input = np.array([[2,4,6],[3,5,7],[1,6,8]], dtype=np.float32) 
eigval_all, eigvec_all = tf.self_adjoint_eig(input)
with tf.compat.v1.Session() as sess:
    print(sess.run(tf.self_adjoint_eig(input)))

OUTPUT:
(array([-0.94226784,  2.6309953 , 13.311278  ], dtype=float32), array([[-0.580067  , -0.7810435 ,  0.2312871 ],
       [ 0.70494777, -0.33907872,  0.62295616],
       [-0.40813133,  0.52440166,  0.7472832 ]], dtype=float32))

Pytorch equivalent:
torch.symeig(torch.tensor(input), eigenvectors=True)
OUTPUT:
torch.return_types.symeig(
eigenvalues=tensor([-1.7482, -0.3348, 17.0831]),
eigenvectors=tensor([[ 0.7941,  0.4292,  0.4304],
        [ 0.1423, -0.8197,  0.5549],
        [-0.5909,  0.3794,  0.7119]]))

Another:
torch.eig(torch.tensor(input),eigenvectors=True)
OUTPUT:
torch.return_types.eig(
eigenvalues=tensor([[14.6176,  0.0000],
        [ 0.1912,  0.6115],
        [ 0.1912, -0.6115]]),
eigenvectors=tensor([[-0.4899,  0.1709,  0.3562],
        [-0.6077,  0.7177,  0.0000],
        [-0.6250, -0.5663, -0.0900]]))