class_docstr: Classifier implementing the k-nearest neighbors vote on manifolds.
  
  Parameters
  ----------
  space : Manifold
      Equipped manifold.
  n_neighbors : int, optional (default = 5)
      Number of neighbors to use by default.
  weights : string or callable, optional (default = 'uniform')
      Weight function used in prediction. Possible values:
  
      - 'uniform' : uniform weights.  All points in each neighborhood
        are weighted equally.
      - 'distance' : weight points by the inverse of their distance.
        in this case, closer neighbors of a query point will have a
        greater influence than neighbors which are further away.
      - [callable] : a user-defined function which accepts an
        array of distances, and returns an array of the same shape
        containing the weights.
  n_jobs : int or None, optional (default = None)
      The number of parallel jobs to run for neighbors search.
      ``None`` means 1; ``-1`` means using all processors.
  
  Attributes
  ----------
  classes_ : array, shape=[n_classes,]
      Class labels known to the classifier
  effective_metric_ : string or callable
      The distance metric used. It will be same as the `distance` parameter
      or a synonym of it, e.g. 'euclidean' if the `distance` parameter set to
      'minkowski' and `p` parameter set to 2.
  effective_metric_params_ : dict
      Additional keyword arguments for the distance function.
      For most distances will be same with `distance_params` parameter,
      but may also contain the `p` parameter value if the
      `effective_metric_` attribute is set to 'minkowski'.
  outputs_2d_ : bool
      False when `y`'s shape is (n_samples, ) or (n_samples, 1) during fit
      otherwise True.
  
  References
  ----------
  This algorithm uses the scikit-learn library:
  https://github.com/scikit-learn/scikit-learn/blob/95d4f0841/sklearn/
  neighbors/_classification.py#L25
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code: def __init__(
      self,
      space,
      n_neighbors=5,
      weights="uniform",
      n_jobs=None,
  ):
      self.space = space
  
      distance = wrap(space.metric.dist)
      super().__init__(
          n_neighbors=n_neighbors,
          weights=weights,
          algorithm="brute",
          metric=distance,
          n_jobs=n_jobs,
      )
  
imports: from sklearn.neighbors import KNeighborsClassifier
  import geomstats.backend
label_desc: Use image node_id 1 to create a new KNearestNeighborsClassifier object from inherited base classes: KNeighborsClassifier with example: obj = KNearestNeighborsClassifier(space, n_neighbors, weights, n_jobs)