code: def _ppf(self, qloc, cache, **kwargs):
          
          xloc = numpy.zeros(qloc.shape)
          for dist in evaluation.sorted_dependencies(self, reverse=True):
              if dist not in self.inverse_map:
                  continue
              idx = self.inverse_map[dist]
              qloc_ = qloc[idx].reshape(1, -1)
              xloc[idx] = evaluation.evaluate_inverse(
                  dist, qloc_, cache=cache)[0]
          return xloc
docs: Example:
              >>> dist = chaospy.J(chaospy.Uniform(), chaospy.Normal())
              >>> print(numpy.around(dist.inv([[0.1, 0.2, 0.3], [0.3, 0.3, 0.4]]), 4))
              [[ 0.1     0.2     0.3   ]
               [-0.5244 -0.5244 -0.2533]]
              >>> d0 = chaospy.Uniform()
              >>> dist = chaospy.J(d0, d0+chaospy.Uniform())
              >>> print(numpy.around(dist.inv([[0.1, 0.2, 0.3], [0.3, 0.3, 0.4]]), 4))
              [[0.1 0.2 0.3]
               [0.4 0.5 0.7]]