TypeError: super(type, obj): obj must be an instance or subt

def compute_energy(self, z, phi=None, mu=None, cov=None, size_average=True):
if phi is None:
phi = to_var(self.phi)
if mu is None:
mu = to_var(self.mu)
if cov is None:
cov = to_var(self.cov)
k, D, _ = cov.size()
z_mu = (z.unsqueeze(1)- mu.unsqueeze(0))
cov_inverse = []
det_cov = []
cov_diag = 0
eps = 1e-12
for i in range(k):
# K x D x D
cov_k = cov[i] + to_var(torch.eye(D)*eps)
cov_inverse.append(torch.inverse(cov_k).unsqueeze(0))
#det_cov.append(np.linalg.det(cov_k.data.cpu().numpy()* (2*np.pi)))
det_cov.append((Cholesky.apply(cov_k.cpu() * (2*np.pi)).diag().prod()).unsqueeze(0))
cov_diag = cov_diag + torch.sum(1 / cov_k.diag())
# K x D x D
cov_inverse = torch.cat(cov_inverse, dim=0)
# K
#det_cov = to_var(torch.from_numpy(np.float32(np.array(det_cov))))
# N x K
exp_term_tmp = -0.5 * torch.sum(torch.sum(z_mu.unsqueeze(-1) * cov_inverse.unsqueeze(0), dim=-2) * z_mu, dim=-1)
# for stability (logsumexp)
max_val = torch.max((exp_term_tmp).clamp(min=0), dim=1, keepdim=True)[0]
exp_term = torch.exp(exp_term_tmp - max_val)
# sample_energy = -max_val.squeeze() - torch.log(torch.sum(phi.unsqueeze(0) * exp_term / (det_cov).unsqueeze(0), dim = 1) + eps)
sample_energy = -max_val.squeeze() - torch.log(torch.sum(phi.unsqueeze(0) * exp_term / (torch.sqrt(det_cov)).unsqueeze(0), dim = 1) + eps)
# sample_energy = -max_val.squeeze() - torch.log(torch.sum(phi.unsqueeze(0) * exp_term / (torch.sqrt((2*np.pi)**D * det_cov)).unsqueeze(0), dim = 1) + eps)
在Py3.6 下我运行上面那段程序的时候报错如下：

请教该如何更改？