class_docstr: Manages issues related to non-iid data distributions.
  
  Parameters
  ----------
  datalab :
      The Datalab instance that this issue manager searches for issues in.
  
  metric :
      The distance metric used to compute the KNN graph of the examples in the dataset.
      If set to `None`, the metric will be automatically selected based on the dimensionality
      of the features used to represent the examples in the dataset.
  
  k :
      The number of nearest neighbors to consider when computing the KNN graph of the examples.
  
  num_permutations :
      The number of trials to run when performing permutation testing to determine whether
      the distribution of index-distances between neighbors in the dataset is IID or not.
  
  Note
  ----
  This class will only flag a single example as an issue if the dataset is considered non-IID. This type of issue
  is more relevant to the entire dataset as a whole, rather than to individual examples.
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code: def collect_info(
      self,
      knn_graph: Optional[csr_matrix] = None,
      knn: Optional[NearestNeighbors] = None,
  ) -> dict:
      issues_dict = {
          "p-value": self.p_value,
      }
  
      params_dict = {
          "metric": self.metric,
          "k": self.k,
      }
      if knn_graph is None:
          assert (
              knn is not None
          ), "If knn_graph is None, knn must be provided."
          knn_graph = knn.kneighbors_graph(mode="distance")  # type: ignore[union-attr]
  
      assert (
          knn_graph is not None
      ), "knn_graph must be provided or computed."
      statistics_dict = self._build_statistics_dictionary(
          knn_graph=knn_graph
      )
  
      info_dict = {
          **issues_dict,
          **params_dict,  # type: ignore[arg-type]
          **statistics_dict,  # type: ignore[arg-type]
      }
      return info_dict
  
imports: from __future__ import annotations
  from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union, cast
  import warnings
  import itertools
  from scipy.stats import gaussian_kde
  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 cleanlab.datalab.internal.issue_manager import IssueManager
label_desc: Use image node_id 6 for calling the NonIIDIssueManager obj's underlying member method code with example usage: obj.collect_info(knn_graph, knn) and returns: info_dict