code: def test_top_k(
      metric_class,
      metric_fn,
      k: int,
      preds: Tensor,
      target: Tensor,
      average: str,
      expected_prec: Tensor,
      expected_recall: Tensor,
  ):
      """A simple test to check that top_k works as expected."""
      class_metric = metric_class(
          top_k=k, average=average, num_classes=3
      )
      class_metric.update(preds, target)
  
      result = (
          expected_prec
          if metric_class.__name__ == "MulticlassPrecision"
          else expected_recall
      )
  
      assert torch.equal(class_metric.compute(), result)
      assert torch.equal(
          metric_fn(
              preds, target, top_k=k, average=average, num_classes=3
          ),
          result,
      )
  
imports: from functools import partial
  import numpy
  import pytest
  import torch
  from scipy.special import expit
  from sklearn.metrics import confusion_matrix
  from sklearn.metrics import precision_score
  from sklearn.metrics import recall_score
  from torch import Tensor, tensor
  from torchmetrics.classification.precision_recall import BinaryPrecision, BinaryRecall, MulticlassPrecision, MulticlassRecall, MultilabelPrecision, MultilabelRecall, Precision, Recall
  from torchmetrics.functional.classification.precision_recall import binary_precision, binary_recall, multiclass_precision, multiclass_recall, multilabel_precision, multilabel_recall
  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 3 for calling a global function with example usage: test_top_k(metric_class, metric_fn, k, preds, target, average, expected_prec, expected_recall) without return types