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 collect_info(
      self,
      knn_graph: csr_matrix,
      n_clusters: int,
      cluster_ids: npt.NDArray[np.int_],
      performed_clustering: bool,
      worst_cluster_id: int,
  ) -> Dict[str, Any]:
      params_dict = {
          "k": self.k,
          "metric": self.metric,
          "threshold": self.threshold,
      }
  
      knn_info_dict = {}
      if knn_graph is not None:
          N = knn_graph.shape[0]
          dists = knn_graph.data.reshape(N, -1)[:, 0]
          nn_ids = knn_graph.indices.reshape(N, -1)[:, 0]
  
          knn_info_dict = {
              "nearest_neighbor": nn_ids.tolist(),
              "distance_to_nearest_neighbor": dists.tolist(),
          }
      statistics_dict = self._build_statistics_dictionary(
          knn_graph=knn_graph
      )
  
      cluster_stat_dict = self._get_cluster_statistics(
          n_clusters=n_clusters,
          cluster_ids=cluster_ids,
          performed_clustering=performed_clustering,
          worst_cluster_id=worst_cluster_id,
      )
      info_dict = {
          **params_dict,
          **knn_info_dict,
          **statistics_dict,
          **cluster_stat_dict,
      }
  
      return info_dict
  
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 8 for calling the UnderperformingGroupIssueManager obj's underlying member method code with example usage: obj.collect_info(knn_graph, n_clusters, cluster_ids, performed_clustering, worst_cluster_id) and returns: info_dict