code: def compute_summary_statistic(iscs, summary_statistic='mean', axis=None):
  
      
  
      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
docs: 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