Get the adjacency matrix for a Squared Grid

Question

In a grid LxL where L is the square root of the number of elements N (L=numpy.sqrt(N)) i want to obtain the adjacency matrix for all the points that are close to each other, including the diagonals.

I already created an algorithm but i think is not the fastest, so i want to ask you how you would solve this problem.

Here is my solution

def get_2D_proximity(sqr_nblocks):
    ncol = sqr_nblocks
    nblocks = sqr_nblocks**2
    adj_mtx = np.zeros([nblocks, nblocks])

    for cell in range(ncol**2):
        ## right
        if (cell+1) % ncol == 0:
            pass
        else:
            adj_mtx[cell, cell+1] = 1
        ## down
        if cell >= ncol*(ncol-1):
            pass
        else:
            adj_mtx[cell, cell+ncol] = 1

        ## right-down diagonal
        if ((cell+1) % ncol == 0) | (cell >= ncol*(ncol-1)):
            pass
        else:
            adj_mtx[cell, cell+1+ncol] = 1

        ## left-down diagonal
        if ((cell) % ncol == 0) | (cell >= ncol*(ncol-1)):
            pass
        else:
            adj_mtx[cell, cell-1+ncol] = 1 

        upper_triangle_idx = np.triu_indices(adj_mtx.shape[0], k=1)
        adj_mtx.T[upper_triangle_idx] = adj_mtx[upper_triangle_idx]
    return adj_mtx 

print(get_2D_proximity(3))


#### OUTPUT ####
array([[0., 1., 0., 1., 1., 0., 0., 0., 0.],
       [1., 0., 1., 1., 1., 1., 0., 0., 0.],
       [0., 1., 0., 0., 1., 1., 0., 0., 0.],
       [1., 1., 0., 0., 1., 0., 1., 1., 0.],
       [1., 1., 1., 1., 0., 1., 1., 1., 1.],
       [0., 1., 1., 0., 1., 0., 0., 1., 1.],
       [0., 0., 0., 1., 1., 0., 0., 1., 0.],
       [0., 0., 0., 1., 1., 1., 1., 0., 1.],
       [0., 0., 0., 0., 1., 1., 0., 1., 0.]])

Below there is a graphical representation of the adjacency matrix.