adds available_device to test_hsic.py #3335 (#3361)

Co-authored-by: vfdev <[email protected]>

Author: BanzaiTokyo

tests/ignite/metrics/test_hsic.py

@@ -87,10 +87,11 @@ def test_case(request) -> Tuple[Tensor, Tensor, int]:
 @pytest.mark.parametrize("n_times", range(3))
 @pytest.mark.parametrize("sigma_x", [-1.0, 1.0])
 @pytest.mark.parametrize("sigma_y", [-1.0, 1.0])
-def test_compute(n_times, sigma_x: float, sigma_y: float, test_case: Tuple[Tensor, Tensor, int]):
+def test_compute(n_times, sigma_x: float, sigma_y: float, test_case: Tuple[Tensor, Tensor, int], available_device):
     x, y, batch_size = test_case
 
-    hsic = HSIC(sigma_x=sigma_x, sigma_y=sigma_y)
+    hsic = HSIC(sigma_x=sigma_x, sigma_y=sigma_y, device=available_device)
+    assert hsic._device == torch.device(available_device)
 
     hsic.reset()
 
@@ -109,8 +110,9 @@ def test_compute(n_times, sigma_x: float, sigma_y: float, test_case: Tuple[Tenso
     assert pytest.approx(expected_hsic, abs=2e-5) == hsic.compute()
 
 
-def test_accumulator_detached():
-    hsic = HSIC()
+def test_accumulator_detached(available_device):
+    hsic = HSIC(device=available_device)
+    assert hsic._device == torch.device(available_device)
 
     x = torch.rand(10, 10, dtype=torch.float)
     y = torch.rand(10, 10, dtype=torch.float)