i did what you just said and i have this

RuntimeError                              Traceback (most recent call last)
 in 
1 spiking_model = SpikingNet(device, n_time_steps=1200, begin_eval=0)
----> 2 train_many_epochs(spiking_model)
 in train_many_epochs(model)
27         epoch = i
28         optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.5)
—> 29         train(model, device, train_set_loader, optimizer, epoch, logging_interval=1)
30         test(model, device, test_set_loader)
31
 in train(model, device, train_set_loader, optimizer, epoch, logging_interval)
10         target = target.long()
11         loss = F.nll_loss(output, target)
—> 12         loss.backward()
13
14         torch.nn.utils.clip_grad_norm_(model.parameters(),0.5)
~.conda\envs\py36\lib\site-packages\torch\tensor.py in backward(self, gradient, retain_graph, create_graph)
193                 products. Defaults to False.
194         “”"
–> 195         torch.autograd.backward(self, gradient, retain_graph, create_graph)
196
197     def register_hook(self, hook):
~.conda\envs\py36\lib\site-packages\torch\autograd_init_.py in backward(tensors, grad_tensors, retain_graph, create_graph, grad_variables)
97     Variable._execution_engine.run_backward(
98         tensors, grad_tensors, retain_graph, create_graph,
—> 99         allow_unreachable=True)  # allow_unreachable flag
100
101
RuntimeError: Function ‘MulBackward0’ returned nan values in its 0th output.
still nothing leading to the forward function
i have also checked the output of the forward function and couldn’t find nans (i might have missed it tho)