code: def compare_medians_ms(group_1, group_2, axis=None):
      """
      Compares the medians from two independent groups along the given axis.
  
      The comparison is performed using the McKean-Schrader estimate of the
      standard error of the medians.
  
      Parameters
      ----------
      group_1 : array_like
          First dataset.  Has to be of size >=7.
      group_2 : array_like
          Second dataset.  Has to be of size >=7.
      axis : int, optional
          Axis along which the medians are estimated. If None, the arrays are
          flattened.  If `axis` is not None, then `group_1` and `group_2`
          should have the same shape.
  
      Returns
      -------
      compare_medians_ms : {float, ndarray}
          If `axis` is None, then returns a float, otherwise returns a 1-D
          ndarray of floats with a length equal to the length of `group_1`
          along `axis`.
  
      """
      (med_1, med_2) = (
          ma.median(group_1, axis=axis),
          ma.median(group_2, axis=axis),
      )
      (std_1, std_2) = (
          mstats.stde_median(group_1, axis=axis),
          mstats.stde_median(group_2, axis=axis),
      )
      W = np.abs(med_1 - med_2) / ma.sqrt(std_1**2 + std_2**2)
      return 1 - norm.cdf(W)
  
file_path: catboost/contrib/python/scipy/py2/scipy/stats/mstats_extras.py
imports: from __future__ import division, print_function, absolute_import
  import numpy
  from numpy import float_, int_, ndarray
  import numpy.ma
  from numpy.ma import MaskedArray
  from .None import mstats_basic
  from scipy.stats.distributions import norm, beta, t, binom
label_desc: Use image node_id 8 for calling a global function with example usage: compare_medians_ms(group_1, group_2, axis) and returns: unknown