code: def test_find_issues_no_underperforming_group(
      self, issue_manager, make_data
  ):
      data = make_data()
      features, labels, pred_probs = (
          data["features"],
          data["labels"],
          data["pred_probs"],
      )
      N = len(labels)
      issue_manager.find_issues(
          features=features, pred_probs=pred_probs
      )
      issues, summary = issue_manager.issues, issue_manager.summary
      assert np.sum(issues["is_underperforming_group_issue"]) == 0
      expected_issue_mask = np.full(N, False, bool)
      assert np.all(
          issues["is_underperforming_group_issue"]
          == expected_issue_mask
      ), "Issue mask should be correct"
      expected_scores = np.ones(N)
      np.testing.assert_allclose(
          issues["underperforming_group_score"],
          expected_scores,
          err_msg="Scores should be correct",
      )
      assert summary["issue_type"][0] == "underperforming_group"
      assert summary["score"][0] == 1.0
      # Check with cluster_ids param
      issue_manager.find_issues(
          features=features, pred_probs=pred_probs, cluster_ids=labels
      )
      issues_with_clabels, summary_with_clabels = (
          issue_manager.issues,
          issue_manager.summary,
      )
      pd.testing.assert_frame_equal(issues_with_clabels, issues)
      pd.testing.assert_frame_equal(summary_with_clabels, summary)
  
file_path: cleanlab/tests/datalab/issue_manager/test_underperforming_group.py
imports: import numpy
  import pytest
  import pandas
  import scipy.sparse
  from cleanlab.datalab.internal.issue_manager.underperforming_group import UnderperformingGroupIssueManager
  from sklearn.datasets import make_blobs, load_iris
label_desc: Use image node_id 5 for calling the TestUnderperformingGroupIssueManager obj's underlying member method code with example usage: obj.test_find_issues_no_underperforming_group(issue_manager, make_data) without return types