code: def test_no_meaningful_clusters(
      self, issue_manager, make_data, monkeypatch
  ):
      np.random.seed(SEED)
      data = make_data()
      k = 10
      N = 20
      # Generate sparse data that cannot be clustered by DBSCAN
      features = np.random.uniform(-100, 100, (N, 2))
      # Dummy pred_probs and labels for running issue manager
      pred_probs = data["pred_probs"][:N]
      monkeypatch.setattr(
          issue_manager.datalab._labels, "labels", data["labels"][:N]
      )
      monkeypatch.setattr(
          issue_manager, "k", k
      )  # Ensure that k is smaller than N
      exception_pattern = "No meaningful clusters"
      with pytest.raises(ValueError, match=exception_pattern):
          issue_manager.find_issues(
              features=features, pred_probs=pred_probs
          )
      # Cluster labels passed containing all outliers
      cluster_ids = np.full(N, -1)  # -1 is the outlier label for DBSCAN
      with pytest.raises(ValueError, match=exception_pattern):
          issue_manager.find_issues(
              features=features,
              pred_probs=pred_probs,
              cluster_ids=cluster_ids,
          )
      # Empty cluster ids
      with pytest.raises(ValueError, match=exception_pattern):
          issue_manager.find_issues(
              features=features,
              pred_probs=pred_probs,
              cluster_ids=np.array([], dtype=int),
          )
  
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 8 for calling the TestUnderperformingGroupIssueManager obj's underlying member method code with example usage: obj.test_no_meaningful_clusters(issue_manager, make_data, monkeypatch) without return types