diff --git a/test/neuron/run_tests.sh b/test/neuron/run_tests.sh
index d8ee9a39b03..a68e0671a3b 100755
--- a/test/neuron/run_tests.sh
+++ b/test/neuron/run_tests.sh
@@ -257,6 +257,7 @@ function run_xla_op_tests3 {
   run_test_multi_device "$_TEST_DIR/spmd/test_dtensor_convert_mesh.py"
   run_test_multi_device "$_TEST_DIR/spmd/test_xla_dtensor_spec_conv.py"
   run_test_multi_device "$_TEST_DIR/spmd/test_dtensor_redistribute.py"
+  run_test_multi_device "$_TEST_DIR/spmd/test_xla_sharded_tensor.py"
   run_test "$_TEST_DIR/spmd/test_xla_auto_sharding.py"
   #run_test "$_TEST_DIR/spmd/test_spmd_parameter_wrapping.py"
   run_test "$_TEST_DIR/spmd/test_train_spmd_linear_model.py"
diff --git a/test/run_tests.sh b/test/run_tests.sh
index 54c893c7b40..0a7002c410d 100755
--- a/test/run_tests.sh
+++ b/test/run_tests.sh
@@ -257,6 +257,7 @@ function run_xla_op_tests3 {
   run_test_multi_devices "$_TEST_DIR/spmd/test_dtensor_convert_mesh.py"
   run_test_multi_devices "$_TEST_DIR/spmd/test_xla_dtensor_spec_conversion.py"
   run_test_multi_devices "$_TEST_DIR/spmd/test_dtensor_redistribute.py"
+  run_test_multi_devices "$_TEST_DIR/spmd/test_xla_sharded_tensor.py"
   run_test "$_TEST_DIR/spmd/test_xla_auto_sharding.py"
   run_test "$_TEST_DIR/spmd/test_spmd_parameter_wrapping.py"
   run_test "$_TEST_DIR/spmd/test_mp_input_sharding.py"
diff --git a/test/spmd/test_xla_sharded_tensor.py b/test/spmd/test_xla_sharded_tensor.py
new file mode 100644
index 00000000000..a101fb9bcd7
--- /dev/null
+++ b/test/spmd/test_xla_sharded_tensor.py
@@ -0,0 +1,38 @@
+import sys
+import unittest
+import test_xla_sharding_base
+from torch.distributed.tensor import DTensor
+from torch_xla.distributed.spmd import XLAShardedTensor
+
+import torch
+
+
+class XlaShardedTensorTest(test_xla_sharding_base.XlaShardingTest):
+
+  @classmethod
+  def setUpClass(cls):
+    super().setUpClass()
+
+  def test_xlashardedtensor_is_dtensor(self):
+    """Test that XLAShardedTensor is a subclass of DTensor."""
+    xt = torch.randn(128, 128).to('xla')
+    xla_tensor = XLAShardedTensor(xt)
+    self.assertIsInstance(xla_tensor, DTensor)
+
+  def test_xlashardedtensor_gradient(self):
+    """Test accessing gradients of an XLAShardedTensor (triggers __torch_function__)."""
+    xt = torch.randn(128, 128).to('xla')
+    xla_tensor = XLAShardedTensor(xt, requires_grad=True)
+    result = xla_tensor.sum()
+    result.backward()
+
+    # this should trigger __torch_function__
+    grad = xla_tensor.grad
+
+    self.assertIsNotNone(grad)
+    self.assertEqual(grad.shape, xla_tensor.shape)
+
+
+if __name__ == '__main__':
+  test = unittest.main()
+  sys.exit(0 if test.result.wasSuccessful() else 1)
diff --git a/test/tpu/run_tests.sh b/test/tpu/run_tests.sh
index 440db8bd28a..e1ad7c0023a 100755
--- a/test/tpu/run_tests.sh
+++ b/test/tpu/run_tests.sh
@@ -63,6 +63,7 @@ run_test "$_TEST_DIR/spmd/test_fsdp_v2.py"
 run_test "$_TEST_DIR/spmd/test_dtensor_convert_mesh.py"
 run_test "$_TEST_DIR/spmd/test_xla_dtensor_spec_conversion.py"
 run_test "$_TEST_DIR/spmd/test_dtensor_redistribute.py"
+run_test "$_TEST_DIR/spmd/test_xla_sharded_tensor.py"
 run_test "$_TEST_DIR/test_gradient_accumulation.py"
 XLA_EXPERIMENTAL=nonzero:masked_select:nms run_test "$_TEST_DIR/ds/test_dynamic_shape_models.py" -v
 run_test "$_TEST_DIR/test_autocast.py"
diff --git a/torch_xla/distributed/spmd/xla_sharded_tensor.py b/torch_xla/distributed/spmd/xla_sharded_tensor.py
index 5a049b5864e..652a2011cbd 100644
--- a/torch_xla/distributed/spmd/xla_sharded_tensor.py
+++ b/torch_xla/distributed/spmd/xla_sharded_tensor.py
@@ -11,6 +11,7 @@
 from torch.distributed.device_mesh import DeviceMesh
 from torch.distributed.tensor.placement_types import Placement, Shard, Replicate, Partial
 from torch.utils._pytree import tree_map_only
+from torch.distributed.tensor import DTensor
 
 
 @dataclass
@@ -63,7 +64,7 @@ def no_dispatch() -> Iterator[None]:
     del guard
 
 
-class XLAShardedTensor(torch.Tensor):
+class XLAShardedTensor(DTensor):
   """
     A wrapper around `torch.Tensor` with sharding annotation
     for XLA SPMD auto-sharding. The wrapped tensors are unwrapped
@@ -300,4 +301,4 @@ def redistribute(self, device_mesh, placements, *, async_op: bool = False):
 
   @classmethod
   def __torch_function__(cls, func, types, args=(), kwargs=None):
-    return super().__torch_function__(func, types, args, kwargs)
+    return super(DTensor, cls).__torch_function__(func, types, args, kwargs)
\ No newline at end of file