diff --git a/test/test_core_aten_ops.py b/test/test_core_aten_ops.py
index 45afbecdb30..220883e6b4e 100644
--- a/test/test_core_aten_ops.py
+++ b/test/test_core_aten_ops.py
@@ -10,19 +10,6 @@
 import unittest
 
 
-def onlyIfTorchSupportsCUDA(fn):
-  return unittest.skipIf(
-      not torch.cuda.is_available(), reason="requires PyTorch CUDA support")(
-          fn)
-
-
-def onlyIfPJRTDeviceIsCUDA(fn):
-  return unittest.skipIf(
-      os.environ.get("PJRT_DEVICE") not in ("GPU", "CUDA"),
-      reason="requires CUDA as PJRT_DEVICE")(
-          fn)
-
-
 def diff_output(testcase, output1, output2, rtol, atol, equal_nan=True):
   if isinstance(output1, torch.Tensor):
     testcase.assertIsInstance(output2, torch.Tensor)
@@ -4726,27 +4713,6 @@ def test_aten_where_self_1(self):
     kwargs = dict()
     run_export_and_compare(self, torch.ops.aten.where.self, args, kwargs)
 
-  def _test_move_tensor_cuda_to_xla(self, cpu_tensor):
-    # Assumes CPU-XLA data movement works.
-    cuda_tensor = cpu_tensor.to("cuda")
-    # Move tensor CUDA -> XLA.
-    xla_tensor = cuda_tensor.to(xm.xla_device())
-    # Move the XLA tensor back to CPU, and check that it is the same as
-    # the original CPU tensor.
-    self.assertTrue(torch.equal(cpu_tensor, xla_tensor.cpu()))
-
-  @onlyIfTorchSupportsCUDA
-  @onlyIfPJRTDeviceIsCUDA
-  def test_aten_move_cuda_to_xla(self):
-    self._test_move_tensor_cuda_to_xla(torch.arange(5))
-
-  @onlyIfTorchSupportsCUDA
-  @onlyIfPJRTDeviceIsCUDA
-  def test_aten_move_scalar_cuda_to_xla(self):
-    # 0-dimensional scalar-tensor
-    # Has a different execution path than other tensors.
-    self._test_move_tensor_cuda_to_xla(torch.tensor(42))
-
 
 if __name__ == '__main__':
   unittest.main()
diff --git a/test/test_operations.py b/test/test_operations.py
index e11c54f86bf..e67722dfec2 100644
--- a/test/test_operations.py
+++ b/test/test_operations.py
@@ -131,6 +131,19 @@ def _zeros_like(tensor_list):
   return zeros_tensors
 
 
+def onlyIfTorchSupportsCUDA(fn):
+  return unittest.skipIf(
+      not torch.cuda.is_available(), reason="requires PyTorch CUDA support")(
+          fn)
+
+
+def onlyIfPJRTDeviceIsCUDA(fn):
+  return unittest.skipIf(
+      os.environ.get("PJRT_DEVICE") not in ("GPU", "CUDA"),
+      reason="requires CUDA as PJRT_DEVICE")(
+          fn)
+
+
 class TestToXlaTensorArena(test_utils.XlaTestCase):
 
   def test(self):
@@ -2356,6 +2369,27 @@ def test_as_strided_input_larger(self):
 
     self.assertEqual(a, former_a)
 
+  def _test_move_tensor_cuda_to_xla(self, cpu_tensor):
+    # Assumes CPU-XLA data movement works.
+    cuda_tensor = cpu_tensor.to("cuda")
+    # Move tensor CUDA -> XLA.
+    xla_tensor = cuda_tensor.to(xm.xla_device())
+    # Move the XLA tensor back to CPU, and check that it is the same as
+    # the original CPU tensor.
+    self.assertTrue(torch.equal(cpu_tensor, xla_tensor.cpu()))
+
+  @onlyIfTorchSupportsCUDA
+  @onlyIfPJRTDeviceIsCUDA
+  def test_aten_move_cuda_to_xla(self):
+    self._test_move_tensor_cuda_to_xla(torch.arange(5))
+
+  @onlyIfTorchSupportsCUDA
+  @onlyIfPJRTDeviceIsCUDA
+  def test_aten_move_scalar_cuda_to_xla(self):
+    # 0-dimensional scalar-tensor
+    # Has a different execution path than other tensors.
+    self._test_move_tensor_cuda_to_xla(torch.tensor(42))
+
 
 if __name__ == '__main__':
   torch.set_default_dtype(torch.float32)