code: def _ttr(self, kloc, cache, **kwargs):
          
          if evaluation.get_dependencies(*list(self.inverse_map)):
              raise StochasticallyDependentError(
                  "Joint distribution with dependencies not supported.")
          output = numpy.zeros((2,)+kloc.shape)
          for dist in evaluation.sorted_dependencies(self):
              if dist not in self.inverse_map:
                  continue
              idx = self.inverse_map[dist]
              kloc_ = kloc[idx].reshape(1)
              values = evaluation.evaluate_recurrence_coefficients(
                  dist, kloc_, cache=cache)
              output.T[idx] = values.T
          return output
docs: Example:
              >>> dist = chaospy.J(chaospy.Uniform(), chaospy.Normal(), chaospy.Exponential())
              >>> print(numpy.around(dist.ttr([[1, 2, 3], [1, 2, 3], [1, 2, 3]]), 4))
              [[[0.5    0.5    0.5   ]
                [0.0833 0.0667 0.0643]
                [0.     0.     0.    ]]
              <BLANKLINE>
               [[1.     2.     3.    ]
                [3.     5.     7.    ]
                [1.     4.     9.    ]]]
              >>> d0 = chaospy.Uniform()
              >>> dist = chaospy.J(d0, d0+chaospy.Uniform())
              >>> print(numpy.around(dist.ttr([1, 1]), 4)) # doctest: +IGNORE_EXCEPTION_DETAIL
              Traceback (most recent call last):
                  ...
              chaospy.distributions.baseclass.StochasticallyDependentError: Joint ...