code: def test_device_and_dtype_transfer_metriccollection(tmpdir):
      """Test that metrics in the collection correctly gets updated their dtype and device."""
      m1 = DummyMetricSum()
      m2 = DummyMetricDiff()
  
      metric_collection = MetricCollection([m1, m2])
      for metric in metric_collection.values():
          assert metric.x.is_cuda is False
          assert metric.x.dtype == torch.float32
  
      metric_collection = metric_collection.to(device="cuda")
      for metric in metric_collection.values():
          assert metric.x.is_cuda
  
      metric_collection = metric_collection.set_dtype(torch.double)
      for metric in metric_collection.values():
          assert metric.x.dtype == torch.float64
  
      metric_collection = metric_collection.set_dtype(torch.half)
      for metric in metric_collection.values():
          assert metric.x.dtype == torch.float16
  
imports: import pickle
  from copy import deepcopy
  from typing import Any
  import pytest
  import torch
  from torchmetrics import Metric, MetricCollection
  from torchmetrics.classification import BinaryAccuracy, MulticlassAccuracy, MulticlassAUROC, MulticlassAveragePrecision, MulticlassCohenKappa, MulticlassConfusionMatrix, MulticlassF1Score, MulticlassMatthewsCorrCoef, MulticlassPrecision, MulticlassRecall, MultilabelAUROC, MultilabelAveragePrecision
  from torchmetrics.utilities.checks import _allclose_recursive
  from unittests.helpers import seed_all
  from unittests.helpers.testers import DummyMetricDiff, DummyMetricMultiOutputDict, DummyMetricSum
label_desc: Use image node_id 2 for calling a global function with example usage: test_device_and_dtype_transfer_metriccollection(tmpdir) without return types