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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def normality(*args, alpha=.05):
 """Shapiro-Wilk univariate normality test. Parameters Array of sample data. May be of different lengths. Returns ------- normal : boolean True if x comes from a normal distribution. p : float P-value. See Also -------- homoscedasticity : Test equality of variance. sphericity : Mauchly's test for sphericity. Notes ----- The Shapiro-Wilk test calculates a :math:`W` statistic that tests whether a The :math:`W` statistic is calculated as follows: .. math:: W = \\frac{(\\sum_{i=1}^n a_i x_{i})^2} {\\sum_{i=1}^n (x_i - \\overline{x})^2} where the :math:`x_i` are the ordered sample values (in ascending order) and the :math:`a_i` are constants generated from the means, variances and covariances of the order statistics of a sample of size :math:`n` from a standard normal distribution. Specifically: expected values of the order statistics of independent and identically distributed random variables sampled from the standard normal distribution, and :math:`V` is the covariance matrix of those order statistics. The null-hypothesis of this test is that the population is normally distributed. Thus, if the p-value is less than the chosen alpha level (typically set at 0.05), then the null hypothesis is rejected and there is evidence that the data tested are not normally distributed. The result of the Shapiro-Wilk test should be interpreted with caution in the case of large sample sizes. Indeed, quoting from Wikipedia: *"Like most statistical significance tests, if the sample size is sufficiently large this test may detect even trivial departures from the null hypothesis (i.e., although there may be some statistically significant effect, it may be too small to be of any practical significance); thus, additional investigation of the effect size is typically advisable, e.g., a Q–Q plot in this case."* References .. [1] Shapiro, S. S., & Wilk, M. B. (1965). An analysis of variance test for normality (complete samples). Biometrika, 52(3/4), 591-611. .. [2] https://www.itl.nist.gov/div898/handbook/prc/section2/prc213.htm .. [3] https://en.wikipedia.org/wiki/Shapiro%E2%80%93Wilk_test Examples -------- 1. Test the normality of one array. True 0.275 2. Test the normality of two arrays. [ True False] [0.275 0.001] """

    from scipy.stats import shapiro
    k = len(args)
    p = np.zeros(k)
    normal = np.zeros(k, 'bool')
    for j in range(k):
        _, p[j] = shapiro(args[j])
        normal[j] = True if p[j] > alpha else False

    if k == 1:
        normal = bool(normal)
        p = float(p)

    return normal, np.round(p, 3)