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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 _apply_adjustments(COEFFS, C_ADJ, tau_ss, mean, stddevs, sites, rup, dists, imt, stddev_types, log_phi_ss, NL=None, tau_value=None):
 """ This method applies adjustments to the mean and standard deviation. The small-magnitude adjustments are applied to mean, whereas the embeded single station sigma logic tree is applied to the total standard deviation. """

    c1_dists = _compute_C1_term(C_ADJ, dists)
    phi_ss = _compute_phi_ss(
        C_ADJ, rup.mag, c1_dists, log_phi_ss, C_ADJ['mean_phi_ss']
    )

    mean_corr = np.exp(mean) * C_ADJ['k_adj'] * \
        _compute_small_mag_correction_term(C_ADJ, rup.mag, dists)

    mean_corr = np.log(mean_corr)

    std_corr = _get_corr_stddevs(COEFFS[imt], tau_ss, stddev_types,
                                 len(sites.vs30), phi_ss, NL, tau_value)

    stddevs = np.array(std_corr)

    return mean_corr, stddevs