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diff --git a/tensorflow/python/autograph/converters/decorators_test.py b/tensorflow/python/autograph/converters/decorators_test.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.
+# ==============================================================================
+"""Tests for decorators module."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+from functools import wraps
+import imp
+
+from tensorflow.python import autograph
+from tensorflow.python.autograph.converters import decorators
+from tensorflow.python.autograph.core import converter_testing
+from tensorflow.python.autograph.pyct import compiler
+from tensorflow.python.autograph.pyct import transformer
+from tensorflow.python.platform import test
+
+
+# The Python parser only briefly captures decorators into the AST.
+# The interpreter desugars them on load, and the decorated function loses any
+# trace of the decorator (which is normally what you would expect, since
+# they are meant to be transparent).
+# However, decorators are still visible when you analyze the function
+# from inside a decorator, before it was applied - as is the case
+# with our conversion decorators.
+
+
+def simple_decorator(f):
+  return lambda a: f(a) + 1
+
+
+def self_transform_decorator(transform):
+
+  def decorator(f):
+    @wraps(f)
+    def wrapper(*args):
+      # This removing wrapper is defined in the test below. This setup is so
+      # intricate in order to simulate how we use the transformer in practice.
+      transformed_f = transform(f, (self_transform_decorator,))
+      return transformed_f(*args) + 1
+    return wrapper
+  return decorator
+
+
+class DecoratorsTest(converter_testing.TestCase):
+
+  def _transform(self, f, autograph_decorators):
+    namespace = {
+        'self_transform_decorator': self_transform_decorator,
+        'simple_decorator': simple_decorator,
+        'converter_testing': converter_testing,
+    }
+    node, ctx = self.prepare(
+        f,
+        namespace,
+        recursive=False,
+        autograph_decorators=autograph_decorators)
+    node = decorators.transform(node, ctx)
+    import_line = '\n'.join(ctx.program.additional_imports)
+    result, _ = compiler.ast_to_object(node, source_prefix=import_line)
+    return getattr(result, f.__name__)
+
+  def test_noop(self):
+
+    def test_fn(a):
+      return a
+
+    with self.converted(test_fn, decorators, {}) as result:
+      self.assertEqual(1, result.test_fn(1))
+
+  def test_function(self):
+
+    @self_transform_decorator(self._transform)
+    def test_fn(a):
+      return a
+
+    # 2 = 1 (a) + 1 (decorator applied exactly once)
+    self.assertEqual(2, test_fn(1))
+
+  def test_method(self):
+
+    class TestClass(object):
+
+      @self_transform_decorator(self._transform)
+      def test_fn(self, a):
+        return a
+
+    # 2 = 1 (a) + 1 (decorator applied exactly once)
+    self.assertEqual(2, TestClass().test_fn(1))
+
+  def test_multiple_decorators(self):
+
+    class TestClass(object):
+
+      # Note that reversing the order of this two doesn't work.
+      @classmethod
+      @self_transform_decorator(self._transform)
+      def test_fn(cls, a):
+        return a
+
+    # 2 = 1 (a) + 1 (decorator applied exactly once)
+    self.assertEqual(2, TestClass.test_fn(1))
+
+  def test_nested_decorators_local(self):
+
+    @self_transform_decorator(self._transform)
+    def test_fn(a):
+      @simple_decorator
+      def inner_fn(b):
+        return b + 11
+      return inner_fn(a)
+
+    # Expected to fail because simple_decorator could not be imported.
+    with self.assertRaises(transformer.AutographParseError):
+      test_fn(1)
+
+  def test_nested_decorators_imported(self):
+
+    @self_transform_decorator(self._transform)
+    def test_fn(a):
+
+      @converter_testing.imported_decorator
+      def inner_fn(b):
+        return b + 11
+
+      return inner_fn(a)
+
+    # Work around TensorFlow's symbol suppression mechanism that causes core to
+    # be invisible in the generated code.
+    core_mod = imp.new_module('core')
+    core_mod.converter_testing = converter_testing
+    autograph.core = core_mod
+
+    # 14 = 1 (a) + 1 (simple_decorator) + 11 (inner_fn)
+    self.assertEqual(14, test_fn(1))
+
+
+if __name__ == '__main__':
+  test.main()