diff --git a/tensorflow/python/ops/parallel_for/control_flow_ops.py b/tensorflow/python/ops/parallel_for/control_flow_ops.py
index ccf2eb82146..ead7ae5478c 100644
--- a/tensorflow/python/ops/parallel_for/control_flow_ops.py
+++ b/tensorflow/python/ops/parallel_for/control_flow_ops.py
@@ -46,6 +46,7 @@ def for_loop(loop_fn, loop_fn_dtypes, iters):
   """
 
   flat_loop_fn_dtypes = nest.flatten(loop_fn_dtypes)
+  is_none_list = []
 
   def while_body(i, *ta_list):
     """Body of while loop."""
@@ -56,10 +57,13 @@ def for_loop(loop_fn, loop_fn_dtypes, iters):
           "actual outputs, %d, from loop_fn" % (len(flat_loop_fn_dtypes),
                                                 len(fn_output)))
     outputs = []
+    del is_none_list[:]
+    is_none_list.extend([x is None for x in fn_output])
     for out, ta in zip(fn_output, ta_list):
       # TODO(agarwal): support returning Operation objects from loop_fn.
-      assert isinstance(out, ops.Tensor)
-      outputs.append(ta.write(i, array_ops.expand_dims(out, 0)))
+      if out is not None:
+        ta = ta.write(i, array_ops.expand_dims(out, 0))
+      outputs.append(ta)
     return tuple([i + 1] + outputs)
 
   ta_list = control_flow_ops.while_loop(
@@ -69,7 +73,10 @@ def for_loop(loop_fn, loop_fn_dtypes, iters):
       ])[1:]
 
   # TODO(rachelim): enable this for sparse tensors
-  return nest.pack_sequence_as(loop_fn_dtypes, [ta.concat() for ta in ta_list])
+
+  output = [None if is_none else ta.concat()
+            for ta, is_none in zip(ta_list, is_none_list)]
+  return nest.pack_sequence_as(loop_fn_dtypes, output)
 
 
 def pfor(loop_fn, iters):
diff --git a/tensorflow/python/ops/parallel_for/gradients.py b/tensorflow/python/ops/parallel_for/gradients.py
index ee3d5c9b86e..460de0a97ff 100644
--- a/tensorflow/python/ops/parallel_for/gradients.py
+++ b/tensorflow/python/ops/parallel_for/gradients.py
@@ -61,9 +61,10 @@ def jacobian(output, inputs, use_pfor=True):
         loop_fn, [output.dtype] * len(flat_inputs), output_size)
 
   for i, out in enumerate(pfor_outputs):
-    new_shape = array_ops.concat(
-        [output_shape, array_ops.shape(out)[1:]], axis=0)
-    out = array_ops.reshape(out, new_shape)
+    if out is not None:
+      new_shape = array_ops.concat(
+          [output_shape, array_ops.shape(out)[1:]], axis=0)
+      out = array_ops.reshape(out, new_shape)
     pfor_outputs[i] = out
 
   return nest.pack_sequence_as(inputs, pfor_outputs)
@@ -119,6 +120,8 @@ def batch_jacobian(output, inp, use_pfor=True):
   else:
     pfor_output = control_flow_ops.for_loop(loop_fn, output.dtype,
                                             output_row_size)
+  if pfor_output is None:
+    return None
   pfor_output = array_ops.reshape(pfor_output,
                                   [output_row_size, batch_size, -1])
   output = array_ops.transpose(pfor_output, [1, 0, 2])
diff --git a/tensorflow/python/ops/parallel_for/gradients_test.py b/tensorflow/python/ops/parallel_for/gradients_test.py
index 3a6d9149ad8..f9cf16f6a46 100644
--- a/tensorflow/python/ops/parallel_for/gradients_test.py
+++ b/tensorflow/python/ops/parallel_for/gradients_test.py
@@ -333,6 +333,13 @@ class GradientsTest(test.TestCase):
     for i in range(n):
       self.assertAllClose(outputs[i], outputs[i + n], rtol=rtol, atol=atol)
 
+  def test_no_path(self):
+    for grad_func in [gradients.jacobian, gradients.batch_jacobian]:
+      for use_pfor in [True, False]:
+        x = constant_op.constant([[1.0]])
+        y = constant_op.constant([[2.0]])
+        self.assertIsNone(grad_func(y, x, use_pfor=use_pfor))
+
   def test_jacobian_fixed_shape(self):
     x = random_ops.random_uniform([2, 2])
     y = math_ops.matmul(x, x, transpose_a=True)
diff --git a/tensorflow/python/ops/parallel_for/pfor.py b/tensorflow/python/ops/parallel_for/pfor.py
index 2e4b2fd64eb..3c914f6ff6d 100644
--- a/tensorflow/python/ops/parallel_for/pfor.py
+++ b/tensorflow/python/ops/parallel_for/pfor.py
@@ -1070,6 +1070,8 @@ class PFor(object):
       If y does not need to be converted, it returns y as is. Else it returns
       the "converted value" corresponding to y.
     """
+    if y is None:
+      return None
     if isinstance(y, sparse_tensor.SparseTensor):
       return self._convert_sparse(y)
     output = self._convert_helper(y)