python – increase efficiency of loops and element-wise operations in PyTorch implementation

For any input matrix W, I have the following implementation in PyTorch. I was wondering if the following can be improved in terms of efficiency,

P.S. Would current implementation break backpropagation?

import torch

W = torch.tensor(((0,1,0,0,0,0,0,0,0),
                  (1,0,1,0,0,1,0,0,0),
                  (0,1,0,3,0,0,0,0,0),
                  (0,0,3,0,1,0,0,0,0),
                  (0,0,0,1,0,1,1,0,0),
                  (0,1,0,0,1,0,0,0,0),
                  (0,0,0,0,1,0,0,1,0),
                  (0,0,0,0,0,0,1,0,1),
                  (0,0,0,0,0,0,0,1,0)))

n = len(W)
C = torch.empty(n, n)
I = torch.eye(n)
for i in range(n):
    for j in range(n):
        B = W.clone()
        B(i, j) = 0
        B(j, i) = 0

        tmp = torch.inverse(n * I - B)

        C(i, j) = tmp(i, j)