code: def compute_connectivities_umap(knn_indices, knn_dists,
          n_obs, n_neighbors, set_op_mix_ratio=1.0,
          local_connectivity=1.0):
      
      from umap.umap_ import fuzzy_simplicial_set
  
      X = coo_matrix(([], ([], [])), shape=(n_obs, 1))
      connectivities = fuzzy_simplicial_set(X, n_neighbors, None, None,
                                            knn_indices=knn_indices, knn_dists=knn_dists,
                                            set_op_mix_ratio=set_op_mix_ratio,
                                            local_connectivity=local_connectivity)
      distances = get_sparse_matrix_from_indices_distances_umap(knn_indices, knn_dists, n_obs, n_neighbors)
  
      return distances, connectivities.tocsr()
docs: This is from umap.fuzzy_simplicial_set [McInnes18]_.
  
      Given a set of data X, a neighborhood size, and a measure of distance
      compute the fuzzy simplicial set (here represented as a fuzzy graph in
      the form of a sparse matrix) associated to the data. This is done by
      locally approximating geodesic distance at each point, creating a fuzzy
      simplicial set for each such point, and then combining all the local
      fuzzy simplicial sets into a global one via a fuzzy union.