Ok, I did:
class LSTM_fixed_len(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.lstm = nn.LSTM(3,3, batch_first=True)

    def forward(self, x, l):
        
        lstm_out, (ht, ct) = self.lstm(x)
        return ht[-1]

#####
torch.manual_seed(60)
torch.cuda.manual_seed(60)
np.random.seed(60)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
random.seed(60)
#####
vec = torch.normal(torch.zeros(2,2,3),torch.ones(2,2,3))#.view(1,3)
vec1 = torch.normal(torch.zeros(2,2,3),torch.ones(2,2,3))#.view(1,3)

model = LSTM_fixed_len()
out = model(vec,vec1)
print(out)

And got determenistic results
What is our next step?