def test_distrib_single_device_xla():
        device = idist.device()
>       _test_distrib_compute(device)

tests/ignite/metrics/regression/test_mean_error.py:233: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
tests/ignite/metrics/regression/test_mean_error.py:127: in _test_distrib_compute
    _test("cpu")
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

metric_device = device(type='cpu')

    def _test(metric_device):
        metric_device = torch.device(metric_device)
        m = MeanError(device=metric_device)
    
        y_pred = torch.rand(size=(100,), device=device)
        y = torch.rand(size=(100,), device=device)
    
        m.update((y_pred, y))
    
        y_pred = idist.all_gather(y_pred)
        y = idist.all_gather(y)
    
        np_y = y.cpu().numpy()
        np_y_pred = y_pred.cpu().numpy()
    
        np_sum = (np_y - np_y_pred).sum()
        np_len = len(np_y_pred)
        np_ans = np_sum / np_len
    
>       assert m.compute() == pytest.approx(np_ans)
E       assert 0.003967249393463134 == 0.003967256546020508 ± 4.0e-09
E         
E         comparison failed
E         Obtained: 0.003967249393463134
E         Expected: 0.003967256546020508 ± 4.0e-09