code: def test_min_cluster_samples(self, lab, issue_manager, make_data):
      data = make_data()
      features, pred_probs, labels = (
          data["features"],
          data["pred_probs"],
          data["labels"],
      )
      labels[:3] = max(labels) + 1  # New cluster with very few samples
      n_clusters = len(set(labels))
      # Check if small cluster is filtered
      issue_manager.find_issues(
          features=features, pred_probs=pred_probs, cluster_ids=labels
      )
      clustering_info = issue_manager.info["clustering"]
      assert clustering_info["stats"]["n_clusters"] == n_clusters - 1
  
      # New issue manager to consider small cluster as well
      issue_manager = UnderperformingGroupIssueManager(
          datalab=lab, threshold=0.2, min_cluster_samples=3
      )
      issue_manager.find_issues(
          features=features, pred_probs=pred_probs, cluster_ids=labels
      )
      clustering_info = issue_manager.info["clustering"]
      assert clustering_info["stats"]["n_clusters"] == n_clusters
  
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 9 for calling the TestUnderperformingGroupIssueManager obj's underlying member method code with example usage: obj.test_min_cluster_samples(lab, issue_manager, make_data) without return types