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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docstr: Given a fitted knn object or a knn graph, returns an (N, k) array in
  which j is in A[i] if item i and j are nearest neighbors.
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code: def _get_neighbors(
      self,
      knn: Optional[NearestNeighbors] = None,
      knn_graph: Optional[csr_matrix] = None,
  ) -> np.ndarray:
      """
      Given a fitted knn object or a knn graph, returns an (N, k) array in
      which j is in A[i] if item i and j are nearest neighbors.
      """
      if knn_graph is not None:
          N = knn_graph.shape[0]
          kneighbors = knn_graph.indices.reshape(N, -1)
      elif knn is not None:
          _, kneighbors = knn.kneighbors()
          N = kneighbors.shape[0]
      else:
          raise ValueError("Must provide either knn or knn_graph")
      self.N = N
      return kneighbors
  
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 10 for calling the NonIIDIssueManager obj's underlying member method code with example usage: obj._get_neighbors(knn, knn_graph) and returns: kneighbors