code: def test_find_label_issues():
      issues = find_label_issues(
          labels, pred_probs, n_jobs=None, batch_size=1000
      )
      assert np.argmax(error) == np.argmax(issues.sum((1, 2)))
  
      issues = find_label_issues(
          labels, pred_probs, downsample=2, batch_size=1739
      )
      assert np.argmax(error) == np.argmax(issues.sum((1, 2)))
  
      issues = find_label_issues(
          labels, pred_probs, downsample=5, n_jobs=None, batch_size=2838
      )
      assert np.argmax(error) == np.argmax(issues.sum((1, 2)))
  
      with pytest.raises(Exception) as e:
          issues = find_label_issues(
              labels,
              pred_probs,
              downsample=4,
              n_jobs=None,
              batch_size=1000,
          )
  
      # Simple tests
      # Test case 1: Test with larger batch_size
      issues = find_label_issues(
          labels, pred_probs, downsample=1, n_jobs=None, batch_size=2000
      )
      assert np.argmax(error) == np.argmax(issues.sum((1, 2)))
  
      # Test case 2: Test with smaller batch_size
      issues = find_label_issues(
          labels, pred_probs, downsample=1, n_jobs=None, batch_size=500
      )
      assert np.argmax(error) == np.argmax(issues.sum((1, 2)))
  
      # Test case 3: Test verbose off
      issues = find_label_issues(
          labels, pred_probs, downsample=1, verbose=False
      )
  
      assert np.argmax(error) == np.argmax(issues.sum((1, 2)))
  
      # Test case 5: Test with invalid downsample value
      with pytest.raises(Exception) as e:
          issues = find_label_issues(
              labels,
              pred_probs,
              downsample=3,
              n_jobs=None,
              batch_size=1000,
          )
  
      # Test case 6: Test with n_jobs parameter
      issues = find_label_issues(
          labels, pred_probs, downsample=1, n_jobs=2, batch_size=1000
      )
      assert np.argmax(error) == np.argmax(issues.sum((1, 2)))
  
      # Test case 7: Test with invalid labels
      with pytest.raises(Exception) as e:
          issues = find_label_issues(
              np.array([[[[1, 2, 3]]]]),
              pred_probs,
              downsample=1,
              n_jobs=None,
              batch_size=1000,
          )
  
      # Test case 8: Test with invalid pred_probs
      with pytest.raises(Exception) as e:
          issues = find_label_issues(
              labels,
              np.array([[[[0.1, 0.2, 0.3]]]]),
              downsample=1,
              n_jobs=None,
              batch_size=1000,
          )
  
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 2 for calling a global function with example usage: test_find_label_issues() without return types