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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 compute_summary_statistic(iscs, summary_statistic='mean', axis=None):
 """Computes summary statistics for ISCs Computes either the 'mean' or 'median' across a set of ISCs. In the case of the mean, ISC values are first Fisher Z transformed (arctanh), averaged, then inverse Fisher Z transformed (tanh). The implementation is based on the work in [SilverDunlap1987]_. .. [SilverDunlap1987] "Averaging corrlelation coefficients: should Fisher's z transformation be used?", N. C. Silver, W. P. Dunlap, 1987, Journal of Applied Psychology, 72, 146-148. https://doi.org/10.1037/0021-9010.72.1.146 Parameters iscs : list or ndarray ISC values summary_statistic : str, default: 'mean' Summary statistic, 'mean' or 'median' axis : None or int or tuple of ints, optional Axis or axes along which the means are computed. The default is to compute the mean of the flattened array. Returns ------- statistic : float or ndarray Summary statistic of ISC values """


    if summary_statistic not in ('mean', 'median'):
        raise ValueError("Summary statistic must be 'mean' or 'median'")

    # Compute summary statistic
    if summary_statistic == 'mean':
        statistic = np.tanh(np.nanmean(np.arctanh(iscs), axis=axis))
    elif summary_statistic == 'median':
        statistic = np.nanmedian(iscs, axis=axis)

    return statistic