code: def convert_distance_to_log_probability(distances, a=1.0, b=1.0):
      """
       convert distance representation into log probability,
          as a function of a, b params
  
      Parameters
      ----------
      distances : array
          euclidean distance between two points in embedding
      a : float, optional
          parameter based on min_dist, by default 1.0
      b : float, optional
          parameter based on min_dist, by default 1.0
  
      Returns
      -------
      float
          log probability in embedding space
      """
      return -tf.math.log1p(a * distances ** (2 * b))
  
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 3 for calling a global function with example usage: convert_distance_to_log_probability(distances, a, b) and returns: unknown