code: def geometric_median(x, eps=1e-5):
      """
      Find the multivariate geometric L1-median by applying
      Vardi and Zhang algorithm.
  
      Parameters
      ----------
      x : array-like, the data points
      eps: float (default=1e-5), a threshold to indicate when to stop
  
      Returns
      -------
      gm : array, Geometric L1-median
      """
      points = np.unique(x, axis=0)
      gm_ = np.mean(points, 0)  # initialize geometric median
      while True:
          D = euclidean(points, gm_, c=True)
          non_zeros = (D != 0)[:, 0]
          Dinv = 1 / D[non_zeros]
          Dinvs = np.sum(Dinv)
          W = Dinv / Dinvs
          T = np.sum(W * points[non_zeros], 0)
          num_zeros = len(points) - np.sum(non_zeros)
          if num_zeros == 0:
              gm1 = T
          elif num_zeros == len(points):
              return gm_
          else:
              R = (T - gm_) * Dinvs
              r = np.linalg.norm(R)
              r_inv = 0 if r == 0 else num_zeros / r
              gm1 = max(0, 1 - r_inv) * T + min(1, r_inv) * gm_
  
          if euclidean(gm_, gm1) < eps:
              return gm1
  
          gm_ = gm1
  
imports: from __future__ import division
  from __future__ import print_function
  import multiprocessing
  from itertools import combinations
  from multiprocessing import Pool
  import numba
  import numpy
  from sklearn.preprocessing import MinMaxScaler, RobustScaler
  from sklearn.utils import check_array
  from .base import BaseDetector
label_desc: Use image node_id 3 for calling a global function with example usage: geometric_median(x, eps) and returns: gm1, gm_