diff --git a/tensorflow/python/eager/forwardprop_test.py b/tensorflow/python/eager/forwardprop_test.py
index addcd96d036..47c6f6b3225 100644
--- a/tensorflow/python/eager/forwardprop_test.py
+++ b/tensorflow/python/eager/forwardprop_test.py
@@ -352,20 +352,6 @@ class ForwardpropTest(test.TestCase, parameterized.TestCase):
     self.assertIsNone(acc.jvp(v))
     self.assertAllClose([[0.]], acc.jvp(v, unconnected_gradients="zero"))
 
-  @test_util.assert_no_new_pyobjects_executing_eagerly
-  def testFunctionReturnsResource(self):
-    v = variables.Variable([[1.]])
-    x = constant_op.constant(1.)
-    xt = constant_op.constant(2.)
-
-    @def_function.function
-    def f(a):
-      return a, v.handle
-
-    with forwardprop.ForwardAccumulator(x, xt) as acc:
-      y, _ = f(x)
-    self.assertAllClose(2., acc.jvp(y))
-
   @test_util.assert_no_new_pyobjects_executing_eagerly
   def testMultipleWatchesAdd(self):
     x = constant_op.constant(-2.)
diff --git a/tensorflow/python/eager/pywrap_tfe_src.cc b/tensorflow/python/eager/pywrap_tfe_src.cc
index 1562969dabe..ccf3bde7c14 100644
--- a/tensorflow/python/eager/pywrap_tfe_src.cc
+++ b/tensorflow/python/eager/pywrap_tfe_src.cc
@@ -2086,14 +2086,6 @@ bool ListContainsNone(PyObject* list) {
   return false;
 }
 
-// As an optimization, the tape generally keeps only the shape and dtype of
-// tensors, and uses this information to generate ones/zeros tensors. However,
-// some tensors require OnesLike/ZerosLike because their gradients do not match
-// their inference shape/dtype.
-bool DTypeNeedsHandleData(tensorflow::DataType dtype) {
-  return dtype == tensorflow::DT_VARIANT || dtype == tensorflow::DT_RESOURCE;
-}
-
 static PyTapeTensor TapeTensorFromTensor(PyObject* tensor) {
   if (EagerTensor_CheckExact(tensor)) {
     tensorflow::ImmediateExecutionTensorHandle* handle =
@@ -2101,7 +2093,7 @@ static PyTapeTensor TapeTensorFromTensor(PyObject* tensor) {
     tensorflow::int64 id = PyEagerTensor_ID(tensor);
     tensorflow::DataType dtype =
         static_cast<tensorflow::DataType>(handle->DataType());
-    if (DTypeNeedsHandleData(dtype)) {
+    if (dtype == tensorflow::DT_VARIANT) {
       return PyTapeTensor(id, dtype, tensor);
     }
 
@@ -2147,7 +2139,7 @@ static PyTapeTensor TapeTensorFromTensor(PyObject* tensor) {
                         tensorflow::TensorShape({}));
   }
 
-  if (ListContainsNone(shape_tuple.get()) || DTypeNeedsHandleData(dtype)) {
+  if (ListContainsNone(shape_tuple.get()) || dtype == tensorflow::DT_VARIANT) {
     return PyTapeTensor(id, dtype, tensor);
   }