code: def test_issues_from_scores():
      image_scores_softmin, pixel_scores = get_label_quality_scores(
          labels, pred_probs, method="softmin"
      )
      issues_from_score = issues_from_scores(
          image_scores_softmin, pixel_scores, threshold=1
      )
      assert np.shape(issues_from_score) == np.shape(pixel_scores)
      assert h * w * num_images == issues_from_score.sum()
  
      issues_from_score = issues_from_scores(
          image_scores_softmin, pixel_scores, threshold=0
      )
      assert 0 == issues_from_score.sum()
  
      issues_from_score = issues_from_scores(
          image_scores_softmin, pixel_scores, threshold=0.5
      )
      assert np.argmax(error) == np.argmax(
          issues_from_score.sum((1, 2))
      )
  
      sort_by_score = issues_from_scores(
          image_scores_softmin, threshold=0.5
      )
      assert error[sort_by_score[0]] == 1
  
imports: import numpy
  import random
  import pytest
  from unittest import mock
  import matplotlib.pyplot
  from pathlib import Path
  from cleanlab.internal.multilabel_scorer import softmin
  from cleanlab.internal.segmentation_utils import _check_input, _get_valid_optional_params, _get_summary_optional_params
  from cleanlab.segmentation.filter import find_label_issues
  from cleanlab.segmentation.rank import get_label_quality_scores, issues_from_scores, _get_label_quality_per_image
  from cleanlab.segmentation.summary import display_issues, common_label_issues, filter_by_class, _generate_colormap
label_desc: Use image node_id 9 for calling a global function with example usage: test_issues_from_scores() without return types