diff --git a/tensorflow/python/ops/custom_gradient.py b/tensorflow/python/ops/custom_gradient.py
index f42e000b4b9..185f0d04782 100644
--- a/tensorflow/python/ops/custom_gradient.py
+++ b/tensorflow/python/ops/custom_gradient.py
@@ -372,6 +372,7 @@ def _graph_mode_decorator(f, args, kwargs):
 
   grad_argspec = tf_inspect.getfullargspec(grad_fn)
   variables_in_signature = ("variables" in grad_argspec.args or
+                            "variables" in grad_argspec.kwonlyargs or
                             grad_argspec.varkw)
   if variables and not variables_in_signature:
     raise TypeError(
@@ -440,6 +441,7 @@ def _eager_mode_decorator(f, args, kwargs):
   ]
   grad_argspec = tf_inspect.getfullargspec(grad_fn)
   if (variables and ("variables" not in grad_argspec.args) and
+      ("variables" not in grad_argspec.kwonlyargs) and
       not grad_argspec.varkw):
     raise TypeError(
         "@tf.custom_gradient grad_fn must accept keyword argument 'variables', "
diff --git a/tensorflow/python/ops/gradients_test.py b/tensorflow/python/ops/gradients_test.py
index 81140af6e79..78fbcdd6e6f 100644
--- a/tensorflow/python/ops/gradients_test.py
+++ b/tensorflow/python/ops/gradients_test.py
@@ -1293,6 +1293,32 @@ class CustomGradientTest(test_util.TensorFlowTestCase, parameterized.TestCase):
     with variable_scope.variable_scope("vs2", use_resource=False):
       FResource(x)
 
+  @parameterized.parameters(True, False)
+  def testCustomGradientVariablesKwonlyArgs(self, anonymous_varargs):
+    with context.eager_mode():
+      x_captured = variables.Variable(3.)  # Used by FuncMult
+      @custom_gradient.custom_gradient
+      def FuncMult(x):
+        def ActualGrad(dy, variables):  # pylint: disable=redefined-outer-name
+          self.assertLen(variables, 1)
+          self.assertIs(variables[0], x_captured)
+          x_captured_grad = 5. * x * dy
+          return (4. * x_captured * dy, [x_captured_grad])
+        # Define the returned GradMult, using varargs; "variables" is kwonlyarg
+        if anonymous_varargs:
+          def GradMult(dy, *, variables=None):  # pylint: disable=redefined-outer-name
+            return ActualGrad(dy, variables)
+        else:
+          def GradMult(*dys, variables=None):  # pylint: disable=redefined-outer-name
+            return ActualGrad(dys[0], variables)
+
+        return x * x_captured, GradMult
+
+      x = variables.Variable(6.)
+      with backprop.GradientTape(persistent=True) as g:
+        y = FuncMult(x)
+      self.assertAllEqual(g.gradient(y, x), 4. * 3.)
+
   def testWithNumpyInputs(self):
     with context.eager_mode():