code: def mannwhitneyu(x, y, use_continuity=True):
      """
      Computes the Mann-Whitney statistic
  
      Missing values in `x` and/or `y` are discarded.
  
      Parameters
      ----------
      x : sequence
          Input
      y : sequence
          Input
      use_continuity : {True, False}, optional
          Whether a continuity correction (1/2.) should be taken into account.
  
      Returns
      -------
      statistic : float
          The Mann-Whitney statistics
      pvalue : float
          Approximate p-value assuming a normal distribution.
  
      """
      x = ma.asarray(x).compressed().view(ndarray)
      y = ma.asarray(y).compressed().view(ndarray)
      ranks = rankdata(np.concatenate([x, y]))
      (nx, ny) = (len(x), len(y))
      nt = nx + ny
      U = ranks[:nx].sum() - nx * (nx + 1) / 2.0
      U = max(U, nx * ny - U)
      u = nx * ny - U
  
      mu = (nx * ny) / 2.0
      sigsq = (nt**3 - nt) / 12.0
      ties = count_tied_groups(ranks)
      sigsq -= (
          sum(v * (k**3 - k) for (k, v) in iteritems(ties)) / 12.0
      )
      sigsq *= nx * ny / float(nt * (nt - 1))
  
      if use_continuity:
          z = (U - 1 / 2.0 - mu) / ma.sqrt(sigsq)
      else:
          z = (U - mu) / ma.sqrt(sigsq)
  
      prob = special.erfc(abs(z) / np.sqrt(2))
      return MannwhitneyuResult(u, prob)
  
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 23 for calling a global function with example usage: mannwhitneyu(x, y, use_continuity) and returns: MannwhitneyuResult