class_docstr: Manages issues related to underperforming group examples.
  
  Note: The `min_cluster_samples` argument should not be confused with the
  `min_samples` argument of sklearn.cluster.DBSCAN.
  
  Examples
  --------
  >>> from cleanlab import Datalab
  >>> import numpy as np
  >>> X = np.random.normal(size=(50, 2))
  >>> y = np.random.randint(2, size=50)
  >>> pred_probs = X / X.sum(axis=1, keepdims=True)
  >>> data = {"X": X, "y": y}
  >>> lab = Datalab(data, label_name="y")
  >>> issue_types={"underperforming_group": {"clustering_kwargs": {"eps": 0.5}}}
  >>> lab.find_issues(pred_probs=pred_probs, features=X, issue_types=issue_types)
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code: def find_issues(
      self,
      features: npt.NDArray,
      pred_probs: npt.NDArray,
      cluster_ids: Optional[npt.NDArray[np.int_]] = None,
      **kwargs: Any,
  ) -> None:
      labels = self.datalab.labels
      if cluster_ids is None:
          knn_graph = self.set_knn_graph(features, kwargs)
          cluster_ids = self.perform_clustering(knn_graph)
          performed_clustering = True
      else:
          if self.clustering_kwargs:
              warnings.warn(
                  "`clustering_kwargs` will not be used since `cluster_ids` have been passed."
              )
          performed_clustering = False
          knn_graph = None
      unique_cluster_ids = self.filter_cluster_ids(cluster_ids)
      if not unique_cluster_ids.size:
          raise ValueError(
              "No meaningful clusters were generated for determining underperforming group."
          )
      n_clusters = len(unique_cluster_ids)
      worst_cluster_id, worst_cluster_ratio = self.get_worst_cluster(
          cluster_ids, unique_cluster_ids, labels, pred_probs
      )
      is_issue_column = cluster_ids == worst_cluster_id
      scores = np.where(is_issue_column, worst_cluster_ratio, 1)
      self.issues = pd.DataFrame(
          {
              f"is_{self.issue_name}_issue": is_issue_column,
              self.issue_score_key: scores,
          },
      )
      self.summary = self.make_summary(score=worst_cluster_ratio)
      self.info = self.collect_info(
          knn_graph=knn_graph,
          n_clusters=n_clusters,
          cluster_ids=cluster_ids,
          performed_clustering=performed_clustering,
          worst_cluster_id=worst_cluster_id,
      )
  
imports: from __future__ import annotations
  from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union, Tuple
  import warnings
  import inspect
  import numpy
  import pandas
  from scipy.sparse import csr_matrix
  from sklearn.neighbors import NearestNeighbors
  from sklearn.exceptions import NotFittedError
  from sklearn.utils.validation import check_is_fitted
  from sklearn.cluster import DBSCAN
  from cleanlab.datalab.internal.issue_manager import IssueManager
  from cleanlab.rank import get_self_confidence_for_each_label
label_desc: Use image node_id 2 for calling the UnderperformingGroupIssueManager obj's underlying member method code with example usage: obj.find_issues(features, pred_probs, cluster_ids) without return types