In the Demo: one SGD step, set the random seed to 2026 and the learning rate to 0.1 then run the demo.

The initial weights should be:

Initial weights:
 tensor([[ 0.3753,  0.1500],
        [ 0.1319, -0.6104]])
Initial biases:
 tensor([ 0.0136, -0.3036])

The gradients should be:

Gradient computation:
grad(W):
 tensor([[ 0.2432, -0.1886],
        [-0.2432,  0.1886]])
grad(b):
 tensor([ 0.0957, -0.0957])
grad(W) norm: 0.4351941645145416
grad(b) norm: 0.13534791767597198

The updated weigths and biases should be:

Updated weights:
 tensor([[ 0.3510,  0.1689],
        [ 0.1562, -0.6293]])
Updated biases:
 tensor([ 0.0041, -0.2940])

Can you explain how these updated weights and biases are calculated? Write down the formula with the for the complete computation for 

initial weight -> updated weight

Also give an example computation for one of the parameters.