code: def test_integration_multiclass(self):
      from ignite.engine import Engine
  
      rank = idist.get_rank()
      torch.manual_seed(12)
  
      def _test(average, n_epochs, metric_device):
          n_iters = 60
          s = 16
          n_classes = 7
  
          offset = n_iters * s
          y_true = torch.randint(
              0, n_classes, size=(offset * idist.get_world_size(),)
          ).to(device)
          y_preds = torch.rand(
              offset * idist.get_world_size(), n_classes
          ).to(device)
  
          def update(engine, i):
              return (
                  y_preds[
                      i * s
                      + rank * offset : (i + 1) * s
                      + rank * offset,
                      :,
                  ],
                  y_true[
                      i * s
                      + rank * offset : (i + 1) * s
                      + rank * offset
                  ],
              )
  
          engine = Engine(update)
  
          pr = Precision(average=average, device=metric_device)
          pr.attach(engine, "pr")
          assert pr._updated is False
  
          data = list(range(n_iters))
          engine.run(data=data, max_epochs=n_epochs)
  
          assert "pr" in engine.state.metrics
          assert pr._updated is True
          res = engine.state.metrics["pr"]
          if isinstance(res, torch.Tensor):
              # Fixes https://github.com/pytorch/ignite/issues/1635#issuecomment-863026919
              assert res.device.type == "cpu"
              res = res.cpu().numpy()
  
          sk_average_parameter = ignite_average_to_scikit_average(
              average, "multiclass"
          )
          true_res = precision_score(
              y_true.cpu().numpy(),
              torch.argmax(y_preds, dim=1).cpu().numpy(),
              average=sk_average_parameter,
          )
  
          assert pytest.approx(res) == true_res
  
      metric_devices = [torch.device("cpu")]
      device = idist.device()
      if device.type != "xla":
          metric_devices.append(idist.device())
      for _ in range(2):
          for metric_device in metric_devices:
              _test(
                  average=False, n_epochs=1, metric_device=metric_device
              )
              _test(
                  average=False, n_epochs=2, metric_device=metric_device
              )
              _test(
                  average="macro",
                  n_epochs=1,
                  metric_device=metric_device,
              )
              _test(
                  average="macro",
                  n_epochs=2,
                  metric_device=metric_device,
              )
              _test(
                  average="weighted",
                  n_epochs=1,
                  metric_device=metric_device,
              )
              _test(
                  average="weighted",
                  n_epochs=2,
                  metric_device=metric_device,
              )
              _test(
                  average="micro",
                  n_epochs=1,
                  metric_device=metric_device,
              )
              _test(
                  average="micro",
                  n_epochs=2,
                  metric_device=metric_device,
              )
  
imports: import warnings
  import pytest
  import torch
  from sklearn.exceptions import UndefinedMetricWarning
  from sklearn.metrics import precision_score
  import ignite.distributed
  from ignite.exceptions import NotComputableError
  from ignite.metrics import Precision
label_desc: Use image node_id 1 for calling the TestDistributed obj's underlying member method code with example usage: obj.test_integration_multiclass() and returns: y_preds,  y_true