code: def stde_median(data, axis=None):
      """Returns the McKean-Schrader estimate of the standard error of the sample
      median along the given axis. masked values are discarded.
  
      Parameters
      ----------
      data : ndarray
          Data to trim.
      axis : {None,int}, optional
          Axis along which to perform the trimming.
          If None, the input array is first flattened.
  
      """
  
      def _stdemed_1D(data):
          data = np.sort(data.compressed())
          n = len(data)
          z = 2.5758293035489004
          k = int(np.round((n + 1) / 2.0 - z * np.sqrt(n / 4.0), 0))
          return (data[n - k] - data[k - 1]) / (2.0 * z)
  
      data = ma.array(data, copy=False, subok=True)
      if axis is None:
          return _stdemed_1D(data)
      else:
          if data.ndim > 2:
              raise ValueError(
                  "Array 'data' must be at most two dimensional, "
                  "but got data.ndim = %d" % data.ndim
              )
          return ma.apply_along_axis(_stdemed_1D, axis, data)
  
imports: from __future__ import division, print_function, absolute_import
  import numpy
  from numpy import ndarray
  import numpy.ma
  from numpy.ma import masked, nomask
  from scipy._lib.six import iteritems
  import itertools
  import warnings
  from collections import namedtuple
  from .None import distributions
  import scipy.special
  from ._stats_mstats_common import _find_repeats, linregress, theilslopes, siegelslopes
label_desc: Use image node_id 47 for calling a global function with example usage: stde_median(data, axis) and returns: unknown, _stdemed_1D, ma