I also did this test with random tensors and got the same result as you (L being the same as L_mean).
I looked into my actual code more, and I realized it was just my own misunderstanding of python and the loss being mutable:
L1 = loss_REC(x_t_hat, x_t)/float(100*64*64)
L1_mean = loss_REC_mean(x_t_hat, x_t)

# Losses match here
print('reconstruction loss:{:.4f}'.format(L1.item()))
print('reconstruction loss mean:{:.4f}'.format(L1_mean.item()))

# Problem in my other code          
loss = L1
loss += 1

# Losses no longer match
print('reconstruction loss:{:.4f}'.format(L1.item()))
print('reconstruction loss mean:{:.4f}'.format(L1_mean.item()))

Thanks for double checking!