code: def log_beta(x, y):
      a = min(x, y)
      b = max(x, y)
      if b < 5:
          value = -np.log(b)
          for i in range(1, int(a)):
              value += np.log(i) - np.log(b + i)
          return value
      else:
          return (
              approx_log_Gamma(x)
              + approx_log_Gamma(y)
              - approx_log_Gamma(x + y)
          )
  
imports: import numba
  import numpy
  import scipy.stats
  from sklearn.metrics import pairwise_distances
label_desc: Use image node_id 40 for calling a global function with example usage: log_beta(x, y) and returns: value, unknown