code: def test_find_top_issues():
      DEFAULT_TOP = 10  # CHANGE THIS IS THE DEFAULT CHANGES
      X_train = data["X_train"]
      X_test = data["X_test"]
      X_ood = np.array(
          [[999999999.0, 999999999.0]]
      )  # Create OOD datapoint
      X_test_with_ood = np.vstack(
          [X_test, X_ood]
      )  # Add OOD datapoint to X_test
  
      # Create OOD object (use knn without cosine metric to identify X_ood correctly)
      knn = NearestNeighbors(n_neighbors=5).fit(X_train)
      ood_outlier = outlier.OutOfDistribution(params={"knn": knn})
      ood_scores = ood_outlier.score(features=X_test_with_ood)
  
      # Get top ood score for outlier example
      top_outlier_indices = rank.find_top_issues(
          quality_scores=ood_scores, top=len(ood_scores)
      )
      top_outlier_indices_more_k = rank.find_top_issues(
          quality_scores=ood_scores, top=100000
      )
  
      ### Check top scores are calculated correctly
  
      # Checking that X_ood has the smallest outlier score among all the datapoints and outlier scores identifies that
      assert np.argmin(ood_scores) == (ood_scores.shape[0] - 1)
      assert len(top_outlier_indices) == len(ood_scores)
      assert top_outlier_indices[0] == np.argmin(ood_scores)
  
      # Checking k > len(ood_scores) is same as sorted list of indices
      assert len(top_outlier_indices) == len(top_outlier_indices_more_k)
      assert (top_outlier_indices == top_outlier_indices_more_k).all()
  
      # Get k = DEFAULT_TOP ood scores
      top_outlier_indices = rank.find_top_issues(ood_scores)
      assert len(top_outlier_indices) == DEFAULT_TOP
  
      # Get k < len(ood_scores) ood scores
      # Assert top k scores are consistent with different length scores vectors
      for k in [0, 1, 3]:
          top_outlier_indices_k = rank.find_top_issues(
              quality_scores=ood_scores, top=k
          )
          assert len(top_outlier_indices_k) == k
          assert (
              top_outlier_indices_k == top_outlier_indices[:k]
          ).all()
  
imports: import numpy
  import pytest
  from cleanlab import rank
  from cleanlab.internal.label_quality_utils import _subtract_confident_thresholds
  from cleanlab.benchmarking.noise_generation import generate_noise_matrix_from_trace
  from cleanlab.benchmarking.noise_generation import generate_noisy_labels
  from cleanlab import count
  from cleanlab import outlier
  from sklearn.neighbors import NearestNeighbors
label_desc: Use image node_id 15 for calling a global function with example usage: test_find_top_issues() without return types