message: [tf.contrib.data] Delete `Dataset.read_batch_features()`.
  Its functionality has been subsumed by the function
  `tf.contrib.data.read_batch_features()`.
diff: @@ -852,55 +852,6 @@ class Dataset(object):
  """
  return PrefetchDataset(self, buffer_size)
  - @staticmethod
  - def read_batch_features(file_pattern,
  - batch_size,
  - features,
  - reader,
  - reader_args=None,
  - randomize_input=True,
  - num_epochs=None,
  - capacity=10000):
  - """Reads batches of Examples.
  -
  - Args:
  - file_pattern: A string pattern or a placeholder with list of filenames.
  - batch_size: A `tf.int64` scalar `tf.Tensor`, representing the number of
  - consecutive elements of this dataset to combine in a single batch.
  - features: A `dict` mapping feature keys to `FixedLenFeature` or
  - `VarLenFeature` values. See `tf.parse_example`.
  - reader: A function or class that can be called with a `filenames` tensor
  - and (optional) `reader_args` and returns a `Dataset` of serialized
  - Examples.
  - reader_args: Additional arguments to pass to the reader class.
  - randomize_input: Whether the input should be randomized.
  - num_epochs: Integer specifying the number of times to read through the
  - dataset. If None, cycles through the dataset forever.
  - capacity: Capacity of the ShuffleDataset.
  -
  - Returns:
  - A `Dataset`.
  - """
  - if isinstance(file_pattern, str):
  - filenames = _get_file_names(file_pattern, randomize_input)
  - else:
  - filenames = file_pattern
  - if reader_args:
  - dataset = reader(filenames, *reader_args)
  - else:
  - dataset = reader(filenames)
  - dataset = dataset.repeat(num_epochs)
  - if dataset.output_types == (dtypes.string, dtypes.string):
  - dataset = dataset.map(lambda unused_k, v: v)
  - elif dataset.output_types != dtypes.string:
  - raise TypeError("`reader` must be a dataset of `tf.string` values, "
  - "or `(tf.string, tf.string)` key-value pairs.")
  - if randomize_input:
  - dataset = dataset.shuffle(capacity)
  - dataset = dataset.map(lambda x: _parse_example(nest.flatten(x), features))
  - dataset = dataset.batch(batch_size)
  - return dataset
  -
  @staticmethod
  def list_files(file_pattern):
  """A dataset of all files matching a pattern.
  