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 _build_statistics_dictionary(
      self, knn_graph: csr_matrix
  ) -> Dict[str, Dict[str, Any]]:
      statistics_dict: Dict[str, Dict[str, Any]] = {"statistics": {}}
  
      # Add the knn graph as a statistic if necessary
      graph_key = "weighted_knn_graph"
      old_knn_graph = self.datalab.get_info("statistics").get(
          graph_key, None
      )
      old_graph_exists = old_knn_graph is not None
      prefer_new_graph = (
          not old_graph_exists
          or (
              isinstance(knn_graph, csr_matrix)
              and knn_graph.nnz > old_knn_graph.nnz
          )
          or self.metric
          != self.datalab.get_info("statistics").get("knn_metric", None)
      )
      if prefer_new_graph:
          if knn_graph is not None:
              statistics_dict["statistics"][graph_key] = knn_graph
              if self.metric is not None:
                  statistics_dict["statistics"][
                      "knn_metric"
                  ] = self.metric
  
      return statistics_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 9 for calling the UnderperformingGroupIssueManager obj's underlying member method code with example usage: obj._build_statistics_dictionary(knn_graph) and returns: statistics_dict