Data Type error Cannot solve

TypeError: expected TensorOptions(dtype=__int64, device=cpu, layout=Strided, requires_grad=false (default), pinned_memory=false (default), memory_format=(nullopt)) (got TensorOptions(dtype=float, device=cpu, layout=Strided, requires_grad=false (default), pinned_memory=false (default), memory_format=(nullopt)))

        loss = criterion(output,targets.view(batch_size*seq_length).type(torch.LongTensor))
        loss.backward()
        nn.utils.clip_grad_norm_(net.parameters(), clip)
        opt.step()

changing the data type to torch.LongTensor does not help. (i am working via CPU only)

Using a LongTensor for nn.CrossEntropyLoss or nn.NLLLoss should work and the error points to a dtype mismatch as a FloatTensor seems to be passed.
Could you post a minimal, executable code snippet which would reproduce the issue, please?