diff --git a/tensorflow/python/autograph/operators/py_builtins.py b/tensorflow/python/autograph/operators/py_builtins.py
index ddf05f73f37..9f54cf4fe5d 100644
--- a/tensorflow/python/autograph/operators/py_builtins.py
+++ b/tensorflow/python/autograph/operators/py_builtins.py
@@ -37,7 +37,7 @@ from tensorflow.python.ops import list_ops
 from tensorflow.python.ops import math_ops
 
 
-UNDEFINED = object()
+UNSPECIFIED = object()
 
 
 def overload_of(f):
@@ -77,14 +77,14 @@ def _py_float(x):
   return float(x)
 
 
-def int_(x=0, base=UNDEFINED):
+def int_(x=0, base=UNSPECIFIED):
   if tensor_util.is_tensor(x):
     return _tf_int(x, base)
   return _py_int(x, base)
 
 
 def _tf_int(x, base):
-  if base not in (10, UNDEFINED):
+  if base not in (10, UNSPECIFIED):
     raise NotImplementedError('base {} not supported for int'.format(base))
 
   # TODO(mdan): We shouldn't assume int32.
@@ -94,7 +94,7 @@ def _tf_int(x, base):
 
 
 def _py_int(x, base):
-  if base is UNDEFINED:
+  if base is UNSPECIFIED:
     return int(x)
   return int(x, base)
 
@@ -167,7 +167,7 @@ def print_(*objects, **kwargs):
 
 def _tf_py_func_print(objects, kwargs):
   """Overload of print_ as a py_func implementation."""
-  override_kwargs = {k: v for k, v in kwargs.items() if v is not UNDEFINED}
+  override_kwargs = {k: v for k, v in kwargs.items() if v is not UNSPECIFIED}
   if 'flush' not in override_kwargs:
     # Defaulting to flushing the console in graph mode, which helps reduce
     # garbled output in IPython.
@@ -187,7 +187,7 @@ def _tf_py_func_print(objects, kwargs):
       print_wrapper, None, objects, use_dummy_return=True)
 
 
-def range_(start_or_stop, stop=UNDEFINED, step=UNDEFINED):
+def range_(start_or_stop, stop=UNSPECIFIED, step=UNSPECIFIED):
   if any(tensor_util.is_tensor(s) for s in (start_or_stop, stop, step)):
     return _tf_range(start_or_stop, stop, step)
   return _py_range(start_or_stop, stop, step)
@@ -200,10 +200,10 @@ def _tf_range(start_or_stop, stop, step):
   # graph construction error aligns the semantics with Python.
 
   # TODO(mdan): We should optimize this when a full tensor is not required.
-  if step is not UNDEFINED:
+  if step is not UNSPECIFIED:
     # TODO(mdan): Add argument coercion similar to other cases.
     return math_ops.range(start_or_stop, stop, step)
-  if stop is not UNDEFINED:
+  if stop is not UNSPECIFIED:
     stop = math_ops.maximum(start_or_stop, stop)
     return math_ops.range(start_or_stop, stop)
   start_or_stop = math_ops.maximum(start_or_stop, 0)
@@ -211,9 +211,9 @@ def _tf_range(start_or_stop, stop, step):
 
 
 def _py_range(start_or_stop, stop, step):
-  if step is not UNDEFINED:
+  if step is not UNSPECIFIED:
     return range(start_or_stop, stop, step)
-  if stop is not UNDEFINED:
+  if stop is not UNSPECIFIED:
     return range(start_or_stop, stop)
   return range(start_or_stop)