value=[19])),
})

example = tf.train.SequenceExample(
    context=customer,
    feature_lists=movies)

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# "example" is of type tf.train.Example.
with tf.python_io.TFRecordWriter('movie_ratings.tfrecord') as writer:
  writer.write(example.SerializeToString())

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