class_docstr: Recursive Neighbor Agglomeration (:term:`ReNA`).
  
  Recursively merges the pair of clusters according to 1-nearest neighbors
  criterion.
  See :footcite:t:`Hoyos2019`.
  
  Parameters
  ----------
  mask_img : Niimg-like object
      Object used for masking the data.
  
  n_clusters : :obj:`int`, default=2
      The number of clusters to find.
  
  scaling : :obj:`bool`, default=False
      If scaling is True, each cluster is scaled by the square root of its
      size, preserving the l2-norm of the image.
  
  n_iter : :obj:`int`, default=10
      Number of iterations of the recursive neighbor agglomeration.
  
  threshold : :obj:`float` in the open interval (0., 1.), default=1e-7
      Threshold used to handle eccentricities.
  %(memory)s
  %(memory_level1)s
  %(verbose0)s
  
  Attributes
  ----------
  labels_ : :class:`numpy.ndarray`, shape = [n_features]
      Cluster labels for each feature.
  
  n_clusters_ : :obj:`int`
      Number of clusters.
  
  sizes_ : :class:`numpy.ndarray`, shape = [n_features]
      It contains the size of each cluster.
  
  References
  ----------
  .. footbibliography::
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code: def _more_tags(self):
      return BaseEstimator._more_tags()
  
imports: import warnings
  import numpy
  from joblib import Memory
  from nibabel import Nifti1Image
  from scipy.sparse import coo_matrix, csgraph, dia_matrix
  from sklearn.base import BaseEstimator, ClusterMixin, TransformerMixin
  from sklearn.utils import check_array
  from sklearn.utils.validation import check_is_fitted
  from nilearn._utils import fill_doc
  from nilearn.image import get_data
  from nilearn.masking import unmask_from_to_3d_array
label_desc: Use image node_id 2 for calling the ReNA obj's underlying member method code with example usage: obj._more_tags() and returns: BaseEstimator