This is extreme difficult to answer since I do not know what your input looks like nor do I know what your desired output or labels look like.
But you can easily debug this yourself. All the error is telling you, is that your networks output should be the same size as your labels, but it currently is not.
Your labels are a one dimensional tensor of size [64] and your output is a two dimensional tensor of size [64, 256].
So you need to make sure that this output



 bbasaran:

output = self.model(x)


Becomes the shape you want it to be (which is torch.Size([64]) guessing by looking at the labels shape).
You are using only the h_n output of your pytorchs lstm.



 bbasaran:

_, (hidden, _) = self.lstm(x)


So I checked the documentation and for the shape of h_n is states:



h_n of shape (num_layers * num_directions, batch, hidden_size): tensor containing the hidden state for t = seq_len. If proj_size > 0 was specified, h_n shape will be (num_layers * num_directions, batch, proj_size).Like output, the layers can be separated using h_n.view(num_layers, num_directions, batch, hidden_size) and similarly for c_n.


You need to either adjust the network or change how you are handling the output (it depends on what you are trying to do) for it to work.
Debugging shapes is not do difficult. You can use something like print(output.shape) or print(output.size()) to print the shapes at different points in your code