Hi,I realize that I have no knowledge of how gradient values we get after calling backward.
for example :
x = torch.randn((4,5),requires_grad = True)
z = x.mean()
z.backward()
#print(x.grad)

Assume I have the values of x
>>> x
tensor([[-0.3571,  0.1481,  0.1713, -1.2597, -0.7667],
        [-0.1553, -0.9620,  0.0103,  3.3494,  0.2220],
        [ 2.1131, -0.2404,  0.4820,  0.3816,  1.9752],
        [ 1.7232, -0.5064, -0.8151,  0.3720,  0.1470]], requires_grad=True)
>>> 

And the result of x.grad returns
tensor([[1.1500, 1.1500, 1.1500, 1.1500, 1.1500],
        [1.1500, 1.1500, 1.1500, 1.1500, 1.1500],
        [1.1500, 1.1500, 1.1500, 1.1500, 1.1500],
        [1.1500, 1.1500, 1.1500, 1.1500, 1.1500]])


And if I change  z = x.mean()  to z = x.sum()  , x.grad becomes
tensor([[2.1500, 2.1500, 2.1500, 2.1500, 2.1500],
                [2.1500, 2.1500, 2.1500, 2.1500, 2.1500],
                [2.1500, 2.1500, 2.1500, 2.1500, 2.1500],
                [2.1500, 2.1500, 2.1500, 2.1500, 2.1500]])

I would like to know how do we compute and get the values both 1.1500 and 2.1500
Thanks in advance !