code: def _cdf(self, xloc, left, right, cache):
          
          left = evaluation.get_forward_cache(left, cache)
          right = evaluation.get_forward_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 numpy.asfarray(left+right <= xloc)
          else:
              left, right = right, left
          xloc = (xloc.T-numpy.asfarray(right).T).T
          output = evaluation.evaluate_forward(left, xloc, cache=cache)
          assert output.shape == xloc.shape
          return output
docs: Cumulative distribution function.
  
          Example:
              >>> print(chaospy.Uniform().fwd([-0.5, 0.5, 1.5, 2.5]))
              [0.  0.5 1.  1. ]
              >>> print(chaospy.Add(chaospy.Uniform(), 1).fwd([-0.5, 0.5, 1.5, 2.5]))
              [0.  0.  0.5 1. ]
              >>> print(chaospy.Add(1, chaospy.Uniform()).fwd([-0.5, 0.5, 1.5, 2.5]))
              [0.  0.  0.5 1. ]
              >>> print(chaospy.Add(1, 1).fwd([-0.5, 0.5, 1.5, 2.5]))
              [0. 0. 0. 1.]