code: def _ppf(self, uloc, left, right, cache):
          
          left = evaluation.get_inverse_cache(left, cache)
          right = evaluation.get_inverse_cache(right, cache)
  
          if isinstance(left, Dist):
              if isinstance(right, Dist):
                  raise evaluation.DependencyError(
                      "under-defined distribution {} or {}".format(left, right))
          elif not isinstance(right, Dist):
              return left+right
          else:
              left, right = right, left
  
          xloc = evaluation.evaluate_inverse(left, uloc, cache=cache)
          output = (xloc.T + numpy.asfarray(right).T).T
          return output
docs: Point percentile function.
  
          Example:
              >>> print(chaospy.Uniform().inv([0.1, 0.2, 0.9]))
              [0.1 0.2 0.9]
              >>> print(chaospy.Add(chaospy.Uniform(), 2).inv([0.1, 0.2, 0.9]))
              [2.1 2.2 2.9]
              >>> print(chaospy.Add(2, chaospy.Uniform()).inv([0.1, 0.2, 0.9]))
              [2.1 2.2 2.9]
              >>> print(chaospy.Add(1, 1).inv([0.1, 0.2, 0.9]))
              [2. 2. 2.]