consider using "torch.func.jvp()" or the low-level forward-mode
     AD API instead.
-[ Example ]-



def exp_reducer(x):
   ...     return x.exp().sum(dim=1)
inputs = torch.rand(4, 4)
v = torch.ones(4, 4)
jvp(exp_reducer, inputs, v)
   (tensor([6.3090, 4.6742, 7.9114, 8.2106]),
    tensor([6.3090, 4.6742, 7.9114, 8.2106]))
jvp(exp_reducer, inputs, v, create_graph=True)
   (tensor([6.3090, 4.6742, 7.9114, 8.2106], grad_fn=),
    tensor([6.3090, 4.6742, 7.9114, 8.2106], grad_fn=))
def adder(x, y):
   ...     return 2 * x + 3 * y
inputs = (torch.rand(2), torch.rand(2))
v = (torch.ones(2), torch.ones(2))
jvp(adder, inputs, v)
   (tensor([2.2399, 2.5005]),
    tensor([5., 5.]))


