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diff --git a/tensorflow/contrib/data/python/kernel_tests/slide_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/slide_dataset_op_test.py
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+++ b/tensorflow/contrib/data/python/kernel_tests/slide_dataset_op_test.py
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+# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ==============================================================================
+"""Tests for the experimental input pipeline ops."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import numpy as np
+
+from tensorflow.contrib.data.python.ops import sliding
+from tensorflow.python.data.ops import dataset_ops
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import errors
+from tensorflow.python.framework import sparse_tensor
+from tensorflow.python.ops import array_ops
+from tensorflow.python.ops import math_ops
+from tensorflow.python.platform import test
+
+
+class SlideDatasetTest(test.TestCase):
+
+  def testSlideDataset(self):
+    """Test an dataset that maps a TF function across its input elements."""
+    components = (np.arange(7),
+                  np.array([[1, 2, 3]]) * np.arange(7)[:, np.newaxis],
+                  np.array(37.0) * np.arange(7))
+
+    count = array_ops.placeholder(dtypes.int64, shape=[])
+    window_size = array_ops.placeholder(dtypes.int64, shape=[])
+    stride = array_ops.placeholder(dtypes.int64, shape=[])
+
+    def _map_fn(x, y, z):
+      return math_ops.square(x), math_ops.square(y), math_ops.square(z)
+
+    # The pipeline is TensorSliceDataset -> MapDataset(square_3) ->
+    # RepeatDataset(count) -> _SlideDataset(window_size, stride).
+    iterator = (dataset_ops.Dataset.from_tensor_slices(components)
+                .map(_map_fn)
+                .repeat(count)
+                .apply(sliding.sliding_window_batch(window_size, stride))
+                .make_initializable_iterator())
+    init_op = iterator.initializer
+    get_next = iterator.get_next()
+
+    self.assertEqual([[None] + list(c.shape[1:]) for c in components],
+                     [t.shape.as_list() for t in get_next])
+
+    with self.test_session() as sess:
+      # Slide over a finite input, where the window_size divides the
+      # total number of elements.
+      sess.run(init_op, feed_dict={count: 20, window_size: 14, stride: 7})
+      # Same formula with convolution layer.
+      num_batches = (20 * 7 - 14) // 7 + 1
+      for i in range(num_batches):
+        result = sess.run(get_next)
+        for component, result_component in zip(components, result):
+          for j in range(14):
+            self.assertAllEqual(component[(i*7 + j) % 7]**2,
+                                result_component[j])
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(get_next)
+
+      # Slide over a finite input, where the window_size does not
+      # divide the total number of elements.
+      sess.run(init_op, feed_dict={count: 20, window_size: 17, stride: 9})
+
+      num_batches = (20 * 7 - 17) // 9 + 1
+      for i in range(num_batches):
+        result = sess.run(get_next)
+        for component, result_component in zip(components, result):
+          for j in range(17):
+            self.assertAllEqual(component[(i*9 + j) % 7]**2,
+                                result_component[j])
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(get_next)
+
+      # Slide over a finite input, which is less than window_size,
+      # should fail straight away.
+      sess.run(init_op, feed_dict={count: 1, window_size: 10, stride: 4})
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(get_next)
+
+      sess.run(init_op, feed_dict={count: 1, window_size: 10, stride: 8})
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(get_next)
+
+      # Slide over an empty input should fail straight away.
+      sess.run(init_op, feed_dict={count: 0, window_size: 8, stride: 4})
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(get_next)
+
+      # Empty window_size should be an initialization time error.
+      with self.assertRaises(errors.InvalidArgumentError):
+        sess.run(init_op, feed_dict={count: 14, window_size: 0, stride: 0})
+
+      # Invalid stride should be an initialization time error.
