The shape mismatch seems to be raise in
combined = torch.cat((input, hidden), dim=1)

which points to a different batch size (dim0).
It seems you are initializing the hidden tensor as torch.zeros(1, self.hidden_size), which would use a fixed batch size of 1.
You could add a batch_size argument to initHidden() and use it to create the right hidden tensor in the forward method via:
x = self.final_classifier1(F.relu(x))
hidden = self.myRNN.initHidden(x.size(0))
x = self.myRNN(x, hidden)

Let me know, if that would work for you.