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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 beta_confidence_intervals(ci_X, ntrials, ci=0.95):
 """ Compute confidence intervals of beta distributions. Parameters ci_X : numpy.array Computed confidence interval estimate from `ntrials` experiments ntrials : int The number of trials that were run. ci : float, optional, default=0.95 Confidence interval to report (e.g. 0.95 for 95% confidence interval) Returns ------- Plow : float The lower bound of the symmetric confidence interval. Phigh : float The upper bound of the symmetric confidence interval. Examples -------- """

    # Compute low and high confidence interval for symmetric CI about mean.
    ci_low = 0.5 - ci/2;
    ci_high = 0.5 + ci/2;

    # Compute for every element of ci_X.
    from scipy.stats import beta
    Plow = ci_X * 0.0;
    Phigh = ci_X * 0.0;
    for i in range(ci_X.shape[0]):
        for j in range(ci_X.shape[1]):
            Plow[i,j] = beta.ppf(ci_low, a = ci_X[i,j] * ntrials, b = (1-ci_X[i,j]) * ntrials);
            Phigh[i,j] = beta.ppf(ci_high, a = ci_X[i,j] * ntrials, b = (1-ci_X[i,j]) * ntrials);

    return [Plow, Phigh]