code: def issues_from_scores(
      label_quality_scores: np.ndarray, *, threshold: float = 0.1
  ) -> np.ndarray:
      """Convert label quality scores to a list of indices of images with issues sorted from most to least severe cut off at threshold.
  
      Returns the list of indices of images with issues sorted from most to least severe cut off at threshold.
  
      Parameters
      ----------
      label_quality_scores:
          Array of shape ``(N, )`` of scores between 0 and 1, one per image in the object detection dataset.
          Lower scores indicate images are more likely to contain a label issue.
  
      threshold:
          Label quality scores above the threshold are not considered to be label issues. The corresponding examples' indices are omitted from the returned array.
  
      Returns
      ---------
      issue_indices:
          Array of issue indices sorted from most to least severe who's label quality scores fall below the threshold if one is provided.
      """
  
      if threshold > 1.0:
          raise ValueError(
              f"""
              Threshold is a cutoff of label_quality_scores and therefore should be <= 1.
              """
          )
  
      issue_indices = np.argwhere(
          label_quality_scores <= threshold
      ).flatten()
      issue_vals = label_quality_scores[issue_indices]
      sorted_idx = issue_vals.argsort()
      return issue_indices[sorted_idx]
  
imports: import warnings
  from cleanlab.internal.constants import ALPHA, CUSTOM_SCORE_WEIGHT_BADLOC, CUSTOM_SCORE_WEIGHT_OVERLOOKED, CUSTOM_SCORE_WEIGHT_SWAP, EUC_FACTOR, HIGH_PROBABILITY_THRESHOLD, LOW_PROBABILITY_THRESHOLD, MAX_ALLOWED_BOX_PRUNE, TINY_VALUE, TEMPERATURE, LABEL_OVERLAP_THRESHOLD
  import copy
  from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, TypeVar
  import numpy
  from cleanlab.internal.object_detection_utils import softmin1d, assert_valid_aggregation_weights, assert_valid_inputs
label_desc: Use image node_id 2 for calling a global function with example usage: issues_from_scores(label_quality_scores) and returns: issue_indices