code: def find_top_issues(
      quality_scores: np.ndarray, *, top: int = 10
  ) -> np.ndarray:
      """Returns the sorted indices of the `top` issues in `quality_scores`, ordered from smallest to largest quality score
      (i.e., from most to least likely to be an issue). For example, the first value returned is the index corresponding
      to the smallest value in `quality_scores` (most likely to be an issue). The second value in the returned array is
      the index corresponding to the second smallest value in `quality-scores` (second-most likely to be an issue), and so forth.
  
      This method assumes that `quality_scores` shares an index with some dataset such that the indices returned by this method
      map to the examples in that dataset.
  
      Parameters
      ----------
      quality_scores :
        Array of shape ``(N,)``, where N is the number of examples, containing one quality score for each example in the dataset.
  
      top :
        The number of indices to return.
  
      Returns
      -------
      top_issue_indices :
        Indices of top examples most likely to suffer from an issue (ranked by issue severity).
      """
  
      if top is None or top > len(quality_scores):
          top = len(quality_scores)
  
      top_outlier_indices = quality_scores.argsort()[:top]
      return top_outlier_indices
  
file_path: cleanlab/cleanlab/rank.py
imports: import numpy
  from sklearn.metrics import log_loss
  from typing import List, Optional
  import warnings
  from cleanlab.internal.validation import assert_valid_inputs
  from cleanlab.internal.constants import CLIPPING_LOWER_BOUND
  from cleanlab.internal.label_quality_utils import _subtract_confident_thresholds, get_normalized_entropy
label_desc: Use image node_id 4 for calling a global function with example usage: find_top_issues(quality_scores) and returns: top_outlier_indices