code: def sokal_sneath(x, y):
      num_true_true = 0.0
      num_not_equal = 0.0
      for i in range(x.shape[0]):
          x_true = x[i] != 0
          y_true = y[i] != 0
          num_true_true += x_true and y_true
          num_not_equal += x_true != y_true
  
      if num_not_equal == 0.0:
          return 0.0
      else:
          return num_not_equal / (0.5 * num_true_true + num_not_equal)
  
imports: import numba
  import numpy
  import scipy.stats
  from sklearn.metrics import pairwise_distances
label_desc: Use image node_id 30 for calling a global function with example usage: sokal_sneath(x, y) and returns: int, unknown