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: Determine if a knn_graph is provided in the kwargs or if one is already stored in the associated Datalab instance.
docstr_tok: ["Determine","if","a","knn_graph","is","provided","in","the","kwargs","or","if","one","is","already","stored","in","the","associated","Datalab","instance","."]
code: def _process_knn_graph_from_inputs(
      self, kwargs: Dict[str, Any]
  ) -> Union[csr_matrix, None]:
      """Determine if a knn_graph is provided in the kwargs or if one is already stored in the associated Datalab instance."""
      knn_graph_kwargs: Optional[csr_matrix] = kwargs.get(
          "knn_graph", None
      )
      knn_graph_stats = self.datalab.get_info("statistics").get(
          "weighted_knn_graph", None
      )
  
      knn_graph: Optional[csr_matrix] = None
      if knn_graph_kwargs is not None:
          knn_graph = knn_graph_kwargs
      elif knn_graph_stats is not None:
          knn_graph = knn_graph_stats
  
      need_to_recompute_knn = isinstance(knn_graph, csr_matrix) and (
          kwargs.get("k", 0) > knn_graph.nnz // knn_graph.shape[0]
          or self.k > knn_graph.nnz // knn_graph.shape[0]
      )
  
      if need_to_recompute_knn:
          # If the provided knn graph is insufficient, then we need to recompute the knn graph
          # with the provided features
          knn_graph = None
      return knn_graph
  
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 5 for calling the NonIIDIssueManager obj's underlying member method code with example usage: obj._process_knn_graph_from_inputs(kwargs) and returns: knn_graph