code: def median1d(self, name, return_errors=False):
          
          if return_errors:
              mid = self.data[name]['best']
              low, high = self.data[name]['err']
              return (mid, low, high)
          else:
              return self.data[name]['best']
docs: Return median 1d marginalized parameters
  
          Parameters
          ----------
          name: str
              The name of the parameter requested
          return_errors: Optional, {bool, False}
              If true, return a second and third parameter that represents the
              lower and upper 90% error on the parameter.
  
          Returns
          -------
          param: float or tuple
              The requested parameter