code: def test_top_k(k, preds, target, average, expected):
      """A simple test to check that top_k works as expected."""
      class_metric = MulticlassAccuracy(
          top_k=k, average=average, num_classes=3
      )
      class_metric.update(preds, target)
      assert torch.isclose(class_metric.compute(), expected)
      assert torch.isclose(
          multiclass_accuracy(
              preds, target, top_k=k, average=average, num_classes=3
          ),
          expected,
      )
  
imports: from functools import partial
  import numpy
  import pytest
  import torch
  from scipy.special import expit
  from sklearn.metrics import accuracy_score
  from sklearn.metrics import confusion_matrix
  from torchmetrics.classification.accuracy import Accuracy, BinaryAccuracy, MulticlassAccuracy, MultilabelAccuracy
  from torchmetrics.functional.classification.accuracy import accuracy, binary_accuracy, multiclass_accuracy, multilabel_accuracy
  from torchmetrics.metric import Metric
  from unittests import NUM_CLASSES, THRESHOLD
  from unittests.classification.inputs import _binary_cases, _input_binary, _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(k, preds, target, average, expected) without return types