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 __init__(
      self,
      datalab: Datalab,
      metric: Optional[str] = None,
      k: int = 10,
      num_permutations: int = 25,
      seed: Optional[int] = 0,
      significance_threshold: float = 0.05,
      **_,
  ):
      super().__init__(datalab)
      self.metric = metric
      self.k = k
      self.num_permutations = num_permutations
      self.tests = {
          "ks": simplified_kolmogorov_smirnov_test,
      }
      self.background_distribution = None
      self.seed = seed
      self.significance_threshold = significance_threshold
  
      # TODO: Temporary flag introduced to decide on storing knn graphs based on pred_probs.
      # Revisit and finalize the implementation.
      self._skip_storing_knn_graph_for_pred_probs: bool = False
  
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 1 to create a new NonIIDIssueManager object from inherited base classes: IssueManager with example: obj = NonIIDIssueManager(datalab, metric, k, num_permutations, seed, significance_threshold)