code: def _log_prior_gradients(self):
          
          if self.priors.size == 0:
              return 0.
          x = self.param_array
          ret = np.zeros(x.size)
          #compute derivate of prior density
          [np.put(ret, ind, p.lnpdf_grad(x[ind])) for p, ind in self.priors.items()]
          #add in jacobian derivatives if transformed
          priored_indexes = np.hstack([i for p, i in self.priors.items()])
          for c,j in self.constraints.items():
              if not isinstance(c, Transformation):continue
              for jj in j:
                  if jj in priored_indexes:
                      ret[jj] += c.log_jacobian_grad(x[jj])
          return ret
docs: evaluate the gradients of the priors