code: def test_top_k_multiclass(k, preds, target, average, expected):
      """A simple test to check that top_k works as expected."""
      class_metric = MulticlassStatScores(
          top_k=k, average=average, num_classes=3
      )
      class_metric.update(preds, target)
  
      assert torch.allclose(class_metric.compute().long(), expected.T)
      assert torch.allclose(
          multiclass_stat_scores(
              preds, target, top_k=k, average=average, num_classes=3
          ).long(),
          expected.T,
      )
  
imports: from functools import partial
  import numpy
  import pytest
  import torch
  from scipy.special import expit
  from sklearn.metrics import confusion_matrix
  from torchmetrics.classification.stat_scores import BinaryStatScores, MulticlassStatScores, MultilabelStatScores, StatScores
  from torchmetrics.functional.classification.stat_scores import binary_stat_scores, multiclass_stat_scores, multilabel_stat_scores
  from torchmetrics.metric import Metric
  from unittests import NUM_CLASSES, THRESHOLD
  from unittests.classification.inputs import _binary_cases, _multiclass_cases, _multilabel_cases
  from unittests.helpers import seed_all
  from unittests.helpers.testers import MetricTester, inject_ignore_index, remove_ignore_index
label_desc: Use image node_id 5 for calling a global function with example usage: test_top_k_multiclass(k, preds, target, average, expected) without return types