message: Remove dependency on contrib dataset ops.
diff: @@ -20,8 +20,6 @@ from __future__ import print_function
  import os
  -from tensorflow.contrib.data.python.ops import batching
  -from tensorflow.contrib.data.python.ops import interleave_ops
  from tensorflow.contrib.distribute.python import input_ops
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.data.ops import readers
  @@ -126,20 +124,6 @@ class AutoShardDatasetTest(test.TestCase):
  # contain records in order of files.
  self._verifySimpleShardingOutput(dataset, self._record)
  - def testParallelInterleave(self):
  - dataset = dataset_ops.Dataset.from_tensor_slices(
  - self._createTFRecordFiles())
  - dataset = dataset.apply(interleave_ops.parallel_interleave(
  - readers.TFRecordDataset,
  - cycle_length=4,
  - block_length=self._num_records))
  - dataset = input_ops.auto_shard_dataset(
  - dataset, self._num_shards, self._shard_index)
  -
  - # Since block_length == num records in each file, the output will still
  - # contain records in order of files.
  - self._verifySimpleShardingOutput(dataset, self._record)
  -
  def testListfiles(self):
  filenames = self._createTFRecordFiles()
  file_pattern = filenames[0].rsplit("/", 1)[0] + "/tf_record.*.txt"
  @@ -171,8 +155,8 @@ class AutoShardDatasetTest(test.TestCase):
  dataset = dataset.prefetch(buffer_size=batch_size)
  dataset = dataset.shuffle(2 * self._num_files * self._num_records)
  dataset = dataset.repeat(num_epochs)
  - dataset = dataset.apply(batching.map_and_batch(
  - lambda x: x, batch_size=batch_size))
  + dataset = dataset.map(lambda x: x)
  + dataset = dataset.batch(batch_size)
  dataset = dataset.prefetch(buffer_size=None)
  # Auto shard.
  