+      with self.assertRaises(errors.InvalidArgumentError):
+        sess.run(init_op, feed_dict={count: 14, window_size: 3, stride: 0})
+      with self.assertRaises(errors.InvalidArgumentError):
+        sess.run(init_op, feed_dict={count: 14, window_size: 3, stride: 3})
+      with self.assertRaises(errors.InvalidArgumentError):
+        sess.run(init_op, feed_dict={count: 14, window_size: 3, stride: 5})
+
+  def assertSparseValuesEqual(self, a, b):
+    self.assertAllEqual(a.indices, b.indices)
+    self.assertAllEqual(a.values, b.values)
+    self.assertAllEqual(a.dense_shape, b.dense_shape)
+
+  def testSlideSparse(self):
+
+    def _sparse(i):
+      return sparse_tensor.SparseTensorValue(
+          indices=[[0]], values=(i * [1]), dense_shape=[1])
+
+    iterator = dataset_ops.Dataset.range(10).map(_sparse).apply(
+        sliding.sliding_window_batch(5, 3)).make_initializable_iterator()
+    init_op = iterator.initializer
+    get_next = iterator.get_next()
+
+    with self.test_session() as sess:
+      sess.run(init_op)
+      num_batches = (10 - 5) // 3 + 1
+      for i in range(num_batches):
+        actual = sess.run(get_next)
+        expected = sparse_tensor.SparseTensorValue(
+            indices=[[0, 0], [1, 0], [2, 0], [3, 0], [4, 0]],
+            values=[i * 3, i * 3 + 1, i * 3 + 2, i * 3 + 3, i * 3 + 4],
+            dense_shape=[5, 1])
+        self.assertTrue(sparse_tensor.is_sparse(actual))
+        self.assertSparseValuesEqual(actual, expected)
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(get_next)
+
+  def testSlideSparseWithDifferentDenseShapes(self):
+
+    def _sparse(i):
+      return sparse_tensor.SparseTensorValue(
+          indices=array_ops.expand_dims(
+              math_ops.range(i, dtype=dtypes.int64), 1),
+          values=array_ops.fill([math_ops.to_int32(i)], i),
+          dense_shape=[i])
+
+    iterator = dataset_ops.Dataset.range(10).map(_sparse).apply(
+        sliding.sliding_window_batch(5, 3)).make_initializable_iterator()
+    init_op = iterator.initializer
+    get_next = iterator.get_next()
+
+    with self.test_session() as sess:
+      sess.run(init_op)
+      num_batches = (10 - 5) // 3 + 1
+      for i in range(num_batches):
+        actual = sess.run(get_next)
+        expected_indices = []
+        expected_values = []
+        for j in range(5):
+          for k in range(i * 3 + j):
+            expected_indices.append([j, k])
+            expected_values.append(i * 3 + j)
+        expected = sparse_tensor.SparseTensorValue(
+            indices=expected_indices,
+            values=expected_values,
+            dense_shape=[5, i * 3 + 5 - 1])
+        self.assertTrue(sparse_tensor.is_sparse(actual))
+        self.assertSparseValuesEqual(actual, expected)
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(get_next)
+
+  def testNestedSlideSparse(self):
+
+    def _sparse(i):
+      return sparse_tensor.SparseTensorValue(
+          indices=[[0]], values=(i * [1]), dense_shape=[1])
+
+    iterator = (dataset_ops.Dataset.range(10)
+                .map(_sparse)
+                .apply(sliding.sliding_window_batch(4, 2))
+                .apply(sliding.sliding_window_batch(3, 1))
+                .make_initializable_iterator())
+    init_op = iterator.initializer
+    get_next = iterator.get_next()
+
+    with self.test_session() as sess:
+      sess.run(init_op)
+      # Slide: 1st batch.
+      actual = sess.run(get_next)
+      expected = sparse_tensor.SparseTensorValue(
+          indices=[[0, 0, 0], [0, 1, 0], [0, 2, 0], [0, 3, 0],
+                   [1, 0, 0], [1, 1, 0], [1, 2, 0], [1, 3, 0],
+                   [2, 0, 0], [2, 1, 0], [2, 2, 0], [2, 3, 0]],
+          values=[0, 1, 2, 3, 2, 3, 4, 5, 4, 5, 6, 7],
+          dense_shape=[3, 4, 1])
+      self.assertTrue(sparse_tensor.is_sparse(actual))
+      self.assertSparseValuesEqual(actual, expected)
+      # Slide: 2nd batch.
+      actual = sess.run(get_next)
+      expected = sparse_tensor.SparseTensorValue(
+          indices=[[0, 0, 0], [0, 1, 0], [0, 2, 0], [0, 3, 0],
+                   [1, 0, 0], [1, 1, 0], [1, 2, 0], [1, 3, 0],
+                   [2, 0, 0], [2, 1, 0], [2, 2, 0], [2, 3, 0]],
+          values=[2, 3, 4, 5, 4, 5, 6, 7, 6, 7, 8, 9],
+          dense_shape=[3, 4, 1])
+      self.assertTrue(sparse_tensor.is_sparse(actual))
+      self.assertSparseValuesEqual(actual, expected)
+      with self.assertRaises(errors.OutOfRangeError):
+        sess.run(get_next)
+
+  def testSlideShapeError(self):
+
+    def generator():
+      yield [1.0, 2.0, 3.0]
+      yield [4.0, 5.0, 6.0]
+      yield [7.0, 8.0, 9.0, 10.0]
+
+    iterator = (dataset_ops.Dataset.from_generator(generator, dtypes.float32,
+                                                   output_shapes=[None])
+                .apply(sliding.sliding_window_batch(3, 1))
+                .make_initializable_iterator())
+    next_element = iterator.get_next()
+
+    with self.test_session() as sess:
+      sess.run(iterator.initializer)
+      with self.assertRaisesRegexp(
+          errors.InvalidArgumentError,
+          r"Cannot batch tensors with different shapes in component 0. "
+          r"First element had shape \[3\] and element 2 had shape \[4\]."):
+        sess.run(next_element)
+
+
+if __name__ == "__main__":
+  test.main()