code: def _test_distrib_accumulator_device(device):
      metric_devices = [torch.device("cpu")]
      if device.type != "xla":
          metric_devices.append(idist.device())
      for metric_device in metric_devices:
          acc = TopKCategoricalAccuracy(2, device=metric_device)
          assert acc._device == metric_device
          assert (
              acc._num_correct.device == metric_device
          ), f"{type(acc._num_correct.device)}:{acc._num_correct.device} vs {type(metric_device)}:{metric_device}"
  
          y_pred = torch.tensor(
              [[0.2, 0.4, 0.6, 0.8], [0.8, 0.6, 0.4, 0.2]]
          )
          y = torch.ones(2).long()
          acc.update((y_pred, y))
  
          assert (
              acc._num_correct.device == metric_device
          ), f"{type(acc._num_correct.device)}:{acc._num_correct.device} vs {type(metric_device)}:{metric_device}"
  
file_path: ignite/tests/ignite/metrics/test_top_k_categorical_accuracy.py
imports: import os
  import pytest
  import torch
  import ignite.distributed
  from ignite.exceptions import NotComputableError
  from ignite.metrics import TopKCategoricalAccuracy
label_desc: Use image node_id 5 for calling a global function with example usage: _test_distrib_accumulator_device(device) without return types