Repackage_var not working

Hello! I built an LSTM and I wanted to test it on a very small sample, to make sure it can overfit it. Here is (part of) my code:

n_hidden = 16
n_classes = 2
bs = 1

class TESS_LSTM(nn.Module):
    def __init__(self, nl):
        super().__init__() = nl
        self.rnn = nn.LSTM(1, n_hidden, nl, dropout=0.1, bidirectional=True)
        self.l_out = nn.Linear(n_hidden*2, n_classes)
    def forward(self, input):
        outp,h = self.rnn(input.view(len(input), bs, -1), self.h)
        #self.h = repackage_var(h)
        return F.log_softmax(self.l_out(outp),dim=2)
    def init_hidden(self, bs):
        self.h = (V(torch.zeros(*2, bs, n_hidden)),
                  V(torch.zeros(*2, bs, n_hidden)))

model = TESS_LSTM(2).cuda()
loss_function = nn.NLLLoss()

optimizer = optim.Adam(model.parameters(), lr=0.0005)
for epoch in range(5551):

    tag_scores = model(trn_x)
    loss = loss_function(tag_scores.reshape(len(trn_x),n_classes), 

In this case the trn_x and trn_y have 100 inputs each (I tried with 10 and got the same result). So here is the problem. If I comment out the self.h = repackage_var(h) line, the code works fine and after enough iterations I am able to reproduce the exact input. If I don’t comment it out, the LSTM doesn’t work (the probabilities for my 2 output classes remain around 50% each, no matter what I do). As far as I understand from the lectures, the purpose of repackage_var is to make sure that at the end of one iteration, the hidden state is saved, but its history is not, which I would totally need when using the actual (long) data set. So why is it not working, in my case, when I use repackage_var? Thank you!