code: def count_distance(x, y, poisson_lambda=1.0, normalisation=1.0):
      lo = int(min(x, y))
      hi = int(max(x, y))
  
      log_lambda = np.log(poisson_lambda)
  
      if lo < 2:
          log_k_factorial = 0.0
      elif lo < 10:
          log_k_factorial = 0.0
          for k in range(2, lo):
              log_k_factorial += np.log(k)
      else:
          log_k_factorial = approx_log_Gamma(lo + 1)
  
      result = 0.0
  
      for k in range(lo, hi):
          result += k * log_lambda - poisson_lambda - log_k_factorial
          log_k_factorial += np.log(k)
  
      return result / normalisation
  
imports: import numba
  import numpy
  import scipy.stats
  from sklearn.metrics import pairwise_distances
label_desc: Use image node_id 55 for calling a global function with example usage: count_distance(x, y, poisson_lambda, normalisation) and returns: unknown