code: def test_common_label_issues(capsys):
      issues = find_label_issues(
          labels, pred_probs, downsample=1, n_jobs=None, batch_size=1000
      )
  
      # test exclude
      df = common_label_issues(issues, labels, pred_probs)
      df_without_0 = common_label_issues(
          issues, labels, pred_probs, exclude=[0]
      )
      assert df.shape != df_without_0.shape
  
      # test verbose
      captured_words = capsys.readouterr()
      df = common_label_issues(
          issues, labels, pred_probs, verbose=False
      )
      captured_no_words = capsys.readouterr()
      assert len(captured_no_words.out) == 0
      assert len(captured_words.out) > 0
  
      # test class names & top
      df_class_names = common_label_issues(
          issues, labels, pred_probs, class_names=["one", "two"]
      )
      captured_top_all = capsys.readouterr()
      df_top_1 = common_label_issues(issues, labels, pred_probs, top=1)
      captured_top_1 = capsys.readouterr()
      assert len(captured_top_1.out) < len(captured_top_all.out)
      assert (
          df_class_names["given_label"].to_list()
          != df["given_label"].to_list()
      )
  
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 20 for calling a global function with example usage: test_common_label_issues(capsys) without return types