code: def parallel_special_metric(X, Y=None, metric=hellinger):
      if Y is None:
          result = np.zeros((X.shape[0], X.shape[0]))
  
          for i in range(X.shape[0]):
              for j in range(i + 1, X.shape[0]):
                  result[i, j] = metric(X[i], X[j])
                  result[j, i] = result[i, j]
      else:
          result = np.zeros((X.shape[0], Y.shape[0]))
  
          for i in range(X.shape[0]):
              for j in range(Y.shape[0]):
                  result[i, j] = metric(X[i], Y[j])
  
      return result
  
imports: import numba
  import numpy
  import scipy.stats
  from sklearn.metrics import pairwise_distances
label_desc: Use image node_id 57 for calling a global function with example usage: parallel_special_metric(X, Y, metric) and returns: result