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diff --git a/tensorflow/contrib/data/python/ops/sliding.py b/tensorflow/contrib/data/python/ops/sliding.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.
+# ==============================================================================
+"""Sliding dataset transformations."""
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.python.data.ops import dataset_ops
+from tensorflow.python.data.util import nest
+from tensorflow.python.data.util import sparse
+from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_shape
+from tensorflow.python.ops import gen_dataset_ops
+
+
+class _SlideDataset(dataset_ops.Dataset):
+  """A `Dataset` that passes a sliding window over its input."""
+
+  def __init__(self, input_dataset, window_size, stride=1):
+    """See `sliding_window_batch` for details."""
+    super(_SlideDataset, self).__init__()
+    self._input_dataset = input_dataset
+    self._window_size = ops.convert_to_tensor(
+        window_size, dtype=dtypes.int64, name="window_size")
+    self._stride = ops.convert_to_tensor(
+        stride, dtype=dtypes.int64, name="stride")
+
+  def _as_variant_tensor(self):
+    return gen_dataset_ops.slide_dataset(
+        self._input_dataset._as_variant_tensor(),  # pylint: disable=protected-access
+        window_size=self._window_size,
+        stride=self._stride,
+        output_shapes=nest.flatten(
+            sparse.as_dense_shapes(self.output_shapes, self.output_classes)),
+        output_types=nest.flatten(
+            sparse.as_dense_types(self.output_types, self.output_classes)))
+
+  @property
+  def output_classes(self):
+    return self._input_dataset.output_classes
+
+  @property
+  def output_shapes(self):
+    input_shapes = self._input_dataset.output_shapes
+    return nest.pack_sequence_as(input_shapes, [
+        tensor_shape.vector(None).concatenate(s)
+        for s in nest.flatten(self._input_dataset.output_shapes)
+    ])
+
+  @property
+  def output_types(self):
+    return self._input_dataset.output_types
+
+
+def sliding_window_batch(window_size, stride=1):
+  """A sliding window with size of `window_size` and step of `stride`.
+
+  This transformation passes a sliding window over this dataset. The
+  window size is `window_size` and step size is `stride`. If the left
+  elements cannot fill up the sliding window, this transformation will
+  drop the final smaller element. For example:
+
+  ```python
+  # NOTE: The following examples use `{ ... }` to represent the
+  # contents of a dataset.
+  a = { [1], [2], [3], [4], [5], [6] }
+
+  a.apply(tf.contrib.data.sliding_window_batch(window_size=3, stride=2)) ==
+  {
+      [[1], [2], [3]],
+      [[3], [4], [5]],
+  }
+  ```
+
+  Args:
+    window_size: A `tf.int64` scalar `tf.Tensor`, representing the number of
+      elements in the sliding window.
+    stride: (Optional.) A `tf.int64` scalar `tf.Tensor`, representing the
+      steps moving the sliding window forward for one iteration. The default
+      is `1`. It must be in `[1, window_size)`.
+
+  Returns:
+    A `Dataset` transformation function, which can be passed to
+    @{tf.data.Dataset.apply}.
+  """
+  def _apply_fn(dataset):
+    return _SlideDataset(dataset, window_size, stride)
+
+  return _apply_fn