Aim of grad_tensor of backward(), and how to compute Jocobian-vector product

autograd
1

b25 = torch.tensor([[1.,2.,3.,4.,5.],[6.,7.,8.,9.,10.]], requires_grad= True)
def func2(x):
y = x**2
return y

y = func2(b25)

vector = torch.tensor([[100., 1., 1., 1., 1.],[1., 1., 1., 1., 1.]])
Jocobian = torch.autograd.functional.jacobian(func2, b25, create_graph=False, strict=False)

y.backward(vector)

The Jocobian is a tensor of (2,5,2,5), because y is (2,5), b25 is also (2,5).
Jocobian is the derivatives of dy/db25
According to the Jocobian-vector product, we can multiply Jocobian with vector to get the result that is same as y.backward(vector). But how could tensor of (2,5,2,5) and vector (2,5) to derive a (2,5) final gradient?

Jocobian = tensor([[
         [[ 2.,  0.,  0.,  0.,  0.],
          [ 0.,  0.,  0.,  0.,  0.]],

         [[ 0.,  4.,  0.,  0.,  0.],
          [ 0.,  0.,  0.,  0.,  0.]],

         [[ 0.,  0.,  6.,  0.,  0.],
          [ 0.,  0.,  0.,  0.,  0.]],

         [[ 0.,  0.,  0.,  8.,  0.],
          [ 0.,  0.,  0.,  0.,  0.]],

         [[ 0.,  0.,  0.,  0., 10.],
          [ 0.,  0.,  0.,  0.,  0.]]],


        [[[ 0.,  0.,  0.,  0.,  0.],
          [12.,  0.,  0.,  0.,  0.]],

         [[ 0.,  0.,  0.,  0.,  0.],
          [ 0., 14.,  0.,  0.,  0.]],

         [[ 0.,  0.,  0.,  0.,  0.],
          [ 0.,  0., 16.,  0.,  0.]],

         [[ 0.,  0.,  0.,  0.,  0.],
          [ 0.,  0.,  0., 18.,  0.]],

         [[ 0.,  0.,  0.,  0.,  0.],
          [ 0.,  0.,  0.,  0., 20.]]]])

This is final gradients compute by pytorch, how can I get the same result by using Jocobian and vector above?

b25.grad
tensor([[200., 4., 6., 8., 10.],
[ 12., 14., 16., 18., 20.]])