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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 crossvalidation_stats(errors1, errors2):
 """Paired difference test of the CV errors of two models Parameters: errors1 : ndarray The CV errors model 1 errors2 : ndarray The CV errors model 2 Returns: -------- pvalue : float Two-sided P-value if the differences between err1 and err2 are significant tscore : float t-statistics se : float Standard Error of the CV-Error-Difference mu : float The average difference between err1 and err2 """

    # load modules
    import numpy as np
    import scipy.stats
    import warnings

    # Number of blocks
    K = errors1.shape[0]

    # display warnings
    if K < 30:
        warnings.warn((
            "The number of blocks is K<30 what is insufficient "
            "for conducting a t-Test to compare both models! "
            "K=40 is suggested."))

    # difference between errors
    delta = errors1 - errors2

    # the average difference
    mu = np.mean(delta)

    # Standard Error of the CV-Error-Difference
    #   se = np.sqrt(np.sum((delta-np.mean(delta))**2)/K)
    se = np.std(delta)

    # t-statistics
    tscore = mu / se

    # Two-sided P-value
    pvalue = scipy.stats.t.sf(np.abs(tscore), K - 1) * 2

    # done
    return pvalue, tscore, se, mu