code: def bray_curtis(x, y):
      numerator = 0.0
      denominator = 0.0
      for i in range(x.shape[0]):
          numerator += np.abs(x[i] - y[i])
          denominator += np.abs(x[i] + y[i])
  
      if denominator > 0.0:
          return float(numerator) / denominator
      else:
          return 0.0
  
imports: import numba
  import numpy
  import scipy.stats
  from sklearn.metrics import pairwise_distances
label_desc: Use image node_id 21 for calling a global function with example usage: bray_curtis(x, y) and returns: unknown, int