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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):
 """Perform forward and backward computation for a batch of src seq and dst seq"""

        (src_seq, tgt_seq, src_valid_length, tgt_valid_length), batch_size = x
        with mx.autograd.record():
            out, _ = self._model(src_seq, tgt_seq[:, :-1],
                                 src_valid_length, tgt_valid_length - 1)
            smoothed_label = self._label_smoothing(tgt_seq[:, 1:])
            ls = self._loss(out, smoothed_label, tgt_valid_length - 1).sum()
            ls = (ls * (tgt_seq.shape[1] - 1)) / batch_size / self._rescale_loss
        ls.backward()
        return ls