Evaluate given the data loader

    Parameters
    ----------
    data_loader : DataLoader

    Returns
    -------
    avg_loss : float
        Average loss
    real_translation_out : list of list of str
        The translation output

def evaluate(data_loader):
    """Evaluate given the data loader

    Parameters
    ----------
    data_loader : DataLoader

    Returns
    -------
    avg_loss : float
        Average loss
    real_translation_out : list of list of str
        The translation output
    """
    translation_out = []
    all_inst_ids = []
    avg_loss_denom = 0
    avg_loss = 0.0
    for _, (src_seq, tgt_seq, src_valid_length, tgt_valid_length, inst_ids) \
            in enumerate(data_loader):
        src_seq = src_seq.as_in_context(ctx)
        tgt_seq = tgt_seq.as_in_context(ctx)
        src_valid_length = src_valid_length.as_in_context(ctx)
        tgt_valid_length = tgt_valid_length.as_in_context(ctx)
        # Calculating Loss
        out, _ = model(src_seq, tgt_seq[:, :-1], src_valid_length, tgt_valid_length - 1)
        loss = loss_function(out, tgt_seq[:, 1:], tgt_valid_length - 1).mean().asscalar()
        all_inst_ids.extend(inst_ids.asnumpy().astype(np.int32).tolist())
        avg_loss += loss * (tgt_seq.shape[1] - 1)
        avg_loss_denom += (tgt_seq.shape[1] - 1)
        # Translate
        samples, _, sample_valid_length =\
            translator.translate(src_seq=src_seq, src_valid_length=src_valid_length)
        max_score_sample = samples[:, 0, :].asnumpy()
        sample_valid_length = sample_valid_length[:, 0].asnumpy()
        for i in range(max_score_sample.shape[0]):
            translation_out.append(
                [tgt_vocab.idx_to_token[ele] for ele in
                 max_score_sample[i][1:(sample_valid_length[i] - 1)]])
    avg_loss = avg_loss / avg_loss_denom
    real_translation_out = [None for _ in range(len(all_inst_ids))]
    for ind, sentence in zip(all_inst_ids, translation_out):
        real_translation_out[ind] = sentence
    return avg_loss, real_translation_out