code: def begin_update(self, x_data, h_0=None, drop=0.0):
          
          x_var = torch.autograd.Variable(xp2torch(x_data), requires_grad=True)
  
          # Make prediction
          out, h_n = self._model(x_var, h_0)
          # Shapes will be:
          # out = seq_len, batch, hidden_size * num_directions
          # h_n = num_layers * num_directions, batch, hidden_size
  
          def backward_pytorch_rnn(d_data, sgd=None):
              dy_data, _ = d_data
              dout = xp2torch(dy_data)
              torch.autograd.backward((out,), grad_tensors=(dout,))
              if sgd is not None:
                  if self._optimizer is None:
                      self._optimizer = self._create_optimizer(sgd)
                  self._optimizer.step()
                  self._optimizer.zero_grad()
              return torch2xp(x_var.grad)
  
          return (torch2xp(out), h_n), backward_pytorch_rnn
docs: Return the output of the wrapped PyTorch model for the given input,
          along with a callback to handle the backward pass.