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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 translate(self, src_seq, src_valid_length):
 """Get the translation result given the input sentence. Parameters src_seq : mx.nd.NDArray Shape (batch_size, length) src_valid_length : mx.nd.NDArray Shape (batch_size,) Returns ------- samples : NDArray Samples draw by beam search. Shape (batch_size, beam_size, length). dtype is int32. scores : NDArray Scores of the samples. Shape (batch_size, beam_size). We make sure that scores[i, :] are in descending order. valid_length : NDArray The valid length of the samples. Shape (batch_size, beam_size). dtype will be int32. """

        batch_size = src_seq.shape[0]
        encoder_outputs, _ = self._model.encode(src_seq, valid_length=src_valid_length)
        decoder_states = self._model.decoder.init_state_from_encoder(encoder_outputs,
                                                                     src_valid_length)
        inputs = mx.nd.full(shape=(batch_size,), ctx=src_seq.context, dtype=np.float32,
                            val=self._model.tgt_vocab.token_to_idx[self._model.tgt_vocab.bos_token])
        samples, scores, sample_valid_length = self._sampler(inputs, decoder_states)
        return samples, scores, sample_valid_length