code: def kulsinski(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:
          return 0.0
      else:
          return float(num_not_equal - num_true_true + x.shape[0]) / (
              num_not_equal + x.shape[0]
          )
  
imports: import numba
  import numpy
  import scipy.stats
  from sklearn.metrics import pairwise_distances
label_desc: Use image node_id 26 for calling a global function with example usage: kulsinski(x, y) and returns: int, unknown