diff --git a/tensorflow/python/ops/gradient_checker_v2.py b/tensorflow/python/ops/gradient_checker_v2.py
index 4edb5adb113..633b5e57d95 100644
--- a/tensorflow/python/ops/gradient_checker_v2.py
+++ b/tensorflow/python/ops/gradient_checker_v2.py
@@ -174,7 +174,6 @@ def _compute_theoretical_jacobian(f, y_shape, y_dtype, xs, param):
     dy_data_flat[row] = 1
     grad = _to_numpy(grad_fn(dy_data, *xs)[0])
     grad = _eval_indexed_slices(grad)
-    dy_data_flat[row] = 0
     if isinstance(grad, ops.IndexedSlicesValue):
       for i, v in zip(grad.indices, grad.values):
         c_begin = i * x_val_size
@@ -182,6 +181,9 @@ def _compute_theoretical_jacobian(f, y_shape, y_dtype, xs, param):
         jacobian[row, c_begin:c_end] += v.flat
     elif grad is not None:
       jacobian[row, :] = grad.ravel().view(jacobian.dtype)
+    # This reset of `dy_data_flat` needs to happen after `grad` is copied to
+    # `jacobian` because `grad` and `dy_data_flat` may share memory.
+    dy_data_flat[row] = 0
 
   # If the output is empty, run the gradients at least once and make sure
   # they produce zeros.
diff --git a/tensorflow/python/ops/gradient_checker_v2_test.py b/tensorflow/python/ops/gradient_checker_v2_test.py
index 96a942d8a90..b77c95d8968 100644
--- a/tensorflow/python/ops/gradient_checker_v2_test.py
+++ b/tensorflow/python/ops/gradient_checker_v2_test.py
@@ -23,6 +23,7 @@ import numpy as np
 from tensorflow.python.eager import backprop
 from tensorflow.python.framework import constant_op
 from tensorflow.python.framework import dtypes
+from tensorflow.python.framework import sparse_tensor
 from tensorflow.python.framework import test_util
 from tensorflow.python.ops import array_ops
 from tensorflow.python.ops import custom_gradient
@@ -30,6 +31,7 @@ from tensorflow.python.ops import \
 gradient_checker_v2 as gradient_checker
 from tensorflow.python.ops import math_ops
 from tensorflow.python.ops import nn_ops
+from tensorflow.python.ops import sparse_ops
 # needs this to register gradient for SoftmaxCrossEntropyWithLogits:
 import tensorflow.python.ops.nn_grad  # pylint: disable=unused-import
 from tensorflow.python.platform import test
@@ -46,6 +48,20 @@ def _random_complex(shape, dtype):
 @test_util.run_all_in_graph_and_eager_modes
 class GradientCheckerTest(test.TestCase):
 
+  def testSparseTensorReshape(self):
+    x = constant_op.constant(2.0, shape=(2,))
+
+    def sparse_tensor_reshape(values):
+      sparse = sparse_tensor.SparseTensor(
+          indices=[[0, 0], [1, 2]], values=values, dense_shape=[3, 4])
+      sparse = sparse_ops.sparse_reshape(sparse, shape=(12,))
+      return sparse.values
+
+    error = gradient_checker.max_error(
+        *gradient_checker.compute_gradient(sparse_tensor_reshape, [x]))
+
+    self.assertLess(error, 1e-4)
+
   def testWithStaticShape(self):
     size = (2, 3)
     constant = constant_op.constant(2.0, shape=size, name="const")