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diff --git a/tensorflow/contrib/signal/python/kernel_tests/test_util.py b/tensorflow/contrib/signal/python/kernel_tests/test_util.py
new file mode 100644
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+++ b/tensorflow/contrib/signal/python/kernel_tests/test_util.py
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+# Copyright 2017 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.
+# ==============================================================================
+"""Test utilities for tf.contrib.signal."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from tensorflow.core.protobuf import rewriter_config_pb2
+from tensorflow.python.grappler import tf_optimizer
+from tensorflow.python.training import saver
+
+
+def grappler_optimize(graph, fetches=None, rewriter_config=None):
+  """Tries to optimize the provided graph using grappler.
+
+  Args:
+    graph: A @{tf.Graph} instance containing the graph to optimize.
+    fetches: An optional list of `Tensor`s to fetch (i.e. not optimize away).
+      Grappler uses the 'train_op' collection to look for fetches, so if not
+      provided this collection should be non-empty.
+    rewriter_config: An optional @{tf.RewriterConfig} to use when rewriting the
+      graph.
+
+  Returns:
+    A @{tf.GraphDef} containing the rewritten graph.
+  """
+  if rewriter_config is None:
+    rewriter_config = rewriter_config_pb2.RewriterConfig()
+  if fetches is not None:
+    for fetch in fetches:
+      graph.add_to_collection('train_op', fetch)
+  metagraph = saver.export_meta_graph(graph_def=graph.as_graph_def())
+  return tf_optimizer.OptimizeGraph(rewriter_config, metagraph)