class_docstr: Use a precomputed distance matrix to construct the KNNG.
  
  Parameters
  ----------
  neighbors: np.ndarray
      A N x K matrix containing the indices of point i's k nearest neighbors.
  
  distances: np.ndarray
      A N x K matrix containing the distances to from data point i to its k
      nearest neighbors.
class_docstr_tok: ["Use","a","precomputed","distance","matrix","to","construct","the","KNNG",".","Parameters","--","--","--","--","--","neighbors",":","np.ndarray","A","N","x","K","matrix","containing","the","indices","of","point","i","'s","k","nearest","neighbors",".","distances",":","np.ndarray","A","N","x","K","matrix","containing","the","distances","to","from","data","point","i","to","its","k","nearest","neighbors","."]
code: def build(self):
      return self.indices, self.distances
  
imports: import logging
  import os
  import warnings
  import numpy
  from scipy.spatial.distance import cdist
  from sklearn import neighbors
  from sklearn.utils import check_random_state
  from openTSNE import utils
label_desc: Use image node_id 2 for calling the PrecomputedNeighbors obj's underlying member method code with example usage: obj.build() and returns: self,  self