code: def _test_distrib_accumulator_device(device, y_test_1):
      metric_devices = [torch.device("cpu")]
      if device.type != "xla":
          metric_devices.append(idist.device())
      for metric_device in metric_devices:
          loss = Loss(nll_loss, device=metric_device)
          assert loss._device == metric_device
          assert (
              loss._sum.device == metric_device
          ), f"{type(loss._sum.device)}:{loss._sum.device} vs {type(metric_device)}:{metric_device}"
  
          y_pred, y, _ = y_test_1
          loss.update((y_pred, y))
  
          assert (
              loss._sum.device == metric_device
          ), f"{type(loss._sum.device)}:{loss._sum.device} vs {type(metric_device)}:{metric_device}"
  
imports: import os
  from unittest.mock import MagicMock
  import pytest
  import torch
  from numpy.testing import assert_almost_equal
  from torch import nn
  from torch.nn.functional import nll_loss
  import ignite.distributed
  from ignite.engine import State
  from ignite.exceptions import NotComputableError
  from ignite.metrics import Loss, Precision
label_desc: Use image node_id 13 for calling a global function with example usage: _test_distrib_accumulator_device(device, y_test_1) without return types