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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def forward_backward(self, x):
 """forward backward implementation"""

        with mx.autograd.record():
            (ls, next_sentence_label, classified, masked_id, decoded, \
             masked_weight, ls1, ls2, valid_length) = forward(x, self._model, self._mlm_loss,
                                                              self._nsp_loss, self._vocab_size,
                                                              args.dtype)
            ls = ls / self._rescale_factor
        if args.dtype == 'float16':
            self._trainer.backward(ls)
        else:
            ls.backward()
        return ls, next_sentence_label, classified, masked_id, decoded, \
               masked_weight, ls1, ls2, valid_length