class_docstr: Class for K-medoids clustering on manifolds.
  
  K-medoids algorithm using Riemannian manifolds.
  
  Parameters
  ----------
  space : Manifold
      Equipped manifold.
  n_clusters : int
      Number of clusters (k value of k-medoids).
      Optional, default: 8.
  max_iter : int
      Maximum number of iterations.
      Optional, default: 100.
  init : str
      How to initialize cluster centers at the beginning of the algorithm. The
      choice 'random' will select training points as initial cluster centers
      uniformly at random.
      Optional, default: 'random'.
  n_jobs : int
      Number of jobs to run in parallel. `-1` means using all processors.
      Optional, default: 1.
  
  Notes
  -----
  * Required metric methods: `dist`, `dist_pairwise`.
  
  Example
  -------
  Available example on the Poincaré Ball and Hypersphere manifolds
  :mod:`examples.plot_kmedoids_manifolds`
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code: def __init__(
      self, space, n_clusters=8, init="random", max_iter=100, n_jobs=1
  ):
      self.space = space
      self.n_clusters = n_clusters
      self.max_iter = max_iter
      self.init = init
      self.n_jobs = n_jobs
  
      self.cluster_centers_ = None
      self.labels_ = None
      self.medoid_indices_ = None
  
file_path: geomstats/geomstats/learning/kmedoids.py
imports: import logging
  from sklearn.base import BaseEstimator, ClusterMixin
  import geomstats.backend
  from geomstats.learning._template import TransformerMixin
label_desc: Use image node_id 1 to create a new RiemannianKMedoids object from inherited base classes: TransformerMixin, ClusterMixin, BaseEstimator with example: obj = RiemannianKMedoids(space, n_clusters, init, max_iter, n_jobs)