code: def _wrap_around_discrepancy(data):
      """Wrap-around Quasi-Monte Carlo discrepancy method"""
  
      n = data.shape[0]
      d = data.shape[1]
  
      disc = np.zeros(n)
  
      for i in prange(n):
          dc = 0.0
          for j in prange(n):
              prod = 1.0
              for k in prange(d):
                  x_kikj = abs(data[i, k] - data[j, k])
                  prod *= 3.0 / 2.0 - x_kikj + x_kikj**2
  
              dc += prod
          disc[i] = dc
  
      return -((4.0 / 3.0) ** d) + 1.0 / (n**2) * disc
  
file_path: pyod/pyod/models/qmcd.py
imports: from __future__ import division
  from __future__ import print_function
  import numpy
  from numba import njit, prange
  from sklearn.preprocessing import MinMaxScaler
  from sklearn.utils import check_array
  from sklearn.utils.validation import check_is_fitted
  from .base import BaseDetector
label_desc: Use image node_id 1 for calling a global function with example usage: _wrap_around_discrepancy(data) and returns: unknown