code: def fit(self, X, y=None, precomputed_distances=None):
      if self.metric == "precomputed":
          if precomputed_distances is None:
              raise ValueError(
                  "Precomputed distances must be supplied if metric \
                      is precomputed."
              )
          # prepare X for training the network
          self._X = X
          # geneate the graph on precomputed distances
          return super().fit(precomputed_distances, y)
      else:
          return super().fit(X, y)
  
imports: import numpy
  from umap import UMAP
  from warnings import warn, catch_warnings, filterwarnings
  from numba import TypingError
  import os
  from umap.spectral import spectral_layout
  from sklearn.utils import check_random_state
  import codecs, pickle
  from sklearn.neighbors import KDTree
  import sys
label_desc: Use image node_id 2 for calling the ParametricUMAP obj's underlying member method code with example usage: obj.fit(X, y, precomputed_distances) and returns: super, super