code: def generate_three_image_dataset(bad_index):
      good_gt = np.zeros((10, 10))
      good_gt[:5, :] = 1.0
      bad_gt = np.ones((10, 10))
      bad_gt[:5, :] = 0.0
      good_pr = np.random.random((2, 10, 10))
      good_pr[0, :5, :] = good_pr[0, :5, :] / 10
      good_pr[1, 5:, :] = good_pr[1, 5:, :] / 10
  
      val = np.binary_repr([4, 2, 1][bad_index], width=3)
      error = [int(case) for case in val]
  
      labels = []
      pred = []
      for case in val:
          if case == "0":
              labels.append(good_gt)
              pred.append(good_pr)
          else:
              labels.append(bad_gt)
              pred.append(good_pr)
  
      labels = np.array(labels)
      pred_probs = np.array(pred)
      return labels, pred_probs, error
  
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 1 for calling a global function with example usage: generate_three_image_dataset(bad_index) and returns: labels,  pred_probs,  error