message: Update TFRecordWriter docstring.
diff: @@ -192,8 +192,64 @@ def tf_record_iterator(path, options=None):
  class TFRecordWriter(object):
  """A class to write records to a TFRecords file.
  + [TFRecords tutorial](https://www.tensorflow.org/tutorials/load_data/tfrecord)
  +
  + TFRecords is a binary format which is optimized for high throughput data
  + retrieval, generally in conjunction with `tf.data`. `TFRecordWriter` is used
  + to write serialized examples to a file for later consumption. The key steps
  + are:
  +
  + Ahead of time:
  +
  + - [Convert data into a serialized format](
  + https://www.tensorflow.org/tutorials/load_data/tfrecord#tfexample)
  + - [Write the serialized data to one or more files](
  + https://www.tensorflow.org/tutorials/load_data/tfrecord#tfrecord_files_in_python)
  +
  + During training or evaluation:
  +
  + - [Read serialized examples into memory](
  + https://www.tensorflow.org/tutorials/load_data/tfrecord#reading_a_tfrecord_file)
  + - [Parse (deserialize) examples](
  + https://www.tensorflow.org/tutorials/load_data/tfrecord#reading_a_tfrecord_file)
  +
  + A minimal example is given below:
  +
  + >>> import tempfile
  + >>> example_path = os.path.join(tempfile.gettempdir(), "example.tfrecords")
  + >>> np.random.seed(0)
  +
  + >>> # Write the records to a file.
  + ... with tf.io.TFRecordWriter(example_path) as file_writer:
  + ... for _ in range(4):
  + ... x, y = np.random.random(), np.random.random()
  + ...
  + ... record_bytes = tf.train.Example(features=tf.train.Features(feature={
  + ... "x": tf.train.Feature(float_list=tf.train.FloatList(value=[x])),
  + ... "y": tf.train.Feature(float_list=tf.train.FloatList(value=[y])),
  + ... })).SerializeToString()
  + ... file_writer.write(record_bytes)
  +
  + >>> # Read the data back out.
  + >>> def decode_fn(record_bytes):
  + ... return tf.io.parse_single_example(
  + ... # Data
  + ... record_bytes,
  + ...
  + ... # Schema
  + ... {"x": tf.io.FixedLenFeature([], dtype=tf.float32),
  + ... "y": tf.io.FixedLenFeature([], dtype=tf.float32)}
  + ... )
  +
  + >>> for batch in tf.data.TFRecordDataset([example_path]).map(decode_fn):
  + ... print("x = {x:.4f}, y = {y:.4f}".format(**batch))
  + x = 0.5488, y = 0.7152
  + x = 0.6028, y = 0.5449
  + x = 0.4237, y = 0.6459
  + x = 0.4376, y = 0.8918
  +
  This class implements `__enter__` and `__exit__`, and can be used
  - in `with` blocks like a normal file.
  + in `with` blocks like a normal file. (See the usage example above.)
  """
  # TODO(josh11b): Support appending?
  