class_docstr: Use a precomputed distance matrix to construct the KNNG.
  
  Parameters
  ----------
  distance_matrix: np.ndarray
      A square, symmetric, and contain only poistive values.
class_docstr_tok: ["Use","a","precomputed","distance","matrix","to","construct","the","KNNG",".","Parameters","--","--","--","--","--","distance_matrix",":","np.ndarray","A","square",",","symmetric",",","and","contain","only","poistive","values","."]
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 PrecomputedDistanceMatrix obj's underlying member method code with example usage: obj.build() and returns: self,  self