code: def normaltest(a, axis=0):
      """
      Tests whether a sample differs from a normal distribution.
  
      Parameters
      ----------
      a : array_like
          The array containing the data to be tested.
      axis : int or None, optional
          Axis along which to compute test. Default is 0. If None,
          compute over the whole array `a`.
  
      Returns
      -------
      statistic : float or array
          ``s^2 + k^2``, where ``s`` is the z-score returned by `skewtest` and
          ``k`` is the z-score returned by `kurtosistest`.
      pvalue : float or array
         A 2-sided chi squared probability for the hypothesis test.
  
      Notes
      -----
      For more details about `normaltest`, see `stats.normaltest`.
  
      """
      a, axis = _chk_asarray(a, axis)
      s, _ = skewtest(a, axis)
      k, _ = kurtosistest(a, axis)
      k2 = s * s + k * k
  
      return NormaltestResult(k2, distributions.chi2.sf(k2, 2))
  
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 50 for calling a global function with example usage: normaltest(a, axis) and returns: NormaltestResult