Input is changed after calling `evaluate`

Somehow, after calling evaluate the initial matrix that I enter is changed by a factor of 2.

from scipy.sparse import diags, eye, csr_matrix, coo_matrix
import numpy as np
from lowdin.block_diagonalization import to_BlockSeries, general

H_0_AA = diags(np.arange(10, dtype=float) - 10)
H_0_BB = diags(np.arange(10, dtype=float) + 10)
h_AB = eye(10)
H_p_AB = {(1,): h_AB}
H_p_AA = {(1,): 0 * eye(10)}
H_p_BB = {(1,): 0 * eye(10)}
H = to_BlockSeries(H_0_AA, H_0_BB, H_p_AA, H_p_BB, H_p_AB)

def sparse_solve_sylvester(Y):
    Y_coo = coo_matrix(Y)
    E_A = H_0_AA.diagonal()
    E_B = H_0_BB.diagonal()
    Y_coo.data /= E_A[Y_coo.col] - E_B[Y_coo.row]
    return csr_matrix(Y_coo)

H_tilde, U, U_adjoint = general(H, solve_sylvester=sparse_solve_sylvester)

print(H_0_AA.diagonal())
print(H_tilde.evaluated[0, 0, 0:1].data[0].diagonal())
print(H_0_AA.diagonal())

The output is:

[-10.  -9.  -8.  -7.  -6.  -5.  -4.  -3.  -2.  -1.]
[-10.  -9.  -8.  -7.  -6.  -5.  -4.  -3.  -2.  -1.]
[-5.  -4.5 -4.  -3.5 -3.  -2.5 -2.  -1.5 -1.  -0.5]